From f6b4c282b6d7557d4b9f69c9c86f468c67bf0ddc Mon Sep 17 00:00:00 2001 From: Ritesh Bansal Date: Tue, 24 May 2016 09:53:52 -0400 Subject: [PATCH 01/11] modified to load preocessed data, wip --- silver_model.ipynb | 13523 ++++++++++++++++++++++++------------------- 1 file changed, 7659 insertions(+), 5864 deletions(-) diff --git a/silver_model.ipynb b/silver_model.ipynb index 83b922a..35daf68 100644 --- a/silver_model.ipynb +++ b/silver_model.ipynb @@ -1,5885 +1,7680 @@ { - "metadata": { - "name": "silver_model" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GitHub link for the talk. You can clone the data and play with it yourself. Please submit any improvements as pull requests\n", - "\n", - "[https://github.com/jseabold/538model](https://github.com/jseabold/538model)" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GitHub link for the talk. You can clone the data and play with it yourself. Please submit any improvements as pull requests\n", + "\n", + "[https://github.com/jseabold/538model](https://github.com/jseabold/538model)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import datetime\n", + "\n", + "import numpy as np\n", + "import statsmodels.api as sm\n", + "import matplotlib.pyplot as plt\n", + "import pandas\n", + "from scipy import stats\n", + "np.set_printoptions(precision=4, suppress=True)\n", + "#pandas.set_options(notebook_repr_html=False,\n", + "# precision=4,\n", + "# max_columns=12, column_space=10,\n", + "# max_colwidth=25)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "today = datetime.datetime(2012, 10, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Methodology was obtained from the old [538 Blog](http://www.fivethirtyeight.com/2008/03/frequently-asked-questions-last-revised.html) with updates at the [new site hosted by the New York Times](http://fivethirtyeight.blogs.nytimes.com/methodology/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Polling Average: Aggregate polling data, and weight it according to our reliability scores.\n", + "\n", + "2. Trend Adjustment: Adjust the polling data for current trends.\n", + "\n", + "3. Regression: Analyze demographic data in each state by means of regression analysis.\n", + "\n", + "4. Snapshot: Combine the polling data with the regression analysis to produce an electoral snapshot. This is our estimate of what would happen if the election were held today.\n", + "\n", + "5. Projection: Translate the snapshot into a projection of what will happen in November, by allocating out undecided voters and applying a discount to current polling leads based on historical trends.\n", + "\n", + "6. Simulation: Simulate our results 10,000 times based on the results of the projection to account for the uncertainty in our estimates. The end result is a robust probabilistic assessment of what will happen in each state as well as in the nation as a whole. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Consensus forecast of GDP growth over the next two economic quarters
(Median of WSJ's monthly forecasting panel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The process for creating an economic index for the 538 model is described [here](http://fivethirtyeight.blogs.nytimes.com/2012/07/05/measuring-the-effect-of-the-economy-on-elections/#more-31732)." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Obtained from WSJ.com on 10/2/12" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Pandas methods are NaN aware, so we can just get the median." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "forecasts = pandas.read_csv(\"data_nuevo/wsj_forecast.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0Unnamed: 0.1ForecasterInstitutiongdp_q3_2012gdp_q4_2012
000Paul AshworthCapital Economics2.01.5
111Nariman BehraveshIHS Global Insight1.51.6
222Richard Berner/ David Greenlaw *Morgan StanleyNaNNaN
333Ram BhagavatulaCombinatorics Capital2.04.0
444Beth Ann Bovino *Standard and Poor'sNaNNaN
555Jay BrinkmannMortgage Bankers Association1.81.9
666Michael CareyCredit Agricole CIB1.71.6
777Joseph CarsonAllianceBernstein2.53.5
888Julia CoronadoBNP Paribas1.41.6
999Mike CosgroveEconoclast1.61.6
101010Lou CrandallWrightson ICAP1.81.8
111111J. Dewey DaaneVanderbilt University1.51.5
121212Douglas DuncanFannie Mae1.81.7
131313Robert DyeComerica Bank2.52.2
141414Maria Fiorini Ramirez/Joshua ShapiroMFR, Inc.1.41.2
151515Ethan HarrisBank of America Securities- Merrill Lynch1.31.0
161616Maury HarrisUBS1.51.8
171717Jan HatziusGoldman, Sachs & Co.2.31.5
181818Tracy HerrickAvidbank1.81.8
191919Stuart Hoffman *PNC Financial Services GroupNaNNaN
202020Gene HuangFedEx Corp.1.91.7
212121William B. HummerWintrust Wealth Management1.71.9
222222Bruce KasmanJP Morgan Chase & Co.1.52.0
232323Joseph LaVorgnaDeutsche Bank Securities Inc.2.72.8
242424Edward Leamer/David ShulmanUCLA Anderson Forecast1.31.5
252525Don Leavens/Tim GillNEMA Business Information Services1.71.7
262626John LonskiMoody's Investors Service1.51.3
272727Dean MakiBarclays Capital2.02.5
282828Aneta Markowska *Societe GeneraleNaNNaN
292929Jim Meil/Arun RahaEaton Corp.1.22.1
303030Mark NielsonMacroEcon Global Advisors2.22.8
313131Michael P. NiemiraInternational Council of Shopping Centers2.32.2
323232Jim O'SullivanHigh Frequency Economics2.52.0
333333Nicholas S. PernaPerna Associates2.21.5
343434Dr. Joel Prakken/ Chris VarvaresMacroeconomic Advisers1.51.4
353535David ReslerNomura Securities International1.91.7
363636John Ryding/Conrad DeQuadrosRDQ Economics2.12.4
373737John SilviaWells Fargo & Co.1.61.7
383838Allen SinaiDecision Economics, Inc.2.12.7
393939James F. SmithParsec Financial Management3.84.8
404040Sean M. SnaithUniversity of Central Florida1.71.9
414141Sung Won SohnCalifornia State University1.81.7
424242Neal SossCSFB1.52.2
434343Stephen StanleyPierpont Securities1.02.1
444444Susan M. SterneEconomic Analysis Associates Inc.2.21.9
454545Diane SwonkMesirow Financial1.31.5
464646Carl TannenbaumThe Northern Trust1.72.0
474747Bart van ArkThe Conference Board1.61.6
484848Brian S. Wesbury/ Robert SteinFirst Trust Advisors, L.P.2.53.0
494949William T. WilsonSkolkovo Institute for Emerging Market Studies1.92.2
505050Lawrence YunNational Association of Realtors1.72.1
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Hummer \n", + "22 22 22 Bruce Kasman \n", + "23 23 23 Joseph LaVorgna \n", + "24 24 24 Edward Leamer/David Shulman \n", + "25 25 25 Don Leavens/Tim Gill \n", + "26 26 26 John Lonski \n", + "27 27 27 Dean Maki \n", + "28 28 28 Aneta Markowska * \n", + "29 29 29 Jim Meil/Arun Raha \n", + "30 30 30 Mark Nielson \n", + "31 31 31 Michael P. Niemira \n", + "32 32 32 Jim O'Sullivan \n", + "33 33 33 Nicholas S. Perna \n", + "34 34 34 Dr. Joel Prakken/ Chris Varvares \n", + "35 35 35 David Resler \n", + "36 36 36 John Ryding/Conrad DeQuadros \n", + "37 37 37 John Silvia \n", + "38 38 38 Allen Sinai \n", + "39 39 39 James F. Smith \n", + "40 40 40 Sean M. Snaith \n", + "41 41 41 Sung Won Sohn \n", + "42 42 42 Neal Soss \n", + "43 43 43 Stephen Stanley \n", + "44 44 44 Susan M. Sterne \n", + "45 45 45 Diane Swonk \n", + "46 46 46 Carl Tannenbaum \n", + "47 47 47 Bart van Ark \n", + "48 48 48 Brian S. Wesbury/ Robert Stein \n", + "49 49 49 William T. Wilson \n", + "50 50 50 Lawrence Yun \n", + "\n", + " Institution gdp_q3_2012 gdp_q4_2012 \n", + "0 Capital Economics 2.0 1.5 \n", + "1 IHS Global Insight 1.5 1.6 \n", + "2 Morgan Stanley NaN NaN \n", + "3 Combinatorics Capital 2.0 4.0 \n", + "4 Standard and Poor's NaN NaN \n", + "5 Mortgage Bankers Association 1.8 1.9 \n", + "6 Credit Agricole CIB 1.7 1.6 \n", + "7 AllianceBernstein 2.5 3.5 \n", + "8 BNP Paribas 1.4 1.6 \n", + "9 Econoclast 1.6 1.6 \n", + "10 Wrightson ICAP 1.8 1.8 \n", + "11 Vanderbilt University 1.5 1.5 \n", + "12 Fannie Mae 1.8 1.7 \n", + "13 Comerica Bank 2.5 2.2 \n", + "14 MFR, Inc. 1.4 1.2 \n", + "15 Bank of America Securities- Merrill Lynch 1.3 1.0 \n", + "16 UBS 1.5 1.8 \n", + "17 Goldman, Sachs & Co. 2.3 1.5 \n", + "18 Avidbank 1.8 1.8 \n", + "19 PNC Financial Services Group NaN NaN \n", + "20 FedEx Corp. 1.9 1.7 \n", + "21 Wintrust Wealth Management 1.7 1.9 \n", + "22 JP Morgan Chase & Co. 1.5 2.0 \n", + "23 Deutsche Bank Securities Inc. 2.7 2.8 \n", + "24 UCLA Anderson Forecast 1.3 1.5 \n", + "25 NEMA Business Information Services 1.7 1.7 \n", + "26 Moody's Investors Service 1.5 1.3 \n", + "27 Barclays Capital 2.0 2.5 \n", + "28 Societe Generale NaN NaN \n", + "29 Eaton Corp. 1.2 2.1 \n", + "30 MacroEcon Global Advisors 2.2 2.8 \n", + "31 International Council of Shopping Centers 2.3 2.2 \n", + "32 High Frequency Economics 2.5 2.0 \n", + "33 Perna Associates 2.2 1.5 \n", + "34 Macroeconomic Advisers 1.5 1.4 \n", + "35 Nomura Securities International 1.9 1.7 \n", + "36 RDQ Economics 2.1 2.4 \n", + "37 Wells Fargo & Co. 1.6 1.7 \n", + "38 Decision Economics, Inc. 2.1 2.7 \n", + "39 Parsec Financial Management 3.8 4.8 \n", + "40 University of Central Florida 1.7 1.9 \n", + "41 California State University 1.8 1.7 \n", + "42 CSFB 1.5 2.2 \n", + "43 Pierpont Securities 1.0 2.1 \n", + "44 Economic Analysis Associates Inc. 2.2 1.9 \n", + "45 Mesirow Financial 1.3 1.5 \n", + "46 The Northern Trust 1.7 2.0 \n", + "47 The Conference Board 1.6 1.6 \n", + "48 First Trust Advisors, L.P. 2.5 3.0 \n", + "49 Skolkovo Institute for Emerging Market Studies 1.9 2.2 \n", + "50 National Association of Realtors 1.7 2.1 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecasts" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "median_forecast = forecasts[['gdp_q3_2012', 'gdp_q4_2012']].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "gdp_q3_2012 1.8\n", + "gdp_q4_2012 1.8\n", + "dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_forecast" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Polling Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used Python to scrape the [Real Clear Politics](realclearpolitics.com) website and download data for the 2004 and 2008 elections. The scraping scripts are available in the github repository for this talk. State by state historical data for the 2004 and 2008 Presidential elections was obtained from [electoral-vote.com](www.electorical-vote.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polling Average" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Details can be found at the 538 blog [here](http://www.fivethirtyeight.com/2008/03/pollster-ratings-updated.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tossup = [\"Colorado\", \"Florida\", \"Iowa\", \"New Hampshire\", \"Nevada\", \n", + " \"Ohio\", \"Virginia\", \"Wisconsin\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "national_data2012 = pandas.read_pickle(\"data_nuevo/2012_poll_data_national.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PollsterSampleMoEObama (D)Romney (R)Spreadobama_spreadStatepoll_date
0RCP AverageNaN--49.145.1Obama +4.04USA2012-09-28
1Rasmussen Tracking15003.048.047.0Obama +11USA2012-09-30
2CNN/Opinion Research7833.550.047.0Obama +33USA2012-09-29
3Gallup Tracking30502.050.044.0Obama +66USA2012-09-28
4Quinnipiac19122.249.045.0Obama +44USA2012-09-28
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" + ], + "text/plain": [ + " Pollster Sample MoE Obama (D) Romney (R) Spread \\\n", + "0 RCP Average NaN -- 49.1 45.1 Obama +4.0 \n", + "1 Rasmussen Tracking 1500 3.0 48.0 47.0 Obama +1 \n", + "2 CNN/Opinion Research 783 3.5 50.0 47.0 Obama +3 \n", + "3 Gallup Tracking 3050 2.0 50.0 44.0 Obama +6 \n", + "4 Quinnipiac 1912 2.2 49.0 45.0 Obama +4 \n", + "\n", + " obama_spread State poll_date \n", + "0 4 USA 2012-09-28 \n", + "1 1 USA 2012-09-30 \n", + "2 3 USA 2012-09-29 \n", + "3 6 USA 2012-09-28 \n", + "4 4 USA 2012-09-28 " + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "national_data2012.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "state_data2012 = pandas.read_pickle(\"data_nuevo/2012_poll_data_states.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PollsterStateMoEObama (D)Romney (R)SampleSpreadobama_spreadpoll_date
0Rasmussen ReportsWA4.55241500Obama +11112012-09-26
1Gravis MarketingWA4.65639625Obama +17172012-09-22
2Elway PollWA5.05336405Obama +17172012-09-11
3SurveyUSAWA4.45438524Obama +16162012-09-08
4SurveyUSAWA4.45437524Obama +17172012-08-02
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" + ], + "text/plain": [ + " Pollster State MoE Obama (D) Romney (R) Sample Spread \\\n", + "0 Rasmussen Reports WA 4.5 52 41 500 Obama +11 \n", + "1 Gravis Marketing WA 4.6 56 39 625 Obama +17 \n", + "2 Elway Poll WA 5.0 53 36 405 Obama +17 \n", + "3 SurveyUSA WA 4.4 54 38 524 Obama +16 \n", + "4 SurveyUSA WA 4.4 54 37 524 Obama +17 \n", + "\n", + " obama_spread poll_date \n", + "0 11 2012-09-26 \n", + "1 17 2012-09-22 \n", + "2 17 2012-09-11 \n", + "3 16 2012-09-08 \n", + "4 17 2012-08-02 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pollsters = state_data2012.Pollster.unique()\n", + "pollsters.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pollsters)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 AFP/Magellan (R)\n", + "1 AIF/McLaughlin (R)\n", + "2 ARG\n", + "3 Albuquerque Journal*\n", + "4 Arizona State\n", + "5 Baltimore Sun\n", + "6 Baydoun/Foster (D)\n", + "7 Behavior Research Center\n", + "8 Bloomberg News\n", + "9 Boston Globe\n", + "10 CBS/NYT/Quinnipiac\n", + "11 CNN/Opinion Research\n", + "12 CNN/Time\n", + "13 CNU/Times-Dispatch\n", + "14 Caddell/McLaughlin/SAN (R)\n", + "15 Castleton State College\n", + "16 Chicago Tribune\n", + "17 Civitas (R)\n", + "18 Clarus Research\n", + "19 Columbus Dispatch*\n", + "20 Courier-Journal/SurveyUSA\n", + "21 Critical Insights\n", + "22 Daily Kos/PPP (D)\n", + "23 Dartmouth\n", + "24 Denver Post/SurveyUSA\n", + "25 Des Moines Register\n", + "26 Deseret News\n", + "27 Deseret News/KSL\n", + "28 Detroit News\n", + "29 EPIC-MRA\n", + " ... \n", + "90 Siena\n", + "91 Sooner Poll\n", + "92 St. Cloud State U.\n", + "93 Star Tribune/Mason-Dixon*\n", + "94 Strategies 360 (D)\n", + "95 Suffolk University\n", + "96 Suffolk/7News\n", + "97 Suffolk/WSVN\n", + "98 Suffolk/WWBT\n", + "99 Sunshine State News/VSS\n", + "100 SurveyUSA\n", + "101 SurveyUSA/Civitas (R)\n", + "102 Talk Business Poll\n", + "103 Tennessean/Vanderbilt\n", + "104 The Simon Poll/SIU\n", + "105 The Washington Poll\n", + "106 Tribune-Review/Susquehanna\n", + "107 UMass/Boston Herald\n", + "108 Virginian-Pilot/ODU\n", + "109 Voter/Consumer Res/TIR (R)\n", + "110 WBUR/MassINC\n", + "111 WMUR/UNH\n", + "112 WPA\n", + "113 WPR/St. Norbert\n", + "114 WPRI\n", + "115 WPRI/Fleming\n", + "116 Washington Post\n", + "117 WeAskAmerica\n", + "118 WeAskAmerica*\n", + "119 Western NE University\n", + "dtype: object\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import datetime\n", + } + ], + "source": [ + "print pandas.Series(pollsters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 538 Pollster Ratings" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "weights = pandas.read_table(\"./data/pollster_weights.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PollsterWeightPIE
0ABC / Washington Post0.951.41
1American Research Group0.651.76
2CBS / New York Times0.661.84
3Chicago Trib. / MarketShares1.161.13
4CNN / Opinion Research0.771.59
5Columbus Dispatch (OH)0.506.76
6EPIC-MRA0.751.65
7Fairleigh-Dickinson (NJ)0.711.72
8Field Poll (CA)1.330.88
9Fox / Opinion Dynamics0.791.60
10Franklin Pierce (NH)0.741.60
11Insider Advantage0.951.29
12Keystone (PA)0.641.55
13LA Times / Bloomberg0.831.44
14Marist (NY)0.691.73
15Mason-Dixon1.101.15
16Mitchell0.961.43
17Ohio Poll1.241.05
18Public Opinion Strategies0.631.81
19Public Policy Polling (PPP)1.051.60
20Quinnipiac0.951.34
21Rasmussen1.300.88
22Research 20001.011.20
23Selzer1.470.92
24Star Tribune (MN)0.812.01
25Strategic Vision0.951.45
26Suffolk (NH/MA)0.771.37
27SurveyUSA1.910.72
28Univ. New Hampshire1.081.26
29USA Today / Gallup0.632.01
30Zogby0.641.72
31Zogby Interactive0.434.74
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" + ], + "text/plain": [ + " Pollster Weight PIE\n", + "0 ABC / Washington Post 0.95 1.41\n", + "1 American Research Group 0.65 1.76\n", + "2 CBS / New York Times 0.66 1.84\n", + "3 Chicago Trib. / MarketShares 1.16 1.13\n", + "4 CNN / Opinion Research 0.77 1.59\n", + "5 Columbus Dispatch (OH) 0.50 6.76\n", + "6 EPIC-MRA 0.75 1.65\n", + "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", + "8 Field Poll (CA) 1.33 0.88\n", + "9 Fox / Opinion Dynamics 0.79 1.60\n", + "10 Franklin Pierce (NH) 0.74 1.60\n", + "11 Insider Advantage 0.95 1.29\n", + "12 Keystone (PA) 0.64 1.55\n", + "13 LA Times / Bloomberg 0.83 1.44\n", + "14 Marist (NY) 0.69 1.73\n", + "15 Mason-Dixon 1.10 1.15\n", + "16 Mitchell 0.96 1.43\n", + "17 Ohio Poll 1.24 1.05\n", + "18 Public Opinion Strategies 0.63 1.81\n", + "19 Public Policy Polling (PPP) 1.05 1.60\n", + "20 Quinnipiac 0.95 1.34\n", + "21 Rasmussen 1.30 0.88\n", + "22 Research 2000 1.01 1.20\n", + "23 Selzer 1.47 0.92\n", + "24 Star Tribune (MN) 0.81 2.01\n", + "25 Strategic Vision 0.95 1.45\n", + "26 Suffolk (NH/MA) 0.77 1.37\n", + "27 SurveyUSA 1.91 0.72\n", + "28 Univ. New Hampshire 1.08 1.26\n", + "29 USA Today / Gallup 0.63 2.01\n", + "30 Zogby 0.64 1.72\n", + "31 Zogby Interactive 0.43 4.74" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Weight 0.907813\n", + "PIE 1.706563\n", + "dtype: float64" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clean up the pollster names a bit so we can merge with the weights." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pickle\n", + "pollster_map = pickle.load(open(\"./data/pollster_map.pkl\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012.Pollster.replace(pollster_map, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "national_data2012.Pollster.replace(pollster_map, inplace=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inner merge the data with the weights" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012 = state_data2012.merge(weights, how=\"inner\", on=\"Pollster\")" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PollsterStateMoEObama (D)Romney (R)SampleSpreadobama_spreadpoll_dateWeightPIE
0RasmussenWA4.55241500Obama +11112012-09-261.30.88
1RasmussenWI4.54946500Obama +332012-09-171.30.88
2RasmussenWI4.54748500Romney +1-12012-08-151.30.88
3RasmussenWI4.54946500Obama +332012-07-251.30.88
4RasmussenWI4.54447500Romney +3-32012-06-121.30.88
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" + ], + "text/plain": [ + " Pollster State MoE Obama (D) Romney (R) Sample Spread \\\n", + "0 Rasmussen WA 4.5 52 41 500 Obama +11 \n", + "1 Rasmussen WI 4.5 49 46 500 Obama +3 \n", + "2 Rasmussen WI 4.5 47 48 500 Romney +1 \n", + "3 Rasmussen WI 4.5 49 46 500 Obama +3 \n", + "4 Rasmussen WI 4.5 44 47 500 Romney +3 \n", + "\n", + " obama_spread poll_date Weight PIE \n", + "0 11 2012-09-26 1.3 0.88 \n", + "1 3 2012-09-17 1.3 0.88 \n", + "2 -1 2012-08-15 1.3 0.88 \n", + "3 3 2012-07-25 1.3 0.88 \n", + "4 -3 2012-06-12 1.3 0.88 " + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pollster object\n", + "State object\n", + "MoE float64\n", + "Obama (D) float64\n", + "Romney (R) float64\n", + "Sample float64\n", + "Spread object\n", + "obama_spread float64\n", + "poll_date datetime64[ns]\n", + "Weight float64\n", + "PIE float64\n", + "ESS float64\n", + "MESS float64\n", + "dtype: object" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### First, we average each pollster for each state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first adjustment is an exponential decay for recency of the poll. Based on research in prior elections, a weight with a half-life of 30 days since the median date the poll has been in the field is assigned to each poll." + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def exp_decay(x):\n", + " # defensive coding, accepts timedeltas\n", + " #days = getattr(days, \"days\", days)\n", + " days = x.astype('timedelta64[D]')/ np.timedelta64(1, 'D')\n", + " # print(\"days is\", type(days))\n", + " return .5 ** (days/30.)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12,8), subplot_kw={\"xlabel\" : \"Days\",\n", + " \"ylabel\" : \"Weight\"})\n", + "days = np.arange(0, 45)\n", + "ax.plot(days, exp_decay(days));\n", + "ax.vlines(30, 0, .99, color='r', linewidth=4)\n", + "ax.set_ylim(0,1)\n", + "ax.set_xlim(0, 45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second adjustment is for the sample size of the poll. Polls with a higher sample size receive a higher weight." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Binomial sampling error = +/- $50 * \\frac{1}{\\sqrt{nobs}}$ where the 50 depends on the underlying probability or population preferences, in this case assumed to be 50:50 (another way of calculating Margin of Error)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def average_error(nobs, p=50.):\n", + " return p*nobs**-.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The thinking here is that having 5 polls of 1200 is a lot like having one poll of 6000. However, we downweight older polls by only including the marginal effective sample size. Where the effective sample size is the size of the methodologically perfect poll for which we would be indifferent between it and the one we have with our current total error. Total error is determined as $TE = \\text{Average Error} + \\text{Long Run Pollster Induced Error}$. See [here](http://www.fivethirtyeight.com/2008/04/pollster-ratings-v30.html) for the detailed calculations of Pollster Induced Error." + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def effective_sample(total_error, p=50.):\n", + " return p**2 * (total_error**-2.)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_pollsters = state_data2012.groupby([\"State\", \"Pollster\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ppp_az = state_pollsters.get_group((\"AZ\", \"Public Policy Polling (PPP)\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PollsterStateObama (D)Romney (R)Samplepoll_date
258Public Policy Polling (PPP)AZ44539932012-09-08
259Public Policy Polling (PPP)AZ41528332012-07-24
260Public Policy Polling (PPP)AZ43505002012-05-19
261Public Policy Polling (PPP)AZ47477432012-02-18
262Public Policy Polling (PPP)AZ42495002011-11-19
263Public Policy Polling (PPP)AZ44486232011-04-30
264Public Policy Polling (PPP)AZ43495992011-01-29
265Public Policy Polling (PPP)AZ43506172010-09-20
\n", + "
" + ], + "text/plain": [ + " Pollster State Obama (D) Romney (R) Sample \\\n", + "258 Public Policy Polling (PPP) AZ 44 53 993 \n", + "259 Public Policy Polling (PPP) AZ 41 52 833 \n", + "260 Public Policy Polling (PPP) AZ 43 50 500 \n", + "261 Public Policy Polling (PPP) AZ 47 47 743 \n", + "262 Public Policy Polling (PPP) AZ 42 49 500 \n", + "263 Public Policy Polling (PPP) AZ 44 48 623 \n", + "264 Public Policy Polling (PPP) AZ 43 49 599 \n", + "265 Public Policy Polling (PPP) AZ 43 50 617 \n", + "\n", + " poll_date \n", + "258 2012-09-08 \n", + "259 2012-07-24 \n", + "260 2012-05-19 \n", + "261 2012-02-18 \n", + "262 2011-11-19 \n", + "263 2011-04-30 \n", + "264 2011-01-29 \n", + "265 2010-09-20 " + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var_idx = [\"Pollster\", \"State\", \"Obama (D)\", \"Romney (R)\", \"Sample\", \"poll_date\"]\n", + "ppp_az[var_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python2.7/site-packages/pandas/core/frame.py:2915: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "import numpy as np\n", - "import statsmodels.api as sm\n", - "import matplotlib.pyplot as plt\n", - "import pandas\n", - "from scipy import stats\n", - "np.set_printoptions(precision=4, suppress=True)\n", - "pandas.set_printoptions(notebook_repr_html=False,\n", - " precision=4,\n", - " max_columns=12, column_space=10,\n", - " max_colwidth=25)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "today = datetime.datetime(2012, 10, 2)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Outline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Methodology was obtained from the old [538 Blog](http://www.fivethirtyeight.com/2008/03/frequently-asked-questions-last-revised.html) with updates at the [new site hosted by the New York Times](http://fivethirtyeight.blogs.nytimes.com/methodology/)" + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " inplace=inplace, kind=kind, na_position=na_position)\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Polling Average: Aggregate polling data, and weight it according to our reliability scores.\n", - "\n", - "2. Trend Adjustment: Adjust the polling data for current trends.\n", - "\n", - "3. Regression: Analyze demographic data in each state by means of regression analysis.\n", + } + ], + "source": [ + "ppp_az.sort(\"poll_date\", ascending=False, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "4. Snapshot: Combine the polling data with the regression analysis to produce an electoral snapshot. This is our estimate of what would happen if the election were held today.\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " if __name__ == '__main__':\n", + "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "5. Projection: Translate the snapshot into a projection of what will happen in November, by allocating out undecided voters and applying a discount to current polling leads based on historical trends.\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from IPython.kernel.zmq import kernelapp as app\n", + "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "6. Simulation: Simulate our results 10,000 times based on the results of the projection to account for the uncertainty in our estimates. The end result is a robust probabilistic assessment of what will happen in each state as well as in the nation as a whole. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Get the Data" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Consensus forecast of GDP growth over the next two economic quarters
(Median of WSJ's monthly forecasting panel)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The process for creating an economic index for the 538 model is described [here](http://fivethirtyeight.blogs.nytimes.com/2012/07/05/measuring-the-effect-of-the-economy-on-elections/#more-31732)." - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Obtained from WSJ.com on 10/2/12" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "forecasts = pandas.read_table(\"/home/skipper/school/seaboldgit/\"\n", - " \"talks/pydata/data/wsj_forecast.csv\", skiprows=2)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "forecasts" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 6, - "text": [ - " Forecaster Institution Q3 2012 Q4 2012\n", - "0 Paul Ashworth Capital Economics 2.0 1.5\n", - "1 Nariman Behravesh IHS Global Insight 1.5 1.6\n", - "2 Richard Berner/ David... Morgan Stanley NaN NaN\n", - "3 Ram Bhagavatula Combinatorics Capital 2.0 4.0\n", - "4 Beth Ann Bovino * Standard and Poor's NaN NaN\n", - "5 Jay Brinkmann Mortgage Bankers Asso... 1.8 1.9\n", - "6 Michael Carey Credit Agricole CIB 1.7 1.6\n", - "7 Joseph Carson AllianceBernstein 2.5 3.5\n", - "8 Julia Coronado BNP Paribas 1.4 1.6\n", - "9 Mike Cosgrove Econoclast 1.6 1.6\n", - "10 Lou Crandall Wrightson ICAP 1.8 1.8\n", - "11 J. Dewey Daane Vanderbilt University 1.5 1.5\n", - "12 Douglas Duncan Fannie Mae 1.8 1.7\n", - "13 Robert Dye Comerica Bank 2.5 2.2\n", - "14 Maria Fiorini Ramirez... MFR, Inc. 1.4 1.2\n", - "15 Ethan Harris Bank of America Secur... 1.3 1.0\n", - "16 Maury Harris UBS 1.5 1.8\n", - "17 Jan Hatzius Goldman, Sachs & Co. 2.3 1.5\n", - "18 Tracy Herrick Avidbank 1.8 1.8\n", - "19 Stuart Hoffman * PNC Financial Service... NaN NaN\n", - "20 Gene Huang FedEx Corp. 1.9 1.7\n", - "21 William B. Hummer Wintrust Wealth Manag... 1.7 1.9\n", - "22 Bruce Kasman JP Morgan Chase & Co. 1.5 2.0\n", - "23 Joseph LaVorgna Deutsche Bank Securit... 2.7 2.8\n", - "24 Edward Leamer/David S... UCLA Anderson Forecast 1.3 1.5\n", - "25 Don Leavens/Tim Gill NEMA Business Informa... 1.7 1.7\n", - "26 John Lonski Moody's Investors Ser... 1.5 1.3\n", - "27 Dean Maki Barclays Capital 2.0 2.5\n", - "28 Aneta Markowska * Societe Generale NaN NaN\n", - "29 Jim Meil/Arun Raha Eaton Corp. 1.2 2.1\n", - "30 Mark Nielson MacroEcon Global Advi... 2.2 2.8\n", - "31 Michael P. Niemira International Council... 2.3 2.2\n", - "32 Jim O'Sullivan High Frequency Economics 2.5 2.0\n", - "33 Nicholas S. Perna Perna Associates 2.2 1.5\n", - "34 Dr. Joel Prakken/ Chr... Macroeconomic Advisers 1.5 1.4\n", - "35 David Resler Nomura Securities Int... 1.9 1.7\n", - "36 John Ryding/Conrad De... RDQ Economics 2.1 2.4\n", - "37 John Silvia Wells Fargo & Co. 1.6 1.7\n", - "38 Allen Sinai Decision Economics, Inc. 2.1 2.7\n", - "39 James F. Smith Parsec Financial Mana... 3.8 4.8\n", - "40 Sean M. Snaith University of Central... 1.7 1.9\n", - "41 Sung Won Sohn California State Univ... 1.8 1.7\n", - "42 Neal Soss CSFB 1.5 2.2\n", - "43 Stephen Stanley Pierpont Securities 1.0 2.1\n", - "44 Susan M. Sterne Economic Analysis Ass... 2.2 1.9\n", - "45 Diane Swonk Mesirow Financial 1.3 1.5\n", - "46 Carl Tannenbaum The Northern Trust 1.7 2.0\n", - "47 Bart van Ark The Conference Board 1.6 1.6\n", - "48 Brian S. Wesbury/ Rob... First Trust Advisors,... 2.5 3.0\n", - "49 William T. Wilson Skolkovo Institute fo... 1.9 2.2\n", - "50 Lawrence Yun National Association ... 1.7 2.1" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "forecasts.rename(columns={\"Q3 2012\" : \"gdp_q3_2012\", \n", - " \"Q4 2012\" : \"gdp_q4_2012\"}, inplace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - " Forecaster Institution gdp_q3_2012 gdp_q4_2012\n", - "0 Paul Ashworth Capital Economics 2.0 1.5\n", - "1 Nariman Behravesh IHS Global Insight 1.5 1.6\n", - "2 Richard Berner/ David... Morgan Stanley NaN NaN\n", - "3 Ram Bhagavatula Combinatorics Capital 2.0 4.0\n", - "4 Beth Ann Bovino * Standard and Poor's NaN NaN\n", - "5 Jay Brinkmann Mortgage Bankers Asso... 1.8 1.9\n", - "6 Michael Carey Credit Agricole CIB 1.7 1.6\n", - "7 Joseph Carson AllianceBernstein 2.5 3.5\n", - "8 Julia Coronado BNP Paribas 1.4 1.6\n", - "9 Mike Cosgrove Econoclast 1.6 1.6\n", - "10 Lou Crandall Wrightson ICAP 1.8 1.8\n", - "11 J. Dewey Daane Vanderbilt University 1.5 1.5\n", - "12 Douglas Duncan Fannie Mae 1.8 1.7\n", - "13 Robert Dye Comerica Bank 2.5 2.2\n", - "14 Maria Fiorini Ramirez... MFR, Inc. 1.4 1.2\n", - "15 Ethan Harris Bank of America Secur... 1.3 1.0\n", - "16 Maury Harris UBS 1.5 1.8\n", - "17 Jan Hatzius Goldman, Sachs & Co. 2.3 1.5\n", - "18 Tracy Herrick Avidbank 1.8 1.8\n", - "19 Stuart Hoffman * PNC Financial Service... NaN NaN\n", - "20 Gene Huang FedEx Corp. 1.9 1.7\n", - "21 William B. Hummer Wintrust Wealth Manag... 1.7 1.9\n", - "22 Bruce Kasman JP Morgan Chase & Co. 1.5 2.0\n", - "23 Joseph LaVorgna Deutsche Bank Securit... 2.7 2.8\n", - "24 Edward Leamer/David S... UCLA Anderson Forecast 1.3 1.5\n", - "25 Don Leavens/Tim Gill NEMA Business Informa... 1.7 1.7\n", - "26 John Lonski Moody's Investors Ser... 1.5 1.3\n", - "27 Dean Maki Barclays Capital 2.0 2.5\n", - "28 Aneta Markowska * Societe Generale NaN NaN\n", - "29 Jim Meil/Arun Raha Eaton Corp. 1.2 2.1\n", - "30 Mark Nielson MacroEcon Global Advi... 2.2 2.8\n", - "31 Michael P. Niemira International Council... 2.3 2.2\n", - "32 Jim O'Sullivan High Frequency Economics 2.5 2.0\n", - "33 Nicholas S. Perna Perna Associates 2.2 1.5\n", - "34 Dr. Joel Prakken/ Chr... Macroeconomic Advisers 1.5 1.4\n", - "35 David Resler Nomura Securities Int... 1.9 1.7\n", - "36 John Ryding/Conrad De... RDQ Economics 2.1 2.4\n", - "37 John Silvia Wells Fargo & Co. 1.6 1.7\n", - "38 Allen Sinai Decision Economics, Inc. 2.1 2.7\n", - "39 James F. Smith Parsec Financial Mana... 3.8 4.8\n", - "40 Sean M. Snaith University of Central... 1.7 1.9\n", - "41 Sung Won Sohn California State Univ... 1.8 1.7\n", - "42 Neal Soss CSFB 1.5 2.2\n", - "43 Stephen Stanley Pierpont Securities 1.0 2.1\n", - "44 Susan M. Sterne Economic Analysis Ass... 2.2 1.9\n", - "45 Diane Swonk Mesirow Financial 1.3 1.5\n", - "46 Carl Tannenbaum The Northern Trust 1.7 2.0\n", - "47 Bart van Ark The Conference Board 1.6 1.6\n", - "48 Brian S. Wesbury/ Rob... First Trust Advisors,... 2.5 3.0\n", - "49 William T. Wilson Skolkovo Institute fo... 1.9 2.2\n", - "50 Lawrence Yun National Association ... 1.7 2.1" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "forecasts" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 8, - "text": [ - " Forecaster Institution gdp_q3_2012 gdp_q4_2012\n", - "0 Paul Ashworth Capital Economics 2.0 1.5\n", - "1 Nariman Behravesh IHS Global Insight 1.5 1.6\n", - "2 Richard Berner/ David... Morgan Stanley NaN NaN\n", - "3 Ram Bhagavatula Combinatorics Capital 2.0 4.0\n", - "4 Beth Ann Bovino * Standard and Poor's NaN NaN\n", - "5 Jay Brinkmann Mortgage Bankers Asso... 1.8 1.9\n", - "6 Michael Carey Credit Agricole CIB 1.7 1.6\n", - "7 Joseph Carson AllianceBernstein 2.5 3.5\n", - "8 Julia Coronado BNP Paribas 1.4 1.6\n", - "9 Mike Cosgrove Econoclast 1.6 1.6\n", - "10 Lou Crandall Wrightson ICAP 1.8 1.8\n", - "11 J. Dewey Daane Vanderbilt University 1.5 1.5\n", - "12 Douglas Duncan Fannie Mae 1.8 1.7\n", - "13 Robert Dye Comerica Bank 2.5 2.2\n", - "14 Maria Fiorini Ramirez... MFR, Inc. 1.4 1.2\n", - "15 Ethan Harris Bank of America Secur... 1.3 1.0\n", - "16 Maury Harris UBS 1.5 1.8\n", - "17 Jan Hatzius Goldman, Sachs & Co. 2.3 1.5\n", - "18 Tracy Herrick Avidbank 1.8 1.8\n", - "19 Stuart Hoffman * PNC Financial Service... NaN NaN\n", - "20 Gene Huang FedEx Corp. 1.9 1.7\n", - "21 William B. Hummer Wintrust Wealth Manag... 1.7 1.9\n", - "22 Bruce Kasman JP Morgan Chase & Co. 1.5 2.0\n", - "23 Joseph LaVorgna Deutsche Bank Securit... 2.7 2.8\n", - "24 Edward Leamer/David S... UCLA Anderson Forecast 1.3 1.5\n", - "25 Don Leavens/Tim Gill NEMA Business Informa... 1.7 1.7\n", - "26 John Lonski Moody's Investors Ser... 1.5 1.3\n", - "27 Dean Maki Barclays Capital 2.0 2.5\n", - "28 Aneta Markowska * Societe Generale NaN NaN\n", - "29 Jim Meil/Arun Raha Eaton Corp. 1.2 2.1\n", - "30 Mark Nielson MacroEcon Global Advi... 2.2 2.8\n", - "31 Michael P. Niemira International Council... 2.3 2.2\n", - "32 Jim O'Sullivan High Frequency Economics 2.5 2.0\n", - "33 Nicholas S. Perna Perna Associates 2.2 1.5\n", - "34 Dr. Joel Prakken/ Chr... Macroeconomic Advisers 1.5 1.4\n", - "35 David Resler Nomura Securities Int... 1.9 1.7\n", - "36 John Ryding/Conrad De... RDQ Economics 2.1 2.4\n", - "37 John Silvia Wells Fargo & Co. 1.6 1.7\n", - "38 Allen Sinai Decision Economics, Inc. 2.1 2.7\n", - "39 James F. Smith Parsec Financial Mana... 3.8 4.8\n", - "40 Sean M. Snaith University of Central... 1.7 1.9\n", - "41 Sung Won Sohn California State Univ... 1.8 1.7\n", - "42 Neal Soss CSFB 1.5 2.2\n", - "43 Stephen Stanley Pierpont Securities 1.0 2.1\n", - "44 Susan M. Sterne Economic Analysis Ass... 2.2 1.9\n", - "45 Diane Swonk Mesirow Financial 1.3 1.5\n", - "46 Carl Tannenbaum The Northern Trust 1.7 2.0\n", - "47 Bart van Ark The Conference Board 1.6 1.6\n", - "48 Brian S. Wesbury/ Rob... First Trust Advisors,... 2.5 3.0\n", - "49 William T. Wilson Skolkovo Institute fo... 1.9 2.2\n", - "50 Lawrence Yun National Association ... 1.7 2.1" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Pandas methods are NaN aware, so we can just get the median." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "median_forecast = forecasts[['gdp_q3_2012', 'gdp_q4_2012']].median()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "median_forecast" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "gdp_q3_2012 1.8\n", - "gdp_q4_2012 1.8" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Economics State Variables from FRED" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Job Growth (Nonfarm-payrolls) **PAYEMS**
\n", - "Personal Income **PI**
\n", - "Industrial production **INDPRO**
\n", - "Consumption **PCEC96**
\n", - "Inflation **CPIAUCSL**
" - ] - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "from pandas.io.data import DataReader" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "series = dict(jobs = \"PAYEMS\",\n", - " income = \"PI\",\n", - " prod = \"INDPRO\",\n", - " cons = \"PCEC96\",\n", - " prices = \"CPIAUCSL\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#indicators = []\n", - "#for variable in series:\n", - "# data = DataReader(series[variable], \"fred\", start=\"2010-1-1\")\n", - "# # renaming not necessary in master\n", - "# data.rename(columns={\"VALUE\" : variable}, inplace=True)\n", - "# indicators.append(data)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#indicators = pandas.concat(indicators, axis=1)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#indicators" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 15 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Polling Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I used Python to scrape the [Real Clear Politics](realclearpolitics.com) website and download data for the 2004 and 2008 elections. The scraping scripts are available in the github repository for this talk. State by state historical data for the 2004 and 2008 Presidential elections was obtained from [electoral-vote.com](www.electorical-vote.com)." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Polling Average" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Details can be found at the 538 blog [here](http://www.fivethirtyeight.com/2008/03/pollster-ratings-updated.html)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tossup = [\"Colorado\", \"Florida\", \"Iowa\", \"New Hampshire\", \"Nevada\", \n", - " \"Ohio\", \"Virginia\", \"Wisconsin\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "national_data2012 = pandas.read_table(\"/home/skipper/school/seaboldgit/talks/pydata/\"\n", - " \"data/2012_poll_data.csv\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "national_data2012" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "\n", - "Int64Index: 290 entries, 0 to 289\n", - "Data columns:\n", - "Poll 290 non-null values\n", - "Date 290 non-null values\n", - "Sample 290 non-null values\n", - "MoE 290 non-null values\n", - "Obama (D) 290 non-null values\n", - "Romney (R) 290 non-null values\n", - "Spread 290 non-null values\n", - "dtypes: float64(2), object(5)" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "national_data2012.rename(columns={\"Poll\" : \"Pollster\"}, inplace=True)\n", - "national_data2012[\"obama_spread\"] = national_data2012[\"Obama (D)\"] - national_data2012[\"Romney (R)\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "national_data2012[\"State\"] = \"USA\"" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012 = pandas.read_table(\"/home/skipper/school/seaboldgit/talks/pydata/data/2012_poll_data_states.csv\")\n", - "state_data2012" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "\n", - "Int64Index: 767 entries, 0 to 766\n", - "Data columns:\n", - "Date 767 non-null values\n", - "MoE 767 non-null values\n", - "Obama (D) 767 non-null values\n", - "Poll 767 non-null values\n", - "Romney (R) 767 non-null values\n", - "Sample 767 non-null values\n", - "Spread 767 non-null values\n", - "State 767 non-null values\n", - "dtypes: float64(2), object(6)" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012[\"obama_spread\"] = state_data2012[\"Obama (D)\"] - state_data2012[\"Romney (R)\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.rename(columns=dict(Poll=\"Pollster\"), inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.MoE" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "0 --\n", - "1 4.5\n", - "2 4.6\n", - "3 5.0\n", - "4 4.4\n", - "5 4.4\n", - "6 4.0\n", - "7 3.0\n", - "8 5.0\n", - "9 4.4\n", - "10 4.2\n", - "11 2.8\n", - "12 4.2\n", - "13 5.0\n", - "14 4.3\n", - "...\n", - "752 2.8\n", - "753 --\n", - "754 4.0\n", - "755 3.8\n", - "756 4.5\n", - "757 4.5\n", - "758 5.0\n", - "759 3.2\n", - "760 4.5\n", - "761 4.5\n", - "762 2.4\n", - "763 3.4\n", - "764 2.9\n", - "765 3.5\n", - "766 3.4\n", - "Name: MoE, Length: 767" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.MoE = state_data2012.MoE.replace(\"--\", \"nan\").astype(float)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 26, - "text": [ - "\n", - "Int64Index: 767 entries, 0 to 766\n", - "Data columns:\n", - "Date 767 non-null values\n", - "MoE 736 non-null values\n", - "Obama (D) 767 non-null values\n", - "Pollster 767 non-null values\n", - "Romney (R) 767 non-null values\n", - "Sample 767 non-null values\n", - "Spread 767 non-null values\n", - "State 767 non-null values\n", - "obama_spread 767 non-null values\n", - "dtypes: float64(4), object(5)" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012 = state_data2012.set_index([\"Pollster\", \"State\", \"Date\"]).drop(\"RCP Average\", level=0).reset_index()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.head(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 28, - "text": [ - " Pollster State Date MoE Obama (D) Romney (R) Sample Spread obama_spread\n", - "0 Rasmussen Reports WA 9/26 - 9/26 4.5 52 41 500 LV Obama +11 11\n", - "1 Gravis Marketing WA 9/21 - 9/22 4.6 56 39 625 LV Obama +17 17\n", - "2 Elway Poll WA 9/9 - 9/12 5.0 53 36 405 RV Obama +17 17\n", - "3 SurveyUSA WA 9/7 - 9/9 4.4 54 38 524 LV Obama +16 16\n", - "4 SurveyUSA WA 8/1 - 8/2 4.4 54 37 524 LV Obama +17 17" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clean up the sample numbers to make it a number." + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " app.launch_new_instance()\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.Sample" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "0 500 LV\n", - "1 625 LV\n", - "2 405 RV\n", - "3 524 LV\n", - "4 524 LV\n", - "5 630 RV\n", - "6 1073 RV\n", - "7 408 RV\n", - "8 500 LV\n", - "9 557 RV\n", - "10 1264 RV\n", - "11 572 RV\n", - "12 405 RV\n", - "13 549 LV\n", - "14 469 RV\n", - "...\n", - "724 600 LV\n", - "725 1224 RV\n", - "726 625 LV\n", - "727 656 LV\n", - "728 500 LV\n", - "729 500 LV\n", - "730 450 LV\n", - "731 934 RV\n", - "732 500 LV\n", - "733 500 LV\n", - "734 1625 RV\n", - "735 819 RV\n", - "736 1176 RV\n", - "737 796 LV\n", - "738 817 RV\n", - "Name: Sample, Length: 739" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.Sample = state_data2012.Sample.str.replace(\"\\s*([L|R]V)|A\", \"\") # 20 RV\n", - "state_data2012.Sample = state_data2012.Sample.str.replace(\"\\s*--\", \"nan\") # --\n", - "state_data2012.Sample = state_data2012.Sample.str.replace(\"^$\", \"nan\")\n", + "data": { + "text/html": [ + "
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PollsterStateObama (D)Romney (R)Samplepoll_datecumulative
258Public Policy Polling (PPP)AZ44539932012-09-08993
259Public Policy Polling (PPP)AZ41528332012-07-241826
260Public Policy Polling (PPP)AZ43505002012-05-192326
261Public Policy Polling (PPP)AZ47477432012-02-183069
262Public Policy Polling (PPP)AZ42495002011-11-193569
263Public Policy Polling (PPP)AZ44486232011-04-304192
264Public Policy Polling (PPP)AZ43495992011-01-294791
265Public Policy Polling (PPP)AZ43506172010-09-205408
\n", + "
" + ], + "text/plain": [ + " Pollster State Obama (D) Romney (R) Sample \\\n", + "258 Public Policy Polling (PPP) AZ 44 53 993 \n", + "259 Public Policy Polling (PPP) AZ 41 52 833 \n", + "260 Public Policy Polling (PPP) AZ 43 50 500 \n", + "261 Public Policy Polling (PPP) AZ 47 47 743 \n", + "262 Public Policy Polling (PPP) AZ 42 49 500 \n", + "263 Public Policy Polling (PPP) AZ 44 48 623 \n", + "264 Public Policy Polling (PPP) AZ 43 49 599 \n", + "265 Public Policy Polling (PPP) AZ 43 50 617 \n", + "\n", + " poll_date cumulative \n", + "258 2012-09-08 993 \n", + "259 2012-07-24 1826 \n", + "260 2012-05-19 2326 \n", + "261 2012-02-18 3069 \n", + "262 2011-11-19 3569 \n", + "263 2011-04-30 4192 \n", + "264 2011-01-29 4791 \n", + "265 2010-09-20 5408 " + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ppp_az[\"cumulative\"] = ppp_az[\"Sample\"].cumsum()\n", + "ppp_az[\"average_error\"] = average_error(ppp_az[\"cumulative\"])\n", + "ppp_az[\"total_error\"] = ppp_az[\"PIE\"] + ppp_az[\"average_error\"]\n", + "ppp_az[var_idx + [\"cumulative\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "national_data2012.Sample = national_data2012.Sample.str.replace(\"\\s*([L|R]V)|A\", \"\") # 20 RV\n", - "national_data2012.Sample = national_data2012.Sample.str.replace(\"\\s*--\", \"nan\") # --\n", - "national_data2012.Sample = national_data2012.Sample.str.replace(\"^$\", \"nan\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 30 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.Sample.astype(float)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "0 500\n", - "1 625\n", - "2 405\n", - "3 524\n", - "4 524\n", - "5 630\n", - "6 1073\n", - "7 408\n", - "8 500\n", - "9 557\n", - "10 1264\n", - "11 572\n", - "12 405\n", - "13 549\n", - "14 469\n", - "...\n", - "724 600\n", - "725 1224\n", - "726 625\n", - "727 656\n", - "728 500\n", - "729 500\n", - "730 450\n", - "731 934\n", - "732 500\n", - "733 500\n", - "734 1625\n", - "735 819\n", - "736 1176\n", - "737 796\n", - "738 817\n", - "Name: Sample, Length: 739" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.Sample = state_data2012.Sample.astype(float)\n", - "national_data2012.Sample = national_data2012.Sample.astype(float)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 32 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 2012 data is currently in order of time by state but doesn't have any years." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#dates2012.get_group((\"OH\", \"NBC News/Marist\"))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012[\"start_date\"] = \"\"\n", - "state_data2012[\"end_date\"] = \"\"\n", - "dates2012 = state_data2012.groupby([\"State\", \"Pollster\"])[\"Date\"]\n", - "for _, date in dates2012:\n", - " year = 2012\n", - " # checked by hand, none straddle years\n", - " changes = np.r_[False, np.diff(map(int, [i[0].split('/')[0] for \n", - " i in date.str.split(' - ')])) > 0]\n", - " for j, (idx, dt) in enumerate(date.iteritems()):\n", - " dt1, dt2 = dt.split(\" - \")\n", - " year -= changes[j]\n", - " # check for ones that haven't polled in a year - soft check\n", - " # could be wrong for some...\n", - " if year == 2012 and (int(dt1.split(\"/\")[0]) > today.month and \n", - " int(dt1.split(\"/\")[1]) > today.day):\n", - " year -= 1\n", - " dt1 += \"/\" + str(year)\n", - " dt2 += \"/\" + str(year)\n", - " state_data2012.set_value(idx, \"start_date\", dt1)\n", - " state_data2012.set_value(idx, \"end_date\", dt2)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "national_data2012[\"start_date\"] = \"\"\n", - "national_data2012[\"end_date\"] = \"\"\n", - "dates2012 = national_data2012.groupby([\"Pollster\"])[\"Date\"]\n", - "for _, date in dates2012:\n", - " year = 2012\n", - " # checked by hand, none straddle years\n", - " changes = np.r_[False, np.diff(map(int, [i[0].split('/')[0] for \n", - " i in date.str.split(' - ')])) > 0]\n", - " for j, (idx, dt) in enumerate(date.iteritems()):\n", - " dt1, dt2 = dt.split(\" - \")\n", - " year -= changes[j]\n", - " dt1 += \"/\" + str(year)\n", - " dt2 += \"/\" + str(year)\n", - " national_data2012.set_value(idx, \"start_date\", dt1)\n", - " national_data2012.set_value(idx, \"end_date\", dt2)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.head(10)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 36, - "text": [ - " Pollster State Date MoE Obama (D) Romney (R) Sample Spread obama_spread start_date end_date\n", - "0 Rasmussen Reports WA 9/26 - 9/26 4.5 52 41 500 Obama +11 11 9/26/2012 9/26/2012\n", - "1 Gravis Marketing WA 9/21 - 9/22 4.6 56 39 625 Obama +17 17 9/21/2012 9/22/2012\n", - "2 Elway Poll WA 9/9 - 9/12 5.0 53 36 405 Obama +17 17 9/9/2012 9/12/2012\n", - "3 SurveyUSA WA 9/7 - 9/9 4.4 54 38 524 Obama +16 16 9/7/2012 9/9/2012\n", - "4 SurveyUSA WA 8/1 - 8/2 4.4 54 37 524 Obama +17 17 8/1/2012 8/2/2012\n", - "5 SurveyUSA WA 7/16 - 7/18 4.0 46 37 630 Obama +9 9 7/16/2012 7/18/2012\n", - "6 PPP (D) WA 6/14 - 6/17 3.0 54 41 1073 Obama +13 13 6/14/2012 6/17/2012\n", - "7 Elway Poll WA 6/13 - 6/16 5.0 49 41 408 Obama +8 8 6/13/2012 6/16/2012\n", - "8 Strategies 360 (D) WA 5/22 - 5/24 4.4 51 40 500 Obama +11 11 5/22/2012 5/24/2012\n", - "9 SurveyUSA WA 5/8 - 5/9 4.2 50 36 557 Obama +14 14 5/8/2012 5/9/2012" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.start_date = state_data2012.start_date.apply(pandas.datetools.parse)\n", - "state_data2012.end_date = state_data2012.end_date.apply(pandas.datetools.parse)\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " if __name__ == '__main__':\n", + "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "national_data2012.start_date = national_data2012.start_date.apply(pandas.datetools.parse)\n", - "national_data2012.end_date = national_data2012.end_date.apply(pandas.datetools.parse)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def median_date(row):\n", - " dates = pandas.date_range(row[\"start_date\"], row[\"end_date\"])\n", - " median_idx = int(np.median(range(len(dates)))+.5)\n", - " return dates[median_idx]\n", - " \n", - "state_data2012[\"poll_date\"] = [median_date(row) for i, row in state_data2012.iterrows()]\n", - "del state_data2012[\"Date\"]\n", - "del state_data2012[\"start_date\"]\n", - "del state_data2012[\"end_date\"]\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from IPython.kernel.zmq import kernelapp as app\n", + "//anaconda/lib/python2.7/site-packages/pandas/core/generic.py:2602: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "national_data2012[\"poll_date\"] = [median_date(row) for i, row in national_data2012.iterrows()]\n", - "del national_data2012[\"Date\"]\n", - "del national_data2012[\"start_date\"]\n", - "del national_data2012[\"end_date\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 38 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.head(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 39, - "text": [ - " Pollster State MoE Obama (D) Romney (R) Sample Spread obama_spread poll_date\n", - "0 Rasmussen Reports WA 4.5 52 41 500 Obama +11 11 2012-09-26 00:00:00\n", - "1 Gravis Marketing WA 4.6 56 39 625 Obama +17 17 2012-09-22 00:00:00\n", - "2 Elway Poll WA 5.0 53 36 405 Obama +17 17 2012-09-11 00:00:00\n", - "3 SurveyUSA WA 4.4 54 38 524 Obama +16 16 2012-09-08 00:00:00\n", - "4 SurveyUSA WA 4.4 54 37 524 Obama +17 17 2012-08-02 00:00:00" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pollsters = state_data2012.Pollster.unique()\n", - "pollsters.sort()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "len(pollsters)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "120" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print pandas.Series(pollsters)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0 AFP/Magellan (R)\n", - "1 AIF/McLaughlin (R)\n", - "2 ARG\n", - "3 Albuquerque Journal*\n", - "4 Arizona State\n", - "5 Baltimore Sun\n", - "6 Baydoun/Foster (D)\n", - "7 Behavior Research Center\n", - "8 Bloomberg News\n", - "9 Boston Globe\n", - "10 CBS/NYT/Quinnipiac\n", - "11 CNN/Opinion Research\n", - "12 CNN/Time\n", - "13 CNU/Times-Dispatch\n", - "14 Caddell/McLaughlin/SA...\n", - "15 Castleton State College\n", - "16 Chicago Tribune\n", - "17 Civitas (R)\n", - "18 Clarus Research\n", - "19 Columbus Dispatch*\n", - "20 Courier-Journal/Surve...\n", - "21 Critical Insights\n", - "22 Daily Kos/PPP (D)\n", - "23 Dartmouth\n", - "24 Denver Post/SurveyUSA\n", - "25 Des Moines Register\n", - "26 Deseret News\n", - "27 Deseret News/KSL\n", - "28 Detroit News\n", - "29 EPIC-MRA\n", - "30 Elon Univ./Charlotte ...\n", - "31 Elway Poll\n", - "32 FOX Chicago/WAA\n", - "33 FOX News\n", - "34 Fairleigh Dickinson\n", - "35 Field\n", - "36 Florida Times-Union/I...\n", - "37 Franklin & Marshall\n", - "38 Glengariff Group (R)\n", - "39 Gonzales Research\n", - "40 Gravis Marketing\n", - "41 Gravis Marketing*\n", - "42 Hartford Courant/UConn\n", - "43 High Point\n", - "44 High Point/SurveyUSA\n", - "45 HighGround/Moore (R)*\n", - "46 Howey/DePauw\n", - "47 Inside MI Politcs/MRG\n", - "48 InsiderAdvantage\n", - "49 KSTP/SurveyUSA\n", - "50 Keating (D)\n", - "51 LA Times/USC\n", - "52 LVRJ/SurveyUSA\n", - "53 Landmark/Rosetta Stone\n", - "54 Las Vegas Review-Journal\n", - "55 MPRC (D)\n", - "56 MPRC (D)*\n", - "57 MRG\n", - "58 Magellan (R)\n", - "59 Magellan Strategies (R)\n", - "60 Marist\n", - "61 Marquette University\n", - "62 Mason-Dixon\n", - "63 Mason-Dixon*\n", - "64 Mass Insight/Opinion ...\n", - "65 Mercyhurst University\n", - "66 Miami Herald/Mason-Dixon\n", - "67 Middle Tn. State U.\n", - "68 Mitchell Research\n", - "69 Monmouth University\n", - "70 Morning Call\n", - "71 NBC News/Marist\n", - "72 NBC/WSJ/Marist\n", - "73 Ohio Newspapers/Univ ...\n", - "74 Ohio Poll/Univ of Cin.\n", - "75 Omaha World-Herald\n", - "76 PPIC\n", - "77 PPP (D)\n", - "78 Philadelphia Inquirer\n", - "79 Post-Dispatch/Mason-D...\n", - "80 Post-Dispatch/Mason-D...\n", - "81 Project New America/K...\n", - "82 Project New America/M...\n", - "83 Project New America/P...\n", - "84 Purple Strategies\n", - "85 Quinnipiac\n", - "86 Rasmussen Reports\n", - "87 Retail Assoc. of Neva...\n", - "88 Roanoke College\n", - "89 Rutgers-Eagleton\n", - "90 Siena\n", - "91 Sooner Poll\n", - "92 St. Cloud State U.\n", - "93 Star Tribune/Mason-Di...\n", - "94 Strategies 360 (D)\n", - "95 Suffolk University\n", - "96 Suffolk/7News\n", - "97 Suffolk/WSVN\n", - "98 Suffolk/WWBT\n", - "99 Sunshine State News/VSS\n", - "100 SurveyUSA\n", - "101 SurveyUSA/Civitas (R)\n", - "102 Talk Business Poll\n", - "103 Tennessean/Vanderbilt\n", - "104 The Simon Poll/SIU\n", - "105 The Washington Poll\n", - "106 Tribune-Review/Susque...\n", - "107 UMass/Boston Herald\n", - "108 Virginian-Pilot/ODU\n", - "109 Voter/Consumer Res/TI...\n", - "110 WBUR/MassINC\n", - "111 WMUR/UNH\n", - "112 WPA\n", - "113 WPR/St. Norbert\n", - "114 WPRI\n", - "115 WPRI/Fleming\n", - "116 Washington Post\n", - "117 WeAskAmerica\n", - "118 WeAskAmerica*\n", - "119 Western NE University\n", - "Length: 120\n" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "538 Pollster Ratings" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "weights = pandas.read_table(\"/home/skipper/school/seaboldgit/talks/pydata/data/pollster_weights.csv\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 43 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "weights" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 44, - "text": [ - " Pollster Weight PIE\n", - "0 ABC / Washington Post 0.95 1.41\n", - "1 American Research Group 0.65 1.76\n", - "2 CBS / New York Times 0.66 1.84\n", - "3 Chicago Trib. / Marke... 1.16 1.13\n", - "4 CNN / Opinion Research 0.77 1.59\n", - "5 Columbus Dispatch (OH) 0.50 6.76\n", - "6 EPIC-MRA 0.75 1.65\n", - "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", - "8 Field Poll (CA) 1.33 0.88\n", - "9 Fox / Opinion Dynamics 0.79 1.60\n", - "10 Franklin Pierce (NH) 0.74 1.60\n", - "11 Insider Advantage 0.95 1.29\n", - "12 Keystone (PA) 0.64 1.55\n", - "13 LA Times / Bloomberg 0.83 1.44\n", - "14 Marist (NY) 0.69 1.73\n", - "15 Mason-Dixon 1.10 1.15\n", - "16 Mitchell 0.96 1.43\n", - "17 Ohio Poll 1.24 1.05\n", - "18 Public Opinion Strate... 0.63 1.81\n", - "19 Public Policy Polling... 1.05 1.60\n", - "20 Quinnipiac 0.95 1.34\n", - "21 Rasmussen 1.30 0.88\n", - "22 Research 2000 1.01 1.20\n", - "23 Selzer 1.47 0.92\n", - "24 Star Tribune (MN) 0.81 2.01\n", - "25 Strategic Vision 0.95 1.45\n", - "26 Suffolk (NH/MA) 0.77 1.37\n", - "27 SurveyUSA 1.91 0.72\n", - "28 Univ. New Hampshire 1.08 1.26\n", - "29 USA Today / Gallup 0.63 2.01\n", - "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74" - ] - } - ], - "prompt_number": 44 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "weights.mean()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 45, - "text": [ - "Weight 0.908\n", - "PIE 1.707" - ] - } - ], - "prompt_number": 45 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clean up the pollster names a bit so we can merge with the weights." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pickle\n", - "pollster_map = pickle.load(open(\"/home/skipper/school/seaboldgit/talks/pydata/data/pollster_map.pkl\", \"rb\"))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 46 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.Pollster.replace(pollster_map, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 47 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "national_data2012.Pollster.replace(pollster_map, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 48 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Inner merge the data with the weights" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012 = state_data2012.merge(weights, how=\"inner\", on=\"Pollster\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 49 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.head(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 50, - "text": [ - " Pollster State MoE Obama (D) Romney (R) Sample Spread obama_spread poll_date Weight PIE\n", - "0 American Research Group FL 4.0 50 45 600 Obama +5 5 2012-09-21 00:00:00 0.65 1.76\n", - "1 American Research Group NH 4.0 50 45 600 Obama +5 5 2012-09-26 00:00:00 0.65 1.76\n", - "2 American Research Group NH 4.5 48 47 463 Obama +1 1 2012-09-16 00:00:00 0.65 1.76\n", - "3 American Research Group NH 4.2 49 46 417 Obama +3 3 2012-06-23 00:00:00 0.65 1.76\n", - "4 American Research Group NH 4.2 48 41 557 Obama +7 7 2012-03-17 00:00:00 0.65 1.76" - ] - } - ], - "prompt_number": 50 - }, - { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "First, we average each pollster for each state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first adjustment is an exponential decay for recency of the poll. Based on research in prior elections, a weight with a half-life of 30 days since the median date the poll has been in the field is assigned to each poll." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def exp_decay(days):\n", - " # defensive coding, accepts timedeltas\n", - " days = getattr(days, \"days\", days)\n", - " return .5 ** (days/30.)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 51 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(figsize=(12,8), subplot_kw={\"xlabel\" : \"Days\",\n", - " \"ylabel\" : \"Weight\"})\n", - "days = np.arange(0, 45)\n", - "ax.plot(days, exp_decay(days));\n", - "ax.vlines(30, 0, .99, color='r', linewidth=4)\n", - "ax.set_ylim(0,1)\n", - "ax.set_xlim(0, 45);" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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/vl+PPx5QLObRFVdU6YILwsrLc3plcAM61oBz6FgDQIrJzpYuuSSs1avLdP/9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\nAAAAIAH+A7iJhoHYvnKbAAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 52 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second adjustment is for the sample size of the poll. Polls with a higher sample size receive a higher weight." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Binomial sampling error = +/- $50 * \\frac{1}{\\sqrt{nobs}}$ where the 50 depends on the underlying probability or population preferences, in this case assumed to be 50:50 (another way of calculating Margin of Error)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def average_error(nobs, p=50.):\n", - " return p*nobs**-.5" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 53 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The thinking here is that having 5 polls of 1200 is a lot like having one poll of 6000. However, we downweight older polls by only including the marginal effective sample size. Where the effective sample size is the size of the methodologically perfect poll for which we would be indifferent between it and the one we have with our current total error. Total error is determined as $TE = \\text{Average Error} + \\text{Long Run Pollster Induced Error}$. See [here](http://www.fivethirtyeight.com/2008/04/pollster-ratings-v30.html) for the detailed calculations of Pollster Induced Error." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def effective_sample(total_error, p=50.):\n", - " return p**2 * (total_error**-2.)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_pollsters = state_data2012.groupby([\"State\", \"Pollster\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 55 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ppp_az = state_pollsters.get_group((\"AZ\", \"Public Policy Polling (PPP)\"))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 56 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "var_idx = [\"Pollster\", \"State\", \"Obama (D)\", \"Romney (R)\", \"Sample\", \"poll_date\"]\n", - "ppp_az[var_idx]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 57, - "text": [ - " Pollster State Obama (D) Romney (R) Sample poll_date\n", - "198 Public Policy Polling... AZ 44 53 993 2012-09-08 00:00:00\n", - "199 Public Policy Polling... AZ 41 52 833 2012-07-24 00:00:00\n", - "200 Public Policy Polling... AZ 43 50 500 2012-05-19 00:00:00\n", - "201 Public Policy Polling... AZ 47 47 743 2012-02-18 00:00:00\n", - "202 Public Policy Polling... AZ 42 49 500 2011-11-19 00:00:00\n", - "203 Public Policy Polling... AZ 44 48 623 2011-04-30 00:00:00\n", - "204 Public Policy Polling... AZ 43 49 599 2011-01-29 00:00:00\n", - "205 Public Policy Polling... AZ 43 50 617 2010-09-20 00:00:00" - ] - } - ], - "prompt_number": 57 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ppp_az.sort(\"poll_date\", ascending=False, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 58 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ppp_az[\"cumulative\"] = ppp_az[\"Sample\"].cumsum()\n", - "ppp_az[\"average_error\"] = average_error(ppp_az[\"cumulative\"])\n", - "ppp_az[\"total_error\"] = ppp_az[\"PIE\"] + ppp_az[\"average_error\"]\n", - "ppp_az[var_idx + [\"cumulative\"]]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 59, - "text": [ - " Pollster State Obama (D) Romney (R) Sample poll_date cumulative\n", - "198 Public Policy Polling... AZ 44 53 993 2012-09-08 00:00:00 993\n", - "199 Public Policy Polling... AZ 41 52 833 2012-07-24 00:00:00 1826\n", - "200 Public Policy Polling... AZ 43 50 500 2012-05-19 00:00:00 2326\n", - "201 Public Policy Polling... AZ 47 47 743 2012-02-18 00:00:00 3069\n", - "202 Public Policy Polling... AZ 42 49 500 2011-11-19 00:00:00 3569\n", - "203 Public Policy Polling... AZ 44 48 623 2011-04-30 00:00:00 4192\n", - "204 Public Policy Polling... AZ 43 49 599 2011-01-29 00:00:00 4791\n", - "205 Public Policy Polling... AZ 43 50 617 2010-09-20 00:00:00 5408" - ] - } - ], - "prompt_number": 59 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ppp_az[\"ESS\"] = effective_sample(ppp_az[\"total_error\"])\n", - "ppp_az[\"MESS\"] = ppp_az[\"ESS\"].diff()\n", - "# fill in first one\n", - "ppp_az[\"MESS\"].fillna(ppp_az[\"ESS\"].head(1).item(), inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 60 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ppp_az[[\"poll_date\", \"Sample\", \"cumulative\", \"ESS\", \"MESS\"]]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 61, - "text": [ - " poll_date Sample cumulative ESS MESS\n", - "198 2012-09-08 00:00:00 993 993 246.182 246.182\n", - "199 2012-07-24 00:00:00 833 1826 325.801 79.618\n", - "200 2012-05-19 00:00:00 500 2326 359.591 33.791\n", - "201 2012-02-18 00:00:00 743 3069 399.185 39.594\n", - "202 2011-11-19 00:00:00 500 3569 420.968 21.783\n", - "203 2011-04-30 00:00:00 623 4192 444.241 23.273\n", - "204 2011-01-29 00:00:00 599 4791 463.531 19.291\n", - "205 2010-09-20 00:00:00 617 5408 480.955 17.424" - ] - } - ], - "prompt_number": 61 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's do it for every polling firm in every state." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def calculate_mess(group):\n", - " cumulative = group[\"Sample\"].cumsum()\n", - " ae = average_error(cumulative)\n", - " total_error = ae + group[\"PIE\"]\n", - " ess = effective_sample(total_error)\n", - " mess = ess.diff()\n", - " mess.fillna(ess.head(1).item(), inplace=True)\n", - " #from IPython.core.debugger import Pdb; Pdb().set_trace()\n", - " return pandas.concat((ess, mess), axis=1)\n", - "\n", - "#state_data2012[\"ESS\", \"MESS\"] \n", - "df = state_pollsters.apply(calculate_mess)\n", - "df.rename(columns={0 : \"ESS\", 1 : \"MESS\"}, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 62 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012 = state_data2012.join(df)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 63 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Give them the time weight" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "td = today - state_data2012[\"poll_date\"].head(1).item()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 64 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012[\"poll_date\"].head(1).item()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 65, - "text": [ - "" - ] - } - ], - "prompt_number": 65 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "td" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 66, - "text": [ - "datetime.timedelta(11)" - ] - } - ], - "prompt_number": 66 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012[\"time_weight\"] = (today - state_data2012[\"poll_date\"]).apply(exp_decay)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 67 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now aggregate all of these. Weight them based on the sample size but also based on the time_weight." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def weighted_mean(group):\n", - " weights1 = group[\"time_weight\"]\n", - " weights2 = group[\"MESS\"]\n", - " return np.sum(weights1*weights2*group[\"obama_spread\"]/(weights1*weights2).sum())" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 68 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_pollsters = state_data2012.groupby([\"State\", \"Pollster\"])\n", - "state_polls = state_pollsters.apply(weighted_mean)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 69 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_polls" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 70, - "text": [ - "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168\n", - " Rasmussen -10.209\n", - "CA Field Poll (CA) 23.344\n", - " Public Policy Polling (PPP) 20.999\n", - " Rasmussen 22.000\n", - " SurveyUSA 22.123\n", - "CO American Research Group 2.000\n", - " Public Policy Polling (PPP) 5.470\n", - " Rasmussen -1.574\n", - "CT Public Policy Polling (PPP) 12.758\n", - " Quinnipiac 7.294\n", - " Rasmussen 8.000\n", - "FL American Research Group 5.000\n", - " Mason-Dixon -3.543\n", - " Public Policy Polling (PPP) 3.125\n", - " Quinnipiac 3.076\n", - " Rasmussen 0.883\n", - " Suffolk (NH/MA) -0.003\n", - " SurveyUSA 4.169\n", - "GA Insider Advantage -19.174\n", - " Mason-Dixon -17.000\n", - " Public Policy Polling (PPP) -3.000\n", - " SurveyUSA -7.984\n", - "HI Public Policy Polling (PPP) 27.000\n", - "IA American Research Group 7.000\n", - " Mason-Dixon -3.000\n", - " Public Policy Polling (PPP) 5.879\n", - " Rasmussen -2.749\n", - "IL Chicago Trib. / MarketShares 21.000\n", - "IN Rasmussen -16.000\n", - "KS SurveyUSA -15.875\n", - "MA Public Policy Polling (PPP) 17.580\n", - " Rasmussen 15.107\n", - "MD Public Policy Polling (PPP) 23.000\n", - "ME Public Policy Polling (PPP) 16.038\n", - " Rasmussen 12.000\n", - "MI CNN / Opinion Research 8.000\n", - " EPIC-MRA 7.430\n", - " Mitchell 0.897\n", - " Public Policy Polling (PPP) 7.694\n", - " Rasmussen 11.072\n", - " SurveyUSA 11.000\n", - "MN Public Policy Polling (PPP) 7.335\n", - "MO Public Policy Polling (PPP) -11.225\n", - " Rasmussen -2.486\n", - " SurveyUSA -1.000\n", - "MS Public Policy Polling (PPP) -17.973\n", - "MT Mason-Dixon -9.000\n", - " Public Policy Polling (PPP) -5.003\n", - " Rasmussen -15.641\n", - "NC American Research Group -4.000\n", - " Public Policy Polling (PPP) 0.261\n", - " Rasmussen -5.676\n", - " SurveyUSA 1.987\n", - "ND Mason-Dixon -13.000\n", - " Rasmussen -15.000\n", - "NE Public Policy Polling (PPP) -12.005\n", - " Rasmussen -14.308\n", - "NH American Research Group 4.150\n", - " LA Times / Bloomberg -10.000\n", - " Mason-Dixon -11.000\n", - " Public Policy Polling (PPP) 6.273\n", - " Rasmussen -2.439\n", - "NJ Fairleigh-Dickinson (NJ) 13.859\n", - " Public Policy Polling (PPP) 14.006\n", - " Quinnipiac 7.504\n", - " Rasmussen 6.000\n", - " SurveyUSA 14.000\n", - "NM Public Policy Polling (PPP) 10.621\n", - " Rasmussen 11.651\n", - "NV American Research Group 7.000\n", - " CNN / Opinion Research 3.000\n", - " Public Policy Polling (PPP) 7.345\n", - " Rasmussen 2.524\n", - "NY Marist (NY) 22.047\n", - " Quinnipiac 27.345\n", - " SurveyUSA 30.000\n", - "OH American Research Group 1.000\n", - " Columbus Dispatch (OH) 8.616\n", - " Ohio Poll 3.000\n", - " Public Policy Polling (PPP) 4.142\n", - " Quinnipiac 7.729\n", - " Rasmussen 0.866\n", - "OR Public Policy Polling (PPP) 9.130\n", - " SurveyUSA 8.676\n", - "PA Public Policy Polling (PPP) 6.160\n", - " Quinnipiac 6.047\n", - " Rasmussen 10.875\n", - " SurveyUSA 0.000\n", - "RI Public Policy Polling (PPP) 17.000\n", - "SC Public Policy Polling (PPP) -14.558\n", - "SD Public Policy Polling (PPP) -6.000\n", - "TN Public Policy Polling (PPP) -7.000\n", - "TX Public Policy Polling (PPP) -6.999\n", - "UT Mason-Dixon -51.000\n", - " Public Policy Polling (PPP) -32.000\n", - "VA American Research Group 2.000\n", - " Mason-Dixon 1.000\n", - " Public Policy Polling (PPP) 5.096\n", - " Quinnipiac 0.578\n", - " Rasmussen 0.892\n", - "VT Public Policy Polling (PPP) 20.000\n", - "WA Public Policy Polling (PPP) 13.051\n", - " Rasmussen 11.000\n", - " SurveyUSA 15.310\n", - "WI CNN / Opinion Research 4.000\n", - " Public Policy Polling (PPP) 5.393\n", - " Rasmussen 2.116\n", - "WV Public Policy Polling (PPP) -19.757\n", - "Length: 109" - ] - } - ], - "prompt_number": 70 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "2004 and 2008 Polls" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004 = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/2004-pres-polls.csv\")\n", - "state_data2004" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 71, - "text": [ - "\n", - "Int64Index: 879 entries, 0 to 878\n", - "Data columns:\n", - "State 879 non-null values\n", - "Kerry 879 non-null values\n", - "Bush 879 non-null values\n", - "Date 879 non-null values\n", - "Pollster 879 non-null values\n", - "dtypes: int64(2), object(3)" - ] - } - ], - "prompt_number": 71 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004.head(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 72, - "text": [ - " State Kerry Bush Date Pollster\n", - "0 AL 39 57 Oct 25 SurveyUSA\n", - "1 AL 32 56 Oct 12 Capital Survey\n", - "2 AL 34 62 Oct 01 SurveyUSA\n", - "3 AL 40 54 Sep 14 ARG\n", - "4 AL 42 53 Sep 06 Rasmussen" - ] - } - ], - "prompt_number": 72 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008 = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/2008-pres-polls.csv\")\n", - "state_data2008" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 73, - "text": [ - "\n", - "Int64Index: 1189 entries, 0 to 1188\n", - "Data columns:\n", - "State 1189 non-null values\n", - "Obama 1189 non-null values\n", - "McCain 1189 non-null values\n", - "Start 1189 non-null values\n", - "End 1189 non-null values\n", - "Pollster 1189 non-null values\n", - "dtypes: int64(2), object(4)" - ] - } - ], - "prompt_number": 73 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008.head(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 74, - "text": [ - " State Obama McCain Start End Pollster\n", - "0 AL 36 61 Oct 27 Oct 28 SurveyUSA\n", - "1 AL 34 54 Oct 15 Oct 16 Capital Survey\n", - "2 AL 35 62 Oct 08 Oct 09 SurveyUSA\n", - "3 AL 35 55 Oct 06 Oct 07 Capital Survey\n", - "4 AL 39 60 Sep 22 Sep 22 Rasmussen" - ] - } - ], - "prompt_number": 74 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008.End + \" 2008\"" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 75, - "text": [ - "0 Oct 28 2008\n", - "1 Oct 16 2008\n", - "2 Oct 09 2008\n", - "3 Oct 07 2008\n", - "4 Sep 22 2008\n", - "5 Sep 17 2008\n", - "6 Sep 16 2008\n", - "7 Sep 15 2008\n", - "8 Sep 09 2008\n", - "9 Aug 04 2008\n", - "10 Jul 31 2008\n", - "11 Jun 26 2008\n", - "12 Jun 26 2008\n", - "13 Jun 02 2008\n", - "14 May 27 2008\n", - "...\n", - "1174 May 05 2008\n", - "1175 Apr 24 2008\n", - "1176 Apr 13 2008\n", - "1177 Apr 05 2008\n", - "1178 Mar 26 2008\n", - "1179 Mar 16 2008\n", - "1180 Feb 28 2008\n", - "1181 Feb 21 2008\n", - "1182 Feb 17 2008\n", - "1183 Oct 19 2008\n", - "1184 Oct 14 2008\n", - "1185 Sep 11 2008\n", - "1186 Sep 10 2008\n", - "1187 Aug 15 2008\n", - "1188 Feb 28 2008\n", - "Name: End, Length: 1189" - ] - } - ], - "prompt_number": 75 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "(state_data2008.End + \" 2008\").apply(pandas.datetools.parse)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 76, - "text": [ - "0 2008-10-28 00:00:00\n", - "1 2008-10-16 00:00:00\n", - "2 2008-10-09 00:00:00\n", - "3 2008-10-07 00:00:00\n", - "4 2008-09-22 00:00:00\n", - "5 2008-09-17 00:00:00\n", - "6 2008-09-16 00:00:00\n", - "7 2008-09-15 00:00:00\n", - "8 2008-09-09 00:00:00\n", - "9 2008-08-04 00:00:00\n", - "10 2008-07-31 00:00:00\n", - "11 2008-06-26 00:00:00\n", - "12 2008-06-26 00:00:00\n", - "13 2008-06-02 00:00:00\n", - "14 2008-05-27 00:00:00\n", - "...\n", - "1174 2008-05-05 00:00:00\n", - "1175 2008-04-24 00:00:00\n", - "1176 2008-04-13 00:00:00\n", - "1177 2008-04-05 00:00:00\n", - "1178 2008-03-26 00:00:00\n", - "1179 2008-03-16 00:00:00\n", - "1180 2008-02-28 00:00:00\n", - "1181 2008-02-21 00:00:00\n", - "1182 2008-02-17 00:00:00\n", - "1183 2008-10-19 00:00:00\n", - "1184 2008-10-14 00:00:00\n", - "1185 2008-09-11 00:00:00\n", - "1186 2008-09-10 00:00:00\n", - "1187 2008-08-15 00:00:00\n", - "1188 2008-02-28 00:00:00\n", - "Name: End, Length: 1189" - ] - } - ], - "prompt_number": 76 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Need to clean some of the dates in this data. Luckily, pandas makes this easy to do." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004.Date = state_data2004.Date.str.replace(\"Nov 00\", \"Nov 01\")\n", - "state_data2004.Date = state_data2004.Date.str.replace(\"Oct 00\", \"Oct 01\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 77 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008[\"poll_date\"] = (state_data2008.End + \" 2008\").apply(pandas.datetools.parse)\n", - "state_data2004[\"poll_date\"] = (state_data2004.Date + \" 2004\").apply(pandas.datetools.parse)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 78 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "del state_data2008[\"End\"]\n", - "del state_data2008[\"Start\"]\n", - "del state_data2004[\"Date\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 79 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 80, - "text": [ - "\n", - "Int64Index: 1189 entries, 0 to 1188\n", - "Data columns:\n", - "State 1189 non-null values\n", - "Obama 1189 non-null values\n", - "McCain 1189 non-null values\n", - "Pollster 1189 non-null values\n", - "poll_date 1189 non-null values\n", - "dtypes: int64(2), object(3)" - ] - } - ], - "prompt_number": 80 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 81, - "text": [ - "\n", - "Int64Index: 879 entries, 0 to 878\n", - "Data columns:\n", - "State 879 non-null values\n", - "Kerry 879 non-null values\n", - "Bush 879 non-null values\n", - "Pollster 879 non-null values\n", - "poll_date 879 non-null values\n", - "dtypes: int64(2), object(3)" - ] - } - ], - "prompt_number": 81 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_groups = state_data2008.groupby(\"State\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 82 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_groups.aggregate(dict(Obama=np.mean, McCain=np.mean))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 83, - "text": [ - " McCain Obama\n", - "State \n", - "AK 52.000 39.429\n", - "AL 56.826 34.348\n", - "AR 51.000 37.250\n", - "AZ 49.333 39.190\n", - "CA 37.633 53.267\n", - "CO 44.467 48.289\n", - "CT 36.923 52.692\n", - "DC 13.000 82.000\n", - "DE 38.625 55.500\n", - "FL 46.394 46.121\n", - "GA 51.346 43.154\n", - "HI 30.000 64.000\n", - "IA 41.407 50.037\n", - "ID 60.000 30.500\n", - "IL 36.900 55.600\n", - "IN 47.500 44.962\n", - "KS 53.562 37.750\n", - "KY 54.842 37.526\n", - "LA 52.167 39.083\n", - "MA 38.800 52.200\n", - "MD 38.667 53.833\n", - "ME 38.188 50.562\n", - "MI 42.053 47.368\n", - "MN 41.739 50.261\n", - "MO 47.429 45.571\n", - "MS 51.200 40.500\n", - "MT 48.214 43.857\n", - "NC 47.523 46.091\n", - "ND 45.571 42.714\n", - "NE 51.714 37.143\n", - "NH 42.757 48.919\n", - "NJ 39.767 49.767\n", - "NM 43.593 48.741\n", - "NV 44.844 46.938\n", - "NY 36.865 52.432\n", - "OH 44.975 46.658\n", - "OK 61.700 32.000\n", - "OR 40.852 50.333\n", - "PA 42.080 48.893\n", - "RI 32.000 53.000\n", - "SC 53.300 41.000\n", - "SD 50.375 39.875\n", - "TN 54.364 36.364\n", - "TX 50.200 40.400\n", - "UT 58.600 30.000\n", - "VA 45.817 47.933\n", - "VT 34.750 59.750\n", - "WA 40.424 51.515\n", - "WI 41.921 49.684\n", - "WV 48.692 42.538\n", - "WY 59.333 32.667" - ] - } - ], - "prompt_number": 83 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Means for the entire country (without weighting by population)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_groups.aggregate(dict(Obama=np.mean, McCain=np.mean)).mean()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 84, - "text": [ - "McCain 45.338\n", - "Obama 46.082" - ] - } - ], - "prompt_number": 84 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004.Pollster.replace(pollster_map, inplace=True)\n", - "state_data2008.Pollster.replace(pollster_map, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 85 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004 = state_data2004.merge(weights, how=\"inner\", on=\"Pollster\")\n", - "state_data2008 = state_data2008.merge(weights, how=\"inner\", on=\"Pollster\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 86 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "len(state_data2004.Pollster.unique())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 87, - "text": [ - "26" - ] - } - ], - "prompt_number": 87 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "len(state_data2008.Pollster.unique())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 88, - "text": [ - "21" - ] - } - ], - "prompt_number": 88 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import datetime" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 89 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "date2004 = datetime.datetime(2004, 11, 2)\n", - "date2004" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 90, - "text": [ - "datetime.datetime(2004, 11, 2, 0, 0)" - ] - } - ], - "prompt_number": 90 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "(date2004 - state_data2004.poll_date) < datetime.timedelta(21)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 91, - "text": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 True\n", - "10 True\n", - "11 False\n", - "12 False\n", - "13 False\n", - "14 False\n", - "...\n", - "719 False\n", - "720 False\n", - "721 False\n", - "722 False\n", - "723 False\n", - "724 True\n", - "725 True\n", - "726 False\n", - "727 False\n", - "728 False\n", - "729 False\n", - "730 False\n", - "731 False\n", - "732 False\n", - "733 False\n", - "Name: poll_date, Length: 734" - ] - } - ], - "prompt_number": 91 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Restrict the samples to the 3 weeks leading up to the election" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004 = state_data2004.ix[(date2004 - state_data2004.poll_date) <= datetime.timedelta(21)]\n", - "state_data2004.reset_index(drop=True, inplace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 92, - "text": [ - "\n", - "Int64Index: 213 entries, 0 to 212\n", - "Data columns:\n", - "State 213 non-null values\n", - "Kerry 213 non-null values\n", - "Bush 213 non-null values\n", - "Pollster 213 non-null values\n", - "poll_date 213 non-null values\n", - "Weight 213 non-null values\n", - "PIE 213 non-null values\n", - "dtypes: float64(2), int64(2), object(3)" - ] - } - ], - "prompt_number": 92 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "date2008 = datetime.datetime(2008, 11, 4)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 93 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008 = state_data2008.ix[(date2008 - state_data2008.poll_date) <= datetime.timedelta(21)]\n", - "state_data2008.reset_index(drop=True, inplace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 94, - "text": [ - "\n", - "Int64Index: 210 entries, 0 to 209\n", - "Data columns:\n", - "State 210 non-null values\n", - "Obama 210 non-null values\n", - "McCain 210 non-null values\n", - "Pollster 210 non-null values\n", - "poll_date 210 non-null values\n", - "Weight 210 non-null values\n", - "PIE 210 non-null values\n", - "dtypes: float64(2), int64(2), object(3)" - ] - } - ], - "prompt_number": 94 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2008" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 95, - "text": [ - "\n", - "Int64Index: 210 entries, 0 to 209\n", - "Data columns:\n", - "State 210 non-null values\n", - "Obama 210 non-null values\n", - "McCain 210 non-null values\n", - "Pollster 210 non-null values\n", - "poll_date 210 non-null values\n", - "Weight 210 non-null values\n", - "PIE 210 non-null values\n", - "dtypes: float64(2), int64(2), object(3)" - ] - } - ], - "prompt_number": 95 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004[\"time_weight\"] =(date2004 - state_data2004.poll_date).apply(exp_decay)\n", - "state_data2008[\"time_weight\"] =(date2008 - state_data2008.poll_date).apply(exp_decay)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 96 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004[[\"time_weight\", \"poll_date\"]].head(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 97, - "text": [ - " time_weight poll_date\n", - "0 0.955 2004-10-31 00:00:00\n", - "1 0.794 2004-10-23 00:00:00\n", - "2 0.831 2004-10-25 00:00:00\n", - "3 0.955 2004-10-31 00:00:00\n", - "4 0.891 2004-10-28 00:00:00" - ] - } - ], - "prompt_number": 97 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def max_date(x):\n", - " return x == x.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 98 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2004[\"newest_poll\"] = state_data2004.groupby((\"State\", \"Pollster\")).poll_date.transform(max_date)\n", - "state_data2008[\"newest_poll\"] = state_data2008.groupby((\"State\", \"Pollster\")).poll_date.transform(max_date)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 99 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Clustering States by Demographics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are notes on trend line adjustment, [here](http://www.fivethirtyeight.com/2008/06/we-know-more-than-we-think-big-change-2.html), [here](http://www.fivethirtyeight.com/2008/06/refinement-to-adjustment-part-i.html), [here](http://www.fivethirtyeight.com/2008/06/refinement-to-adjustment-part-ii.html), [here](http://www.fivethirtyeight.com/2008/06/trendline-now-calculated-from-daily.html), and [here](http://www.fivethirtyeight.com/2008/06/construction-season-over-technical.html). However, to the best of my knowledge, the similar state \"nearest neighbor\" clustering remains a black box." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Partican Voting Index data obtained from [Wikipedia](http://en.wikipedia.org/wiki/Cook_Partisan_Voting_Index)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pvi = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/partisan_voting.csv\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 100 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pvi.set_index(\"State\", inplace=True);\n", - "pvi" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 101, - "text": [ - " PVI\n", - "State \n", - "Alabama R+13\n", - "Alaska R+13\n", - "Arizona R+6 \n", - "Arkansas R+9 \n", - "California D+7 \n", - "Colorado EVEN\n", - "Connecticut D+7 \n", - "Delaware D+7 \n", - "District of Columbia D+39\n", - "Florida R+2 \n", - "Georgia R+7 \n", - "Hawaii D+12\n", - "Idaho R+17\n", - "Illinois D+8 \n", - "Indiana R+6 \n", - "Iowa D+1 \n", - "Kansas R+12\n", - "Kentucky R+10\n", - "Louisiana R+10\n", - "Maine D+5 \n", - "Maryland D+9 \n", - "Massachusetts D+12\n", - "Michigan D+4 \n", - "Minnesota D+2 \n", - "Mississippi R+10\n", - "Missouri R+3 \n", - "Montana R+7 \n", - "Nebraska R+13\n", - "Nevada D+1 \n", - "New Hampshire D+2 \n", - "New Jersey D+4 \n", - "New Mexico D+2 \n", - "New York D+10\n", - "North Carolina R+4 \n", - "North Dakota R+10\n", - "Ohio R+1 \n", - "Oklahoma R+17\n", - "Oregon D+4 \n", - "Pennsylvania D+2 \n", - "Rhode Island D+11\n", - "South Carolina R+8 \n", - "South Dakota R+9 \n", - "Tennessee R+9 \n", - "Texas R+10\n", - "Utah R+20\n", - "Vermont D+13\n", - "Virginia R+2 \n", - "Washington D+5 \n", - "West Virginia R+8 \n", - "Wisconsin D+2 \n", - "Wyoming R+20" - ] - } - ], - "prompt_number": 101 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pvi.PVI = pvi.PVI.replace({\"EVEN\" : \"0\"})\n", - "pvi.PVI = pvi.PVI.str.replace(\"R\\+\", \"-\")\n", - "pvi.PVI = pvi.PVI.str.replace(\"D\\+\", \"\")\n", - "pvi.PVI = pvi.PVI.astype(float)\n", - "pvi.PVI" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 102, - "text": [ - "State\n", - "Alabama -13\n", - "Alaska -13\n", - "Arizona -6\n", - "Arkansas -9\n", - "California 7\n", - "Colorado 0\n", - "Connecticut 7\n", - "Delaware 7\n", - "District of Columbia 39\n", - "Florida -2\n", - "Georgia -7\n", - "Hawaii 12\n", - "Idaho -17\n", - "Illinois 8\n", - "Indiana -6\n", - "Iowa 1\n", - "Kansas -12\n", - "Kentucky -10\n", - "Louisiana -10\n", - "Maine 5\n", - "Maryland 9\n", - "Massachusetts 12\n", - "Michigan 4\n", - "Minnesota 2\n", - "Mississippi -10\n", - "Missouri -3\n", - "Montana -7\n", - "Nebraska -13\n", - "Nevada 1\n", - "New Hampshire 2\n", - "New Jersey 4\n", - "New Mexico 2\n", - "New York 10\n", - "North Carolina -4\n", - "North Dakota -10\n", - "Ohio -1\n", - "Oklahoma -17\n", - "Oregon 4\n", - "Pennsylvania 2\n", - "Rhode Island 11\n", - "South Carolina -8\n", - "South Dakota -9\n", - "Tennessee -9\n", - "Texas -10\n", - "Utah -20\n", - "Vermont 13\n", - "Virginia -2\n", - "Washington 5\n", - "West Virginia -8\n", - "Wisconsin 2\n", - "Wyoming -20\n", - "Name: PVI, Length: 51" - ] - } - ], - "prompt_number": 102 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Party affliation of electorate obtained from [Gallup](http://www.gallup.com/poll/156437/Heavily-Democratic-States-Concentrated-East.aspx#2)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "party_affil = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/gallup_electorate.csv\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 103 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "party_affil.Democrat = party_affil.Democrat.str.replace(\"%\", \"\").astype(float)\n", - "party_affil.Republican = party_affil.Republican.str.replace(\"%\", \"\").astype(float)\n", - "party_affil.set_index(\"State\", inplace=True);\n", - "party_affil.rename(columns={\"Democrat Advantage\" : \"dem_adv\"}, inplace=True);\n", - "party_affil[\"no_party\"] = 100 - party_affil.Democrat - party_affil.Republican" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 104 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "party_affil" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 105, - "text": [ - " Democrat Republican dem_adv N no_party\n", - "State \n", - "District of Columbia 79.0 12.7 66.30 416 8.3\n", - "Rhode Island 52.5 26.5 26.00 623 21.0\n", - "Hawaii 54.3 28.7 25.60 466 17.0\n", - "New York 52.0 30.8 21.20 8674 17.2\n", - "Maryland 54.0 33.8 20.20 3571 12.2\n", - "Massachusetts 52.5 33.4 19.10 3583 14.1\n", - "Delaware 50.5 33.1 17.40 540 16.4\n", - "Connecticut 49.8 34.4 15.40 2020 15.8\n", - "Vermont 48.8 34.9 13.90 550 16.3\n", - "California 48.3 34.6 13.70 16197 17.1\n", - "Illinois 48.4 35.8 12.60 5888 15.8\n", - "New Jersey 47.4 35.9 11.50 4239 16.7\n", - "Michigan 47.7 36.6 11.10 5056 15.7\n", - "Minnesota 48.4 38.2 10.20 3873 13.4\n", - "Washington 47.5 37.7 9.80 5333 14.8\n", - "Oregon 47.2 39.1 8.10 3002 13.7\n", - "Pennsylvania 46.4 41.2 5.20 8443 12.4\n", - "Maine 43.8 39.4 4.40 1040 16.8\n", - "New Mexico 44.7 41.1 3.60 1555 14.2\n", - "Ohio 44.1 40.5 3.60 6426 15.4\n", - "West Virginia 45.3 41.9 3.40 1202 12.8\n", - "Wisconsin 45.0 42.2 2.80 4140 12.8\n", - "Iowa 43.2 41.4 1.80 2337 15.4\n", - "Florida 43.0 42.3 0.70 9965 14.7\n", - "Arkansas 41.5 40.8 0.70 2071 17.7\n", - "Kentucky 43.5 43.1 0.40 2898 13.4\n", - "North Carolina 43.4 43.2 0.20 6213 13.4\n", - "New Hampshire 42.3 43.8 -1.50 873 13.9\n", - "Virginia 41.2 44.2 -3.00 5313 14.6\n", - "Missouri 40.1 44.0 -3.90 3727 15.9\n", - "Georgia 40.3 44.3 -4.00 5110 15.4\n", - "Nevada 39.2 43.4 -4.20 1348 17.4\n", - "Louisiana 40.3 45.1 -4.80 2655 14.6\n", - "Colorado 39.9 45.1 -5.20 3671 15.0\n", - "Texas 38.3 44.1 -5.80 11325 17.6\n", - "South Dakota 41.5 47.5 -6.00 607 11.0\n", - "Indiana 39.0 45.7 -6.70 4197 15.3\n", - "Mississippi 40.1 47.1 -7.00 1763 12.8\n", - "Arizona 39.8 47.3 -7.50 4325 12.9\n", - "Tennessee 38.1 46.5 -8.40 4231 15.4\n", - "Alaska 35.9 44.3 -8.44 NaN 19.8\n", - "Oklahoma 38.6 48.0 -9.40 2583 13.4\n", - "South Carolina 36.9 48.8 -11.90 2858 14.3\n", - "North Dakota 35.8 49.0 -13.20 547 15.2\n", - "Alabama 36.0 49.6 -13.60 3197 14.4\n", - "Montana 35.9 49.6 -13.70 1137 14.5\n", - "Kansas 34.4 51.3 -16.90 1937 14.3\n", - "Nebraska 33.1 52.1 -19.00 1351 14.8\n", - "Wyoming 26.7 56.6 -29.90 600 16.7\n", - "Idaho 27.5 57.8 -30.30 1336 14.7\n", - "Utah 24.5 63.8 -39.30 2256 11.7" - ] - } - ], - "prompt_number": 105 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "census_data = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/census_demographics.csv\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 106 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def capitalize(s):\n", - " s = s.title()\n", - " s = s.replace(\"Of\", \"of\")\n", - " return s" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 107 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "census_data[\"State\"] = census_data.state.map(capitalize)\n", - "del census_data[\"state\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 108 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "census_data.set_index(\"State\", inplace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 109, - "text": [ - " per_black per_hisp per_white educ_hs educ_coll average_income median_income pop_density vote_pop older_pop\n", - "State \n", - "Alabama 26.5 4.0 66.8 81.4 21.7 22984 42081 94.4 3001712.500 672383.600\n", - "Alaska 3.6 5.8 63.7 90.7 27.0 30726 66521 1.2 475548.444 58540.158\n", - "Arizona 4.5 30.1 57.4 85.0 26.3 25680 50448 56.3 3934880.535 920515.710\n", - "Arkansas 15.6 6.6 74.2 81.9 19.1 21274 39267 56.0 1798043.148 428944.934\n", - "California 6.6 38.1 39.7 80.7 30.1 29188 60883 239.1 24009747.944 4409953.704\n", - "Colorado 4.3 20.9 69.7 89.3 35.9 30151 56456 48.5 3310567.012 578197.948\n", - "Connecticut 11.1 13.8 70.9 88.4 35.2 36775 67740 738.1 2263008.088 515622.096\n", - "Delaware 21.9 8.4 65.1 87.0 27.7 29007 57599 460.8 568773.645 133348.845\n", - "District of Columbia 50.7 9.5 35.3 86.5 49.2 42078 58526 9856.5 442485.136 70451.544\n", - "Florida 16.5 22.9 57.5 85.3 25.9 26551 47661 350.6 11701330.788 3354127.392\n", - "Georgia 31.0 9.1 55.5 83.5 27.2 25134 49347 168.4 6242473.560 1079673.100\n", - "Hawaii 2.0 9.2 22.9 89.8 29.4 28882 66420 211.8 867505.110 202097.070\n", - "Idaho 0.8 11.5 83.6 88.2 24.3 22518 46423 19.0 954160.970 202878.080\n", - "Illinois 14.8 16.2 63.3 86.2 30.3 28782 55735 231.1 8133370.424 1634395.639\n", - "Indiana 9.4 6.2 81.3 86.2 22.4 24058 47697 181.0 4060042.406 860233.704\n", - "Iowa 3.1 5.2 88.4 89.9 24.5 25335 48872 54.5 1880257.726 456284.041\n", - "Kansas 6.1 10.8 77.8 89.2 29.3 25907 49424 34.9 1765811.370 381874.654\n", - "Kentucky 8.0 3.2 86.1 81.0 20.3 22515 41576 109.9 2757063.636 589863.060\n", - "Louisiana 32.4 4.4 60.1 81.0 20.9 23094 43445 104.9 2886721.516 571854.500\n", - "Maine 1.3 1.4 94.3 89.8 26.5 25385 46933 43.1 842071.192 216494.644\n", - "Maryland 30.0 8.4 54.4 87.8 35.7 34849 70647 594.8 3753418.116 728536.125\n", - "Massachusetts 7.8 9.9 76.4 88.7 38.3 33966 64509 839.4 4262135.792 922255.040\n", - "Michigan 14.3 4.5 76.4 88.0 25.0 25135 48432 174.8 6192369.249 1392542.367\n", - "Minnesota 5.4 4.9 82.8 91.3 31.4 29582 57243 66.6 3367262.430 700176.791\n", - "Mississippi 37.3 2.9 57.7 79.6 19.5 19977 37881 63.2 1840720.416 387206.560\n", - "Missouri 11.7 3.7 80.8 86.2 25.0 24724 46262 87.1 3744658.624 853517.696\n", - "Montana 0.5 3.1 87.5 91.0 27.9 23836 43872 6.8 623874.375 151726.248\n", - "Nebraska 4.7 9.5 81.8 90.0 27.7 25229 49342 23.8 1131381.574 250599.176\n", - "Nevada 8.6 27.1 53.6 84.3 21.8 27589 55726 24.6 1718416.182 340415.250\n", - "New Hampshire 1.3 2.9 92.2 90.9 32.9 31422 63277 147.0 854189.712 184547.160\n", - "New Jersey 14.6 18.1 58.9 87.3 34.6 34858 69811 1195.5 5566148.805 1208498.235\n", - "New Mexico 2.5 46.7 40.2 82.7 25.5 22966 43820 17.0 1280567.760 283182.464\n", - "New York 17.5 18.0 58.0 84.4 32.1 30948 55603 411.2 12516121.671 2666731.989\n", - "North Carolina 22.0 8.6 65.0 83.6 26.1 24745 45570 196.1 6093189.031 1274644.932\n", - "North Dakota 1.3 2.2 88.6 89.4 26.3 25803 46781 9.7 434296.820 98486.208\n", - "Ohio 12.4 3.2 81.0 87.4 24.1 25113 47358 282.3 7204049.424 1650927.993\n", - "Oklahoma 7.7 9.2 68.2 85.4 22.6 23094 42979 54.7 2335568.928 519436.596\n", - "Oregon 2.0 12.0 78.1 88.6 28.6 26171 49260 39.9 2454758.606 553675.837\n", - "Pennsylvania 11.3 5.9 79.2 87.4 26.4 27049 50398 283.9 7989789.522 1987890.216\n", - "Rhode Island 7.2 12.8 76.5 83.7 30.3 28707 54902 1018.1 677038.488 154541.394\n", - "South Carolina 28.1 5.3 64.0 83.0 24.0 23443 43939 153.9 2938556.440 659771.430\n", - "South Dakota 1.4 2.9 84.4 89.3 25.3 24110 46369 10.7 501865.938 118667.808\n", - "Tennessee 16.9 4.7 75.4 82.5 22.7 23722 43314 153.9 4034112.390 877259.361\n", - "Texas 12.2 38.1 44.8 80.0 25.8 24870 49646 96.3 16021000.944 2695841.505\n", - "Utah 1.3 13.2 80.1 90.6 29.4 23139 56330 33.6 1679064.312 259184.424\n", - "Vermont 1.1 1.6 94.2 90.6 33.3 27478 51841 67.9 406553.719 93964.650\n", - "Virginia 19.8 8.2 64.5 86.1 33.8 32145 61406 202.6 5230406.184 1012075.500\n", - "Washington 3.8 11.6 72.1 89.6 31.0 29733 57244 101.2 4378054.358 867414.826\n", - "West Virginia 3.5 1.3 93.0 81.9 17.3 21232 38380 77.1 1170734.684 300568.968\n", - "Wisconsin 6.5 6.1 83.1 89.4 25.8 26624 51598 105.0 3592701.443 793935.613\n", - "Wyoming 1.1 9.1 85.5 91.3 23.6 27860 53802 5.8 361348.488 72156.066" - ] - } - ], - "prompt_number": 109 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#loadpy https://raw.github.com/gist/3912533/d958b515f602f6e73f7b16d8bc412bc8d1f433d9/state_abbrevs.py;" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 110 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "states_abbrev_dict = {\n", - " 'AK': 'Alaska',\n", - " 'AL': 'Alabama',\n", - " 'AR': 'Arkansas',\n", - " 'AS': 'American Samoa',\n", - " 'AZ': 'Arizona',\n", - " 'CA': 'California',\n", - " 'CO': 'Colorado',\n", - " 'CT': 'Connecticut',\n", - " 'DC': 'District of Columbia',\n", - " 'DE': 'Delaware',\n", - " 'FL': 'Florida',\n", - " 'GA': 'Georgia',\n", - " 'GU': 'Guam',\n", - " 'HI': 'Hawaii',\n", - " 'IA': 'Iowa',\n", - " 'ID': 'Idaho',\n", - " 'IL': 'Illinois',\n", - " 'IN': 'Indiana',\n", - " 'KS': 'Kansas',\n", - " 'KY': 'Kentucky',\n", - " 'LA': 'Louisiana',\n", - " 'MA': 'Massachusetts',\n", - " 'MD': 'Maryland',\n", - " 'ME': 'Maine',\n", - " 'MI': 'Michigan',\n", - " 'MN': 'Minnesota',\n", - " 'MO': 'Missouri',\n", - " 'MP': 'Northern Mariana Islands',\n", - " 'MS': 'Mississippi',\n", - " 'MT': 'Montana',\n", - " 'NA': 'National',\n", - " 'NC': 'North Carolina',\n", - " 'ND': 'North Dakota',\n", - " 'NE': 'Nebraska',\n", - " 'NH': 'New Hampshire',\n", - " 'NJ': 'New Jersey',\n", - " 'NM': 'New Mexico',\n", - " 'NV': 'Nevada',\n", - " 'NY': 'New York',\n", - " 'OH': 'Ohio',\n", - " 'OK': 'Oklahoma',\n", - " 'OR': 'Oregon',\n", - " 'PA': 'Pennsylvania',\n", - " 'PR': 'Puerto Rico',\n", - " 'RI': 'Rhode Island',\n", - " 'SC': 'South Carolina',\n", - " 'SD': 'South Dakota',\n", - " 'TN': 'Tennessee',\n", - " 'TX': 'Texas',\n", - " 'UT': 'Utah',\n", - " 'VA': 'Virginia',\n", - " 'VI': 'Virgin Islands',\n", - " 'VT': 'Vermont',\n", - " 'WA': 'Washington',\n", - " 'WI': 'Wisconsin',\n", - " 'WV': 'West Virginia',\n", - " 'WY': 'Wyoming'\n", - "}" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 111 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Campaign Contributions from FEC." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "obama_give = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/obama_indiv_state.csv\", \n", - " header=None, names=[\"State\", \"obama_give\"])\n", - "romney_give = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/romney_indiv_state.csv\",\n", - " header=None, names=[\"State\", \"romney_give\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 112 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "obama_give.State.replace(states_abbrev_dict, inplace=True);\n", - "romney_give.State.replace(states_abbrev_dict, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 113 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "obama_give.set_index(\"State\", inplace=True)\n", - "romney_give.set_index(\"State\", inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 114 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data = census_data.join(party_affil[[\"dem_adv\", \"no_party\"]]).join(pvi)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 115 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data = demo_data.join(obama_give).join(romney_give)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 116 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "giving = demo_data[[\"obama_give\", \"romney_give\"]].div(demo_data[[\"vote_pop\", \"older_pop\"]].sum(1), axis=0)\n", - "giving" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 117, - "text": [ - " obama_give romney_give\n", - "State \n", - "Alabama 0.245 0.366\n", - "Alaska 1.112 0.499\n", - "Arizona 0.569 0.673\n", - "Arkansas 0.247 0.217\n", - "California 1.128 0.618\n", - "Colorado 1.056 0.797\n", - "Connecticut 1.207 1.545\n", - "Delaware 0.767 0.359\n", - "District of Columbia 326.864 2.535\n", - "Florida 0.503 0.875\n", - "Georgia 0.468 0.526\n", - "Hawaii 1.007 0.225\n", - "Idaho 0.366 0.990\n", - "Illinois 0.934 0.590\n", - "Indiana 0.341 0.257\n", - "Iowa 0.487 0.286\n", - "Kansas 0.392 0.470\n", - "Kentucky 0.289 0.393\n", - "Louisiana 0.260 0.529\n", - "Maine 0.800 0.246\n", - "Maryland 1.518 0.594\n", - "Massachusetts 1.735 1.105\n", - "Michigan 0.512 0.501\n", - "Minnesota 0.660 0.232\n", - "Mississippi 0.189 0.327\n", - "Missouri 0.413 0.482\n", - "Montana 0.764 0.534\n", - "Nebraska 0.336 0.351\n", - "Nevada 0.484 0.639\n", - "New Hampshire 0.962 0.734\n", - "New Jersey 0.736 0.704\n", - "New Mexico 1.052 0.379\n", - "New York 1.199 0.809\n", - "North Carolina 0.549 0.355\n", - "North Dakota 0.238 0.343\n", - "Ohio 0.378 0.428\n", - "Oklahoma 0.325 0.801\n", - "Oregon 0.971 0.342\n", - "Pennsylvania 0.588 0.467\n", - "Rhode Island 0.713 0.358\n", - "South Carolina 0.317 0.351\n", - "South Dakota 0.271 0.519\n", - "Tennessee 0.377 0.522\n", - "Texas 0.477 0.691\n", - "Utah 0.379 2.395\n", - "Vermont 1.602 0.250\n", - "Virginia 1.000 0.939\n", - "Washington 1.191 0.476\n", - "West Virginia 0.260 0.321\n", - "Wisconsin 0.455 0.238\n", - "Wyoming 0.746 1.080" - ] - } - ], - "prompt_number": 117 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data[[\"obama_give\", \"romney_give\"]] = giving" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 118 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import cluster as sp_cluster\n", - "from sklearn import cluster, neighbors" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 119 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "clean_data = sp_cluster.vq.whiten(demo_data.values)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 120 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "clean_data.var(axis=0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 121, - "text": [ - "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", - " 1., 1.])" - ] - } - ], - "prompt_number": 121 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "KNN = neighbors.NearestNeighbors(n_neighbors=7)\n", - "KNN.fit(clean_data)\n", - "KNN.kneighbors(clean_data[0], return_distance=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 122, - "text": [ - "(array([[ 0. , 0.8763, 1.0233, 1.3971, 1.8694, 2.1093, 2.2603]]),\n", - " array([[ 0, 40, 18, 42, 24, 33, 10]], dtype=int32))" - ] - } - ], - "prompt_number": 122 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "idx = _[1]\n", - "demo_data.index[0], demo_data.index[idx]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 123, - "text": [ - "('Alabama',\n", - " array([['Alabama', 'South Carolina', 'Louisiana', 'Tennessee',\n", - " 'Mississippi', 'North Carolina', 'Georgia']], dtype=object))" - ] - } - ], - "prompt_number": 123 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nearest_neighbor = {}\n", - "for i, state in enumerate(demo_data.index):\n", - " neighborhood = KNN.kneighbors(clean_data[i], return_distance=True)\n", - " nearest_neighbor.update({state : (demo_data.index[neighborhood[1]],\n", - " neighborhood[0])})" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 124 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nearest_neighbor" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 125, - "text": [ - "{'Alabama': (array([['Alabama', 'South Carolina', 'Louisiana', 'Tennessee',\n", - " 'Mississippi', 'North Carolina', 'Georgia']], dtype=object),\n", - " array([[ 0. , 0.8763, 1.0233, 1.3971, 1.8694, 2.1093, 2.2603]])),\n", - " 'Alaska': (array([['Alaska', 'Wyoming', 'Nebraska', 'Washington', 'Kansas',\n", - " 'Delaware', 'Colorado']], dtype=object),\n", - " array([[ 0. , 3.3349, 3.6854, 3.7395, 3.7689, 3.7821, 3.8213]])),\n", - " 'Arizona': (array([['Arizona', 'Nevada', 'Oklahoma', 'New Mexico', 'Oregon',\n", - " 'Colorado', 'Kansas']], dtype=object),\n", - " array([[ 0. , 2.6992, 2.8728, 2.8766, 3.0299, 3.0351, 3.0367]])),\n", - " 'Arkansas': (array([['Arkansas', 'Tennessee', 'Alabama', 'Kentucky', 'Indiana',\n", - " 'Missouri', 'South Carolina']], dtype=object),\n", - " array([[ 0. , 1.8792, 2.3171, 2.4043, 2.4555, 2.5235, 2.5635]])),\n", - " 'California': (array([['California', 'Texas', 'New York', 'Florida', 'Illinois',\n", - " 'New Jersey', 'Pennsylvania']], dtype=object),\n", - " array([[ 0. , 3.9121, 4.461 , 4.7217, 5.8944, 6.7702, 7.0775]])),\n", - " 'Colorado': (array([['Colorado', 'Washington', 'Virginia', 'Oregon', 'Kansas',\n", - " 'Minnesota', 'New Hampshire']], dtype=object),\n", - " array([[ 0. , 1.8541, 2.4517, 2.5881, 2.655 , 2.7096, 2.7496]])),\n", - " 'Connecticut': (array([['Connecticut', 'Massachusetts', 'New Jersey', 'Virginia',\n", - " 'Colorado', 'Washington', 'New Hampshire']], dtype=object),\n", - " array([[ 0. , 1.882 , 2.4986, 2.8973, 3.2209, 3.4531, 3.4642]])),\n", - " 'Delaware': (array([['Delaware', 'Washington', 'Michigan', 'Illinois', 'Oregon',\n", - " 'Virginia', 'Rhode Island']], dtype=object),\n", - " array([[ 0. , 2.5376, 2.7875, 2.8076, 2.8882, 2.9771, 3.0112]])),\n", - " 'District of Columbia': (array([['District of Columbia', 'Maryland', 'Massachusetts', 'Connecticut',\n", - " 'New Jersey', 'Virginia', 'Delaware']], dtype=object),\n", - " array([[ 0. , 12.3449, 12.6305, 12.6762, 13.2193, 13.5237,\n", - " 13.7589]])),\n", - " 'Florida': (array([['Florida', 'New York', 'Illinois', 'Texas', 'Pennsylvania',\n", - " 'North Carolina', 'Ohio']], dtype=object),\n", - " array([[ 0. , 2.9107, 3.038 , 3.1893, 3.3073, 3.4896, 3.5918]])),\n", - " 'Georgia': (array([['Georgia', 'North Carolina', 'South Carolina', 'Louisiana',\n", - " 'Alabama', 'Tennessee', 'Missouri']], dtype=object),\n", - " array([[ 0. , 1.5857, 1.7042, 2.0122, 2.2603, 2.2821, 2.7322]])),\n", - " 'Hawaii': (array([['Hawaii', 'Delaware', 'Washington', 'New Jersey', 'Illinois',\n", - " 'Nevada', 'Alaska']], dtype=object),\n", - " array([[ 0. , 3.5885, 3.861 , 4.0249, 4.2819, 4.3453, 4.3871]])),\n", - " 'Idaho': (array([['Idaho', 'Nebraska', 'Kansas', 'Wyoming', 'Oklahoma', 'Montana',\n", - " 'North Dakota']], dtype=object),\n", - " array([[ 0. , 1.9803, 2.0754, 2.0787, 2.1757, 2.2378, 2.3297]])),\n", - " 'Illinois': (array([['Illinois', 'New York', 'Washington', 'Michigan', 'Virginia',\n", - " 'Pennsylvania', 'New Jersey']], dtype=object),\n", - " array([[ 0. , 2.0673, 2.137 , 2.2883, 2.4543, 2.59 , 2.6832]])),\n", - " 'Indiana': (array([['Indiana', 'Missouri', 'Tennessee', 'Ohio', 'Iowa', 'Michigan',\n", - " 'Wisconsin']], dtype=object),\n", - " array([[ 0. , 0.907 , 1.5921, 1.6033, 1.7819, 1.9781, 2.0616]])),\n", - " 'Iowa': (array([['Iowa', 'Maine', 'Wisconsin', 'Oregon', 'North Dakota', 'Missouri',\n", - " 'Indiana']], dtype=object),\n", - " array([[ 0. , 1.1152, 1.5243, 1.594 , 1.6018, 1.755 , 1.7819]])),\n", - " 'Kansas': (array([['Kansas', 'Nebraska', 'North Dakota', 'Montana', 'Idaho',\n", - " 'Indiana', 'Missouri']], dtype=object),\n", - " array([[ 0. , 0.6719, 1.5316, 1.6431, 2.0754, 2.154 , 2.2018]])),\n", - " 'Kentucky': (array([['Kentucky', 'West Virginia', 'Tennessee', 'Indiana', 'Oklahoma',\n", - " 'Alabama', 'Arkansas']], dtype=object),\n", - " array([[ 0. , 1.1876, 1.7593, 2.1843, 2.2642, 2.3361, 2.4043]])),\n", - " 'Louisiana': (array([['Louisiana', 'Alabama', 'South Carolina', 'Mississippi',\n", - " 'Tennessee', 'Georgia', 'North Carolina']], dtype=object),\n", - " array([[ 0. , 1.0233, 1.136 , 1.5793, 1.9074, 2.0122, 2.2222]])),\n", - " 'Maine': (array([['Maine', 'Iowa', 'Vermont', 'North Dakota', 'Montana', 'Oregon',\n", - " 'Missouri']], dtype=object),\n", - " array([[ 0. , 1.1152, 1.7709, 2.0009, 2.1852, 2.2556, 2.2738]])),\n", - " 'Maryland': (array([['Maryland', 'Virginia', 'New Jersey', 'Massachusetts',\n", - " 'Connecticut', 'Delaware', 'Washington']], dtype=object),\n", - " array([[ 0. , 2.9192, 2.9716, 3.0229, 3.4926, 3.5658, 3.7933]])),\n", - " 'Massachusetts': (array([['Massachusetts', 'Connecticut', 'New Jersey', 'Washington',\n", - " 'Virginia', 'Colorado', 'New Hampshire']], dtype=object),\n", - " array([[ 0. , 1.882 , 2.5823, 2.6111, 2.7211, 2.8532, 2.8816]])),\n", - " 'Michigan': (array([['Michigan', 'Ohio', 'Missouri', 'Pennsylvania', 'Indiana',\n", - " 'Wisconsin', 'Iowa']], dtype=object),\n", - " array([[ 0. , 0.914 , 1.5797, 1.9268, 1.9781, 2.1194, 2.1783]])),\n", - " 'Minnesota': (array([['Minnesota', 'Washington', 'Wisconsin', 'Oregon', 'New Hampshire',\n", - " 'Iowa', 'Vermont']], dtype=object),\n", - " array([[ 0. , 1.4419, 1.6072, 1.8303, 1.9445, 2.2889, 2.363 ]])),\n", - " 'Mississippi': (array([['Mississippi', 'Louisiana', 'Alabama', 'South Carolina',\n", - " 'Tennessee', 'North Carolina', 'Georgia']], dtype=object),\n", - " array([[ 0. , 1.5793, 1.8694, 2.1346, 3.0436, 3.163 , 3.2884]])),\n", - " 'Missouri': (array([['Missouri', 'Indiana', 'Ohio', 'Tennessee', 'Michigan', 'Iowa',\n", - " 'North Dakota']], dtype=object),\n", - " array([[ 0. , 0.907 , 1.414 , 1.537 , 1.5797, 1.755 , 2.1177]])),\n", - " 'Montana': (array([['Montana', 'North Dakota', 'Nebraska', 'Kansas', 'Iowa',\n", - " 'South Dakota', 'Maine']], dtype=object),\n", - " array([[ 0. , 1.0132, 1.4011, 1.6431, 1.7944, 1.8935, 2.1852]])),\n", - " 'Nebraska': (array([['Nebraska', 'Kansas', 'North Dakota', 'Montana', 'Idaho', 'Iowa',\n", - " 'Indiana']], dtype=object),\n", - " array([[ 0. , 0.6719, 1.1533, 1.4011, 1.9803, 2.0716, 2.1521]])),\n", - " 'Nevada': (array([['Nevada', 'Arizona', 'Delaware', 'Illinois', 'New Mexico',\n", - " 'Colorado', 'Indiana']], dtype=object),\n", - " array([[ 0. , 2.6992, 3.2186, 3.3355, 3.4181, 3.4779, 3.4977]])),\n", - " 'New Hampshire': (array([['New Hampshire', 'Minnesota', 'Washington', 'Vermont', 'Colorado',\n", - " 'Wisconsin', 'Massachusetts']], dtype=object),\n", - " array([[ 0. , 1.9445, 2.3854, 2.6837, 2.7496, 2.8483, 2.8816]])),\n", - " 'New Jersey': (array([['New Jersey', 'Virginia', 'Connecticut', 'Massachusetts',\n", - " 'Illinois', 'Washington', 'Maryland']], dtype=object),\n", - " array([[ 0. , 2.4212, 2.4986, 2.5823, 2.6832, 2.9395, 2.9716]])),\n", - " 'New Mexico': (array([['New Mexico', 'Arizona', 'Nevada', 'Oregon', 'Oklahoma',\n", - " 'North Carolina', 'Colorado']], dtype=object),\n", - " array([[ 0. , 2.8766, 3.4181, 4.7554, 4.8641, 4.8808, 4.9258]])),\n", - " 'New York': (array([['New York', 'Illinois', 'Florida', 'New Jersey', 'Virginia',\n", - " 'Michigan', 'Pennsylvania']], dtype=object),\n", - " array([[ 0. , 2.0673, 2.9107, 3.3612, 3.6949, 3.819 , 3.9422]])),\n", - " 'North Carolina': (array([['North Carolina', 'Georgia', 'South Carolina', 'Tennessee',\n", - " 'Alabama', 'Ohio', 'Missouri']], dtype=object),\n", - " array([[ 0. , 1.5857, 1.6217, 1.8337, 2.1093, 2.1905, 2.1934]])),\n", - " 'North Dakota': (array([['North Dakota', 'Montana', 'Nebraska', 'Kansas', 'Iowa', 'Maine',\n", - " 'Indiana']], dtype=object),\n", - " array([[ 0. , 1.0132, 1.1533, 1.5316, 1.6018, 2.0009, 2.0738]])),\n", - " 'Ohio': (array([['Ohio', 'Michigan', 'Missouri', 'Indiana', 'Pennsylvania',\n", - " 'Wisconsin', 'North Carolina']], dtype=object),\n", - " array([[ 0. , 0.914 , 1.414 , 1.6033, 1.6991, 2.1596, 2.1905]])),\n", - " 'Oklahoma': (array([['Oklahoma', 'Tennessee', 'Idaho', 'Indiana', 'Kentucky', 'Kansas',\n", - " 'Missouri']], dtype=object),\n", - " array([[ 0. , 2.0111, 2.1757, 2.21 , 2.2642, 2.3057, 2.3747]])),\n", - " 'Oregon': (array([['Oregon', 'Wisconsin', 'Iowa', 'Washington', 'Minnesota',\n", - " 'Missouri', 'Kansas']], dtype=object),\n", - " array([[ 0. , 1.2415, 1.594 , 1.6631, 1.8303, 2.2442, 2.2534]])),\n", - " 'Pennsylvania': (array([['Pennsylvania', 'Ohio', 'Michigan', 'Wisconsin', 'North Carolina',\n", - " 'Oregon', 'Illinois']], dtype=object),\n", - " array([[ 0. , 1.6991, 1.9268, 1.9716, 2.297 , 2.5221, 2.59 ]])),\n", - " 'Rhode Island': (array([['Rhode Island', 'Delaware', 'Vermont', 'Maine', 'Nevada',\n", - " 'Illinois', 'Washington']], dtype=object),\n", - " array([[ 0. , 3.0112, 3.6719, 3.8235, 3.8411, 3.8785, 3.887 ]])),\n", - " 'South Carolina': (array([['South Carolina', 'Alabama', 'Louisiana', 'Tennessee',\n", - " 'North Carolina', 'Georgia', 'Mississippi']], dtype=object),\n", - " array([[ 0. , 0.8763, 1.136 , 1.4947, 1.6217, 1.7042, 2.1346]])),\n", - " 'South Dakota': (array([['South Dakota', 'Montana', 'North Dakota', 'Wisconsin', 'Kansas',\n", - " 'Nebraska', 'Oklahoma']], dtype=object),\n", - " array([[ 0. , 1.8935, 2.1282, 2.1654, 2.2243, 2.2675, 2.4938]])),\n", - " 'Tennessee': (array([['Tennessee', 'Alabama', 'South Carolina', 'Missouri', 'Indiana',\n", - " 'Kentucky', 'North Carolina']], dtype=object),\n", - " array([[ 0. , 1.3971, 1.4947, 1.537 , 1.5921, 1.7593, 1.8337]])),\n", - " 'Texas': (array([['Texas', 'Florida', 'California', 'New York', 'Arizona',\n", - " 'Illinois', 'Georgia']], dtype=object),\n", - " array([[ 0. , 3.1893, 3.9121, 4.2069, 4.5867, 4.6339, 4.7973]])),\n", - " 'Utah': (array([['Utah', 'Idaho', 'Wyoming', 'Kansas', 'Oklahoma', 'Nebraska',\n", - " 'South Dakota']], dtype=object),\n", - " array([[ 0. , 3.8272, 4.1158, 4.8072, 4.8669, 5.0276, 5.046 ]])),\n", - " 'Vermont': (array([['Vermont', 'Maine', 'Iowa', 'Minnesota', 'Oregon', 'Washington',\n", - " 'New Hampshire']], dtype=object),\n", - " array([[ 0. , 1.7709, 2.3538, 2.363 , 2.5073, 2.6424, 2.6837]])),\n", - " 'Virginia': (array([['Virginia', 'New Jersey', 'Colorado', 'Illinois', 'Washington',\n", - " 'Massachusetts', 'Connecticut']], dtype=object),\n", - " array([[ 0. , 2.4212, 2.4517, 2.4543, 2.5525, 2.7211, 2.8973]])),\n", - " 'Washington': (array([['Washington', 'Minnesota', 'Oregon', 'Colorado', 'Wisconsin',\n", - " 'Illinois', 'New Hampshire']], dtype=object),\n", - " array([[ 0. , 1.4419, 1.6631, 1.8541, 2.0414, 2.137 , 2.3854]])),\n", - " 'West Virginia': (array([['West Virginia', 'Kentucky', 'Tennessee', 'Indiana', 'Oklahoma',\n", - " 'Arkansas', 'Missouri']], dtype=object),\n", - " array([[ 0. , 1.1876, 2.7545, 2.8103, 2.8824, 2.9104, 3.1176]])),\n", - " 'Wisconsin': (array([['Wisconsin', 'Oregon', 'Iowa', 'Minnesota', 'Pennsylvania',\n", - " 'Washington', 'Indiana']], dtype=object),\n", - " array([[ 0. , 1.2415, 1.5243, 1.6072, 1.9716, 2.0414, 2.0616]])),\n", - " 'Wyoming': (array([['Wyoming', 'Idaho', 'Nebraska', 'Kansas', 'North Dakota',\n", - " 'Montana', 'Alaska']], dtype=object),\n", - " array([[ 0. , 2.0787, 2.4089, 2.6214, 2.6543, 2.8861, 3.3349]]))}" - ] - } - ], - "prompt_number": 125 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "k_means = cluster.KMeans(n_clusters=5, n_init=50)\n", - "k_means.fit(clean_data)\n", - "values = k_means.cluster_centers_.squeeze()\n", - "labels = k_means.labels_" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 126 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "clusters = sp_cluster.vq.kmeans(clean_data, 5)[0]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 127 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def choose_group(data, clusters):\n", - " \"\"\"\n", - " Return the index of the cluster to which the rows in data\n", - " are \"closest\" (in the sense of the L2-norm)\n", - " \"\"\"\n", - " data = data[:,None] # add an axis for broadcasting\n", - " distances = data - clusters\n", - " groups = []\n", - " for row in distances:\n", - " dists = map(np.linalg.norm, row)\n", - " groups.append(np.argmin(dists))\n", - " return groups" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 128 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "groups = choose_group(clean_data, clusters)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 129 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "np.array(groups)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 130, - "text": [ - "array([1, 0, 3, 1, 4, 0, 0, 0, 2, 4, 1, 0, 3, 0, 3, 3, 3, 1, 1, 3, 0, 0, 3,\n", - " 0, 1, 3, 3, 3, 0, 0, 0, 1, 4, 1, 3, 3, 1, 3, 3, 0, 1, 3, 1, 4, 3, 0,\n", - " 0, 0, 1, 3, 3])" - ] - } - ], - "prompt_number": 130 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or use a one-liner" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "groups = [np.argmin(map(np.linalg.norm, (clean_data[:,None] - clusters)[i])) for i in range(51)]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 131 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data[\"kmeans_group\"] = groups\n", - "demo_data[\"kmeans_labels\"] = labels" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 132 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for _, group in demo_data.groupby(\"kmeans_group\"):\n", - " group = group.index\n", - " group.values.sort()\n", - " print group.values" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", - " 'Maryland' 'Massachusetts' 'Minnesota' 'Nevada' 'New Hampshire'\n", - " 'New Jersey' 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n", - "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi'\n", - " 'New Mexico' 'North Carolina' 'Oklahoma' 'South Carolina' 'Tennessee'\n", - " 'West Virginia']\n", - "['District of Columbia']\n", - "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri'\n", - " 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania'\n", - " 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", - "['California' 'Florida' 'New York' 'Texas']\n" - ] - } - ], - "prompt_number": 133 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "labels" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 134, - "text": [ - "array([0, 1, 0, 0, 3, 1, 1, 1, 2, 3, 0, 1, 4, 1, 4, 4, 4, 0, 0, 4, 1, 1, 4,\n", - " 4, 0, 4, 4, 4, 1, 4, 1, 0, 3, 0, 4, 4, 0, 4, 4, 1, 0, 4, 0, 3, 4, 4,\n", - " 1, 1, 0, 4, 4])" - ] - } - ], - "prompt_number": 134 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data[\"kmeans_labels\"] = labels\n", - "for _, group in demo_data.groupby(\"kmeans_labels\"):\n", - " group = group.index.copy()\n", - " group.values.sort()\n", - " print group.values" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana'\n", - " 'Mississippi' 'New Mexico' 'North Carolina' 'Oklahoma' 'South Carolina'\n", - " 'Tennessee' 'West Virginia']\n", - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", - " 'Maryland' 'Massachusetts' 'Nevada' 'New Jersey' 'Rhode Island' 'Virginia'\n", - " 'Washington']\n", - "['District of Columbia']\n", - "['California' 'Florida' 'New York' 'Texas']\n", - "['Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Minnesota'\n", - " 'Missouri' 'Montana' 'Nebraska' 'New Hampshire' 'North Dakota' 'Ohio'\n", - " 'Oregon' 'Pennsylvania' 'South Dakota' 'Utah' 'Vermont' 'Wisconsin'\n", - " 'Wyoming']\n" - ] - } - ], - "prompt_number": 135 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data = demo_data.reset_index()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 136 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.State.replace(states_abbrev_dict, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 137 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012 = state_data2012.merge(demo_data[[\"State\", \"kmeans_labels\"]], on=\"State\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 138 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "kmeans_groups = state_data2012.groupby(\"kmeans_labels\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 139 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "group = kmeans_groups.get_group(kmeans_groups.groups.keys()[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 140 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "group.State.unique()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 141, - "text": [ - "array(['California', 'Florida', 'New York', 'Texas'], dtype=object)" - ] - } - ], - "prompt_number": 141 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def edit_tick_label(tick_val, tick_pos):\n", - " if tick_val < 0:\n", - " text = str(int(tick_val)).replace(\"-\", \"Romney+\")\n", - " else:\n", - " text = \"Obama+\"+str(int(tick_val))\n", - " return text" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 142 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import lib\n", - "from matplotlib.ticker import FuncFormatter\n", - "fig, axes = plt.subplots(figsize=(12,8))\n", - "\n", - "data = group[[\"poll_date\", \"obama_spread\"]]\n", - "data = pandas.concat((data, national_data2012[[\"poll_date\", \"obama_spread\"]]))\n", - " \n", - "data.sort(\"poll_date\", inplace=True)\n", - "dates = pandas.DatetimeIndex(data.poll_date).asi8\n", - "\n", - "loess_res = sm.nonparametric.lowess(data.obama_spread.values, dates, \n", - " frac=.2, it=3)\n", - "\n", - "dates_x = lib.ints_to_pydatetime(dates)\n", - "axes.scatter(dates_x, data[\"obama_spread\"])\n", - "axes.plot(dates_x, loess_res[:,1], color='r')\n", - "axes.yaxis.get_major_locator().set_params(nbins=12)\n", - "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n", - "axes.grid(False, axis='x')\n", - "axes.hlines(0, dates_x[0], dates_x[-1], color='black', lw=3)\n", - "axes.margins(0, .05)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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cAW/x8cdgGOD1RrqS3tPYCHV1Bl6v2W/ugBcVFZGfn3/Wv/fPHv0iIiIi3eB0wkUX9b17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\ndVxUVL2O8/IU1q0L7/+HH6yUlNR93+npKrou9mUYCmlpVetq1Sqxr7Z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5dRyOyjLGxZ0Z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\n5/XXX2f58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- } - ], - "prompt_number": 143 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "loess_res[-7:,1].mean()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 144, - "text": [ - "2.3144535643345003" - ] - } - ], - "prompt_number": 144 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import lib\n", - "from matplotlib.ticker import FuncFormatter\n", - "fig, axes = plt.subplots(figsize=(12,8))\n", - "\n", - "national_data2012.sort(\"poll_date\", inplace=True)\n", - "dates = pandas.DatetimeIndex(national_data2012.poll_date).asi8\n", - "\n", - "loess_res = sm.nonparametric.lowess(national_data2012.obama_spread.values, dates, \n", - " frac=.075, it=3)\n", - "\n", - "dates_x = lib.ints_to_pydatetime(dates)\n", - "axes.scatter(dates_x, national_data2012[\"obama_spread\"])\n", - "axes.plot(dates_x, loess_res[:,1], color='r')\n", - "axes.yaxis.get_major_locator().set_params(nbins=12)\n", - "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n", - "axes.grid(False, axis='x')\n", - "axes.hlines(0, dates_x[0], dates_x[-1], color='black', lw=3)\n", - "axes.margins(0, .05)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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Vx/nnJ/ODHySwfbvR+FhBgcFttyVw/vnJ/Pa3sZSXG8dYkxxq71743e98nH9+MrfcksDW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\nTcxqF0OGtO7XTvPzTTZscJG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rduPQOC4eGr+hxairO6GanMi1eTPhPn0Ip6dHZ1y8euJF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/J6bVyuE+Xk5va/nKP3/xmGHZHzD9UZZ5w4p2HYQz6n2x0a+8elptphjeO8vBPHJCTYJPb/rDMk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\n" - } - ], - "prompt_number": 145 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends = []\n", - "for i, group in kmeans_groups:\n", - " data = group[[\"poll_date\", \"obama_spread\"]]\n", - " data = pandas.concat((data, national_data2012[[\"poll_date\", \"obama_spread\"]]))\n", - " \n", - " data.sort(\"poll_date\", inplace=True)\n", - " dates = pandas.DatetimeIndex(data.poll_date).asi8\n", - "\n", - " loess_res = sm.nonparametric.lowess(data.obama_spread.values, dates, \n", - " frac=.1, it=3)\n", - " states = group.State.unique()\n", - " for state in states:\n", - " trends.append([state, loess_res[-7:,1].mean()])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 146 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 147, - "text": [ - "[['Arizona', 2.3149200179538716],\n", - " ['Georgia', 2.3149200179538716],\n", - " ['Mississippi', 2.3149200179538716],\n", - " ['New Mexico', 2.3149200179538716],\n", - " ['North Carolina', 2.3149200179538716],\n", - " ['South Carolina', 2.3149200179538716],\n", - " ['Tennessee', 2.3149200179538716],\n", - " ['West Virginia', 2.3149200179538716],\n", - " ['Colorado', 18.412063676088412],\n", - " ['Connecticut', 18.412063676088412],\n", - " ['Hawaii', 18.412063676088412],\n", - " ['Illinois', 18.412063676088412],\n", - " ['Maryland', 18.412063676088412],\n", - " ['Massachusetts', 18.412063676088412],\n", - " ['Nevada', 18.412063676088412],\n", - " ['New Jersey', 18.412063676088412],\n", - " ['Rhode Island', 18.412063676088412],\n", - " ['Virginia', 18.412063676088412],\n", - " ['Washington', 18.412063676088412],\n", - " ['California', 2.73263672729736],\n", - " ['Florida', 2.73263672729736],\n", - " ['New York', 2.73263672729736],\n", - " ['Texas', 2.73263672729736],\n", - " ['Indiana', 6.5865280433068092],\n", - " ['Iowa', 6.5865280433068092],\n", - " ['Kansas', 6.5865280433068092],\n", - " ['Maine', 6.5865280433068092],\n", - " ['Michigan', 6.5865280433068092],\n", - " ['Minnesota', 6.5865280433068092],\n", - " ['Missouri', 6.5865280433068092],\n", - " ['Montana', 6.5865280433068092],\n", - " ['Nebraska', 6.5865280433068092],\n", - " ['New Hampshire', 6.5865280433068092],\n", - " ['North Dakota', 6.5865280433068092],\n", - " ['Ohio', 6.5865280433068092],\n", - " ['Oregon', 6.5865280433068092],\n", - " ['Pennsylvania', 6.5865280433068092],\n", - " ['South Dakota', 6.5865280433068092],\n", - " ['Utah', 6.5865280433068092],\n", - " ['Vermont', 6.5865280433068092],\n", - " ['Wisconsin', 6.5865280433068092]]" - ] - } - ], - "prompt_number": 147 - }, - { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "Adjust for sensitivity to time-trends" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\\text{Margin}=X_i+Z_t+\\epsilon$$\n", - "\n", - "where $S_i$ are Pollster:State dummies. In a state with a time-dependent trend, you might write\n", - "\n", - "$$\\text{Margin}=X_i+m*Z_t$$\n", - "\n", - "where $m$ is a multiplier representing uncertainty in the time-trend parameter. Solving for $m$ gives\n", - "\n", - "$$m=\\text{Margin}-\\frac{X_i}{Z_t}$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from statsmodels.formula.api import ols, wls" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 148 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#pollster_state_dummy = state_data2012.groupby([\"Pollster\", \"State\"])[\"obama_spread\"].mean()\n", - "#daily_dummy = state_data2012.groupby([\"poll_date\"])[\"obama_spread\"].mean()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 149 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012[\"pollster_state\"] = state_data2012[\"Pollster\"] + \"-\" + state_data2012[\"State\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 150 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There's actually a bug in pandas when you merge on datetimes. In order to avoid it, we need to sort our data now and once again after we merge on dates." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 151 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dummy_model = ols(\"obama_spread ~ C(pollster_state) + C(poll_date)\", data=state_data2012).fit()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 152 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The base case is American Research Group-Colorado" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.irow(0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 153, - "text": [ - "Pollster American Research Group\n", - "State Colorado\n", - "MoE 4\n", - "Obama (D) 49\n", - "Romney (R) 47\n", - "Sample 600\n", - "Spread Obama +2\n", - "obama_spread 2\n", - "poll_date 2012-09-11 00:00:00\n", - "Weight 0.65\n", - "PIE 1.76\n", - "ESS 173\n", - "MESS 173\n", - "time_weight 0.6156\n", - "kmeans_labels 1\n", - "pollster_state American Research Gro...\n", - "Name: 25" - ] - } - ], - "prompt_number": 153 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pollster_state = state_data2012[\"pollster_state\"].unique()\n", - "pollster_state.sort()\n", - "pollster_state_params = dummy_model.params[1:len(pollster_state)] + dummy_model.params[0]\n", - "intercept = dummy_model.params[0]\n", - "X = pandas.DataFrame(zip(pollster_state, np.r_[intercept, pollster_state_params]), \n", - " columns=[\"pollster_state\", \"X\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 154 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dates = state_data2012.poll_date.unique()\n", - "dates.sort()\n", - "dates_params = intercept + dummy_model.params[-len(dates):]\n", - "Z = pandas.DataFrame(zip(dates, dates_params), columns=[\"poll_date\", \"Z\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 155 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Drop the ones less than 1." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Z = Z.ix[np.abs(Z.Z) > 1]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 156 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012 = state_data2012.merge(X, on=\"pollster_state\", sort=False)\n", - "state_data2012 = state_data2012.merge(Z, on=\"poll_date\", sort=False)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 157 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 158 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_data2012[\"m\"] = state_data2012[\"obama_spread\"].sub(state_data2012[\"X\"].div(state_data2012[\"Z\"]))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 159 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#m_dataframe.ix[m_dataframe.pollster_state == \"American Research Group-New Hampshire\"].values" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 160 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_dataframe = state_data2012[[\"State\", \"m\", \"poll_date\", \"Pollster\", \"pollster_state\"]]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 161 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_dataframe[\"m\"].describe()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 162, - "text": [ - "count 355.000\n", - "mean 3.281\n", - "std 9.168\n", - "min -52.000\n", - "25% -0.808\n", - "50% 2.697\n", - "75% 8.145\n", - "max 38.723" - ] - } - ], - "prompt_number": 162 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_size = m_dataframe.groupby(\"pollster_state\").size()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 163 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_size" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 164, - "text": [ - "pollster_state\n", - "American Research Group-Colorado 1\n", - "American Research Group-Florida 1\n", - "American Research Group-Iowa 1\n", - "American Research Group-Nevada 1\n", - "American Research Group-New Hampshire 3\n", - "American Research Group-North Carolina 1\n", - "American Research Group-Ohio 1\n", - "American Research Group-Virginia 1\n", - "CNN / Opinion Research-Wisconsin 1\n", - "Chicago Trib. / MarketShares-Illinois 1\n", - "Columbus Dispatch (OH)-Ohio 2\n", - "EPIC-MRA-Michigan 8\n", - "Fairleigh-Dickinson (NJ)-New Jersey 3\n", - "Field Poll (CA)-California 6\n", - "Insider Advantage-Georgia 2\n", - "LA Times / Bloomberg-New Hampshire 1\n", - "Marist (NY)-New York 3\n", - "Mason-Dixon-Florida 3\n", - "Mason-Dixon-Georgia 1\n", - "Mason-Dixon-New Hampshire 1\n", - "Mason-Dixon-North Dakota 1\n", - "Mason-Dixon-Utah 1\n", - "Mason-Dixon-Virginia 1\n", - "Mitchell-Michigan 3\n", - "Ohio Poll-Ohio 2\n", - "Public Policy Polling (PPP)-Arizona 7\n", - "Public Policy Polling (PPP)-California 2\n", - "Public Policy Polling (PPP)-Colorado 6\n", - "Public Policy Polling (PPP)-Connecticut 3\n", - "Public Policy Polling (PPP)-Florida 8\n", - "Public Policy Polling (PPP)-Georgia 1\n", - "Public Policy Polling (PPP)-Hawaii 1\n", - "Public Policy Polling (PPP)-Iowa 8\n", - "Public Policy Polling (PPP)-Maine 2\n", - "Public Policy Polling (PPP)-Maryland 1\n", - "Public Policy Polling (PPP)-Massachusetts 6\n", - "Public Policy Polling (PPP)-Michigan 6\n", - "Public Policy Polling (PPP)-Minnesota 5\n", - "Public Policy Polling (PPP)-Mississippi 2\n", - "Public Policy Polling (PPP)-Missouri 7\n", - "Public Policy Polling (PPP)-Montana 3\n", - "Public Policy Polling (PPP)-Nebraska 1\n", - "Public Policy Polling (PPP)-Nevada 4\n", - "Public Policy Polling (PPP)-New Hampshire 3\n", - "Public Policy Polling (PPP)-New Mexico 6\n", - "Public Policy Polling (PPP)-North Carolina 22\n", - "Public Policy Polling (PPP)-Ohio 9\n", - "Public Policy Polling (PPP)-Oregon 2\n", - "Public Policy Polling (PPP)-Pennsylvania 5\n", - "Public Policy Polling (PPP)-Rhode Island 1\n", - "Public Policy Polling (PPP)-South Carolina 3\n", - "Public Policy Polling (PPP)-South Dakota 1\n", - "Public Policy Polling (PPP)-Tennessee 1\n", - "Public Policy Polling (PPP)-Texas 3\n", - "Public Policy Polling (PPP)-Utah 1\n", - "Public Policy Polling (PPP)-Virginia 7\n", - "Public Policy Polling (PPP)-Washington 3\n", - "Public Policy Polling (PPP)-West Virginia 3\n", - "Public Policy Polling (PPP)-Wisconsin 6\n", - "Quinnipiac-Connecticut 4\n", - "Quinnipiac-Florida 12\n", - "Quinnipiac-New Jersey 8\n", - "Quinnipiac-New York 5\n", - "Quinnipiac-Ohio 11\n", - "Quinnipiac-Pennsylvania 9\n", - "Quinnipiac-Virginia 5\n", - "Rasmussen-Arizona 3\n", - "Rasmussen-California 1\n", - "Rasmussen-Colorado 3\n", - "Rasmussen-Connecticut 1\n", - "Rasmussen-Florida 5\n", - "Rasmussen-Indiana 1\n", - "Rasmussen-Iowa 3\n", - "Rasmussen-Maine 1\n", - "Rasmussen-Massachusetts 4\n", - "Rasmussen-Michigan 2\n", - "Rasmussen-Missouri 6\n", - "Rasmussen-Montana 5\n", - "Rasmussen-Nebraska 2\n", - "Rasmussen-Nevada 3\n", - "Rasmussen-New Hampshire 1\n", - "Rasmussen-New Jersey 1\n", - "Rasmussen-New Mexico 3\n", - "Rasmussen-North Carolina 4\n", - "Rasmussen-North Dakota 1\n", - "Rasmussen-Ohio 7\n", - "Rasmussen-Pennsylvania 4\n", - "Rasmussen-Virginia 5\n", - "Rasmussen-Washington 1\n", - "Rasmussen-Wisconsin 7\n", - "Suffolk (NH/MA)-Florida 2\n", - "SurveyUSA-California 4\n", - "SurveyUSA-Florida 2\n", - "SurveyUSA-Georgia 4\n", - "SurveyUSA-Kansas 2\n", - "SurveyUSA-Michigan 1\n", - "SurveyUSA-New Jersey 1\n", - "SurveyUSA-New York 1\n", - "SurveyUSA-North Carolina 2\n", - "SurveyUSA-Oregon 4\n", - "SurveyUSA-Pennsylvania 1\n", - "SurveyUSA-Washington 4\n", - "Length: 102" - ] - } - ], - "prompt_number": 164 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "drop_idx = m_size.ix[m_size == 1]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 165 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_dataframe = m_dataframe.set_index([\"pollster_state\", \"poll_date\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 166 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_dataframe.xs(\"American Research Group-New Hampshire\", level=0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 167, - "text": [ - " State m Pollster\n", - "poll_date \n", - "2012-03-17 New Hampshire 6.437 American Research Group\n", - "2012-06-23 New Hampshire 0.071 American Research Group\n", - "2012-09-26 New Hampshire 4.055 American Research Group" - ] - } - ], - "prompt_number": 167 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_dataframe = m_dataframe.drop(drop_idx.index, level=0).reset_index()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 168 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_dataframe" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 169, - "text": [ - "\n", - "Int64Index: 320 entries, 0 to 319\n", - "Data columns:\n", - "pollster_state 320 non-null values\n", - "poll_date 320 non-null values\n", - "State 320 non-null values\n", - "m 320 non-null values\n", - "Pollster 320 non-null values\n", - "dtypes: datetime64[ns](1), float64(1), object(3)" - ] - } - ], - "prompt_number": 169 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_regression_data = m_dataframe.merge(demo_data, on=\"State\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 170 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_regression_data" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 171, - "text": [ - "\n", - "Int64Index: 320 entries, 0 to 319\n", - "Data columns:\n", - "pollster_state 320 non-null values\n", - "poll_date 320 non-null values\n", - "State 320 non-null values\n", - "m 320 non-null values\n", - "Pollster 320 non-null values\n", - "per_black 320 non-null values\n", - "per_hisp 320 non-null values\n", - "per_white 320 non-null values\n", - "educ_hs 320 non-null values\n", - "educ_coll 320 non-null values\n", - "average_income 320 non-null values\n", - "median_income 320 non-null values\n", - "pop_density 320 non-null values\n", - "vote_pop 320 non-null values\n", - "older_pop 320 non-null values\n", - "dem_adv 320 non-null values\n", - "no_party 320 non-null values\n", - "PVI 320 non-null values\n", - "obama_give 320 non-null values\n", - "romney_give 320 non-null values\n", - "kmeans_group 320 non-null values\n", - "kmeans_labels 320 non-null values\n", - "dtypes: datetime64[ns](1), float64(14), int64(4), object(3)" - ] - } - ], - "prompt_number": 171 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_regression_data[[\"PVI\", \"per_black\", \"per_hisp\", \"older_pop\", \"average_income\", \n", - " \"romney_give\", \"obama_give\", \"educ_coll\", \"educ_hs\"]].corr()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 172, - "text": [ - " PVI per_black per_hisp older_pop average_income romney_give obama_give educ_coll educ_hs\n", - "PVI 1.000 -0.295 0.115 0.150 0.594 0.291 0.669 0.494 0.226\n", - "per_black -0.295 1.000 -0.174 0.279 -0.064 0.111 -0.281 -0.111 -0.497\n", - "per_hisp 0.115 -0.174 1.000 0.403 0.098 0.289 0.306 0.112 -0.566\n", - "older_pop 0.150 0.279 0.403 1.000 0.022 0.237 -0.038 -0.076 -0.479\n", - "average_income 0.594 -0.064 0.098 0.022 1.000 0.718 0.704 0.888 0.250\n", - "romney_give 0.291 0.111 0.289 0.237 0.718 1.000 0.555 0.630 -0.025\n", - "obama_give 0.669 -0.281 0.306 -0.038 0.704 0.555 1.000 0.835 0.085\n", - "educ_coll 0.494 -0.111 0.112 -0.076 0.888 0.630 0.835 1.000 0.273\n", - "educ_hs 0.226 -0.497 -0.566 -0.479 0.250 -0.025 0.085 0.273 1.000" - ] - } - ], - "prompt_number": 172 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "(today - m_regression_data[\"poll_date\"].astype('O'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 173, - "text": [ - "0 743 days, 0:00:00\n", - "1 612 days, 0:00:00\n", - "2 521 days, 0:00:00\n", - "3 227 days, 0:00:00\n", - "4 136 days, 0:00:00\n", - "5 70 days, 0:00:00\n", - "6 24 days, 0:00:00\n", - "7 203 days, 0:00:00\n", - "8 98 days, 0:00:00\n", - "9 7 days, 0:00:00\n", - "10 391 days, 0:00:00\n", - "11 316 days, 0:00:00\n", - "12 235 days, 0:00:00\n", - "13 130 days, 0:00:00\n", - "14 97 days, 0:00:00\n", - "...\n", - "305 29 days, 0:00:00\n", - "306 1 day, 0:00:00\n", - "307 584 days, 0:00:00\n", - "308 500 days, 0:00:00\n", - "309 409 days, 0:00:00\n", - "310 220 days, 0:00:00\n", - "311 87 days, 0:00:00\n", - "312 13 days, 0:00:00\n", - "313 342 days, 0:00:00\n", - "314 218 days, 0:00:00\n", - "315 189 days, 0:00:00\n", - "316 146 days, 0:00:00\n", - "317 112 days, 0:00:00\n", - "318 69 days, 0:00:00\n", - "319 15 days, 0:00:00\n", - "Name: poll_date, Length: 320" - ] - } - ], - "prompt_number": 173 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "time_weights = (today - m_regression_data[\"poll_date\"].astype('O')).apply(exp_decay)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 174 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_model = wls(\"m ~ PVI + per_hisp + per_black + average_income + educ_coll\", data=m_regression_data, weights=time_weights).fit()\n", - "m_model.summary()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
WLS Regression Results
Dep. Variable: m R-squared: 0.704
Model: WLS Adj. R-squared: 0.699
Method: Least Squares F-statistic: 149.4
Date: Fri, 26 Oct 2012 Prob (F-statistic): 8.75e-81
Time: 08:31:09 Log-Likelihood: -632.76
No. Observations: 320 AIC: 1278.
Df Residuals: 314 BIC: 1300.
Df Model: 5
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.5623 2.504 1.822 0.069 -0.364 9.488
PVI 1.5534 0.076 20.565 0.000 1.405 1.702
per_hisp 0.1672 0.023 7.351 0.000 0.122 0.212
per_black 0.1972 0.040 4.954 0.000 0.119 0.275
average_income -0.0003 0.000 -1.836 0.067 -0.001 2.17e-05
educ_coll 0.0612 0.121 0.506 0.613 -0.177 0.299
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 113.511 Durbin-Watson: 1.677
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1263.298
Skew: -1.115 Prob(JB): 4.77e-275
Kurtosis: 12.475 Cond. No. 2.71e+05
" - ], - "output_type": "pyout", - "prompt_number": 175, - "text": [ - "\n", - "\"\"\"\n", - " WLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: m R-squared: 0.704\n", - "Model: WLS Adj. R-squared: 0.699\n", - "Method: Least Squares F-statistic: 149.4\n", - "Date: Fri, 26 Oct 2012 Prob (F-statistic): 8.75e-81\n", - "Time: 08:31:09 Log-Likelihood: -632.76\n", - "No. Observations: 320 AIC: 1278.\n", - "Df Residuals: 314 BIC: 1300.\n", - "Df Model: 5 \n", - "==================================================================================\n", - " coef std err t P>|t| [95.0% Conf. Int.]\n", - "----------------------------------------------------------------------------------\n", - "Intercept 4.5623 2.504 1.822 0.069 -0.364 9.488\n", - "PVI 1.5534 0.076 20.565 0.000 1.405 1.702\n", - "per_hisp 0.1672 0.023 7.351 0.000 0.122 0.212\n", - "per_black 0.1972 0.040 4.954 0.000 0.119 0.275\n", - "average_income -0.0003 0.000 -1.836 0.067 -0.001 2.17e-05\n", - "educ_coll 0.0612 0.121 0.506 0.613 -0.177 0.299\n", - "==============================================================================\n", - "Omnibus: 113.511 Durbin-Watson: 1.677\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1263.298\n", - "Skew: -1.115 Prob(JB): 4.77e-275\n", - "Kurtosis: 12.475 Cond. No. 2.71e+05\n", - "==============================================================================\n", - "\n", - "The condition number is large, 2.71e+05. This might indicate that there are\n", - "strong multicollinearity or other numerical problems.\n", - "\"\"\"" - ] - } - ], - "prompt_number": 175 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_resid = pandas.DataFrame(zip(m_model.resid, m_regression_data.State), \n", - " columns=[\"resid\", \"State\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 176 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_resid_group = state_resid.groupby(\"State\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 177 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, axes = plt.subplots(figsize=(12,8), subplot_kw={\"ylabel\" : \"Residual\",\n", - " \"xlabel\" : \"State\"})\n", - "i = 0\n", - "for state, group in state_resid_group:\n", - " x = [i] * len(group)\n", - " axes.scatter(x, group[\"resid\"], s=91)\n", - " i += 1\n", - "states = m_regression_data.State.unique()\n", - "states.sort()\n", - "#axes.xaxis.get_major_locator().set_params(nbins=len(states))\n", - "axes.margins(.05, .05)\n", - "axes.xaxis.set_ticks(range(31))\n", - "axes.xaxis.set_ticklabels(states);\n", - "for label in axes.xaxis.get_ticklabels():\n", - " label.set_rotation(90)\n", - " label.set_fontsize('large')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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jMxHz644fH31xismTbezeXfzDdDq8honOO3pUZe3a2ONKP/xg4dixovUA79ql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8fnj3PjcvnfTOL6dSXy7PtnlAr2SpX8s5mL4FGunMQ//2njnXcyj1hNmGBTleb1h58ZFSXnKFnH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\nExXGjrVx5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ZEjub8gc9uYLxCEi1zfBO3bt6dly5bUrFmTs2fPMnToUJo3b54fbStwSpdW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Pd+/exYgRI/QTNSncRW3FihU1fr+NvUaP6jbFWbFiBV5//XUMGjSo2uvc2rVrG2UT1pdffsnk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\nmlxeU6qiyTN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- } - ], - "prompt_number": 178 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "demo_data = demo_data.drop(demo_data.index[demo_data['State'] == 'District of Columbia'])\n", - "demo_data.reset_index(drop=True, inplace=True);" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 179 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "exog = demo_data[[\"PVI\", \"per_hisp\", \"per_black\", \"average_income\", \"educ_coll\"]]\n", - "exog[\"const\"] = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 180 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_m = m_model.predict(exog)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 181 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_m" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 182, - "text": [ - "array([-15.3603, -21.5913, -4.9947, -10.5032, 16.1222, 1.9831,\n", - " 10.9647, 14.0814, 2.0932, -4.6139, 18.2001, -25.0872,\n", - " 15.766 , -7.7724, 1.4341, -17.1113, -14.4254, -9.5522,\n", - " 6.7645, 17.5081, 18.4677, 8.2765, 2.5286, -7.98 ,\n", - " -3.1192, -11.1954, -19.0509, 5.3332, 0.9212, 8.2565,\n", - " 10.5848, 19.1612, -1.7616, -16.5408, -0.1307, -24.3894,\n", - " 7.0121, 4.3192, 18.3818, -7.0588, -14.4 , -11.0846,\n", - " -8.1381, -29.2403, 18.9684, -0.9221, 7.9232, -12.3191,\n", - " 3.498 , -31.7474])" - ] - } - ], - "prompt_number": 182 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "unit_m = (state_m - state_m.min())/(state_m.max() - state_m.min())" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 183 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "unit_m *= 2" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 184 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_correction = zip(demo_data.State, unit_m)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 185 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, axes = plt.subplots(figsize=(12,8), subplot_kw={\"ylabel\" : \"Time Uncertainty\",\n", - " \"xlabel\" : \"State\"})\n", - "\n", - "axes.scatter(range(len(unit_m)), unit_m, s=91)\n", - "\n", - "axes.margins(.05, .05)\n", - "axes.xaxis.set_ticks(range(len(unit_m)))\n", - "axes.xaxis.set_ticklabels(demo_data.State);\n", - "for label in axes.xaxis.get_ticklabels():\n", - " label.set_rotation(90)\n", - " label.set_fontsize('large')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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sP+L0r25HMcjnA7791o577kmB13vmBaULFjjQurUXs2YVon59bqRJPnxCLUxW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\nIRiGQWFhIS/7WKukp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- } - ], - "prompt_number": 186 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m_correction" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 187, - "text": [ - "[('Alabama', 0.64378739293503406),\n", - " ('Alaska', 0.39899365289624461),\n", - " ('Arizona', 1.0510086330835007),\n", - " ('Arkansas', 0.83460144787235913),\n", - " ('California', 1.8806123579140055),\n", - " ('Colorado', 1.3251405679451049),\n", - " ('Connecticut', 1.6779924829159023),\n", - " ('Delaware', 1.8004372432963116),\n", - " ('Florida', 1.329465611597048),\n", - " ('Georgia', 1.0659702728954288),\n", - " ('Hawaii', 1.9622429470675198),\n", - " ('Idaho', 0.26165481506057381),\n", - " ('Illinois', 1.8666167182198876),\n", - " ('Indiana', 0.94188345895010406),\n", - " ('Iowa', 1.3035708167337279),\n", - " ('Kansas', 0.57499466689906564),\n", - " ('Kentucky', 0.68051576608007691),\n", - " ('Louisiana', 0.87196446188500021),\n", - " ('Maine', 1.5129826861514035),\n", - " ('Maryland', 1.9350556925421645),\n", - " ('Massachusetts', 1.972758329102086),\n", - " ('Michigan', 1.572382623529095),\n", - " ('Minnesota', 1.3465700819595314),\n", - " ('Mississippi', 0.9337304745438173),\n", - " ('Missouri', 1.1246911889623949),\n", - " ('Montana', 0.80740786234605832),\n", - " ('Nebraska', 0.49879580201830931),\n", - " ('Nevada', 1.4567544736098006),\n", - " ('New Hampshire', 1.2834218995227675),\n", - " ('New Jersey', 1.5715997823676553),\n", - " ('New Mexico', 1.6630695718979507),\n", - " ('New York', 2.0),\n", - " ('North Carolina', 1.1780268665681453),\n", - " ('North Dakota', 0.59740688290763611),\n", - " ('Ohio', 1.2420974975622283),\n", - " ('Oklahoma', 0.28906929360446137),\n", - " ('Oregon', 1.5227100227420458),\n", - " ('Pennsylvania', 1.4169161758937938),\n", - " ('Rhode Island', 1.9693830170636866),\n", - " ('South Carolina', 0.96992120743772237),\n", - " ('South Dakota', 0.68151306828176339),\n", - " ('Tennessee', 0.81176165562541391),\n", - " ('Texas', 0.92751616650445501),\n", - " ('Utah', 0.098492882545137092),\n", - " ('Vermont', 1.9924269957578287),\n", - " ('Virginia', 1.2110060089821313),\n", - " ('Washington', 1.5585050874076187),\n", - " ('West Virginia', 0.76326334710489518),\n", - " ('Wisconsin', 1.3846558069742687),\n", - " ('Wyoming', 0.0)]" - ] - } - ], - "prompt_number": 187 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends = pandas.DataFrame(trends, columns=[\"State\", \"trend\"])\n", - "m_correction = pandas.DataFrame(m_correction, columns=[\"State\", \"m_correction\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 188 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends = trends.merge(m_correction, on=\"State\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 189 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends.set_index(\"State\", inplace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 190, - "text": [ - " trend m_correction\n", - "State \n", - "Arizona 2.315 1.051\n", - "California 2.733 1.881\n", - "Colorado 18.412 1.325\n", - "Connecticut 18.412 1.678\n", - "Florida 2.733 1.329\n", - "Georgia 2.315 1.066\n", - "Hawaii 18.412 1.962\n", - "Illinois 18.412 1.867\n", - "Indiana 6.587 0.942\n", - "Iowa 6.587 1.304\n", - "Kansas 6.587 0.575\n", - "Maine 6.587 1.513\n", - "Maryland 18.412 1.935\n", - "Massachusetts 18.412 1.973\n", - "Michigan 6.587 1.572\n", - "Minnesota 6.587 1.347\n", - "Mississippi 2.315 0.934\n", - "Missouri 6.587 1.125\n", - "Montana 6.587 0.807\n", - "Nebraska 6.587 0.499\n", - "Nevada 18.412 1.457\n", - "New Hampshire 6.587 1.283\n", - "New Jersey 18.412 1.572\n", - "New Mexico 2.315 1.663\n", - "New York 2.733 2.000\n", - "North Carolina 2.315 1.178\n", - "North Dakota 6.587 0.597\n", - "Ohio 6.587 1.242\n", - "Oregon 6.587 1.523\n", - "Pennsylvania 6.587 1.417\n", - "Rhode Island 18.412 1.969\n", - "South Carolina 2.315 0.970\n", - "South Dakota 6.587 0.682\n", - "Tennessee 2.315 0.812\n", - "Texas 2.733 0.928\n", - "Utah 6.587 0.098\n", - "Vermont 6.587 1.992\n", - "Virginia 18.412 1.211\n", - "Washington 18.412 1.559\n", - "West Virginia 2.315 0.763\n", - "Wisconsin 6.587 1.385" - ] - } - ], - "prompt_number": 190 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends = trends.product(axis=1)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 191 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Snapshot: Combine Trend Estimates and State Polls" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_polls.name = \"poll\"\n", - "state_polls" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 192, - "text": [ - "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168\n", - " Rasmussen -10.209\n", - "CA Field Poll (CA) 23.344\n", - " Public Policy Polling (PPP) 20.999\n", - " Rasmussen 22.000\n", - " SurveyUSA 22.123\n", - "CO American Research Group 2.000\n", - " Public Policy Polling (PPP) 5.470\n", - " Rasmussen -1.574\n", - "CT Public Policy Polling (PPP) 12.758\n", - " Quinnipiac 7.294\n", - " Rasmussen 8.000\n", - "FL American Research Group 5.000\n", - " Mason-Dixon -3.543\n", - " Public Policy Polling (PPP) 3.125\n", - " Quinnipiac 3.076\n", - " Rasmussen 0.883\n", - " Suffolk (NH/MA) -0.003\n", - " SurveyUSA 4.169\n", - "GA Insider Advantage -19.174\n", - " Mason-Dixon -17.000\n", - " Public Policy Polling (PPP) -3.000\n", - " SurveyUSA -7.984\n", - "HI Public Policy Polling (PPP) 27.000\n", - "IA American Research Group 7.000\n", - " Mason-Dixon -3.000\n", - " Public Policy Polling (PPP) 5.879\n", - " Rasmussen -2.749\n", - "IL Chicago Trib. / MarketShares 21.000\n", - "IN Rasmussen -16.000\n", - "KS SurveyUSA -15.875\n", - "MA Public Policy Polling (PPP) 17.580\n", - " Rasmussen 15.107\n", - "MD Public Policy Polling (PPP) 23.000\n", - "ME Public Policy Polling (PPP) 16.038\n", - " Rasmussen 12.000\n", - "MI CNN / Opinion Research 8.000\n", - " EPIC-MRA 7.430\n", - " Mitchell 0.897\n", - " Public Policy Polling (PPP) 7.694\n", - " Rasmussen 11.072\n", - " SurveyUSA 11.000\n", - "MN Public Policy Polling (PPP) 7.335\n", - "MO Public Policy Polling (PPP) -11.225\n", - " Rasmussen -2.486\n", - " SurveyUSA -1.000\n", - "MS Public Policy Polling (PPP) -17.973\n", - "MT Mason-Dixon -9.000\n", - " Public Policy Polling (PPP) -5.003\n", - " Rasmussen -15.641\n", - "NC American Research Group -4.000\n", - " Public Policy Polling (PPP) 0.261\n", - " Rasmussen -5.676\n", - " SurveyUSA 1.987\n", - "ND Mason-Dixon -13.000\n", - " Rasmussen -15.000\n", - "NE Public Policy Polling (PPP) -12.005\n", - " Rasmussen -14.308\n", - "NH American Research Group 4.150\n", - " LA Times / Bloomberg -10.000\n", - " Mason-Dixon -11.000\n", - " Public Policy Polling (PPP) 6.273\n", - " Rasmussen -2.439\n", - "NJ Fairleigh-Dickinson (NJ) 13.859\n", - " Public Policy Polling (PPP) 14.006\n", - " Quinnipiac 7.504\n", - " Rasmussen 6.000\n", - " SurveyUSA 14.000\n", - "NM Public Policy Polling (PPP) 10.621\n", - " Rasmussen 11.651\n", - "NV American Research Group 7.000\n", - " CNN / Opinion Research 3.000\n", - " Public Policy Polling (PPP) 7.345\n", - " Rasmussen 2.524\n", - "NY Marist (NY) 22.047\n", - " Quinnipiac 27.345\n", - " SurveyUSA 30.000\n", - "OH American Research Group 1.000\n", - " Columbus Dispatch (OH) 8.616\n", - " Ohio Poll 3.000\n", - " Public Policy Polling (PPP) 4.142\n", - " Quinnipiac 7.729\n", - " Rasmussen 0.866\n", - "OR Public Policy Polling (PPP) 9.130\n", - " SurveyUSA 8.676\n", - "PA Public Policy Polling (PPP) 6.160\n", - " Quinnipiac 6.047\n", - " Rasmussen 10.875\n", - " SurveyUSA 0.000\n", - "RI Public Policy Polling (PPP) 17.000\n", - "SC Public Policy Polling (PPP) -14.558\n", - "SD Public Policy Polling (PPP) -6.000\n", - "TN Public Policy Polling (PPP) -7.000\n", - "TX Public Policy Polling (PPP) -6.999\n", - "UT Mason-Dixon -51.000\n", - " Public Policy Polling (PPP) -32.000\n", - "VA American Research Group 2.000\n", - " Mason-Dixon 1.000\n", - " Public Policy Polling (PPP) 5.096\n", - " Quinnipiac 0.578\n", - " Rasmussen 0.892\n", - "VT Public Policy Polling (PPP) 20.000\n", - "WA Public Policy Polling (PPP) 13.051\n", - " Rasmussen 11.000\n", - " SurveyUSA 15.310\n", - "WI CNN / Opinion Research 4.000\n", - " Public Policy Polling (PPP) 5.393\n", - " Rasmussen 2.116\n", - "WV Public Policy Polling (PPP) -19.757\n", - "Name: poll, Length: 109" - ] - } - ], - "prompt_number": 192 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "state_polls = state_polls.reset_index()\n", - "state_polls.State = state_polls.State.replace(states_abbrev_dict)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 193 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends.name = \"poll\"\n", - "trends" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 194, - "text": [ - "State\n", - "Arizona 2.433\n", - "California 5.139\n", - "Colorado 24.399\n", - "Connecticut 30.895\n", - "Florida 3.633\n", - "Georgia 2.468\n", - "Hawaii 36.129\n", - "Illinois 34.368\n", - "Indiana 6.204\n", - "Iowa 8.586\n", - "Kansas 3.787\n", - "Maine 9.965\n", - "Maryland 35.628\n", - "Massachusetts 36.323\n", - "Michigan 10.357\n", - "Minnesota 8.869\n", - "Mississippi 2.162\n", - "Missouri 7.408\n", - "Montana 5.318\n", - "Nebraska 3.285\n", - "Nevada 26.822\n", - "New Hampshire 8.453\n", - "New Jersey 28.936\n", - "New Mexico 3.850\n", - "New York 5.465\n", - "North Carolina 2.727\n", - "North Dakota 3.935\n", - "Ohio 8.181\n", - "Oregon 10.029\n", - "Pennsylvania 9.333\n", - "Rhode Island 36.260\n", - "South Carolina 2.245\n", - "South Dakota 4.489\n", - "Tennessee 1.879\n", - "Texas 2.535\n", - "Utah 0.649\n", - "Vermont 13.123\n", - "Virginia 22.297\n", - "Washington 28.695\n", - "West Virginia 1.767\n", - "Wisconsin 9.120\n", - "Name: poll" - ] - } - ], - "prompt_number": 194 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends = trends.reset_index()\n", - "trends[\"Pollster\"] = \"National\"" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 195 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trends" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 196, - "text": [ - " State poll Pollster\n", - "0 Arizona 2.433 National\n", - "1 California 5.139 National\n", - "2 Colorado 24.399 National\n", - "3 Connecticut 30.895 National\n", - "4 Florida 3.633 National\n", - "5 Georgia 2.468 National\n", - "6 Hawaii 36.129 National\n", - "7 Illinois 34.368 National\n", - "8 Indiana 6.204 National\n", - "9 Iowa 8.586 National\n", - "10 Kansas 3.787 National\n", - "11 Maine 9.965 National\n", - "12 Maryland 35.628 National\n", - "13 Massachusetts 36.323 National\n", - "14 Michigan 10.357 National\n", - "15 Minnesota 8.869 National\n", - "16 Mississippi 2.162 National\n", - "17 Missouri 7.408 National\n", - "18 Montana 5.318 National\n", - "19 Nebraska 3.285 National\n", - "20 Nevada 26.822 National\n", - "21 New Hampshire 8.453 National\n", - "22 New Jersey 28.936 National\n", - "23 New Mexico 3.850 National\n", - "24 New York 5.465 National\n", - "25 North Carolina 2.727 National\n", - "26 North Dakota 3.935 National\n", - "27 Ohio 8.181 National\n", - "28 Oregon 10.029 National\n", - "29 Pennsylvania 9.333 National\n", - "30 Rhode Island 36.260 National\n", - "31 South Carolina 2.245 National\n", - "32 South Dakota 4.489 National\n", - "33 Tennessee 1.879 National\n", - "34 Texas 2.535 National\n", - "35 Utah 0.649 National\n", - "36 Vermont 13.123 National\n", - "37 Virginia 22.297 National\n", - "38 Washington 28.695 National\n", - "39 West Virginia 1.767 National\n", - "40 Wisconsin 9.120 National" - ] - } - ], - "prompt_number": 196 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "polls = pandas.concat((state_polls, trends))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 197 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "weights" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 198, - "text": [ - " Pollster Weight PIE\n", - "0 ABC / Washington Post 0.95 1.41\n", - "1 American Research Group 0.65 1.76\n", - "2 CBS / New York Times 0.66 1.84\n", - "3 Chicago Trib. / Marke... 1.16 1.13\n", - "4 CNN / Opinion Research 0.77 1.59\n", - "5 Columbus Dispatch (OH) 0.50 6.76\n", - "6 EPIC-MRA 0.75 1.65\n", - "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", - "8 Field Poll (CA) 1.33 0.88\n", - "9 Fox / Opinion Dynamics 0.79 1.60\n", - "10 Franklin Pierce (NH) 0.74 1.60\n", - "11 Insider Advantage 0.95 1.29\n", - "12 Keystone (PA) 0.64 1.55\n", - "13 LA Times / Bloomberg 0.83 1.44\n", - "14 Marist (NY) 0.69 1.73\n", - "15 Mason-Dixon 1.10 1.15\n", - "16 Mitchell 0.96 1.43\n", - "17 Ohio Poll 1.24 1.05\n", - "18 Public Opinion Strate... 0.63 1.81\n", - "19 Public Policy Polling... 1.05 1.60\n", - "20 Quinnipiac 0.95 1.34\n", - "21 Rasmussen 1.30 0.88\n", - "22 Research 2000 1.01 1.20\n", - "23 Selzer 1.47 0.92\n", - "24 Star Tribune (MN) 0.81 2.01\n", - "25 Strategic Vision 0.95 1.45\n", - "26 Suffolk (NH/MA) 0.77 1.37\n", - "27 SurveyUSA 1.91 0.72\n", - "28 Univ. New Hampshire 1.08 1.26\n", - "29 USA Today / Gallup 0.63 2.01\n", - "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74" - ] - } - ], - "prompt_number": 198 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "natl_weight = pandas.DataFrame([[\"National\", weights.Weight.mean(), weights.PIE.mean()]],\n", - " columns=[\"Pollster\", \"Weight\", \"PIE\"])\n", - "weights = pandas.concat((weights, natl_weight)).reset_index(drop=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 199 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "polls = polls.merge(weights, on=\"Pollster\", how=\"left\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 200 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "polls = polls.sort(\"State\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 201 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def weighted_mean(group):\n", - " return (group[\"poll\"] * group[\"Weight\"] / group[\"Weight\"].sum()).sum()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 202 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "group" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 203, - "text": [ - " resid State\n", - "307 5.193 Wisconsin\n", - "308 7.402 Wisconsin\n", - "309 0.246 Wisconsin\n", - "310 9.971 Wisconsin\n", - "311 0.697 Wisconsin\n", - "312 2.648 Wisconsin\n", - "313 -0.300 Wisconsin\n", - "314 0.859 Wisconsin\n", - "315 7.209 Wisconsin\n", - "316 -0.676 Wisconsin\n", - "317 -5.412 Wisconsin\n", - "318 -1.565 Wisconsin\n", - "319 -1.565 Wisconsin" - ] - } - ], - "prompt_number": 203 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results = polls.groupby(\"State\").aggregate(weighted_mean)[\"poll\"]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 204 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 205, - "text": [ - "State\n", - "Arizona -6.351\n", - "California 19.794\n", - "Colorado 6.947\n", - "Connecticut 13.967\n", - "Florida 2.079\n", - "Georgia -8.969\n", - "Hawaii 31.233\n", - "Illinois 26.869\n", - "Indiana -6.870\n", - "Iowa 2.325\n", - "Kansas -9.541\n", - "Maine 12.734\n", - "Maryland 28.856\n", - "Massachusetts 21.816\n", - "Michigan 8.561\n", - "Minnesota 8.046\n", - "Mississippi -8.637\n", - "Missouri -1.974\n", - "Montana -7.035\n", - "Nebraska -8.663\n", - "Nevada 9.022\n", - "New Hampshire -1.133\n", - "New Jersey 13.545\n", - "New Mexico 9.145\n", - "New York 23.207\n", - "North Carolina -0.590\n", - "North Dakota -9.138\n", - "Ohio 4.384\n", - "Oregon 9.117\n", - "Pennsylvania 5.692\n", - "Rhode Island 25.931\n", - "South Carolina -6.767\n", - "South Dakota -1.136\n", - "Tennessee -2.883\n", - "Texas -2.578\n", - "Utah -29.142\n", - "Vermont 16.811\n", - "Virginia 4.985\n", - "Washington 16.118\n", - "West Virginia -9.776\n", - "Wisconsin 4.909\n", - "Name: poll" - ] - } - ], - "prompt_number": 205 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results = results.reset_index()\n", - "results[\"obama\"] = 0\n", - "results[\"romney\"] = 0\n", - "results.ix[results[\"poll\"] > 0, [\"obama\"]] = 1\n", - "results.ix[results[\"poll\"] < 0, [\"romney\"]] = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 206 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results[[\"State\", \"poll\"]].to_csv(\"/home/skipper/school/talks/538model/2012-predicted.csv\", index=False)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "electoral_votes = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/electoral_votes.csv\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 207 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "electoral_votes.sort(\"State\", inplace=True).reset_index(drop=True, inplace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 208, - "text": [ - " State Votes\n", - "0 Alabama 9\n", - "1 Alaska 3\n", - "2 Arizona 11\n", - "3 Arkansas 6\n", - "4 California 55\n", - "5 Colorado 9\n", - "6 Connecticut 7\n", - "7 Delaware 3\n", - "8 District of Columbia 3\n", - "9 Florida 29\n", - "10 Georgia 16\n", - "11 Hawaii 4\n", - "12 Idaho 4\n", - "13 Illinois 20\n", - "14 Indiana 11\n", - "15 Iowa 6\n", - "16 Kansas 6\n", - "17 Kentucky 8\n", - "18 Louisiana 8\n", - "19 Maine 4\n", - "20 Maryland 10\n", - "21 Massachusetts 11\n", - "22 Michigan 16\n", - "23 Minnesota 10\n", - "24 Mississippi 6\n", - "25 Missouri 10\n", - "26 Montana 3\n", - "27 Nebraska 5\n", - "28 Nevada 6\n", - "29 New Hampshire 4\n", - "30 New Jersey 14\n", - "31 New Mexico 5\n", - "32 New York 29\n", - "33 North Carolina 15\n", - "34 North Dakota 3\n", - "35 Ohio 18\n", - "36 Oklahoma 7\n", - "37 Oregon 7\n", - "38 Pennsylvania 20\n", - "39 Rhode Island 4\n", - "40 South Carolina 9\n", - "41 South Dakota 3\n", - "42 Tennessee 11\n", - "43 Texas 38\n", - "44 Utah 6\n", - "45 Vermont 3\n", - "46 Virginia 13\n", - "47 Washington 12\n", - "48 West Virginia 5\n", - "49 Wisconsin 10\n", - "50 Wyoming 3" - ] - } - ], - "prompt_number": 208 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results = electoral_votes.merge(results, on=\"State\", how=\"left\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 209 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results = results.set_index(\"State\")\n", - "red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Kentucky\", \"Louisiana\",\n", - " \"Oklahoma\", \"Wyoming\"]\n", - "blue_states = [\"Delaware\", \"District of Columbia\"]\n", - "results.ix[red_states, [\"romney\"]] = 1\n", - "results.ix[red_states, [\"obama\"]] = 0\n", - "results.ix[blue_states, [\"obama\"]] = 1\n", - "results.ix[blue_states, [\"romney\"]] = 0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 210 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 211, - "text": [ - " Votes poll obama romney\n", - "State \n", - "Alabama 9 NaN 0 1\n", - "Alaska 3 NaN 0 1\n", - "Arizona 11 -6.351 0 1\n", - "Arkansas 6 NaN 0 1\n", - "California 55 19.794 1 0\n", - "Colorado 9 6.947 1 0\n", - "Connecticut 7 13.967 1 0\n", - "Delaware 3 NaN 1 0\n", - "District of Columbia 3 NaN 1 0\n", - "Florida 29 2.079 1 0\n", - "Georgia 16 -8.969 0 1\n", - "Hawaii 4 31.233 1 0\n", - "Idaho 4 NaN 0 1\n", - "Illinois 20 26.869 1 0\n", - "Indiana 11 -6.870 0 1\n", - "Iowa 6 2.325 1 0\n", - "Kansas 6 -9.541 0 1\n", - "Kentucky 8 NaN 0 1\n", - "Louisiana 8 NaN 0 1\n", - "Maine 4 12.734 1 0\n", - "Maryland 10 28.856 1 0\n", - "Massachusetts 11 21.816 1 0\n", - "Michigan 16 8.561 1 0\n", - "Minnesota 10 8.046 1 0\n", - "Mississippi 6 -8.637 0 1\n", - "Missouri 10 -1.974 0 1\n", - "Montana 3 -7.035 0 1\n", - "Nebraska 5 -8.663 0 1\n", - "Nevada 6 9.022 1 0\n", - "New Hampshire 4 -1.133 0 1\n", - "New Jersey 14 13.545 1 0\n", - "New Mexico 5 9.145 1 0\n", - "New York 29 23.207 1 0\n", - "North Carolina 15 -0.590 0 1\n", - "North Dakota 3 -9.138 0 1\n", - "Ohio 18 4.384 1 0\n", - "Oklahoma 7 NaN 0 1\n", - "Oregon 7 9.117 1 0\n", - "Pennsylvania 20 5.692 1 0\n", - "Rhode Island 4 25.931 1 0\n", - "South Carolina 9 -6.767 0 1\n", - "South Dakota 3 -1.136 0 1\n", - "Tennessee 11 -2.883 0 1\n", - "Texas 38 -2.578 0 1\n", - "Utah 6 -29.142 0 1\n", - "Vermont 3 16.811 1 0\n", - "Virginia 13 4.985 1 0\n", - "Washington 12 16.118 1 0\n", - "West Virginia 5 -9.776 0 1\n", - "Wisconsin 10 4.909 1 0\n", - "Wyoming 3 NaN 0 1" - ] - } - ], - "prompt_number": 211 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results[\"Votes\"].mul(results[\"obama\"]).sum()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 212, - "text": [ - "328.0" - ] - } - ], - "prompt_number": 212 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results[\"Votes\"].mul(results[\"romney\"]).sum()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 213, - "text": [ - "210.0" - ] - } - ], - "prompt_number": 213 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 214, - "text": [ - " Votes poll obama romney\n", - "State \n", - "Alabama 9 NaN 0 1\n", - "Alaska 3 NaN 0 1\n", - "Arizona 11 -6.351 0 1\n", - "Arkansas 6 NaN 0 1\n", - "California 55 19.794 1 0\n", - "Colorado 9 6.947 1 0\n", - "Connecticut 7 13.967 1 0\n", - "Delaware 3 NaN 1 0\n", - "District of Columbia 3 NaN 1 0\n", - "Florida 29 2.079 1 0\n", - "Georgia 16 -8.969 0 1\n", - "Hawaii 4 31.233 1 0\n", - "Idaho 4 NaN 0 1\n", - "Illinois 20 26.869 1 0\n", - "Indiana 11 -6.870 0 1\n", - "Iowa 6 2.325 1 0\n", - "Kansas 6 -9.541 0 1\n", - "Kentucky 8 NaN 0 1\n", - "Louisiana 8 NaN 0 1\n", - "Maine 4 12.734 1 0\n", - "Maryland 10 28.856 1 0\n", - "Massachusetts 11 21.816 1 0\n", - "Michigan 16 8.561 1 0\n", - "Minnesota 10 8.046 1 0\n", - "Mississippi 6 -8.637 0 1\n", - "Missouri 10 -1.974 0 1\n", - "Montana 3 -7.035 0 1\n", - "Nebraska 5 -8.663 0 1\n", - "Nevada 6 9.022 1 0\n", - "New Hampshire 4 -1.133 0 1\n", - "New Jersey 14 13.545 1 0\n", - "New Mexico 5 9.145 1 0\n", - "New York 29 23.207 1 0\n", - "North Carolina 15 -0.590 0 1\n", - "North Dakota 3 -9.138 0 1\n", - "Ohio 18 4.384 1 0\n", - "Oklahoma 7 NaN 0 1\n", - "Oregon 7 9.117 1 0\n", - "Pennsylvania 20 5.692 1 0\n", - "Rhode Island 4 25.931 1 0\n", - "South Carolina 9 -6.767 0 1\n", - "South Dakota 3 -1.136 0 1\n", - "Tennessee 11 -2.883 0 1\n", - "Texas 38 -2.578 0 1\n", - "Utah 6 -29.142 0 1\n", - "Vermont 3 16.811 1 0\n", - "Virginia 13 4.985 1 0\n", - "Washington 12 16.118 1 0\n", - "West Virginia 5 -9.776 0 1\n", - "Wisconsin 10 4.909 1 0\n", - "Wyoming 3 NaN 0 1" - ] - } - ], - "prompt_number": 214 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TODO:" + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self._update_inplace(new_data)\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Divide undecided voters probabilistically." + } + ], + "source": [ + "ppp_az[\"ESS\"] = effective_sample(ppp_az[\"total_error\"])\n", + "ppp_az[\"MESS\"] = ppp_az[\"ESS\"].diff()\n", + "# fill in first one\n", + "ppp_az[\"MESS\"].fillna(ppp_az[\"ESS\"].head(1).item(), inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " poll_date Sample cumulative ESS MESS\n", + "198 2012-09-08 00:00:00 993 993 246.182 246.182\n", + "199 2012-07-24 00:00:00 833 1826 325.801 79.618\n", + "200 2012-05-19 00:00:00 500 2326 359.591 33.791\n", + "201 2012-02-18 00:00:00 743 3069 399.185 39.594\n", + "202 2011-11-19 00:00:00 500 3569 420.968 21.783\n", + "203 2011-04-30 00:00:00 623 4192 444.241 23.273\n", + "204 2011-01-29 00:00:00 599 4791 463.531 19.291\n", + "205 2010-09-20 00:00:00 617 5408 480.955 17.424" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ppp_az[[\"poll_date\", \"Sample\", \"cumulative\", \"ESS\", \"MESS\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's do it for every polling firm in every state." + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def calculate_mess(group):\n", + " cumulative = group[\"Sample\"].cumsum()\n", + " ae = average_error(cumulative)\n", + " total_error = ae + group[\"PIE\"]\n", + " ess = effective_sample(total_error)\n", + " mess = ess.diff()\n", + " mess.fillna(ess.head(1).item(), inplace=True)\n", + " #from IPython.core.debugger import Pdb; Pdb().set_trace()\n", + " return pandas.concat((ess, mess), axis=1)\n", + "\n", + "#state_data2012[\"ESS\", \"MESS\"] \n", + "df = state_pollsters.apply(calculate_mess)\n", + "df.rename(columns={0 : \"ESS\", 1 : \"MESS\"}, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012 = state_data2012.join(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Give them the time weight" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2012-09-26\n", + "Name: poll_date, dtype: datetime64[ns]" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012[\"poll_date\"].head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012[\"time_weight\"] = (today - state_data2012[\"poll_date\"]).apply(exp_decay)" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.870551\n", + "1 0.707107\n", + "2 0.329877\n", + "3 0.203063\n", + "4 0.075189\n", + "5 0.034276\n", + "6 0.012691\n", + "7 0.006494\n", + "8 0.000370\n", + "9 0.629961\n", + "10 0.329877\n", + "11 0.140308\n", + "12 0.024803\n", + "13 0.009184\n", + "14 0.004284\n", + "15 0.000615\n", + "16 0.723635\n", + "17 0.090454\n", + "18 0.050766\n", + "19 0.890899\n", + "20 0.378929\n", + "21 0.014919\n", + "22 0.004809\n", + "23 0.644685\n", + "24 0.238710\n", + "25 0.101532\n", + "26 0.038473\n", + "27 0.017538\n", + "28 0.146943\n", + "29 0.040293\n", + " ... \n", + "393 0.017948\n", + "394 0.002637\n", + "395 0.000370\n", + "396 0.629961\n", + "397 0.106333\n", + "398 0.049606\n", + "399 0.004385\n", + "400 0.000675\n", + "401 0.000119\n", + "402 0.723635\n", + "403 0.198425\n", + "404 0.046284\n", + "405 0.831238\n", + "406 0.370274\n", + "407 0.361817\n", + "408 0.000147\n", + "409 0.003817\n", + "410 0.601513\n", + "411 0.445449\n", + "412 0.217638\n", + "413 0.062500\n", + "414 0.014579\n", + "415 0.002893\n", + "416 0.000587\n", + "417 0.000031\n", + "418 0.000001\n", + "419 0.396850\n", + "420 0.314980\n", + "421 0.193893\n", + "422 0.086370\n", + "Name: time_weight, dtype: float64" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012[\"time_weight\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now aggregate all of these. Weight them based on the sample size but also based on the time_weight." + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def weighted_mean(group):\n", + " weights1 = group[\"time_weight\"]\n", + " weights2 = group[\"MESS\"]\n", + " return np.sum(weights1*weights2*group[\"obama_spread\"]/(weights1*weights2).sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_pollsters = state_data2012.groupby([\"State\", \"Pollster\"])\n", + "state_polls = state_pollsters.apply(weighted_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State Pollster \n", + "AZ Public Policy Polling (PPP) -9.168494\n", + " Rasmussen -10.209446\n", + "CA Field Poll (CA) 23.343924\n", + " Public Policy Polling (PPP) 20.999075\n", + " Rasmussen 22.000000\n", + " SurveyUSA 22.123414\n", + "CO American Research Group 2.000000\n", + " Public Policy Polling (PPP) 5.469907\n", + " Rasmussen -1.573788\n", + "CT Public Policy Polling (PPP) 12.757757\n", + " Quinnipiac 7.293983\n", + " Rasmussen 8.000000\n", + "FL American Research Group 5.000000\n", + " Mason-Dixon -3.543178\n", + " Public Policy Polling (PPP) 3.125154\n", + " Quinnipiac 3.075653\n", + " Rasmussen 0.882884\n", + " Suffolk (NH/MA) -0.003377\n", + " SurveyUSA 4.168952\n", + "GA Insider Advantage -19.174054\n", + " Mason-Dixon -17.000000\n", + " Public Policy Polling (PPP) -3.000000\n", + " SurveyUSA -7.983856\n", + "HI Public Policy Polling (PPP) 27.000000\n", + "IA American Research Group 7.000000\n", + " Mason-Dixon -3.000000\n", + " Public Policy Polling (PPP) 5.878693\n", + " Rasmussen -2.749416\n", + "IL Chicago Trib. / MarketShares 21.000000\n", + "IN Rasmussen -16.000000\n", + " ... \n", + "OH Ohio Poll 3.000406\n", + " Public Policy Polling (PPP) 4.141640\n", + " Quinnipiac 7.729397\n", + " Rasmussen 0.865613\n", + "OR Public Policy Polling (PPP) 9.130153\n", + " SurveyUSA 8.675504\n", + "PA Public Policy Polling (PPP) 6.160027\n", + " Quinnipiac 6.047221\n", + " Rasmussen 10.874768\n", + " SurveyUSA 0.000000\n", + "RI Public Policy Polling (PPP) 17.000000\n", + "SC Public Policy Polling (PPP) -14.558484\n", + "SD Public Policy Polling (PPP) -6.000000\n", + "TN Public Policy Polling (PPP) -7.000000\n", + "TX Public Policy Polling (PPP) -6.998595\n", + "UT Mason-Dixon -51.000000\n", + " Public Policy Polling (PPP) -32.000000\n", + "VA American Research Group 2.000000\n", + " Mason-Dixon 1.000000\n", + " Public Policy Polling (PPP) 5.095802\n", + " Quinnipiac 0.578138\n", + " Rasmussen 0.891780\n", + "VT Public Policy Polling (PPP) 20.000000\n", + "WA Public Policy Polling (PPP) 13.050886\n", + " Rasmussen 11.000000\n", + " SurveyUSA 15.310208\n", + "WI CNN / Opinion Research 4.000000\n", + " Public Policy Polling (PPP) 5.392554\n", + " Rasmussen 2.116005\n", + "WV Public Policy Polling (PPP) -19.756631\n", + "dtype: float64" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_polls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2004 and 2008 Polls" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "state_data2008= pandas.read_pickle(\"data_nuevo/state_data_2008.pkl\"); state_data2004 = pandas.read_pickle(\"data_nuevo/state_data_2004.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State object\n", + "Obama int64\n", + "McCain int64\n", + "Pollster object\n", + "poll_date datetime64[ns]\n", + "dtype: object" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2008.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State object\n", + "Kerry int64\n", + "Bush int64\n", + "Pollster object\n", + "poll_date datetime64[ns]\n", + "dtype: object" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2004.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateObamaMcCainPollsterpoll_date
0AL3661SurveyUSA2008-10-28
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" + ], + "text/plain": [ + " State Obama McCain Pollster poll_date\n", + "0 AL 36 61 SurveyUSA 2008-10-28\n", + "1 AL 34 54 Capital Survey 2008-10-16\n", + "2 AL 35 62 SurveyUSA 2008-10-09\n", + "3 AL 35 55 Capital Survey 2008-10-07\n", + "4 AL 39 60 Rasmussen 2008-09-22" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2008.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_groups = state_data2008.groupby(\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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McCainObama
State
AK52.00000039.428571
AL56.82608734.347826
AR51.00000037.250000
AZ49.33333339.190476
CA37.63333353.266667
CO44.46666748.288889
CT36.92307752.692308
DC13.00000082.000000
DE38.62500055.500000
FL46.39393946.121212
GA51.34615443.153846
HI30.00000064.000000
IA41.40740750.037037
ID60.00000030.500000
IL36.90000055.600000
IN47.50000044.961538
KS53.56250037.750000
KY54.84210537.526316
LA52.16666739.083333
MA38.80000052.200000
MD38.66666753.833333
ME38.18750050.562500
MI42.05263247.368421
MN41.73913050.260870
MO47.42857145.571429
MS51.20000040.500000
MT48.21428643.857143
NC47.52272746.090909
ND45.57142942.714286
NE51.71428637.142857
NH42.75675748.918919
NJ39.76666749.766667
NM43.59259348.740741
NV44.84375046.937500
NY36.86486552.432432
OH44.97468446.658228
OK61.70000032.000000
OR40.85185250.333333
PA42.08000048.893333
RI32.00000053.000000
SC53.30000041.000000
SD50.37500039.875000
TN54.36363636.363636
TX50.20000040.400000
UT58.60000030.000000
VA45.81666747.933333
VT34.75000059.750000
WA40.42424251.515152
WI41.92105349.684211
WV48.69230842.538462
WY59.33333332.666667
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" + ], + "text/plain": [ + " McCain Obama\n", + "State \n", + "AK 52.000000 39.428571\n", + "AL 56.826087 34.347826\n", + "AR 51.000000 37.250000\n", + "AZ 49.333333 39.190476\n", + "CA 37.633333 53.266667\n", + "CO 44.466667 48.288889\n", + "CT 36.923077 52.692308\n", + "DC 13.000000 82.000000\n", + "DE 38.625000 55.500000\n", + "FL 46.393939 46.121212\n", + "GA 51.346154 43.153846\n", + "HI 30.000000 64.000000\n", + "IA 41.407407 50.037037\n", + "ID 60.000000 30.500000\n", + "IL 36.900000 55.600000\n", + "IN 47.500000 44.961538\n", + "KS 53.562500 37.750000\n", + "KY 54.842105 37.526316\n", + "LA 52.166667 39.083333\n", + "MA 38.800000 52.200000\n", + "MD 38.666667 53.833333\n", + "ME 38.187500 50.562500\n", + "MI 42.052632 47.368421\n", + "MN 41.739130 50.260870\n", + "MO 47.428571 45.571429\n", + "MS 51.200000 40.500000\n", + "MT 48.214286 43.857143\n", + "NC 47.522727 46.090909\n", + "ND 45.571429 42.714286\n", + "NE 51.714286 37.142857\n", + "NH 42.756757 48.918919\n", + "NJ 39.766667 49.766667\n", + "NM 43.592593 48.740741\n", + "NV 44.843750 46.937500\n", + "NY 36.864865 52.432432\n", + "OH 44.974684 46.658228\n", + "OK 61.700000 32.000000\n", + "OR 40.851852 50.333333\n", + "PA 42.080000 48.893333\n", + "RI 32.000000 53.000000\n", + "SC 53.300000 41.000000\n", + "SD 50.375000 39.875000\n", + "TN 54.363636 36.363636\n", + "TX 50.200000 40.400000\n", + "UT 58.600000 30.000000\n", + "VA 45.816667 47.933333\n", + "VT 34.750000 59.750000\n", + "WA 40.424242 51.515152\n", + "WI 41.921053 49.684211\n", + "WV 48.692308 42.538462\n", + "WY 59.333333 32.666667" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_groups.aggregate(dict(Obama=np.mean, McCain=np.mean))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Means for the entire country (without weighting by population)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "McCain 45.337861\n", + "Obama 46.082498\n", + "dtype: float64" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_groups.aggregate(dict(Obama=np.mean, McCain=np.mean)).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2004.Pollster.replace(pollster_map, inplace=True)\n", + "state_data2008.Pollster.replace(pollster_map, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2004 = state_data2004.merge(weights, how=\"inner\", on=\"Pollster\")\n", + "state_data2008 = state_data2008.merge(weights, how=\"inner\", on=\"Pollster\")" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "26" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(state_data2004.Pollster.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(state_data2008.Pollster.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2004, 11, 2, 0, 0)" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date2004 = datetime.datetime(2004, 11, 2)\n", + "date2004" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": 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poll_date, dtype: bool" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(date2004 - state_data2004.poll_date) < datetime.timedelta(21)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Restrict the samples to the 3 weeks leading up to the election" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2004 = state_data2004.ix[(date2004 - state_data2004.poll_date) <= datetime.timedelta(21)]\n", + "state_data2004.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "date2008 = datetime.datetime(2008, 11, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2008 = state_data2008.ix[(date2008 - state_data2008.poll_date) <= datetime.timedelta(21)]\n", + "state_data2008.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State object\n", + "Obama int64\n", + "McCain int64\n", + "Pollster object\n", + "poll_date datetime64[ns]\n", + "Weight float64\n", + "PIE float64\n", + "dtype: object" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2008.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2004[\"time_weight\"] =(date2004 - state_data2004.poll_date).apply(exp_decay)\n", + "state_data2008[\"time_weight\"] =(date2008 - state_data2008.poll_date).apply(exp_decay)" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " time_weight poll_date\n", + "0 0.831238 2004-10-25\n", + "1 0.890899 2004-10-28\n", + "2 0.690956 2004-10-17\n", + "3 0.977160 2004-11-01\n", + "4 0.793701 2004-10-23" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2004[[\"time_weight\", \"poll_date\"]].head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def max_date(x):\n", + " return x == x.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2004[\"newest_poll\"] = state_data2004.groupby((\"State\", \"Pollster\")).poll_date.transform(max_date)\n", + "state_data2008[\"newest_poll\"] = state_data2008.groupby((\"State\", \"Pollster\")).poll_date.transform(max_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clustering States by Demographics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are notes on trend line adjustment, [here](http://www.fivethirtyeight.com/2008/06/we-know-more-than-we-think-big-change-2.html), [here](http://www.fivethirtyeight.com/2008/06/refinement-to-adjustment-part-i.html), [here](http://www.fivethirtyeight.com/2008/06/refinement-to-adjustment-part-ii.html), [here](http://www.fivethirtyeight.com/2008/06/trendline-now-calculated-from-daily.html), and [here](http://www.fivethirtyeight.com/2008/06/construction-season-over-technical.html). However, to the best of my knowledge, the similar state \"nearest neighbor\" clustering remains a black box." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Partican Voting Index data obtained from [Wikipedia](http://en.wikipedia.org/wiki/Cook_Partisan_Voting_Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pvi = pandas.read_csv(\"./data/partisan_voting.csv\") \n" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PVI
State
AlabamaR+13
AlaskaR+13
ArizonaR+6
ArkansasR+9
CaliforniaD+7
ColoradoEVEN
ConnecticutD+7
DelawareD+7
District of ColumbiaD+39
FloridaR+2
GeorgiaR+7
HawaiiD+12
IdahoR+17
IllinoisD+8
IndianaR+6
IowaD+1
KansasR+12
KentuckyR+10
LouisianaR+10
MaineD+5
MarylandD+9
MassachusettsD+12
MichiganD+4
MinnesotaD+2
MississippiR+10
MissouriR+3
MontanaR+7
NebraskaR+13
NevadaD+1
New HampshireD+2
New JerseyD+4
New MexicoD+2
New YorkD+10
North CarolinaR+4
North DakotaR+10
OhioR+1
OklahomaR+17
OregonD+4
PennsylvaniaD+2
Rhode IslandD+11
South CarolinaR+8
South DakotaR+9
TennesseeR+9
TexasR+10
UtahR+20
VermontD+13
VirginiaR+2
WashingtonD+5
West VirginiaR+8
WisconsinD+2
WyomingR+20
\n", + "
" + ], + "text/plain": [ + " PVI\n", + "State \n", + "Alabama R+13\n", + "Alaska R+13\n", + "Arizona R+6 \n", + "Arkansas R+9 \n", + "California D+7 \n", + "Colorado EVEN\n", + "Connecticut D+7 \n", + "Delaware D+7 \n", + "District of Columbia D+39\n", + "Florida R+2 \n", + "Georgia R+7 \n", + "Hawaii D+12\n", + "Idaho R+17\n", + "Illinois D+8 \n", + "Indiana R+6 \n", + "Iowa D+1 \n", + "Kansas R+12\n", + "Kentucky R+10\n", + "Louisiana R+10\n", + "Maine D+5 \n", + "Maryland D+9 \n", + "Massachusetts D+12\n", + "Michigan D+4 \n", + "Minnesota D+2 \n", + "Mississippi R+10\n", + "Missouri R+3 \n", + "Montana R+7 \n", + "Nebraska R+13\n", + "Nevada D+1 \n", + "New Hampshire D+2 \n", + "New Jersey D+4 \n", + "New Mexico D+2 \n", + "New York D+10\n", + "North Carolina R+4 \n", + "North Dakota R+10\n", + "Ohio R+1 \n", + "Oklahoma R+17\n", + "Oregon D+4 \n", + "Pennsylvania D+2 \n", + "Rhode Island D+11\n", + "South Carolina R+8 \n", + "South Dakota R+9 \n", + "Tennessee R+9 \n", + "Texas R+10\n", + "Utah R+20\n", + "Vermont D+13\n", + "Virginia R+2 \n", + "Washington D+5 \n", + "West Virginia R+8 \n", + "Wisconsin D+2 \n", + "Wyoming R+20" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pvi.set_index(\"State\", inplace=True);\n", + "pvi" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State\n", + "Alabama -13\n", + "Alaska -13\n", + "Arizona -6\n", + "Arkansas -9\n", + "California 7\n", + "Colorado 0\n", + "Connecticut 7\n", + "Delaware 7\n", + "District of Columbia 39\n", + "Florida -2\n", + "Georgia -7\n", + "Hawaii 12\n", + "Idaho -17\n", + "Illinois 8\n", + "Indiana -6\n", + "Iowa 1\n", + "Kansas -12\n", + "Kentucky -10\n", + "Louisiana -10\n", + "Maine 5\n", + "Maryland 9\n", + "Massachusetts 12\n", + "Michigan 4\n", + "Minnesota 2\n", + "Mississippi -10\n", + "Missouri -3\n", + "Montana -7\n", + "Nebraska -13\n", + "Nevada 1\n", + "New Hampshire 2\n", + "New Jersey 4\n", + "New Mexico 2\n", + "New York 10\n", + "North Carolina -4\n", + "North Dakota -10\n", + "Ohio -1\n", + "Oklahoma -17\n", + "Oregon 4\n", + "Pennsylvania 2\n", + "Rhode Island 11\n", + "South Carolina -8\n", + "South Dakota -9\n", + "Tennessee -9\n", + "Texas -10\n", + "Utah -20\n", + "Vermont 13\n", + "Virginia -2\n", + "Washington 5\n", + "West Virginia -8\n", + "Wisconsin 2\n", + "Wyoming -20\n", + "Name: PVI, dtype: float64" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pvi.PVI = pvi.PVI.replace({\"EVEN\" : \"0\"})\n", + "pvi.PVI = pvi.PVI.str.replace(\"R\\+\", \"-\")\n", + "pvi.PVI = pvi.PVI.str.replace(\"D\\+\", \"\")\n", + "pvi.PVI = pvi.PVI.astype(float)\n", + "pvi.PVI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Party affliation of electorate obtained from [Gallup](http://www.gallup.com/poll/156437/Heavily-Democratic-States-Concentrated-East.aspx#2)." + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "party_affil = pandas.read_csv(\"./data/gallup_electorate.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "party_affil.Democrat = party_affil.Democrat.str.replace(\"%\", \"\").astype(float)\n", + "party_affil.Republican = party_affil.Republican.str.replace(\"%\", \"\").astype(float)\n", + "party_affil.set_index(\"State\", inplace=True);\n", + "party_affil.rename(columns={\"Democrat Advantage\" : \"dem_adv\"}, inplace=True);\n", + "party_affil[\"no_party\"] = 100 - party_affil.Democrat - party_affil.Republican" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DemocratRepublicandem_advNno_party
State
District of Columbia79.012.766.30004168.3
Rhode Island52.526.526.000062321.0
Hawaii54.328.725.600046617.0
New York52.030.821.2000867417.2
Maryland54.033.820.2000357112.2
Massachusetts52.533.419.1000358314.1
Delaware50.533.117.400054016.4
Connecticut49.834.415.4000202015.8
Vermont48.834.913.900055016.3
California48.334.613.70001619717.1
Illinois48.435.812.6000588815.8
New Jersey47.435.911.5000423916.7
Michigan47.736.611.1000505615.7
Minnesota48.438.210.2000387313.4
Washington47.537.79.8000533314.8
Oregon47.239.18.1000300213.7
Pennsylvania46.441.25.2000844312.4
Maine43.839.44.4000104016.8
New Mexico44.741.13.6000155514.2
Ohio44.140.53.6000642615.4
West Virginia45.341.93.4000120212.8
Wisconsin45.042.22.8000414012.8
Iowa43.241.41.8000233715.4
Florida43.042.30.7000996514.7
Arkansas41.540.80.7000207117.7
Kentucky43.543.10.4000289813.4
North Carolina43.443.20.2000621313.4
New Hampshire42.343.8-1.500087313.9
Virginia41.244.2-3.0000531314.6
Missouri40.144.0-3.9000372715.9
Georgia40.344.3-4.0000511015.4
Nevada39.243.4-4.2000134817.4
Louisiana40.345.1-4.8000265514.6
Colorado39.945.1-5.2000367115.0
Texas38.344.1-5.80001132517.6
South Dakota41.547.5-6.000060711.0
Indiana39.045.7-6.7000419715.3
Mississippi40.147.1-7.0000176312.8
Arizona39.847.3-7.5000432512.9
Tennessee38.146.5-8.4000423115.4
Alaska35.944.3-8.4402NaN19.8
Oklahoma38.648.0-9.4000258313.4
South Carolina36.948.8-11.9000285814.3
North Dakota35.849.0-13.200054715.2
Alabama36.049.6-13.6000319714.4
Montana35.949.6-13.7000113714.5
Kansas34.451.3-16.9000193714.3
Nebraska33.152.1-19.0000135114.8
Wyoming26.756.6-29.900060016.7
Idaho27.557.8-30.3000133614.7
Utah24.563.8-39.3000225611.7
\n", + "
" + ], + "text/plain": [ + " Democrat Republican dem_adv N no_party\n", + "State \n", + "District of Columbia 79.0 12.7 66.3000 416 8.3\n", + "Rhode Island 52.5 26.5 26.0000 623 21.0\n", + "Hawaii 54.3 28.7 25.6000 466 17.0\n", + "New York 52.0 30.8 21.2000 8674 17.2\n", + "Maryland 54.0 33.8 20.2000 3571 12.2\n", + "Massachusetts 52.5 33.4 19.1000 3583 14.1\n", + "Delaware 50.5 33.1 17.4000 540 16.4\n", + "Connecticut 49.8 34.4 15.4000 2020 15.8\n", + "Vermont 48.8 34.9 13.9000 550 16.3\n", + "California 48.3 34.6 13.7000 16197 17.1\n", + "Illinois 48.4 35.8 12.6000 5888 15.8\n", + "New Jersey 47.4 35.9 11.5000 4239 16.7\n", + "Michigan 47.7 36.6 11.1000 5056 15.7\n", + "Minnesota 48.4 38.2 10.2000 3873 13.4\n", + "Washington 47.5 37.7 9.8000 5333 14.8\n", + "Oregon 47.2 39.1 8.1000 3002 13.7\n", + "Pennsylvania 46.4 41.2 5.2000 8443 12.4\n", + "Maine 43.8 39.4 4.4000 1040 16.8\n", + "New Mexico 44.7 41.1 3.6000 1555 14.2\n", + "Ohio 44.1 40.5 3.6000 6426 15.4\n", + "West Virginia 45.3 41.9 3.4000 1202 12.8\n", + "Wisconsin 45.0 42.2 2.8000 4140 12.8\n", + "Iowa 43.2 41.4 1.8000 2337 15.4\n", + "Florida 43.0 42.3 0.7000 9965 14.7\n", + "Arkansas 41.5 40.8 0.7000 2071 17.7\n", + "Kentucky 43.5 43.1 0.4000 2898 13.4\n", + "North Carolina 43.4 43.2 0.2000 6213 13.4\n", + "New Hampshire 42.3 43.8 -1.5000 873 13.9\n", + "Virginia 41.2 44.2 -3.0000 5313 14.6\n", + "Missouri 40.1 44.0 -3.9000 3727 15.9\n", + "Georgia 40.3 44.3 -4.0000 5110 15.4\n", + "Nevada 39.2 43.4 -4.2000 1348 17.4\n", + "Louisiana 40.3 45.1 -4.8000 2655 14.6\n", + "Colorado 39.9 45.1 -5.2000 3671 15.0\n", + "Texas 38.3 44.1 -5.8000 11325 17.6\n", + "South Dakota 41.5 47.5 -6.0000 607 11.0\n", + "Indiana 39.0 45.7 -6.7000 4197 15.3\n", + "Mississippi 40.1 47.1 -7.0000 1763 12.8\n", + "Arizona 39.8 47.3 -7.5000 4325 12.9\n", + "Tennessee 38.1 46.5 -8.4000 4231 15.4\n", + "Alaska 35.9 44.3 -8.4402 NaN 19.8\n", + "Oklahoma 38.6 48.0 -9.4000 2583 13.4\n", + "South Carolina 36.9 48.8 -11.9000 2858 14.3\n", + "North Dakota 35.8 49.0 -13.2000 547 15.2\n", + "Alabama 36.0 49.6 -13.6000 3197 14.4\n", + "Montana 35.9 49.6 -13.7000 1137 14.5\n", + "Kansas 34.4 51.3 -16.9000 1937 14.3\n", + "Nebraska 33.1 52.1 -19.0000 1351 14.8\n", + "Wyoming 26.7 56.6 -29.9000 600 16.7\n", + "Idaho 27.5 57.8 -30.3000 1336 14.7\n", + "Utah 24.5 63.8 -39.3000 2256 11.7" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "party_affil" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "census_data = pandas.read_csv(\"./data/census_demographics.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def capitalize(s):\n", + " s = s.title()\n", + " s = s.replace(\"Of\", \"of\")\n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "census_data[\"State\"] = census_data.state.map(capitalize)\n", + "del census_data[\"state\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "census_data.set_index(\"State\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "states_abbrev_dict = {\n", + " 'AK': 'Alaska',\n", + " 'AL': 'Alabama',\n", + " 'AR': 'Arkansas',\n", + " 'AS': 'American Samoa',\n", + " 'AZ': 'Arizona',\n", + " 'CA': 'California',\n", + " 'CO': 'Colorado',\n", + " 'CT': 'Connecticut',\n", + " 'DC': 'District of Columbia',\n", + " 'DE': 'Delaware',\n", + " 'FL': 'Florida',\n", + " 'GA': 'Georgia',\n", + " 'GU': 'Guam',\n", + " 'HI': 'Hawaii',\n", + " 'IA': 'Iowa',\n", + " 'ID': 'Idaho',\n", + " 'IL': 'Illinois',\n", + " 'IN': 'Indiana',\n", + " 'KS': 'Kansas',\n", + " 'KY': 'Kentucky',\n", + " 'LA': 'Louisiana',\n", + " 'MA': 'Massachusetts',\n", + " 'MD': 'Maryland',\n", + " 'ME': 'Maine',\n", + " 'MI': 'Michigan',\n", + " 'MN': 'Minnesota',\n", + " 'MO': 'Missouri',\n", + " 'MP': 'Northern Mariana Islands',\n", + " 'MS': 'Mississippi',\n", + " 'MT': 'Montana',\n", + " 'NA': 'National',\n", + " 'NC': 'North Carolina',\n", + " 'ND': 'North Dakota',\n", + " 'NE': 'Nebraska',\n", + " 'NH': 'New Hampshire',\n", + " 'NJ': 'New Jersey',\n", + " 'NM': 'New Mexico',\n", + " 'NV': 'Nevada',\n", + " 'NY': 'New York',\n", + " 'OH': 'Ohio',\n", + " 'OK': 'Oklahoma',\n", + " 'OR': 'Oregon',\n", + " 'PA': 'Pennsylvania',\n", + " 'PR': 'Puerto Rico',\n", + " 'RI': 'Rhode Island',\n", + " 'SC': 'South Carolina',\n", + " 'SD': 'South Dakota',\n", + " 'TN': 'Tennessee',\n", + " 'TX': 'Texas',\n", + " 'UT': 'Utah',\n", + " 'VA': 'Virginia',\n", + " 'VI': 'Virgin Islands',\n", + " 'VT': 'Vermont',\n", + " 'WA': 'Washington',\n", + " 'WI': 'Wisconsin',\n", + " 'WV': 'West Virginia',\n", + " 'WY': 'Wyoming'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Campaign Contributions from FEC." + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "obama_give = pandas.read_csv(\"./data/obama_indiv_state.csv\", \n", + " header=None, names=[\"State\", \"obama_give\"])\n", + "romney_give = pandas.read_csv(\"./data/romney_indiv_state.csv\",\n", + " header=None, names=[\"State\", \"romney_give\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "obama_give.State.replace(states_abbrev_dict, inplace=True);\n", + "romney_give.State.replace(states_abbrev_dict, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "obama_give.set_index(\"State\", inplace=True)\n", + "romney_give.set_index(\"State\", inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data = census_data.join(party_affil[[\"dem_adv\", \"no_party\"]]).join(pvi)" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data = demo_data.join(obama_give).join(romney_give)" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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obama_giveromney_give
State
Alabama0.2446510.365672
Alaska1.1118700.498678
Arizona0.5686340.672651
Arkansas0.2467810.216652
California1.1281450.617581
Colorado1.0563660.796661
Connecticut1.2066981.544816
Delaware0.7668600.358712
District of Columbia326.8636212.535392
Florida0.5031800.874699
Georgia0.4675290.526246
Hawaii1.0066320.225184
Idaho0.3663430.990299
Illinois0.9335370.589753
Indiana0.3413420.256904
Iowa0.4874320.285937
Kansas0.3920000.469934
Kentucky0.2890650.393258
Louisiana0.2604820.528613
Maine0.8003840.245619
Maryland1.5182990.593536
Massachusetts1.7348211.104913
Michigan0.5119530.500678
Minnesota0.6596530.231681
Mississippi0.1891200.327144
Missouri0.4125070.482069
Montana0.7636070.533750
Nebraska0.3356300.351093
Nevada0.4841820.638822
New Hampshire0.9615630.733997
New Jersey0.7362190.703930
New Mexico1.0523080.379238
New York1.1986320.809349
North Carolina0.5492090.355353
North Dakota0.2383110.343288
Ohio0.3775480.427662
Oklahoma0.3248380.800888
Oregon0.9713270.342027
Pennsylvania0.5876880.467086
Rhode Island0.7132000.358394
South Carolina0.3172500.351393
South Dakota0.2709700.518931
Tennessee0.3765230.522332
Texas0.4767290.690927
Utah0.3794362.394654
Vermont1.6022220.249971
Virginia1.0001850.938508
Washington1.1905900.475625
West Virginia0.2604370.321333
Wisconsin0.4554100.237802
Wyoming0.7461221.080021
\n", + "
" + ], + "text/plain": [ + " obama_give romney_give\n", + "State \n", + "Alabama 0.244651 0.365672\n", + "Alaska 1.111870 0.498678\n", + "Arizona 0.568634 0.672651\n", + "Arkansas 0.246781 0.216652\n", + "California 1.128145 0.617581\n", + "Colorado 1.056366 0.796661\n", + "Connecticut 1.206698 1.544816\n", + "Delaware 0.766860 0.358712\n", + "District of Columbia 326.863621 2.535392\n", + "Florida 0.503180 0.874699\n", + "Georgia 0.467529 0.526246\n", + "Hawaii 1.006632 0.225184\n", + "Idaho 0.366343 0.990299\n", + "Illinois 0.933537 0.589753\n", + "Indiana 0.341342 0.256904\n", + "Iowa 0.487432 0.285937\n", + "Kansas 0.392000 0.469934\n", + "Kentucky 0.289065 0.393258\n", + "Louisiana 0.260482 0.528613\n", + "Maine 0.800384 0.245619\n", + "Maryland 1.518299 0.593536\n", + "Massachusetts 1.734821 1.104913\n", + "Michigan 0.511953 0.500678\n", + "Minnesota 0.659653 0.231681\n", + "Mississippi 0.189120 0.327144\n", + "Missouri 0.412507 0.482069\n", + "Montana 0.763607 0.533750\n", + "Nebraska 0.335630 0.351093\n", + "Nevada 0.484182 0.638822\n", + "New Hampshire 0.961563 0.733997\n", + "New Jersey 0.736219 0.703930\n", + "New Mexico 1.052308 0.379238\n", + "New York 1.198632 0.809349\n", + "North Carolina 0.549209 0.355353\n", + "North Dakota 0.238311 0.343288\n", + "Ohio 0.377548 0.427662\n", + "Oklahoma 0.324838 0.800888\n", + "Oregon 0.971327 0.342027\n", + "Pennsylvania 0.587688 0.467086\n", + "Rhode Island 0.713200 0.358394\n", + "South Carolina 0.317250 0.351393\n", + "South Dakota 0.270970 0.518931\n", + "Tennessee 0.376523 0.522332\n", + "Texas 0.476729 0.690927\n", + "Utah 0.379436 2.394654\n", + "Vermont 1.602222 0.249971\n", + "Virginia 1.000185 0.938508\n", + "Washington 1.190590 0.475625\n", + "West Virginia 0.260437 0.321333\n", + "Wisconsin 0.455410 0.237802\n", + "Wyoming 0.746122 1.080021" + ] + }, + "execution_count": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "giving = demo_data[[\"obama_give\", \"romney_give\"]].div(demo_data[[\"vote_pop\", \"older_pop\"]].sum(1), axis=0)\n", + "giving" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data[[\"obama_give\", \"romney_give\"]] = giving" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy import cluster as sp_cluster\n", + "from sklearn import cluster, neighbors" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "clean_data = sp_cluster.vq.whiten(demo_data.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1.])" + ] + }, + "execution_count": 249, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean_data.var(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0. , 0.895 , 1.4131, 1.4377, 2.0061, 2.1917, 2.3806]]),\n", + " array([[ 0, 40, 18, 42, 24, 33, 17]]))" + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "KNN = neighbors.NearestNeighbors(n_neighbors=7)\n", + "KNN.fit(clean_data)\n", + "KNN.kneighbors(clean_data[0], return_distance=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Alabama',\n", + " Index([[u'Alabama', u'South Carolina', u'Louisiana', u'Tennessee', u'Mississippi', u'North Carolina', u'Kentucky']], dtype='object', name=u'State'))" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = _[1]\n", + "demo_data.index[0], demo_data.index[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "nearest_neighbor = {}\n", + "for i, state in enumerate(demo_data.index):\n", + " neighborhood = KNN.kneighbors(clean_data[i], return_distance=True)\n", + " nearest_neighbor.update({state : (demo_data.index[neighborhood[1]],\n", + " neighborhood[0])})" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Alabama': (Index([[u'Alabama', u'South Carolina', u'Louisiana', u'Tennessee', u'Mississippi', u'North Carolina', u'Kentucky']], dtype='object', name=u'State'),\n", + " array([[ 0. , 0.895 , 1.4131, 1.4377, 2.0061, 2.1917, 2.3806]])),\n", + " 'Alaska': (Index([[u'Alaska', u'Colorado', u'Wyoming', u'Washington', u'Virginia', u'Nevada', u'New Hampshire']], dtype='object', name=u'State'),\n", + " array([[ 0. , 4.3342, 4.5308, 4.7701, 4.9261, 4.9871, 5.2991]])),\n", + " 'Arizona': (Index([[u'Arizona', u'Oklahoma', u'New Mexico', u'Kansas', u'Nevada', u'North Carolina', u'Oregon']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.9343, 2.9355, 3.1193, 3.1995, 3.3985, 3.4008]])),\n", + " 'Arkansas': (Index([[u'Arkansas', u'Tennessee', u'Alabama', u'Missouri', u'Indiana', u'Kentucky', u'South Carolina']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.2112, 2.4606, 2.612 , 2.6739, 2.7217, 2.7387]])),\n", + " 'California': (Index([[u'California', u'Texas', u'New York', u'Illinois', u'Florida', u'New Jersey', u'Georgia']], dtype='object', name=u'State'),\n", + " array([[ 0. , 4.0483, 4.6365, 5.9325, 6.0743, 6.8872, 7.3305]])),\n", + " 'Colorado': (Index([[u'Colorado', u'Washington', u'Virginia', u'Minnesota', u'Illinois', u'New Hampshire', u'Oregon']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.0688, 2.5587, 3.0797, 3.091 , 3.203 , 3.2526]])),\n", + " 'Connecticut': (Index([[u'Connecticut', u'Massachusetts', u'New Jersey', u'Virginia', u'New Hampshire', u'Washington', u'Maryland']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.0824, 2.5353, 3.2202, 3.5911, 3.6411, 3.7422]])),\n", + " 'Delaware': (Index([[u'Delaware', u'Michigan', u'Washington', u'Oregon', u'Missouri', u'Illinois', u'Rhode Island']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.8113, 2.9256, 2.926 , 3.0637, 3.0731, 3.1641]])),\n", + " 'District of Columbia': (Index([[u'District of Columbia', u'Maryland', u'Massachusetts', u'Connecticut', u'Virginia', u'New Jersey', u'New York']], dtype='object', name=u'State'),\n", + " array([[ 0. , 13.03 , 13.3261, 13.6773, 14.119 , 14.1531,\n", + " 14.5409]])),\n", + " 'Florida': (Index([[u'Florida', u'Pennsylvania', u'New York', u'Ohio', u'Arizona', u'Michigan', u'Illinois']], dtype='object', name=u'State'),\n", + " array([[ 0. , 3.6013, 4.1043, 4.154 , 4.3315, 4.3721, 4.3781]])),\n", + " 'Georgia': (Index([[u'Georgia', u'North Carolina', u'Louisiana', u'South Carolina', u'Tennessee', u'Alabama', u'Illinois']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.0943, 2.2277, 2.5821, 2.8322, 2.972 , 3.1716]])),\n", + " 'Hawaii': (Index([[u'Hawaii', u'Delaware', u'New Jersey', u'Washington', u'Illinois', u'Nevada', u'Rhode Island']], dtype='object', name=u'State'),\n", + " array([[ 0. , 3.5958, 4.0706, 4.0882, 4.4516, 4.5466, 4.6198]])),\n", + " 'Idaho': (Index([[u'Idaho', u'Nebraska', u'Kansas', u'Oklahoma', u'Wyoming', u'Montana', u'South Dakota']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.1515, 2.2253, 2.3819, 2.8463, 2.9776, 3.0037]])),\n", + " 'Illinois': (Index([[u'Illinois', u'Washington', u'New York', u'Michigan', u'Virginia', u'New Jersey', u'North Carolina']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.1981, 2.2448, 2.4582, 2.5843, 2.7518, 2.9154]])),\n", + " 'Indiana': (Index([[u'Indiana', u'Missouri', u'Tennessee', u'Ohio', u'Michigan', u'Iowa', u'Wisconsin']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.092 , 1.6695, 1.7382, 2.0651, 2.1233, 2.1329]])),\n", + " 'Iowa': (Index([[u'Iowa', u'Maine', u'Wisconsin', u'Missouri', u'Montana', u'Oregon', u'North Dakota']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.8098, 1.8516, 1.8777, 1.9101, 1.9952, 2.0246]])),\n", + " 'Kansas': (Index([[u'Kansas', u'Nebraska', u'North Dakota', u'Montana', u'Indiana', u'Idaho', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 0.6986, 2.0249, 2.0884, 2.2029, 2.2253, 2.3144]])),\n", + " 'Kentucky': (Index([[u'Kentucky', u'Tennessee', u'West Virginia', u'Indiana', u'Alabama', u'Oklahoma', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.7644, 2.0265, 2.2391, 2.3806, 2.4241, 2.5181]])),\n", + " 'Louisiana': (Index([[u'Louisiana', u'Alabama', u'South Carolina', u'Mississippi', u'Tennessee', u'Georgia', u'North Carolina']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.4131, 1.5056, 1.7716, 2.043 , 2.2277, 2.2626]])),\n", + " 'Maine': (Index([[u'Maine', u'Iowa', u'Vermont', u'North Dakota', u'Montana', u'Oregon', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.8098, 2.1204, 2.3112, 2.3425, 2.5626, 2.6826]])),\n", + " 'Maryland': (Index([[u'Maryland', u'Virginia', u'New Jersey', u'Massachusetts', u'Connecticut', u'Washington', u'Illinois']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.9214, 3.1488, 3.1622, 3.7422, 3.7991, 3.878 ]])),\n", + " 'Massachusetts': (Index([[u'Massachusetts', u'Connecticut', u'New Jersey', u'Washington', u'Virginia', u'New Hampshire', u'Maryland']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.0824, 2.7455, 2.7496, 2.8705, 2.8822, 3.1622]])),\n", + " 'Michigan': (Index([[u'Michigan', u'Ohio', u'Missouri', u'Indiana', u'Wisconsin', u'Pennsylvania', u'Oregon']], dtype='object', name=u'State'),\n", + " array([[ 0. , 0.9378, 1.5973, 2.0651, 2.1259, 2.1318, 2.2958]])),\n", + " 'Minnesota': (Index([[u'Minnesota', u'Washington', u'Wisconsin', u'Oregon', u'New Hampshire', u'Iowa', u'Vermont']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.5917, 1.6802, 1.9837, 2.267 , 2.697 , 2.8457]])),\n", + " 'Mississippi': (Index([[u'Mississippi', u'Louisiana', u'Alabama', u'South Carolina', u'Tennessee', u'North Carolina', u'Kentucky']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.7716, 2.0061, 2.3089, 3.1488, 3.2514, 3.4804]])),\n", + " 'Missouri': (Index([[u'Missouri', u'Indiana', u'Ohio', u'Michigan', u'Tennessee', u'Iowa', u'Wisconsin']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.092 , 1.4165, 1.5973, 1.617 , 1.8777, 2.1894]])),\n", + " 'Montana': (Index([[u'Montana', u'North Dakota', u'Nebraska', u'Iowa', u'Kansas', u'South Dakota', u'Maine']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.261 , 1.818 , 1.9101, 2.0884, 2.1583, 2.3425]])),\n", + " 'Nebraska': (Index([[u'Nebraska', u'Kansas', u'North Dakota', u'Montana', u'Idaho', u'Iowa', u'Indiana']], dtype='object', name=u'State'),\n", + " array([[ 0. , 0.6986, 1.7342, 1.818 , 2.1515, 2.2173, 2.2261]])),\n", + " 'Nevada': (Index([[u'Nevada', u'Arizona', u'Illinois', u'Delaware', u'Indiana', u'New Mexico', u'Washington']], dtype='object', name=u'State'),\n", + " array([[ 0. , 3.1995, 3.3382, 3.493 , 3.5531, 3.6011, 3.6074]])),\n", + " 'New Hampshire': (Index([[u'New Hampshire', u'Minnesota', u'Washington', u'Vermont', u'Massachusetts', u'Oregon', u'Wisconsin']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.267 , 2.5447, 2.7523, 2.8822, 3.0272, 3.0489]])),\n", + " 'New Jersey': (Index([[u'New Jersey', u'Connecticut', u'Virginia', u'Massachusetts', u'Illinois', u'Washington', u'Maryland']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.5353, 2.6702, 2.7455, 2.7518, 3.0557, 3.1488]])),\n", + " 'New Mexico': (Index([[u'New Mexico', u'Arizona', u'Nevada', u'Oklahoma', u'Oregon', u'North Carolina', u'Illinois']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.9355, 3.6011, 4.8648, 4.8963, 4.9717, 5.1184]])),\n", + " 'New York': (Index([[u'New York', u'Illinois', u'New Jersey', u'Virginia', u'Michigan', u'Washington', u'Florida']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.2448, 3.4305, 3.7701, 3.9345, 4.0056, 4.1043]])),\n", + " 'North Carolina': (Index([[u'North Carolina', u'South Carolina', u'Tennessee', u'Georgia', u'Alabama', u'Louisiana', u'Indiana']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.7201, 1.8596, 2.0943, 2.1917, 2.2626, 2.2931]])),\n", + " 'North Dakota': (Index([[u'North Dakota', u'Montana', u'Nebraska', u'Iowa', u'Kansas', u'Missouri', u'Indiana']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.261 , 1.7342, 2.0246, 2.0249, 2.2294, 2.3029]])),\n", + " 'Ohio': (Index([[u'Ohio', u'Michigan', u'Missouri', u'Indiana', u'Pennsylvania', u'Wisconsin', u'North Carolina']], dtype='object', name=u'State'),\n", + " array([[ 0. , 0.9378, 1.4165, 1.7382, 1.8819, 2.192 , 2.3251]])),\n", + " 'Oklahoma': (Index([[u'Oklahoma', u'Tennessee', u'Indiana', u'Kansas', u'Idaho', u'Kentucky', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.1644, 2.2664, 2.3192, 2.3819, 2.4241, 2.4273]])),\n", + " 'Oregon': (Index([[u'Oregon', u'Wisconsin', u'Washington', u'Minnesota', u'Iowa', u'Michigan', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.297 , 1.9679, 1.9837, 1.9952, 2.2958, 2.3314]])),\n", + " 'Pennsylvania': (Index([[u'Pennsylvania', u'Ohio', u'Michigan', u'Wisconsin', u'Oregon', u'North Carolina', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.8819, 2.1318, 2.2291, 2.6733, 2.731 , 2.7411]])),\n", + " 'Rhode Island': (Index([[u'Rhode Island', u'Delaware', u'Vermont', u'Maine', u'Washington', u'Illinois', u'Nevada']], dtype='object', name=u'State'),\n", + " array([[ 0. , 3.1641, 3.6876, 3.9865, 4.0764, 4.1222, 4.1348]])),\n", + " 'South Carolina': (Index([[u'South Carolina', u'Alabama', u'Louisiana', u'Tennessee', u'North Carolina', u'Mississippi', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 0.895 , 1.5056, 1.5187, 1.7201, 2.3089, 2.3979]])),\n", + " 'South Dakota': (Index([[u'South Dakota', u'Montana', u'Nebraska', u'Kansas', u'Wisconsin', u'Iowa', u'Oklahoma']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.1583, 2.3366, 2.3479, 2.4675, 2.5475, 2.5613]])),\n", + " 'Tennessee': (Index([[u'Tennessee', u'Alabama', u'South Carolina', u'Missouri', u'Indiana', u'Kentucky', u'North Carolina']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.4377, 1.5187, 1.617 , 1.6695, 1.7644, 1.8596]])),\n", + " 'Texas': (Index([[u'Texas', u'California', u'New York', u'Illinois', u'Georgia', u'Nevada', u'Arizona']], dtype='object', name=u'State'),\n", + " array([[ 0. , 4.0483, 4.7608, 4.8448, 4.8556, 5.077 , 5.2005]])),\n", + " 'Utah': (Index([[u'Utah', u'Idaho', u'Wyoming', u'Kansas', u'Oklahoma', u'Nebraska', u'South Dakota']], dtype='object', name=u'State'),\n", + " array([[ 0. , 4.4224, 5.167 , 5.5232, 5.6994, 5.7875, 6.0012]])),\n", + " 'Vermont': (Index([[u'Vermont', u'Maine', u'Oregon', u'New Hampshire', u'Minnesota', u'Washington', u'Iowa']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.1204, 2.6838, 2.7523, 2.8457, 3.0245, 3.0897]])),\n", + " 'Virginia': (Index([[u'Virginia', u'Colorado', u'Washington', u'Illinois', u'New Jersey', u'Massachusetts', u'Maryland']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.5587, 2.5714, 2.5843, 2.6702, 2.8705, 2.9214]])),\n", + " 'Washington': (Index([[u'Washington', u'Minnesota', u'Oregon', u'Colorado', u'Illinois', u'Wisconsin', u'New Hampshire']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.5917, 1.9679, 2.0688, 2.1981, 2.2739, 2.5447]])),\n", + " 'West Virginia': (Index([[u'West Virginia', u'Kentucky', u'Tennessee', u'Arkansas', u'Oklahoma', u'Missouri', u'Indiana']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.0265, 3.1467, 3.2553, 3.3697, 3.3775, 3.3816]])),\n", + " 'Wisconsin': (Index([[u'Wisconsin', u'Oregon', u'Minnesota', u'Iowa', u'Michigan', u'Indiana', u'Missouri']], dtype='object', name=u'State'),\n", + " array([[ 0. , 1.297 , 1.6802, 1.8516, 2.1259, 2.1329, 2.1894]])),\n", + " 'Wyoming': (Index([[u'Wyoming', u'Nebraska', u'Idaho', u'North Dakota', u'Kansas', u'Montana', u'Indiana']], dtype='object', name=u'State'),\n", + " array([[ 0. , 2.772 , 2.8463, 2.8491, 2.9062, 3.3221, 3.7427]]))}" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nearest_neighbor" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "k_means = cluster.KMeans(n_clusters=5, n_init=50)\n", + "k_means.fit(clean_data)\n", + "values = k_means.cluster_centers_.squeeze()\n", + "labels = k_means.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "clusters = sp_cluster.vq.kmeans(clean_data, 5)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def choose_group(data, clusters):\n", + " \"\"\"\n", + " Return the index of the cluster to which the rows in data\n", + " are \"closest\" (in the sense of the L2-norm)\n", + " \"\"\"\n", + " data = data[:,None] # add an axis for broadcasting\n", + " distances = data - clusters\n", + " groups = []\n", + " for row in distances:\n", + " dists = map(np.linalg.norm, row)\n", + " groups.append(np.argmin(dists))\n", + " return groups" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "groups = choose_group(clean_data, clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3, 1, 2, 0, 3, 3, 3, 4, 0, 2, 3, 1, 3, 1, 1, 1, 2, 2, 1, 3, 3, 1,\n", + " 3, 2, 1, 1, 1, 3, 3, 3, 0, 0, 2, 1, 1, 2, 1, 1, 3, 2, 1, 2, 0, 1, 3,\n", + " 3, 3, 2, 1, 1])" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or use a one-liner" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "groups = [np.argmin(map(np.linalg.norm, (clean_data[:,None] - clusters)[i])) for i in range(51)]" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data[\"kmeans_group\"] = groups\n", + "demo_data[\"kmeans_labels\"] = labels" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['California' 'Florida' 'New Mexico' 'New York' 'Texas']\n", + "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri'\n", + " 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania'\n", + " 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", + "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi'\n", + " 'North Carolina' 'Oklahoma' 'South Carolina' 'Tennessee' 'West Virginia']\n", + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", + " 'Maryland' 'Massachusetts' 'Minnesota' 'Nevada' 'New Hampshire'\n", + " 'New Jersey' 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n", + "['District of Columbia']\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do historical adjustments based on how polls changed in the past conditional on \"election environment\"" + } + ], + "source": [ + "for _, group in demo_data.groupby(\"kmeans_group\"):\n", + " group = group.index\n", + " group.values.sort()\n", + " print group.values" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 2, 2, 3, 1, 1, 1, 4, 3, 2, 1, 0, 1, 2, 0, 0, 2, 2, 0, 1, 1, 2,\n", + " 1, 2, 2, 0, 0, 2, 1, 1, 2, 3, 2, 0, 2, 2, 0, 2, 1, 2, 0, 2, 3, 0, 1,\n", + " 1, 1, 2, 0, 0], dtype=int32)" + ] + }, + "execution_count": 262, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Idaho' 'Iowa' 'Kansas' 'Maine' 'Montana' 'Nebraska' 'North Dakota'\n", + " 'Oregon' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", + " 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey'\n", + " 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n", + "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Indiana' 'Kentucky' 'Louisiana'\n", + " 'Michigan' 'Mississippi' 'Missouri' 'Nevada' 'New Mexico' 'North Carolina'\n", + " 'Ohio' 'Oklahoma' 'Pennsylvania' 'South Carolina' 'Tennessee'\n", + " 'West Virginia']\n", + "['California' 'Florida' 'New York' 'Texas']\n", + "['District of Columbia']\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Error analysis\"" + } + ], + "source": [ + "demo_data[\"kmeans_labels\"] = labels\n", + "for _, group in demo_data.groupby(\"kmeans_labels\"):\n", + " group = group.index.copy()\n", + " group.values.sort()\n", + " print group.values" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data = demo_data.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012.State.replace(states_abbrev_dict, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012 = state_data2012.merge(demo_data[[\"State\", \"kmeans_labels\"]], on=\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "kmeans_groups = state_data2012.groupby(\"kmeans_labels\")" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "group = kmeans_groups.get_group(kmeans_groups.groups.keys()[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['New Mexico', 'North Carolina', 'Nevada', 'Ohio', 'Pennsylvania',\n", + " 'Indiana', 'Arizona', 'Missouri', 'Michigan', 'Georgia',\n", + " 'West Virginia', 'South Carolina', 'Tennessee', 'Mississippi'], dtype=object)" + ] + }, + "execution_count": 269, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group.State.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def edit_tick_label(tick_val, tick_pos):\n", + " if tick_val < 0:\n", + " text = str(int(tick_val)).replace(\"-\", \"Romney+\")\n", + " else:\n", + " text = \"Obama+\"+str(int(tick_val))\n", + " return text" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'module' object has no attribute 'ints_to_pydatetime'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m frac=.2, it=3)\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mdates_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mints_to_pydatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"obama_spread\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloess_res\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'ints_to_pydatetime'" ] } ], - "metadata": {} + "source": [ + "from pandas import lib\n", + "from matplotlib.ticker import FuncFormatter\n", + "fig, axes = plt.subplots(figsize=(12,8))\n", + "\n", + "data = group[[\"poll_date\", \"obama_spread\"]]\n", + "data = pandas.concat((data, national_data2012[[\"poll_date\", \"obama_spread\"]]))\n", + " \n", + "data.sort(\"poll_date\", inplace=True)\n", + "dates = pandas.DatetimeIndex(data.poll_date).asi8\n", + "\n", + "loess_res = sm.nonparametric.lowess(data.obama_spread.values, dates, \n", + " frac=.2, it=3)\n", + "\n", + "dates_x = lib.ints_to_pydatetime(dates)\n", + "axes.scatter(dates_x, data[\"obama_spread\"])\n", + "axes.plot(dates_x, loess_res[:,1], color='r')\n", + "axes.yaxis.get_major_locator().set_params(nbins=12)\n", + "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n", + "axes.grid(False, axis='x')\n", + "axes.hlines(0, dates_x[0], dates_x[-1], color='black', lw=3)\n", + "axes.margins(0, .05)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2.3144535643345003" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loess_res[-7:,1].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Vx/nnJ/ODHySwfbvR+FhBgcFttyVw/vnJ/Pa3sZSXG8dYkxxq71743e98nH9+MrfcksDW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\nTcxqF0OGtO7XTvPzTTZscJG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rduPQOC4eGr+hxairO6GanMi1eTPhPn0Ip6dHZ1y8euJF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/J6bVyuE+Xk5va/nKP3/xmGHZHzD9UZZ5w4p2HYQz6n2x0a+8elptphjeO8vBPHJCTYJPb/rDMk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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pandas import lib\n", + "from matplotlib.ticker import FuncFormatter\n", + "fig, axes = plt.subplots(figsize=(12,8))\n", + "\n", + "national_data2012.sort(\"poll_date\", inplace=True)\n", + "dates = pandas.DatetimeIndex(national_data2012.poll_date).asi8\n", + "\n", + "loess_res = sm.nonparametric.lowess(national_data2012.obama_spread.values, dates, \n", + " frac=.075, it=3)\n", + "\n", + "dates_x = lib.ints_to_pydatetime(dates)\n", + "axes.scatter(dates_x, national_data2012[\"obama_spread\"])\n", + "axes.plot(dates_x, loess_res[:,1], color='r')\n", + "axes.yaxis.get_major_locator().set_params(nbins=12)\n", + "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n", + "axes.grid(False, axis='x')\n", + "axes.hlines(0, dates_x[0], dates_x[-1], color='black', lw=3)\n", + "axes.margins(0, .05)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trends = []\n", + "for i, group in kmeans_groups:\n", + " data = group[[\"poll_date\", \"obama_spread\"]]\n", + " data = pandas.concat((data, national_data2012[[\"poll_date\", \"obama_spread\"]]))\n", + " \n", + " data.sort(\"poll_date\", inplace=True)\n", + " dates = pandas.DatetimeIndex(data.poll_date).asi8\n", + "\n", + " loess_res = sm.nonparametric.lowess(data.obama_spread.values, dates, \n", + " frac=.1, it=3)\n", + " states = group.State.unique()\n", + " for state in states:\n", + " trends.append([state, loess_res[-7:,1].mean()])" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[['Arizona', 2.3149200179538716],\n", + " ['Georgia', 2.3149200179538716],\n", + " ['Mississippi', 2.3149200179538716],\n", + " ['New Mexico', 2.3149200179538716],\n", + " ['North Carolina', 2.3149200179538716],\n", + " ['South Carolina', 2.3149200179538716],\n", + " ['Tennessee', 2.3149200179538716],\n", + " ['West Virginia', 2.3149200179538716],\n", + " ['Colorado', 18.412063676088412],\n", + " ['Connecticut', 18.412063676088412],\n", + " ['Hawaii', 18.412063676088412],\n", + " ['Illinois', 18.412063676088412],\n", + " ['Maryland', 18.412063676088412],\n", + " ['Massachusetts', 18.412063676088412],\n", + " ['Nevada', 18.412063676088412],\n", + " ['New Jersey', 18.412063676088412],\n", + " ['Rhode Island', 18.412063676088412],\n", + " ['Virginia', 18.412063676088412],\n", + " ['Washington', 18.412063676088412],\n", + " ['California', 2.73263672729736],\n", + " ['Florida', 2.73263672729736],\n", + " ['New York', 2.73263672729736],\n", + " ['Texas', 2.73263672729736],\n", + " ['Indiana', 6.5865280433068092],\n", + " ['Iowa', 6.5865280433068092],\n", + " ['Kansas', 6.5865280433068092],\n", + " ['Maine', 6.5865280433068092],\n", + " ['Michigan', 6.5865280433068092],\n", + " ['Minnesota', 6.5865280433068092],\n", + " ['Missouri', 6.5865280433068092],\n", + " ['Montana', 6.5865280433068092],\n", + " ['Nebraska', 6.5865280433068092],\n", + " ['New Hampshire', 6.5865280433068092],\n", + " ['North Dakota', 6.5865280433068092],\n", + " ['Ohio', 6.5865280433068092],\n", + " ['Oregon', 6.5865280433068092],\n", + " ['Pennsylvania', 6.5865280433068092],\n", + " ['South Dakota', 6.5865280433068092],\n", + " ['Utah', 6.5865280433068092],\n", + " ['Vermont', 6.5865280433068092],\n", + " ['Wisconsin', 6.5865280433068092]]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trends" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adjust for sensitivity to time-trends" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\text{Margin}=X_i+Z_t+\\epsilon$$\n", + "\n", + "where $S_i$ are Pollster:State dummies. In a state with a time-dependent trend, you might write\n", + "\n", + "$$\\text{Margin}=X_i+m*Z_t$$\n", + "\n", + "where $m$ is a multiplier representing uncertainty in the time-trend parameter. Solving for $m$ gives\n", + "\n", + "$$m=\\text{Margin}-\\frac{X_i}{Z_t}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from statsmodels.formula.api import ols, wls" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#pollster_state_dummy = state_data2012.groupby([\"Pollster\", \"State\"])[\"obama_spread\"].mean()\n", + "#daily_dummy = state_data2012.groupby([\"poll_date\"])[\"obama_spread\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012[\"pollster_state\"] = state_data2012[\"Pollster\"] + \"-\" + state_data2012[\"State\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's actually a bug in pandas when you merge on datetimes. In order to avoid it, we need to sort our data now and once again after we merge on dates." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dummy_model = ols(\"obama_spread ~ C(pollster_state) + C(poll_date)\", data=state_data2012).fit()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The base case is American Research Group-Colorado" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pollster American Research Group\n", + "State Colorado\n", + "MoE 4\n", + "Obama (D) 49\n", + "Romney (R) 47\n", + "Sample 600\n", + "Spread Obama +2\n", + "obama_spread 2\n", + "poll_date 2012-09-11 00:00:00\n", + "Weight 0.65\n", + "PIE 1.76\n", + "ESS 173\n", + "MESS 173\n", + "time_weight 0.6156\n", + "kmeans_labels 1\n", + "pollster_state American Research Gro...\n", + "Name: 25" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012.irow(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pollster_state = state_data2012[\"pollster_state\"].unique()\n", + "pollster_state.sort()\n", + "pollster_state_params = dummy_model.params[1:len(pollster_state)] + dummy_model.params[0]\n", + "intercept = dummy_model.params[0]\n", + "X = pandas.DataFrame(zip(pollster_state, np.r_[intercept, pollster_state_params]), \n", + " columns=[\"pollster_state\", \"X\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dates = state_data2012.poll_date.unique()\n", + "dates.sort()\n", + "dates_params = intercept + dummy_model.params[-len(dates):]\n", + "Z = pandas.DataFrame(zip(dates, dates_params), columns=[\"poll_date\", \"Z\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Drop the ones less than 1." + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Z = Z.ix[np.abs(Z.Z) > 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012 = state_data2012.merge(X, on=\"pollster_state\", sort=False)\n", + "state_data2012 = state_data2012.merge(Z, on=\"poll_date\", sort=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_data2012[\"m\"] = state_data2012[\"obama_spread\"].sub(state_data2012[\"X\"].div(state_data2012[\"Z\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#m_dataframe.ix[m_dataframe.pollster_state == \"American Research Group-New Hampshire\"].values" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_dataframe = state_data2012[[\"State\", \"m\", \"poll_date\", \"Pollster\", \"pollster_state\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 355.000\n", + "mean 3.281\n", + "std 9.168\n", + "min -52.000\n", + "25% -0.808\n", + "50% 2.697\n", + "75% 8.145\n", + "max 38.723" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_dataframe[\"m\"].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_size = m_dataframe.groupby(\"pollster_state\").size()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pollster_state\n", + "American Research Group-Colorado 1\n", + "American Research Group-Florida 1\n", + "American Research Group-Iowa 1\n", + "American Research Group-Nevada 1\n", + "American Research Group-New Hampshire 3\n", + "American Research Group-North Carolina 1\n", + "American Research Group-Ohio 1\n", + "American Research Group-Virginia 1\n", + "CNN / Opinion Research-Wisconsin 1\n", + "Chicago Trib. / MarketShares-Illinois 1\n", + "Columbus Dispatch (OH)-Ohio 2\n", + "EPIC-MRA-Michigan 8\n", + "Fairleigh-Dickinson (NJ)-New Jersey 3\n", + "Field Poll (CA)-California 6\n", + "Insider Advantage-Georgia 2\n", + "LA Times / Bloomberg-New Hampshire 1\n", + "Marist (NY)-New York 3\n", + "Mason-Dixon-Florida 3\n", + "Mason-Dixon-Georgia 1\n", + "Mason-Dixon-New Hampshire 1\n", + "Mason-Dixon-North Dakota 1\n", + "Mason-Dixon-Utah 1\n", + "Mason-Dixon-Virginia 1\n", + "Mitchell-Michigan 3\n", + "Ohio Poll-Ohio 2\n", + "Public Policy Polling (PPP)-Arizona 7\n", + "Public Policy Polling (PPP)-California 2\n", + "Public Policy Polling (PPP)-Colorado 6\n", + "Public Policy Polling (PPP)-Connecticut 3\n", + "Public Policy Polling (PPP)-Florida 8\n", + "Public Policy Polling (PPP)-Georgia 1\n", + "Public Policy Polling (PPP)-Hawaii 1\n", + "Public Policy Polling (PPP)-Iowa 8\n", + "Public Policy Polling (PPP)-Maine 2\n", + "Public Policy Polling (PPP)-Maryland 1\n", + "Public Policy Polling (PPP)-Massachusetts 6\n", + "Public Policy Polling (PPP)-Michigan 6\n", + "Public Policy Polling (PPP)-Minnesota 5\n", + "Public Policy Polling (PPP)-Mississippi 2\n", + "Public Policy Polling (PPP)-Missouri 7\n", + "Public Policy Polling (PPP)-Montana 3\n", + "Public Policy Polling (PPP)-Nebraska 1\n", + "Public Policy Polling (PPP)-Nevada 4\n", + "Public Policy Polling (PPP)-New Hampshire 3\n", + "Public Policy Polling (PPP)-New Mexico 6\n", + "Public Policy Polling (PPP)-North Carolina 22\n", + "Public Policy Polling (PPP)-Ohio 9\n", + "Public Policy Polling (PPP)-Oregon 2\n", + "Public Policy Polling (PPP)-Pennsylvania 5\n", + "Public Policy Polling (PPP)-Rhode Island 1\n", + "Public Policy Polling (PPP)-South Carolina 3\n", + "Public Policy Polling (PPP)-South Dakota 1\n", + "Public Policy Polling (PPP)-Tennessee 1\n", + "Public Policy Polling (PPP)-Texas 3\n", + "Public Policy Polling (PPP)-Utah 1\n", + "Public Policy Polling (PPP)-Virginia 7\n", + "Public Policy Polling (PPP)-Washington 3\n", + "Public Policy Polling (PPP)-West Virginia 3\n", + "Public Policy Polling (PPP)-Wisconsin 6\n", + "Quinnipiac-Connecticut 4\n", + "Quinnipiac-Florida 12\n", + "Quinnipiac-New Jersey 8\n", + "Quinnipiac-New York 5\n", + "Quinnipiac-Ohio 11\n", + "Quinnipiac-Pennsylvania 9\n", + "Quinnipiac-Virginia 5\n", + "Rasmussen-Arizona 3\n", + "Rasmussen-California 1\n", + "Rasmussen-Colorado 3\n", + "Rasmussen-Connecticut 1\n", + "Rasmussen-Florida 5\n", + "Rasmussen-Indiana 1\n", + "Rasmussen-Iowa 3\n", + "Rasmussen-Maine 1\n", + "Rasmussen-Massachusetts 4\n", + "Rasmussen-Michigan 2\n", + "Rasmussen-Missouri 6\n", + "Rasmussen-Montana 5\n", + "Rasmussen-Nebraska 2\n", + "Rasmussen-Nevada 3\n", + "Rasmussen-New Hampshire 1\n", + "Rasmussen-New Jersey 1\n", + "Rasmussen-New Mexico 3\n", + "Rasmussen-North Carolina 4\n", + "Rasmussen-North Dakota 1\n", + "Rasmussen-Ohio 7\n", + "Rasmussen-Pennsylvania 4\n", + "Rasmussen-Virginia 5\n", + "Rasmussen-Washington 1\n", + "Rasmussen-Wisconsin 7\n", + "Suffolk (NH/MA)-Florida 2\n", + "SurveyUSA-California 4\n", + "SurveyUSA-Florida 2\n", + "SurveyUSA-Georgia 4\n", + "SurveyUSA-Kansas 2\n", + "SurveyUSA-Michigan 1\n", + "SurveyUSA-New Jersey 1\n", + "SurveyUSA-New York 1\n", + "SurveyUSA-North Carolina 2\n", + "SurveyUSA-Oregon 4\n", + "SurveyUSA-Pennsylvania 1\n", + "SurveyUSA-Washington 4\n", + "Length: 102" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_size" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "drop_idx = m_size.ix[m_size == 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_dataframe = m_dataframe.set_index([\"pollster_state\", \"poll_date\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " State m Pollster\n", + "poll_date \n", + "2012-03-17 New Hampshire 6.437 American Research Group\n", + "2012-06-23 New Hampshire 0.071 American Research Group\n", + "2012-09-26 New Hampshire 4.055 American Research Group" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_dataframe.xs(\"American Research Group-New Hampshire\", level=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_dataframe = m_dataframe.drop(drop_idx.index, level=0).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Int64Index: 320 entries, 0 to 319\n", + "Data columns:\n", + "pollster_state 320 non-null values\n", + "poll_date 320 non-null values\n", + "State 320 non-null values\n", + "m 320 non-null values\n", + "Pollster 320 non-null values\n", + "dtypes: datetime64[ns](1), float64(1), object(3)" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_regression_data = m_dataframe.merge(demo_data, on=\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Int64Index: 320 entries, 0 to 319\n", + "Data columns:\n", + "pollster_state 320 non-null values\n", + "poll_date 320 non-null values\n", + "State 320 non-null values\n", + "m 320 non-null values\n", + "Pollster 320 non-null values\n", + "per_black 320 non-null values\n", + "per_hisp 320 non-null values\n", + "per_white 320 non-null values\n", + "educ_hs 320 non-null values\n", + "educ_coll 320 non-null values\n", + "average_income 320 non-null values\n", + "median_income 320 non-null values\n", + "pop_density 320 non-null values\n", + "vote_pop 320 non-null values\n", + "older_pop 320 non-null values\n", + "dem_adv 320 non-null values\n", + "no_party 320 non-null values\n", + "PVI 320 non-null values\n", + "obama_give 320 non-null values\n", + "romney_give 320 non-null values\n", + "kmeans_group 320 non-null values\n", + "kmeans_labels 320 non-null values\n", + "dtypes: datetime64[ns](1), float64(14), int64(4), object(3)" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_regression_data" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " PVI per_black per_hisp older_pop average_income romney_give obama_give educ_coll educ_hs\n", + "PVI 1.000 -0.295 0.115 0.150 0.594 0.291 0.669 0.494 0.226\n", + "per_black -0.295 1.000 -0.174 0.279 -0.064 0.111 -0.281 -0.111 -0.497\n", + "per_hisp 0.115 -0.174 1.000 0.403 0.098 0.289 0.306 0.112 -0.566\n", + "older_pop 0.150 0.279 0.403 1.000 0.022 0.237 -0.038 -0.076 -0.479\n", + "average_income 0.594 -0.064 0.098 0.022 1.000 0.718 0.704 0.888 0.250\n", + "romney_give 0.291 0.111 0.289 0.237 0.718 1.000 0.555 0.630 -0.025\n", + "obama_give 0.669 -0.281 0.306 -0.038 0.704 0.555 1.000 0.835 0.085\n", + "educ_coll 0.494 -0.111 0.112 -0.076 0.888 0.630 0.835 1.000 0.273\n", + "educ_hs 0.226 -0.497 -0.566 -0.479 0.250 -0.025 0.085 0.273 1.000" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_regression_data[[\"PVI\", \"per_black\", \"per_hisp\", \"older_pop\", \"average_income\", \n", + " \"romney_give\", \"obama_give\", \"educ_coll\", \"educ_hs\"]].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 743 days, 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WLS Regression Results
Dep. Variable: m R-squared: 0.704
Model: WLS Adj. R-squared: 0.699
Method: Least Squares F-statistic: 149.4
Date: Fri, 26 Oct 2012 Prob (F-statistic): 8.75e-81
Time: 08:31:09 Log-Likelihood: -632.76
No. Observations: 320 AIC: 1278.
Df Residuals: 314 BIC: 1300.
Df Model: 5
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.5623 2.504 1.822 0.069 -0.364 9.488
PVI 1.5534 0.076 20.565 0.000 1.405 1.702
per_hisp 0.1672 0.023 7.351 0.000 0.122 0.212
per_black 0.1972 0.040 4.954 0.000 0.119 0.275
average_income -0.0003 0.000 -1.836 0.067 -0.001 2.17e-05
educ_coll 0.0612 0.121 0.506 0.613 -0.177 0.299
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 113.511 Durbin-Watson: 1.677
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1263.298
Skew: -1.115 Prob(JB): 4.77e-275
Kurtosis: 12.475 Cond. No. 2.71e+05
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " WLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: m R-squared: 0.704\n", + "Model: WLS Adj. R-squared: 0.699\n", + "Method: Least Squares F-statistic: 149.4\n", + "Date: Fri, 26 Oct 2012 Prob (F-statistic): 8.75e-81\n", + "Time: 08:31:09 Log-Likelihood: -632.76\n", + "No. Observations: 320 AIC: 1278.\n", + "Df Residuals: 314 BIC: 1300.\n", + "Df Model: 5 \n", + "==================================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "----------------------------------------------------------------------------------\n", + "Intercept 4.5623 2.504 1.822 0.069 -0.364 9.488\n", + "PVI 1.5534 0.076 20.565 0.000 1.405 1.702\n", + "per_hisp 0.1672 0.023 7.351 0.000 0.122 0.212\n", + "per_black 0.1972 0.040 4.954 0.000 0.119 0.275\n", + "average_income -0.0003 0.000 -1.836 0.067 -0.001 2.17e-05\n", + "educ_coll 0.0612 0.121 0.506 0.613 -0.177 0.299\n", + "==============================================================================\n", + "Omnibus: 113.511 Durbin-Watson: 1.677\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1263.298\n", + "Skew: -1.115 Prob(JB): 4.77e-275\n", + "Kurtosis: 12.475 Cond. No. 2.71e+05\n", + "==============================================================================\n", + "\n", + "The condition number is large, 2.71e+05. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\"\"\"" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_model = wls(\"m ~ PVI + per_hisp + per_black + average_income + educ_coll\", data=m_regression_data, weights=time_weights).fit()\n", + "m_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_resid = pandas.DataFrame(zip(m_model.resid, m_regression_data.State), \n", + " columns=[\"resid\", \"State\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_resid_group = state_resid.groupby(\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jMxHz644fH31xismTbezeXfzDdDq8honOO3pUZe3a2ONKP/xg4dixovUA79ql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8fnj3PjcvnfTOL6dSXy7PtnlAr2SpX8s5mL4FGunMQ//2njnXcyj1hNmGBTleb1h58ZFSXnKFnH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\nExXGjrVx5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ZEjub8gc9uYLxCEi1zfBO3bt6dly5bUrFmTs2fPMnToUJo3b54fbStwSpdW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Pd+/exYgRI/QTNSncRW3FihU1fr+NvUaP6jbFWbFiBV5//XUMGjSo2uvc2rVrG2UT1pdffsnk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\nERERkYUYnomIiIiILPQPhlIF2kcamm0AAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize=(12,8), subplot_kw={\"ylabel\" : \"Residual\",\n", + " \"xlabel\" : \"State\"})\n", + "i = 0\n", + "for state, group in state_resid_group:\n", + " x = [i] * len(group)\n", + " axes.scatter(x, group[\"resid\"], s=91)\n", + " i += 1\n", + "states = m_regression_data.State.unique()\n", + "states.sort()\n", + "#axes.xaxis.get_major_locator().set_params(nbins=len(states))\n", + "axes.margins(.05, .05)\n", + "axes.xaxis.set_ticks(range(31))\n", + "axes.xaxis.set_ticklabels(states);\n", + "for label in axes.xaxis.get_ticklabels():\n", + " label.set_rotation(90)\n", + " label.set_fontsize('large')" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data = demo_data.drop(demo_data.index[demo_data['State'] == 'District of Columbia'])\n", + "demo_data.reset_index(drop=True, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "exog = demo_data[[\"PVI\", \"per_hisp\", \"per_black\", \"average_income\", \"educ_coll\"]]\n", + "exog[\"const\"] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_m = m_model.predict(exog)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-15.3603, -21.5913, -4.9947, -10.5032, 16.1222, 1.9831,\n", + " 10.9647, 14.0814, 2.0932, -4.6139, 18.2001, -25.0872,\n", + " 15.766 , -7.7724, 1.4341, -17.1113, -14.4254, -9.5522,\n", + " 6.7645, 17.5081, 18.4677, 8.2765, 2.5286, -7.98 ,\n", + " -3.1192, -11.1954, -19.0509, 5.3332, 0.9212, 8.2565,\n", + " 10.5848, 19.1612, -1.7616, -16.5408, -0.1307, -24.3894,\n", + " 7.0121, 4.3192, 18.3818, -7.0588, -14.4 , -11.0846,\n", + " -8.1381, -29.2403, 18.9684, -0.9221, 7.9232, -12.3191,\n", + " 3.498 , -31.7474])" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_m" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "unit_m = (state_m - state_m.min())/(state_m.max() - state_m.min())" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "unit_m *= 2" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_correction = zip(demo_data.State, unit_m)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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sP+L0r25HMcjnA7791o577kmB13vmBaULFjjQurUXs2YVon59bqRJPnxCLUxW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\nCCEcURFNCCGEEEIIR/8HPK6BHULNBFoAAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize=(12,8), subplot_kw={\"ylabel\" : \"Time Uncertainty\",\n", + " \"xlabel\" : \"State\"})\n", + "\n", + "axes.scatter(range(len(unit_m)), unit_m, s=91)\n", + "\n", + "axes.margins(.05, .05)\n", + "axes.xaxis.set_ticks(range(len(unit_m)))\n", + "axes.xaxis.set_ticklabels(demo_data.State);\n", + "for label in axes.xaxis.get_ticklabels():\n", + " label.set_rotation(90)\n", + " label.set_fontsize('large')" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Alabama', 0.64378739293503406),\n", + " ('Alaska', 0.39899365289624461),\n", + " ('Arizona', 1.0510086330835007),\n", + " ('Arkansas', 0.83460144787235913),\n", + " ('California', 1.8806123579140055),\n", + " ('Colorado', 1.3251405679451049),\n", + " ('Connecticut', 1.6779924829159023),\n", + " ('Delaware', 1.8004372432963116),\n", + " ('Florida', 1.329465611597048),\n", + " ('Georgia', 1.0659702728954288),\n", + " ('Hawaii', 1.9622429470675198),\n", + " ('Idaho', 0.26165481506057381),\n", + " ('Illinois', 1.8666167182198876),\n", + " ('Indiana', 0.94188345895010406),\n", + " ('Iowa', 1.3035708167337279),\n", + " ('Kansas', 0.57499466689906564),\n", + " ('Kentucky', 0.68051576608007691),\n", + " ('Louisiana', 0.87196446188500021),\n", + " ('Maine', 1.5129826861514035),\n", + " ('Maryland', 1.9350556925421645),\n", + " ('Massachusetts', 1.972758329102086),\n", + " ('Michigan', 1.572382623529095),\n", + " ('Minnesota', 1.3465700819595314),\n", + " ('Mississippi', 0.9337304745438173),\n", + " ('Missouri', 1.1246911889623949),\n", + " ('Montana', 0.80740786234605832),\n", + " ('Nebraska', 0.49879580201830931),\n", + " ('Nevada', 1.4567544736098006),\n", + " ('New Hampshire', 1.2834218995227675),\n", + " ('New Jersey', 1.5715997823676553),\n", + " ('New Mexico', 1.6630695718979507),\n", + " ('New York', 2.0),\n", + " ('North Carolina', 1.1780268665681453),\n", + " ('North Dakota', 0.59740688290763611),\n", + " ('Ohio', 1.2420974975622283),\n", + " ('Oklahoma', 0.28906929360446137),\n", + " ('Oregon', 1.5227100227420458),\n", + " ('Pennsylvania', 1.4169161758937938),\n", + " ('Rhode Island', 1.9693830170636866),\n", + " ('South Carolina', 0.96992120743772237),\n", + " ('South Dakota', 0.68151306828176339),\n", + " ('Tennessee', 0.81176165562541391),\n", + " ('Texas', 0.92751616650445501),\n", + " ('Utah', 0.098492882545137092),\n", + " ('Vermont', 1.9924269957578287),\n", + " ('Virginia', 1.2110060089821313),\n", + " ('Washington', 1.5585050874076187),\n", + " ('West Virginia', 0.76326334710489518),\n", + " ('Wisconsin', 1.3846558069742687),\n", + " ('Wyoming', 0.0)]" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_correction" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trends = pandas.DataFrame(trends, columns=[\"State\", \"trend\"])\n", + "m_correction = pandas.DataFrame(m_correction, columns=[\"State\", \"m_correction\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trends = trends.merge(m_correction, on=\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " trend m_correction\n", + "State \n", + "Arizona 2.315 1.051\n", + "California 2.733 1.881\n", + "Colorado 18.412 1.325\n", + "Connecticut 18.412 1.678\n", + "Florida 2.733 1.329\n", + "Georgia 2.315 1.066\n", + "Hawaii 18.412 1.962\n", + "Illinois 18.412 1.867\n", + "Indiana 6.587 0.942\n", + "Iowa 6.587 1.304\n", + "Kansas 6.587 0.575\n", + "Maine 6.587 1.513\n", + "Maryland 18.412 1.935\n", + "Massachusetts 18.412 1.973\n", + "Michigan 6.587 1.572\n", + "Minnesota 6.587 1.347\n", + "Mississippi 2.315 0.934\n", + "Missouri 6.587 1.125\n", + "Montana 6.587 0.807\n", + "Nebraska 6.587 0.499\n", + "Nevada 18.412 1.457\n", + "New Hampshire 6.587 1.283\n", + "New Jersey 18.412 1.572\n", + "New Mexico 2.315 1.663\n", + "New York 2.733 2.000\n", + "North Carolina 2.315 1.178\n", + "North Dakota 6.587 0.597\n", + "Ohio 6.587 1.242\n", + "Oregon 6.587 1.523\n", + "Pennsylvania 6.587 1.417\n", + "Rhode Island 18.412 1.969\n", + "South Carolina 2.315 0.970\n", + "South Dakota 6.587 0.682\n", + "Tennessee 2.315 0.812\n", + "Texas 2.733 0.928\n", + "Utah 6.587 0.098\n", + "Vermont 6.587 1.992\n", + "Virginia 18.412 1.211\n", + "Washington 18.412 1.559\n", + "West Virginia 2.315 0.763\n", + "Wisconsin 6.587 1.385" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trends.set_index(\"State\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trends = trends.product(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Snapshot: Combine Trend Estimates and State Polls" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State Pollster \n", + "AZ Public Policy Polling (PPP) -9.168\n", + " Rasmussen -10.209\n", + "CA Field Poll (CA) 23.344\n", + " Public Policy Polling (PPP) 20.999\n", + " Rasmussen 22.000\n", + " SurveyUSA 22.123\n", + "CO American Research Group 2.000\n", + " Public Policy Polling (PPP) 5.470\n", + " Rasmussen -1.574\n", + "CT Public Policy Polling (PPP) 12.758\n", + " Quinnipiac 7.294\n", + " Rasmussen 8.000\n", + "FL American Research Group 5.000\n", + " Mason-Dixon -3.543\n", + " Public Policy Polling (PPP) 3.125\n", + " Quinnipiac 3.076\n", + " Rasmussen 0.883\n", + " Suffolk (NH/MA) -0.003\n", + " SurveyUSA 4.169\n", + "GA Insider Advantage -19.174\n", + " Mason-Dixon -17.000\n", + " Public Policy Polling (PPP) -3.000\n", + " SurveyUSA -7.984\n", + "HI Public Policy Polling (PPP) 27.000\n", + "IA American Research Group 7.000\n", + " Mason-Dixon -3.000\n", + " Public Policy Polling (PPP) 5.879\n", + " Rasmussen -2.749\n", + "IL Chicago Trib. / MarketShares 21.000\n", + "IN Rasmussen -16.000\n", + "KS SurveyUSA -15.875\n", + "MA Public Policy Polling (PPP) 17.580\n", + " Rasmussen 15.107\n", + "MD Public Policy Polling (PPP) 23.000\n", + "ME Public Policy Polling (PPP) 16.038\n", + " Rasmussen 12.000\n", + "MI CNN / Opinion Research 8.000\n", + " EPIC-MRA 7.430\n", + " Mitchell 0.897\n", + " Public Policy Polling (PPP) 7.694\n", + " Rasmussen 11.072\n", + " SurveyUSA 11.000\n", + "MN Public Policy Polling (PPP) 7.335\n", + "MO Public Policy Polling (PPP) -11.225\n", + " Rasmussen -2.486\n", + " SurveyUSA -1.000\n", + "MS Public Policy Polling (PPP) -17.973\n", + "MT Mason-Dixon -9.000\n", + " Public Policy Polling (PPP) -5.003\n", + " Rasmussen -15.641\n", + "NC American Research Group -4.000\n", + " Public Policy Polling (PPP) 0.261\n", + " Rasmussen -5.676\n", + " SurveyUSA 1.987\n", + "ND Mason-Dixon -13.000\n", + " Rasmussen -15.000\n", + "NE Public Policy Polling (PPP) -12.005\n", + " Rasmussen -14.308\n", + "NH American Research Group 4.150\n", + " LA Times / Bloomberg -10.000\n", + " Mason-Dixon -11.000\n", + " Public Policy Polling (PPP) 6.273\n", + " Rasmussen -2.439\n", + "NJ Fairleigh-Dickinson (NJ) 13.859\n", + " Public Policy Polling (PPP) 14.006\n", + " Quinnipiac 7.504\n", + " Rasmussen 6.000\n", + " SurveyUSA 14.000\n", + "NM Public Policy Polling (PPP) 10.621\n", + " Rasmussen 11.651\n", + "NV American Research Group 7.000\n", + " CNN / Opinion Research 3.000\n", + " Public Policy Polling (PPP) 7.345\n", + " Rasmussen 2.524\n", + "NY Marist (NY) 22.047\n", + " Quinnipiac 27.345\n", + " SurveyUSA 30.000\n", + "OH American Research Group 1.000\n", + " Columbus Dispatch (OH) 8.616\n", + " Ohio Poll 3.000\n", + " Public Policy Polling (PPP) 4.142\n", + " Quinnipiac 7.729\n", + " Rasmussen 0.866\n", + "OR Public Policy Polling (PPP) 9.130\n", + " SurveyUSA 8.676\n", + "PA Public Policy Polling (PPP) 6.160\n", + " Quinnipiac 6.047\n", + " Rasmussen 10.875\n", + " SurveyUSA 0.000\n", + "RI Public Policy Polling (PPP) 17.000\n", + "SC Public Policy Polling (PPP) -14.558\n", + "SD Public Policy Polling (PPP) -6.000\n", + "TN Public Policy Polling (PPP) -7.000\n", + "TX Public Policy Polling (PPP) -6.999\n", + "UT Mason-Dixon -51.000\n", + " Public Policy Polling (PPP) -32.000\n", + "VA American Research Group 2.000\n", + " Mason-Dixon 1.000\n", + " Public Policy Polling (PPP) 5.096\n", + " Quinnipiac 0.578\n", + " Rasmussen 0.892\n", + "VT Public Policy Polling (PPP) 20.000\n", + "WA Public Policy Polling (PPP) 13.051\n", + " Rasmussen 11.000\n", + " SurveyUSA 15.310\n", + "WI CNN / Opinion Research 4.000\n", + " Public Policy Polling (PPP) 5.393\n", + " Rasmussen 2.116\n", + "WV Public Policy Polling (PPP) -19.757\n", + "Name: poll, Length: 109" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_polls.name = \"poll\"\n", + "state_polls" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_polls = state_polls.reset_index()\n", + "state_polls.State = state_polls.State.replace(states_abbrev_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State\n", + "Arizona 2.433\n", + "California 5.139\n", + "Colorado 24.399\n", + "Connecticut 30.895\n", + "Florida 3.633\n", + "Georgia 2.468\n", + "Hawaii 36.129\n", + "Illinois 34.368\n", + "Indiana 6.204\n", + "Iowa 8.586\n", + "Kansas 3.787\n", + "Maine 9.965\n", + "Maryland 35.628\n", + "Massachusetts 36.323\n", + "Michigan 10.357\n", + "Minnesota 8.869\n", + "Mississippi 2.162\n", + "Missouri 7.408\n", + "Montana 5.318\n", + "Nebraska 3.285\n", + "Nevada 26.822\n", + "New Hampshire 8.453\n", + "New Jersey 28.936\n", + "New Mexico 3.850\n", + "New York 5.465\n", + "North Carolina 2.727\n", + "North Dakota 3.935\n", + "Ohio 8.181\n", + "Oregon 10.029\n", + "Pennsylvania 9.333\n", + "Rhode Island 36.260\n", + "South Carolina 2.245\n", + "South Dakota 4.489\n", + "Tennessee 1.879\n", + "Texas 2.535\n", + "Utah 0.649\n", + "Vermont 13.123\n", + "Virginia 22.297\n", + "Washington 28.695\n", + "West Virginia 1.767\n", + "Wisconsin 9.120\n", + "Name: poll" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trends.name = \"poll\"\n", + "trends" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "trends = trends.reset_index()\n", + "trends[\"Pollster\"] = \"National\"" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " State poll Pollster\n", + "0 Arizona 2.433 National\n", + "1 California 5.139 National\n", + "2 Colorado 24.399 National\n", + "3 Connecticut 30.895 National\n", + "4 Florida 3.633 National\n", + "5 Georgia 2.468 National\n", + "6 Hawaii 36.129 National\n", + "7 Illinois 34.368 National\n", + "8 Indiana 6.204 National\n", + "9 Iowa 8.586 National\n", + "10 Kansas 3.787 National\n", + "11 Maine 9.965 National\n", + "12 Maryland 35.628 National\n", + "13 Massachusetts 36.323 National\n", + "14 Michigan 10.357 National\n", + "15 Minnesota 8.869 National\n", + "16 Mississippi 2.162 National\n", + "17 Missouri 7.408 National\n", + "18 Montana 5.318 National\n", + "19 Nebraska 3.285 National\n", + "20 Nevada 26.822 National\n", + "21 New Hampshire 8.453 National\n", + "22 New Jersey 28.936 National\n", + "23 New Mexico 3.850 National\n", + "24 New York 5.465 National\n", + "25 North Carolina 2.727 National\n", + "26 North Dakota 3.935 National\n", + "27 Ohio 8.181 National\n", + "28 Oregon 10.029 National\n", + "29 Pennsylvania 9.333 National\n", + "30 Rhode Island 36.260 National\n", + "31 South Carolina 2.245 National\n", + "32 South Dakota 4.489 National\n", + "33 Tennessee 1.879 National\n", + "34 Texas 2.535 National\n", + "35 Utah 0.649 National\n", + "36 Vermont 13.123 National\n", + "37 Virginia 22.297 National\n", + "38 Washington 28.695 National\n", + "39 West Virginia 1.767 National\n", + "40 Wisconsin 9.120 National" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trends" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "polls = pandas.concat((state_polls, trends))" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " Pollster Weight PIE\n", + "0 ABC / Washington Post 0.95 1.41\n", + "1 American Research Group 0.65 1.76\n", + "2 CBS / New York Times 0.66 1.84\n", + "3 Chicago Trib. / Marke... 1.16 1.13\n", + "4 CNN / Opinion Research 0.77 1.59\n", + "5 Columbus Dispatch (OH) 0.50 6.76\n", + "6 EPIC-MRA 0.75 1.65\n", + "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", + "8 Field Poll (CA) 1.33 0.88\n", + "9 Fox / Opinion Dynamics 0.79 1.60\n", + "10 Franklin Pierce (NH) 0.74 1.60\n", + "11 Insider Advantage 0.95 1.29\n", + "12 Keystone (PA) 0.64 1.55\n", + "13 LA Times / Bloomberg 0.83 1.44\n", + "14 Marist (NY) 0.69 1.73\n", + "15 Mason-Dixon 1.10 1.15\n", + "16 Mitchell 0.96 1.43\n", + "17 Ohio Poll 1.24 1.05\n", + "18 Public Opinion Strate... 0.63 1.81\n", + "19 Public Policy Polling... 1.05 1.60\n", + "20 Quinnipiac 0.95 1.34\n", + "21 Rasmussen 1.30 0.88\n", + "22 Research 2000 1.01 1.20\n", + "23 Selzer 1.47 0.92\n", + "24 Star Tribune (MN) 0.81 2.01\n", + "25 Strategic Vision 0.95 1.45\n", + "26 Suffolk (NH/MA) 0.77 1.37\n", + "27 SurveyUSA 1.91 0.72\n", + "28 Univ. New Hampshire 1.08 1.26\n", + "29 USA Today / Gallup 0.63 2.01\n", + "30 Zogby 0.64 1.72\n", + "31 Zogby Interactive 0.43 4.74" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "natl_weight = pandas.DataFrame([[\"National\", weights.Weight.mean(), weights.PIE.mean()]],\n", + " columns=[\"Pollster\", \"Weight\", \"PIE\"])\n", + "weights = pandas.concat((weights, natl_weight)).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "polls = polls.merge(weights, on=\"Pollster\", how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "polls = polls.sort(\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def weighted_mean(group):\n", + " return (group[\"poll\"] * group[\"Weight\"] / group[\"Weight\"].sum()).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " resid State\n", + "307 5.193 Wisconsin\n", + "308 7.402 Wisconsin\n", + "309 0.246 Wisconsin\n", + "310 9.971 Wisconsin\n", + "311 0.697 Wisconsin\n", + "312 2.648 Wisconsin\n", + "313 -0.300 Wisconsin\n", + "314 0.859 Wisconsin\n", + "315 7.209 Wisconsin\n", + "316 -0.676 Wisconsin\n", + "317 -5.412 Wisconsin\n", + "318 -1.565 Wisconsin\n", + "319 -1.565 Wisconsin" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = polls.groupby(\"State\").aggregate(weighted_mean)[\"poll\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State\n", + "Arizona -6.351\n", + "California 19.794\n", + "Colorado 6.947\n", + "Connecticut 13.967\n", + "Florida 2.079\n", + "Georgia -8.969\n", + "Hawaii 31.233\n", + "Illinois 26.869\n", + "Indiana -6.870\n", + "Iowa 2.325\n", + "Kansas -9.541\n", + "Maine 12.734\n", + "Maryland 28.856\n", + "Massachusetts 21.816\n", + "Michigan 8.561\n", + "Minnesota 8.046\n", + "Mississippi -8.637\n", + "Missouri -1.974\n", + "Montana -7.035\n", + "Nebraska -8.663\n", + "Nevada 9.022\n", + "New Hampshire -1.133\n", + "New Jersey 13.545\n", + "New Mexico 9.145\n", + "New York 23.207\n", + "North Carolina -0.590\n", + "North Dakota -9.138\n", + "Ohio 4.384\n", + "Oregon 9.117\n", + "Pennsylvania 5.692\n", + "Rhode Island 25.931\n", + "South Carolina -6.767\n", + "South Dakota -1.136\n", + "Tennessee -2.883\n", + "Texas -2.578\n", + "Utah -29.142\n", + "Vermont 16.811\n", + "Virginia 4.985\n", + "Washington 16.118\n", + "West Virginia -9.776\n", + "Wisconsin 4.909\n", + "Name: poll" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = results.reset_index()\n", + "results[\"obama\"] = 0\n", + "results[\"romney\"] = 0\n", + "results.ix[results[\"poll\"] > 0, [\"obama\"]] = 1\n", + "results.ix[results[\"poll\"] < 0, [\"romney\"]] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results[[\"State\", \"poll\"]].to_csv(\"/home/skipper/school/talks/538model/2012-predicted.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "electoral_votes = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/electoral_votes.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " State Votes\n", + "0 Alabama 9\n", + "1 Alaska 3\n", + "2 Arizona 11\n", + "3 Arkansas 6\n", + "4 California 55\n", + "5 Colorado 9\n", + "6 Connecticut 7\n", + "7 Delaware 3\n", + "8 District of Columbia 3\n", + "9 Florida 29\n", + "10 Georgia 16\n", + "11 Hawaii 4\n", + "12 Idaho 4\n", + "13 Illinois 20\n", + "14 Indiana 11\n", + "15 Iowa 6\n", + "16 Kansas 6\n", + "17 Kentucky 8\n", + "18 Louisiana 8\n", + "19 Maine 4\n", + "20 Maryland 10\n", + "21 Massachusetts 11\n", + "22 Michigan 16\n", + "23 Minnesota 10\n", + "24 Mississippi 6\n", + "25 Missouri 10\n", + "26 Montana 3\n", + "27 Nebraska 5\n", + "28 Nevada 6\n", + "29 New Hampshire 4\n", + "30 New Jersey 14\n", + "31 New Mexico 5\n", + "32 New York 29\n", + "33 North Carolina 15\n", + "34 North Dakota 3\n", + "35 Ohio 18\n", + "36 Oklahoma 7\n", + "37 Oregon 7\n", + "38 Pennsylvania 20\n", + "39 Rhode Island 4\n", + "40 South Carolina 9\n", + "41 South Dakota 3\n", + "42 Tennessee 11\n", + "43 Texas 38\n", + "44 Utah 6\n", + "45 Vermont 3\n", + "46 Virginia 13\n", + "47 Washington 12\n", + "48 West Virginia 5\n", + "49 Wisconsin 10\n", + "50 Wyoming 3" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "electoral_votes.sort(\"State\", inplace=True).reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = electoral_votes.merge(results, on=\"State\", how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = results.set_index(\"State\")\n", + "red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Kentucky\", \"Louisiana\",\n", + " \"Oklahoma\", \"Wyoming\"]\n", + "blue_states = [\"Delaware\", \"District of Columbia\"]\n", + "results.ix[red_states, [\"romney\"]] = 1\n", + "results.ix[red_states, [\"obama\"]] = 0\n", + "results.ix[blue_states, [\"obama\"]] = 1\n", + "results.ix[blue_states, [\"romney\"]] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " Votes poll obama romney\n", + "State \n", + "Alabama 9 NaN 0 1\n", + "Alaska 3 NaN 0 1\n", + "Arizona 11 -6.351 0 1\n", + "Arkansas 6 NaN 0 1\n", + "California 55 19.794 1 0\n", + "Colorado 9 6.947 1 0\n", + "Connecticut 7 13.967 1 0\n", + "Delaware 3 NaN 1 0\n", + "District of Columbia 3 NaN 1 0\n", + "Florida 29 2.079 1 0\n", + "Georgia 16 -8.969 0 1\n", + "Hawaii 4 31.233 1 0\n", + "Idaho 4 NaN 0 1\n", + "Illinois 20 26.869 1 0\n", + "Indiana 11 -6.870 0 1\n", + "Iowa 6 2.325 1 0\n", + "Kansas 6 -9.541 0 1\n", + "Kentucky 8 NaN 0 1\n", + "Louisiana 8 NaN 0 1\n", + "Maine 4 12.734 1 0\n", + "Maryland 10 28.856 1 0\n", + "Massachusetts 11 21.816 1 0\n", + "Michigan 16 8.561 1 0\n", + "Minnesota 10 8.046 1 0\n", + "Mississippi 6 -8.637 0 1\n", + "Missouri 10 -1.974 0 1\n", + "Montana 3 -7.035 0 1\n", + "Nebraska 5 -8.663 0 1\n", + "Nevada 6 9.022 1 0\n", + "New Hampshire 4 -1.133 0 1\n", + "New Jersey 14 13.545 1 0\n", + "New Mexico 5 9.145 1 0\n", + "New York 29 23.207 1 0\n", + "North Carolina 15 -0.590 0 1\n", + "North Dakota 3 -9.138 0 1\n", + "Ohio 18 4.384 1 0\n", + "Oklahoma 7 NaN 0 1\n", + "Oregon 7 9.117 1 0\n", + "Pennsylvania 20 5.692 1 0\n", + "Rhode Island 4 25.931 1 0\n", + "South Carolina 9 -6.767 0 1\n", + "South Dakota 3 -1.136 0 1\n", + "Tennessee 11 -2.883 0 1\n", + "Texas 38 -2.578 0 1\n", + "Utah 6 -29.142 0 1\n", + "Vermont 3 16.811 1 0\n", + "Virginia 13 4.985 1 0\n", + "Washington 12 16.118 1 0\n", + "West Virginia 5 -9.776 0 1\n", + "Wisconsin 10 4.909 1 0\n", + "Wyoming 3 NaN 0 1" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "328.0" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[\"Votes\"].mul(results[\"obama\"]).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "210.0" + ] + }, + "execution_count": 213, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[\"Votes\"].mul(results[\"romney\"]).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " Votes poll obama romney\n", + "State \n", + "Alabama 9 NaN 0 1\n", + "Alaska 3 NaN 0 1\n", + "Arizona 11 -6.351 0 1\n", + "Arkansas 6 NaN 0 1\n", + "California 55 19.794 1 0\n", + "Colorado 9 6.947 1 0\n", + "Connecticut 7 13.967 1 0\n", + "Delaware 3 NaN 1 0\n", + "District of Columbia 3 NaN 1 0\n", + "Florida 29 2.079 1 0\n", + "Georgia 16 -8.969 0 1\n", + "Hawaii 4 31.233 1 0\n", + "Idaho 4 NaN 0 1\n", + "Illinois 20 26.869 1 0\n", + "Indiana 11 -6.870 0 1\n", + "Iowa 6 2.325 1 0\n", + "Kansas 6 -9.541 0 1\n", + "Kentucky 8 NaN 0 1\n", + "Louisiana 8 NaN 0 1\n", + "Maine 4 12.734 1 0\n", + "Maryland 10 28.856 1 0\n", + "Massachusetts 11 21.816 1 0\n", + "Michigan 16 8.561 1 0\n", + "Minnesota 10 8.046 1 0\n", + "Mississippi 6 -8.637 0 1\n", + "Missouri 10 -1.974 0 1\n", + "Montana 3 -7.035 0 1\n", + "Nebraska 5 -8.663 0 1\n", + "Nevada 6 9.022 1 0\n", + "New Hampshire 4 -1.133 0 1\n", + "New Jersey 14 13.545 1 0\n", + "New Mexico 5 9.145 1 0\n", + "New York 29 23.207 1 0\n", + "North Carolina 15 -0.590 0 1\n", + "North Dakota 3 -9.138 0 1\n", + "Ohio 18 4.384 1 0\n", + "Oklahoma 7 NaN 0 1\n", + "Oregon 7 9.117 1 0\n", + "Pennsylvania 20 5.692 1 0\n", + "Rhode Island 4 25.931 1 0\n", + "South Carolina 9 -6.767 0 1\n", + "South Dakota 3 -1.136 0 1\n", + "Tennessee 11 -2.883 0 1\n", + "Texas 38 -2.578 0 1\n", + "Utah 6 -29.142 0 1\n", + "Vermont 3 16.811 1 0\n", + "Virginia 13 4.985 1 0\n", + "Washington 12 16.118 1 0\n", + "West Virginia 5 -9.776 0 1\n", + "Wisconsin 10 4.909 1 0\n", + "Wyoming 3 NaN 0 1" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Divide undecided voters probabilistically." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do historical adjustments based on how polls changed in the past conditional on \"election environment\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Error analysis\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 991a936ec05301b5de12adcc70b56a41641e61be Mon Sep 17 00:00:00 2001 From: Ritesh Bansal Date: Tue, 24 May 2016 09:58:21 -0400 Subject: [PATCH 02/11] added processed data files --- data_nuevo/2012_poll_data_national.pkl | Bin 0 -> 20419 bytes data_nuevo/2012_poll_data_states.csv | 740 +++++++++++++++++++++++++ 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+172,Quinnipiac,NJ,2.4,49.0,40.0,1607.0,Obama +9,9.0,2012-04-06 +173,Fairleigh Dickinson,NJ,3.5,50.0,37.0,800.0,Obama +13,13.0,2012-03-08 +174,SurveyUSA,NJ,4.3,52.0,38.0,533.0,Obama +14,14.0,2012-02-25 +175,Quinnipiac,NJ,2.6,49.0,39.0,1396.0,Obama +10,10.0,2012-02-24 +176,Quinnipiac,NJ,2.6,48.0,38.0,1460.0,Obama +10,10.0,2012-01-13 +177,Quinnipiac,NJ,2.3,49.0,40.0,1788.0,Obama +9,9.0,2011-11-12 +178,Quinnipiac,NJ,2.9,47.0,41.0,1186.0,Obama +6,6.0,2011-10-08 +179,PPP (D),NJ,4.5,53.0,39.0,480.0,Obama +14,14.0,2012-07-17 +180,Rasmussen Reports,NJ,4.5,49.0,43.0,500.0,Obama +6,6.0,2012-05-26 +181,PPP (D),NJ,4.3,52.0,37.0,520.0,Obama +15,15.0,2012-01-08 +182,Rasmussen Reports,NM,4.5,51.0,40.0,500.0,Obama +11,11.0,2012-09-27 +183,WeAskAmerica*,NM,2.9,51.0,41.0,1258.0,Obama +10,10.0,2012-09-26 +184,PPP (D),NM,2.9,53.0,42.0,1122.0,Obama +11,11.0,2012-09-08 +185,Albuquerque Journal*,NM,3.8,45.0,40.0,667.0,Obama +5,5.0,2012-09-05 +186,Rasmussen Reports,NM,4.5,52.0,38.0,500.0,Obama +14,14.0,2012-08-21 +187,PPP (D),NM,3.6,49.0,44.0,724.0,Obama +5,5.0,2012-07-15 +188,WeAskAmerica,NM,2.8,51.0,40.0,1295.0,Obama +11,11.0,2012-07-10 +189,PPP (D),NM,4.3,54.0,40.0,526.0,Obama +14,14.0,2012-04-21 +190,Rasmussen Reports,NM,4.5,52.0,36.0,500.0,Obama +16,16.0,2012-04-03 +191,Rasmussen Reports,NM,4.5,55.0,36.0,500.0,Obama +19,19.0,2012-02-14 +192,PPP (D),NM,4.4,53.0,38.0,500.0,Obama +15,15.0,2011-12-11 +193,PPP (D),NM,3.6,49.0,42.0,732.0,Obama +7,7.0,2011-06-25 +194,PPP (D),NM,4.2,53.0,37.0,545.0,Obama +16,16.0,2011-02-05 +195,SurveyUSA,NC,4.2,49.0,47.0,573.0,Obama +2,2.0,2012-09-30 +196,ARG,NC,4.0,46.0,50.0,600.0,Romney +4,-4.0,2012-09-29 +197,PPP (D),NC,3.1,48.0,48.0,981.0,Tie,0.0,2012-09-29 +198,NBC/WSJ/Marist,NC,3.1,48.0,46.0,1035.0,Obama +2,2.0,2012-09-24 +199,Civitas (R),NC,4.0,49.0,45.0,600.0,Obama +4,4.0,2012-09-19 +200,Purple Strategies,NC,4.0,48.0,46.0,600.0,Obama +2,2.0,2012-09-17 +201,Rasmussen Reports,NC,4.5,45.0,51.0,500.0,Romney +6,-6.0,2012-09-13 +202,High Point,NC,4.7,48.0,44.0,448.0,Obama +4,4.0,2012-09-13 +203,PPP (D),NC,3.0,49.0,48.0,1087.0,Obama +1,1.0,2012-09-08 +204,SurveyUSA/Civitas (R),NC,4.5,43.0,53.0,500.0,Romney +10,-10.0,2012-09-05 +205,PPP (D),NC,3.1,48.0,48.0,1012.0,Tie,0.0,2012-09-01 +206,Elon Univ./Charlotte Observer,NC,3.0,43.0,47.0,1089.0,Romney +4,-4.0,2012-08-28 +207,High Point/SurveyUSA,NC,4.3,43.0,46.0,543.0,Romney +3,-3.0,2012-08-28 +208,CNN/Time,NC,3.0,47.0,48.0,766.0,Romney +1,-1.0,2012-08-24 +209,High Point/SurveyUSA,NC,4.3,43.0,43.0,540.0,Tie,0.0,2012-08-21 +210,PPP (D),NC,3.4,49.0,46.0,813.0,Obama +3,3.0,2012-08-04 +211,Rasmussen Reports,NC,4.5,44.0,49.0,500.0,Romney +5,-5.0,2012-08-01 +212,Civitas (R),NC,4.0,48.0,49.0,600.0,Romney +1,-1.0,2012-07-17 +213,PPP (D),NC,3.5,47.0,46.0,775.0,Obama +1,1.0,2012-07-07 +214,Project New America/Myers (D),NC,4.4,48.0,49.0,500.0,Romney +1,-1.0,2012-07-05 +215,SurveyUSA/Civitas (R),NC,4.2,45.0,50.0,558.0,Romney +5,-5.0,2012-06-30 +216,NBC News/Marist,NC,3.1,46.0,44.0,1019.0,Obama +2,2.0,2012-06-25 +217,Rasmussen Reports,NC,4.5,44.0,47.0,500.0,Romney +3,-3.0,2012-06-25 +218,PPP (D),NC,3.4,46.0,48.0,810.0,Romney +2,-2.0,2012-06-09 +219,Civitas (R),NC,4.0,45.0,47.0,600.0,Romney +2,-2.0,2012-05-20 +220,SurveyUSA,NC,4.4,44.0,45.0,524.0,Romney +1,-1.0,2012-05-20 +221,Rasmussen Reports,NC,4.5,43.0,51.0,500.0,Romney +8,-8.0,2012-05-14 +222,PPP (D),NC,3.8,48.0,47.0,666.0,Obama +1,1.0,2012-05-12 +223,SurveyUSA,NC,2.5,47.0,43.0,1636.0,Obama +4,4.0,2012-04-28 +224,Rasmussen Reports,NC,4.5,44.0,46.0,500.0,Romney +2,-2.0,2012-04-10 +225,PPP (D),NC,3.1,49.0,44.0,975.0,Obama +5,5.0,2012-04-06 +226,PPP (D),NC,3.5,49.0,46.0,804.0,Obama +3,3.0,2012-03-10 +227,Civitas (R),NC,4.0,48.0,46.0,600.0,Obama +2,2.0,2012-02-28 +228,PPP (D),NC,3.0,47.0,46.0,1052.0,Obama +1,1.0,2012-02-04 +229,Civitas (R),NC,4.0,39.0,48.0,300.0,Romney +9,-9.0,2012-01-10 +230,PPP (D),NC,3.5,46.0,45.0,780.0,Obama +1,1.0,2012-01-07 +231,PPP (D),NC,3.3,46.0,46.0,865.0,Tie,0.0,2011-12-03 +232,PPP (D),NC,4.0,45.0,46.0,615.0,Romney +1,-1.0,2011-10-29 +233,PPP (D),NC,3.6,46.0,45.0,760.0,Obama +1,1.0,2011-10-02 +234,Civitas (R),NC,4.0,39.0,50.0,600.0,Romney +11,-11.0,2011-09-24 +235,Magellan Strategies (R),NC,3.2,45.0,43.0,923.0,Obama +2,2.0,2012-09-08 +236,PPP (D),NC,4.3,45.0,44.0,520.0,Obama +1,1.0,2011-09-03 +237,PPP (D),NC,3.5,46.0,43.0,780.0,Obama +3,3.0,2011-08-06 +238,PPP (D),NC,3.8,45.0,45.0,651.0,Tie,0.0,2011-07-09 +239,PPP (D),NC,4.1,45.0,44.0,563.0,Obama +1,1.0,2011-06-10 +240,PPP (D),NC,3.4,46.0,43.0,835.0,Obama +3,3.0,2011-05-14 +241,PPP (D),NC,4.4,47.0,44.0,507.0,Obama +3,3.0,2011-04-16 +242,PPP (D),NC,4.1,44.0,42.0,584.0,Obama +2,2.0,2011-03-19 +243,PPP (D),NC,3.8,47.0,44.0,650.0,Obama +3,3.0,2011-02-19 +244,PPP (D),NC,4.1,47.0,44.0,575.0,Obama +3,3.0,2011-01-22 +245,PPP (D),NC,4.3,46.0,43.0,520.0,Obama +3,3.0,2010-12-18 +246,PPP (D),NC,4.3,44.0,44.0,517.0,Tie,0.0,2010-11-20 +247,Rasmussen Reports,ND,5.0,36.0,51.0,400.0,Romney +15,-15.0,2012-07-11 +248,Mason-Dixon,ND,4.0,39.0,52.0,625.0,Romney +13,-13.0,2012-06-05 +249,Omaha World-Herald,NE,3.8,39.0,53.0,656.0,Romney +14,-14.0,2012-09-19 +250,Rasmussen Reports,NE,4.5,39.0,53.0,500.0,Romney +14,-14.0,2012-05-16 +251,PPP (D),NE,3.1,39.0,51.0,1028.0,Romney +12,-12.0,2012-03-24 +252,Rasmussen Reports,NE,4.5,35.0,52.0,500.0,Romney +17,-17.0,2012-03-05 +253,PPP (D),NE,3.6,38.0,51.0,739.0,Romney +13,-13.0,2011-10-01 +254,PPP (D),NE,3.1,37.0,49.0,977.0,Romney +12,-12.0,2011-01-27 +255,Quinnipiac,NY,2.5,62.0,34.0,1486.0,Obama +28,28.0,2012-09-07 +256,Siena,NY,3.8,62.0,33.0,671.0,Obama +29,29.0,2012-08-17 +257,Quinnipiac,NY,2.3,55.0,32.0,1779.0,Obama +23,23.0,2012-07-20 +258,Siena,NY,3.6,61.0,34.0,758.0,Obama +27,27.0,2012-07-13 +259,Siena,NY,3.4,59.0,35.0,807.0,Obama +24,24.0,2012-06-05 +260,Quinnipiac,NY,2.5,56.0,31.0,1504.0,Obama +25,25.0,2012-05-25 +261,Siena,NY,3.5,57.0,37.0,766.0,Obama +20,20.0,2012-05-08 +262,Marist,NY,4.0,57.0,35.0,632.0,Obama +22,22.0,2012-04-11 +263,Siena,NY,3.4,60.0,35.0,808.0,Obama +25,25.0,2012-04-03 +264,Quinnipiac,NY,2.5,56.0,33.0,1597.0,Obama +23,23.0,2012-03-31 +265,Siena,NY,3.4,60.0,34.0,808.0,Obama +26,26.0,2012-02-28 +266,SurveyUSA,NY,4.3,60.0,30.0,518.0,Obama +30,30.0,2012-02-25 +267,Quinnipiac,NY,2.8,52.0,35.0,1233.0,Obama +17,17.0,2012-02-11 +268,Siena,NY,3.4,63.0,31.0,807.0,Obama +32,32.0,2012-01-31 +269,Marist,NY,6.0,58.0,35.0,296.0,Obama +23,23.0,2012-01-19 +270,Siena,NY,3.5,59.0,34.0,803.0,Obama +25,25.0,2011-11-11 +271,Marist,NY,3.5,59.0,35.0,855.0,Obama +24,24.0,2011-10-26 +272,Siena,NY,3.5,55.0,37.0,800.0,Obama +18,18.0,2011-10-11 +273,Siena,NY,3.4,56.0,36.0,808.0,Obama +20,20.0,2011-09-18 +274,Siena,NY,3.1,55.0,37.0,1008.0,Obama +18,18.0,2011-08-12 +275,WPRI/Fleming,RI,4.4,57.0,33.0,501.0,Obama +24,24.0,2012-09-28 +276,PPP (D),RI,4.2,54.0,37.0,544.0,Obama +17,17.0,2012-02-19 +277,WeAskAmerica,NV,3.1,53.0,42.0,1078.0,Obama +11,11.0,2012-09-26 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+293,Rasmussen Reports,NV,4.5,50.0,44.0,500.0,Obama +6,6.0,2012-03-19 +294,Las Vegas Review-Journal,NV,4.0,46.0,40.0,600.0,Obama +6,6.0,2011-12-16 +295,PPP (D),NV,4.4,46.0,46.0,500.0,Tie,0.0,2011-10-22 +296,PPP (D),NV,4.0,47.0,46.0,601.0,Obama +1,1.0,2011-07-30 +297,PPP (D),NV,4.4,43.0,46.0,491.0,Romney +3,-3.0,2011-04-23 +298,PPP (D),NV,3.2,47.0,46.0,932.0,Obama +1,1.0,2011-01-04 +299,WeAskAmerica,CO,2.8,49.0,46.0,1273.0,Obama +3,3.0,2012-09-26 +300,Gravis Marketing,CO,3.4,50.0,46.0,765.0,Obama +4,4.0,2012-09-22 +301,PPP (D),CO,3.2,51.0,45.0,940.0,Obama +6,6.0,2012-09-22 +302,Purple Strategies,CO,4.0,48.0,45.0,600.0,Obama +3,3.0,2012-09-17 +303,Rasmussen Reports,CO,4.5,45.0,47.0,500.0,Romney +2,-2.0,2012-09-17 +304,NBC/WSJ/Marist,CO,3.1,50.0,45.0,971.0,Obama +5,5.0,2012-09-17 +305,CBS/NYT/Quinnipiac,CO,3.0,48.0,47.0,1497.0,Obama +1,1.0,2012-09-14 +306,ARG,CO,4.0,49.0,47.0,600.0,Obama +2,2.0,2012-09-11 +307,Denver Post/SurveyUSA,CO,4.0,47.0,46.0,615.0,Obama +1,1.0,2012-09-11 +308,Project New America/Keating (D),CO,4.4,49.0,44.0,503.0,Obama +5,5.0,2012-09-11 +309,PPP (D),CO,3.1,49.0,46.0,1001.0,Obama +3,3.0,2012-09-01 +310,Keating (D),CO,4.4,48.0,44.0,500.0,Obama +4,4.0,2012-08-22 +311,Purple Strategies,CO,4.0,49.0,46.0,600.0,Obama +3,3.0,2012-08-14 +312,Gravis Marketing,CO,4.0,45.0,46.0,607.0,Romney +1,-1.0,2012-08-09 +313,Rasmussen Reports,CO,4.5,47.0,47.0,500.0,Tie,0.0,2012-08-06 +314,CBS/NYT/Quinnipiac,CO,3.0,45.0,50.0,1463.0,Romney +5,-5.0,2012-08-03 +315,PPP (D),CO,3.5,49.0,43.0,779.0,Obama +6,6.0,2012-08-04 +316,Purple Strategies,CO,4.0,45.0,44.0,600.0,Obama +1,1.0,2012-07-11 +317,WeAskAmerica,CO,3.0,47.0,43.0,1083.0,Obama +4,4.0,2012-06-25 +318,PPP (D),CO,3.5,49.0,42.0,799.0,Obama +7,7.0,2012-06-16 +319,Rasmussen Reports,CO,4.5,45.0,45.0,500.0,Tie,0.0,2012-06-06 +320,Purple Strategies,CO,4.0,48.0,46.0,600.0,Obama +2,2.0,2012-06-03 +321,NBC News/Marist,CO,3.0,46.0,45.0,1030.0,Obama +1,1.0,2012-05-23 +322,Project New America/Keating (D),CO,4.0,48.0,44.0,601.0,Obama +4,4.0,2012-05-23 +323,Purple Strategies,CO,4.1,47.0,47.0,600.0,Tie,0.0,2012-04-21 +324,PPP (D),CO,4.2,53.0,40.0,542.0,Obama +13,13.0,2012-04-06 +325,PPP (D),CO,3.5,47.0,45.0,793.0,Obama +2,2.0,2011-12-03 +326,PPP (D),CO,4.3,48.0,41.0,510.0,Obama +7,7.0,2011-08-06 +327,PPP (D),CO,4.3,47.0,41.0,517.0,Obama +6,6.0,2011-02-05 +328,PPIC,CA,4.4,53.0,39.0,995.0,Obama +14,14.0,2012-09-13 +329,Field,CA,3.4,58.0,34.0,891.0,Obama +24,24.0,2012-09-12 +330,SurveyUSA,CA,4.3,57.0,35.0,524.0,Obama +22,22.0,2012-09-10 +331,Field,CA,3.4,55.0,37.0,848.0,Obama +18,18.0,2012-06-27 +332,SurveyUSA,CA,2.5,57.0,36.0,1575.0,Obama +21,21.0,2012-05-28 +333,Field,CA,3.8,48.0,32.0,710.0,Obama +16,16.0,2012-05-25 +334,LA Times/USC,CA,3.5,56.0,37.0,1002.0,Obama +19,19.0,2012-05-19 +335,PPIC,CA,4.2,50.0,39.0,894.0,Obama +11,11.0,2012-05-17 +336,SurveyUSA,CA,2.2,62.0,31.0,1995.0,Obama +31,31.0,2012-03-31 +337,LA Times/USC,CA,2.9,57.0,36.0,1500.0,Obama +21,21.0,2012-03-17 +338,Rasmussen Reports,CA,4.5,57.0,35.0,500.0,Obama +22,22.0,2012-02-12 +339,Field,CA,3.1,55.0,35.0,1003.0,Obama +20,20.0,2012-02-10 +340,SurveyUSA,CA,2.1,60.0,31.0,2088.0,Obama +29,29.0,2012-02-09 +341,Field,CA,3.1,50.0,40.0,1000.0,Obama +10,10.0,2011-11-21 +342,PPP (D),CA,4.4,57.0,36.0,500.0,Obama +21,21.0,2011-11-12 +343,SurveyUSA,CA,3.5,50.0,39.0,800.0,Obama +11,11.0,2011-11-10 +344,LA Times/USC,CA,2.5,52.0,35.0,1500.0,Obama +17,17.0,2011-11-04 +345,Field,CA,3.2,51.0,38.0,1001.0,Obama +13,13.0,2011-09-07 +346,LA Times/USC,CA,,54.0,35.0,1408.0,Obama +19,19.0,2011-08-23 +347,PPP (D),CA,3.3,56.0,36.0,892.0,Obama +20,20.0,2011-01-29 +348,InsiderAdvantage,GA,4.5,35.0,56.0,483.0,Romney +21,-21.0,2012-09-18 +349,SurveyUSA,GA,2.9,42.0,50.0,1169.0,Romney +8,-8.0,2012-07-29 +350,InsiderAdvantage,GA,4.0,41.0,50.0,591.0,Romney +9,-9.0,2012-07-24 +351,InsiderAdvantage,GA,5.0,40.0,52.0,438.0,Romney +12,-12.0,2012-05-22 +352,Landmark/Rosetta Stone,GA,4.0,40.0,51.0,600.0,Romney +11,-11.0,2012-05-10 +353,SurveyUSA,GA,2.9,42.0,49.0,1156.0,Romney +7,-7.0,2012-02-25 +354,SurveyUSA,GA,3.0,43.0,51.0,1144.0,Romney +8,-8.0,2012-02-02 +355,Mason-Dixon,GA,4.0,38.0,55.0,625.0,Romney +17,-17.0,2011-12-13 +356,SurveyUSA,GA,2.9,42.0,49.0,1176.0,Romney +7,-7.0,2011-12-07 +357,PPP (D),GA,3.5,43.0,46.0,790.0,Romney +3,-3.0,2012-04-02 +358,PPP (D),CT,3.5,54.0,41.0,801.0,Obama +13,13.0,2012-09-25 +359,Hartford Courant/UConn,CT,4.4,53.0,32.0,508.0,Obama +21,21.0,2012-09-14 +360,Quinnipiac,CT,2.6,52.0,45.0,1472.0,Obama +7,7.0,2012-08-24 +361,PPP (D),CT,3.3,53.0,40.0,881.0,Obama +13,13.0,2012-08-23 +362,Rasmussen Reports,CT,4.5,51.0,43.0,500.0,Obama +8,8.0,2012-08-21 +363,PPP (D),CT,3.5,51.0,43.0,771.0,Obama +8,8.0,2012-07-28 +364,Quinnipiac,CT,2.6,50.0,38.0,1408.0,Obama +12,12.0,2012-06-01 +365,Quinnipiac,CT,2.4,53.0,37.0,1622.0,Obama +16,16.0,2012-03-17 +366,PPP (D),CT,4.0,47.0,45.0,592.0,Obama +2,2.0,2011-09-24 +367,Quinnipiac,CT,2.8,49.0,36.0,1230.0,Obama +13,13.0,2011-09-11 +368,Sooner Poll,OK,4.4,29.0,58.0,495.0,Romney +29,-29.0,2012-08-05 +369,Sooner Poll,OK,4.4,27.0,62.0,504.0,Romney +35,-35.0,2012-05-09 +370,PPP (D),OH,3.3,49.0,45.0,897.0,Obama +4,4.0,2012-09-29 +371,Columbus Dispatch*,OH,2.2,51.0,42.0,1662.0,Obama +9,9.0,2012-09-24 +372,Gravis Marketing,OH,4.3,45.0,44.0,594.0,Obama +1,1.0,2012-09-22 +373,Washington Post,OH,4.5,52.0,44.0,759.0,Obama +8,8.0,2012-09-21 +374,CBS/NYT/Quinnipiac,OH,3.0,53.0,43.0,1162.0,Obama +10,10.0,2012-09-21 +375,Purple Strategies,OH,4.0,48.0,44.0,600.0,Obama +4,4.0,2012-09-17 +376,FOX News,OH,3.0,49.0,42.0,1009.0,Obama +7,7.0,2012-09-17 +377,Ohio Newspapers/Univ of Cin.,OH,3.3,51.0,46.0,861.0,Obama +5,5.0,2012-09-16 +378,Caddell/McLaughlin/SAN (R),OH,4.0,47.0,44.0,600.0,Obama +3,3.0,2012-09-14 +379,Rasmussen Reports,OH,4.5,47.0,46.0,500.0,Obama +1,1.0,2012-09-12 +380,ARG,OH,4.0,48.0,47.0,600.0,Obama +1,1.0,2012-09-11 +381,NBC/WSJ/Marist,OH,3.1,50.0,43.0,979.0,Obama +7,7.0,2012-09-10 +382,PPP (D),OH,3.0,50.0,45.0,1072.0,Obama +5,5.0,2012-09-08 +383,Gravis Marketing,OH,2.7,47.0,43.0,1548.0,Obama +4,4.0,2012-09-08 +384,Gravis Marketing,OH,2.9,44.0,47.0,1381.0,Romney +3,-3.0,2012-09-02 +385,Gravis Marketing,OH,3.0,45.0,44.0,1397.0,Obama +1,1.0,2012-08-27 +386,Columbus Dispatch*,OH,2.1,45.0,45.0,1758.0,Tie,0.0,2012-08-20 +387,Ohio Poll/Univ of Cin.,OH,3.4,49.0,46.0,847.0,Obama +3,3.0,2012-08-19 +388,CBS/NYT/Quinnipiac,OH,3.0,50.0,44.0,1253.0,Obama +6,6.0,2012-08-18 +389,Purple Strategies,OH,4.0,44.0,46.0,600.0,Romney +2,-2.0,2012-08-14 +390,Rasmussen Reports,OH,4.5,45.0,45.0,500.0,Tie,0.0,2012-08-13 +391,PPP (D),OH,3.2,48.0,45.0,961.0,Obama +3,3.0,2012-08-11 +392,CBS/NYT/Quinnipiac,OH,3.0,50.0,44.0,1193.0,Obama +6,6.0,2012-07-27 +393,WeAskAmerica*,OH,3.0,48.0,40.0,1115.0,Obama +8,8.0,2012-07-24 +394,Rasmussen Reports,OH,4.5,47.0,45.0,500.0,Obama +2,2.0,2012-07-18 +395,Purple Strategies,OH,4.0,48.0,45.0,600.0,Obama +3,3.0,2012-07-11 +396,Quinnipiac,OH,2.8,47.0,38.0,1237.0,Obama +9,9.0,2012-06-22 +397,PPP (D),OH,3.8,47.0,44.0,673.0,Obama +3,3.0,2012-06-23 +398,Purple Strategies,OH,4.0,45.0,48.0,600.0,Romney +3,-3.0,2012-06-03 +399,Rasmussen Reports,OH,4.5,44.0,46.0,500.0,Romney +2,-2.0,2012-05-29 +400,NBC News/Marist,OH,3.0,48.0,42.0,1103.0,Obama +6,6.0,2012-05-19 +401,Quinnipiac,OH,3.0,45.0,44.0,1069.0,Obama +1,1.0,2012-05-05 +402,PPP (D),OH,3.3,50.0,43.0,875.0,Obama +7,7.0,2012-05-05 +403,Quinnipiac,OH,2.9,44.0,42.0,1130.0,Obama +2,2.0,2012-04-28 +404,Purple Strategies,OH,4.1,49.0,44.0,600.0,Obama +5,5.0,2012-04-21 +405,Rasmussen Reports,OH,4.5,46.0,42.0,500.0,Obama +4,4.0,2012-04-18 +406,FOX News,OH,4.0,45.0,39.0,606.0,Obama +6,6.0,2012-04-16 +407,Rasmussen Reports,OH,4.5,48.0,40.0,500.0,Obama +8,8.0,2012-03-26 +408,Quinnipiac,OH,2.8,47.0,41.0,1246.0,Obama +6,6.0,2012-03-23 +409,NBC News/Marist,OH,2.5,50.0,38.0,1573.0,Obama +12,12.0,2012-03-01 +410,FOX News,OH,4.5,38.0,44.0,505.0,Romney +6,-6.0,2012-02-12 +411,Quinnipiac,OH,2.6,46.0,44.0,1421.0,Obama +2,2.0,2012-02-10 +412,Rasmussen Reports,OH,4.5,45.0,41.0,500.0,Obama +4,4.0,2012-02-08 +413,PPP (D),OH,3.4,49.0,42.0,820.0,Obama +7,7.0,2012-01-29 +414,Quinnipiac,OH,2.4,44.0,42.0,1610.0,Obama +2,2.0,2012-01-13 +415,Quinnipiac,OH,2.6,42.0,43.0,1437.0,Romney +1,-1.0,2011-12-02 +416,PPP (D),OH,3.1,50.0,41.0,1022.0,Obama +9,9.0,2011-11-05 +417,Quinnipiac,OH,2.7,45.0,42.0,1312.0,Obama +3,3.0,2011-11-04 +418,Quinnipiac,OH,2.4,45.0,41.0,1668.0,Obama +4,4.0,2011-10-20 +419,PPP (D),OH,4.1,46.0,46.0,581.0,Tie,0.0,2011-10-15 +420,Quinnipiac,OH,2.7,44.0,42.0,1301.0,Obama +2,2.0,2011-09-23 +421,PPP (D),OH,3.5,45.0,43.0,792.0,Obama +2,2.0,2011-08-13 +422,Quinnipiac,OH,2.4,45.0,41.0,1659.0,Obama +4,4.0,2011-07-15 +423,PPP (D),OH,4.1,46.0,42.0,565.0,Obama +4,4.0,2011-05-21 +424,PPP (D),OH,4.1,46.0,40.0,559.0,Obama +6,6.0,2011-03-12 +425,PPP (D),OH,4.3,44.0,42.0,510.0,Obama +2,2.0,2010-12-11 +426,Ohio Poll/Univ of Cin.,OH,3.3,51.0,46.0,861.0,Obama +5,5.0,2011-09-16 +427,SurveyUSA,KS,4.4,39.0,48.0,510.0,Romney +9,-9.0,2011-11-20 +428,SurveyUSA,KS,3.5,31.0,56.0,800.0,Romney +25,-25.0,2011-11-10 +429,NBC News/Marist,SC,2.1,45.0,42.0,2107.0,Obama +3,3.0,2011-12-05 +430,NBC News/Marist,SC,2.1,40.0,46.0,2131.0,Romney +6,-6.0,2011-10-12 +431,PPP (D),SC,4.0,38.0,53.0,587.0,Romney +15,-15.0,2012-08-27 +432,PPP (D),SC,3.6,41.0,50.0,741.0,Romney +9,-9.0,2012-06-04 +433,PPP (D),SC,2.9,42.0,49.0,1167.0,Romney +7,-7.0,2012-01-29 +434,Courier-Journal/SurveyUSA,KY,4.1,39.0,53.0,606.0,Romney +14,-14.0,2012-09-12 +435,SurveyUSA,OR,4.3,50.0,41.0,552.0,Obama +9,9.0,2012-09-12 +436,PPP (D),OR,3.7,50.0,42.0,686.0,Obama +8,8.0,2012-06-23 +437,SurveyUSA,OR,2.6,47.0,43.0,1468.0,Obama +4,4.0,2012-05-09 +438,SurveyUSA,OR,2.5,50.0,39.0,1615.0,Obama +11,11.0,2012-03-17 +439,SurveyUSA,OR,4.4,48.0,40.0,528.0,Obama +8,8.0,2011-11-20 +440,PPP (D),OR,3.7,50.0,38.0,701.0,Obama +12,12.0,2012-06-20 +441,PPP (D),SD,3.0,40.0,46.0,1045.0,Romney +6,-6.0,2012-01-29 +442,PPP (D),HI,4.1,59.0,32.0,568.0,Obama +27,27.0,2012-10-15 +443,WPA,TX,3.1,40.0,55.0,1000.0,Romney +15,-15.0,2012-09-10 +444,PPP (D),TX,4.0,43.0,50.0,591.0,Romney +7,-7.0,2012-04-21 +445,PPP (D),TX,3.7,42.0,49.0,700.0,Romney +7,-7.0,2012-01-14 +446,PPP (D),TX,4.1,41.0,47.0,569.0,Romney +6,-6.0,2011-09-17 +447,PPP (D),TX,3.5,42.0,50.0,795.0,Romney +8,-8.0,2011-06-26 +448,PPP (D),TX,3.3,42.0,49.0,892.0,Romney +7,-7.0,2011-01-15 +449,Clarus Research,LA,4.0,37.0,53.0,602.0,Romney +16,-16.0,2012-10-06 +450,Tennessean/Vanderbilt,TN,,40.0,47.0,,Romney +7,-7.0,2012-05-06 +451,Middle Tn. State U.,TN,5.0,41.0,47.0,416.0,Romney +6,-6.0,2012-02-19 +452,Tennessean/Vanderbilt,TN,3.0,39.0,42.0,1508.0,Romney +3,-3.0,2012-02-19 +453,PPP (D),TN,4.4,41.0,48.0,500.0,Romney +7,-7.0,2012-02-11 +454,Morning Call,PA,5.0,49.0,42.0,427.0,Obama +7,7.0,2012-09-24 +455,CBS/NYT/Quinnipiac,PA,3.0,54.0,42.0,1162.0,Obama +12,12.0,2012-09-21 +456,Franklin & Marshall,PA,4.9,52.0,43.0,392.0,Obama +9,9.0,2012-09-21 +457,Tribune-Review/Susquehanna,PA,3.5,47.0,45.0,800.0,Obama +2,2.0,2012-09-19 +458,Rasmussen Reports,PA,4.5,51.0,39.0,500.0,Obama +12,12.0,2012-09-19 +459,WeAskAmerica,PA,2.9,48.0,42.0,1214.0,Obama +6,6.0,2012-09-18 +460,Mercyhurst University,PA,4.3,48.0,40.0,522.0,Obama +8,8.0,2012-09-16 +461,Morning Call,PA,4.0,50.0,41.0,640.0,Obama +9,9.0,2012-09-13 +462,Philadelphia Inquirer,PA,4.0,50.0,39.0,600.0,Obama +11,11.0,2012-09-11 +463,Philadelphia Inquirer,PA,4.0,51.0,42.0,601.0,Obama +9,9.0,2012-08-22 +464,Morning Call,PA,5.0,49.0,40.0,422.0,Obama +9,9.0,2012-08-21 +465,Franklin & Marshall,PA,3.8,47.0,42.0,681.0,Obama +5,5.0,2012-08-10 +466,CBS/NYT/Quinnipiac,PA,3.0,53.0,42.0,1168.0,Obama +11,11.0,2012-07-27 +467,PPP (D),PA,3.6,49.0,43.0,758.0,Obama +6,6.0,2012-07-22 +468,Rasmussen Reports,PA,4.5,48.0,44.0,500.0,Obama +4,4.0,2012-07-18 +469,WeAskAmerica,PA,2.8,47.0,40.0,1227.0,Obama +7,7.0,2012-07-10 +470,Quinnipiac,PA,2.8,45.0,39.0,1252.0,Obama +6,6.0,2012-06-22 +471,Quinnipiac,PA,3.1,46.0,40.0,997.0,Obama +6,6.0,2012-06-08 +472,Franklin & Marshall,PA,4.8,48.0,36.0,412.0,Obama +12,12.0,2012-06-01 +473,Rasmussen Reports,PA,4.5,47.0,41.0,500.0,Obama +6,6.0,2012-05-21 +474,PPP (D),PA,3.8,50.0,42.0,671.0,Obama +8,8.0,2012-05-19 +475,Quinnipiac,PA,2.9,47.0,39.0,1168.0,Obama +8,8.0,2012-04-28 +476,Morning Call,PA,5.0,45.0,40.0,492.0,Obama +5,5.0,2012-03-28 +477,Quinnipiac,PA,2.8,45.0,42.0,1232.0,Obama +3,3.0,2012-03-23 +478,Quinnipiac,PA,2.8,46.0,40.0,1256.0,Obama +6,6.0,2012-03-10 +479,PPP (D),PA,3.7,49.0,42.0,689.0,Obama +7,7.0,2012-03-10 +480,Morning Call,PA,4.0,48.0,37.0,625.0,Obama +11,11.0,2012-02-18 +481,Franklin & Marshall,PA,4.0,41.0,33.0,592.0,Obama +8,8.0,2012-02-17 +482,Rasmussen Reports,PA,4.5,45.0,44.0,438.0,Obama +1,1.0,2012-02-16 +483,Tribune-Review/Susquehanna,PA,3.5,43.0,45.0,800.0,Romney +2,-2.0,2012-02-04 +484,Morning Call,PA,5.0,45.0,41.0,422.0,Obama +4,4.0,2011-12-03 +485,Quinnipiac,PA,2.6,46.0,43.0,1453.0,Obama +3,3.0,2011-12-02 +486,PPP (D),PA,4.4,45.0,45.0,500.0,Tie,0.0,2011-11-19 +487,SurveyUSA,PA,3.5,44.0,44.0,800.0,Tie,0.0,2011-11-10 +488,Quinnipiac,PA,2.6,44.0,43.0,1436.0,Obama +1,1.0,2011-11-04 +489,Franklin & Marshall,PA,4.8,35.0,26.0,419.0,Obama +9,9.0,2011-10-27 +490,Quinnipiac,PA,2.7,45.0,43.0,1370.0,Obama +2,2.0,2011-09-24 +491,Magellan Strategies (R),PA,3.7,50.0,40.0,702.0,Obama +10,10.0,2012-09-13 +492,Franklin & Marshall,PA,4.9,36.0,30.0,407.0,Obama +6,6.0,2011-08-26 +493,Quinnipiac,PA,2.7,42.0,44.0,1358.0,Romney +2,-2.0,2011-07-28 +494,PPP (D),PA,4.2,44.0,44.0,545.0,Tie,0.0,2011-07-03 +495,Quinnipiac,PA,2.7,47.0,40.0,1277.0,Obama +7,7.0,2011-06-10 +496,PPP (D),PA,4.0,42.0,43.0,593.0,Romney +1,-1.0,2011-04-09 +497,Morning Call,PA,5.5,43.0,36.0,395.0,Obama +7,7.0,2011-02-19 +498,PPP (D),PA,4.2,46.0,42.0,547.0,Obama +4,4.0,2011-01-04 +499,ARG,VA,4.0,49.0,47.0,600.0,Obama +2,2.0,2012-09-26 +500,Suffolk/WWBT,VA,4.0,46.0,44.0,600.0,Obama +2,2.0,2012-09-25 +501,Roanoke College,VA,4.0,48.0,40.0,589.0,Obama +8,8.0,2012-09-24 +502,WeAskAmerica,VA,2.8,49.0,46.0,1238.0,Obama +3,3.0,2012-09-17 +503,Purple Strategies,VA,4.0,46.0,43.0,600.0,Obama +3,3.0,2012-09-17 +504,FOX News,VA,3.0,50.0,43.0,1006.0,Obama +7,7.0,2012-09-17 +505,PPP (D),VA,3.1,51.0,46.0,1021.0,Obama +5,5.0,2012-09-15 +506,CBS/NYT/Quinnipiac,VA,3.0,50.0,46.0,1474.0,Obama +4,4.0,2012-09-14 +507,Rasmussen Reports,VA,4.5,49.0,48.0,500.0,Obama +1,1.0,2012-09-13 +508,Washington Post,VA,4.0,52.0,44.0,847.0,Obama +8,8.0,2012-09-14 +509,NBC/WSJ/Marist,VA,3.1,49.0,44.0,996.0,Obama +5,5.0,2012-09-10 +510,Gravis Marketing,VA,2.2,44.0,49.0,2238.0,Romney +5,-5.0,2012-09-09 +511,Rasmussen Reports,VA,4.5,47.0,47.0,500.0,Tie,0.0,2012-08-23 +512,PPP (D),VA,3.4,50.0,45.0,855.0,Obama +5,5.0,2012-08-18 +513,Purple Strategies,VA,4.0,45.0,48.0,600.0,Romney +3,-3.0,2012-08-14 +514,Rasmussen Reports,VA,4.5,48.0,46.0,500.0,Obama +2,2.0,2012-08-07 +515,CBS/NYT/Quinnipiac,VA,3.0,49.0,45.0,1412.0,Obama +4,4.0,2012-08-03 +516,Rasmussen Reports,VA,4.5,47.0,46.0,500.0,Obama +1,1.0,2012-07-17 +517,Quinnipiac,VA,2.4,44.0,44.0,1673.0,Tie,0.0,2012-07-13 +518,Purple Strategies,VA,4.0,46.0,44.0,600.0,Obama +2,2.0,2012-07-11 +519,PPP (D),VA,3.9,50.0,42.0,647.0,Obama +8,8.0,2012-07-07 +520,WeAskAmerica,VA,3.0,43.0,48.0,1106.0,Romney +5,-5.0,2012-06-25 +521,Rasmussen Reports,VA,4.5,47.0,47.0,500.0,Tie,0.0,2012-06-03 +522,Purple Strategies,VA,4.0,49.0,46.0,600.0,Obama +3,3.0,2012-06-03 +523,Quinnipiac,VA,2.7,47.0,42.0,1282.0,Obama +5,5.0,2012-06-02 +524,Virginian-Pilot/ODU,VA,3.5,49.0,42.0,776.0,Obama +7,7.0,2012-05-31 +525,NBC News/Marist,VA,3.0,48.0,44.0,1076.0,Obama +4,4.0,2012-05-19 +526,Washington Post,VA,4.0,51.0,44.0,964.0,Obama +7,7.0,2012-04-30 +527,PPP (D),VA,3.8,51.0,43.0,680.0,Obama +8,8.0,2012-04-28 +528,Rasmussen Reports,VA,4.5,44.0,45.0,500.0,Romney +1,-1.0,2012-04-23 +529,Purple Strategies,VA,4.1,48.0,46.0,600.0,Obama +2,2.0,2012-04-21 +530,Roanoke College,VA,4.2,41.0,46.0,537.0,Romney +5,-5.0,2012-03-31 +531,Rasmussen Reports,VA,4.5,51.0,42.0,500.0,Obama +9,9.0,2012-03-20 +532,Quinnipiac,VA,3.1,50.0,42.0,1034.0,Obama +8,8.0,2012-03-16 +533,NBC News/Marist,VA,2.8,52.0,35.0,1273.0,Obama +17,17.0,2012-03-01 +534,Roanoke College,VA,4.0,42.0,43.0,607.0,Romney +1,-1.0,2012-02-20 +535,Rasmussen Reports,VA,4.5,49.0,43.0,500.0,Obama +6,6.0,2012-02-21 +536,CNU/Times-Dispatch,VA,3.1,43.0,46.0,1018.0,Romney +3,-3.0,2012-02-09 +537,Quinnipiac,VA,2.5,47.0,43.0,1544.0,Obama +4,4.0,2012-02-04 +538,Mason-Dixon,VA,3.9,45.0,44.0,625.0,Obama +1,1.0,2012-01-17 +539,Quinnipiac,VA,2.9,42.0,44.0,1135.0,Romney +2,-2.0,2011-12-16 +540,PPP (D),VA,4.0,48.0,42.0,600.0,Obama +6,6.0,2011-12-12 +541,Quinnipiac,VA,2.6,44.0,45.0,1459.0,Romney +1,-1.0,2011-10-06 +542,CNU/Times-Dispatch,VA,3.1,42.0,46.0,1027.0,Romney +4,-4.0,2011-10-06 +543,Rasmussen Reports,VA,4.5,45.0,46.0,500.0,Romney +1,-1.0,2011-09-28 +544,Roanoke College,VA,4.0,37.0,45.0,601.0,Romney +8,-8.0,2011-09-12 +545,Quinnipiac,VA,2.7,42.0,44.0,1368.0,Romney +2,-2.0,2011-09-10 +546,PPP (D),VA,4.4,47.0,43.0,500.0,Obama +4,4.0,2011-07-23 +547,PPP (D),VA,4.2,51.0,40.0,547.0,Obama +11,11.0,2011-05-07 +548,Washington Post,VA,3.5,50.0,44.0,1040.0,Obama +6,6.0,2012-05-01 +549,PPP (D),VA,3.5,48.0,42.0,524.0,Obama +6,6.0,2011-02-26 +550,PPP (D),VA,4.2,48.0,43.0,551.0,Obama +5,5.0,2010-11-12 +551,Talk Business Poll,AR,2.0,35.0,56.0,2228.0,Romney +21,-21.0,2012-09-17 +552,Talk Business Poll,AR,3.6,33.0,57.0,759.0,Romney +24,-24.0,2012-03-26 +553,Talk Business Poll,AR,2.1,34.0,50.0,2101.0,Romney +16,-16.0,2011-09-15 +554,Castleton State College,VT,4.5,62.0,25.0,477.0,Obama +37,37.0,2012-08-16 +555,Castleton State College,VT,4.0,59.0,28.0,607.0,Obama +31,31.0,2012-05-12 +556,Castleton State College,VT,3.5,58.0,33.0,800.0,Obama +25,25.0,2012-02-17 +557,PPP (D),VT,2.8,54.0,34.0,1233.0,Obama +20,20.0,2012-07-30 +558,The Simon Poll/SIU,IL,2.8,47.0,34.0,1261.0,Obama +13,13.0,2012-09-07 +559,WeAskAmerica,IL,2.8,54.0,37.0,1382.0,Obama +17,17.0,2012-09-05 +560,Chicago Tribune,IL,4.0,56.0,35.0,600.0,Obama +21,21.0,2012-02-04 +561,The Simon Poll/SIU,IL,3.0,46.0,39.0,1000.0,Obama +7,7.0,2011-10-14 +562,FOX Chicago/WAA,IL,2.3,50.0,35.0,1815.0,Obama +15,15.0,2012-09-28 +563,Howey/DePauw,IN,3.5,40.0,52.0,800.0,Romney +12,-12.0,2012-09-21 +564,Rasmussen Reports,IN,5.0,35.0,51.0,400.0,Romney +16,-16.0,2012-08-01 +565,Howey/DePauw,IN,4.5,40.0,49.0,503.0,Romney +9,-9.0,2012-03-27 +566,WeAskAmerica,IA,2.8,48.0,44.0,1273.0,Obama +4,4.0,2012-09-26 +567,Des Moines Register,IA,3.8,49.0,45.0,650.0,Obama +4,4.0,2012-09-25 +568,PPP (D),IA,3.6,51.0,44.0,754.0,Obama +7,7.0,2012-09-25 +569,Voter/Consumer Res/TIR (R),IA,4.4,46.0,47.0,500.0,Romney +1,-1.0,2012-09-24 +570,ARG,IA,4.0,51.0,44.0,600.0,Obama +7,7.0,2012-09-22 +571,Rasmussen Reports,IA,4.5,44.0,47.0,500.0,Romney +3,-3.0,2012-09-19 +572,NBC/WSJ/Marist,IA,3.3,50.0,42.0,898.0,Obama +8,8.0,2012-09-17 +573,PPP (D),IA,2.8,47.0,45.0,1244.0,Obama +2,2.0,2012-08-25 +574,Rasmussen Reports,IA,4.5,44.0,46.0,500.0,Romney +2,-2.0,2012-08-08 +575,PPP (D),IA,2.9,48.0,43.0,1131.0,Obama +5,5.0,2012-07-14 +576,WeAskAmerica,IA,3.0,45.0,44.0,1086.0,Obama +1,1.0,2012-06-18 +577,Rasmussen Reports,IA,4.5,46.0,47.0,500.0,Romney +1,-1.0,2012-06-11 +578,NBC News/Marist,IA,3.0,44.0,44.0,1106.0,Tie,0.0,2012-05-23 +579,PPP (D),IA,2.9,51.0,41.0,1181.0,Obama +10,10.0,2012-05-05 +580,Des Moines Register,IA,4.0,44.0,46.0,611.0,Romney +2,-2.0,2012-02-14 +581,NBC News/Marist,IA,2.5,46.0,39.0,1503.0,Obama +7,7.0,2011-11-28 +582,PPP (D),IA,3.6,46.0,42.0,749.0,Obama +4,4.0,2011-10-09 +583,NBC News/Marist,IA,1.8,43.0,40.0,2836.0,Obama +3,3.0,2011-10-04 +584,PPP (D),IA,3.5,49.0,39.0,798.0,Obama +10,10.0,2011-08-20 +585,Mason-Dixon,IA,3.9,44.0,47.0,629.0,Romney +3,-3.0,2012-07-06 +586,PPP (D),IA,2.6,49.0,40.0,1387.0,Obama +9,9.0,2011-05-29 +587,PPP (D),IA,2.9,45.0,41.0,1109.0,Obama +4,4.0,2011-04-16 +588,PPP (D),IA,3.0,47.0,41.0,1077.0,Obama +6,6.0,2011-01-08 +589,HighGround/Moore (R)*,AZ,4.0,42.0,46.0,500.0,Romney +4,-4.0,2012-09-26 +590,Rasmussen Reports,AZ,4.5,42.0,52.0,500.0,Romney +10,-10.0,2012-09-25 +591,Purple Strategies,AZ,4.0,45.0,48.0,600.0,Romney +3,-3.0,2012-09-17 +592,PPP (D),AZ,3.1,44.0,53.0,993.0,Romney +9,-9.0,2012-09-08 +593,PPP (D),AZ,3.4,41.0,52.0,833.0,Romney +11,-11.0,2012-07-24 +594,Rasmussen Reports,AZ,4.5,41.0,54.0,500.0,Romney +13,-13.0,2012-06-26 +595,Project New America/PPP (D),AZ,3.5,46.0,49.0,791.0,Romney +3,-3.0,2012-06-05 +596,PPP (D),AZ,4.4,43.0,50.0,500.0,Romney +7,-7.0,2012-05-19 +597,Magellan (R),AZ,3.3,43.0,52.0,909.0,Romney +9,-9.0,2012-05-01 +598,Arizona State,AZ,4.4,40.0,42.0,488.0,Romney +2,-2.0,2012-04-15 +599,Behavior Research Center,AZ,4.4,42.0,40.0,511.0,Obama +2,2.0,2012-04-13 +600,Rasmussen Reports,AZ,4.5,40.0,51.0,500.0,Romney +11,-11.0,2012-03-13 +601,NBC News/Marist,AZ,2.8,40.0,45.0,1265.0,Romney +5,-5.0,2012-02-20 +602,PPP (D),AZ,3.6,47.0,47.0,743.0,Tie,0.0,2012-02-18 +603,Behavior Research Center,AZ,4.3,37.0,43.0,553.0,Romney +6,-6.0,2012-01-07 +604,PPP (D),AZ,4.4,42.0,49.0,500.0,Romney +7,-7.0,2011-11-19 +605,Behavior Research Center,AZ,4.1,45.0,40.0,581.0,Obama +5,5.0,2011-10-19 +606,PPP (D),AZ,3.9,44.0,48.0,623.0,Romney +4,-4.0,2011-04-30 +607,PPP (D),AZ,4.0,43.0,49.0,599.0,Romney +6,-6.0,2011-01-29 +608,PPP (D),AZ,3.9,43.0,50.0,617.0,Romney +7,-7.0,2010-09-20 +609,Rasmussen Reports,ME,4.5,52.0,40.0,500.0,Obama +12,12.0,2012-09-25 +610,PPP (D),ME,3.5,55.0,39.0,804.0,Obama +16,16.0,2012-09-18 +611,MPRC (D)*,ME,3.4,54.0,37.0,856.0,Obama +17,17.0,2012-09-16 +612,Critical Insights,ME,4.0,52.0,36.0,618.0,Obama +16,16.0,2012-09-14 +613,Critical Insights,ME,4.0,49.0,35.0,615.0,Obama +14,14.0,2012-06-23 +614,WBUR/MassINC,ME,4.4,48.0,34.0,506.0,Obama +14,14.0,2012-06-14 +615,Critical Insights,ME,4.0,50.0,42.0,600.0,Obama +8,8.0,2012-05-05 +616,MPRC (D),ME,3.1,55.0,37.0,993.0,Obama +18,18.0,2012-04-01 +617,PPP (D),ME,2.8,58.0,35.0,1256.0,Obama +23,23.0,2012-03-03 +618,PPP (D),ME,3.8,49.0,38.0,673.0,Obama +11,11.0,2011-10-30 +619,Critical Insights,ME,4.0,41.0,40.0,600.0,Obama +1,1.0,2011-10-21 +620,PPP (D),ME,2.8,49.0,41.0,1247.0,Obama +8,8.0,2011-03-05 +621,Baltimore Sun,MD,3.5,57.0,34.0,804.0,Obama +23,23.0,2012-09-26 +622,Gonzales Research,MD,3.5,55.0,36.0,813.0,Obama +19,19.0,2012-09-20 +623,PPP (D),MD,3.4,58.0,35.0,852.0,Obama +23,23.0,2012-05-18 +624,WBUR/MassINC,MA,4.4,60.0,32.0,504.0,Obama +28,28.0,2012-09-27 +625,Boston Globe,MA,4.4,57.0,30.0,502.0,Obama +27,27.0,2012-09-24 +626,Rasmussen Reports,MA,4.5,55.0,40.0,500.0,Obama +15,15.0,2012-09-24 +627,WBUR/MassINC,MA,4.4,59.0,31.0,507.0,Obama +28,28.0,2012-09-16 +628,UMass/Boston Herald,MA,5.5,59.0,36.0,497.0,Obama +23,23.0,2012-09-15 +629,Suffolk/7News,MA,4.0,64.0,31.0,600.0,Obama +33,33.0,2012-09-15 +630,PPP (D),MA,3.3,57.0,39.0,876.0,Obama +18,18.0,2012-09-15 +631,Western NE University,MA,4.6,60.0,38.0,444.0,Obama +22,22.0,2012-09-10 +632,PPP (D),MA,2.9,55.0,39.0,1115.0,Obama +16,16.0,2012-08-18 +633,PPP (D),MA,3.3,55.0,39.0,902.0,Obama +16,16.0,2012-06-23 +634,Western NE University,MA,4.4,56.0,34.0,504.0,Obama +22,22.0,2012-05-30 +635,Boston Globe,MA,3.8,46.0,34.0,651.0,Obama +12,12.0,2012-05-28 +636,Suffolk/7News,MA,4.0,59.0,34.0,600.0,Obama +25,25.0,2012-05-21 +637,Rasmussen Reports,MA,4.5,56.0,35.0,500.0,Obama +21,21.0,2012-05-07 +638,Rasmussen Reports,MA,4.5,51.0,40.0,500.0,Obama +11,11.0,2012-04-09 +639,Boston Globe,MA,4.2,49.0,33.0,544.0,Obama +16,16.0,2012-03-24 +640,PPP 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++++++++++++++++++++++++++++++--------- 3 files changed, 5331 insertions(+), 1468 deletions(-) delete mode 100644 2012-predicted.csv create mode 100644 data/2012-predicted.csv diff --git a/2012-predicted.csv b/2012-predicted.csv deleted file mode 100644 index e8ce3a5..0000000 --- a/2012-predicted.csv +++ /dev/null @@ -1,42 +0,0 @@ -State,poll -Arizona,-6.35103758203 -California,19.7939605433 -Colorado,6.94680413392 -Connecticut,13.9673695823 -Florida,2.07942320278 -Georgia,-8.9685783511 -Hawaii,31.2329731476 -Illinois,26.8689454989 -Indiana,-6.87022364116 -Iowa,2.32494127675 -Kansas,-9.54075722393 -Maine,12.7344881691 -Maryland,28.8556122688 -Massachusetts,21.8158972511 -Michigan,8.56148458235 -Minnesota,8.04648458474 -Mississippi,-8.63666431435 -Missouri,-1.97440258249 -Montana,-7.03529525259 -Nebraska,-8.66305659485 -Nevada,9.02203805722 -New Hampshire,-1.13269055622 -New Jersey,13.5452010098 -New Mexico,9.14499594544 -New York,23.2067488327 -North Carolina,-0.590242725331 -North Dakota,-9.13833707796 -Ohio,4.38365889772 -Oregon,9.11669401207 -Pennsylvania,5.69195264214 -Rhode Island,25.9308025562 -South Carolina,-6.76679008144 -South Dakota,-1.13647592348 -Tennessee,-2.88284606462 -Texas,-2.57819116911 -Utah,-29.142100213 -Vermont,16.8113052033 -Virginia,4.9851756192 -Washington,16.1182074603 -West Virginia,-9.77644909265 -Wisconsin,4.90894385251 diff --git a/data/2012-predicted.csv b/data/2012-predicted.csv new file mode 100644 index 0000000..48379a1 --- /dev/null +++ b/data/2012-predicted.csv @@ -0,0 +1,42 @@ +State,poll +Arizona,-6.07214167012 +California,19.9664750649 +Colorado,2.67118129814 +Connecticut,8.9401551505 +Florida,2.17096302191 +Georgia,-8.81344162236 +Hawaii,18.5946671349 +Illinois,15.4867527001 +Indiana,-7.34289753635 +Iowa,2.03682437111 +Kansas,-9.76720266945 +Maine,12.2201232519 +Maryland,16.3940261257 +Massachusetts,14.1805921769 +Michigan,8.33397983927 +Minnesota,7.28605122763 +Mississippi,-8.22714240889 +Missouri,-2.21556524617 +Montana,-7.2414126942 +Nebraska,-8.8332297192 +Nevada,5.09619740646 +New Hampshire,-1.54489284704 +New Jersey,10.6434861203 +New Mexico,9.58640543396 +New York,23.4735500439 +North Carolina,-0.415848216015 +North Dakota,-9.33913293328 +Ohio,4.17520432162 +Oregon,8.68138312385 +Pennsylvania,5.435867347 +Rhode Island,13.2467532472 +South Carolina,-6.34067005172 +South Dakota,-1.52369274353 +Tennessee,-2.52634864683 +Texas,-2.29550733867 +Utah,-29.1794130385 +Vermont,15.6848389883 +Virginia,2.42224505406 +Washington,12.3154731284 +West Virginia,-9.44182914139 +Wisconsin,4.528761127 diff --git a/silver_model.ipynb b/silver_model.ipynb index 35daf68..e5e50d8 100644 --- a/silver_model.ipynb +++ b/silver_model.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -25,6 +25,8 @@ "import pandas\n", "from scipy import stats\n", "np.set_printoptions(precision=4, suppress=True)\n", + "%matplotlib inline\n", + "\n", "#pandas.set_options(notebook_repr_html=False,\n", "# precision=4,\n", "# max_columns=12, column_space=10,\n", @@ -33,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -110,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -121,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -714,7 +716,7 @@ "50 National Association of Realtors 1.7 2.1 " ] }, - "execution_count": 19, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -725,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -736,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -749,7 +751,7 @@ "dtype: float64" ] }, - "execution_count": 18, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -788,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -800,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -811,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -916,7 +918,7 @@ "4 4 USA 2012-09-28 " ] }, - "execution_count": 107, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -927,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -938,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -1043,7 +1045,7 @@ "4 17 2012-08-02 " ] }, - "execution_count": 109, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -1066,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -1077,7 +1079,7 @@ "120" ] }, - "execution_count": 48, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1088,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -1175,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -1186,7 +1188,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -1437,7 +1439,7 @@ "31 Zogby Interactive 0.43 4.74" ] }, - "execution_count": 111, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1448,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -1461,7 +1463,7 @@ "dtype: float64" ] }, - "execution_count": 116, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1479,7 +1481,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -1491,7 +1493,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -1502,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -1520,7 +1522,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -1531,7 +1533,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -1648,7 +1650,7 @@ "4 -3 2012-06-12 1.3 0.88 " ] }, - "execution_count": 118, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1659,7 +1661,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -1678,12 +1680,10 @@ "poll_date datetime64[ns]\n", "Weight float64\n", "PIE float64\n", - "ESS float64\n", - "MESS float64\n", "dtype: object" ] }, - "execution_count": 134, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1708,7 +1708,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1724,11 +1724,32 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 25, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 45)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Phn32gcmTc1cmSZLKxTAtlVinTnDssfDCC9CrF+y4IxxzDLzySu7KJElSqRmmpTJZZhn4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\nb7+dTls89NB23Gb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(12,8), subplot_kw={\"xlabel\" : \"Days\",\n", " \"ylabel\" : \"Weight\"})\n", @@ -1736,8 +1757,7 @@ "ax.plot(days, exp_decay(days));\n", "ax.vlines(30, 0, .99, color='r', linewidth=4)\n", "ax.set_ylim(0,1)\n", - "ax.set_xlim(0, 45)\n", - "plt.show()" + "ax.set_xlim(0, 45)\n" ] }, { @@ -1756,7 +1776,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -1775,7 +1795,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1787,7 +1807,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1798,7 +1818,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1809,7 +1829,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -1929,7 +1949,7 @@ "265 2010-09-20 " ] }, - "execution_count": 127, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1941,7 +1961,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -1964,7 +1984,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -2117,7 +2137,7 @@ "265 2010-09-20 5408 " ] }, - "execution_count": 129, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -2131,7 +2151,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -2169,26 +2189,108 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "

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2652010-09-206175408480.95502017.423881
\n", + "
" + ], "text/plain": [ - " poll_date Sample cumulative ESS MESS\n", - "198 2012-09-08 00:00:00 993 993 246.182 246.182\n", - "199 2012-07-24 00:00:00 833 1826 325.801 79.618\n", - "200 2012-05-19 00:00:00 500 2326 359.591 33.791\n", - "201 2012-02-18 00:00:00 743 3069 399.185 39.594\n", - "202 2011-11-19 00:00:00 500 3569 420.968 21.783\n", - "203 2011-04-30 00:00:00 623 4192 444.241 23.273\n", - "204 2011-01-29 00:00:00 599 4791 463.531 19.291\n", - "205 2010-09-20 00:00:00 617 5408 480.955 17.424" + " poll_date Sample cumulative ESS MESS\n", + "258 2012-09-08 993 993 246.182451 246.182451\n", + "259 2012-07-24 833 1826 325.800656 79.618204\n", + "260 2012-05-19 500 2326 359.591232 33.790577\n", + "261 2012-02-18 743 3069 399.185055 39.593822\n", + "262 2011-11-19 500 3569 420.967611 21.782557\n", + "263 2011-04-30 623 4192 444.240505 23.272893\n", + "264 2011-01-29 599 4791 463.531139 19.290634\n", + "265 2010-09-20 617 5408 480.955020 17.423881" ] }, - "execution_count": 61, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -2206,7 +2308,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -2229,7 +2331,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -2247,7 +2349,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -2259,7 +2361,7 @@ "Name: poll_date, dtype: datetime64[ns]" ] }, - "execution_count": 142, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2270,7 +2372,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -2281,7 +2383,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -2353,7 +2455,7 @@ "Name: time_weight, dtype: float64" ] }, - "execution_count": 183, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2371,7 +2473,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 40, "metadata": { "collapsed": false }, @@ -2385,7 +2487,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -2397,7 +2499,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -2470,7 +2572,7 @@ "dtype: float64" ] }, - "execution_count": 190, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2488,7 +2590,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -2499,7 +2601,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -2515,7 +2617,7 @@ "dtype: object" ] }, - "execution_count": 204, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2526,7 +2628,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 45, "metadata": { "collapsed": false }, @@ -2542,7 +2644,7 @@ "dtype: object" ] }, - "execution_count": 205, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -2553,7 +2655,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 46, "metadata": { "collapsed": false }, @@ -2627,7 +2729,7 @@ "4 AL 42 53 Rasmussen 2004-09-06" ] }, - "execution_count": 207, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2638,7 +2740,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -2712,7 +2814,7 @@ "4 AL 39 60 Rasmussen 2008-09-22" ] }, - "execution_count": 208, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -2723,7 +2825,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 48, "metadata": { "collapsed": false }, @@ -2734,7 +2836,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 49, "metadata": { "collapsed": false }, @@ -3072,7 +3174,7 @@ "WY 59.333333 32.666667" ] }, - "execution_count": 210, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -3090,7 +3192,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 50, "metadata": { "collapsed": false }, @@ -3103,7 +3205,7 @@ "dtype: float64" ] }, - "execution_count": 211, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -3114,7 +3216,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 51, "metadata": { "collapsed": false }, @@ -3126,7 +3228,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 52, "metadata": { "collapsed": false }, @@ -3138,7 +3240,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 53, "metadata": { "collapsed": false }, @@ -3149,7 +3251,7 @@ "26" ] }, - "execution_count": 214, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -3160,7 +3262,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 54, "metadata": { "collapsed": false }, @@ -3171,7 +3273,7 @@ "21" ] }, - "execution_count": 215, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -3182,7 +3284,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 55, "metadata": { "collapsed": false }, @@ -3193,7 +3295,7 @@ "datetime.datetime(2004, 11, 2, 0, 0)" ] }, - "execution_count": 217, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -3205,7 +3307,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 56, "metadata": { "collapsed": false }, @@ -3277,7 +3379,7 @@ "Name: poll_date, dtype: bool" ] }, - "execution_count": 218, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -3295,7 +3397,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 57, "metadata": { "collapsed": false }, @@ -3307,7 +3409,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 58, "metadata": { "collapsed": false }, @@ -3318,7 +3420,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 59, "metadata": { "collapsed": false }, @@ -3330,7 +3432,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 60, "metadata": { "collapsed": false }, @@ -3348,7 +3450,7 @@ "dtype: object" ] }, - "execution_count": 222, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -3359,7 +3461,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 61, "metadata": { "collapsed": false }, @@ -3371,7 +3473,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 62, "metadata": { "collapsed": false }, @@ -3427,7 +3529,7 @@ "4 0.793701 2004-10-23" ] }, - "execution_count": 225, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -3438,7 +3540,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 63, "metadata": { "collapsed": false }, @@ -3450,7 +3552,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 64, "metadata": { "collapsed": false }, @@ -3483,7 +3585,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 65, "metadata": { "collapsed": false }, @@ -3494,7 +3596,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 66, "metadata": { "collapsed": false }, @@ -3779,7 +3881,7 @@ "Wyoming R+20" ] }, - "execution_count": 229, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -3791,7 +3893,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 67, "metadata": { "collapsed": false }, @@ -3854,7 +3956,7 @@ "Name: PVI, dtype: float64" ] }, - "execution_count": 230, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -3876,7 +3978,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 68, "metadata": { "collapsed": false }, @@ -3887,7 +3989,7 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 69, "metadata": { "collapsed": false }, @@ -3902,7 +4004,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 70, "metadata": { "collapsed": false }, @@ -4399,7 +4501,7 @@ "Utah 24.5 63.8 -39.3000 2256 11.7" ] }, - "execution_count": 233, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -4410,7 +4512,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 71, "metadata": { "collapsed": false }, @@ -4421,7 +4523,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 72, "metadata": { "collapsed": false }, @@ -4435,7 +4537,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 73, "metadata": { "collapsed": false }, @@ -4447,7 +4549,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 74, "metadata": { "collapsed": false }, @@ -4458,7 +4560,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 75, "metadata": { "collapsed": false }, @@ -4534,7 +4636,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 76, "metadata": { "collapsed": false }, @@ -4548,7 +4650,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 77, "metadata": { "collapsed": false }, @@ -4560,7 +4662,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 78, "metadata": { "collapsed": false }, @@ -4572,7 +4674,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 79, "metadata": { "collapsed": false }, @@ -4583,7 +4685,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 80, "metadata": { "collapsed": false }, @@ -4594,7 +4696,7 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 81, "metadata": { "collapsed": false }, @@ -4932,7 +5034,7 @@ "Wyoming 0.746122 1.080021" ] }, - "execution_count": 244, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -4944,7 +5046,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 82, "metadata": { "collapsed": false }, @@ -4955,7 +5057,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 83, "metadata": { "collapsed": false }, @@ -4967,7 +5069,7 @@ }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 84, "metadata": { "collapsed": false }, @@ -4978,7 +5080,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 85, "metadata": { "collapsed": false }, @@ -4990,7 +5092,7 @@ " 1., 1., 1., 1.])" ] }, - "execution_count": 249, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -5001,7 +5103,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 86, "metadata": { "collapsed": false }, @@ -5013,7 +5115,7 @@ " array([[ 0, 40, 18, 42, 24, 33, 17]]))" ] }, - "execution_count": 250, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -5026,7 +5128,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 87, "metadata": { "collapsed": false }, @@ -5038,7 +5140,7 @@ " Index([[u'Alabama', u'South Carolina', u'Louisiana', u'Tennessee', u'Mississippi', u'North Carolina', u'Kentucky']], dtype='object', name=u'State'))" ] }, - "execution_count": 251, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -5050,7 +5152,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 88, "metadata": { "collapsed": false }, @@ -5065,7 +5167,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 89, "metadata": { "collapsed": false }, @@ -5178,7 +5280,7 @@ " array([[ 0. , 2.772 , 2.8463, 2.8491, 2.9062, 3.3221, 3.7427]]))}" ] }, - "execution_count": 253, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -5189,7 +5291,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 90, "metadata": { "collapsed": false }, @@ -5203,7 +5305,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 91, "metadata": { "collapsed": false }, @@ -5214,7 +5316,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 92, "metadata": { "collapsed": false }, @@ -5236,7 +5338,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 93, "metadata": { "collapsed": false }, @@ -5247,7 +5349,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 94, "metadata": { "collapsed": false }, @@ -5255,12 +5357,12 @@ { "data": { "text/plain": [ - "array([2, 3, 1, 2, 0, 3, 3, 3, 4, 0, 2, 3, 1, 3, 1, 1, 1, 2, 2, 1, 3, 3, 1,\n", - " 3, 2, 1, 1, 1, 3, 3, 3, 0, 0, 2, 1, 1, 2, 1, 1, 3, 2, 1, 2, 0, 1, 3,\n", - " 3, 3, 2, 1, 1])" + "array([2, 4, 0, 2, 3, 4, 4, 4, 1, 3, 2, 4, 0, 4, 0, 0, 0, 2, 2, 0, 4, 4, 0,\n", + " 4, 2, 0, 0, 0, 0, 4, 4, 0, 3, 2, 0, 0, 0, 0, 0, 4, 2, 0, 2, 3, 0, 4,\n", + " 4, 4, 2, 0, 0])" ] }, - "execution_count": 258, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -5278,7 +5380,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 95, "metadata": { "collapsed": false }, @@ -5289,7 +5391,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 96, "metadata": { "collapsed": false }, @@ -5301,7 +5403,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 97, "metadata": { "collapsed": false }, @@ -5310,16 +5412,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "['California' 'Florida' 'New Mexico' 'New York' 'Texas']\n", "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri'\n", - " 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania'\n", - " 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", + " 'Montana' 'Nebraska' 'Nevada' 'New Mexico' 'North Dakota' 'Ohio'\n", + " 'Oklahoma' 'Oregon' 'Pennsylvania' 'South Dakota' 'Utah' 'Wisconsin'\n", + " 'Wyoming']\n", + "['District of Columbia']\n", "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi'\n", - " 'North Carolina' 'Oklahoma' 'South Carolina' 'Tennessee' 'West Virginia']\n", + " 'North Carolina' 'South Carolina' 'Tennessee' 'West Virginia']\n", + "['California' 'Florida' 'New York' 'Texas']\n", "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", - " 'Maryland' 'Massachusetts' 'Minnesota' 'Nevada' 'New Hampshire'\n", - " 'New Jersey' 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n", - "['District of Columbia']\n" + " 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey'\n", + " 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n" ] } ], @@ -5332,7 +5435,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 98, "metadata": { "collapsed": false }, @@ -5340,12 +5443,12 @@ { "data": { "text/plain": [ - "array([2, 1, 2, 2, 3, 1, 1, 1, 4, 3, 2, 1, 0, 1, 2, 0, 0, 2, 2, 0, 1, 1, 2,\n", - " 1, 2, 2, 0, 0, 2, 1, 1, 2, 3, 2, 0, 2, 2, 0, 2, 1, 2, 0, 2, 3, 0, 1,\n", - " 1, 1, 2, 0, 0], dtype=int32)" + "array([1, 0, 1, 1, 2, 0, 0, 0, 4, 2, 1, 0, 3, 0, 3, 3, 3, 1, 1, 3, 0, 0, 3,\n", + " 3, 1, 3, 3, 3, 0, 0, 0, 1, 2, 1, 3, 3, 1, 3, 3, 0, 1, 3, 1, 2, 3, 3,\n", + " 0, 0, 1, 3, 3], dtype=int32)" ] }, - "execution_count": 262, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -5356,7 +5459,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 99, "metadata": { "collapsed": false }, @@ -5365,16 +5468,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Idaho' 'Iowa' 'Kansas' 'Maine' 'Montana' 'Nebraska' 'North Dakota'\n", - " 'Oregon' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", - " 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey'\n", - " 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n", - "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Indiana' 'Kentucky' 'Louisiana'\n", - " 'Michigan' 'Mississippi' 'Missouri' 'Nevada' 'New Mexico' 'North Carolina'\n", - " 'Ohio' 'Oklahoma' 'Pennsylvania' 'South Carolina' 'Tennessee'\n", - " 'West Virginia']\n", + " 'Maryland' 'Massachusetts' 'Nevada' 'New Hampshire' 'New Jersey'\n", + " 'Rhode Island' 'Virginia' 'Washington']\n", + "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana'\n", + " 'Mississippi' 'New Mexico' 'North Carolina' 'Oklahoma' 'South Carolina'\n", + " 'Tennessee' 'West Virginia']\n", "['California' 'Florida' 'New York' 'Texas']\n", + "['Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Minnesota'\n", + " 'Missouri' 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon'\n", + " 'Pennsylvania' 'South Dakota' 'Utah' 'Vermont' 'Wisconsin' 'Wyoming']\n", "['District of Columbia']\n" ] } @@ -5389,7 +5492,7 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 100, "metadata": { "collapsed": false }, @@ -5400,7 +5503,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 101, "metadata": { "collapsed": false }, @@ -5411,7 +5514,7 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": 102, "metadata": { "collapsed": false }, @@ -5422,7 +5525,7 @@ }, { "cell_type": "code", - "execution_count": 267, + "execution_count": 103, "metadata": { "collapsed": false }, @@ -5433,7 +5536,7 @@ }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 104, "metadata": { "collapsed": false }, @@ -5444,7 +5547,7 @@ }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 105, "metadata": { "collapsed": false }, @@ -5452,12 +5555,10 @@ { "data": { "text/plain": [ - "array(['New Mexico', 'North Carolina', 'Nevada', 'Ohio', 'Pennsylvania',\n", - " 'Indiana', 'Arizona', 'Missouri', 'Michigan', 'Georgia',\n", - " 'West Virginia', 'South Carolina', 'Tennessee', 'Mississippi'], dtype=object)" + "array(['Florida', 'California', 'New York', 'Texas'], dtype=object)" ] }, - "execution_count": 269, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -5468,7 +5569,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 106, "metadata": { "collapsed": false }, @@ -5484,21 +5585,20 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "AttributeError", - "evalue": "'module' object has no attribute 'ints_to_pydatetime'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m frac=.2, it=3)\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mdates_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mints_to_pydatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"obama_spread\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates_x\u001b[0m\u001b[0;34m,\u001b[0m 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++lXpK1+R+vcvTKAAAAAoapTB5Eu3btIxx0g33SS99ZZ0/vnSn/8s7b23VFkp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\nzcKFn2RsbM2UBZWuueaauqkL+4FpRHwVeL6kSwB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -5515,7 +5615,7 @@ "loess_res = sm.nonparametric.lowess(data.obama_spread.values, dates, \n", " frac=.2, it=3)\n", "\n", - "dates_x = lib.ints_to_pydatetime(dates)\n", + "dates_x = pandas.to_datetime(dates)\n", "axes.scatter(dates_x, data[\"obama_spread\"])\n", "axes.plot(dates_x, loess_res[:,1], color='r')\n", "axes.yaxis.get_major_locator().set_params(nbins=12)\n", @@ -5527,7 +5627,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 108, "metadata": { "collapsed": false }, @@ -5535,10 +5635,10 @@ { "data": { "text/plain": [ - "2.3144535643345003" + "4.1600466106699434" ] }, - "execution_count": 144, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -5549,14 +5649,17 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Vx/nnJ/ODHySwfbvR+FhBgcFttyVw/vnJ/Pa3sZSXG8dYkxxq71743e98nH9+MrfcksDW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\nTcxqF0OGtO7XTvPzTTZscJG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rduPQOC4eGr+hxairO6GanMi1eTPhPn0Ip6dHZ1y8euJF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/J6bVyuE+Xk5va/nKP3/xmGHZHzD9UZZ5w4p2HYQz6n2x0a+8elptphjeO8vBPHJCTYJPb/rDMk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\n" 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Ff/yii+yT/fdXWUva+1NZi8paVNaS15OS81bmww/NfvITs3PPNVu3LuhoRCRg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\nJalQQhaI0KBoFS6jIlmgoqCU3ZOZaisIhdp9lC5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C0047bWGDnYNuPM8RQWbGNustRh9g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\n1G9LaMwvau5VHvMUH966BbgT2Ar8FNi1B8f81yk+FPaHcp/BHhrzbc+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+ "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -5573,7 +5676,7 @@ "loess_res = sm.nonparametric.lowess(national_data2012.obama_spread.values, dates, \n", " frac=.075, it=3)\n", "\n", - "dates_x = lib.ints_to_pydatetime(dates)\n", + "dates_x = pandas.to_datetime(dates)\n", "axes.scatter(dates_x, national_data2012[\"obama_spread\"])\n", "axes.plot(dates_x, loess_res[:,1], color='r')\n", "axes.yaxis.get_major_locator().set_params(nbins=12)\n", @@ -5585,7 +5688,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 110, "metadata": { "collapsed": false }, @@ -5608,7 +5711,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 111, "metadata": { "collapsed": false }, @@ -5616,50 +5719,50 @@ { "data": { "text/plain": [ - "[['Arizona', 2.3149200179538716],\n", - " ['Georgia', 2.3149200179538716],\n", - " ['Mississippi', 2.3149200179538716],\n", - " ['New Mexico', 2.3149200179538716],\n", - " ['North Carolina', 2.3149200179538716],\n", - " ['South Carolina', 2.3149200179538716],\n", - " ['Tennessee', 2.3149200179538716],\n", - " ['West Virginia', 2.3149200179538716],\n", - " ['Colorado', 18.412063676088412],\n", - " ['Connecticut', 18.412063676088412],\n", - " ['Hawaii', 18.412063676088412],\n", - " ['Illinois', 18.412063676088412],\n", - " ['Maryland', 18.412063676088412],\n", - " ['Massachusetts', 18.412063676088412],\n", - " ['Nevada', 18.412063676088412],\n", - " ['New Jersey', 18.412063676088412],\n", - " ['Rhode Island', 18.412063676088412],\n", - " ['Virginia', 18.412063676088412],\n", - " ['Washington', 18.412063676088412],\n", - " ['California', 2.73263672729736],\n", - " ['Florida', 2.73263672729736],\n", - " ['New York', 2.73263672729736],\n", - " ['Texas', 2.73263672729736],\n", - " ['Indiana', 6.5865280433068092],\n", - " ['Iowa', 6.5865280433068092],\n", - " ['Kansas', 6.5865280433068092],\n", - " ['Maine', 6.5865280433068092],\n", - " ['Michigan', 6.5865280433068092],\n", - " ['Minnesota', 6.5865280433068092],\n", - " ['Missouri', 6.5865280433068092],\n", - " ['Montana', 6.5865280433068092],\n", - " ['Nebraska', 6.5865280433068092],\n", - " ['New Hampshire', 6.5865280433068092],\n", - " ['North Dakota', 6.5865280433068092],\n", - " ['Ohio', 6.5865280433068092],\n", - " ['Oregon', 6.5865280433068092],\n", - " ['Pennsylvania', 6.5865280433068092],\n", - " ['South Dakota', 6.5865280433068092],\n", - " ['Utah', 6.5865280433068092],\n", - " ['Vermont', 6.5865280433068092],\n", - " ['Wisconsin', 6.5865280433068092]]" + "[['Washington', 4.5234550021334803],\n", + " ['New Hampshire', 4.5234550021334803],\n", + " ['New Jersey', 4.5234550021334803],\n", + " ['Nevada', 4.5234550021334803],\n", + " ['Colorado', 4.5234550021334803],\n", + " ['Connecticut', 4.5234550021334803],\n", + " ['Virginia', 4.5234550021334803],\n", + " ['Massachusetts', 4.5234550021334803],\n", + " ['Rhode Island', 4.5234550021334803],\n", + " ['Hawaii', 4.5234550021334803],\n", + " ['Maryland', 4.5234550021334803],\n", + " ['Illinois', 4.5234550021334803],\n", + " ['New Mexico', 3.266461905439225],\n", + " ['North Carolina', 3.266461905439225],\n", + " ['Arizona', 3.266461905439225],\n", + " ['Georgia', 3.266461905439225],\n", + " ['West Virginia', 3.266461905439225],\n", + " ['South Carolina', 3.266461905439225],\n", + " ['Tennessee', 3.266461905439225],\n", + " ['Mississippi', 3.266461905439225],\n", + " ['Florida', 3.3877002862540975],\n", + " ['California', 3.3877002862540975],\n", + " ['New York', 3.3877002862540975],\n", + " ['Texas', 3.3877002862540975],\n", + " ['Wisconsin', 5.3717701704680234],\n", + " ['North Dakota', 5.3717701704680234],\n", + " ['Nebraska', 5.3717701704680234],\n", + " ['Ohio', 5.3717701704680234],\n", + " ['Pennsylvania', 5.3717701704680234],\n", + " ['Indiana', 5.3717701704680234],\n", + " ['Iowa', 5.3717701704680234],\n", + " ['Maine', 5.3717701704680234],\n", + " ['Missouri', 5.3717701704680234],\n", + " ['Michigan', 5.3717701704680234],\n", + " ['Montana', 5.3717701704680234],\n", + " ['Kansas', 5.3717701704680234],\n", + " ['Oregon', 5.3717701704680234],\n", + " ['South Dakota', 5.3717701704680234],\n", + " ['Vermont', 5.3717701704680234],\n", + " ['Utah', 5.3717701704680234],\n", + " ['Minnesota', 5.3717701704680234]]" ] }, - "execution_count": 147, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -5692,7 +5795,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 112, "metadata": { "collapsed": false }, @@ -5703,7 +5806,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 113, "metadata": { "collapsed": false }, @@ -5715,7 +5818,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 114, "metadata": { "collapsed": false }, @@ -5733,7 +5836,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 115, "metadata": { "collapsed": false }, @@ -5744,7 +5847,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 116, "metadata": { "collapsed": false }, @@ -5762,7 +5865,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 117, "metadata": { "collapsed": false }, @@ -5770,26 +5873,26 @@ { "data": { "text/plain": [ - "Pollster American Research Group\n", - "State Colorado\n", - "MoE 4\n", - "Obama (D) 49\n", - "Romney (R) 47\n", - "Sample 600\n", - "Spread Obama +2\n", - "obama_spread 2\n", - "poll_date 2012-09-11 00:00:00\n", - "Weight 0.65\n", - "PIE 1.76\n", - "ESS 173\n", - "MESS 173\n", - "time_weight 0.6156\n", - "kmeans_labels 1\n", - "pollster_state American Research Gro...\n", - "Name: 25" + "Pollster American Research Group\n", + "State Colorado\n", + "MoE 4\n", + "Obama (D) 49\n", + "Romney (R) 47\n", + "Sample 600\n", + "Spread Obama +2\n", + "obama_spread 2\n", + "poll_date 2012-09-11 00:00:00\n", + "Weight 0.65\n", + "PIE 1.76\n", + "ESS 173.0171\n", + "MESS 173.0171\n", + "time_weight 0.6155722\n", + "kmeans_labels 0\n", + "pollster_state American Research Group-Colorado\n", + "Name: 168, dtype: object" ] }, - "execution_count": 153, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -5800,7 +5903,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 118, "metadata": { "collapsed": false }, @@ -5816,7 +5919,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 119, "metadata": { "collapsed": false }, @@ -5837,7 +5940,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 120, "metadata": { "collapsed": false }, @@ -5848,7 +5951,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 121, "metadata": { "collapsed": false }, @@ -5860,7 +5963,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 122, "metadata": { "collapsed": false }, @@ -5871,7 +5974,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 123, "metadata": { "collapsed": false }, @@ -5882,7 +5985,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 124, "metadata": { "collapsed": false }, @@ -5893,7 +5996,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 125, "metadata": { "collapsed": false }, @@ -5904,7 +6007,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 126, "metadata": { "collapsed": false }, @@ -5912,17 +6015,18 @@ { "data": { "text/plain": [ - "count 355.000\n", - "mean 3.281\n", - "std 9.168\n", - "min -52.000\n", - "25% -0.808\n", - "50% 2.697\n", - "75% 8.145\n", - "max 38.723" + "count 357.000000\n", + "mean 3.364920\n", + "std 9.414260\n", + "min -52.000000\n", + "25% -0.807689\n", + "50% 2.696995\n", + "75% 8.172477\n", + "max 44.255926\n", + "Name: m, dtype: float64" ] }, - "execution_count": 162, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -5933,7 +6037,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 127, "metadata": { "collapsed": false }, @@ -5944,7 +6048,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 128, "metadata": { "collapsed": false }, @@ -5953,112 +6057,71 @@ "data": { "text/plain": [ "pollster_state\n", - "American Research Group-Colorado 1\n", - "American Research Group-Florida 1\n", - "American Research Group-Iowa 1\n", - "American Research Group-Nevada 1\n", - "American Research Group-New Hampshire 3\n", - "American Research Group-North Carolina 1\n", - "American Research Group-Ohio 1\n", - "American Research Group-Virginia 1\n", - "CNN / Opinion Research-Wisconsin 1\n", - "Chicago Trib. / MarketShares-Illinois 1\n", - "Columbus Dispatch (OH)-Ohio 2\n", - "EPIC-MRA-Michigan 8\n", - "Fairleigh-Dickinson (NJ)-New Jersey 3\n", - "Field Poll (CA)-California 6\n", - "Insider Advantage-Georgia 2\n", - "LA Times / Bloomberg-New Hampshire 1\n", - "Marist (NY)-New York 3\n", - "Mason-Dixon-Florida 3\n", - "Mason-Dixon-Georgia 1\n", - "Mason-Dixon-New Hampshire 1\n", - "Mason-Dixon-North Dakota 1\n", - "Mason-Dixon-Utah 1\n", - "Mason-Dixon-Virginia 1\n", - "Mitchell-Michigan 3\n", - "Ohio Poll-Ohio 2\n", - "Public Policy Polling (PPP)-Arizona 7\n", - "Public Policy Polling (PPP)-California 2\n", - "Public Policy Polling (PPP)-Colorado 6\n", - "Public Policy Polling (PPP)-Connecticut 3\n", - "Public Policy Polling (PPP)-Florida 8\n", - "Public Policy Polling (PPP)-Georgia 1\n", - "Public Policy Polling (PPP)-Hawaii 1\n", - "Public Policy Polling (PPP)-Iowa 8\n", - "Public Policy Polling (PPP)-Maine 2\n", - "Public Policy Polling (PPP)-Maryland 1\n", - "Public Policy Polling (PPP)-Massachusetts 6\n", - "Public Policy Polling (PPP)-Michigan 6\n", - "Public Policy Polling (PPP)-Minnesota 5\n", - "Public Policy Polling (PPP)-Mississippi 2\n", - "Public Policy Polling (PPP)-Missouri 7\n", - "Public Policy Polling (PPP)-Montana 3\n", - "Public Policy Polling (PPP)-Nebraska 1\n", - "Public Policy Polling (PPP)-Nevada 4\n", - "Public Policy Polling (PPP)-New Hampshire 3\n", - "Public Policy Polling (PPP)-New Mexico 6\n", - "Public Policy Polling (PPP)-North Carolina 22\n", - "Public Policy Polling (PPP)-Ohio 9\n", - "Public Policy Polling (PPP)-Oregon 2\n", - "Public Policy Polling (PPP)-Pennsylvania 5\n", - "Public Policy Polling (PPP)-Rhode Island 1\n", - "Public Policy Polling (PPP)-South Carolina 3\n", - "Public Policy Polling (PPP)-South Dakota 1\n", - "Public Policy Polling (PPP)-Tennessee 1\n", - "Public Policy Polling (PPP)-Texas 3\n", - "Public Policy Polling (PPP)-Utah 1\n", - "Public Policy Polling (PPP)-Virginia 7\n", - "Public Policy Polling (PPP)-Washington 3\n", - "Public Policy Polling (PPP)-West Virginia 3\n", - "Public Policy Polling (PPP)-Wisconsin 6\n", - "Quinnipiac-Connecticut 4\n", - "Quinnipiac-Florida 12\n", - "Quinnipiac-New Jersey 8\n", - "Quinnipiac-New York 5\n", - "Quinnipiac-Ohio 11\n", - "Quinnipiac-Pennsylvania 9\n", - "Quinnipiac-Virginia 5\n", - "Rasmussen-Arizona 3\n", - "Rasmussen-California 1\n", - "Rasmussen-Colorado 3\n", - "Rasmussen-Connecticut 1\n", - "Rasmussen-Florida 5\n", - "Rasmussen-Indiana 1\n", - "Rasmussen-Iowa 3\n", - "Rasmussen-Maine 1\n", - "Rasmussen-Massachusetts 4\n", - "Rasmussen-Michigan 2\n", - "Rasmussen-Missouri 6\n", - "Rasmussen-Montana 5\n", - "Rasmussen-Nebraska 2\n", - "Rasmussen-Nevada 3\n", - "Rasmussen-New Hampshire 1\n", - "Rasmussen-New Jersey 1\n", - "Rasmussen-New Mexico 3\n", - "Rasmussen-North Carolina 4\n", - "Rasmussen-North Dakota 1\n", - "Rasmussen-Ohio 7\n", - "Rasmussen-Pennsylvania 4\n", - "Rasmussen-Virginia 5\n", - "Rasmussen-Washington 1\n", - "Rasmussen-Wisconsin 7\n", - "Suffolk (NH/MA)-Florida 2\n", - "SurveyUSA-California 4\n", - "SurveyUSA-Florida 2\n", - "SurveyUSA-Georgia 4\n", - "SurveyUSA-Kansas 2\n", - "SurveyUSA-Michigan 1\n", - "SurveyUSA-New Jersey 1\n", - "SurveyUSA-New York 1\n", - "SurveyUSA-North Carolina 2\n", - "SurveyUSA-Oregon 4\n", - "SurveyUSA-Pennsylvania 1\n", - "SurveyUSA-Washington 4\n", - "Length: 102" + "American Research Group-Colorado 1\n", + "American Research Group-Florida 1\n", + "American Research Group-Iowa 1\n", + "American Research Group-Nevada 1\n", + "American Research Group-New Hampshire 3\n", + "American Research Group-North Carolina 1\n", + "American Research Group-Ohio 1\n", + "American Research Group-Virginia 1\n", + "CNN / Opinion Research-Wisconsin 1\n", + "Chicago Trib. / MarketShares-Illinois 1\n", + "Columbus Dispatch (OH)-Ohio 2\n", + "EPIC-MRA-Michigan 8\n", + "Fairleigh-Dickinson (NJ)-New Jersey 3\n", + "Field Poll (CA)-California 6\n", + "Insider Advantage-Georgia 2\n", + "LA Times / Bloomberg-New Hampshire 1\n", + "Marist (NY)-New York 3\n", + "Mason-Dixon-Florida 3\n", + "Mason-Dixon-Georgia 1\n", + "Mason-Dixon-New Hampshire 1\n", + "Mason-Dixon-North Dakota 1\n", + "Mason-Dixon-Utah 1\n", + "Mason-Dixon-Virginia 1\n", + "Mitchell-Michigan 3\n", + "Ohio Poll-Ohio 2\n", + "Public Policy Polling (PPP)-Arizona 7\n", + "Public Policy Polling (PPP)-California 2\n", + "Public Policy Polling (PPP)-Colorado 6\n", + "Public Policy Polling (PPP)-Connecticut 3\n", + "Public Policy Polling (PPP)-Florida 8\n", + " ..\n", + "Rasmussen-Iowa 3\n", + "Rasmussen-Maine 1\n", + "Rasmussen-Massachusetts 4\n", + "Rasmussen-Michigan 2\n", + "Rasmussen-Missouri 7\n", + "Rasmussen-Montana 5\n", + "Rasmussen-Nebraska 2\n", + "Rasmussen-Nevada 3\n", + "Rasmussen-New Hampshire 1\n", + "Rasmussen-New Jersey 1\n", + "Rasmussen-New Mexico 3\n", + "Rasmussen-North Carolina 4\n", + "Rasmussen-North Dakota 1\n", + "Rasmussen-Ohio 7\n", + "Rasmussen-Pennsylvania 4\n", + "Rasmussen-Virginia 5\n", + "Rasmussen-Washington 1\n", + "Rasmussen-Wisconsin 7\n", + "Suffolk (NH/MA)-Florida 2\n", + "SurveyUSA-California 4\n", + "SurveyUSA-Florida 2\n", + "SurveyUSA-Georgia 4\n", + "SurveyUSA-Kansas 2\n", + "SurveyUSA-Michigan 1\n", + "SurveyUSA-New Jersey 1\n", + "SurveyUSA-New York 1\n", + "SurveyUSA-North Carolina 2\n", + "SurveyUSA-Oregon 4\n", + "SurveyUSA-Pennsylvania 1\n", + "SurveyUSA-Washington 4\n", + "dtype: int64" ] }, - "execution_count": 164, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -6069,7 +6132,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 129, "metadata": { "collapsed": false }, @@ -6080,7 +6143,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 130, "metadata": { "collapsed": false }, @@ -6091,22 +6154,62 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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StatemPollster
poll_date
2012-03-17New Hampshire6.436534American Research Group
2012-06-23New Hampshire0.071010American Research Group
2012-09-26New Hampshire4.054884American Research Group
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" + ], "text/plain": [ - " State m Pollster\n", - "poll_date \n", - "2012-03-17 New Hampshire 6.437 American Research Group\n", - "2012-06-23 New Hampshire 0.071 American Research Group\n", - "2012-09-26 New Hampshire 4.055 American Research Group" + " State m Pollster\n", + "poll_date \n", + "2012-03-17 New Hampshire 6.436534 American Research Group\n", + "2012-06-23 New Hampshire 0.071010 American Research Group\n", + "2012-09-26 New Hampshire 4.054884 American Research Group" ] }, - "execution_count": 167, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -6117,7 +6220,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 132, "metadata": { "collapsed": false }, @@ -6128,191 +6231,673 @@ }, { "cell_type": "code", - "execution_count": 169, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Int64Index: 320 entries, 0 to 319\n", - "Data columns:\n", - "pollster_state 320 non-null values\n", - "poll_date 320 non-null values\n", - "State 320 non-null values\n", - "m 320 non-null values\n", - "Pollster 320 non-null values\n", - "dtypes: datetime64[ns](1), float64(1), object(3)" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m_dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "m_regression_data = m_dataframe.merge(demo_data, on=\"State\")" - ] - }, - { - "cell_type": "code", - "execution_count": 171, + "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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pollster_statepoll_dateStatemPollster
0American Research Group-New Hampshire2012-03-17New Hampshire6.436534American Research Group
1American Research Group-New Hampshire2012-06-23New Hampshire0.071010American Research Group
2American Research Group-New Hampshire2012-09-26New Hampshire4.054884American Research Group
3Columbus Dispatch (OH)-Ohio2012-08-20Ohio1.875520Columbus Dispatch (OH)
4Columbus Dispatch (OH)-Ohio2012-09-24Ohio7.679307Columbus Dispatch (OH)
5EPIC-MRA-Michigan2011-02-15Michigan-4.201071EPIC-MRA
6EPIC-MRA-Michigan2011-07-10Michigan-3.096961EPIC-MRA
7EPIC-MRA-Michigan2011-11-15Michigan-4.201071EPIC-MRA
8EPIC-MRA-Michigan2012-01-23Michigan6.398112EPIC-MRA
9EPIC-MRA-Michigan2012-04-02Michigan-0.219418EPIC-MRA
10EPIC-MRA-Michigan2012-06-04Michigan1.427470EPIC-MRA
11EPIC-MRA-Michigan2012-07-28Michigan2.361416EPIC-MRA
12EPIC-MRA-Michigan2012-09-10Michigan8.081481EPIC-MRA
13Fairleigh-Dickinson (NJ)-New Jersey2012-03-08New Jersey11.012846Fairleigh-Dickinson (NJ)
14Fairleigh-Dickinson (NJ)-New Jersey2012-07-26New Jersey11.012846Fairleigh-Dickinson (NJ)
15Fairleigh-Dickinson (NJ)-New Jersey2012-09-09New Jersey12.321317Fairleigh-Dickinson (NJ)
16Field Poll (CA)-California2011-09-07California26.901821Field Poll (CA)
17Field Poll (CA)-California2011-11-21California14.111741Field Poll (CA)
18Field Poll (CA)-California2012-02-10California16.323488Field Poll (CA)
19Field Poll (CA)-California2012-05-25California12.879358Field Poll (CA)
20Field Poll (CA)-California2012-06-27California13.316641Field Poll (CA)
21Field Poll (CA)-California2012-09-12California21.440282Field Poll (CA)
22Insider Advantage-Georgia2012-05-22Georgia-8.785026Insider Advantage
23Insider Advantage-Georgia2012-07-24Georgia-4.598910Insider Advantage
24Marist (NY)-New York2011-10-26New York18.229994Marist (NY)
25Marist (NY)-New York2012-01-19New York15.323173Marist (NY)
26Marist (NY)-New York2012-04-11New York10.533985Marist (NY)
27Mason-Dixon-Florida2012-04-06Florida-4.277919Mason-Dixon
28Mason-Dixon-Florida2012-07-10Florida1.514054Mason-Dixon
29Mason-Dixon-Florida2012-08-20Florida-9.210613Mason-Dixon
..................
291Rasmussen-Wisconsin2012-02-27Wisconsin4.357351Rasmussen
292Rasmussen-Wisconsin2012-03-27Wisconsin10.707061Rasmussen
293Rasmussen-Wisconsin2012-05-09Wisconsin2.821784Rasmussen
294Rasmussen-Wisconsin2012-06-12Wisconsin-1.913946Rasmussen
295Rasmussen-Wisconsin2012-07-25Wisconsin1.932597Rasmussen
296Rasmussen-Wisconsin2012-09-17Wisconsin1.932597Rasmussen
297Suffolk (NH/MA)-Florida2012-04-11Florida-1.113497Suffolk (NH/MA)
298Suffolk (NH/MA)-Florida2012-10-28Florida-0.075990Suffolk (NH/MA)
299SurveyUSA-California2011-11-10California13.393160SurveyUSA
300SurveyUSA-California2012-02-09California25.638748SurveyUSA
301SurveyUSA-California2012-03-31California27.921344SurveyUSA
302SurveyUSA-California2012-09-10California15.256589SurveyUSA
303SurveyUSA-Florida2012-07-18Florida3.754219SurveyUSA
304SurveyUSA-Florida2012-09-08Florida1.921633SurveyUSA
305SurveyUSA-Georgia2011-12-07Georgia-5.667851SurveyUSA
306SurveyUSA-Georgia2012-02-02Georgia-6.538168SurveyUSA
307SurveyUSA-Georgia2012-02-25Georgia-5.667851SurveyUSA
308SurveyUSA-Georgia2012-07-29Georgia-6.538168SurveyUSA
309SurveyUSA-Kansas2011-11-10Kansas-26.128872SurveyUSA
310SurveyUSA-Kansas2011-11-20Kansas-6.973400SurveyUSA
311SurveyUSA-North Carolina2012-04-28North Carolina3.435788SurveyUSA
312SurveyUSA-North Carolina2012-09-30North Carolina1.008145SurveyUSA
313SurveyUSA-Oregon2011-11-20Oregon7.001203SurveyUSA
314SurveyUSA-Oregon2012-03-17Oregon10.204604SurveyUSA
315SurveyUSA-Oregon2012-05-09Oregon1.934380SurveyUSA
316SurveyUSA-Oregon2012-09-12Oregon8.172477SurveyUSA
317SurveyUSA-Washington2011-11-22Washington12.315353SurveyUSA
318SurveyUSA-Washington2012-05-09Washington8.655616SurveyUSA
319SurveyUSA-Washington2012-08-02Washington14.386038SurveyUSA
320SurveyUSA-Washington2012-09-08Washington9.553699SurveyUSA
\n", + "

321 rows × 5 columns

\n", + "
" + ], "text/plain": [ - "\n", - "Int64Index: 320 entries, 0 to 319\n", - "Data columns:\n", - "pollster_state 320 non-null values\n", - "poll_date 320 non-null values\n", - "State 320 non-null values\n", - "m 320 non-null values\n", - "Pollster 320 non-null values\n", - "per_black 320 non-null values\n", - "per_hisp 320 non-null values\n", - "per_white 320 non-null values\n", - "educ_hs 320 non-null values\n", - "educ_coll 320 non-null values\n", - "average_income 320 non-null values\n", - "median_income 320 non-null values\n", - "pop_density 320 non-null values\n", - "vote_pop 320 non-null values\n", - "older_pop 320 non-null values\n", - "dem_adv 320 non-null values\n", - "no_party 320 non-null values\n", - "PVI 320 non-null values\n", - "obama_give 320 non-null values\n", - "romney_give 320 non-null values\n", - "kmeans_group 320 non-null values\n", - "kmeans_labels 320 non-null values\n", - "dtypes: datetime64[ns](1), float64(14), int64(4), object(3)" + " pollster_state poll_date State \\\n", + "0 American Research Group-New Hampshire 2012-03-17 New Hampshire \n", + "1 American Research Group-New Hampshire 2012-06-23 New Hampshire \n", + "2 American Research Group-New Hampshire 2012-09-26 New Hampshire \n", + "3 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio \n", + "4 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio \n", + "5 EPIC-MRA-Michigan 2011-02-15 Michigan \n", + "6 EPIC-MRA-Michigan 2011-07-10 Michigan \n", + "7 EPIC-MRA-Michigan 2011-11-15 Michigan \n", + "8 EPIC-MRA-Michigan 2012-01-23 Michigan \n", + "9 EPIC-MRA-Michigan 2012-04-02 Michigan \n", + "10 EPIC-MRA-Michigan 2012-06-04 Michigan \n", + "11 EPIC-MRA-Michigan 2012-07-28 Michigan \n", + "12 EPIC-MRA-Michigan 2012-09-10 Michigan \n", + "13 Fairleigh-Dickinson (NJ)-New Jersey 2012-03-08 New Jersey \n", + "14 Fairleigh-Dickinson (NJ)-New Jersey 2012-07-26 New Jersey \n", + "15 Fairleigh-Dickinson (NJ)-New Jersey 2012-09-09 New Jersey \n", + "16 Field Poll (CA)-California 2011-09-07 California \n", + "17 Field Poll (CA)-California 2011-11-21 California \n", + "18 Field Poll (CA)-California 2012-02-10 California \n", + "19 Field Poll (CA)-California 2012-05-25 California \n", + "20 Field Poll (CA)-California 2012-06-27 California \n", + "21 Field Poll (CA)-California 2012-09-12 California \n", + "22 Insider Advantage-Georgia 2012-05-22 Georgia \n", + "23 Insider Advantage-Georgia 2012-07-24 Georgia \n", + "24 Marist (NY)-New York 2011-10-26 New York \n", + "25 Marist (NY)-New York 2012-01-19 New York \n", + "26 Marist (NY)-New York 2012-04-11 New York \n", + "27 Mason-Dixon-Florida 2012-04-06 Florida \n", + "28 Mason-Dixon-Florida 2012-07-10 Florida \n", + "29 Mason-Dixon-Florida 2012-08-20 Florida \n", + ".. ... ... ... \n", + "291 Rasmussen-Wisconsin 2012-02-27 Wisconsin \n", + "292 Rasmussen-Wisconsin 2012-03-27 Wisconsin \n", + "293 Rasmussen-Wisconsin 2012-05-09 Wisconsin \n", + "294 Rasmussen-Wisconsin 2012-06-12 Wisconsin \n", + "295 Rasmussen-Wisconsin 2012-07-25 Wisconsin \n", + "296 Rasmussen-Wisconsin 2012-09-17 Wisconsin \n", + "297 Suffolk (NH/MA)-Florida 2012-04-11 Florida \n", + "298 Suffolk (NH/MA)-Florida 2012-10-28 Florida \n", + "299 SurveyUSA-California 2011-11-10 California \n", + "300 SurveyUSA-California 2012-02-09 California \n", + "301 SurveyUSA-California 2012-03-31 California \n", + "302 SurveyUSA-California 2012-09-10 California \n", + "303 SurveyUSA-Florida 2012-07-18 Florida \n", + "304 SurveyUSA-Florida 2012-09-08 Florida \n", + "305 SurveyUSA-Georgia 2011-12-07 Georgia \n", + "306 SurveyUSA-Georgia 2012-02-02 Georgia \n", + "307 SurveyUSA-Georgia 2012-02-25 Georgia \n", + "308 SurveyUSA-Georgia 2012-07-29 Georgia \n", + "309 SurveyUSA-Kansas 2011-11-10 Kansas \n", + "310 SurveyUSA-Kansas 2011-11-20 Kansas \n", + "311 SurveyUSA-North Carolina 2012-04-28 North Carolina \n", + "312 SurveyUSA-North Carolina 2012-09-30 North Carolina \n", + "313 SurveyUSA-Oregon 2011-11-20 Oregon \n", + "314 SurveyUSA-Oregon 2012-03-17 Oregon \n", + "315 SurveyUSA-Oregon 2012-05-09 Oregon \n", + "316 SurveyUSA-Oregon 2012-09-12 Oregon \n", + "317 SurveyUSA-Washington 2011-11-22 Washington \n", + "318 SurveyUSA-Washington 2012-05-09 Washington \n", + "319 SurveyUSA-Washington 2012-08-02 Washington \n", + "320 SurveyUSA-Washington 2012-09-08 Washington \n", + "\n", + " m Pollster \n", + "0 6.436534 American Research Group \n", + "1 0.071010 American Research Group \n", + "2 4.054884 American Research Group \n", + "3 1.875520 Columbus Dispatch (OH) \n", + "4 7.679307 Columbus Dispatch (OH) \n", + "5 -4.201071 EPIC-MRA \n", + "6 -3.096961 EPIC-MRA \n", + "7 -4.201071 EPIC-MRA \n", + "8 6.398112 EPIC-MRA \n", + "9 -0.219418 EPIC-MRA \n", + "10 1.427470 EPIC-MRA \n", + "11 2.361416 EPIC-MRA \n", + "12 8.081481 EPIC-MRA \n", + "13 11.012846 Fairleigh-Dickinson (NJ) \n", + "14 11.012846 Fairleigh-Dickinson (NJ) \n", + "15 12.321317 Fairleigh-Dickinson (NJ) \n", + "16 26.901821 Field Poll (CA) \n", + "17 14.111741 Field Poll (CA) \n", + "18 16.323488 Field Poll (CA) \n", + "19 12.879358 Field Poll (CA) \n", + "20 13.316641 Field Poll (CA) \n", + "21 21.440282 Field Poll (CA) \n", + "22 -8.785026 Insider Advantage \n", + "23 -4.598910 Insider Advantage \n", + "24 18.229994 Marist (NY) \n", + "25 15.323173 Marist (NY) \n", + "26 10.533985 Marist (NY) \n", + "27 -4.277919 Mason-Dixon \n", + "28 1.514054 Mason-Dixon \n", + "29 -9.210613 Mason-Dixon \n", + ".. ... ... \n", + "291 4.357351 Rasmussen \n", + "292 10.707061 Rasmussen \n", + "293 2.821784 Rasmussen \n", + "294 -1.913946 Rasmussen \n", + "295 1.932597 Rasmussen \n", + "296 1.932597 Rasmussen \n", + "297 -1.113497 Suffolk (NH/MA) \n", + "298 -0.075990 Suffolk (NH/MA) \n", + "299 13.393160 SurveyUSA \n", + "300 25.638748 SurveyUSA \n", + "301 27.921344 SurveyUSA \n", + "302 15.256589 SurveyUSA \n", + "303 3.754219 SurveyUSA \n", + "304 1.921633 SurveyUSA \n", + "305 -5.667851 SurveyUSA \n", + "306 -6.538168 SurveyUSA \n", + "307 -5.667851 SurveyUSA \n", + "308 -6.538168 SurveyUSA \n", + "309 -26.128872 SurveyUSA \n", + "310 -6.973400 SurveyUSA \n", + "311 3.435788 SurveyUSA \n", + "312 1.008145 SurveyUSA \n", + "313 7.001203 SurveyUSA \n", + "314 10.204604 SurveyUSA \n", + "315 1.934380 SurveyUSA \n", + "316 8.172477 SurveyUSA \n", + "317 12.315353 SurveyUSA \n", + "318 8.655616 SurveyUSA \n", + "319 14.386038 SurveyUSA \n", + "320 9.553699 SurveyUSA \n", + "\n", + "[321 rows x 5 columns]" ] }, - "execution_count": 171, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m_regression_data" + "m_dataframe" ] }, { "cell_type": "code", - "execution_count": 172, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - " PVI per_black per_hisp older_pop average_income romney_give obama_give educ_coll educ_hs\n", - "PVI 1.000 -0.295 0.115 0.150 0.594 0.291 0.669 0.494 0.226\n", - "per_black -0.295 1.000 -0.174 0.279 -0.064 0.111 -0.281 -0.111 -0.497\n", - "per_hisp 0.115 -0.174 1.000 0.403 0.098 0.289 0.306 0.112 -0.566\n", - "older_pop 0.150 0.279 0.403 1.000 0.022 0.237 -0.038 -0.076 -0.479\n", - "average_income 0.594 -0.064 0.098 0.022 1.000 0.718 0.704 0.888 0.250\n", - "romney_give 0.291 0.111 0.289 0.237 0.718 1.000 0.555 0.630 -0.025\n", - "obama_give 0.669 -0.281 0.306 -0.038 0.704 0.555 1.000 0.835 0.085\n", - "educ_coll 0.494 -0.111 0.112 -0.076 0.888 0.630 0.835 1.000 0.273\n", - "educ_hs 0.226 -0.497 -0.566 -0.479 0.250 -0.025 0.085 0.273 1.000" - ] - }, - "execution_count": 172, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m_regression_data[[\"PVI\", \"per_black\", \"per_hisp\", \"older_pop\", \"average_income\", \n", - " \"romney_give\", \"obama_give\", \"educ_coll\", \"educ_hs\"]].corr()" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 743 days, 0:00:00\n", - "1 612 days, 0:00:00\n", - "2 521 days, 0:00:00\n", - "3 227 days, 0:00:00\n", - "4 136 days, 0:00:00\n", - "5 70 days, 0:00:00\n", - "6 24 days, 0:00:00\n", - "7 203 days, 0:00:00\n", - "8 98 days, 0:00:00\n", - "9 7 days, 0:00:00\n", - "10 391 days, 0:00:00\n", - "11 316 days, 0:00:00\n", - "12 235 days, 0:00:00\n", - "13 130 days, 0:00:00\n", - "14 97 days, 0:00:00\n", - "...\n", - "305 29 days, 0:00:00\n", - "306 1 day, 0:00:00\n", - "307 584 days, 0:00:00\n", - "308 500 days, 0:00:00\n", - "309 409 days, 0:00:00\n", - "310 220 days, 0:00:00\n", - "311 87 days, 0:00:00\n", - "312 13 days, 0:00:00\n", - "313 342 days, 0:00:00\n", - "314 218 days, 0:00:00\n", - "315 189 days, 0:00:00\n", - "316 146 days, 0:00:00\n", - "317 112 days, 0:00:00\n", - "318 69 days, 0:00:00\n", - "319 15 days, 0:00:00\n", - "Name: poll_date, Length: 320" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(today - m_regression_data[\"poll_date\"].astype('O'))" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "time_weights = (today - m_regression_data[\"poll_date\"].astype('O')).apply(exp_decay)" - ] - }, - { - "cell_type": "code", - "execution_count": 175, + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m_regression_data = m_dataframe.merge(demo_data, on=\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 135, "metadata": { "collapsed": false }, @@ -6320,265 +6905,2390 @@ { "data": { "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
WLS Regression Results
Dep. Variable: m R-squared: 0.704
Model: WLS Adj. R-squared: 0.699
Method: Least Squares F-statistic: 149.4
Date: Fri, 26 Oct 2012 Prob (F-statistic): 8.75e-81
Time: 08:31:09 Log-Likelihood: -632.76
No. Observations: 320 AIC: 1278.
Df Residuals: 314 BIC: 1300.
Df Model: 5
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coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.5623 2.504 1.822 0.069 -0.364 9.488
PVI 1.5534 0.076 20.565 0.000 1.405 1.702
per_hisp 0.1672 0.023 7.351 0.000 0.122 0.212
per_black 0.1972 0.040 4.954 0.000 0.119 0.275
average_income -0.0003 0.000 -1.836 0.067 -0.001 2.17e-05
educ_coll 0.0612 0.121 0.506 0.613 -0.177 0.299
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Omnibus: 113.511 Durbin-Watson: 1.677
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1263.298
Skew: -1.115 Prob(JB): 4.77e-275
Kurtosis: 12.475 Cond. No. 2.71e+05
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " WLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: m R-squared: 0.704\n", - "Model: WLS Adj. R-squared: 0.699\n", - "Method: Least Squares F-statistic: 149.4\n", - "Date: Fri, 26 Oct 2012 Prob (F-statistic): 8.75e-81\n", - "Time: 08:31:09 Log-Likelihood: -632.76\n", - "No. Observations: 320 AIC: 1278.\n", - "Df Residuals: 314 BIC: 1300.\n", - "Df Model: 5 \n", - "==================================================================================\n", - " coef std err t P>|t| [95.0% Conf. Int.]\n", - "----------------------------------------------------------------------------------\n", - "Intercept 4.5623 2.504 1.822 0.069 -0.364 9.488\n", - "PVI 1.5534 0.076 20.565 0.000 1.405 1.702\n", - "per_hisp 0.1672 0.023 7.351 0.000 0.122 0.212\n", - "per_black 0.1972 0.040 4.954 0.000 0.119 0.275\n", - "average_income -0.0003 0.000 -1.836 0.067 -0.001 2.17e-05\n", - "educ_coll 0.0612 0.121 0.506 0.613 -0.177 0.299\n", - "==============================================================================\n", - "Omnibus: 113.511 Durbin-Watson: 1.677\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1263.298\n", - "Skew: -1.115 Prob(JB): 4.77e-275\n", - "Kurtosis: 12.475 Cond. No. 2.71e+05\n", - "==============================================================================\n", - "\n", - "The condition number is large, 2.71e+05. This might indicate that there are\n", - "strong multicollinearity or other numerical problems.\n", - "\"\"\"" - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m_model = wls(\"m ~ PVI + per_hisp + per_black + average_income + educ_coll\", data=m_regression_data, weights=time_weights).fit()\n", - "m_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "state_resid = pandas.DataFrame(zip(m_model.resid, m_regression_data.State), \n", - " columns=[\"resid\", \"State\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "state_resid_group = state_resid.groupby(\"State\")" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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jMxHz644fH31xismTbezeXfzDdDq8honOO3pUZe3a2ONKP/xg4dixovUA79ql\nMHq0nS5dcujRI4cuXXK47bYsli0zEY7z2ijefT7//DC33BJrzmCNq6+O74FBo73PpSVv506FV16x\n07lzDp07l+GSS3J47LEM1q6Nv3wz4j5v3arSv38mf/97Bm63QtWqGocPKzzySCYPPpjJnj2JeR4k\nXlI8i7TkcsErr9gZNiyTw4f//Bps3Wrm7rszmTnTmsStE+niZG9pdAobNshwX0kym8Fkij265XBo\nRRqF3b9fYciQTF56yXHag3cKixdbuPbabH7+ObnvsdUKQ4Z4adgwmM9PNV55JY8mTVKnTaW02LNH\n4f77M3nhBQe5uZHzVDCoMHmyjeuuy05IAW00s2ZZMZng2Wfz+OtfvVxxRYCHH/by9NMetm9XWbzY\nGEPu0vMs0lKkxy+baCN+OTlhvv7aScOGcrtc6GfaNAuDB0frrY149FEPzzyTgCkeRKH4fDBkSAaf\nfmqL+jvDhnl4/HEvSiEHwebPN3PLLdH7M9q1C/Dppy5ycqL+SonYsUNl6VIzEyZYyc1VueSSALfc\n4qdFi1CRW1RE/D7/3MKAAdGPD7fd5mPs2LyUaeHYs0fhsccyaNQozBtv2AiH//yCWa0aTzzhZfly\nlTfeyKNcOX23ReZ5FiIfK1aYiVY4A+TmqqxfLyN+Ql81ahR8cdawoYz4lSSbDQYN8mG35z+uVKZM\nmGuu8Re6cNY0mDQpeiEOsGyZhW3bkn86rl07TN++fmbOdDFnTi6jR3vo0EEK52Rwu+Gdd2J/bqZP\nt7JjR/I/N4mSlwedOgUZP95+RuEM4PcrPP+8na5dg+TlJWkDT5M6r7pOpGfQeHmJyDx0qOAzXzx9\nz+nwGpa2PD0y481r2jREkyb53SqPyM7WaN26+MVzOryGeuS1bBni88+dNGp05nvTtm2AWbOcXHhh\n4e9IBQJw4EDBp1qPp/i9nInsr50xw8LNN2fRq1cOzzzjYNmy1Jzb2uh5Xq/CwYOxPzfBoIIn+to2\nBTLaPlutMGNG9GF0TVNYvNhsiIs5mW1DpKX69Qs++VWoUCo6mkQpVr48vPWWm5tuyubQoTNPlHa7\nxr/+5eL886V1qKQpCnToEOKLL1ysWOFEVctStqxGgwYhypQpWpbVCv/3fwGWL49+urXZtKQfb7Zs\nUbnzzkzWrftzOzdsMPH++zZeey2PPn38hihaAE6cAKu1Ebt2KVSrpmFOwUomK0ujadMQO3dGvwOa\nna0lvdVsLKgaAAAgAElEQVQnkQIBhV9+id2D8sMPlj+mT0zu90V6nkVaWr7cRO/e2VEf1mraNMhH\nH7moV69UfD1EKbd5s8r//mdm0iQbgQBcf32A7t39NG0aLnR7gDCuFStMdO8e/RmLgQO9PP+8J2lF\nYCAAjz/u4F//ijZFn8aCBc647oIkwokT8OOPFsaOtbFihRmbDW6/3Uf//n6aN0+99qbvvjNz003R\ne+WffDLSe58qtm5VaNu2DJoW/aBXqVKYRYtyqVpV33OzzPMsRD4aNw4xdqybhx7KPOd2adWqYUaM\n8EjhLEpM/fph6tf307evH02DjIxkb5FIpCZNQowbl8eQIRmcXUBfdFGQQYO8SR093bJFZerUWP21\nCvPmWZJaPLvd8N57dl588c/hb58P3n/fzrRpNmbNSn5xn2itWgV56CEPs2ZZ6d3bj9UauSvy7bcW\nNC3Sn55KqlXTuPLKAPPmRZ/tql8/H5UrJ//cLD3PBZCeQePlJSIzMxM6dQrwwQcuBg/20KpVkHbt\nggwblsfbb7tp2zZ6H2pJbJ/eeXpkGj1Pj8xE561YsTihhXM6voZGzLPboU8fP1995WTQIC9Nmwbp\n1CnARx+5mDjRRd268RUD8c9trZxaICWaH3+0EIqjNo13G9evN/Hii/mPjLvdCkOHZnDsWPHzjfi5\nKVsWbrvNT9++PrZtM3HsmML27SoNGwYZNcpL7drxtXQZbZ8dDnjoIW/UqSIzMjRuvDGAaoDK1QCb\nIETJCwQiV+8PPZRBVhYMGODlrru8HD6scvfdmaxcKTdlhBCJY7dDu3YhXnzRw7hxy/nsMxfXXx+g\nZs34Cue8vEj/77ZtarEfci5ML3P16uGkrjD4/fcWYs2Q9MsvZrZsSa0ZknbvVpg61YLDobBnj8pn\nn1lZscJM5coa8+ebWbMm9Uq4Vq1CTJvmonLlMy8MatUKMXOm0zDtOdLzLNLS+vUqXbrkRB1tqV49\nzDff6N9XJYQQxeHzRXqpx42zs2BB5CGrnj0DPPiglzZtQliLsM7T8ePQp08WK1ZEf1jrs8+cdOsW\n3x25eAwYkMnnn8feqRkznFx2WfK2MdG++cbMJ5/Y8t3vhg1DPPKIhxtuCBTpvT5p1y6FfftUVBVq\n1QobohXidHv2KGzcaMLpVChXTqNhw1CJbqP0PAuRj9WrzTFvU+7dq7Jhg4mqVVPnQCyESA3hMMyb\nZ2HAgMwzHq76+msr8+db+PBDN9ddFyj0w6Zly8LIkR5uuMGM33/u/3TFFX6aNUvuiF/jxgX/+2XK\nGKsAjIfbDdu2qVEvGDZsMLFkiZm2bYNFavs5eFBh1iwLL73k4NixyMh1nTpBRo700rVrgMzMhGx+\n3GrU0KhRw7jn39Qb808w6Rk0Xl4iMgszsfzx48mfd1WvvCNHFJYsOcLOnWpcfYynM/o+65GZbnl6\nZKZbXiIyt21TePDBzHxnJdC0yM+KuuhKu3YhvvjCyaWXBjg5DViZMmGefTaPMWPy4h71i3efT9+u\n/LRpE6B+fePMiR5vXl5e5AIplpkzbUU6T7lcMHasnWHDMk8VzgDbt5u5/fZMvvgivqUKjfYa6klG\nnkVaqlWr4ActypZNnVGMkw4dUvj+ezNjxtjZsqUsNpvGXXf56N/fR5MmMp+wEKXB+vVm8vKiF00u\nl8Lvv5uKNEe4qkLbtiH+9S8XK1ceJyurPBUqaNSqZYzjYKNGIUaM8PDss+c+UZuTE+allzxFnoPb\nyMJhhW3bYvdwu1xKvncKotm40cSECdFmVVEYNiyDDh2c1Kkj54KCSM+zSEvr1kV6nkOh6D3PCxbk\nUq1aqfh6FMrRozByZAYTJ5578MzJCTNnjpPmzeWgKYTRTZ5s5aGHYt9ff/NNN/36pdZUZi4XLF1q\n5o037CxZYsbhiFz89+4dmRM9leTlQe/eWSxdGn002GrV+N//cqlXr3D7/s47Np56KvZ0PtOnO+ne\n3bjtEiVFep6FyEf9+mHGjs1/3lWrVePNN90pVTgDrFljzrdwBsjNVXnxRQcffOA2TM+bECJ/FSoU\nXCyVL59axSRAVhZ06xakfXsXx48rqCpUraoZYuqyRMvIgEGDfDGL5379fIW6i3rSkSMFj1InYin2\ndJCCH7nEMmK/W7rnJSLTaoXevf38+98uLrssgKJoWCwat97qY948J506pd48z598EvuR7PnzLWzd\nWvxDQqL3+ZdffklYP/ZJRnxfTrdp01b8CRwsNOJ3L93zEpHZqFGY7OzoF/dlyoRp1Kh4xXMwCMuW\nHWLzZoXDhxO3vGUiX8esLNi+/QeqV09c4WzEz0379sE/er3PValSmIEDfViK0KZ84YUFH1CrVCn+\noJERX0O9yMizSFsZGdClS5A2bVysW3eIqlUrU6WKVqxpf4wuEKAQc6AquFzJXwt6/36FX34xMXly\nB44cMdG1a5AePQI0aRIq0omiNPntN5Xly83MmtWMYFChW7cAnToFadvWGHOaCmOpWzfMhAku+vfP\nOqf1zGzWmDDBXawFNNavV1m3zsT27TUJBhVycsI0bhyiSZMQlSolautFYVWrpjFunJsvv7Qydqyd\nQ4dU7HaNe+7xcdttPho3Ltp73Lx5iOxsDacz/+N8q1YBLrhAjjmFIT3PQqSJxx5z8NFH+a/QBaCq\nGv/9by4XXpi82707dqgMHpzBjz+eWSWrqsZ777m55ppAyhXQy5ebuOuuTPbuPfPiJjNT4+OPXUmd\nW1cYVzAIq1ebmDjRxuzZVhQFbrjBz+23+7joolCRFzT59VeV6dNtvP++jUDgz+KqTp0Q//iHh44d\nA5Qvn+CdEIW2d6+C06lgt0PNmsVbsGbTJoWFCy384x8ZeDxnFtDVq4f529/yuOKKAJUrJ2ijSzHp\neY6D1ws7d6r4/VCunEaNGqXiOkOIfPXu7Y9ZPF9/vb/QD57oIRSC99+3nVM4Q+TJ8/vuy+Sbb5y0\nbJk6IyN79sDQoY5zCmeILDk8YEAmc+Y4adYs9fpXRXzMZmjTJkSLFnkMG+ZBUaBSJa1YF5duN3z3\nnYW33z73+LB9u4mhQzOYONHFxRenznevtKleXSPWVH2FsXy5hbFjHfztbx4OHVJZtsyMxaLRsWMQ\nTYMnnsikbl0XlSvLBXtBpOc5ilWrTNx/fwaXXJJD585luOyyHCZMsLFvX/y3tY3eF2T0PD0y0yGv\nadMQQ4d68v1ZtWohHn/ciy3aLEaFEO82bt2q8v770TcgHFaYPz+15iFdt87Mr79G36cTJ1TWrCn+\nksPy3TNeXqIzLRbYti3S/1vcuzKbNqn5Fs4nHT4caSuKh9HfF6PnJSJz61aVAwdUhg/PYOZMKzk5\nGjYbvP22nZdfduDzKSm9vkEiSfGcjxUrTFxzTTZz5tgIhyMfpEOHVIYNy+CppxwcOpT8vlAhiion\nBwYP9jJ9upNOnQJkZWlUrx5m+PA8Zs1yFfsBo0Q5dEjB54v93fr+e0vCHyJMpgMHCj6WbNxY/OJZ\niMI4cEDl0KHY5cCCBeaU+u6loxo1/jzGHzig8t13FhYutJzxrEusB1HFn6Tn+SxOJ9x2WxaLF0e/\nhJ8xw8lll8ltDVF6uVxw4oSCxULcK4cFg7Bli8rOnZGTb+3aYerVK3pP3sqVJi6/PCfm71x1lZ/J\nk93F3VTD+ewzC4MGZcX8neeey2PIEF8JbVHJCYUin5s9e1RUVaNOHa1YD7mJ+H37rZk+fbJj/s7/\n/V+A2bNdKTktXLpYs0ala9ecU4OCZ6taNcw33+T+0SKS3grqeZavwVm2blVZvDj27alp0+K4ty2E\nAWRlQY0aWtyF8/79Ci+9ZKdr1xxuvjmbm2/OpmvXHEaPtrN/f9Hu0NStG6Jp09gXpf37p1YR2bBh\nmKysWO+BllI93ift2aPw/PN2unTJoXfvbHr1yuHSS7P5+GMrx44le+vSz/nnhwqcO/qGG/xSOJdy\n9euHef75/Fv3zGaN8ePdUjgXknwVzhJZ8jT2SX/nTpVA/lMvFigYhJUrNxFM4MC10fuMjNj7le55\nicjMy4NXX7XzyiuOM9otvF6FMWMcjBtnJy+v8HnlysELL3hQ1fwP3h06BGjRIr5C0mjvS9OmIYYN\ny/9kBtC/v58LLyz+wcKIn5vjx2H4cAfjxp35uTl+XOXRRzOZPt1GOI4BaKO9xyWRGW9e3boaTzwR\n/XNYrlyYSy5JvbnvS0vevn0K331nZvJkjXnzzGzdqlKcngG7PTIA8cknTlq3jhQxiqJx7bV+5s51\n0rVrar/HiSSzbZylXDkNs1kjGIxeQLdpEyzygxnHj8Mvv5iZPNnK+vWtqFMnzF13+WjRIkSlSnKl\nJ0qfzZtNMR/wmzDBRr9+viIt+d2hQ5B//9vFU085WLs2cniy2TTuvtvHoEG+lFv1UVWhTx8fOTka\nL73kYM+eyHhG2bJhBg/20auXL+Xm19240cSMGdE/N88/7+DyywPUry8tHCWpVy8/Bw6ovPqq/Yzb\n+tWrh/n4Y1eR5xQWifHTTyYGDMhi794/xzozMzX++U83vXoFyIi92vY5srKgR48g7dq5WL/+CFWq\nVKRaNQ2HI8EbnuKk5/ksfj889lgGU6ZEO7hrfPWVk3btCj8CdvQovPKKnbffPvfTedNNPkaM8FC1\naql4G4Q4ZcYMCwMHxu7XnTDBxU03Ff02zbFjsHWrCZ8PKlbUOP/8MOYUv9Rfvx727zcRDitUrBii\nRYvUPCa8+aaNv/899hl/6lQnV1whz5UURNMirYa7dkUKq1q1wtStG0Yp5jPtXm+kD33tWhNut8J5\n54W58MKQ3MpPkt9+U7n66pwoi1dpTJvmomfP1PyeOJ2RARq3G8qU0ahXL1zkC4V4yDzPRWS1wpAh\nXpYuNbF589kvj8YLL3ho0qRot46XLjXnWzgDzJhho0uXILfdlsA1eYUoAX5/wWfo0xdbKIpy5aB1\n69Tr9c2P0xl5WHLdOjObNpkIBqFevRB79oRo3TpIlSrJ3sLEKswqlsVti0snBw8qTJ1q5ZVXHLjd\nkdc0K0vjscc89OvnL9bzDHY7NGgQxuHQ/ljfIL7lmkV8vvzSGuP7ojBqlIN27ZyUK1eim6W7VatM\nPPOMgyVLzICComj07Bng73/3GOYOiPQ856N+/TCffupm/Hg3F14YpGbNEL17+5gzx8Udd/jIzCx8\nltMJ48dHnz8TYPRoe1zT3xm5z+jgQYX//MfNN9+YWbnShMuVmFwj73NpyEtEZq1aBR/ECvM70Rhx\nnxOdp2nw889mRozI4OmnM/j4YxuTJ9sYPjyDJ57IZNkyS1zfGSO+hk2bFnRRpMU10mm091iPTI8H\n3njDxogRGacKZ4hcmAwfnsHbb9vweoue++uvJh59NIMOHcpwySVl6d49m8mTrRw8KOsblHTekSMw\nc6Y15u/8+quZbduKX8YZbZ8h8hm8/vpsliyxcPL5M01T+OorK716ZbNxozHKVhl5jqJOnTB16vi5\n8MJ11KrVgJwcDWvsz3G+cnMV1qyJ/TLv2mUiN5eU6m0MBuGHH8w88kgGO3eW/eNvNTp3DjJqVJ5h\nrh5F8TVqFKJJk+Cp3uSzNWsWpFGj9Bg9Lq5t2xTeesvGqlXnvoZ79qg884yDiRNDtGyZOt+XZs2C\nVKwY5vDh/E+CXbsGueAC+dzEsnmzyptvRh+Uef11O717+2natPCfm1WrIkXL6SOdu3ebeOihTG67\nzcc//pFHhQpxbbYoAo9HKdRDgV5v6qw7EQzCRx9FH20/eFDl668tXHBB8mddMkTP85tvvsnevXux\nWq106dKFrl27nvM7JdXznGiHDilcdlnOqQeB8uNwaCxZcoJatZL+ViTMkiUmrrsum1Do3C9BtWoh\nZs92yQNBKeD331Vuuy2TbdvOLP7q1QsyaZI76QuvGF1kft0sYs3w8847Lvr2Ta0+hhUrTPTtm8Wx\nY2ceFxs3DvLxx24aNJDPTSyFed7gvfdc9O5duM+NyxVZ3+CHH6I/CT9rlpPOnVOzv9aInM7IrDQf\nfhj9IumCC4J8/LGLRo1So3bYvl2lffucmO1+1auH+f77XN0nWigVPc+KovDII49QsWLFZG9KwlWq\npDF4sJennore6T5ggJeaNVPjww+RL/0//+nIt3AG2LfPxH//a6Z+fenzLu0aNQozZ46LNWtMLFwY\nOfF27RqgadMQNWqkzmdaL/v2FTw15vr1JiC1iufWrUMsWOBkxQozCxaYsdngmmv88rkppMKMNhbl\neYOtW1V++CF2OTB9ulWK5xKUnQ3duweYMsUWdeXVAQN8nHde6nxfgsGCP7culzGeiTBG8whggAHw\nfCWih6d7dz+1a+d/G7JChTC33BLf5PNG61vatavgA/GHH9pwOov/bxhtn0tbXiIza9TQ6NkzSO/e\nPzBqlIeePYMJKYCMvM+JyrPHfhwCoIBFVGIz8mt4/vlh+vTx85e//I/XX88z7OfGiK9hYZ4lOH0p\n5oJEbpPHLlq2bjXFtTy30d8XI+ZdcEGI55/Po0yZM99Ls1njgQc8tGoVLNIzWGcz2j6XLatRr17s\nC7R27YKUKZP8etEQI88Oh4Nx48Zx3nnn0bt376gj0IsXL6Zjx46n/hvQ/c+n/9vFzatXT+Odd3Yy\ndWolPvkkk0BAwWTSuP56D48+GqBx43CJ7U9J/DmyAEzsA7HXq7Bx41Zatz6/WP/eb7/9ltDtT7e8\nxYsX89tvvyUkT9Ng6dLDbN9eG7fbxHnnaRw5sgyfz2uI7Tv9zycZJa9hw85YLFrM0ZbWrUOG2V89\n/ux2uw21PSXx53i/zxkZ22jQ4EI2bcr/FN6wYRCHYwtQu1B54fARVDU76rLNAM2aeTGZKPb+J/r7\nnC55nToFePppDadT5cQJlcxMjZycMA0bumjRwhpX/klGOR527NiRv/3Ny6BB0VuS7r/fx6pV+n9f\nMwqYF88QPc8nrVmzhiVLlnDfffed87PS2vN8ukAAtm1Tyc1VyMrSqFs3jC0FV/ret0/h8stz2Lcv\n+nD6wIFeXnjBc+pgnGxuNygKJTqPZCo4eFBh+nQrY8Y4Tj3kkZ2t8fjjHm6+2S8LABUgEICXXrIz\ndmz+U1l27+7n7bfdlC9fwhsmDG/tWpWbb85i794zD6I1a4aYPr1oi5r4fDB4cAaffx59fYP//MdJ\n+/byIGcyuFyRh0SPHVOw2zUaNNCoWDE1j62HDyu89JKdDz44+7acxrPPerj3Xh/Z2fpvR6noeT7J\nZrNhS8Vq8g8WC1xwQeo/CFOtWmSp14cfzv9+kqpq9O7tT3rhrGmwZo3K/PlW5syxYDbD7bf76Ngx\nKA8zFoLXC2+/bWPcuDMLP6dT4dlnMzh6VOFvf/MWqjUhXVksMGiQD4sFXnvNfqq3UVU1+vb1M2yY\nRwpnka8mTcLMm+di2TIT8+dbUBTo2TNAmzbBIj98brPBY495WbbMzO7d5x6Yn37aU4gpBoVesrIw\n/Iw7bnekZTMYjDzrVdz5wStW1Hj6aQ833BDg3/+2sGmTSsuWIa6+OkCTJqG42lQSyRA9z++++y4j\nR45k/vz5XHfddcnenDOcfTvCiJlGzLvyygD33HPuRKMmk8aECW5atozvQJyIbVy0yEyPHjm88IKD\n334zs2qVmUcfzeTqq7P57bf4vhpGfE8Snbl5s8rrr0evjMePt7Nli3HmIE1kptMZmTHik08CfPWV\nmc2bVcLFPLdVqqQxdKiXRYty+fDDg0ye7GLhwlzGjs2LewYeI7+G6ZqXqMy9eyMX/8eORZa1b98+\nyJEjsHatiX37ipOn0L+/jwcf9FKnTogKFcJ07Rpg5Mg8du6MjHrGw+jvi9HzEp25Z4/CN9/ksnq1\nypEjxX9vg8HIEuK3357FJZfk0LlzGS6/PIepU60cPly83LJl4f/+L8itt/6P2bPdDB/upV074xTO\nYJCR50GDBiV7E0SCVaqk8fe/e+jTx89XX2kcOGDnootCXHxxgIYNw1iiz4hUIrZuVbnjjqx8n2I+\ndEhl8OBMZs9OvZWbEun3300xeyTDYYXffzfRpImxR0yKavNmlSeeyOD7781ADhCZbvKppzzcequv\nWJ8ZkylyV+rgwaWn+u6EiMbjgRUrzEyebGfBAjN/PmOiccUVAUIh6N49WOi2wEOHFIYNy2DLFjMV\nKwa5/34f5cuHWbTIwrPPOtA0he7dg9SsaYBpDkRcDh9W+PJLC6NGOTh4MLIGQ8OGQYYP99CxY7DI\nrYuLF0em2zx9dq09e1QeeCCTQYM8PPmkl5yc4m1rXl5e8f7HEmConudYUqHnWRjHv/9t4d57Y8+T\n+uWXuVx8sdyqjGbKFCsPPhh7KODNN93065c6UxLu26fQt29W1IVhxoxxc++9qbO/wphWrlR58UUH\n332X/8pd3bv7efJJT6Fv9S9fbqJHjxyefTaP2rXD/PSTmaNHFVq0CFG3bpgxY+xUqxZm2jR3IndD\nlDC3O/KMxRtv5PeMReSu8E03Ff4C6eBBhZ49s9mxI3oP5ldf5dKuXek7jxbU82yItg0hStqmTQU3\nXB86JF+PWM47r+ATc82aqTXqvGaNKWrhDPD88w527JDPjdDXvn0mvvsu+u27BQssMR/YPpvfD2+9\n5WLpUjP33pvFe+/ZmTnTxrPPZnDffZkMGeKlcuVQXFPVieTbuNHEG29Ea7VTePzxjCIdvzZvVmMW\nzkDMz2lpJkf5Ahi9bykd8xKRWaFCwUWdw2Gc+XWN+Bo2ahSiQYNg1J83aBDf8txG3Oe5c2OfCE6c\nUNm61Th93kZ8DfXM8/ngp58Os3FjfH2cpzPiaxhZsTbW/ikxV7U9W40aIdatM/H11+eOZHu9Cn/9\nayY33BCM6yFvI39uSkNeIjJ//vn0Fp9znTihsmlT4T83bnfB37Hdu41zPEwkKZ5Lue3bVfLyWrB6\ndeTBEVE4rVuHgOjFcZkyYVkiuACVK2t88IGbatXOLZCrVw/x4YduQ01Vd/Sogs3WgEOHil9UFWZl\nt8KcUERiaRqsXGnir3/N4Jpr6tOhQxmuuSaLWbMsKXlcVJSCv1dFWXjr0CETH30U/eFfn09h6VJD\nPCJVKuzZo3DsWGvmzTOzbJmJ48fjy8vLg99+Uzl2rBU//2wq9me6MMc+j6fweRUqaMQ6jwK0aJGa\ntyuk57mU2rdP4bPPrIwdayc3N3KUbNo0yIgRHi6+uPAPiqQrtxteecXOa6/l3/v17rtu+vSRh2MK\nY8cOldWrTcyda0VRNK66KsBFF4WoXdsYFx979yosXGjh1Vdt7NhhomrVMA8/7OXyywNFns3i7bet\nPP109D5vm03js8+cdOyYmicMo1qyxESvXtn4/ecWB48+6uHhh71kxX7EoVT59lszffrEnux25kwn\nl14a/c7Q6ebONXPHHbHzLr44wL//7cKaf5u1IHLn47vvzDzySCYHD/559XLRRQHGjcujadOiHxM3\nbFB5/nkH8+ZZ0LTI57tJkyCjR+fRvn2oSBdJn39uYcCA2F+EefNy6dChcMcvpxNuvTWLPXtU+vaN\nTD8bDoPdrvHDDxYWLTLx/ffOYu13spWqeZ5F4Rw9Cs8952DGjDMr5DVrzPTuncW0aS569CjcQTNd\nZWbC4MFezj8/zIsvOti/P3IEOv2pY1E4tWuHyczUqFMncsCtUcM4E/jv2aPw8MOZfPvtn+0Wu3eb\nGDo0k1atgnzwgbtIRX7Fihrly4c5ejT/M1afPn4pLkrYkSMKDz+ckW/hDDB2rJ2ePQO0bZs6FzRl\nyoTo2DHA4sX5txF16RIgO7vw+1uYz6zdrmGWiiGmpUvN9O+fdarIPWnVKgu9e2czd66zSHc0t25V\n6dMn65y5t9euNXPDDZG8onyuW7QIkpmpRb071qJFkIYNC5+XnQ2jRuUxa5aVt96yn1ooS1E0unUL\nMHOmi4YNS1/hXBjStlEAI/YtrV9vPqdwPknTFB57LJMDB4p/6zgder8AKlSA/v39fPNNLp99tp3v\nvz/Bl19GLjziXWkwXV5DpxO++MLClVdmcemlZbj00jJcdVUWc+dacDqTv30//GA5o3A+3cqVZr78\nsmgPs1SuHObJJ71UrnzuCaF79wAVK4apVq34J4t0+dwkMm/TJjXqMtURkTsPxWXE1zAjA+6+28vF\nF597d6xjxwC33160kfbzzw9Rp07sAYNevQJFGuU8m9E+N4nOO3ECRo60n1M4n3TokMqiRUW7+vjh\nh/wXrQEIBBTGjrXjLsIEKPXqaUya5MJmO3dwo0qVMK+/7i7yVJvLlpkZO/bPFWYhUod8842V5593\nxPXsgZF7nuU6shSKzOsZ3Z49Kps3q1SpkjojLXqqXl1j69bfaNFC5tctikAApk2zMmzYmW0Mmzeb\nueOOLEaPdnPXXf6kjVYdPQqvvRa7f+nVV+306uWnWrXCjZQ3bBjm1VfN9OvnJztbY+9eBbs9UlT/\n/LOZ+vXDnHeeMUbd08XpJ+1otm1LrXGi+vU1vv5apW7dEPfd52P/fgVFgSpVNBYsMLN7t8q11xb+\n7lmdOhrDhnm5//5M8nugrH79+B7+TQe7d6usWBH7Iu3jj23cfLO/UMtL5+bChAmxl2f9+msLu3ap\nNGpU+Av2Ll2CzJ+fy4IFVmbPtmC1wh13+LjkkgD16hXt2LV9u8qIEfm1PkYsX25h/XoTVaum3p1c\n6XkuhQYNyuCzz2IXBZ984pTWDaGrDRtUunTJwe9XUFXt1LR0u3erhMMKVqvGokW5Sbttt3u3Qvv2\nZfB4YhdXP/98vEgnjW3bFF580cGcORbKlYv0OQKMGOHh+uv9xV4QQBTPyTmKYxk5Mo/Bg30ltEUl\n49ixSPE0YkQGR49GPuMVKmg8+2wePXsGKFu28FmrVpn4+GMrrVuHePFFx6l+XUXRuOyyAAMH+li+\nXOXJJ2UO82h++UXl0kvLxPyd+vVDzJ+fW6j35sgRhcsvjz2HMsCiRSdo1qx4x9i8vMiDpfbYNXpU\nC2DsaloAACAASURBVBeaufHG2FcCgwZ5GTWqCE8hGoT0PKegCy+MPQKgKBoZGaXimkiUYpEVBuEv\nf/FSqVKYTZtMKErkBHHokMp779nYsMGUtOI5M1OjZs1QzFv65cqFcUQfOMlX3boa48fnMWSIyt69\nKhYLnH9+2DAPSKabCy4I0a5dgGXL8h/1UxSNTp1SbyChXDm45ZYAnTvnnpqWrmbNcKHvopzuwAGF\nSZPsLF0a5Pnn8wgEIm0BZctqrFxpol+/TNq1CzF0qD/pq8MaVZUqGtWrh9m7N/pdjquuKvzFdZky\nGp07B5k0KXrxXLVqOK7nS+JtTwwW4mtl4EUC45Ja97J0YMR+t6pVw1gs0b8wXbsGY/68IEbrJSuJ\nzHTLS0Sm2w3Dh3tOjX5Nm2Zj6lQbI0Zk8PXXFv7xD09cB854t69cORgyJPZo48MPe6levejfFYcD\nmjYNk5GxkEsvDSascE6Hz02i83JyYPToPMqVy+890Bg7Nq9ID0GdzeivYfXqGj7fItq2DRWrcIY/\nHxjcuNHMwIFZDB6cxcMPZ3LXXVmMH+8gHFbJytKKVTjv26fw/fdmpk4N8u23ZnbtMub82/GflzWe\neir6CKvZrHH99YXvGzeboV8/H7Gmgnv8cU+x33OIf59r1AhjNsf+9zt3Lv6Fq5F7nqV4LoX27FF4\n9llPvh/aunVDdOsWKNQVoRDxOP/8MFOmWNm69dyRka1bTUybZqVOneSOxnbuHOCSS/KfcrBJkyDX\nXFP86Qg1DRyOKik7slKaNG8e5ssvnTz9tIfq1cOUKxfm+ut9fPGFk759/TJ1ZwHq1w9Rv37sk8aN\nNxatZUPTIg+8XX55Dr17Z/PAA5Xo0yebbt1ymD/fnJLnqJ49/Tz0kIezC16bTWPiRBfNmxftIq5l\nyxDjx+flO6/3bbf5uOqq+I5fGRlVivTA4dnq1Qtz553RByjKlQvTqlVq9spLz3Mp9OOPJh58MJO7\n7/axfbvKqlVm7HaNLl2CeDwKs2eb+c9/XFSpUireWlFKff+9md69Y/e7ff65k65dk3uW3L1bYf58\nC2PHOti7V6Vixcg8z1ddFShWcR8IwG+/mZg928KCBVYyMsLcc4+fiy8OUreutG4k28GDCuEwlC2r\nFbuXMx3NmWPh7rsz850t4sILI9M6FqUFa/VqE1demY3Pd26eyaTx5ZdO2rVLvcLK5Yosg/3jj2b2\n7VNo0iRM69ZBGjQIF2u2Ep8P1q+PLMf+888matcOc801AZo0CRZ5ZgyIzMO8Zo2JefMszJljxWLR\nuPNOH506BYu1MNiuXZFlvefPP3O+wwoVwnzyieuPBclKH+l5TkGNG4do1y7Is89mUKtWiMaNQ/j9\nCuPG2fF6Ydo0KZyF/vbtK/hMcHL+7GSqWVPjnnv8XH11gLy8SMtF1arF+34Eg/DllxYGDMgkHD5Z\nFJhYudJCrVohpk9P3XlNS4vKleXYVxyXXhrg/ffdDB/uYOfOyN0kk0nj2mv9PPqot0if62AQPvnE\nis+nUKdOiF69IvOfB4PwxRdWNm408dZbNpo1yyvyMwdGl5UFrVqFEjbiarNFRqBbtow/T9MiD/nd\nemvWGfOiDx1qplKlMDNmOIv88OF552m8+aabDRt8LF5sxuWCNm1CtGhhnIWy9JD8M5vBGbHfrVw5\neO45DyNH5pGbq/D111a+/97CBReE+PxzV7FH+tzuyGjBpEkhpk+3sHRp8ZcBPZ0RX8N0z0tEZmH6\n6gvqh4sl0ftcpYrGnj0/FLtwhsgMI/fdd3rh/KedO00895wjrtug6fC5Ocnng7VrVWbM8DF/vpnN\nm1XCCTjXJnKfQyH46adDrF2rsmOHSqLu0xrxfc7Ojszl/PnnTiZPPs5HHzmZO9fJ+PFFXxnv0CGF\nGTMsDBvmoWvXIO+9Z+ellxy89ZadNm2CPPdcHvPnWzhwoPgliBFfQz3zEpG5ZYvKHXdk5bug0KFD\nKg88kMnRo0XPrVABLrkkSNeu/2XECC/XXRdISOFs5J5nGXkupapW1Rg82Me11/rZuPEElSqVpVat\ncLFu40Ckj/qllxxMnmzl9Hk+O3QIMH58HvXrF/+LkJVVDpcrMuJnij3rjihFLrggTKS3L/8HgBRF\n44ILSuctu2h+/NFCKBT9gaf58y1s3myiRYvU2u9E27lT4ZVX7EyZYiMcjkzv5XBoPP64h/79/YZY\noXLjRpWJE2189FEDvF6F7GyNv/zFy803+6hbN/nbp4dduxRWrzbz/vs2jh9X6dQpgKr6adYsVKQW\nmHAYBgzw8/nnkVHmkzwehalTbdSsGeKxx7yEw6n5OhrVL7+YyMuLfvz67TczGzeaCr0899n8/vSZ\nylB6ngU+H/z97w7efz//o2OrVkGmTXNRqVLRPir79yusXGnmgw+sHDigctFFIfr189O8ebBIq18J\nY3K7I8vEf/hh/p+be+/1Mny4J+7pkIykMHOsf/aZk27dUu9pKI8nMnJ19KhCZmZkVbriXKwfPQqD\nB2fy9df5rwn99NN5DBniS+pS0Bs3qtx4YxZ79557td+0aZBJk4q2rHtpsHWrwl13ZbFmzdkvvMbr\nr+fRu7e/0AW0zwfjxtn55z+j92Tcf7+Xp57yyLmgEA4dUvj9dxNHjihkZGg0bFi8qTFfeMHOK6/E\n7pOZONHFtdcW/0HEVCE9z6JAmzerfPhh9IJg5Uozv/9uolKlwhcEu3YpDBmSecayuOvWmZkyxcZz\nz+Vx770+OWiWcpmZMHSol+xsjbfftp+6FWi1avz1r14GDvSlVOEMFGrpbYejVIxHFMnGjSovvmjn\niy+spx4oa948yD//mUe7dqEiPQi1YYMpauEM8PLLDq69NvDHnY2Sp2nw6afWfAtngDVrzCxcaObO\nO40zynZyCEwp5ixwgQC88YY9n8IZQOGhhzJo1ChU6Ie/nE6FKVOiv8cAU6bY+OtfvWRlFe37cvBg\npJDcuVPFbo/c3apfP5xyx5qTVq40MWhQBlu2/PnelC0bZuzYPK64IlCkOwKVKxf8nZI1IgpHep4L\nYMS+pUTnbdum5tvDebrly4vWbzFjhvWMwvl0w4dnsGpVfNdtRnsNS1teojIjc5t6WbQol/fe28cn\nnzhZtCiXp57yFru32OuFNWtUJk4M88EHVr75xsyePcaYG7ZHj9gjMnXqBONqcTLi52brVpVbbslk\nzhzbGTMx/PqrmRtuyGblyqIdG5Yvj/3d9/kUtmwp/qlp5cqVxf5/ITI7y7vvxq5Ixo2zc+RI8T+T\niXqft2xR+ewzC//4h43hw23MmGFhy5aib9eWLSpTpkQfQNE0hf/8p/CTPHu9cPhw7PfQ6VROrc5Z\nWGvXqlx7bRY33JDNQw9lMnBgFpdemsMLL9g5cCC+Y4QRv3vr10fugJxeOAMcP65yzz2ZLF1atPNo\n27YhYs0bXa5cmAYNjDMnupF7nqV4FoV6CCa/6Yui2blTYfz42CefiROtBAxyZ2j3buX/2TvvKCnK\nrI3/qjpPJIch55wVVCQHEcRFlKiAwEpQAckr4AISFFQWUURkQSR8CEh2ZQEBQRBBSUqQPDAw5NTT\nPZ2rvj9qBxhmuqeru4dux3nO2XMWnLm8VfWG+9773Odis9XkwAENN26Exkn7K0Gng0qVJAoX/pXW\nrd1UqiQFnHK/dQtmzDDStGkcQ4fmZ+TIaDp3jqVVqzj27w8/Yb5KFQ8vv5z5iS+KMh9+aMtxag+7\nd2tJTMz8gzqdCndZTZFkVu3Swb/OZQ8jKQk2b9ayYcNTjBplYtkyPUePqj/iHA4Bq9X3GG/cELHb\n1Y8xlPj9d5GNG3UcO6bl55/1/PyznmPHNHz3nZ4jR9TtY7duCbhcvn/np5+0ePz0q2Ji5CydsIQE\nSVX28cIFgS5dYjJ0DJVlgTlzTCxdqg9JwWkkYcsWHWaztzksMHWqkbt3/bdXoYKHt97yNnFlPvww\nlZIlc9b+lV3I5Tx7gculRFxOnNBgtysRtsqVPRF1MF66BKdPa7h8WUSrVRqkVKmiPn115IhI06Zx\nPqPPa9ak0KSJfyfa4cMizZrF+/yZMmU8bNliJl8+VUMNKW7cEPj2Wx1Tp5ruRUnKlXMzaZKNRo3c\nREeHb2x/VSxcqGfYsMxffHy8xKZNKWFL56fhyhWB9et1fPCBiZs3lXlTv76LceNsNGjgyVHti1NS\noG3bWI4e9XUbktmzx+y3lNnmzVq6dvWlDy6zbVuKKmmu48dFxo0zsX17Rq3ZBQusqtpzX70q0KxZ\nnE+Zxbp1XaxebfG71XKocfUq/Pe/eiZMMHH3bvpx5s0rMXFiKq1auShc2D97Bw9qaNEiDp1Opl07\nF1WrevB4lMK/1av1nDmj4YUXnMyf7/8tac0aHX37eveOZ8yw8uqr/lNf1q/X8eqr3u1FRcns2GGm\nXLmc4UHfuQPPPBPHqVO+gwa7dt2lalX/n/nWLWXuTJ1qutdKvHp1N+PH22jY0B0x2uhmM5w5o8Fi\nUTTby5aVHumZnMt5DgB378KSJQYmTTKlk3QpV04Riq9ZM/yL88ABkU8+UTiIaU5v3rwSI0bYaN3a\nRbly/jv55ctL9OzpYOHCzFdNzZpuqlTx/yAzGpUonC9nPG9eKayL1GqFWbMMfPpp+uKJM2cUDczP\nPrPStWuEhMb/IkhKEpgyxXsxy927Inv2aKlYMbxc0yJFZPr1c9K2rYvr1wV0OihZUgqbI5WdcLvJ\nMgoL6tLvVat6SEjweOUUt2vnUqXSYrPBnDnGDI4zwM2bIq++Gs2aNf53dytcWGbYMBujRnk/qYcM\ncYT1eycmavjgg4yOM8Dt2yLTp5uoWFGicGH/nrlMGQ9t2zpo2tTDsmV61q5V3mV0tEynTk7at3d6\n7dTpDQ0buunUyZFpgW3Lli5atVJnb9Mm37fS1FSBCxfEHOM8S5J/GRi10fZ8+aB7dyfNmrm4elVE\nFGWKF5fCGsh6GAcOaBg3zsTPP2sBAUGQeeYZJUCh5qKQncilbWSC//5XxzvvRGXQQjxzRstLL8UG\nxceD4Hk8R4+KjBoVxbp1hnQO6u3bImPHRrN1q07VgjIaYdgwO507O3iYD/XYYy7mzbOqirgrHZB8\nOzgDBgRXTBbsOzx5UsOnn3rz3gX+8Y8oEhNzrgZpKG2ePSuyfLmezp2j6NIlmhUr9Jw9q/7dXbok\n3ovkesM33+j9Th1nhlC+x+LFZazWnVSvHjrHOdLmTWwsPP207xO8UCGJ/Pn93x+KF5dZtsySafFl\n/fou3n03VdXecOSIhq+/9l6cdvu2qJqX/eyzLpo3z9y5697dwRNPBK6lf/iwhtWr7ezcqSU5OTCa\nWFKSeC9qmBkuXtRw8aL/azBPHnj1VSdjxpiw2WDyZCszZljp0sXB8uV69u7Vqu7GWaiQzOTJNpYs\nsdCwoYsiRSQee8zFl19a+PhjK8WKhT6LG2jBJETe2suTB9q3932OlivnDri2pGhRGYtlBzVrhs5x\nDsU7/O03DR06xPLzzzrSZFBlWeC//9XTsWMsJ09GhtuaG3l+CMnJAhMnet+5b9wQ2btXS7ly4Yt+\nnTypdDTzhg8+MFG/vpvatf3f7IoXl/ngg1T69XPw++8utFojZcp4qFRJ3cEIijM+dKidrVv1mUat\natRwU79+eKW8lMPU+05rNoucPCkG1L75r4TffhPp1CmW69fvb2hbtugpWFBi5coUVVkafw4+UZSD\nOiBzoQ5areIsPqz//iBGjrSpdoRq1JDYtMnMb79p+fFHmagoLY0bKxkutdS4pCQRt9v3pNi9W6uK\nIlCsmMwnn1jZu1fL7Nl6Ll/WUr68hwED7Dz2mCcgHepTp0TGjzfx3//qAOW2VaSIxLRpqbRs6VLV\naS+rSybA7dv+L5Tbt2HmTCPz5llJSYHdu3WYzQJVq3qYO9fKzp1azp3TUL68un27YEGZtm1dNG3q\n4tixC1SuXDJglaU2bVwsW+a9qDEmRqZkyZyzX4sidOjgYs4co1c++tix9ojQRA8VXC5YsECPxZL5\n8167JvLf/+qoWFFlpWk2IJfz/BD27dPQpo3vMNLTT7tYs8YStoYfgwZF+ayMBpg/38ILL4SPdnDq\nlMDu3TrWr9ezY4cWWRYwmWQ6dnTyxBMuWrRwUaRI2IbH9Om+NUgBFi608PzzudQNb7h6VeDZZ2O8\nFpOVKePmu+/8bxWfnCzQokWcz65jn35qpXv3yJEI+yvAblek2956K4qHHejOnR1MmGALqmtjsFi3\nTkfv3r49spdfdvDJJ6kB2bdaFepKTIwccLbswgWBTp0yFrspkFm+3EKrVv47psuX6xg40Pczf/65\nhc6d/du/DhwQuXZNZO5cAzt2pI/i6/VKYMVigYEDw7f2kpIEnnsulqSkzA/ed95J5a23HDnqcu3x\nwLZtWnr3jknX3EQQZMaOtdO3r5143+VFfyokJoo0aBDns3g1IUFi2zZzttef5XKeVcIfukMwaeNQ\nINWPMyCQavVQwe2Gzz83snChgaZN3bz9th2PR1H1WLdOz9KlBpYssdC2bfgcU384lf5oYv6Vcfy4\nxqvjDHDunKIPXriwf5MxIUFmwoRUr05BwYISDRrkvOYjkQ6jETp3VrrMff+9jr17NZQqJdGhg4uq\nVd3kzx/e8ZUr5yEuTvKhSgAtWgS+10RHK9zfYLB/v9aL4wwgMG6ciTp1LH5HEUuWlNBoZK/dLnU6\ndVFYjUbmhx90GRxnUBRVRoyIYtEiCx5P+LrEarUyI0famT3byIkT9wchijLduzupVMmToxxnUN51\ny5Zuduww89tvGs6e1VCokETt2m4qVAhv3VB2wO0mS9UXiyW8/k0aIoM8EkEoVkyiQAHfm06nTs6g\nNpBgeUFZ8e00GpkSJcKnNZuYKLJkiaIJu327omYxbZqJ6dNN9za9uXMN2GyB/xvBjrFmTQ+xsd4P\nqpo13UG1lo40/lx22Lx0KevtwxcvMzM884yLSZNS0evTf5vy5d2sXJkSdDFQpH+XSLVnNEKdOh5G\njrTzz3/u4aOPFEWaYBzn5GSB77/XMmWKyMyZBnbv1gaknVy9usTw4d5146pXd1OjRuCnbbDvUJJg\n8WLfDUNOndJy/rz/a8Vmg8GDvT/z4MF2VfKBqanKnu0NLpfA3r3asJ57v/+uZfjwKOrXdzNhQiqj\nR9sYM8bGuHE2/vhDw5tvRqt6h6EeX3bZEwTlslSliofq1W9Ss6ab0qVD4zhH2jPnzStTrpzvtVq/\nvpv4+PATJnIjzw+hRAmZsWNtDB3qXS7rqafCe+2pW9dNbKxMSkrmB81LLzn9rizPDlitWd8ek5JE\nbDZU8fxCibJlJRYvttC1awx2e/qxFi4s8emn1oiqPo5E+NNJz2hUt8nlyQP9+zto2dLFoUMORDGK\nwoVlqlTxqG4Pn4vsgRQCMd1jx0R69Ijm3Ln0R1DTpi7+9a9U1a2HO3Z04HbDjBmmB+osZFq2dPHO\nOzZV6kOhhscDVmvWTp0a3XuXS2kkM2WKlU8+Md2T1StaVGLQIBt79mh5/HH/LyI3bwrpaAGZYe9e\nLQ4HGHwzBrMN332nw+USWLw4bQAyD9OIEhPFHNc2/fRpkVmzjHz9tR63Ox6QadjQzYQJNurWzVnR\n9vz5ZUaNstO/v3dK0oABjoiQkc3lPGeCmzdh3jwjH35oTKdmUaSI4nD526I0O7F1q5Y+fWIyONAN\nGriYPj2VGjXCt4GcPSvSqFEc5csrguypqQJ2O8THKw7/pElKQeNXX1nDthGDQiM5elRk2zYd69bp\n0WqhRw8HTz7pzjFyR9mJo0dFmjTxrg+u0Si6q5EiLZSLwOF0KjSdHTu0HDigpXhxibZtnVSt6iFP\nHnW2kpMFnnvOO1e+Y0cHs2apU9wAJcL7++8aTp0ScbuVLGK1ap6gL8HXryv7V3S0HLCtmTMNvPuu\n9weKjVXWir8FykuW6EhJEfnmGx1PPOEhLk4ppL1zR2DfPg0dO7qIj5d4+WX/PPKtW7V06uRLexta\ntnSyYoWKcHaIMWBAFCtW+D4wVq1KoVmzCMjphwiJiUp3z5MnM64Vk0nm229TqFMn/P5IKHHzpsD0\n6UbmzXs4tC4zfryNPn0cxPqeqiFBLuc5AOTPD2+9Zee555wcP67BbBYoVUpJm2SHvE4gaNHCzbp1\nZg4c0LJvn5boaJnmzd1Uq+amdOnwjrF0aYnp0y243SIjRkRx+/b9qEvFim7mzrViMEhhdZxBSYdV\nry5RvbqDfv0ciCLofWdXcwRu3hT44w+RK1dEjEaZihUlypaVVKdky5VT0uUffJB5+mD4cHvuJSQH\nwOFQGl688UZ0uk6jn31mpGdPB2PGqOuqeOyYb678mjV6Bg92qM6eiSLUquWhVq3QOBPnz4ts3apl\n5kwjV66IlC7tYdgwB02auChaVN0e26KFi/fflzPIn6ZhyBCbKmWf/Pll5swxcPy4hoMHMyovOZ0C\nY8b4z4urUsVD2bJuzp71/l1efDG8hbotWrhZscJAvnwSnTs7iY+XEUVFOWnLFh1arVJMlpPw00/a\nTB1nUDp1zpxp4PPPU8OWwc0O5M8v8/bbNp5/3sX69TpOnhSpXdvDs8+6qFbNExFRZ8jlPHuF0ag4\nVkWLbqdvXyctW7pD5jiHimdUu7ZEnz5OBg5UOIjt2rlC4jgHOz5RVNLvQ4emd5wBTp7U0r9/NCZT\ncLmmUHO1fv11V0gd50jjkqXhyBGR9u1jaN8+jtdei6FHj1iaNIljwQI9ZrM6W0Yj9Otn54MPrOTN\ne//QyptX4oMPrPTrZw/qghSJPO+/or3DhzUZHOc0LFpkYONGdS0VDx70HbORZSHs3NXz55XmKiNG\nRHPxoga3W+D0aS2vvx7NW29Fc/myuv2rWjWJJUssGAwZ9+cXX3TQrZs6x9TtFjh+3Ptt98gRbZby\nfQ8iIUFm/Hg7D+v8p6FKFTc1awYX0Q32u9Sr56J3bzu9eztYu1bPtGkm3nvPyN27Au+/b2PkSFtQ\nl/VIW3spKUptkC98+62epKScx/POk0dpstO58y7WrLEyfryd+vUjx3GG3MhzjkBKSkq4h5AOly/D\nhx8a8aYLe+eOyA8/6Hj88ZyVbop0nD0r8tJLsVy7ln6ztdsFRo+OJm9emZdeUqdKkD8/9O3rpFUr\nF0eOpJAnTzzFi0uULBkZGZpcBAe3G77+Wp+p45yGadNMtGrlIiHBv2+u02X9cxpNeOfP+vU6Dh/O\n/HjcskXH7t1aVWtFFJXI6fbtZn75RcvPP0ORIiItWrioXFk9teT69awdYzXFl7dvw/btAvPnW3nn\nnah7hb6iKPP88y769LFz6ZKoqtNsqJEvn4zbDR999GCYVeDnn3X8+quWZcssaAP0aNxuMBjKcfGi\nQP78ckREcp1Ogbt3fX9DWVbX3fPPBlswqgLZjFzOcy5Cjj17NLRrp2hlGwwyjRu7yZNHIjFRwy+/\nKM1JKlXysHatmcKFwzvWvxL+7//0vPmm96t7QoLE99+bw6rZm4vIwq1b0LJlHImJvjk9P/xw1++G\nOD/9pOW557yTFvV6hf9bqVJ4UvDJyQKNG8dx65b3iF7Vqm6+/TZFNd87VFiyRMfgwb51nmfPttCt\nm786zwJOp8h77xmpWlVpjOXxKDS27du1dOjgwGSS6dYtfHziB8+VzFCunJuNG/2X+wOl7uXQIQ3L\nlulZvtyA261wu/v3d1Cvnies1EKHAwYMiGbdOu8p0QIFJL77LoXy5XMWXSUSkMt5zsUjh6KDLfPa\naw4KFJD5/nsdZ88qHbomTnSyY4eO8+fFsOtl/5Xg8eCzhTEosnIXLogUKZL7YXKhQKeDqCjFGSlc\nWOKll5zExMjIsqJ+cOSIFpBVUZ4qVfLQpImLHTsyp3sMGWKnbFn1zsDVqwJHjmjYuFFHSopA48Zu\nHn/cTcWK6mxZrYJPxxngwgUNVqtAnjzhuWgqBeEZ1SbSIAgy1ar5v46jo2UWLjTw4496fvwx43//\n6SctX38dXp3nzZt904POnNFy6pRIgQL+P/fu3Ro6dYrF4bj/HjdsMPDtt3rmzrXSoYMr4Gh2sLBa\n4amnXKxbd79N9cPo2tWJ261+Dt65o3QqPn9eRKuF8uU9lC8vRUTE/c+CXM5zFsjlXapHsWIS//yn\njf37tbz3nolfftFy5oyGTZv0jB8fRenSHvr2tVOoUPjG+Fe054+wfDAqZJH4zNltM6fbi42Fv//d\nzptv2unY0cnXXytc05kzjVSu7GHy5FTatXOp0pXPn19m5sxUOnRw8CDHVqeTGTHCRt++DnTqaNQk\nJgr06RNNp06xLFhgZOVKA4MGRdOyZRw//aTO24uKkomP9/08CQmee5eKQBDsdylf3kPv3t7z9X37\nOlRFI2/d0rBihfcbkCQJbN8eXp1nb50FH4TN5j9V5fJlgQEDYtI5zmmQZYE33ojmzJnw8YkFQZEH\nfPPN9OskDc2auXA48Kp25A1nzoi8+moMbdrE0b9/DH37xtC0aRzjx5tUc/kfRqTtX9mJXOc5FyFH\nmTKKJN2BA5lf2RcsMFKihBy2G/1fERoNdOzouygpf36JYsVy03+5SI/HH/ewb5+GOXOM3LypHBlO\np8A33xj417+MvP66XXUhT6lSEp98ksr27Sl89tlVlixJYedOMyNH2lW33XU44MMPTezZk9HjtlgE\nunaN5dQp/4+6YsVknw1IAIYOdZA3r6phAgq3eO9eDRcvNmDTJi3nzokEQpyMjoYRI+wMHmxLxyHX\n62XeesvGsGF2VVJ/N24IXpVA0nDwoBa779eSrahbN6vbv0y+fP6/zJMnNT6bOLlcglfe+6NA3rxQ\nvbqHvXu1TJxoo1s3B4895qZNGyfjx6dSsKDEgQMaVQoj168LDBgQxc6d6deKLAv8+99GZs824gyv\nqMqfBrmc51yEHOfPizRuHOe1iQvACy84mDMn9S8hDRcpOHFC5JlnYmnQwMOTT7pwOAQ0GjCbe2ze\n8gAAIABJREFUBZYv1zN6tI0+fXJ3zlykx7p1Onr39s6v7dHDzocf2lRHi0OFrPTGAT77zErXrv7P\n7bNnBV55JYY//sjoPDVq5OKzz6yq1ZeOHxcZPDiKxESlvbnVKpCcLDBlio0OHZzE+KYwZwqXSykE\nTkwUEQTlUlKunKQ6MLFli5YuXXyL57Zp42TxYmvYaBt792po1y7W63du0sTJV19ZifNOi06HtWt1\n9Onj+6WPGWNjxIjw3RiOHRN59lnlLK1QwUOJEhJ37wocOKBBlmHVKosqXeudO7V06OD9O2u1Sr1B\nlSq5QZRcznMuHjnu3MGn4wxKFMNiIbeL3yNEpUoSK1damDzZyLvvmkjj0RUsKPGPf9ho2zbXcfYX\nZ8+KHDigYe9eLbGxMs2aualSxaOqWOnPgNTUrOWyvv7awKBB6mgCocTly2KWqevdu7WqnOeyZWWW\nLLHy3Xc6Pv5YibgnJEgMG2ajdWuXasf53DmRt96Kol07Fzdvejh5UkOZMhJdu7rZtUtLfLxM+/bq\nlG5A4aRXqiQFXVxZtKiHKlXc3L4t0qWLk+hopWDQaJTZt0/Lpk06XnjBGdZudleuiPzjH3bee8+I\nRgOFCsk4HNz7Nm3auEhOFoiL8+/b+PNz4c7EVa0qsXp1Cn//ezSnTmk4dUq5ucTFSXz0USpPPqmu\ngHPfPt8un9stcPasmOs8+4Fc2kYWyOVdqodGo0gc+UKBAhLGhxsIqUCkPfOfwd6FCwJvvBHFjz/q\nebAA5fp1keHDozh0KLi7dCQ+c3bY3LtXQ/PmsfTrF8P8+UZmzjTxt7/FMnBgNElJOYszaLcrzqkv\nuFwCqamB/xvBjtGfiHdsrPpLTdmyEm++6WDZsmPs33+HbdvM9OnjpHhx9baOHtXQvr2LqVNNzJ5t\nZMsWHatW6Zk4MYrLl0WOHRO5ejXwuRPsO/R4ZKZMSaVPHzuLFumZOtXEtGkmJk40cfeuwMcfWylZ\nMjiljT179gT1+//5j44tW7QsXWphypRUWrd20rWrky++sDB2bCoTJkSRnOx/WLxyZQ+FC3t3ErVa\nmdq1A3/mUK3levU8bNqUwrp1KXzyyXWWLUvhhx9SePFFl+oz1J+almC4CJG2f2UnciPPuQg59HqJ\n9u1dPiV2unVz4nSiugVvLgLH/v1aTp/2tuQFxo0zUbeuhYIFc1b0NJQ4fVqkc+fYTDMrW7fq+OQT\nI1OmhI/CEGpER0PVqh7On/fulMTEyH6nyrMDZct6yJdP8qmQ8cwz6qO6abDbz1GmTLGAf9/jgVu3\nBN5915Rp45Iff9RRoYKHK1cEChcOz9rLn1+hdU2dej8jpUDgp590XLki8PHHqYiiOiUej0cpUDty\nRENi4lNcvKilRg0P5cpJqtdIwYIS9et7GDQo+h73Pg3Nm7vo08eBVuv/+0tIkJk920rXrjGZfBeZ\nDz9MjZgOqYUKyRQq5GbXrp95+umnA7ZTr57vy4Aoyqo6Xf6Vkct5zkXIcewYHDqk4+23ozN1MmrW\ndPPGG3ZatXKFTSf1rwZJgs6do9m2zTfJfPNmM489litV5w3Ll+sZONB7dZxOJ/PDDzmLM7h1q5ZO\nnbzzJEeOtPGPf9jDmtJfvlzHwIGZ81ebNHHxxRfWsF0K3W4YP97EnDnew4TR0TKrVqVQv3541t7V\nqwKtW8f6VLT4/HMLnTv7fwlxuRQ5w4EDo7Hb708OnU7mo49SefFFpypptJ9+0vDKKzHcuZP5JemV\nV+yMHm2jmIp7jscDBw9qmDvXwNq1ejweaNzYzVtv2alf353jgjtXrgh07Jg5lx+gZ087779vCyor\nnFOQFec5l7bxCCFJClfyt99EzpwRcQUeDIloOBwi48ZFMXasjWefdd6jcMTEyPTta6dNGxf/+pcx\nrIftXw2SBFZr1svdHzm7vzJ27NCi13uYPz+FpUtT+OgjK7NmWVmzxsxrr9lxuYQsaQ5/NtSp42bA\ngMw7fdWs6aZbt/ByYQHatXMxd66FAgXuX1q0WplXX7Uzc2ZqWLMpkgS//uo7yWu1Bk59sdng5EmR\nkydFAm3IduGCmKUU3MqVelUp/QMHNPTpk95xBoXmM3hwVJb824dx/rzGq+MMsGKFAYtF3drTaOCx\nxzx8+mkqv/5q5sABM0uWWGjaNOc5zgBFisgsXGilRo2HN3qZF15wMHy4Pddx9hM5a5fPBoSKc3Pm\njMikSUYaN46jadN4GjaMY8wYE3/8EfwniDSekSAovKlixdzUq+fm7bdtjB5tY/BgGxUqeChaVMLp\njAxNYadTqWhevdrO999rOXNGDGpcaYi0b6LVwrPP+i6Yio2Vg0obR9ozZ4fNvHndrF5tZc4cIy+/\nHMvw4dEMHhxNly6xREfLfP65JaxauNlhL18+RRbt669TaNbMRaFCElWruvnsMyuLFlmCTvOGYowx\nMdCpk4tt28wsXnyJb75JYccOM++/b6NUqfCOT6tVCryyglrqi8Oh8O/XrtWxfr3yv3XrdOzbJ6qW\nG/Pn0myzCX7vjQ4HLFhg8NHWXWDmTCMWi99D5IcffDvbTqfgU3rOFwwGSEraSenSUkCqJ5khEvcv\nBTLPPutk4sRUhg61MXKkjQkTbFSv7g76EhzqZz548GDEBhlzOc+PAOfOibz8cjQnT95/3U6nwPz5\nRv7zHz1r1qSErRVtdsBgkPniCyuvvx6TgZsG0LChi4kTbWGXqUtKEpg1y8jChQY8nnhASZ+OHq1o\naubPH97xhRrNm7t47z0506YAAEOH2ihTJufMw+zAiy+66d8/mjNn0m+dTqfAzJkmxoyx0axZGMVw\nswn58kHr1m6eftrCsWNJVKhQgvj4cI8qI4oXl0lMPBAULzTUEEXo2dPJ1q3eN7yqVd2ULq2OsrF7\nt5Y1a3T/ayutrGmdTqZLFwepqS6aNvU/jVSkiExsrOxTJem551x+Xwxv3BDYuNH3Br9jh5Zr1wRi\nYvy7sD+oZ+0NoQh8RCpSU8FgKInZrP6ilYarVwV6947h+HFl/9LrFVUVj0f57klJdt57zxbWtuQA\nN2/CkSNali59gvPn9dSo4aZjRyc1aniI9a2o+MiQy3l+BPj3v/WMGuWdJzlsmI2xY8PLGQwlTp+G\nadOiWLXK+wqcNctC27ausEnV3b4NgwdH8Z//ZD7Gd95JZdAgR45q5CLLSvTmlVdiMnTi6tTJwYQJ\nNooW/VNsB2HDihU6BgzwHpqKjZVZs8ZM3bo5+BTPhWpcvCjQvXvM/9qZp4cgyHzzjTq93mPHBD78\n0MTatZnvXy+95GDYMBuVK/u3nmUZZswwMGVK5lwFk0lmyxYzVav6N6+TkwWeeioOs9l7JFgQZPbv\nv0vp0v6NcfFiPUOGeD9H8+SRWLbMQoMGOatm4/Zt+OUXLV98YeToUQ0FCki88YaDhg1dlCihbr/e\nvl3Liy/Gkj+/ROfOTuLiZAQBkpJEVq/W43DADz+YqV49fPvXtWsCU6aYWLw449wePdrGgAH2R3Jx\nz+U8BwirFQ4d0vDll3pmzDDy7bc6EhPVv64bNwQ+/dQ3iWjuXCOXLuUQzxm4fVvjU2kDYOlSQ1gv\nCydParw6zgDTp5s4ezZnLQ9BgKZN3Wzfbmb2bCt9+tgZNcrGd9+Zef/91FzH2Q9s2+ZbIiAlRSAx\nMUxdJHIRcty9C/v3a9ixQ8PhwyJWa2B2ihdXuKadOjnSyXiWKuVm+XILDRuqKza4eFHD2rXe99hV\nq/R+tbNOgyBArVqeTLXeo6NlJk1K9Svym4ZChWQ6d/bNHXnuOZcqmpjdDpUqeXeM//53B3fu+G3u\nT4Fbt+CDD4x07RrLtm06rl4VOXpUy+uvR/PqqzGqfZJff9XSsaOT3r0drF6tZ9o0E++/b+LXX7WM\nHWujTh0P58+H99zbvl2XqeMMMG2aKcv6gUeFnOUdhAjXrwtMnWqkeXOF0zh5somePWNo0SKWn39W\ndzCmibj7gtUqZCiqUINI40na7WQqyfQgrl4N/CCC4Mf4yy++F6DDIXDmTODLI9K+SRoEASpWlOjW\nzUm3brv4xz/sPPGEJ6BWww8jUp85lDb94YZGApc/u+xlh81Itbd/v4bOnWNo1SqOF16Io1mzOF59\nNYYjRwLbF8qWlZg1K5UdO8wsXHiJ//zHzKZNFlq2dKumsF24IJImKVepkoeePR307OmgYkXFuZRl\ngaQk/8d5/rzAW29FI8swcWIqr71mp2tXB6NH2xg0yM60aSZ+/tl/bTmtVpEjNRgyd441Gpk33rCr\nUtu4fl2ga1cHjRu7gPt2o6Nlhgyx89tvGnS6nHOOgtJM7PPPM39JBw9q+fprdUWcRYp4MBplPvzQ\nxNWr9+fHiRMaxo0z0aaNy+s38wfBPvP16wLTp/vmjMyebQxKVz5UiAwXPsKwZo2OOXMyTtjbtxWN\n182bzVSu7N8JGRsrU6mSmwMHvG88CQlSQCL+kYr4eJnoaBmr1ftGVq6cJ6zasP5cVlyunJMNyAy2\nQEvz/8Jo1MjN6tXeN3ejUaZkyZyVNv6zITYEpMjDhzV06BD70B4msHWrjsOHY9mwIbA6FYMBqlWT\nuH37AE8+GTgv2+MRKFZMYuBAO8eOadiyRYcgQLNmLnr2dPDZZ0ZVTtWpUxqSk0WSk/Vs3KinYEGJ\nqCiZtWvFe3vl6tU6XnnFf2WV2rU9rFhhoV+/6HSOWr58ErNnW6lbV906qV1bolevKFq2dDN+vA27\nXUCjUeTmVqzQU6yY76YnfzbY7TB/vm9Hcs4cI926Of0uii1eXGbIEG83NYHZsw2sWRO+TrN37gic\nO+fbLT1wQMOdOwJRUeH1mXKd54dw4YLAtGner8MWi8CuXVoqV/ZvgsXFwVtvOejZ07vzPGKELSiV\ng1AXxwRrr3JliZdfdvDFF97pKt27O4NynoMdY9WqWYUQZYoXD3wjjrRv8ihsRrq9UNisWdNNwYIS\n169nHtXr1ctOrVq58yYc9i5dEjh0SMvy5U9hNkObNm4aN3ZRpYqkiiLmcilKEd4u/zduiHz3nY5K\nlRwBjzXYZ65YUdHKnzDBhNN5f5xLlxowGGQmTrRRvrz/zunDGZXM5rfLJeB2+9fREZQsV6NGbr7/\n3syJE4rDExsrU7myRzVXF6BECTejR9t57z0TmzaldwATEiReecUZVMFzpM1rq1Xg2DHfme6UFIG7\nd/23+ccfGtI3wUmPO3dErl0TgcDeY7DPLElKEeODc/ph+Nt+PbuR6zw/hORkkdu3fae7Vq/W07u3\n0+/K4yeecNOrl4Ovvsp4i2zTxknr1pGhxWI2KxGIP/7Q4HZD+fISlSp5KFBA3WTV66FbNwc7dmg5\ncSLjFHvlFQd164ZXULhwYYmEBMmrtFGTJi6/5KVy8ddCnToSCxZY6N8/5qG5I9Ohg5OXX3aEvVL9\nr4izZ0V6946iYEGoX9+NLCt72YwZRubOtdK0qf8yXJcuiSxb5ptHMW+eke7dnWHrCJiQINO3rzFT\nJ8PhEJg+3cjGjWa/7RUtKhEXJ+F0CrzwguKEejyKA7x+vY5jx7Q0a+YKqHNmsWIyxYoFv997PAL5\n8kl88onC97t+XUSvlylYUObGDQGPR6FJ5hR9ZpNJplAhiQsXvDsaOp2s6pvcupX1IrCHUSxIr5dp\n397FqlXe11+HDs50dQPhQkDkrdOnT4d6HBED0Y83otPJqiIZBQrIjBuXyooVKbRs6aRUKQ8NG7pY\nvNjCjBmpJCQENxFCwa1KShIYNiyKbt2i2bJFx86dOgYNiqJTpxhOnlQ/TWrVkpg718qkSan/c8Al\nnnzSxZw5FoYOtVGqVHif+cABLW+9Zc80zVe9uptGjTycOpXzOM/ZaTPS7YXKZsOGHr7+OoUvvrAw\ncqSN8eNTWbHCwsSJqVSvHv61nJ32ssNmsPZsNpgzR88rr7jweOD9941Mm2Zi2zYtf/+7g+XL9ar2\nMLc765oNq1Vx1gJFsM987pzos2HIrVsi5875X59TpYrEqFGK3u/Bg1qmTjUxbZqJf/3LSK1aHsaM\nsdGwYXBBnmCfedMmHWazyJEjWkaMiGbixCjGjo1myhQjcXHw1VcGTp8OvFg31PP6wIGzXLkSePMb\nq1UpqvSFtm2duFz+7zmVK2c9aYNpKBR8jwiBevXcXmmsCQkShQrJiGL4KZUBRZ4XL17MxIkTQz2W\niEDJkhLFiklcuuR9Y3r5ZZdfTvaDyJ8fKlTwMGSIDbNZIjZWQ9GiHgoVCnLAIYDdDp98YqBKFYli\nxVxs3arF4xFo0sRN+fIexowxMXt2quooS82aEjVqOHj66Rvo9XHkySNTtGg2PYRK3Lkj8vHHRvr3\ntxMVpXTo0umgYkUP589rmDLFyJdfeoBc/mouMqJ6dYnq1SX27/+ZevXqhXs4f2mcPStQoYLE2LFR\n6Zze8+c1vP++iS5dHJw6JfrNUc6XT6ZiRXc6Xf6H8cQTLuLjwxf98kYbehBZFao/CL0eKlSQ6NYt\nBkm6/w4dDoFlywzUr++ic+fAaSqhQEyM4kDv2pU+1Hr+vJY339QwcaIt4A6LocT58yI//qhl9uza\n3LwpUL26hwED7Dz2mFuVNKvdLnD3rsDjj7v45ZeM4eX8+SUef9xDSoqIv+dU7dpun/VINWu6fSqa\nZDeKFZM4f17gnXdsrF+vY9cuLSAgijKtW7to1MiNJMkUKhT+yLNXnedp06Z5/aUTJ06wYMGCbBtU\nZniUOs9r1ujo2zdzLdeiRT1s2GChbFn/U/oeD2zbpuW116LT6V5GRcnMmmWlXTtXWFO9R48K7Nmj\n4/33Tdy6lX7D1etlJkywUbOmm6eeyjmO5MaNWl5+WSks0ulkSpRQ0pQXLoj/64ols2VLCvXq5Zxn\nzkUuciL27hV5440Yzp71HnWcPdtCt27+R05Xr9bx97971/NetSpFlS5zqLFhg45evXy3wlu0yJJl\n5DINt29D+/axHDvm/cKwbFkKzzwTvmdeuVJH//7enzkhQWLx4hTq1Akf3e7cOYGePWM4ejTje3zj\nDRvDh9vJk8c/WzYbTJlioEEDDz//rGX5cgO3bilUleefV+ie+/dr6N3bScWK/j/z9u1aunePydAs\nq1AhiW++SQmrxjMoXTM7d46hQQMPDRq4cbuVNurbt2s5fVpk+XILtWtn/xiz0nn2ulKuX79Onz59\nyMy3bt++fWhGF6Fo2dLFRx9Z+ec/o9Ld0GrWdDN7tlWV4wyKXvTLL8dkSAWmpgq89lo069al8PTT\n4XPSbt8WmT/fmMFxBqVz2pQpJj7/XEUf1WyEJEFiosjdu2AyQalSkiq5ozRUr+6hUCGJa9dEXC4h\nw8HbrJn7nuxTLnLxZ4XZrESw4uJkjL7l5v+0uHVL9Ok4A/8rvPLfeW7a1MXAgbZMVJdk3n3XxuOP\nh7dmo3JlRXLMm2pQVJTsV4o+DWfPanw6zqAUIwbqPLtcijNoNBJwZ9l9+3yPLzlZxGwOXzpfkmDR\nIkOmjjPA7Nkmmjd3+33pMpmgaVMPXbrEULiwTLduTmJiZCQJNm7U8c03BqZOTVXlOIOi9b95s5nN\nm/WsW6dDr4eePR089ZSb8uXD6zjLMvz8s5Z33rGzfLmeKVOMKAWOMk2auBk+3MEff2geifOcFbzO\nxlKlSlG1atVHOZaIQWwsvPqqkyZN3Bw8aEOjiaFIEaV4Tq0erssFS5bovXLoZFlp61unjoVo782T\nfGLXrl1BVbmazQInTng/fKxWIZ3UkBokJoocPGhDEGIoUECmUiVPwJyq8+dFFi3SM3eukdRUAUGQ\nadfOxYgRNmrWVLeYSpSQWbbMQufOGVuIV6vmZtq01KDagAb7TbLbXnbYjHR72WEzUu0lJQn89JOO\n2bMN3LwpUr26m379HNSr5/Y78pXdYwyVPX+0txUFAf+RLx+MGmXnuedcrFsnkJhooGZND61bu6hS\nxRPwXp2GYJ+5bFmJDz9M5c03o8ioniDz4YfqgjwOPxgZaUV5/hbKg9Lk4/BhLQsXGjh5UkPJkh76\n9nVQp476c+DOHRFRlHnxRSdVq3qw2wVEURnP2rU6jhzR3mszHQiC/SaJiSLz5vm+oS5caKBhQ/90\nvd1uhaYiywJXrgjMnp3R9uzZRl54QV3hqiBAjRoSNWrYad78MFWqVArZxTrYd3jxosCMGSZsNnj2\nWRdt2rjuKbz89JOWt982Ub68ROvW4etOnAavzvOgQYMe2SAuXrzIypUrAejUqRPFixd/ZP+2NwiC\nskElJ/8S1GS4fl3w2QkKFErH1asCZcuGh8djsWS94XhTpfAGux2++07HqFEmqlWLIk8emcREkdRU\nmblzbao1Pi9eFOjbN5oDB+5PWVkW+PZbPTt26Niwwazaga5Tx8OmTSkcOqRh82aB6GgNbdq4qFbN\nE3QRZy5yES4kJor07h3N4cP318rly3q2bNEzeLCNt97yP3X8Z4DiyMr4kuAKJIsUHw9Fi8o0a2ZD\nq5XR6xWVnmAd51BAo1FUB4oWlZg2zci+fTpApkEDRc6tQQO3qrqcAgVkDAY5Qyr/QTRr5lblON+8\nCe+/b2L+/Pue2YkTGrZs0fPiiw4mT1Yn0fr0007q1HHzzTd6Vq68z3PU6WR69XJQvrySTQwXUlKU\nbLIvHD2qwWr1L/puNsPWrb6lNC5dErl2TQhY9cVqvY7RWCmg380O2O0CKSnKO9ywQc+GDRl/5soV\nEZtNiUaHExEhVffVV1/x+uuvAzBv3jxGjRoV5hHdR3bopIYawY4xT56sJ2GRIuo2pT17NOzYoeX1\n153s3Knl7FmRChUk6tZ1M22akYkTbX43mgHYu1ebznF+ECkpAp98YuTTT1NVc8fLlpUoW1aiY0d1\nv5cVIk0zNDtt3r6ttDtPTGxOYqJSGFupkidoBy2SnzlS7cmykul60HF+ELNmmWja1E3TpoHTDiLt\nmaOjZR5/3J1pURWAICg1DWrgdCpRvzffjCYlJf7e3xcoIDFvnpVGjdQ5pw8jFO8wKkpxaOvVs3D5\nsoggKPt0IPr5ZctK9OnjYM6czEOQoijTqpU6tY09e3TpHOcHsWqVgebN3XTr5n9Djrp1PfTqZSQx\nMf3cdrkE/v1vI6NG2ahQIXw6z1FRoNXKPpVaEhL8pxlqNPhxnqmTqnsYkbaWY2JkChSQuHHD++Iq\nW9a7GsejRJbOsyzLHD9+nLNnzyLLMrIsc/fuXXr06BGSAdjtdrRaLXkf4EM4nU70gRKjIgyFCin6\nr1995T0v0qKFmyJFwjcZKlWSiI+XuHvX24SVefxx/yM3d+7A779rOXlSy+LF96fY0aOwdq2eAQPs\nHD6s8dt5Tk2Fzz/3vYusWaNn5Ei7av5XLoLDuXMCI0ZEs317+h28WTMXH35opUyZ8G9y2YGUFOXC\ncOmSiE4nU768cglTE5nLDiQmisydm3Xq+Mkn3TlGjzouTqJTJyeJiZpMVChkRo+2Ex2tbh7++quG\nV1+N/l/x8H3cuCHSpUsMGzemUKdO+GsiPB64elXk4kXluQUBYmIk1Y69Vgv9+jk4fFjDTz+lX8ui\nKDNvnpVq1fx/XrMZZs3yPcGmTzfSsqXLb/rGxYtiBsf5QcyZY6RLl+AapQSDUqWUebhsmffn7tfP\n4TdFIj4e+vRxMHq0d+HqJk3cQTXzijQULSozdKidsWNNNG7s5umn3Xg8ioTw5s069u/XMGSII6zd\nidOQpfP8xRdfcPnyZTQaDUWKFCExMZFatWqFbACXL1+mQIECfPXVVwDky5eP5ORkSpcuneFnH+TT\npOkJZvef0/4uGHs9ejhZutSQ6Y1UEGSGDLFx4EDg9h8eq9rfL1VKYurUW7zxRn4yS32OHGnH4TjM\nrl13/LJ37ZrAlSsie/dmPr0+/9zItGlWDhw4Td265bO0Z7dnLbskSQLJybeoWDGP6ucHmDNnDjVq\n1AjZ/Il0e7t27eL3339n4MCBAf++0ViE99+vncFxBti+XceIEUYWLLARHx+e8WX257S/C8beqVMi\nI0ca2blTT9p6MRplhg+/S+/eMvnyhW98sbGNvcpQpeH33zUcOXIWmy05LPtNqO2VKyfz5ZcS/fo5\nuH1b4L//1WG3K3qxDRq4+e47HY0bX2LXruN+2bNaYcYMfQbHOQ1Op8DSpSIez6889lidgJ4/FOtZ\nry/Jtm0VmTXLeK9w0GRSnI9GjU7gciWptj9vXiN++03DokVabt/W0qyZm1atXDidv7B3b6rf9k6c\nuMLRo5UzfX9pOH9eg9kMBQtmbe/AgQOsX/+ET3spKQLnz4uUKSOFbb95443GbNqky7T4vlkzRa5O\njb2mTRvfK2x/GBqNzOjRNmJiwrsfhtpe+/YO8uSRWLXKwHvvGZFlAa1Wpm1bF/362XnySdcj8f+i\nsui241WqLg0jR45k+vTpbN26lYIFC1K+fHk+++wzRo4c6dOwv3A4HMycOZOhQ4ciy/K9//9w5PlR\nStWBUhl8+rTI/v1uXC4jpUtLVKnioXhx9ZE0j0eRWXntteh00d3oaJmPPw5eqi4UBTx2O+zerWXS\nJBO//aY4vcWKSYwbl0qrVurI+QcPinTsGOsjkq2Iu8+aZfXLrtMJAwdGsWaN95cUFyexc6eZkiUD\ni3SG4h3+meyFwua+fRratPEdAti0yawqa/EgIvGZk5MFOneO8apM8MEHVvr29T8V/TCCHd/JkyIN\nG8b5LJx68kkXK1daAu7EFolzOylJYMIEExUqSFSo4EEQlFqOlSt1/POfdlVz8OxZgccei8cXhzpv\nXoldu8wULRqe/SY1Fd5918QXX2Qexnz9dRtjx9oDUiICOHz4MNWr1wo4k3LtmkDz5nE+a2Wio2X2\n7Lnr95k6cGAUy5f7PiiDkRAM1bw+dkxk4UIDixYZcDoFChaUGDbMznPPOSlWTP18OXpUZPToKH76\nSUvanCxRwsO//pVK48ZutFmGQL0jEtfy0aMi7dvHZtoEqGRJD6tWWShXLoKl6tJQtmwnPpB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8fnj3PjcvnfTOL6dSXy7PtnlAr2SpX8s5mL4FGunMQ//2njnXcyj1hNmGBTleb1h58ZFSXnKFnH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\nExXGjrVx5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ZEjub8gc9uYLxCEi1zfBO3bt6dly5bUrFmTs2fPMnToUJo3b54fbStwSpdW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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(figsize=(12,8), subplot_kw={\"ylabel\" : \"Residual\",\n", - " \"xlabel\" : \"State\"})\n", - "i = 0\n", - "for state, group in state_resid_group:\n", - " x = [i] * len(group)\n", - " axes.scatter(x, group[\"resid\"], s=91)\n", - " i += 1\n", - "states = m_regression_data.State.unique()\n", - "states.sort()\n", - "#axes.xaxis.get_major_locator().set_params(nbins=len(states))\n", - "axes.margins(.05, .05)\n", - "axes.xaxis.set_ticks(range(31))\n", - "axes.xaxis.set_ticklabels(states);\n", - "for label in axes.xaxis.get_ticklabels():\n", - " label.set_rotation(90)\n", - " label.set_fontsize('large')" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "demo_data = demo_data.drop(demo_data.index[demo_data['State'] == 'District of Columbia'])\n", - "demo_data.reset_index(drop=True, inplace=True);" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "exog = demo_data[[\"PVI\", \"per_hisp\", \"per_black\", \"average_income\", \"educ_coll\"]]\n", - "exog[\"const\"] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "state_m = m_model.predict(exog)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-15.3603, -21.5913, -4.9947, -10.5032, 16.1222, 1.9831,\n", - " 10.9647, 14.0814, 2.0932, -4.6139, 18.2001, -25.0872,\n", - " 15.766 , -7.7724, 1.4341, -17.1113, -14.4254, -9.5522,\n", - " 6.7645, 17.5081, 18.4677, 8.2765, 2.5286, -7.98 ,\n", - " -3.1192, -11.1954, -19.0509, 5.3332, 0.9212, 8.2565,\n", - " 10.5848, 19.1612, -1.7616, -16.5408, -0.1307, -24.3894,\n", - " 7.0121, 4.3192, 18.3818, -7.0588, -14.4 , -11.0846,\n", - " -8.1381, -29.2403, 18.9684, -0.9221, 7.9232, -12.3191,\n", - " 3.498 , -31.7474])" - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state_m" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "unit_m = (state_m - state_m.min())/(state_m.max() - state_m.min())" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "unit_m *= 2" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": { - "collapsed": false - }, + "
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pollster_statepoll_dateStatemPollsterper_blackper_hispper_whiteeduc_hseduc_coll...older_popper_olderper_votedem_advno_partyPVIobama_giveromney_givekmeans_groupkmeans_labels
0American Research Group-New Hampshire2012-03-17New Hampshire6.436534American Research Group1.32.992.290.932.9...184547.1600.1400.648-1.513.920.9615630.73399740
1American Research Group-New Hampshire2012-06-23New Hampshire0.071010American Research Group1.32.992.290.932.9...184547.1600.1400.648-1.513.920.9615630.73399740
2American Research Group-New Hampshire2012-09-26New Hampshire4.054884American Research Group1.32.992.290.932.9...184547.1600.1400.648-1.513.920.9615630.73399740
3Public Policy Polling (PPP)-New Hampshire2011-04-02New Hampshire3.118546Public Policy Polling (PPP)1.32.992.290.932.9...184547.1600.1400.648-1.513.920.9615630.73399740
4Public Policy Polling (PPP)-New Hampshire2011-07-03New Hampshire-0.240062Public Policy Polling (PPP)1.32.992.290.932.9...184547.1600.1400.648-1.513.920.9615630.73399740
5Public Policy Polling (PPP)-New Hampshire2012-05-12New Hampshire10.469450Public Policy Polling (PPP)1.32.992.290.932.9...184547.1600.1400.648-1.513.920.9615630.73399740
6Columbus Dispatch (OH)-Ohio2012-08-20Ohio1.875520Columbus Dispatch (OH)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
7Columbus Dispatch (OH)-Ohio2012-09-24Ohio7.679307Columbus Dispatch (OH)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
8Ohio Poll-Ohio2011-09-16Ohio4.166959Ohio Poll12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
9Ohio Poll-Ohio2012-08-19Ohio1.501578Ohio Poll12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
10Public Policy Polling (PPP)-Ohio2011-03-12Ohio4.430867Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
11Public Policy Polling (PPP)-Ohio2011-05-21Ohio2.960337Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
12Public Policy Polling (PPP)-Ohio2011-10-15Ohio2.358188Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
13Public Policy Polling (PPP)-Ohio2011-11-05Ohio6.859215Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
14Public Policy Polling (PPP)-Ohio2012-01-29Ohio4.969091Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
15Public Policy Polling (PPP)-Ohio2012-05-05Ohio5.842069Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
16Public Policy Polling (PPP)-Ohio2012-06-23Ohio-1.127675Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
17Public Policy Polling (PPP)-Ohio2012-09-08Ohio2.512656Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
18Public Policy Polling (PPP)-Ohio2012-09-29Ohio0.678406Public Policy Polling (PPP)12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
19Quinnipiac-Ohio2011-07-15Ohio3.746496Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
20Quinnipiac-Ohio2011-09-23Ohio1.612668Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
21Quinnipiac-Ohio2011-10-20Ohio3.746496Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
22Quinnipiac-Ohio2011-11-04Ohio2.340016Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
23Quinnipiac-Ohio2011-12-02Ohio-1.942619Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
24Quinnipiac-Ohio2012-01-13Ohio1.694036Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
25Quinnipiac-Ohio2012-02-10Ohio1.691996Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
26Quinnipiac-Ohio2012-03-23Ohio5.796853Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
27Quinnipiac-Ohio2012-04-28Ohio1.683659Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
28Quinnipiac-Ohio2012-05-05Ohio0.699432Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
29Quinnipiac-Ohio2012-06-22Ohio8.821557Quinnipiac12.43.281.087.424.1...1650927.9930.1430.6243.615.4-10.3775480.42766203
..................................................................
291Rasmussen-Virginia2012-08-07Virginia1.245810Rasmussen19.88.264.586.133.8...1012075.5000.1250.646-3.014.6-21.0001850.93850840
292Rasmussen-Virginia2012-08-23Virginia-0.707960Rasmussen19.88.264.586.133.8...1012075.5000.1250.646-3.014.6-21.0001850.93850840
293Rasmussen-Virginia2012-09-13Virginia-0.132000Rasmussen19.88.264.586.133.8...1012075.5000.1250.646-3.014.6-21.0001850.93850840
294Public Policy Polling (PPP)-Washington2011-05-14Washington8.921781Public Policy Polling (PPP)3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
295Public Policy Polling (PPP)-Washington2012-02-18Washington13.966914Public Policy Polling (PPP)3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
296Public Policy Polling (PPP)-Washington2012-06-16Washington11.253592Public Policy Polling (PPP)3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
297SurveyUSA-Washington2011-11-22Washington12.315353SurveyUSA3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
298SurveyUSA-Washington2012-05-09Washington8.655616SurveyUSA3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
299SurveyUSA-Washington2012-08-02Washington14.386038SurveyUSA3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
300SurveyUSA-Washington2012-09-08Washington9.553699SurveyUSA3.811.672.189.631.0...867414.8260.1270.6419.814.851.1905900.47562540
301Public Policy Polling (PPP)-West Virginia2012-01-22West Virginia-7.852240Public Policy Polling (PPP)3.51.393.081.917.3...300568.9680.1620.6313.412.8-80.2604370.32133321
302Public Policy Polling (PPP)-West Virginia2012-09-03West Virginia-8.389235Public Policy Polling (PPP)3.51.393.081.917.3...300568.9680.1620.6313.412.8-80.2604370.32133321
303Public Policy Polling (PPP)-West Virginia2012-10-01West Virginia-23.140121Public Policy Polling (PPP)3.51.393.081.917.3...300568.9680.1620.6313.412.8-80.2604370.32133321
304Public Policy Polling (PPP)-Wisconsin2011-02-26Wisconsin8.691364Public Policy Polling (PPP)6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
305Public Policy Polling (PPP)-Wisconsin2011-05-21Wisconsin10.899564Public Policy Polling (PPP)6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
306Public Policy Polling (PPP)-Wisconsin2011-08-20Wisconsin3.743916Public Policy Polling (PPP)6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
307Public Policy Polling (PPP)-Wisconsin2012-02-25Wisconsin13.469173Public Policy Polling (PPP)6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
308Public Policy Polling (PPP)-Wisconsin2012-07-07Wisconsin4.195163Public Policy Polling (PPP)6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
309Public Policy Polling (PPP)-Wisconsin2012-09-19Wisconsin6.146157Public Policy Polling (PPP)6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
310Rasmussen-Wisconsin2011-10-26Wisconsin3.197725Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
311Rasmussen-Wisconsin2012-02-27Wisconsin4.357351Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
312Rasmussen-Wisconsin2012-03-27Wisconsin10.707061Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
313Rasmussen-Wisconsin2012-05-09Wisconsin2.821784Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
314Rasmussen-Wisconsin2012-06-12Wisconsin-1.913946Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
315Rasmussen-Wisconsin2012-07-25Wisconsin1.932597Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
316Rasmussen-Wisconsin2012-09-17Wisconsin1.932597Rasmussen6.56.183.189.425.8...793935.6130.1390.6292.812.820.4554100.23780203
317Rasmussen-Nebraska2012-03-05Nebraska-19.056922Rasmussen4.79.581.890.027.7...250599.1760.1360.614-19.014.8-130.3356300.35109303
318Rasmussen-Nebraska2012-05-16Nebraska-20.181630Rasmussen4.79.581.890.027.7...250599.1760.1360.614-19.014.8-130.3356300.35109303
319SurveyUSA-Kansas2011-11-10Kansas-26.128872SurveyUSA6.110.877.889.229.3...381874.6540.1330.615-16.914.3-120.3920000.46993403
320SurveyUSA-Kansas2011-11-20Kansas-6.973400SurveyUSA6.110.877.889.229.3...381874.6540.1330.615-16.914.3-120.3920000.46993403
\n", + "

321 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " pollster_state poll_date State \\\n", + "0 American Research Group-New Hampshire 2012-03-17 New Hampshire \n", + "1 American Research Group-New Hampshire 2012-06-23 New Hampshire \n", + "2 American Research Group-New Hampshire 2012-09-26 New Hampshire \n", + "3 Public Policy Polling (PPP)-New Hampshire 2011-04-02 New Hampshire \n", + "4 Public Policy Polling (PPP)-New Hampshire 2011-07-03 New Hampshire \n", + "5 Public Policy Polling (PPP)-New Hampshire 2012-05-12 New Hampshire \n", + "6 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio \n", + "7 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio \n", + "8 Ohio Poll-Ohio 2011-09-16 Ohio \n", + "9 Ohio Poll-Ohio 2012-08-19 Ohio \n", + "10 Public Policy Polling (PPP)-Ohio 2011-03-12 Ohio \n", + "11 Public Policy Polling (PPP)-Ohio 2011-05-21 Ohio \n", + "12 Public Policy Polling (PPP)-Ohio 2011-10-15 Ohio \n", + "13 Public Policy Polling (PPP)-Ohio 2011-11-05 Ohio \n", + "14 Public Policy Polling (PPP)-Ohio 2012-01-29 Ohio \n", + "15 Public Policy Polling (PPP)-Ohio 2012-05-05 Ohio \n", + "16 Public Policy Polling (PPP)-Ohio 2012-06-23 Ohio \n", + "17 Public Policy Polling (PPP)-Ohio 2012-09-08 Ohio \n", + "18 Public Policy Polling (PPP)-Ohio 2012-09-29 Ohio \n", + "19 Quinnipiac-Ohio 2011-07-15 Ohio \n", + "20 Quinnipiac-Ohio 2011-09-23 Ohio \n", + "21 Quinnipiac-Ohio 2011-10-20 Ohio \n", + "22 Quinnipiac-Ohio 2011-11-04 Ohio \n", + "23 Quinnipiac-Ohio 2011-12-02 Ohio \n", + "24 Quinnipiac-Ohio 2012-01-13 Ohio \n", + "25 Quinnipiac-Ohio 2012-02-10 Ohio \n", + "26 Quinnipiac-Ohio 2012-03-23 Ohio \n", + "27 Quinnipiac-Ohio 2012-04-28 Ohio \n", + "28 Quinnipiac-Ohio 2012-05-05 Ohio \n", + "29 Quinnipiac-Ohio 2012-06-22 Ohio \n", + ".. ... ... ... \n", + "291 Rasmussen-Virginia 2012-08-07 Virginia \n", + "292 Rasmussen-Virginia 2012-08-23 Virginia \n", + "293 Rasmussen-Virginia 2012-09-13 Virginia \n", + "294 Public Policy Polling (PPP)-Washington 2011-05-14 Washington \n", + "295 Public Policy Polling (PPP)-Washington 2012-02-18 Washington \n", + "296 Public Policy Polling (PPP)-Washington 2012-06-16 Washington \n", + "297 SurveyUSA-Washington 2011-11-22 Washington \n", + "298 SurveyUSA-Washington 2012-05-09 Washington \n", + "299 SurveyUSA-Washington 2012-08-02 Washington \n", + "300 SurveyUSA-Washington 2012-09-08 Washington \n", + "301 Public Policy Polling (PPP)-West Virginia 2012-01-22 West Virginia \n", + "302 Public Policy Polling (PPP)-West Virginia 2012-09-03 West Virginia \n", + "303 Public Policy Polling (PPP)-West Virginia 2012-10-01 West Virginia \n", + "304 Public Policy Polling (PPP)-Wisconsin 2011-02-26 Wisconsin \n", + "305 Public Policy Polling (PPP)-Wisconsin 2011-05-21 Wisconsin \n", + "306 Public Policy Polling (PPP)-Wisconsin 2011-08-20 Wisconsin \n", + "307 Public Policy Polling (PPP)-Wisconsin 2012-02-25 Wisconsin \n", + "308 Public Policy Polling (PPP)-Wisconsin 2012-07-07 Wisconsin \n", + "309 Public Policy Polling (PPP)-Wisconsin 2012-09-19 Wisconsin \n", + "310 Rasmussen-Wisconsin 2011-10-26 Wisconsin \n", + "311 Rasmussen-Wisconsin 2012-02-27 Wisconsin \n", + "312 Rasmussen-Wisconsin 2012-03-27 Wisconsin \n", + "313 Rasmussen-Wisconsin 2012-05-09 Wisconsin \n", + "314 Rasmussen-Wisconsin 2012-06-12 Wisconsin \n", + "315 Rasmussen-Wisconsin 2012-07-25 Wisconsin \n", + "316 Rasmussen-Wisconsin 2012-09-17 Wisconsin \n", + "317 Rasmussen-Nebraska 2012-03-05 Nebraska \n", + "318 Rasmussen-Nebraska 2012-05-16 Nebraska \n", + "319 SurveyUSA-Kansas 2011-11-10 Kansas \n", + "320 SurveyUSA-Kansas 2011-11-20 Kansas \n", + "\n", + " m Pollster per_black per_hisp per_white \\\n", + "0 6.436534 American Research Group 1.3 2.9 92.2 \n", + "1 0.071010 American Research Group 1.3 2.9 92.2 \n", + "2 4.054884 American Research Group 1.3 2.9 92.2 \n", + "3 3.118546 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", + "4 -0.240062 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", + "5 10.469450 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", + "6 1.875520 Columbus Dispatch (OH) 12.4 3.2 81.0 \n", + "7 7.679307 Columbus Dispatch (OH) 12.4 3.2 81.0 \n", + "8 4.166959 Ohio Poll 12.4 3.2 81.0 \n", + "9 1.501578 Ohio Poll 12.4 3.2 81.0 \n", + "10 4.430867 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "11 2.960337 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "12 2.358188 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "13 6.859215 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "14 4.969091 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "15 5.842069 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "16 -1.127675 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "17 2.512656 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "18 0.678406 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", + "19 3.746496 Quinnipiac 12.4 3.2 81.0 \n", + "20 1.612668 Quinnipiac 12.4 3.2 81.0 \n", + "21 3.746496 Quinnipiac 12.4 3.2 81.0 \n", + "22 2.340016 Quinnipiac 12.4 3.2 81.0 \n", + "23 -1.942619 Quinnipiac 12.4 3.2 81.0 \n", + "24 1.694036 Quinnipiac 12.4 3.2 81.0 \n", + "25 1.691996 Quinnipiac 12.4 3.2 81.0 \n", + "26 5.796853 Quinnipiac 12.4 3.2 81.0 \n", + "27 1.683659 Quinnipiac 12.4 3.2 81.0 \n", + "28 0.699432 Quinnipiac 12.4 3.2 81.0 \n", + "29 8.821557 Quinnipiac 12.4 3.2 81.0 \n", + ".. ... ... ... ... ... \n", + "291 1.245810 Rasmussen 19.8 8.2 64.5 \n", + "292 -0.707960 Rasmussen 19.8 8.2 64.5 \n", + "293 -0.132000 Rasmussen 19.8 8.2 64.5 \n", + "294 8.921781 Public Policy Polling (PPP) 3.8 11.6 72.1 \n", + "295 13.966914 Public Policy Polling (PPP) 3.8 11.6 72.1 \n", + "296 11.253592 Public Policy Polling (PPP) 3.8 11.6 72.1 \n", + "297 12.315353 SurveyUSA 3.8 11.6 72.1 \n", + "298 8.655616 SurveyUSA 3.8 11.6 72.1 \n", + "299 14.386038 SurveyUSA 3.8 11.6 72.1 \n", + "300 9.553699 SurveyUSA 3.8 11.6 72.1 \n", + "301 -7.852240 Public Policy Polling (PPP) 3.5 1.3 93.0 \n", + "302 -8.389235 Public Policy Polling (PPP) 3.5 1.3 93.0 \n", + "303 -23.140121 Public Policy Polling (PPP) 3.5 1.3 93.0 \n", + "304 8.691364 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", + "305 10.899564 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", + "306 3.743916 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", + "307 13.469173 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", + "308 4.195163 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", + "309 6.146157 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", + "310 3.197725 Rasmussen 6.5 6.1 83.1 \n", + "311 4.357351 Rasmussen 6.5 6.1 83.1 \n", + "312 10.707061 Rasmussen 6.5 6.1 83.1 \n", + "313 2.821784 Rasmussen 6.5 6.1 83.1 \n", + "314 -1.913946 Rasmussen 6.5 6.1 83.1 \n", + "315 1.932597 Rasmussen 6.5 6.1 83.1 \n", + "316 1.932597 Rasmussen 6.5 6.1 83.1 \n", + "317 -19.056922 Rasmussen 4.7 9.5 81.8 \n", + "318 -20.181630 Rasmussen 4.7 9.5 81.8 \n", + "319 -26.128872 SurveyUSA 6.1 10.8 77.8 \n", + "320 -6.973400 SurveyUSA 6.1 10.8 77.8 \n", + "\n", + " educ_hs educ_coll ... older_pop per_older per_vote \\\n", + "0 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "1 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "2 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "3 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "4 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "5 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "6 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "7 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "8 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "9 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "10 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "11 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "12 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "13 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "14 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "15 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "16 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "17 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "18 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "19 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "20 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "21 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "22 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "23 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "24 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "25 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "26 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "27 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "28 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "29 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + ".. ... ... ... ... ... ... \n", + "291 86.1 33.8 ... 1012075.500 0.125 0.646 \n", + "292 86.1 33.8 ... 1012075.500 0.125 0.646 \n", + "293 86.1 33.8 ... 1012075.500 0.125 0.646 \n", + "294 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "295 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "296 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "297 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "298 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "299 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "300 89.6 31.0 ... 867414.826 0.127 0.641 \n", + "301 81.9 17.3 ... 300568.968 0.162 0.631 \n", + "302 81.9 17.3 ... 300568.968 0.162 0.631 \n", + "303 81.9 17.3 ... 300568.968 0.162 0.631 \n", + "304 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "305 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "306 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "307 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "308 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "309 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "310 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "311 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "312 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "313 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "314 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "315 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "316 89.4 25.8 ... 793935.613 0.139 0.629 \n", + "317 90.0 27.7 ... 250599.176 0.136 0.614 \n", + "318 90.0 27.7 ... 250599.176 0.136 0.614 \n", + "319 89.2 29.3 ... 381874.654 0.133 0.615 \n", + "320 89.2 29.3 ... 381874.654 0.133 0.615 \n", + "\n", + " dem_adv no_party PVI obama_give romney_give kmeans_group \\\n", + "0 -1.5 13.9 2 0.961563 0.733997 4 \n", + "1 -1.5 13.9 2 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PVIper_blackper_hispolder_popaverage_incomeromney_giveobama_giveeduc_colleduc_hs
PVI1.000000-0.2947990.1164180.1505100.5950830.2919970.6691930.4942910.225624
per_black-0.2947991.000000-0.1733550.278531-0.0641760.111333-0.280984-0.110643-0.497133
per_hisp0.116418-0.1733551.0000000.4033860.0999820.2896530.3078530.113554-0.564734
older_pop0.1505100.2785310.4033861.0000000.0231830.237119-0.036660-0.074438-0.478205
average_income0.595083-0.0641760.0999820.0231831.0000000.7178600.7046090.8883440.249691
romney_give0.2919970.1113330.2896530.2371190.7178601.0000000.5549000.630611-0.024673
obama_give0.669193-0.2809840.307853-0.0366600.7046090.5549001.0000000.8354240.084808
educ_coll0.494291-0.1106430.113554-0.0744380.8883440.6306110.8354241.0000000.272766
educ_hs0.225624-0.497133-0.564734-0.4782050.249691-0.0246730.0848080.2727661.000000
\n", + "
" + ], + "text/plain": [ + " PVI per_black per_hisp older_pop average_income \\\n", + "PVI 1.000000 -0.294799 0.116418 0.150510 0.595083 \n", + "per_black -0.294799 1.000000 -0.173355 0.278531 -0.064176 \n", + "per_hisp 0.116418 -0.173355 1.000000 0.403386 0.099982 \n", + "older_pop 0.150510 0.278531 0.403386 1.000000 0.023183 \n", + "average_income 0.595083 -0.064176 0.099982 0.023183 1.000000 \n", + "romney_give 0.291997 0.111333 0.289653 0.237119 0.717860 \n", + "obama_give 0.669193 -0.280984 0.307853 -0.036660 0.704609 \n", + "educ_coll 0.494291 -0.110643 0.113554 -0.074438 0.888344 \n", + "educ_hs 0.225624 -0.497133 -0.564734 -0.478205 0.249691 \n", + "\n", + " romney_give obama_give educ_coll educ_hs \n", + "PVI 0.291997 0.669193 0.494291 0.225624 \n", + "per_black 0.111333 -0.280984 -0.110643 -0.497133 \n", + "per_hisp 0.289653 0.307853 0.113554 -0.564734 \n", + "older_pop 0.237119 -0.036660 -0.074438 -0.478205 \n", + "average_income 0.717860 0.704609 0.888344 0.249691 \n", + "romney_give 1.000000 0.554900 0.630611 -0.024673 \n", + "obama_give 0.554900 1.000000 0.835424 0.084808 \n", + "educ_coll 0.630611 0.835424 1.000000 0.272766 \n", + "educ_hs -0.024673 0.084808 0.272766 1.000000 " + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_regression_data[[\"PVI\", \"per_black\", \"per_hisp\", \"older_pop\", \"average_income\", \n", + " \"romney_give\", \"obama_give\", \"educ_coll\", \"educ_hs\"]].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 199 days\n", + "1 101 days\n", + "2 6 days\n", + "3 549 days\n", + "4 457 days\n", + "5 143 days\n", + "6 43 days\n", + "7 8 days\n", + "8 382 days\n", + "9 44 days\n", + "10 570 days\n", + "11 500 days\n", + "12 353 days\n", + "13 332 days\n", + "14 247 days\n", + "15 150 days\n", + "16 101 days\n", + "17 24 days\n", + "18 3 days\n", + "19 445 days\n", + "20 375 days\n", + "21 348 days\n", + "22 333 days\n", + "23 305 days\n", + "24 263 days\n", + "25 235 days\n", + "26 193 days\n", + "27 157 days\n", + "28 150 days\n", + "29 102 days\n", + " ... \n", + "291 56 days\n", + "292 40 days\n", + "293 19 days\n", + "294 507 days\n", + "295 227 days\n", + "296 108 days\n", + "297 315 days\n", + "298 146 days\n", + "299 61 days\n", + "300 24 days\n", + "301 254 days\n", + "302 29 days\n", + "303 1 days\n", + "304 584 days\n", + "305 500 days\n", + "306 409 days\n", + "307 220 days\n", + "308 87 days\n", + "309 13 days\n", + "310 342 days\n", + "311 218 days\n", + "312 189 days\n", + "313 146 days\n", + "314 112 days\n", + "315 69 days\n", + "316 15 days\n", + "317 211 days\n", + "318 139 days\n", + "319 327 days\n", + "320 317 days\n", + "Name: poll_date, dtype: timedelta64[ns]" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(today - m_regression_data[\"poll_date\"].astype('O'))" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "time_weights = (today - m_regression_data[\"poll_date\"].astype('O')).apply(exp_decay)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
WLS Regression Results
Dep. Variable: m R-squared: 0.705
Model: WLS Adj. R-squared: 0.700
Method: Least Squares F-statistic: 150.4
Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81
Time: 10:23:58 Log-Likelihood: -1457.5
No. Observations: 321 AIC: 2927.
Df Residuals: 315 BIC: 2950.
Df Model: 5
Covariance Type: nonrobust
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coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.4405 2.504 1.773 0.077 -0.486 9.367
PVI 1.5550 0.076 20.571 0.000 1.406 1.704
per_hisp 0.1687 0.023 7.422 0.000 0.124 0.213
per_black 0.1975 0.040 4.958 0.000 0.119 0.276
average_income -0.0003 0.000 -1.807 0.072 -0.001 2.64e-05
educ_coll 0.0594 0.121 0.491 0.624 -0.179 0.298
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Omnibus: 110.772 Durbin-Watson: 1.687
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1222.490
Skew: -1.077 Prob(JB): 3.47e-266
Kurtosis: 12.315 Cond. No. 2.71e+05
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " WLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: m R-squared: 0.705\n", + "Model: WLS Adj. R-squared: 0.700\n", + "Method: Least Squares F-statistic: 150.4\n", + "Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", + "Time: 10:23:58 Log-Likelihood: -1457.5\n", + "No. Observations: 321 AIC: 2927.\n", + "Df Residuals: 315 BIC: 2950.\n", + "Df Model: 5 \n", + "Covariance Type: nonrobust \n", + "==================================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "----------------------------------------------------------------------------------\n", + "Intercept 4.4405 2.504 1.773 0.077 -0.486 9.367\n", + "PVI 1.5550 0.076 20.571 0.000 1.406 1.704\n", + "per_hisp 0.1687 0.023 7.422 0.000 0.124 0.213\n", + "per_black 0.1975 0.040 4.958 0.000 0.119 0.276\n", + "average_income -0.0003 0.000 -1.807 0.072 -0.001 2.64e-05\n", + "educ_coll 0.0594 0.121 0.491 0.624 -0.179 0.298\n", + "==============================================================================\n", + "Omnibus: 110.772 Durbin-Watson: 1.687\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1222.490\n", + "Skew: -1.077 Prob(JB): 3.47e-266\n", + "Kurtosis: 12.315 Cond. No. 2.71e+05\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 2.71e+05. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\"\"\"" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_model = wls(\"m ~ PVI + per_hisp + per_black + average_income + educ_coll\", data=m_regression_data, weights=time_weights).fit()\n", + "m_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_resid = pandas.DataFrame(zip(m_model.resid, m_regression_data.State), \n", + " columns=[\"resid\", \"State\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_resid_group = state_resid.groupby(\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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baGhYwqJFi0rVrJKRlLn8uNEhUw6BTLn5q7/ayfz587CsHmAp0OIsS7GsHubP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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize=(12,8), subplot_kw={\"ylabel\" : \"Residual\",\n", + " \"xlabel\" : \"State\"})\n", + "i = 0\n", + "for state, group in state_resid_group:\n", + " x = [i] * len(group)\n", + " axes.scatter(x, group[\"resid\"], s=91)\n", + " i += 1\n", + "states = m_regression_data.State.unique()\n", + "states.sort()\n", + "#axes.xaxis.get_major_locator().set_params(nbins=len(states))\n", + "axes.margins(.05, .05)\n", + "axes.xaxis.set_ticks(range(31))\n", + "axes.xaxis.set_ticklabels(states);\n", + "for label in axes.xaxis.get_ticklabels():\n", + " label.set_rotation(90)\n", + " label.set_fontsize('large')" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "demo_data = demo_data.drop(demo_data.index[demo_data['State'] == 'District of Columbia'])\n", + "demo_data.reset_index(drop=True, inplace=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from IPython.kernel.zmq import kernelapp as app\n" + ] + } + ], + "source": [ + "exog = demo_data[[\"PVI\", \"per_hisp\", \"per_black\", \"average_income\", \"educ_coll\"]]\n", + "exog[\"const\"] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "state_m = m_model.predict(exog)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-15.4217, -21.6313, -5.0082, -10.561 , 16.1516, 1.9687,\n", + " 10.9842, 14.074 , 2.0836, -4.6565, 18.1919, -25.1584,\n", + " 15.7641, -7.8211, 1.3951, -17.1674, -14.4888, -9.6044,\n", + " 6.7223, 17.5189, 18.47 , 8.243 , 2.4987, -8.0447,\n", + " -3.1677, -11.2639, -19.1112, 5.3443, 0.8928, 8.2712,\n", + " 10.5977, 19.1728, -1.8028, -16.6034, -0.1732, -24.4563,\n", + " 6.9845, 4.29 , 18.3768, -7.1117, -14.4658, -11.14 ,\n", + " -8.1462, -29.3198, 18.9368, -0.9413, 7.9092, -12.3842,\n", + " 3.4667, -31.8014])" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_m" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "unit_m = (state_m - state_m.min())/(state_m.max() - state_m.min())" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "unit_m *= 2" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "m_correction = zip(demo_data.State, unit_m)" @@ -6586,14 +9296,17 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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sP+L0r25HMcjnA7791o577kmB13vmBaULFjjQurUXs2YVon59bqRJPnxCLUxW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\n9tprCAsLM1lwD+Tr64s//vGP1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c7pRz0jrmlHNS5zn9mNTFU86FEDaFEEJ6/bkkSfwnazxM0jrjZPtS5zn9mFRdS3uaQwh/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\n6g5c5rnR4Hg2evYGcLPdvUiDFDN7H1oR2hY9U+MR931SpOzZaCBeBN2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70xYMOAytzncE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Dakota', 0.59740688290763611),\n", - " ('Ohio', 1.2420974975622283),\n", - " ('Oklahoma', 0.28906929360446137),\n", - " ('Oregon', 1.5227100227420458),\n", - " ('Pennsylvania', 1.4169161758937938),\n", - " ('Rhode Island', 1.9693830170636866),\n", - " ('South Carolina', 0.96992120743772237),\n", - " ('South Dakota', 0.68151306828176339),\n", - " ('Tennessee', 0.81176165562541391),\n", - " ('Texas', 0.92751616650445501),\n", - " ('Utah', 0.098492882545137092),\n", - " ('Vermont', 1.9924269957578287),\n", - " ('Virginia', 1.2110060089821313),\n", - " ('Washington', 1.5585050874076187),\n", - " ('West Virginia', 0.76326334710489518),\n", - " ('Wisconsin', 1.3846558069742687),\n", + " ('North Carolina', 1.1770116288991195),\n", + " ('North Dakota', 0.59630134352740394),\n", + " ('Ohio', 1.2409505020643061),\n", + " ('Oklahoma', 0.28819001562205282),\n", + " ('Oregon', 1.5217875821850069),\n", + " ('Pennsylvania', 1.4160660286631208),\n", + " ('Rhode Island', 1.9687713407242649),\n", + " ('South Carolina', 0.96871534442524843),\n", + " ('South Dakota', 0.68017110979691953),\n", + " ('Tennessee', 0.81066155479860558),\n", + " ('Texas', 0.92812466205019228),\n", + " ('Utah', 0.097369058861876509),\n", + " ('Vermont', 1.9907421659896687),\n", + " ('Virginia', 1.2108135939642795),\n", + " ('Washington', 1.558068638876154),\n", + " ('West Virginia', 0.7618468941505212),\n", + " ('Wisconsin', 1.3837649018595277),\n", " ('Wyoming', 0.0)]" ] }, - "execution_count": 187, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -6686,7 +9399,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 152, "metadata": { "collapsed": false }, @@ -6698,7 +9411,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 153, "metadata": { "collapsed": false }, @@ -6709,71 +9422,18 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 154, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - " trend m_correction\n", - "State \n", - "Arizona 2.315 1.051\n", - "California 2.733 1.881\n", - "Colorado 18.412 1.325\n", - "Connecticut 18.412 1.678\n", - "Florida 2.733 1.329\n", - "Georgia 2.315 1.066\n", - "Hawaii 18.412 1.962\n", - "Illinois 18.412 1.867\n", - "Indiana 6.587 0.942\n", - "Iowa 6.587 1.304\n", - "Kansas 6.587 0.575\n", - "Maine 6.587 1.513\n", - "Maryland 18.412 1.935\n", - "Massachusetts 18.412 1.973\n", - "Michigan 6.587 1.572\n", - "Minnesota 6.587 1.347\n", - "Mississippi 2.315 0.934\n", - "Missouri 6.587 1.125\n", - "Montana 6.587 0.807\n", - "Nebraska 6.587 0.499\n", - "Nevada 18.412 1.457\n", - "New Hampshire 6.587 1.283\n", - "New Jersey 18.412 1.572\n", - "New Mexico 2.315 1.663\n", - "New York 2.733 2.000\n", - "North Carolina 2.315 1.178\n", - "North Dakota 6.587 0.597\n", - "Ohio 6.587 1.242\n", - "Oregon 6.587 1.523\n", - "Pennsylvania 6.587 1.417\n", - "Rhode Island 18.412 1.969\n", - "South Carolina 2.315 0.970\n", - "South Dakota 6.587 0.682\n", - "Tennessee 2.315 0.812\n", - "Texas 2.733 0.928\n", - "Utah 6.587 0.098\n", - "Vermont 6.587 1.992\n", - "Virginia 18.412 1.211\n", - "Washington 18.412 1.559\n", - "West Virginia 2.315 0.763\n", - "Wisconsin 6.587 1.385" - ] - }, - "execution_count": 190, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "trends.set_index(\"State\", inplace=True)" ] }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 155, "metadata": { "collapsed": false }, @@ -6791,7 +9451,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 156, "metadata": { "collapsed": false }, @@ -6800,119 +9460,71 @@ "data": { "text/plain": [ "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168\n", - " Rasmussen -10.209\n", - "CA Field Poll (CA) 23.344\n", - " Public Policy Polling (PPP) 20.999\n", - " Rasmussen 22.000\n", - " SurveyUSA 22.123\n", - "CO American Research Group 2.000\n", - " Public Policy Polling (PPP) 5.470\n", - " Rasmussen -1.574\n", - "CT Public Policy Polling (PPP) 12.758\n", - " Quinnipiac 7.294\n", - " Rasmussen 8.000\n", - "FL American Research Group 5.000\n", - " Mason-Dixon -3.543\n", - " Public Policy Polling (PPP) 3.125\n", - " Quinnipiac 3.076\n", - " Rasmussen 0.883\n", - " Suffolk (NH/MA) -0.003\n", - " SurveyUSA 4.169\n", - "GA Insider Advantage -19.174\n", - " Mason-Dixon -17.000\n", - " Public Policy Polling (PPP) -3.000\n", - " SurveyUSA -7.984\n", - "HI Public Policy Polling (PPP) 27.000\n", - "IA American Research Group 7.000\n", - " Mason-Dixon -3.000\n", - " Public Policy Polling (PPP) 5.879\n", - " Rasmussen -2.749\n", - "IL Chicago Trib. / MarketShares 21.000\n", - "IN Rasmussen -16.000\n", - "KS SurveyUSA -15.875\n", - "MA Public Policy Polling (PPP) 17.580\n", - " Rasmussen 15.107\n", - "MD Public Policy Polling (PPP) 23.000\n", - "ME Public Policy Polling (PPP) 16.038\n", - " Rasmussen 12.000\n", - "MI CNN / Opinion Research 8.000\n", - " EPIC-MRA 7.430\n", - " Mitchell 0.897\n", - " Public Policy Polling (PPP) 7.694\n", - " Rasmussen 11.072\n", - " SurveyUSA 11.000\n", - "MN Public Policy Polling (PPP) 7.335\n", - "MO Public Policy Polling (PPP) -11.225\n", - " Rasmussen -2.486\n", - " SurveyUSA -1.000\n", - "MS Public Policy Polling (PPP) -17.973\n", - "MT Mason-Dixon -9.000\n", - " Public Policy Polling (PPP) -5.003\n", - " Rasmussen -15.641\n", - "NC American Research Group -4.000\n", - " Public Policy Polling (PPP) 0.261\n", - " Rasmussen -5.676\n", - " SurveyUSA 1.987\n", - "ND Mason-Dixon -13.000\n", - " Rasmussen -15.000\n", - "NE Public Policy Polling (PPP) -12.005\n", - " Rasmussen -14.308\n", - "NH American Research Group 4.150\n", - " LA Times / Bloomberg -10.000\n", - " Mason-Dixon -11.000\n", - " Public Policy Polling (PPP) 6.273\n", - " Rasmussen -2.439\n", - "NJ Fairleigh-Dickinson (NJ) 13.859\n", - " Public Policy Polling (PPP) 14.006\n", - " Quinnipiac 7.504\n", - " Rasmussen 6.000\n", - " SurveyUSA 14.000\n", - "NM Public Policy Polling (PPP) 10.621\n", - " Rasmussen 11.651\n", - "NV American Research Group 7.000\n", - " CNN / Opinion Research 3.000\n", - " Public Policy Polling (PPP) 7.345\n", - " Rasmussen 2.524\n", - "NY Marist (NY) 22.047\n", - " Quinnipiac 27.345\n", - " SurveyUSA 30.000\n", - "OH American Research Group 1.000\n", - " Columbus Dispatch (OH) 8.616\n", - " Ohio Poll 3.000\n", - " Public Policy Polling (PPP) 4.142\n", - " Quinnipiac 7.729\n", - " Rasmussen 0.866\n", - "OR Public Policy Polling (PPP) 9.130\n", - " SurveyUSA 8.676\n", - "PA Public Policy Polling (PPP) 6.160\n", - " Quinnipiac 6.047\n", - " Rasmussen 10.875\n", - " SurveyUSA 0.000\n", - "RI Public Policy Polling (PPP) 17.000\n", - "SC Public Policy Polling (PPP) -14.558\n", - "SD Public Policy Polling (PPP) -6.000\n", - "TN Public Policy Polling (PPP) -7.000\n", - "TX Public Policy Polling (PPP) -6.999\n", - "UT Mason-Dixon -51.000\n", - " Public Policy Polling (PPP) -32.000\n", - "VA American Research Group 2.000\n", - " Mason-Dixon 1.000\n", - " Public Policy Polling (PPP) 5.096\n", - " Quinnipiac 0.578\n", - " Rasmussen 0.892\n", - "VT Public Policy Polling (PPP) 20.000\n", - "WA Public Policy Polling (PPP) 13.051\n", - " Rasmussen 11.000\n", - " SurveyUSA 15.310\n", - "WI CNN / Opinion Research 4.000\n", - " Public Policy Polling (PPP) 5.393\n", - " Rasmussen 2.116\n", - "WV Public Policy Polling (PPP) -19.757\n", - "Name: poll, Length: 109" + "AZ Public Policy Polling (PPP) -9.168494\n", + " Rasmussen -10.209446\n", + "CA Field Poll (CA) 23.343924\n", + " Public Policy Polling (PPP) 20.999075\n", + " Rasmussen 22.000000\n", + " SurveyUSA 22.123414\n", + "CO American Research Group 2.000000\n", + " Public Policy Polling (PPP) 5.469907\n", + " Rasmussen -1.573788\n", + "CT Public Policy Polling (PPP) 12.757757\n", + " Quinnipiac 7.293983\n", + " Rasmussen 8.000000\n", + "FL American Research Group 5.000000\n", + " Mason-Dixon -3.543178\n", + " Public Policy Polling (PPP) 3.125154\n", + " Quinnipiac 3.075653\n", + " Rasmussen 0.882884\n", + " Suffolk (NH/MA) -0.003377\n", + " SurveyUSA 4.168952\n", + "GA Insider Advantage -19.174054\n", + " Mason-Dixon -17.000000\n", + " Public Policy Polling (PPP) -3.000000\n", + " SurveyUSA -7.983856\n", + "HI Public Policy Polling (PPP) 27.000000\n", + "IA American Research Group 7.000000\n", + " Mason-Dixon -3.000000\n", + " Public Policy Polling (PPP) 5.878693\n", + " Rasmussen -2.749416\n", + "IL Chicago Trib. / MarketShares 21.000000\n", + "IN Rasmussen -16.000000\n", + " ... \n", + "OH Ohio Poll 3.000406\n", + " Public Policy Polling (PPP) 4.141640\n", + " Quinnipiac 7.729397\n", + " Rasmussen 0.865613\n", + "OR Public Policy Polling (PPP) 9.130153\n", + " SurveyUSA 8.675504\n", + "PA Public Policy Polling (PPP) 6.160027\n", + " Quinnipiac 6.047221\n", + " Rasmussen 10.874768\n", + " SurveyUSA 0.000000\n", + "RI Public Policy Polling (PPP) 17.000000\n", + "SC Public Policy Polling (PPP) -14.558484\n", + "SD Public Policy Polling (PPP) -6.000000\n", + "TN Public Policy Polling (PPP) -7.000000\n", + "TX Public Policy Polling (PPP) -6.998595\n", + "UT Mason-Dixon -51.000000\n", + " Public Policy Polling (PPP) -32.000000\n", + "VA American Research Group 2.000000\n", + " Mason-Dixon 1.000000\n", + " Public Policy Polling (PPP) 5.095802\n", + " Quinnipiac 0.578138\n", + " Rasmussen 0.891780\n", + "VT Public Policy Polling (PPP) 20.000000\n", + "WA Public Policy Polling (PPP) 13.050886\n", + " Rasmussen 11.000000\n", + " SurveyUSA 15.310208\n", + "WI CNN / Opinion Research 4.000000\n", + " Public Policy Polling (PPP) 5.392554\n", + " Rasmussen 2.116005\n", + "WV Public Policy Polling (PPP) -19.756631\n", + "Name: poll, dtype: float64" ] }, - "execution_count": 192, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -6924,7 +9536,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 157, "metadata": { "collapsed": false }, @@ -6936,7 +9548,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 158, "metadata": { "collapsed": false }, @@ -6945,51 +9557,51 @@ "data": { "text/plain": [ "State\n", - "Arizona 2.433\n", - "California 5.139\n", - "Colorado 24.399\n", - "Connecticut 30.895\n", - "Florida 3.633\n", - "Georgia 2.468\n", - "Hawaii 36.129\n", - "Illinois 34.368\n", - "Indiana 6.204\n", - "Iowa 8.586\n", - "Kansas 3.787\n", - "Maine 9.965\n", - "Maryland 35.628\n", - "Massachusetts 36.323\n", - "Michigan 10.357\n", - "Minnesota 8.869\n", - "Mississippi 2.162\n", - "Missouri 7.408\n", - "Montana 5.318\n", - "Nebraska 3.285\n", - "Nevada 26.822\n", - "New Hampshire 8.453\n", - "New Jersey 28.936\n", - "New Mexico 3.850\n", - "New York 5.465\n", - "North Carolina 2.727\n", - "North Dakota 3.935\n", - "Ohio 8.181\n", - "Oregon 10.029\n", - "Pennsylvania 9.333\n", - "Rhode Island 36.260\n", - "South Carolina 2.245\n", - "South Dakota 4.489\n", - "Tennessee 1.879\n", - "Texas 2.535\n", - "Utah 0.649\n", - "Vermont 13.123\n", - "Virginia 22.297\n", - "Washington 28.695\n", - "West Virginia 1.767\n", - "Wisconsin 9.120\n", - "Name: poll" + "Washington 7.047853\n", + "New Hampshire 5.802571\n", + "New Jersey 7.112103\n", + "Nevada 6.592628\n", + "Colorado 5.993525\n", + "Connecticut 7.593603\n", + "Virginia 5.477061\n", + "Massachusetts 8.922189\n", + "Rhode Island 8.905649\n", + "Hawaii 8.872836\n", + "Maryland 8.753382\n", + "Illinois 8.441942\n", + "New Mexico 5.433933\n", + "North Carolina 3.844664\n", + "Arizona 3.433858\n", + "Georgia 3.478935\n", + "West Virginia 2.488544\n", + "South Carolina 3.164272\n", + "Tennessee 2.647995\n", + "Mississippi 3.044697\n", + "Florida 4.503946\n", + "California 6.373831\n", + "New York 6.775401\n", + "Texas 3.144208\n", + "Wisconsin 7.433267\n", + "North Dakota 3.203194\n", + "Nebraska 2.674642\n", + "Ohio 6.666101\n", + "Pennsylvania 7.606781\n", + "Indiana 5.054192\n", + "Iowa 6.996652\n", + "Maine 8.119432\n", + "Missouri 6.034968\n", + "Michigan 8.439942\n", + "Montana 4.328580\n", + "Kansas 3.084341\n", + "Oregon 8.174693\n", + "South Dakota 3.653723\n", + "Vermont 10.693809\n", + "Utah 0.523044\n", + "Minnesota 7.229251\n", + "Name: poll, dtype: float64" ] }, - "execution_count": 194, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -7001,7 +9613,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 159, "metadata": { "collapsed": false }, @@ -7013,59 +9625,321 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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StatepollPollster
0Washington7.047853National
1New Hampshire5.802571National
2New Jersey7.112103National
3Nevada6.592628National
4Colorado5.993525National
5Connecticut7.593603National
6Virginia5.477061National
7Massachusetts8.922189National
8Rhode Island8.905649National
9Hawaii8.872836National
10Maryland8.753382National
11Illinois8.441942National
12New Mexico5.433933National
13North Carolina3.844664National
14Arizona3.433858National
15Georgia3.478935National
16West Virginia2.488544National
17South Carolina3.164272National
18Tennessee2.647995National
19Mississippi3.044697National
20Florida4.503946National
21California6.373831National
22New York6.775401National
23Texas3.144208National
24Wisconsin7.433267National
25North Dakota3.203194National
26Nebraska2.674642National
27Ohio6.666101National
28Pennsylvania7.606781National
29Indiana5.054192National
30Iowa6.996652National
31Maine8.119432National
32Missouri6.034968National
33Michigan8.439942National
34Montana4.328580National
35Kansas3.084341National
36Oregon8.174693National
37South Dakota3.653723National
38Vermont10.693809National
39Utah0.523044National
40Minnesota7.229251National
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" + ], "text/plain": [ - " State poll Pollster\n", - "0 Arizona 2.433 National\n", - "1 California 5.139 National\n", - "2 Colorado 24.399 National\n", - "3 Connecticut 30.895 National\n", - "4 Florida 3.633 National\n", - "5 Georgia 2.468 National\n", - "6 Hawaii 36.129 National\n", - "7 Illinois 34.368 National\n", - "8 Indiana 6.204 National\n", - "9 Iowa 8.586 National\n", - "10 Kansas 3.787 National\n", - "11 Maine 9.965 National\n", - "12 Maryland 35.628 National\n", - "13 Massachusetts 36.323 National\n", - "14 Michigan 10.357 National\n", - "15 Minnesota 8.869 National\n", - "16 Mississippi 2.162 National\n", - "17 Missouri 7.408 National\n", - "18 Montana 5.318 National\n", - "19 Nebraska 3.285 National\n", - "20 Nevada 26.822 National\n", - "21 New Hampshire 8.453 National\n", - "22 New Jersey 28.936 National\n", - "23 New Mexico 3.850 National\n", - "24 New York 5.465 National\n", - "25 North Carolina 2.727 National\n", - "26 North Dakota 3.935 National\n", - "27 Ohio 8.181 National\n", - "28 Oregon 10.029 National\n", - "29 Pennsylvania 9.333 National\n", - "30 Rhode Island 36.260 National\n", - "31 South Carolina 2.245 National\n", - "32 South Dakota 4.489 National\n", - "33 Tennessee 1.879 National\n", - "34 Texas 2.535 National\n", - "35 Utah 0.649 National\n", - "36 Vermont 13.123 National\n", - "37 Virginia 22.297 National\n", - "38 Washington 28.695 National\n", - "39 West Virginia 1.767 National\n", - "40 Wisconsin 9.120 National" + " State poll Pollster\n", + "0 Washington 7.047853 National\n", + "1 New Hampshire 5.802571 National\n", + "2 New Jersey 7.112103 National\n", + "3 Nevada 6.592628 National\n", + "4 Colorado 5.993525 National\n", + "5 Connecticut 7.593603 National\n", + "6 Virginia 5.477061 National\n", + "7 Massachusetts 8.922189 National\n", + "8 Rhode Island 8.905649 National\n", + "9 Hawaii 8.872836 National\n", + "10 Maryland 8.753382 National\n", + "11 Illinois 8.441942 National\n", + "12 New Mexico 5.433933 National\n", + "13 North Carolina 3.844664 National\n", + "14 Arizona 3.433858 National\n", + "15 Georgia 3.478935 National\n", + "16 West Virginia 2.488544 National\n", + "17 South Carolina 3.164272 National\n", + "18 Tennessee 2.647995 National\n", + "19 Mississippi 3.044697 National\n", + "20 Florida 4.503946 National\n", + "21 California 6.373831 National\n", + "22 New York 6.775401 National\n", + "23 Texas 3.144208 National\n", + "24 Wisconsin 7.433267 National\n", + "25 North Dakota 3.203194 National\n", + "26 Nebraska 2.674642 National\n", + "27 Ohio 6.666101 National\n", + "28 Pennsylvania 7.606781 National\n", + "29 Indiana 5.054192 National\n", + "30 Iowa 6.996652 National\n", + "31 Maine 8.119432 National\n", + "32 Missouri 6.034968 National\n", + "33 Michigan 8.439942 National\n", + "34 Montana 4.328580 National\n", + "35 Kansas 3.084341 National\n", + "36 Oregon 8.174693 National\n", + "37 South Dakota 3.653723 National\n", + "38 Vermont 10.693809 National\n", + "39 Utah 0.523044 National\n", + "40 Minnesota 7.229251 National" ] }, - "execution_count": 196, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -7076,7 +9950,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 161, "metadata": { "collapsed": false }, @@ -7087,50 +9961,258 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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PollsterWeightPIE
0ABC / Washington Post0.951.41
1American Research Group0.651.76
2CBS / New York Times0.661.84
3Chicago Trib. / MarketShares1.161.13
4CNN / Opinion Research0.771.59
5Columbus Dispatch (OH)0.506.76
6EPIC-MRA0.751.65
7Fairleigh-Dickinson (NJ)0.711.72
8Field Poll (CA)1.330.88
9Fox / Opinion Dynamics0.791.60
10Franklin Pierce (NH)0.741.60
11Insider Advantage0.951.29
12Keystone (PA)0.641.55
13LA Times / Bloomberg0.831.44
14Marist (NY)0.691.73
15Mason-Dixon1.101.15
16Mitchell0.961.43
17Ohio Poll1.241.05
18Public Opinion Strategies0.631.81
19Public Policy Polling (PPP)1.051.60
20Quinnipiac0.951.34
21Rasmussen1.300.88
22Research 20001.011.20
23Selzer1.470.92
24Star Tribune (MN)0.812.01
25Strategic Vision0.951.45
26Suffolk (NH/MA)0.771.37
27SurveyUSA1.910.72
28Univ. New Hampshire1.081.26
29USA Today / Gallup0.632.01
30Zogby0.641.72
31Zogby Interactive0.434.74
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" + ], "text/plain": [ - " Pollster Weight PIE\n", - "0 ABC / Washington Post 0.95 1.41\n", - "1 American Research Group 0.65 1.76\n", - "2 CBS / New York Times 0.66 1.84\n", - "3 Chicago Trib. / Marke... 1.16 1.13\n", - "4 CNN / Opinion Research 0.77 1.59\n", - "5 Columbus Dispatch (OH) 0.50 6.76\n", - "6 EPIC-MRA 0.75 1.65\n", - "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", - "8 Field Poll (CA) 1.33 0.88\n", - "9 Fox / Opinion Dynamics 0.79 1.60\n", - "10 Franklin Pierce (NH) 0.74 1.60\n", - "11 Insider Advantage 0.95 1.29\n", - "12 Keystone (PA) 0.64 1.55\n", - "13 LA Times / Bloomberg 0.83 1.44\n", - "14 Marist (NY) 0.69 1.73\n", - "15 Mason-Dixon 1.10 1.15\n", - "16 Mitchell 0.96 1.43\n", - "17 Ohio Poll 1.24 1.05\n", - "18 Public Opinion Strate... 0.63 1.81\n", - "19 Public Policy Polling... 1.05 1.60\n", - "20 Quinnipiac 0.95 1.34\n", - "21 Rasmussen 1.30 0.88\n", - "22 Research 2000 1.01 1.20\n", - "23 Selzer 1.47 0.92\n", - "24 Star Tribune (MN) 0.81 2.01\n", - "25 Strategic Vision 0.95 1.45\n", - "26 Suffolk (NH/MA) 0.77 1.37\n", - "27 SurveyUSA 1.91 0.72\n", - "28 Univ. New Hampshire 1.08 1.26\n", - "29 USA Today / Gallup 0.63 2.01\n", - "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74" + " Pollster Weight PIE\n", + "0 ABC / Washington Post 0.95 1.41\n", + "1 American Research Group 0.65 1.76\n", + "2 CBS / New York Times 0.66 1.84\n", + "3 Chicago Trib. / MarketShares 1.16 1.13\n", + "4 CNN / Opinion Research 0.77 1.59\n", + "5 Columbus Dispatch (OH) 0.50 6.76\n", + "6 EPIC-MRA 0.75 1.65\n", + "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", + "8 Field Poll (CA) 1.33 0.88\n", + "9 Fox / Opinion Dynamics 0.79 1.60\n", + "10 Franklin Pierce (NH) 0.74 1.60\n", + "11 Insider Advantage 0.95 1.29\n", + "12 Keystone (PA) 0.64 1.55\n", + "13 LA Times / Bloomberg 0.83 1.44\n", + "14 Marist (NY) 0.69 1.73\n", + "15 Mason-Dixon 1.10 1.15\n", + "16 Mitchell 0.96 1.43\n", + "17 Ohio Poll 1.24 1.05\n", + "18 Public Opinion Strategies 0.63 1.81\n", + "19 Public Policy Polling (PPP) 1.05 1.60\n", + "20 Quinnipiac 0.95 1.34\n", + "21 Rasmussen 1.30 0.88\n", + "22 Research 2000 1.01 1.20\n", + "23 Selzer 1.47 0.92\n", + "24 Star Tribune (MN) 0.81 2.01\n", + "25 Strategic Vision 0.95 1.45\n", + "26 Suffolk (NH/MA) 0.77 1.37\n", + "27 SurveyUSA 1.91 0.72\n", + "28 Univ. New Hampshire 1.08 1.26\n", + "29 USA Today / Gallup 0.63 2.01\n", + "30 Zogby 0.64 1.72\n", + "31 Zogby Interactive 0.43 4.74" ] }, - "execution_count": 198, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -7141,7 +10223,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 163, "metadata": { "collapsed": false }, @@ -7154,7 +10236,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 164, "metadata": { "collapsed": false }, @@ -7165,7 +10247,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 165, "metadata": { "collapsed": false }, @@ -7176,7 +10258,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 166, "metadata": { "collapsed": false }, @@ -7188,31 +10270,111 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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residState
3045.224651Wisconsin
3057.432851Wisconsin
3060.277203Wisconsin
30710.002459Wisconsin
3080.728450Wisconsin
3092.679444Wisconsin
310-0.268988Wisconsin
3110.890637Wisconsin
3127.240347Wisconsin
313-0.644929Wisconsin
314-5.380660Wisconsin
315-1.534117Wisconsin
316-1.534117Wisconsin
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" + ], "text/plain": [ - " resid State\n", - "307 5.193 Wisconsin\n", - "308 7.402 Wisconsin\n", - "309 0.246 Wisconsin\n", - "310 9.971 Wisconsin\n", - "311 0.697 Wisconsin\n", - "312 2.648 Wisconsin\n", - "313 -0.300 Wisconsin\n", - "314 0.859 Wisconsin\n", - "315 7.209 Wisconsin\n", - "316 -0.676 Wisconsin\n", - "317 -5.412 Wisconsin\n", - "318 -1.565 Wisconsin\n", - "319 -1.565 Wisconsin" + " resid State\n", + "304 5.224651 Wisconsin\n", + "305 7.432851 Wisconsin\n", + "306 0.277203 Wisconsin\n", + "307 10.002459 Wisconsin\n", + "308 0.728450 Wisconsin\n", + "309 2.679444 Wisconsin\n", + "310 -0.268988 Wisconsin\n", + "311 0.890637 Wisconsin\n", + "312 7.240347 Wisconsin\n", + "313 -0.644929 Wisconsin\n", + "314 -5.380660 Wisconsin\n", + "315 -1.534117 Wisconsin\n", + "316 -1.534117 Wisconsin" ] }, - "execution_count": 203, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -7223,7 +10385,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 168, "metadata": { "collapsed": false }, @@ -7234,7 +10396,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 169, "metadata": { "collapsed": false }, @@ -7243,51 +10405,51 @@ "data": { "text/plain": [ "State\n", - "Arizona -6.351\n", - "California 19.794\n", - "Colorado 6.947\n", - "Connecticut 13.967\n", - "Florida 2.079\n", - "Georgia -8.969\n", - "Hawaii 31.233\n", - "Illinois 26.869\n", - "Indiana -6.870\n", - "Iowa 2.325\n", - "Kansas -9.541\n", - "Maine 12.734\n", - "Maryland 28.856\n", - "Massachusetts 21.816\n", - "Michigan 8.561\n", - "Minnesota 8.046\n", - "Mississippi -8.637\n", - "Missouri -1.974\n", - "Montana -7.035\n", - "Nebraska -8.663\n", - "Nevada 9.022\n", - "New Hampshire -1.133\n", - "New Jersey 13.545\n", - "New Mexico 9.145\n", - "New York 23.207\n", - "North Carolina -0.590\n", - "North Dakota -9.138\n", - "Ohio 4.384\n", - "Oregon 9.117\n", - "Pennsylvania 5.692\n", - "Rhode Island 25.931\n", - "South Carolina -6.767\n", - "South Dakota -1.136\n", - "Tennessee -2.883\n", - "Texas -2.578\n", - "Utah -29.142\n", - "Vermont 16.811\n", - "Virginia 4.985\n", - "Washington 16.118\n", - "West Virginia -9.776\n", - "Wisconsin 4.909\n", - "Name: poll" + "Arizona -6.072142\n", + "California 19.966475\n", + "Colorado 2.671181\n", + "Connecticut 8.940155\n", + "Florida 2.170963\n", + "Georgia -8.813442\n", + "Hawaii 18.594667\n", + "Illinois 15.486753\n", + "Indiana -7.342898\n", + "Iowa 2.036824\n", + "Kansas -9.767203\n", + "Maine 12.220123\n", + "Maryland 16.394026\n", + "Massachusetts 14.180592\n", + "Michigan 8.333980\n", + "Minnesota 7.286051\n", + "Mississippi -8.227142\n", + "Missouri -2.215565\n", + "Montana -7.241413\n", + "Nebraska -8.833230\n", + "Nevada 5.096197\n", + "New Hampshire -1.544893\n", + "New Jersey 10.643486\n", + "New Mexico 9.586405\n", + "New York 23.473550\n", + "North Carolina -0.415848\n", + "North Dakota -9.339133\n", + "Ohio 4.175204\n", + "Oregon 8.681383\n", + "Pennsylvania 5.435867\n", + "Rhode Island 13.246753\n", + "South Carolina -6.340670\n", + "South Dakota -1.523693\n", + "Tennessee -2.526349\n", + "Texas -2.295507\n", + "Utah -29.179413\n", + "Vermont 15.684839\n", + "Virginia 2.422245\n", + "Washington 12.315473\n", + "West Virginia -9.441829\n", + "Wisconsin 4.528761\n", + "Name: poll, dtype: float64" ] }, - "execution_count": 205, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -7298,7 +10460,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 170, "metadata": { "collapsed": false }, @@ -7313,102 +10475,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "results[[\"State\", \"poll\"]].to_csv(\"/home/skipper/school/talks/538model/2012-predicted.csv\", index=False)" + "results[[\"State\", \"poll\"]].to_csv(\"./data/2012-predicted.csv\", index=False)" ] }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "electoral_votes = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/electoral_votes.csv\")" + "electoral_votes = pandas.read_csv(\"./data/electoral_votes.csv\")" ] }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 173, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - " State Votes\n", - "0 Alabama 9\n", - "1 Alaska 3\n", - "2 Arizona 11\n", - "3 Arkansas 6\n", - "4 California 55\n", - "5 Colorado 9\n", - "6 Connecticut 7\n", - "7 Delaware 3\n", - "8 District of Columbia 3\n", - "9 Florida 29\n", - "10 Georgia 16\n", - "11 Hawaii 4\n", - "12 Idaho 4\n", - "13 Illinois 20\n", - "14 Indiana 11\n", - "15 Iowa 6\n", - "16 Kansas 6\n", - "17 Kentucky 8\n", - "18 Louisiana 8\n", - "19 Maine 4\n", - "20 Maryland 10\n", - "21 Massachusetts 11\n", - "22 Michigan 16\n", - "23 Minnesota 10\n", - "24 Mississippi 6\n", - "25 Missouri 10\n", - "26 Montana 3\n", - "27 Nebraska 5\n", - "28 Nevada 6\n", - "29 New Hampshire 4\n", - "30 New Jersey 14\n", - "31 New Mexico 5\n", - "32 New York 29\n", - "33 North Carolina 15\n", - "34 North Dakota 3\n", - "35 Ohio 18\n", - "36 Oklahoma 7\n", - "37 Oregon 7\n", - "38 Pennsylvania 20\n", - "39 Rhode Island 4\n", - "40 South Carolina 9\n", - "41 South Dakota 3\n", - "42 Tennessee 11\n", - "43 Texas 38\n", - "44 Utah 6\n", - "45 Vermont 3\n", - "46 Virginia 13\n", - "47 Washington 12\n", - "48 West Virginia 5\n", - "49 Wisconsin 10\n", - "50 Wyoming 3" - ] - }, - "execution_count": 208, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "electoral_votes.sort(\"State\", inplace=True).reset_index(drop=True, inplace=True)" + "electoral_votes.sort(\"State\", inplace=True)\n", + "electoral_votes.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 174, "metadata": { "collapsed": false }, @@ -7419,7 +10520,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 175, "metadata": { "collapsed": false }, @@ -7437,70 +10538,451 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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Votespollobamaromney
State
Alabama9NaN01
Alaska3NaN01
Arizona11-6.07214201
Arkansas6NaN01
California5519.96647510
Colorado92.67118110
Connecticut78.94015510
Delaware3NaN10
District of Columbia3NaN10
Florida292.17096310
Georgia16-8.81344201
Hawaii418.59466710
Idaho4NaN01
Illinois2015.48675310
Indiana11-7.34289801
Iowa62.03682410
Kansas6-9.76720301
Kentucky8NaN01
Louisiana8NaN01
Maine412.22012310
Maryland1016.39402610
Massachusetts1114.18059210
Michigan168.33398010
Minnesota107.28605110
Mississippi6-8.22714201
Missouri10-2.21556501
Montana3-7.24141301
Nebraska5-8.83323001
Nevada65.09619710
New Hampshire4-1.54489301
New Jersey1410.64348610
New Mexico59.58640510
New York2923.47355010
North Carolina15-0.41584801
North Dakota3-9.33913301
Ohio184.17520410
Oklahoma7NaN01
Oregon78.68138310
Pennsylvania205.43586710
Rhode Island413.24675310
South Carolina9-6.34067001
South Dakota3-1.52369301
Tennessee11-2.52634901
Texas38-2.29550701
Utah6-29.17941301
Vermont315.68483910
Virginia132.42224510
Washington1212.31547310
West Virginia5-9.44182901
Wisconsin104.52876110
Wyoming3NaN01
\n", + "
" + ], "text/plain": [ - " Votes poll obama romney\n", - "State \n", - "Alabama 9 NaN 0 1\n", - "Alaska 3 NaN 0 1\n", - "Arizona 11 -6.351 0 1\n", - "Arkansas 6 NaN 0 1\n", - "California 55 19.794 1 0\n", - "Colorado 9 6.947 1 0\n", - "Connecticut 7 13.967 1 0\n", - "Delaware 3 NaN 1 0\n", - "District of Columbia 3 NaN 1 0\n", - "Florida 29 2.079 1 0\n", - "Georgia 16 -8.969 0 1\n", - "Hawaii 4 31.233 1 0\n", - "Idaho 4 NaN 0 1\n", - "Illinois 20 26.869 1 0\n", - "Indiana 11 -6.870 0 1\n", - "Iowa 6 2.325 1 0\n", - "Kansas 6 -9.541 0 1\n", - "Kentucky 8 NaN 0 1\n", - "Louisiana 8 NaN 0 1\n", - "Maine 4 12.734 1 0\n", - "Maryland 10 28.856 1 0\n", - "Massachusetts 11 21.816 1 0\n", - "Michigan 16 8.561 1 0\n", - "Minnesota 10 8.046 1 0\n", - "Mississippi 6 -8.637 0 1\n", - "Missouri 10 -1.974 0 1\n", - "Montana 3 -7.035 0 1\n", - "Nebraska 5 -8.663 0 1\n", - "Nevada 6 9.022 1 0\n", - "New Hampshire 4 -1.133 0 1\n", - "New Jersey 14 13.545 1 0\n", - "New Mexico 5 9.145 1 0\n", - "New York 29 23.207 1 0\n", - "North Carolina 15 -0.590 0 1\n", - "North Dakota 3 -9.138 0 1\n", - "Ohio 18 4.384 1 0\n", - "Oklahoma 7 NaN 0 1\n", - "Oregon 7 9.117 1 0\n", - "Pennsylvania 20 5.692 1 0\n", - "Rhode Island 4 25.931 1 0\n", - "South Carolina 9 -6.767 0 1\n", - "South Dakota 3 -1.136 0 1\n", - "Tennessee 11 -2.883 0 1\n", - "Texas 38 -2.578 0 1\n", - "Utah 6 -29.142 0 1\n", - "Vermont 3 16.811 1 0\n", - "Virginia 13 4.985 1 0\n", - "Washington 12 16.118 1 0\n", - "West Virginia 5 -9.776 0 1\n", - "Wisconsin 10 4.909 1 0\n", - "Wyoming 3 NaN 0 1" + " Votes poll obama romney\n", + "State \n", + "Alabama 9 NaN 0 1\n", + "Alaska 3 NaN 0 1\n", + "Arizona 11 -6.072142 0 1\n", + "Arkansas 6 NaN 0 1\n", + "California 55 19.966475 1 0\n", + "Colorado 9 2.671181 1 0\n", + "Connecticut 7 8.940155 1 0\n", + "Delaware 3 NaN 1 0\n", + "District of Columbia 3 NaN 1 0\n", + "Florida 29 2.170963 1 0\n", + "Georgia 16 -8.813442 0 1\n", + "Hawaii 4 18.594667 1 0\n", + "Idaho 4 NaN 0 1\n", + "Illinois 20 15.486753 1 0\n", + "Indiana 11 -7.342898 0 1\n", + "Iowa 6 2.036824 1 0\n", + "Kansas 6 -9.767203 0 1\n", + "Kentucky 8 NaN 0 1\n", + "Louisiana 8 NaN 0 1\n", + "Maine 4 12.220123 1 0\n", + "Maryland 10 16.394026 1 0\n", + "Massachusetts 11 14.180592 1 0\n", + "Michigan 16 8.333980 1 0\n", + "Minnesota 10 7.286051 1 0\n", + "Mississippi 6 -8.227142 0 1\n", + "Missouri 10 -2.215565 0 1\n", + "Montana 3 -7.241413 0 1\n", + "Nebraska 5 -8.833230 0 1\n", + "Nevada 6 5.096197 1 0\n", + "New Hampshire 4 -1.544893 0 1\n", + "New Jersey 14 10.643486 1 0\n", + "New Mexico 5 9.586405 1 0\n", + "New York 29 23.473550 1 0\n", + "North Carolina 15 -0.415848 0 1\n", + "North Dakota 3 -9.339133 0 1\n", + "Ohio 18 4.175204 1 0\n", + "Oklahoma 7 NaN 0 1\n", + "Oregon 7 8.681383 1 0\n", + "Pennsylvania 20 5.435867 1 0\n", + "Rhode Island 4 13.246753 1 0\n", + "South Carolina 9 -6.340670 0 1\n", + "South Dakota 3 -1.523693 0 1\n", + "Tennessee 11 -2.526349 0 1\n", + "Texas 38 -2.295507 0 1\n", + "Utah 6 -29.179413 0 1\n", + "Vermont 3 15.684839 1 0\n", + "Virginia 13 2.422245 1 0\n", + "Washington 12 12.315473 1 0\n", + "West Virginia 5 -9.441829 0 1\n", + "Wisconsin 10 4.528761 1 0\n", + "Wyoming 3 NaN 0 1" ] }, - "execution_count": 211, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -7511,7 +10993,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 177, "metadata": { "collapsed": false }, @@ -7522,7 +11004,7 @@ "328.0" ] }, - "execution_count": 212, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -7533,7 +11015,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 178, "metadata": { "collapsed": false }, @@ -7544,7 +11026,7 @@ "210.0" ] }, - "execution_count": 213, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -7555,70 +11037,451 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 179, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "text/html": [ + "
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Votespollobamaromney
State
Alabama9NaN01
Alaska3NaN01
Arizona11-6.07214201
Arkansas6NaN01
California5519.96647510
Colorado92.67118110
Connecticut78.94015510
Delaware3NaN10
District of Columbia3NaN10
Florida292.17096310
Georgia16-8.81344201
Hawaii418.59466710
Idaho4NaN01
Illinois2015.48675310
Indiana11-7.34289801
Iowa62.03682410
Kansas6-9.76720301
Kentucky8NaN01
Louisiana8NaN01
Maine412.22012310
Maryland1016.39402610
Massachusetts1114.18059210
Michigan168.33398010
Minnesota107.28605110
Mississippi6-8.22714201
Missouri10-2.21556501
Montana3-7.24141301
Nebraska5-8.83323001
Nevada65.09619710
New Hampshire4-1.54489301
New Jersey1410.64348610
New Mexico59.58640510
New York2923.47355010
North Carolina15-0.41584801
North Dakota3-9.33913301
Ohio184.17520410
Oklahoma7NaN01
Oregon78.68138310
Pennsylvania205.43586710
Rhode Island413.24675310
South Carolina9-6.34067001
South Dakota3-1.52369301
Tennessee11-2.52634901
Texas38-2.29550701
Utah6-29.17941301
Vermont315.68483910
Virginia132.42224510
Washington1212.31547310
West Virginia5-9.44182901
Wisconsin104.52876110
Wyoming3NaN01
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" + ], "text/plain": [ - " Votes poll obama romney\n", - "State \n", - "Alabama 9 NaN 0 1\n", - "Alaska 3 NaN 0 1\n", - "Arizona 11 -6.351 0 1\n", - "Arkansas 6 NaN 0 1\n", - "California 55 19.794 1 0\n", - "Colorado 9 6.947 1 0\n", - "Connecticut 7 13.967 1 0\n", - "Delaware 3 NaN 1 0\n", - "District of Columbia 3 NaN 1 0\n", - "Florida 29 2.079 1 0\n", - "Georgia 16 -8.969 0 1\n", - "Hawaii 4 31.233 1 0\n", - "Idaho 4 NaN 0 1\n", - "Illinois 20 26.869 1 0\n", - "Indiana 11 -6.870 0 1\n", - "Iowa 6 2.325 1 0\n", - "Kansas 6 -9.541 0 1\n", - "Kentucky 8 NaN 0 1\n", - "Louisiana 8 NaN 0 1\n", - "Maine 4 12.734 1 0\n", - "Maryland 10 28.856 1 0\n", - "Massachusetts 11 21.816 1 0\n", - "Michigan 16 8.561 1 0\n", - "Minnesota 10 8.046 1 0\n", - "Mississippi 6 -8.637 0 1\n", - "Missouri 10 -1.974 0 1\n", - "Montana 3 -7.035 0 1\n", - "Nebraska 5 -8.663 0 1\n", - "Nevada 6 9.022 1 0\n", - "New Hampshire 4 -1.133 0 1\n", - "New Jersey 14 13.545 1 0\n", - "New Mexico 5 9.145 1 0\n", - "New York 29 23.207 1 0\n", - "North Carolina 15 -0.590 0 1\n", - "North Dakota 3 -9.138 0 1\n", - "Ohio 18 4.384 1 0\n", - "Oklahoma 7 NaN 0 1\n", - "Oregon 7 9.117 1 0\n", - "Pennsylvania 20 5.692 1 0\n", - "Rhode Island 4 25.931 1 0\n", - "South Carolina 9 -6.767 0 1\n", - "South Dakota 3 -1.136 0 1\n", - "Tennessee 11 -2.883 0 1\n", - "Texas 38 -2.578 0 1\n", - "Utah 6 -29.142 0 1\n", - "Vermont 3 16.811 1 0\n", - "Virginia 13 4.985 1 0\n", - "Washington 12 16.118 1 0\n", - "West Virginia 5 -9.776 0 1\n", - "Wisconsin 10 4.909 1 0\n", - "Wyoming 3 NaN 0 1" + " Votes poll obama romney\n", + "State \n", + "Alabama 9 NaN 0 1\n", + "Alaska 3 NaN 0 1\n", + "Arizona 11 -6.072142 0 1\n", + "Arkansas 6 NaN 0 1\n", + "California 55 19.966475 1 0\n", + "Colorado 9 2.671181 1 0\n", + "Connecticut 7 8.940155 1 0\n", + "Delaware 3 NaN 1 0\n", + "District of Columbia 3 NaN 1 0\n", + "Florida 29 2.170963 1 0\n", + "Georgia 16 -8.813442 0 1\n", + "Hawaii 4 18.594667 1 0\n", + "Idaho 4 NaN 0 1\n", + "Illinois 20 15.486753 1 0\n", + "Indiana 11 -7.342898 0 1\n", + "Iowa 6 2.036824 1 0\n", + "Kansas 6 -9.767203 0 1\n", + "Kentucky 8 NaN 0 1\n", + "Louisiana 8 NaN 0 1\n", + "Maine 4 12.220123 1 0\n", + "Maryland 10 16.394026 1 0\n", + "Massachusetts 11 14.180592 1 0\n", + "Michigan 16 8.333980 1 0\n", + "Minnesota 10 7.286051 1 0\n", + "Mississippi 6 -8.227142 0 1\n", + "Missouri 10 -2.215565 0 1\n", + "Montana 3 -7.241413 0 1\n", + "Nebraska 5 -8.833230 0 1\n", + "Nevada 6 5.096197 1 0\n", + "New Hampshire 4 -1.544893 0 1\n", + "New Jersey 14 10.643486 1 0\n", + "New Mexico 5 9.586405 1 0\n", + "New York 29 23.473550 1 0\n", + "North Carolina 15 -0.415848 0 1\n", + "North Dakota 3 -9.339133 0 1\n", + "Ohio 18 4.175204 1 0\n", + "Oklahoma 7 NaN 0 1\n", + "Oregon 7 8.681383 1 0\n", + "Pennsylvania 20 5.435867 1 0\n", + "Rhode Island 4 13.246753 1 0\n", + "South Carolina 9 -6.340670 0 1\n", + "South Dakota 3 -1.523693 0 1\n", + "Tennessee 11 -2.526349 0 1\n", + "Texas 38 -2.295507 0 1\n", + "Utah 6 -29.179413 0 1\n", + "Vermont 3 15.684839 1 0\n", + "Virginia 13 2.422245 1 0\n", + "Washington 12 12.315473 1 0\n", + "West Virginia 5 -9.441829 0 1\n", + "Wisconsin 10 4.528761 1 0\n", + "Wyoming 3 NaN 0 1" ] }, - "execution_count": 214, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } From 835aab58334df392cbbb5ba30a6aaff00b4c9f8c Mon Sep 17 00:00:00 2001 From: Jim Date: Tue, 24 May 2016 17:08:34 -0400 Subject: [PATCH 04/11] Simulation and Slides --- Simulation.ipynb | 548 +++++++++++++++++++++++++++++++++++++++++++++++ Slides.ipynb | 112 ++++++++++ 2 files changed, 660 insertions(+) create mode 100644 Simulation.ipynb create mode 100644 Slides.ipynb diff --git a/Simulation.ipynb b/Simulation.ipynb new file mode 100644 index 0000000..d7b0492 --- /dev/null +++ b/Simulation.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from pandas import Series, DataFrame\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "N = 10000\n", + "states = [\"s1\", \"s2\", \"s3\", \"s4\", \"s5\"]\n", + "state_electorial_college_votes = Series(np.array([5, 10, 15, 20, 25]), index=states)\n", + "local_vote_predictions = Series(np.array([0.53, 0.54, 0.56, 0.52, 0.46]), index=states)\n", + "local_margin_of_error = Series(np.array([0.07, 0.05, 0.04, 0.02, 0.04]), index=states)\n", + "national_margin_of_error = 0.03" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "local_error_sim = DataFrame(np.random.randn(N, len(states)), columns=states).multiply(local_margin_of_error)\n", + "national_error_sim = Series(np.random.randn(N) * national_margin_of_error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "LocalError = \\sqrt{TotalError^{2} + NationalError^{2}}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "TotalError = \\sqrt{LocalError^{2} + NationalError^{2}}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# total_error_signs = DataFrame(np.random.choice([-1, 1], size=local_error_sim.shape), columns=states)\n", + "# total_error_sim = (local_error_sim**2).add(national_error_sim**2, axis='rows') * total_error_signs\n", + "total_error_sim = local_error_sim.add(national_error_sim, axis='rows')" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "simulated_vote_predictions = total_error_sim.add(local_vote_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " s1 s2 s3 s4 s5\n", + "s1 1.000000 0.199057 0.219443 0.320138 0.239901\n", + "s2 0.199057 1.000000 0.307852 0.431101 0.298213\n", + "s3 0.219443 0.307852 1.000000 0.507409 0.356670\n", + "s4 0.320138 0.431101 0.507409 1.000000 0.486735\n", + "s5 0.239901 0.298213 0.356670 0.486735 1.000000" + ] + }, + "execution_count": 319, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simulated_vote_predictions.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bins = np.arange(0, 1, 0.01)\n", + "histograms = {s: np.histogram(simulated_vote_predictions[s], bins=bins, density=True)[0] for s in states}" + ] + }, + { + "cell_type": "code", + "execution_count": 359, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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/4oQTTsi6KElSOysUChQKhTafpy09o6OAGSmlzZrffxw4PaV0SIt29oxKkipe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\n8UPATufcQ2Z2B9DKOXfnAd7rtPG3iIgkO7Nv7/Wt7z4REUk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rep_electorial_college_votes.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 409, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.65849999999999997" + ] + }, + "execution_count": 409, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DEM chance of winning\n", + "sum(dem_simulated_electorial_college['total'] > rep_simulated_electorial_college['total']) / float(N)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Slides.ipynb b/Slides.ipynb new file mode 100644 index 0000000..5802643 --- /dev/null +++ b/Slides.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction - Nate Silver's Election Prediction Meta-Analysis\n", + "\n", + "* Presenters: Ritesh Bansal and James Schmitz\n", + " * We don't know Nate Silver, but we are motivated by curiousity and respect of his work\n", + "* Skipper Seabold put together original presentation and Jupyter Notebooks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Nate Silver's Analysis Goal\n", + "\n", + "\"...but somewhat contrary to the media portrayal of election forecasters as wizards who conjure up spells from their spreadsheets, our goal is not to divine some magic formula that miraculously predicts every election. Instead, it’s to make sense of publicly available information in a rigorous and disciplined way.\" - Nate Silver" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Meta-Analysis\n", + "\n", + "(insert definition)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Polling - Combining Poll Results\n", + "\n", + "* Remove historical biases and de-emphasize old polls or polls with methodology flaws\n", + "* Why are polls not accurate?\n", + "* Likely votes vs Actual Voters\n", + "* Is the sample of atual voters representative of the actual population?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Poll Time Decay\n", + "\n", + "* Polls decay over time\n", + "* Adjust older polls for trends" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Macroeconomic Prediction\n", + "\n", + "(is this the right term for this? how about fundamental analysis based on incumbant status, etc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combine Poll results with Macroeconomic Prediction\n", + "\n", + "* ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Project out by allocating the undecided voters\n", + "\n", + "* ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Prediction Uncertainty and Simulate\n", + "\n", + "* Guestimate Electorial college votes" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 80cf6effdb0b591b572a28f10be4599e5fc48f8d Mon Sep 17 00:00:00 2001 From: Ritesh Bansal Date: Tue, 24 May 2016 19:09:37 -0400 Subject: [PATCH 05/11] added env --- pydata_env_export.env | 74 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 pydata_env_export.env diff --git a/pydata_env_export.env b/pydata_env_export.env new file mode 100644 index 0000000..8db0028 --- /dev/null +++ b/pydata_env_export.env @@ -0,0 +1,74 @@ +name: pydata +dependencies: +- appnope=0.1.0=py27_0 +- backports=1.0=py27_0 +- backports_abc=0.4=py27_0 +- configparser=3.5.0b2=py27_1 +- decorator=4.0.9=py27_0 +- entrypoints=0.2=py27_1 +- freetype=2.5.5=0 +- functools32=3.2.3.2=py27_0 +- get_terminal_size=1.0.0=py27_0 +- ipykernel=4.3.1=py27_0 +- ipython=4.2.0=py27_1 +- ipython_genutils=0.1.0=py27_0 +- ipywidgets=4.1.1=py27_0 +- jinja2=2.8=py27_0 +- jsonschema=2.5.1=py27_0 +- jupyter=1.0.0=py27_2 +- jupyter_client=4.2.2=py27_0 +- jupyter_console=4.1.1=py27_0 +- jupyter_core=4.1.0=py27_0 +- libpng=1.6.17=0 +- markupsafe=0.23=py27_0 +- matplotlib=1.4.3=np19py27_3 +- mistune=0.7.2=py27_1 +- mkl=11.3.3=0 +- nbconvert=4.2.0=py27_0 +- nbformat=4.0.1=py27_0 +- notebook=4.2.0=py27_0 +- numpy=1.11.0=py27_1 +- openssl=1.0.2h=0 +- pandas=0.16.2=np19py27_0 +- path.py=8.2.1=py27_0 +- patsy=0.4.1=py27_0 +- pexpect=4.0.1=py27_0 +- pickleshare=0.5=py27_0 +- pip=8.1.1=py27_1 +- ptyprocess=0.5=py27_0 +- pygments=2.1.3=py27_0 +- pyparsing=2.0.3=py27_0 +- pyqt=4.11.4=py27_1 +- python=2.7.11=0 +- python-dateutil=2.5.3=py27_0 +- python.app=1.2=py27_4 +- pytz=2016.4=py27_0 +- pyzmq=15.2.0=py27_0 +- qt=4.8.7=1 +- qtconsole=4.2.1=py27_0 +- readline=6.2=2 +- scikit-learn=0.17.1=np111py27_1 +- scipy=0.17.1=np111py27_0 +- setuptools=21.2.1=py27_0 +- simplegeneric=0.8.1=py27_0 +- singledispatch=3.4.0.3=py27_0 +- sip=4.16.9=py27_0 +- six=1.10.0=py27_0 +- sqlite=3.9.2=0 +- ssl_match_hostname=3.4.0.2=py27_1 +- statsmodels=0.6.1=np19py27_0 +- terminado=0.5=py27_1 +- tk=8.5.18=0 +- tornado=4.3=py27_0 +- traitlets=4.2.1=py27_0 +- wheel=0.29.0=py27_0 +- zlib=1.2.8=0 +- pip: + - backports-abc==0.4 + - backports.shutil-get-terminal-size==1.0.0 + - backports.ssl-match-hostname==3.4.0.2 + - ipython-genutils==0.1.0 + - jupyter-client==4.2.2 + - jupyter-console==4.1.1 + - jupyter-core==4.1.0 + From b814485adccc9e655982739e2fa2d6778e9e08ed Mon Sep 17 00:00:00 2001 From: Ritesh Bansal Date: Tue, 24 May 2016 19:11:07 -0400 Subject: [PATCH 06/11] moved 2012-predicted.csv to data dir --- data/2012-predicted.csv | 74 ++++++++++++++++++++--------------------- 1 file changed, 37 insertions(+), 37 deletions(-) diff --git a/data/2012-predicted.csv b/data/2012-predicted.csv index 48379a1..566dede 100644 --- a/data/2012-predicted.csv +++ b/data/2012-predicted.csv @@ -1,42 +1,42 @@ State,poll -Arizona,-6.07214167012 +Arizona,-5.47338235568 California,19.9664750649 -Colorado,2.67118129814 -Connecticut,8.9401551505 +Colorado,2.70582862386 +Connecticut,8.98092251576 Florida,2.17096302191 -Georgia,-8.81344162236 -Hawaii,18.5946671349 -Illinois,15.4867527001 -Indiana,-7.34289753635 -Iowa,2.03682437111 -Kansas,-9.76720266945 -Maine,12.2201232519 -Maryland,16.3940261257 -Massachusetts,14.1805921769 -Michigan,8.33397983927 -Minnesota,7.28605122763 -Mississippi,-8.22714240889 -Missouri,-2.21556524617 -Montana,-7.2414126942 -Nebraska,-8.8332297192 -Nevada,5.09619740646 -New Hampshire,-1.54489284704 -New Jersey,10.6434861203 -New Mexico,9.58640543396 +Georgia,-8.79052658316 +Hawaii,18.6970465167 +Illinois,15.5789784841 +Indiana,-7.36662226941 +Iowa,2.02234485831 +Kansas,-9.77854656124 +Maine,12.1942940297 +Maryland,16.4950271772 +Massachusetts,14.2424602294 +Michigan,8.32254279018 +Minnesota,6.8269245287 +Mississippi,-8.16652344925 +Missouri,-2.22766788175 +Montana,-7.25170680045 +Nebraska,-8.84173818768 +Nevada,4.78033849449 +New Hampshire,-1.52243896396 +New Jersey,10.6670169688 +New Mexico,9.65142194494 New York,23.4735500439 -North Carolina,-0.415848216015 -North Dakota,-9.33913293328 -Ohio,4.17520432162 -Oregon,8.68138312385 -Pennsylvania,5.435867347 -Rhode Island,13.2467532472 -South Carolina,-6.34067005172 -South Dakota,-1.52369274353 -Tennessee,-2.52634864683 +North Carolina,-0.39008891323 +North Dakota,-9.34916878139 +Ohio,4.16473342997 +Oregon,8.65947940788 +Pennsylvania,5.42298139475 +Rhode Island,13.3495112334 +South Carolina,-6.27767039939 +South Dakota,-1.54303362486 +Tennessee,-2.47362790033 Texas,-2.29550733867 -Utah,-29.1794130385 -Vermont,15.6848389883 -Virginia,2.42224505406 -Washington,12.3154731284 -West Virginia,-9.44182914139 -Wisconsin,4.528761127 +Utah,-29.181185755 +Vermont,15.0056796275 +Virginia,2.44301244125 +Washington,12.3462816771 +West Virginia,-8.71977421619 +Wisconsin,4.50963521112 From 1468badae1bdf03c0238c24d7383ae2d64f05189 Mon Sep 17 00:00:00 2001 From: Jim Date: Tue, 24 May 2016 22:16:37 -0400 Subject: [PATCH 07/11] graft simulation --- silver_model.ipynb | 7007 ++++++++++++++++---------------------------- 1 file changed, 2517 insertions(+), 4490 deletions(-) diff --git a/silver_model.ipynb b/silver_model.ipynb index e5e50d8..c66ffc3 100644 --- a/silver_model.ipynb +++ b/silver_model.ipynb @@ -254,247 +254,13 @@ " 1.5\n", " \n", " \n", - " 12\n", - " 12\n", - " 12\n", - " Douglas Duncan\n", - " Fannie Mae\n", - " 1.8\n", - " 1.7\n", - " \n", - " \n", - " 13\n", - " 13\n", - " 13\n", - " Robert Dye\n", - " Comerica Bank\n", - " 2.5\n", - " 2.2\n", - " \n", - " \n", - " 14\n", - " 14\n", - " 14\n", - " Maria Fiorini Ramirez/Joshua Shapiro\n", - " MFR, Inc.\n", - " 1.4\n", - " 1.2\n", - " \n", - " \n", - " 15\n", - " 15\n", - " 15\n", - " Ethan Harris\n", - " Bank of America Securities- Merrill Lynch\n", - " 1.3\n", - " 1.0\n", - " \n", - " \n", - " 16\n", - " 16\n", - " 16\n", - " Maury Harris\n", - " UBS\n", - " 1.5\n", - " 1.8\n", - " \n", - " \n", - " 17\n", - " 17\n", - " 17\n", - " Jan Hatzius\n", - " Goldman, Sachs & Co.\n", - " 2.3\n", - " 1.5\n", - " \n", - " \n", - " 18\n", - " 18\n", - " 18\n", - " Tracy Herrick\n", - " Avidbank\n", - " 1.8\n", - " 1.8\n", - " \n", - " \n", - " 19\n", - " 19\n", - " 19\n", - " Stuart Hoffman *\n", - " PNC Financial Services Group\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 20\n", - " 20\n", - " 20\n", - " Gene Huang\n", - " FedEx Corp.\n", - " 1.9\n", - " 1.7\n", - " \n", - " \n", - " 21\n", - " 21\n", - " 21\n", - " William B. Hummer\n", - " Wintrust Wealth Management\n", - " 1.7\n", - " 1.9\n", - " \n", - " \n", - " 22\n", - " 22\n", - " 22\n", - " Bruce Kasman\n", - " JP Morgan Chase & Co.\n", - " 1.5\n", - " 2.0\n", - " \n", - " \n", - " 23\n", - " 23\n", - " 23\n", - " Joseph LaVorgna\n", - " Deutsche Bank Securities Inc.\n", - " 2.7\n", - " 2.8\n", - " \n", - " \n", - " 24\n", - " 24\n", - " 24\n", - " Edward Leamer/David Shulman\n", - " UCLA Anderson Forecast\n", - " 1.3\n", - " 1.5\n", - " \n", - " \n", - " 25\n", - " 25\n", - " 25\n", - " Don Leavens/Tim Gill\n", - " NEMA Business Information Services\n", - " 1.7\n", - " 1.7\n", - " \n", - " \n", - " 26\n", - " 26\n", - " 26\n", - " John Lonski\n", - " Moody's Investors Service\n", - " 1.5\n", - " 1.3\n", - " \n", - " \n", - " 27\n", - " 27\n", - " 27\n", - " Dean Maki\n", - " Barclays Capital\n", - " 2.0\n", - " 2.5\n", - " \n", - " \n", - " 28\n", - " 28\n", - " 28\n", - " Aneta Markowska *\n", - " Societe Generale\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 29\n", - " 29\n", - " 29\n", - " Jim Meil/Arun Raha\n", - " Eaton Corp.\n", - " 1.2\n", - " 2.1\n", - " \n", - " \n", - " 30\n", - " 30\n", - " 30\n", - " Mark Nielson\n", - " MacroEcon Global Advisors\n", - " 2.2\n", - " 2.8\n", - " \n", - " \n", - " 31\n", - " 31\n", - " 31\n", - " Michael P. Niemira\n", - " International Council of Shopping Centers\n", - " 2.3\n", - " 2.2\n", - " \n", - " \n", - " 32\n", - " 32\n", - " 32\n", - " Jim O'Sullivan\n", - " High Frequency Economics\n", - " 2.5\n", - " 2.0\n", - " \n", - " \n", - " 33\n", - " 33\n", - " 33\n", - " Nicholas S. Perna\n", - " Perna Associates\n", - " 2.2\n", - " 1.5\n", - " \n", - " \n", - " 34\n", - " 34\n", - " 34\n", - " Dr. Joel Prakken/ Chris Varvares\n", - " Macroeconomic Advisers\n", - " 1.5\n", - " 1.4\n", - " \n", - " \n", - " 35\n", - " 35\n", - " 35\n", - " David Resler\n", - " Nomura Securities International\n", - " 1.9\n", - " 1.7\n", - " \n", - " \n", - " 36\n", - " 36\n", - " 36\n", - " John Ryding/Conrad DeQuadros\n", - " RDQ Economics\n", - " 2.1\n", - " 2.4\n", - " \n", - " \n", - " 37\n", - " 37\n", - " 37\n", - " John Silvia\n", - " Wells Fargo & Co.\n", - " 1.6\n", - " 1.7\n", - " \n", - " \n", - " 38\n", - " 38\n", - " 38\n", - " Allen Sinai\n", - " Decision Economics, Inc.\n", - " 2.1\n", - " 2.7\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 39\n", @@ -606,114 +372,65 @@ " \n", " \n", "\n", + "

51 rows × 6 columns

\n", "" ], "text/plain": [ - " Unnamed: 0 Unnamed: 0.1 Forecaster \\\n", - "0 0 0 Paul Ashworth \n", - "1 1 1 Nariman Behravesh \n", - "2 2 2 Richard Berner/ David Greenlaw * \n", - "3 3 3 Ram Bhagavatula \n", - "4 4 4 Beth Ann Bovino * \n", - "5 5 5 Jay Brinkmann \n", - "6 6 6 Michael Carey \n", - "7 7 7 Joseph Carson \n", - "8 8 8 Julia Coronado \n", - "9 9 9 Mike Cosgrove \n", - "10 10 10 Lou Crandall \n", - "11 11 11 J. Dewey Daane \n", - "12 12 12 Douglas Duncan \n", - "13 13 13 Robert Dye \n", - "14 14 14 Maria Fiorini Ramirez/Joshua Shapiro \n", - "15 15 15 Ethan Harris \n", - "16 16 16 Maury Harris \n", - "17 17 17 Jan Hatzius \n", - "18 18 18 Tracy Herrick \n", - "19 19 19 Stuart Hoffman * \n", - "20 20 20 Gene Huang \n", - "21 21 21 William B. Hummer \n", - "22 22 22 Bruce Kasman \n", - "23 23 23 Joseph LaVorgna \n", - "24 24 24 Edward Leamer/David Shulman \n", - "25 25 25 Don Leavens/Tim Gill \n", - "26 26 26 John Lonski \n", - "27 27 27 Dean Maki \n", - "28 28 28 Aneta Markowska * \n", - "29 29 29 Jim Meil/Arun Raha \n", - "30 30 30 Mark Nielson \n", - "31 31 31 Michael P. Niemira \n", - "32 32 32 Jim O'Sullivan \n", - "33 33 33 Nicholas S. Perna \n", - "34 34 34 Dr. Joel Prakken/ Chris Varvares \n", - "35 35 35 David Resler \n", - "36 36 36 John Ryding/Conrad DeQuadros \n", - "37 37 37 John Silvia \n", - "38 38 38 Allen Sinai \n", - "39 39 39 James F. Smith \n", - "40 40 40 Sean M. Snaith \n", - "41 41 41 Sung Won Sohn \n", - "42 42 42 Neal Soss \n", - "43 43 43 Stephen Stanley \n", - "44 44 44 Susan M. Sterne \n", - "45 45 45 Diane Swonk \n", - "46 46 46 Carl Tannenbaum \n", - "47 47 47 Bart van Ark \n", - "48 48 48 Brian S. Wesbury/ Robert Stein \n", - "49 49 49 William T. Wilson \n", - "50 50 50 Lawrence Yun \n", + " Unnamed: 0 Unnamed: 0.1 Forecaster Institution \\\n", + "0 0 0 Paul Ashworth Capital Economics \n", + "1 1 1 Nariman Behravesh IHS Global Insight \n", + "2 2 2 Richard Berner/ David Greenlaw * Morgan Stanley \n", + "3 3 3 Ram Bhagavatula Combinatorics Capital \n", + "4 4 4 Beth Ann Bovino * Standard and Poor's \n", + "5 5 5 Jay Brinkmann Mortgage Bankers Association \n", + "6 6 6 Michael Carey Credit Agricole CIB \n", + "7 7 7 Joseph Carson AllianceBernstein \n", + "8 8 8 Julia Coronado BNP Paribas \n", + "9 9 9 Mike Cosgrove Econoclast \n", + "10 10 10 Lou Crandall Wrightson ICAP \n", + "11 11 11 J. Dewey Daane Vanderbilt University \n", + ".. ... ... ... ... \n", + "39 39 39 James F. Smith Parsec Financial Management \n", + "40 40 40 Sean M. Snaith University of Central Florida \n", + "41 41 41 Sung Won Sohn California State University \n", + "42 42 42 Neal Soss CSFB \n", + "43 43 43 Stephen Stanley Pierpont Securities \n", + "44 44 44 Susan M. Sterne Economic Analysis Associates Inc. \n", + "45 45 45 Diane Swonk Mesirow Financial \n", + "46 46 46 Carl Tannenbaum The Northern Trust \n", + "47 47 47 Bart van Ark The Conference Board \n", + "48 48 48 Brian S. Wesbury/ Robert Stein First Trust Advisors, L.P. \n", + "49 49 49 William T. Wilson Skolkovo Institute for Emerging Market Studies \n", + "50 50 50 Lawrence Yun National Association of Realtors \n", + "\n", + " gdp_q3_2012 gdp_q4_2012 \n", + "0 2.0 1.5 \n", + "1 1.5 1.6 \n", + "2 NaN NaN \n", + "3 2.0 4.0 \n", + "4 NaN NaN \n", + "5 1.8 1.9 \n", + "6 1.7 1.6 \n", + "7 2.5 3.5 \n", + "8 1.4 1.6 \n", + "9 1.6 1.6 \n", + "10 1.8 1.8 \n", + "11 1.5 1.5 \n", + ".. ... ... \n", + "39 3.8 4.8 \n", + "40 1.7 1.9 \n", + "41 1.8 1.7 \n", + "42 1.5 2.2 \n", + "43 1.0 2.1 \n", + "44 2.2 1.9 \n", + "45 1.3 1.5 \n", + "46 1.7 2.0 \n", + "47 1.6 1.6 \n", + "48 2.5 3.0 \n", + "49 1.9 2.2 \n", + "50 1.7 2.1 \n", "\n", - " Institution gdp_q3_2012 gdp_q4_2012 \n", - "0 Capital Economics 2.0 1.5 \n", - "1 IHS Global Insight 1.5 1.6 \n", - "2 Morgan Stanley NaN NaN \n", - "3 Combinatorics Capital 2.0 4.0 \n", - "4 Standard and Poor's NaN NaN \n", - "5 Mortgage Bankers Association 1.8 1.9 \n", - "6 Credit Agricole CIB 1.7 1.6 \n", - "7 AllianceBernstein 2.5 3.5 \n", - "8 BNP Paribas 1.4 1.6 \n", - "9 Econoclast 1.6 1.6 \n", - "10 Wrightson ICAP 1.8 1.8 \n", - "11 Vanderbilt University 1.5 1.5 \n", - "12 Fannie Mae 1.8 1.7 \n", - "13 Comerica Bank 2.5 2.2 \n", - "14 MFR, Inc. 1.4 1.2 \n", - "15 Bank of America Securities- Merrill Lynch 1.3 1.0 \n", - "16 UBS 1.5 1.8 \n", - "17 Goldman, Sachs & Co. 2.3 1.5 \n", - "18 Avidbank 1.8 1.8 \n", - "19 PNC Financial Services Group NaN NaN \n", - "20 FedEx Corp. 1.9 1.7 \n", - "21 Wintrust Wealth Management 1.7 1.9 \n", - "22 JP Morgan Chase & Co. 1.5 2.0 \n", - "23 Deutsche Bank Securities Inc. 2.7 2.8 \n", - "24 UCLA Anderson Forecast 1.3 1.5 \n", - "25 NEMA Business Information Services 1.7 1.7 \n", - "26 Moody's Investors Service 1.5 1.3 \n", - "27 Barclays Capital 2.0 2.5 \n", - "28 Societe Generale NaN NaN \n", - "29 Eaton Corp. 1.2 2.1 \n", - "30 MacroEcon Global Advisors 2.2 2.8 \n", - "31 International Council of Shopping Centers 2.3 2.2 \n", - "32 High Frequency Economics 2.5 2.0 \n", - "33 Perna Associates 2.2 1.5 \n", - "34 Macroeconomic Advisers 1.5 1.4 \n", - "35 Nomura Securities International 1.9 1.7 \n", - "36 RDQ Economics 2.1 2.4 \n", - "37 Wells Fargo & Co. 1.6 1.7 \n", - "38 Decision Economics, Inc. 2.1 2.7 \n", - "39 Parsec Financial Management 3.8 4.8 \n", - "40 University of Central Florida 1.7 1.9 \n", - "41 California State University 1.8 1.7 \n", - "42 CSFB 1.5 2.2 \n", - "43 Pierpont Securities 1.0 2.1 \n", - "44 Economic Analysis Associates Inc. 2.2 1.9 \n", - "45 Mesirow Financial 1.3 1.5 \n", - "46 The Northern Trust 1.7 2.0 \n", - "47 The Conference Board 1.6 1.6 \n", - "48 First Trust Advisors, L.P. 2.5 3.0 \n", - "49 Skolkovo Institute for Emerging Market Studies 1.9 2.2 \n", - "50 National Association of Realtors 1.7 2.1 " + "[51 rows x 6 columns]" ] }, "execution_count": 4, @@ -903,19 +620,12 @@ "" ], "text/plain": [ - " Pollster Sample MoE Obama (D) Romney (R) Spread \\\n", - "0 RCP Average NaN -- 49.1 45.1 Obama +4.0 \n", - "1 Rasmussen Tracking 1500 3.0 48.0 47.0 Obama +1 \n", - "2 CNN/Opinion Research 783 3.5 50.0 47.0 Obama +3 \n", - "3 Gallup Tracking 3050 2.0 50.0 44.0 Obama +6 \n", - "4 Quinnipiac 1912 2.2 49.0 45.0 Obama +4 \n", - "\n", - " obama_spread State poll_date \n", - "0 4 USA 2012-09-28 \n", - "1 1 USA 2012-09-30 \n", - "2 3 USA 2012-09-29 \n", - "3 6 USA 2012-09-28 \n", - "4 4 USA 2012-09-28 " + " Pollster Sample MoE Obama (D) Romney (R) Spread obama_spread State poll_date\n", + "0 RCP Average NaN -- 49.1 45.1 Obama +4.0 4 USA 2012-09-28\n", + "1 Rasmussen Tracking 1500 3.0 48.0 47.0 Obama +1 1 USA 2012-09-30\n", + "2 CNN/Opinion Research 783 3.5 50.0 47.0 Obama +3 3 USA 2012-09-29\n", + "3 Gallup Tracking 3050 2.0 50.0 44.0 Obama +6 6 USA 2012-09-28\n", + "4 Quinnipiac 1912 2.2 49.0 45.0 Obama +4 4 USA 2012-09-28" ] }, "execution_count": 9, @@ -1030,19 +740,12 @@ "" ], "text/plain": [ - " Pollster State MoE Obama (D) Romney (R) Sample Spread \\\n", - "0 Rasmussen Reports WA 4.5 52 41 500 Obama +11 \n", - "1 Gravis Marketing WA 4.6 56 39 625 Obama +17 \n", - "2 Elway Poll WA 5.0 53 36 405 Obama +17 \n", - "3 SurveyUSA WA 4.4 54 38 524 Obama +16 \n", - "4 SurveyUSA WA 4.4 54 37 524 Obama +17 \n", - "\n", - " obama_spread poll_date \n", - "0 11 2012-09-26 \n", - "1 17 2012-09-22 \n", - "2 17 2012-09-11 \n", - "3 16 2012-09-08 \n", - "4 17 2012-08-02 " + " Pollster State MoE Obama (D) Romney (R) Sample Spread obama_spread poll_date\n", + "0 Rasmussen Reports WA 4.5 52 41 500 Obama +11 11 2012-09-26\n", + "1 Gravis Marketing WA 4.6 56 39 625 Obama +17 17 2012-09-22\n", + "2 Elway Poll WA 5.0 53 36 405 Obama +17 17 2012-09-11\n", + "3 SurveyUSA WA 4.4 54 38 524 Obama +16 16 2012-09-08\n", + "4 SurveyUSA WA 4.4 54 37 524 Obama +17 17 2012-08-02" ] }, "execution_count": 11, @@ -1111,43 +814,7 @@ "9 Boston Globe\n", "10 CBS/NYT/Quinnipiac\n", "11 CNN/Opinion Research\n", - "12 CNN/Time\n", - "13 CNU/Times-Dispatch\n", - "14 Caddell/McLaughlin/SAN (R)\n", - "15 Castleton State College\n", - "16 Chicago Tribune\n", - "17 Civitas (R)\n", - "18 Clarus Research\n", - "19 Columbus Dispatch*\n", - "20 Courier-Journal/SurveyUSA\n", - "21 Critical Insights\n", - "22 Daily Kos/PPP (D)\n", - "23 Dartmouth\n", - "24 Denver Post/SurveyUSA\n", - "25 Des Moines Register\n", - "26 Deseret News\n", - "27 Deseret News/KSL\n", - "28 Detroit News\n", - "29 EPIC-MRA\n", " ... \n", - "90 Siena\n", - "91 Sooner Poll\n", - "92 St. Cloud State U.\n", - "93 Star Tribune/Mason-Dixon*\n", - "94 Strategies 360 (D)\n", - "95 Suffolk University\n", - "96 Suffolk/7News\n", - "97 Suffolk/WSVN\n", - "98 Suffolk/WWBT\n", - "99 Sunshine State News/VSS\n", - "100 SurveyUSA\n", - "101 SurveyUSA/Civitas (R)\n", - "102 Talk Business Poll\n", - "103 Tennessean/Vanderbilt\n", - "104 The Simon Poll/SIU\n", - "105 The Washington Poll\n", - "106 Tribune-Review/Susquehanna\n", - "107 UMass/Boston Herald\n", "108 Virginian-Pilot/ODU\n", "109 Voter/Consumer Res/TIR (R)\n", "110 WBUR/MassINC\n", @@ -1280,52 +947,10 @@ " 1.29\n", " \n", " \n", - " 12\n", - " Keystone (PA)\n", - " 0.64\n", - " 1.55\n", - " \n", - " \n", - " 13\n", - " LA Times / Bloomberg\n", - " 0.83\n", - " 1.44\n", - " \n", - " \n", - " 14\n", - " Marist (NY)\n", - " 0.69\n", - " 1.73\n", - " \n", - " \n", - " 15\n", - " Mason-Dixon\n", - " 1.10\n", - " 1.15\n", - " \n", - " \n", - " 16\n", - " Mitchell\n", - " 0.96\n", - " 1.43\n", - " \n", - " \n", - " 17\n", - " Ohio Poll\n", - " 1.24\n", - " 1.05\n", - " \n", - " \n", - " 18\n", - " Public Opinion Strategies\n", - " 0.63\n", - " 1.81\n", - " \n", - " \n", - " 19\n", - " Public Policy Polling (PPP)\n", - " 1.05\n", - " 1.60\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 20\n", @@ -1401,6 +1026,7 @@ " \n", " \n", "\n", + "

32 rows × 3 columns

\n", "" ], "text/plain": [ @@ -1417,14 +1043,7 @@ "9 Fox / Opinion Dynamics 0.79 1.60\n", "10 Franklin Pierce (NH) 0.74 1.60\n", "11 Insider Advantage 0.95 1.29\n", - "12 Keystone (PA) 0.64 1.55\n", - "13 LA Times / Bloomberg 0.83 1.44\n", - "14 Marist (NY) 0.69 1.73\n", - "15 Mason-Dixon 1.10 1.15\n", - "16 Mitchell 0.96 1.43\n", - "17 Ohio Poll 1.24 1.05\n", - "18 Public Opinion Strategies 0.63 1.81\n", - "19 Public Policy Polling (PPP) 1.05 1.60\n", + ".. ... ... ...\n", "20 Quinnipiac 0.95 1.34\n", "21 Rasmussen 1.30 0.88\n", "22 Research 2000 1.01 1.20\n", @@ -1436,7 +1055,9 @@ "28 Univ. New Hampshire 1.08 1.26\n", "29 USA Today / Gallup 0.63 2.01\n", "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74" + "31 Zogby Interactive 0.43 4.74\n", + "\n", + "[32 rows x 3 columns]" ] }, "execution_count": 16, @@ -1635,19 +1256,12 @@ "" ], "text/plain": [ - " Pollster State MoE Obama (D) Romney (R) Sample Spread \\\n", - "0 Rasmussen WA 4.5 52 41 500 Obama +11 \n", - "1 Rasmussen WI 4.5 49 46 500 Obama +3 \n", - "2 Rasmussen WI 4.5 47 48 500 Romney +1 \n", - "3 Rasmussen WI 4.5 49 46 500 Obama +3 \n", - "4 Rasmussen WI 4.5 44 47 500 Romney +3 \n", - "\n", - " obama_spread poll_date Weight PIE \n", - "0 11 2012-09-26 1.3 0.88 \n", - "1 3 2012-09-17 1.3 0.88 \n", - "2 -1 2012-08-15 1.3 0.88 \n", - "3 3 2012-07-25 1.3 0.88 \n", - "4 -3 2012-06-12 1.3 0.88 " + " Pollster State MoE Obama (D) Romney (R) Sample Spread obama_spread poll_date Weight PIE\n", + "0 Rasmussen WA 4.5 52 41 500 Obama +11 11 2012-09-26 1.3 0.88\n", + "1 Rasmussen WI 4.5 49 46 500 Obama +3 3 2012-09-17 1.3 0.88\n", + "2 Rasmussen WI 4.5 47 48 500 Romney +1 -1 2012-08-15 1.3 0.88\n", + "3 Rasmussen WI 4.5 49 46 500 Obama +3 3 2012-07-25 1.3 0.88\n", + "4 Rasmussen WI 4.5 44 47 500 Romney +3 -3 2012-06-12 1.3 0.88" ] }, "execution_count": 22, @@ -1739,11 +1353,19 @@ "metadata": {}, "output_type": "execute_result" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, { "data": { - "image/png": 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Phn32gcmTc1cmSZLKxTAtlVinTnDssfDCC9CrF+y4IxxzDLzySu7KJElSqRmmpTJZZhn4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\npiVJkqSCDNOSJElSQYZpSZI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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1928,25 +1550,15 @@ "" ], "text/plain": [ - " Pollster State Obama (D) Romney (R) Sample \\\n", - "258 Public Policy Polling (PPP) AZ 44 53 993 \n", - "259 Public Policy Polling (PPP) AZ 41 52 833 \n", - "260 Public Policy Polling (PPP) AZ 43 50 500 \n", - "261 Public Policy Polling (PPP) AZ 47 47 743 \n", - "262 Public Policy Polling (PPP) AZ 42 49 500 \n", - "263 Public Policy Polling (PPP) AZ 44 48 623 \n", - "264 Public Policy Polling (PPP) AZ 43 49 599 \n", - "265 Public Policy Polling (PPP) AZ 43 50 617 \n", - "\n", - " poll_date \n", - "258 2012-09-08 \n", - "259 2012-07-24 \n", - "260 2012-05-19 \n", - "261 2012-02-18 \n", - "262 2011-11-19 \n", - "263 2011-04-30 \n", - "264 2011-01-29 \n", - "265 2010-09-20 " + " Pollster State Obama (D) Romney (R) Sample poll_date\n", + "258 Public Policy Polling (PPP) AZ 44 53 993 2012-09-08\n", + "259 Public Policy Polling (PPP) AZ 41 52 833 2012-07-24\n", + "260 Public Policy Polling (PPP) AZ 43 50 500 2012-05-19\n", + "261 Public Policy Polling (PPP) AZ 47 47 743 2012-02-18\n", + "262 Public Policy Polling (PPP) AZ 42 49 500 2011-11-19\n", + "263 Public Policy Polling (PPP) AZ 44 48 623 2011-04-30\n", + "264 Public Policy Polling (PPP) AZ 43 49 599 2011-01-29\n", + "265 Public Policy Polling (PPP) AZ 43 50 617 2010-09-20" ] }, "execution_count": 30, @@ -1970,10 +1582,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "//anaconda/lib/python2.7/site-packages/pandas/core/frame.py:2915: SettingWithCopyWarning: \n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", + " if __name__ == '__main__':\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/pandas/core/frame.py:3167: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " inplace=inplace, kind=kind, na_position=na_position)\n" ] } @@ -1993,23 +1607,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n", - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " from IPython.kernel.zmq import kernelapp as app\n", - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:3: SettingWithCopyWarning: \n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from ipykernel import kernelapp as app\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " app.launch_new_instance()\n" ] }, @@ -2116,25 +1730,15 @@ "" ], "text/plain": [ - " Pollster State Obama (D) Romney (R) Sample \\\n", - "258 Public Policy Polling (PPP) AZ 44 53 993 \n", - "259 Public Policy Polling (PPP) AZ 41 52 833 \n", - "260 Public Policy Polling (PPP) AZ 43 50 500 \n", - "261 Public Policy Polling (PPP) AZ 47 47 743 \n", - "262 Public Policy Polling (PPP) AZ 42 49 500 \n", - "263 Public Policy Polling (PPP) AZ 44 48 623 \n", - "264 Public Policy Polling (PPP) AZ 43 49 599 \n", - "265 Public Policy Polling (PPP) AZ 43 50 617 \n", - "\n", - " poll_date cumulative \n", - "258 2012-09-08 993 \n", - "259 2012-07-24 1826 \n", - "260 2012-05-19 2326 \n", - "261 2012-02-18 3069 \n", - "262 2011-11-19 3569 \n", - "263 2011-04-30 4192 \n", - "264 2011-01-29 4791 \n", - "265 2010-09-20 5408 " + " Pollster State Obama (D) Romney (R) Sample poll_date cumulative\n", + "258 Public Policy Polling (PPP) AZ 44 53 993 2012-09-08 993\n", + "259 Public Policy Polling (PPP) AZ 41 52 833 2012-07-24 1826\n", + "260 Public Policy Polling (PPP) AZ 43 50 500 2012-05-19 2326\n", + "261 Public Policy Polling (PPP) AZ 47 47 743 2012-02-18 3069\n", + "262 Public Policy Polling (PPP) AZ 42 49 500 2011-11-19 3569\n", + "263 Public Policy Polling (PPP) AZ 44 48 623 2011-04-30 4192\n", + "264 Public Policy Polling (PPP) AZ 43 49 599 2011-01-29 4791\n", + "265 Public Policy Polling (PPP) AZ 43 50 617 2010-09-20 5408" ] }, "execution_count": 32, @@ -2160,22 +1764,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n", - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " from IPython.kernel.zmq import kernelapp as app\n", - "//anaconda/lib/python2.7/site-packages/pandas/core/generic.py:2602: SettingWithCopyWarning: \n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from ipykernel import kernelapp as app\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/pandas/core/generic.py:2862: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self._update_inplace(new_data)\n" ] } @@ -2403,43 +2007,7 @@ "9 0.629961\n", "10 0.329877\n", "11 0.140308\n", - "12 0.024803\n", - "13 0.009184\n", - "14 0.004284\n", - "15 0.000615\n", - "16 0.723635\n", - "17 0.090454\n", - "18 0.050766\n", - "19 0.890899\n", - "20 0.378929\n", - "21 0.014919\n", - "22 0.004809\n", - "23 0.644685\n", - "24 0.238710\n", - "25 0.101532\n", - "26 0.038473\n", - "27 0.017538\n", - "28 0.146943\n", - "29 0.040293\n", " ... \n", - "393 0.017948\n", - "394 0.002637\n", - "395 0.000370\n", - "396 0.629961\n", - "397 0.106333\n", - "398 0.049606\n", - "399 0.004385\n", - "400 0.000675\n", - "401 0.000119\n", - "402 0.723635\n", - "403 0.198425\n", - "404 0.046284\n", - "405 0.831238\n", - "406 0.370274\n", - "407 0.361817\n", - "408 0.000147\n", - "409 0.003817\n", - "410 0.601513\n", "411 0.445449\n", "412 0.217638\n", "413 0.062500\n", @@ -2507,68 +2075,32 @@ { "data": { "text/plain": [ - "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168494\n", - " Rasmussen -10.209446\n", - "CA Field Poll (CA) 23.343924\n", - " Public Policy Polling (PPP) 20.999075\n", - " Rasmussen 22.000000\n", - " SurveyUSA 22.123414\n", - "CO American Research Group 2.000000\n", - " Public Policy Polling (PPP) 5.469907\n", - " Rasmussen -1.573788\n", - "CT Public Policy Polling (PPP) 12.757757\n", - " Quinnipiac 7.293983\n", - " Rasmussen 8.000000\n", - "FL American Research Group 5.000000\n", - " Mason-Dixon -3.543178\n", - " Public Policy Polling (PPP) 3.125154\n", - " Quinnipiac 3.075653\n", - " Rasmussen 0.882884\n", - " Suffolk (NH/MA) -0.003377\n", - " SurveyUSA 4.168952\n", - "GA Insider Advantage -19.174054\n", - " Mason-Dixon -17.000000\n", - " Public Policy Polling (PPP) -3.000000\n", - " SurveyUSA -7.983856\n", - "HI Public Policy Polling (PPP) 27.000000\n", - "IA American Research Group 7.000000\n", - " Mason-Dixon -3.000000\n", - " Public Policy Polling (PPP) 5.878693\n", - " Rasmussen -2.749416\n", - "IL Chicago Trib. / MarketShares 21.000000\n", - "IN Rasmussen -16.000000\n", - " ... \n", - "OH Ohio Poll 3.000406\n", - " Public Policy Polling (PPP) 4.141640\n", - " Quinnipiac 7.729397\n", - " Rasmussen 0.865613\n", - "OR Public Policy Polling (PPP) 9.130153\n", - " SurveyUSA 8.675504\n", - "PA Public Policy Polling (PPP) 6.160027\n", - " Quinnipiac 6.047221\n", - " Rasmussen 10.874768\n", - " SurveyUSA 0.000000\n", - "RI Public Policy Polling (PPP) 17.000000\n", - "SC Public Policy Polling (PPP) -14.558484\n", - "SD Public Policy Polling (PPP) -6.000000\n", - "TN Public Policy Polling (PPP) -7.000000\n", - "TX Public Policy Polling (PPP) -6.998595\n", - "UT Mason-Dixon -51.000000\n", - " Public Policy Polling (PPP) -32.000000\n", - "VA American Research Group 2.000000\n", - " Mason-Dixon 1.000000\n", - " Public Policy Polling (PPP) 5.095802\n", - " Quinnipiac 0.578138\n", - " Rasmussen 0.891780\n", - "VT Public Policy Polling (PPP) 20.000000\n", - "WA Public Policy Polling (PPP) 13.050886\n", - " Rasmussen 11.000000\n", - " SurveyUSA 15.310208\n", - "WI CNN / Opinion Research 4.000000\n", - " Public Policy Polling (PPP) 5.392554\n", - " Rasmussen 2.116005\n", - "WV Public Policy Polling (PPP) -19.756631\n", + "State Pollster \n", + "AZ Public Policy Polling (PPP) -9.168494\n", + " Rasmussen -10.209446\n", + "CA Field Poll (CA) 23.343924\n", + " Public Policy Polling (PPP) 20.999075\n", + " Rasmussen 22.000000\n", + " SurveyUSA 22.123414\n", + "CO American Research Group 2.000000\n", + " Public Policy Polling (PPP) 5.469907\n", + " Rasmussen -1.573788\n", + "CT Public Policy Polling (PPP) 12.757757\n", + " Quinnipiac 7.293983\n", + " Rasmussen 8.000000\n", + " ... \n", + "VA Mason-Dixon 1.000000\n", + " Public Policy Polling (PPP) 5.095802\n", + " Quinnipiac 0.578138\n", + " Rasmussen 0.891780\n", + "VT Public Policy Polling (PPP) 20.000000\n", + "WA Public Policy Polling (PPP) 13.050886\n", + " Rasmussen 11.000000\n", + " SurveyUSA 15.310208\n", + "WI CNN / Opinion Research 4.000000\n", + " Public Policy Polling (PPP) 5.392554\n", + " Rasmussen 2.116005\n", + "WV Public Policy Polling (PPP) -19.756631\n", "dtype: float64" ] }, @@ -2920,139 +2452,9 @@ " 64.000000\n", " \n", " \n", - " IA\n", - " 41.407407\n", - " 50.037037\n", - " \n", - " \n", - " ID\n", - " 60.000000\n", - " 30.500000\n", - " \n", - " \n", - " IL\n", - " 36.900000\n", - " 55.600000\n", - " \n", - " \n", - " IN\n", - " 47.500000\n", - " 44.961538\n", - " \n", - " \n", - " KS\n", - " 53.562500\n", - " 37.750000\n", - " \n", - " \n", - " KY\n", - " 54.842105\n", - " 37.526316\n", - " \n", - " \n", - " LA\n", - " 52.166667\n", - " 39.083333\n", - " \n", - " \n", - " MA\n", - " 38.800000\n", - " 52.200000\n", - " \n", - " \n", - " MD\n", - " 38.666667\n", - " 53.833333\n", - " \n", - " \n", - " ME\n", - " 38.187500\n", - " 50.562500\n", - " \n", - " \n", - " MI\n", - " 42.052632\n", - " 47.368421\n", - " \n", - " \n", - " MN\n", - " 41.739130\n", - " 50.260870\n", - " \n", - " \n", - " MO\n", - " 47.428571\n", - " 45.571429\n", - " \n", - " \n", - " MS\n", - " 51.200000\n", - " 40.500000\n", - " \n", - " \n", - " MT\n", - " 48.214286\n", - " 43.857143\n", - " \n", - " \n", - " NC\n", - " 47.522727\n", - " 46.090909\n", - " \n", - " \n", - " ND\n", - " 45.571429\n", - " 42.714286\n", - " \n", - " \n", - " NE\n", - " 51.714286\n", - " 37.142857\n", - " \n", - " \n", - " NH\n", - " 42.756757\n", - " 48.918919\n", - " \n", - " \n", - " NJ\n", - " 39.766667\n", - " 49.766667\n", - " \n", - " \n", - " NM\n", - " 43.592593\n", - " 48.740741\n", - " \n", - " \n", - " NV\n", - " 44.843750\n", - " 46.937500\n", - " \n", - " \n", - " NY\n", - " 36.864865\n", - " 52.432432\n", - " \n", - " \n", - " OH\n", - " 44.974684\n", - " 46.658228\n", - " \n", - " \n", - " OK\n", - " 61.700000\n", - " 32.000000\n", - " \n", - " \n", - " OR\n", - " 40.851852\n", - " 50.333333\n", - " \n", - " \n", - " PA\n", - " 42.080000\n", - " 48.893333\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " RI\n", @@ -3116,6 +2518,7 @@ " \n", " \n", "\n", + "

51 rows × 2 columns

\n", "" ], "text/plain": [ @@ -3133,33 +2536,7 @@ "FL 46.393939 46.121212\n", "GA 51.346154 43.153846\n", "HI 30.000000 64.000000\n", - "IA 41.407407 50.037037\n", - "ID 60.000000 30.500000\n", - "IL 36.900000 55.600000\n", - "IN 47.500000 44.961538\n", - "KS 53.562500 37.750000\n", - "KY 54.842105 37.526316\n", - "LA 52.166667 39.083333\n", - "MA 38.800000 52.200000\n", - "MD 38.666667 53.833333\n", - "ME 38.187500 50.562500\n", - "MI 42.052632 47.368421\n", - "MN 41.739130 50.260870\n", - "MO 47.428571 45.571429\n", - "MS 51.200000 40.500000\n", - "MT 48.214286 43.857143\n", - "NC 47.522727 46.090909\n", - "ND 45.571429 42.714286\n", - "NE 51.714286 37.142857\n", - "NH 42.756757 48.918919\n", - "NJ 39.766667 49.766667\n", - "NM 43.592593 48.740741\n", - "NV 44.843750 46.937500\n", - "NY 36.864865 52.432432\n", - "OH 44.974684 46.658228\n", - "OK 61.700000 32.000000\n", - "OR 40.851852 50.333333\n", - "PA 42.080000 48.893333\n", + "... ... ...\n", "RI 32.000000 53.000000\n", "SC 53.300000 41.000000\n", "SD 50.375000 39.875000\n", @@ -3171,7 +2548,9 @@ "WA 40.424242 51.515152\n", "WI 41.921053 49.684211\n", "WV 48.692308 42.538462\n", - "WY 59.333333 32.666667" + "WY 59.333333 32.666667\n", + "\n", + "[51 rows x 2 columns]" ] }, "execution_count": 49, @@ -3327,43 +2706,7 @@ "9 True\n", "10 True\n", "11 False\n", - "12 False\n", - "13 False\n", - "14 True\n", - "15 True\n", - "16 False\n", - "17 False\n", - "18 False\n", - "19 False\n", - "20 True\n", - "21 True\n", - "22 True\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 True\n", - "27 True\n", - "28 True\n", - "29 False\n", " ... \n", - "704 True\n", - "705 True\n", - "706 True\n", - "707 True\n", - "708 True\n", - "709 True\n", - "710 False\n", - "711 False\n", - "712 False\n", - "713 True\n", - "714 False\n", - "715 True\n", - "716 True\n", - "717 False\n", - "718 True\n", - "719 True\n", - "720 False\n", - "721 True\n", "722 True\n", "723 False\n", "724 False\n", @@ -3666,124 +3009,20 @@ " D+12\n", " \n", " \n", - " Idaho\n", - " R+17\n", + " ...\n", + " ...\n", " \n", " \n", - " Illinois\n", - " D+8\n", + " Rhode Island\n", + " D+11\n", " \n", " \n", - " Indiana\n", - " R+6\n", + " South Carolina\n", + " R+8\n", " \n", " \n", - " Iowa\n", - " D+1\n", - " \n", - " \n", - " Kansas\n", - " R+12\n", - " \n", - " \n", - " Kentucky\n", - " R+10\n", - " \n", - " \n", - " Louisiana\n", - " R+10\n", - " \n", - " \n", - " Maine\n", - " D+5\n", - " \n", - " \n", - " Maryland\n", - " D+9\n", - " \n", - " \n", - " Massachusetts\n", - " D+12\n", - " \n", - " \n", - " Michigan\n", - " D+4\n", - " \n", - " \n", - " Minnesota\n", - " D+2\n", - " \n", - " \n", - " Mississippi\n", - " R+10\n", - " \n", - " \n", - " Missouri\n", - " R+3\n", - " \n", - " \n", - " Montana\n", - " R+7\n", - " \n", - " \n", - " Nebraska\n", - " R+13\n", - " \n", - " \n", - " Nevada\n", - " D+1\n", - " \n", - " \n", - " New Hampshire\n", - " D+2\n", - " \n", - " \n", - " New Jersey\n", - " D+4\n", - " \n", - " \n", - " New Mexico\n", - " D+2\n", - " \n", - " \n", - " New York\n", - " D+10\n", - " \n", - " \n", - " North Carolina\n", - " R+4\n", - " \n", - " \n", - " North Dakota\n", - " R+10\n", - " \n", - " \n", - " Ohio\n", - " R+1\n", - " \n", - " \n", - " Oklahoma\n", - " R+17\n", - " \n", - " \n", - " Oregon\n", - " D+4\n", - " \n", - " \n", - " Pennsylvania\n", - " D+2\n", - " \n", - " \n", - " Rhode Island\n", - " D+11\n", - " \n", - " \n", - " South Carolina\n", - " R+8\n", - " \n", - " \n", - " South Dakota\n", - " R+9\n", + " South Dakota\n", + " R+9\n", " \n", " \n", " Tennessee\n", @@ -3823,6 +3062,7 @@ " \n", " \n", "\n", + "

51 rows × 1 columns

\n", "" ], "text/plain": [ @@ -3840,33 +3080,7 @@ "Florida R+2 \n", "Georgia R+7 \n", "Hawaii D+12\n", - "Idaho R+17\n", - "Illinois D+8 \n", - "Indiana R+6 \n", - "Iowa D+1 \n", - "Kansas R+12\n", - "Kentucky R+10\n", - "Louisiana R+10\n", - "Maine D+5 \n", - "Maryland D+9 \n", - "Massachusetts D+12\n", - "Michigan D+4 \n", - "Minnesota D+2 \n", - "Mississippi R+10\n", - "Missouri R+3 \n", - "Montana R+7 \n", - "Nebraska R+13\n", - "Nevada D+1 \n", - "New Hampshire D+2 \n", - "New Jersey D+4 \n", - "New Mexico D+2 \n", - "New York D+10\n", - "North Carolina R+4 \n", - "North Dakota R+10\n", - "Ohio R+1 \n", - "Oklahoma R+17\n", - "Oregon D+4 \n", - "Pennsylvania D+2 \n", + "... ...\n", "Rhode Island D+11\n", "South Carolina R+8 \n", "South Dakota R+9 \n", @@ -3878,7 +3092,9 @@ "Washington D+5 \n", "West Virginia R+8 \n", "Wisconsin D+2 \n", - "Wyoming R+20" + "Wyoming R+20\n", + "\n", + "[51 rows x 1 columns]" ] }, "execution_count": 66, @@ -3914,33 +3130,7 @@ "Florida -2\n", "Georgia -7\n", "Hawaii 12\n", - "Idaho -17\n", - "Illinois 8\n", - "Indiana -6\n", - "Iowa 1\n", - "Kansas -12\n", - "Kentucky -10\n", - "Louisiana -10\n", - "Maine 5\n", - "Maryland 9\n", - "Massachusetts 12\n", - "Michigan 4\n", - "Minnesota 2\n", - "Mississippi -10\n", - "Missouri -3\n", - "Montana -7\n", - "Nebraska -13\n", - "Nevada 1\n", - "New Hampshire 2\n", - "New Jersey 4\n", - "New Mexico 2\n", - "New York 10\n", - "North Carolina -4\n", - "North Dakota -10\n", - "Ohio -1\n", - "Oklahoma -17\n", - "Oregon 4\n", - "Pennsylvania 2\n", + " ..\n", "Rhode Island 11\n", "South Carolina -8\n", "South Dakota -9\n", @@ -4130,220 +3320,12 @@ " 16.7\n", " \n", " \n", - " Michigan\n", - " 47.7\n", - " 36.6\n", - " 11.1000\n", - " 5056\n", - " 15.7\n", - " \n", - " \n", - " Minnesota\n", - " 48.4\n", - " 38.2\n", - " 10.2000\n", - " 3873\n", - " 13.4\n", - " \n", - " \n", - " Washington\n", - " 47.5\n", - " 37.7\n", - " 9.8000\n", - " 5333\n", - " 14.8\n", - " \n", - " \n", - " Oregon\n", - " 47.2\n", - " 39.1\n", - " 8.1000\n", - " 3002\n", - " 13.7\n", - " \n", - " \n", - " Pennsylvania\n", - " 46.4\n", - " 41.2\n", - " 5.2000\n", - " 8443\n", - " 12.4\n", - " \n", - " \n", - " Maine\n", - " 43.8\n", - " 39.4\n", - " 4.4000\n", - " 1040\n", - " 16.8\n", - " \n", - " \n", - " New Mexico\n", - " 44.7\n", - " 41.1\n", - " 3.6000\n", - " 1555\n", - " 14.2\n", - " \n", - " \n", - " Ohio\n", - " 44.1\n", - " 40.5\n", - " 3.6000\n", - " 6426\n", - " 15.4\n", - " \n", - " \n", - " West Virginia\n", - " 45.3\n", - " 41.9\n", - " 3.4000\n", - " 1202\n", - " 12.8\n", - " \n", - " \n", - " Wisconsin\n", - " 45.0\n", - " 42.2\n", - " 2.8000\n", - " 4140\n", - " 12.8\n", - " \n", - " \n", - " Iowa\n", - " 43.2\n", - " 41.4\n", - " 1.8000\n", - " 2337\n", - " 15.4\n", - " \n", - " \n", - " Florida\n", - " 43.0\n", - " 42.3\n", - " 0.7000\n", - " 9965\n", - " 14.7\n", - " \n", - " \n", - " Arkansas\n", - " 41.5\n", - " 40.8\n", - " 0.7000\n", - " 2071\n", - " 17.7\n", - " \n", - " \n", - " Kentucky\n", - " 43.5\n", - " 43.1\n", - " 0.4000\n", - " 2898\n", - " 13.4\n", - " \n", - " \n", - " North Carolina\n", - " 43.4\n", - " 43.2\n", - " 0.2000\n", - " 6213\n", - " 13.4\n", - " \n", - " \n", - " New Hampshire\n", - " 42.3\n", - " 43.8\n", - " -1.5000\n", - " 873\n", - " 13.9\n", - " \n", - " \n", - " Virginia\n", - " 41.2\n", - " 44.2\n", - " -3.0000\n", - " 5313\n", - " 14.6\n", - " \n", - " \n", - " Missouri\n", - " 40.1\n", - " 44.0\n", - " -3.9000\n", - " 3727\n", - " 15.9\n", - " \n", - " \n", - " Georgia\n", - " 40.3\n", - " 44.3\n", - " -4.0000\n", - " 5110\n", - " 15.4\n", - " \n", - " \n", - " Nevada\n", - " 39.2\n", - " 43.4\n", - " -4.2000\n", - " 1348\n", - " 17.4\n", - " \n", - " \n", - " Louisiana\n", - " 40.3\n", - " 45.1\n", - " -4.8000\n", - " 2655\n", - " 14.6\n", - " \n", - " \n", - " Colorado\n", - " 39.9\n", - " 45.1\n", - " -5.2000\n", - " 3671\n", - " 15.0\n", - " \n", - " \n", - " Texas\n", - " 38.3\n", - " 44.1\n", - " -5.8000\n", - " 11325\n", - " 17.6\n", - " \n", - " \n", - " South Dakota\n", - " 41.5\n", - " 47.5\n", - " -6.0000\n", - " 607\n", - " 11.0\n", - " \n", - " \n", - " Indiana\n", - " 39.0\n", - " 45.7\n", - " -6.7000\n", - " 4197\n", - " 15.3\n", - " \n", - " \n", - " Mississippi\n", - " 40.1\n", - " 47.1\n", - " -7.0000\n", - " 1763\n", - " 12.8\n", - " \n", - " \n", - " Arizona\n", - " 39.8\n", - " 47.3\n", - " -7.5000\n", - " 4325\n", - " 12.9\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " Tennessee\n", @@ -4443,6 +3425,7 @@ " \n", " \n", "\n", + "

51 rows × 5 columns

\n", "" ], "text/plain": [ @@ -4460,33 +3443,7 @@ "California 48.3 34.6 13.7000 16197 17.1\n", "Illinois 48.4 35.8 12.6000 5888 15.8\n", "New Jersey 47.4 35.9 11.5000 4239 16.7\n", - "Michigan 47.7 36.6 11.1000 5056 15.7\n", - "Minnesota 48.4 38.2 10.2000 3873 13.4\n", - "Washington 47.5 37.7 9.8000 5333 14.8\n", - "Oregon 47.2 39.1 8.1000 3002 13.7\n", - "Pennsylvania 46.4 41.2 5.2000 8443 12.4\n", - "Maine 43.8 39.4 4.4000 1040 16.8\n", - "New Mexico 44.7 41.1 3.6000 1555 14.2\n", - "Ohio 44.1 40.5 3.6000 6426 15.4\n", - "West Virginia 45.3 41.9 3.4000 1202 12.8\n", - "Wisconsin 45.0 42.2 2.8000 4140 12.8\n", - "Iowa 43.2 41.4 1.8000 2337 15.4\n", - "Florida 43.0 42.3 0.7000 9965 14.7\n", - "Arkansas 41.5 40.8 0.7000 2071 17.7\n", - "Kentucky 43.5 43.1 0.4000 2898 13.4\n", - "North Carolina 43.4 43.2 0.2000 6213 13.4\n", - "New Hampshire 42.3 43.8 -1.5000 873 13.9\n", - "Virginia 41.2 44.2 -3.0000 5313 14.6\n", - "Missouri 40.1 44.0 -3.9000 3727 15.9\n", - "Georgia 40.3 44.3 -4.0000 5110 15.4\n", - "Nevada 39.2 43.4 -4.2000 1348 17.4\n", - "Louisiana 40.3 45.1 -4.8000 2655 14.6\n", - "Colorado 39.9 45.1 -5.2000 3671 15.0\n", - "Texas 38.3 44.1 -5.8000 11325 17.6\n", - "South Dakota 41.5 47.5 -6.0000 607 11.0\n", - "Indiana 39.0 45.7 -6.7000 4197 15.3\n", - "Mississippi 40.1 47.1 -7.0000 1763 12.8\n", - "Arizona 39.8 47.3 -7.5000 4325 12.9\n", + "... ... ... ... ... ...\n", "Tennessee 38.1 46.5 -8.4000 4231 15.4\n", "Alaska 35.9 44.3 -8.4402 NaN 19.8\n", "Oklahoma 38.6 48.0 -9.4000 2583 13.4\n", @@ -4498,7 +3455,9 @@ "Nebraska 33.1 52.1 -19.0000 1351 14.8\n", "Wyoming 26.7 56.6 -29.9000 600 16.7\n", "Idaho 27.5 57.8 -30.3000 1336 14.7\n", - "Utah 24.5 63.8 -39.3000 2256 11.7" + "Utah 24.5 63.8 -39.3000 2256 11.7\n", + "\n", + "[51 rows x 5 columns]" ] }, "execution_count": 70, @@ -4780,139 +3739,9 @@ " 0.225184\n", " \n", " \n", - " Idaho\n", - " 0.366343\n", - " 0.990299\n", - " \n", - " \n", - " Illinois\n", - " 0.933537\n", - " 0.589753\n", - " \n", - " \n", - " Indiana\n", - " 0.341342\n", - " 0.256904\n", - " \n", - " \n", - " Iowa\n", - " 0.487432\n", - " 0.285937\n", - " \n", - " \n", - " Kansas\n", - " 0.392000\n", - " 0.469934\n", - " \n", - " \n", - " Kentucky\n", - " 0.289065\n", - " 0.393258\n", - " \n", - " \n", - " Louisiana\n", - " 0.260482\n", - " 0.528613\n", - " \n", - " \n", - " Maine\n", - " 0.800384\n", - " 0.245619\n", - " \n", - " \n", - " Maryland\n", - " 1.518299\n", - " 0.593536\n", - " \n", - " \n", - " Massachusetts\n", - " 1.734821\n", - " 1.104913\n", - " \n", - " \n", - " Michigan\n", - " 0.511953\n", - " 0.500678\n", - " \n", - " \n", - " Minnesota\n", - " 0.659653\n", - " 0.231681\n", - " \n", - " \n", - " Mississippi\n", - " 0.189120\n", - " 0.327144\n", - " \n", - " \n", - " Missouri\n", - " 0.412507\n", - " 0.482069\n", - " \n", - " \n", - " Montana\n", - " 0.763607\n", - " 0.533750\n", - " \n", - " \n", - " Nebraska\n", - " 0.335630\n", - " 0.351093\n", - " \n", - " \n", - " Nevada\n", - " 0.484182\n", - " 0.638822\n", - " \n", - " \n", - " New Hampshire\n", - " 0.961563\n", - " 0.733997\n", - " \n", - " \n", - " New Jersey\n", - " 0.736219\n", - " 0.703930\n", - " \n", - " \n", - " New Mexico\n", - " 1.052308\n", - " 0.379238\n", - " \n", - " \n", - " New York\n", - " 1.198632\n", - " 0.809349\n", - " \n", - " \n", - " North Carolina\n", - " 0.549209\n", - " 0.355353\n", - " \n", - " \n", - " North Dakota\n", - " 0.238311\n", - " 0.343288\n", - " \n", - " \n", - " Ohio\n", - " 0.377548\n", - " 0.427662\n", - " \n", - " \n", - " Oklahoma\n", - " 0.324838\n", - " 0.800888\n", - " \n", - " \n", - " Oregon\n", - " 0.971327\n", - " 0.342027\n", - " \n", - " \n", - " Pennsylvania\n", - " 0.587688\n", - " 0.467086\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " Rhode Island\n", @@ -4976,6 +3805,7 @@ " \n", " \n", "\n", + "

51 rows × 2 columns

\n", "" ], "text/plain": [ @@ -4993,33 +3823,7 @@ "Florida 0.503180 0.874699\n", "Georgia 0.467529 0.526246\n", "Hawaii 1.006632 0.225184\n", - "Idaho 0.366343 0.990299\n", - "Illinois 0.933537 0.589753\n", - "Indiana 0.341342 0.256904\n", - "Iowa 0.487432 0.285937\n", - "Kansas 0.392000 0.469934\n", - "Kentucky 0.289065 0.393258\n", - "Louisiana 0.260482 0.528613\n", - "Maine 0.800384 0.245619\n", - "Maryland 1.518299 0.593536\n", - "Massachusetts 1.734821 1.104913\n", - "Michigan 0.511953 0.500678\n", - "Minnesota 0.659653 0.231681\n", - "Mississippi 0.189120 0.327144\n", - "Missouri 0.412507 0.482069\n", - "Montana 0.763607 0.533750\n", - "Nebraska 0.335630 0.351093\n", - "Nevada 0.484182 0.638822\n", - "New Hampshire 0.961563 0.733997\n", - "New Jersey 0.736219 0.703930\n", - "New Mexico 1.052308 0.379238\n", - "New York 1.198632 0.809349\n", - "North Carolina 0.549209 0.355353\n", - "North Dakota 0.238311 0.343288\n", - "Ohio 0.377548 0.427662\n", - "Oklahoma 0.324838 0.800888\n", - "Oregon 0.971327 0.342027\n", - "Pennsylvania 0.587688 0.467086\n", + "... ... ...\n", "Rhode Island 0.713200 0.358394\n", "South Carolina 0.317250 0.351393\n", "South Dakota 0.270970 0.518931\n", @@ -5031,7 +3835,9 @@ "Washington 1.190590 0.475625\n", "West Virginia 0.260437 0.321333\n", "Wisconsin 0.455410 0.237802\n", - "Wyoming 0.746122 1.080021" + "Wyoming 0.746122 1.080021\n", + "\n", + "[51 rows x 2 columns]" ] }, "execution_count": 81, @@ -5088,8 +3894,7 @@ { "data": { "text/plain": [ - "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", - " 1., 1., 1., 1.])" + "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 85, @@ -5108,6 +3913,14 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n" + ] + }, { "data": { "text/plain": [ @@ -5156,8 +3969,117 @@ "metadata": { "collapsed": false }, - "outputs": [], - "source": [ + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n" + ] + } + ], + "source": [ "nearest_neighbor = {}\n", "for i, state in enumerate(demo_data.index):\n", " neighborhood = KNN.kneighbors(clean_data[i], return_distance=True)\n", @@ -5192,8 +4114,7 @@ " 'Delaware': (Index([[u'Delaware', u'Michigan', u'Washington', u'Oregon', u'Missouri', u'Illinois', u'Rhode Island']], dtype='object', name=u'State'),\n", " array([[ 0. , 2.8113, 2.9256, 2.926 , 3.0637, 3.0731, 3.1641]])),\n", " 'District of Columbia': (Index([[u'District of Columbia', u'Maryland', u'Massachusetts', u'Connecticut', u'Virginia', u'New Jersey', u'New York']], dtype='object', name=u'State'),\n", - " array([[ 0. , 13.03 , 13.3261, 13.6773, 14.119 , 14.1531,\n", - " 14.5409]])),\n", + " array([[ 0. , 13.03 , 13.3261, 13.6773, 14.119 , 14.1531, 14.5409]])),\n", " 'Florida': (Index([[u'Florida', u'Pennsylvania', u'New York', u'Ohio', u'Arizona', u'Michigan', u'Illinois']], dtype='object', name=u'State'),\n", " array([[ 0. , 3.6013, 4.1043, 4.154 , 4.3315, 4.3721, 4.3781]])),\n", " 'Georgia': (Index([[u'Georgia', u'North Carolina', u'Louisiana', u'South Carolina', u'Tennessee', u'Alabama', u'Illinois']], dtype='object', name=u'State'),\n", @@ -5357,9 +4278,7 @@ { "data": { "text/plain": [ - "array([2, 4, 0, 2, 3, 4, 4, 4, 1, 3, 2, 4, 0, 4, 0, 0, 0, 2, 2, 0, 4, 4, 0,\n", - " 4, 2, 0, 0, 0, 0, 4, 4, 0, 3, 2, 0, 0, 0, 0, 0, 4, 2, 0, 2, 3, 0, 4,\n", - " 4, 4, 2, 0, 0])" + "array([2, 4, 1, 2, 3, 4, 4, 4, 0, 3, 2, 4, 1, 4, 1, 1, 1, 2, 2, 1, 4, 4, 1, 4, 2, 1, 1, 1, 1, 4, 4, 1, 3, 2, 1, 1, 1, 1, 1, 4, 2, 1, 2, 3, 1, 4, 4, 4, 2, 1, 1])" ] }, "execution_count": 94, @@ -5412,17 +4331,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri'\n", - " 'Montana' 'Nebraska' 'Nevada' 'New Mexico' 'North Dakota' 'Ohio'\n", - " 'Oklahoma' 'Oregon' 'Pennsylvania' 'South Dakota' 'Utah' 'Wisconsin'\n", - " 'Wyoming']\n", "['District of Columbia']\n", - "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi'\n", - " 'North Carolina' 'South Carolina' 'Tennessee' 'West Virginia']\n", + "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri' 'Montana' 'Nebraska' 'Nevada' 'New Mexico' 'North Dakota' 'Ohio' 'Oklahoma' 'Oregon'\n", + " 'Pennsylvania' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", + "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi' 'North Carolina' 'South Carolina' 'Tennessee' 'West Virginia']\n", "['California' 'Florida' 'New York' 'Texas']\n", - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", - " 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey'\n", - " 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n" + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey' 'Rhode Island' 'Vermont'\n", + " 'Virginia' 'Washington']\n" ] } ], @@ -5443,9 +4358,7 @@ { "data": { "text/plain": [ - "array([1, 0, 1, 1, 2, 0, 0, 0, 4, 2, 1, 0, 3, 0, 3, 3, 3, 1, 1, 3, 0, 0, 3,\n", - " 3, 1, 3, 3, 3, 0, 0, 0, 1, 2, 1, 3, 3, 1, 3, 3, 0, 1, 3, 1, 2, 3, 3,\n", - " 0, 0, 1, 3, 3], dtype=int32)" + "array([4, 3, 0, 4, 2, 3, 3, 3, 1, 2, 4, 3, 0, 3, 0, 0, 0, 4, 4, 0, 3, 3, 0, 3, 4, 0, 0, 0, 3, 3, 3, 4, 2, 4, 0, 0, 4, 0, 0, 3, 4, 0, 4, 2, 0, 3, 3, 3, 4, 0, 0], dtype=int32)" ] }, "execution_count": 98, @@ -5468,17 +4381,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", - " 'Maryland' 'Massachusetts' 'Nevada' 'New Hampshire' 'New Jersey'\n", - " 'Rhode Island' 'Virginia' 'Washington']\n", - "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana'\n", - " 'Mississippi' 'New Mexico' 'North Carolina' 'Oklahoma' 'South Carolina'\n", - " 'Tennessee' 'West Virginia']\n", + "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri' 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania' 'South Dakota'\n", + " 'Utah' 'Wisconsin' 'Wyoming']\n", + "['District of Columbia']\n", "['California' 'Florida' 'New York' 'Texas']\n", - "['Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Minnesota'\n", - " 'Missouri' 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon'\n", - " 'Pennsylvania' 'South Dakota' 'Utah' 'Vermont' 'Wisconsin' 'Wyoming']\n", - "['District of Columbia']\n" + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'Minnesota' 'Nevada' 'New Hampshire' 'New Jersey' 'Rhode Island'\n", + " 'Vermont' 'Virginia' 'Washington']\n", + "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi' 'New Mexico' 'North Carolina' 'Oklahoma' 'South Carolina' 'Tennessee' 'West Virginia']\n" ] } ], @@ -5555,7 +4464,8 @@ { "data": { "text/plain": [ - "array(['Florida', 'California', 'New York', 'Texas'], dtype=object)" + "array(['Washington', 'New Hampshire', 'New Jersey', 'Nevada', 'Colorado', 'Connecticut', 'Virginia', 'Massachusetts', 'Rhode Island', 'Hawaii', 'Vermont',\n", + " 'Maryland', 'Minnesota', 'Illinois'], dtype=object)" ] }, "execution_count": 105, @@ -5590,11 +4500,18 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" + ] + }, { "data": { - "image/png": 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0swfN7NHkcliauNK+UXD3e1J+/Luk3ZO3J0hqHMPfJO1sZgMyPBcAgDQaOxzW1o5Xbe14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\ngg==\n", 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Y8Wk0mBwoWRcREZGoKecqnYlK0Y5s9lns+DQajIiIiIhImVEFUxEREZEy0luqdJaiHdns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\nu/N+nTNnZ+6988z3ufebyXfuPHMfAACAQWhVrDO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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -5635,7 +4552,7 @@ { "data": { "text/plain": [ - "4.1600466106699434" + "5.0108108613106328" ] }, "execution_count": 108, @@ -5654,11 +4571,18 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" + ] + }, { "data": { - "image/png": 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Ff/yii+yT/fdXWUva+1NZi8paVNaS15OS81bmww/NfvITs3PPNVu3LuhoRCRg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\n1G9LaMwvau5VHvMUH966BbgT2Ar8FNi1B8f81yk+FPaHcp/BHhrzbc+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OuadiZ9XPr1nJOTfdOfeSc26ec+74WvO/c85dHps/0zm3h3NutnPufefcyNgy2zrnnnHO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\ne6UoSZUSskCEBkWrcBkVyQIVBaVsJzPVVhAKtfs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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -5692,7 +4616,15 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" + ] + } + ], "source": [ "trends = []\n", "for i, group in kmeans_groups:\n", @@ -5719,47 +4651,47 @@ { "data": { "text/plain": [ - "[['Washington', 4.5234550021334803],\n", - " ['New Hampshire', 4.5234550021334803],\n", - " ['New Jersey', 4.5234550021334803],\n", - " ['Nevada', 4.5234550021334803],\n", - " ['Colorado', 4.5234550021334803],\n", - " ['Connecticut', 4.5234550021334803],\n", - " ['Virginia', 4.5234550021334803],\n", - " ['Massachusetts', 4.5234550021334803],\n", - " ['Rhode Island', 4.5234550021334803],\n", - " ['Hawaii', 4.5234550021334803],\n", - " ['Maryland', 4.5234550021334803],\n", - " ['Illinois', 4.5234550021334803],\n", - " ['New Mexico', 3.266461905439225],\n", - " ['North Carolina', 3.266461905439225],\n", - " ['Arizona', 3.266461905439225],\n", - " ['Georgia', 3.266461905439225],\n", - " ['West Virginia', 3.266461905439225],\n", - " ['South Carolina', 3.266461905439225],\n", - " ['Tennessee', 3.266461905439225],\n", - " ['Mississippi', 3.266461905439225],\n", + "[['Wisconsin', 5.3767534315390426],\n", + " ['North Dakota', 5.3767534315390426],\n", + " ['Nebraska', 5.3767534315390426],\n", + " ['Ohio', 5.3767534315390426],\n", + " ['Pennsylvania', 5.3767534315390426],\n", + " ['Indiana', 5.3767534315390426],\n", + " ['Iowa', 5.3767534315390426],\n", + " ['Arizona', 5.3767534315390426],\n", + " ['Maine', 5.3767534315390426],\n", + " ['Missouri', 5.3767534315390426],\n", + " ['Michigan', 5.3767534315390426],\n", + " ['Montana', 5.3767534315390426],\n", + " ['Kansas', 5.3767534315390426],\n", + " ['Oregon', 5.3767534315390426],\n", + " ['South Dakota', 5.3767534315390426],\n", + " ['Utah', 5.3767534315390426],\n", " ['Florida', 3.3877002862540975],\n", " ['California', 3.3877002862540975],\n", " ['New York', 3.3877002862540975],\n", " ['Texas', 3.3877002862540975],\n", - " ['Wisconsin', 5.3717701704680234],\n", - " ['North Dakota', 5.3717701704680234],\n", - " ['Nebraska', 5.3717701704680234],\n", - " ['Ohio', 5.3717701704680234],\n", - " ['Pennsylvania', 5.3717701704680234],\n", - " ['Indiana', 5.3717701704680234],\n", - " ['Iowa', 5.3717701704680234],\n", - " ['Maine', 5.3717701704680234],\n", - " ['Missouri', 5.3717701704680234],\n", - " ['Michigan', 5.3717701704680234],\n", - " ['Montana', 5.3717701704680234],\n", - " ['Kansas', 5.3717701704680234],\n", - " ['Oregon', 5.3717701704680234],\n", - " ['South Dakota', 5.3717701704680234],\n", - " ['Vermont', 5.3717701704680234],\n", - " ['Utah', 5.3717701704680234],\n", - " ['Minnesota', 5.3717701704680234]]" + " ['Washington', 4.5126101123209397],\n", + " ['New Hampshire', 4.5126101123209397],\n", + " ['New Jersey', 4.5126101123209397],\n", + " ['Nevada', 4.5126101123209397],\n", + " ['Colorado', 4.5126101123209397],\n", + " ['Connecticut', 4.5126101123209397],\n", + " ['Virginia', 4.5126101123209397],\n", + " ['Massachusetts', 4.5126101123209397],\n", + " ['Rhode Island', 4.5126101123209397],\n", + " ['Hawaii', 4.5126101123209397],\n", + " ['Vermont', 4.5126101123209397],\n", + " ['Maryland', 4.5126101123209397],\n", + " ['Minnesota', 4.5126101123209397],\n", + " ['Illinois', 4.5126101123209397],\n", + " ['New Mexico', 3.2804611900008411],\n", + " ['North Carolina', 3.2804611900008411],\n", + " ['Georgia', 3.2804611900008411],\n", + " ['West Virginia', 3.2804611900008411],\n", + " ['South Carolina', 3.2804611900008411],\n", + " ['Tennessee', 3.2804611900008411],\n", + " ['Mississippi', 3.2804611900008411]]" ] }, "execution_count": 111, @@ -5840,7 +4772,16 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", + " if __name__ == '__main__':\n" + ] + } + ], "source": [ "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" ] @@ -5870,6 +4811,14 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: irow(i) is deprecated. Please use .iloc[i]\n", + " if __name__ == '__main__':\n" + ] + }, { "data": { "text/plain": [ @@ -5884,10 +4833,10 @@ "poll_date 2012-09-11 00:00:00\n", "Weight 0.65\n", "PIE 1.76\n", - "ESS 173.0171\n", - "MESS 173.0171\n", - "time_weight 0.6155722\n", - "kmeans_labels 0\n", + "ESS 173.017\n", + "MESS 173.017\n", + "time_weight 0.615572\n", + "kmeans_labels 3\n", "pollster_state American Research Group-Colorado\n", "Name: 168, dtype: object" ] @@ -5967,7 +4916,16 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", + " if __name__ == '__main__':\n" + ] + } + ], "source": [ "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" ] @@ -6057,67 +5015,31 @@ "data": { "text/plain": [ "pollster_state\n", - "American Research Group-Colorado 1\n", - "American Research Group-Florida 1\n", - "American Research Group-Iowa 1\n", - "American Research Group-Nevada 1\n", - "American Research Group-New Hampshire 3\n", - "American Research Group-North Carolina 1\n", - "American Research Group-Ohio 1\n", - "American Research Group-Virginia 1\n", - "CNN / Opinion Research-Wisconsin 1\n", - "Chicago Trib. / MarketShares-Illinois 1\n", - "Columbus Dispatch (OH)-Ohio 2\n", - "EPIC-MRA-Michigan 8\n", - "Fairleigh-Dickinson (NJ)-New Jersey 3\n", - "Field Poll (CA)-California 6\n", - "Insider Advantage-Georgia 2\n", - "LA Times / Bloomberg-New Hampshire 1\n", - "Marist (NY)-New York 3\n", - "Mason-Dixon-Florida 3\n", - "Mason-Dixon-Georgia 1\n", - "Mason-Dixon-New Hampshire 1\n", - "Mason-Dixon-North Dakota 1\n", - "Mason-Dixon-Utah 1\n", - "Mason-Dixon-Virginia 1\n", - "Mitchell-Michigan 3\n", - "Ohio Poll-Ohio 2\n", - "Public Policy Polling (PPP)-Arizona 7\n", - "Public Policy Polling (PPP)-California 2\n", - "Public Policy Polling (PPP)-Colorado 6\n", - "Public Policy Polling (PPP)-Connecticut 3\n", - "Public Policy Polling (PPP)-Florida 8\n", - " ..\n", - "Rasmussen-Iowa 3\n", - "Rasmussen-Maine 1\n", - "Rasmussen-Massachusetts 4\n", - "Rasmussen-Michigan 2\n", - "Rasmussen-Missouri 7\n", - "Rasmussen-Montana 5\n", - "Rasmussen-Nebraska 2\n", - "Rasmussen-Nevada 3\n", - "Rasmussen-New Hampshire 1\n", - "Rasmussen-New Jersey 1\n", - "Rasmussen-New Mexico 3\n", - "Rasmussen-North Carolina 4\n", - "Rasmussen-North Dakota 1\n", - "Rasmussen-Ohio 7\n", - "Rasmussen-Pennsylvania 4\n", - "Rasmussen-Virginia 5\n", - "Rasmussen-Washington 1\n", - "Rasmussen-Wisconsin 7\n", - "Suffolk (NH/MA)-Florida 2\n", - "SurveyUSA-California 4\n", - "SurveyUSA-Florida 2\n", - "SurveyUSA-Georgia 4\n", - "SurveyUSA-Kansas 2\n", - "SurveyUSA-Michigan 1\n", - "SurveyUSA-New Jersey 1\n", - "SurveyUSA-New York 1\n", - "SurveyUSA-North Carolina 2\n", - "SurveyUSA-Oregon 4\n", - "SurveyUSA-Pennsylvania 1\n", - "SurveyUSA-Washington 4\n", + "American Research Group-Colorado 1\n", + "American Research Group-Florida 1\n", + "American Research Group-Iowa 1\n", + "American Research Group-Nevada 1\n", + "American Research Group-New Hampshire 3\n", + "American Research Group-North Carolina 1\n", + "American Research Group-Ohio 1\n", + "American Research Group-Virginia 1\n", + "CNN / Opinion Research-Wisconsin 1\n", + "Chicago Trib. / MarketShares-Illinois 1\n", + "Columbus Dispatch (OH)-Ohio 2\n", + "EPIC-MRA-Michigan 8\n", + " ..\n", + "Suffolk (NH/MA)-Florida 2\n", + "SurveyUSA-California 4\n", + "SurveyUSA-Florida 2\n", + "SurveyUSA-Georgia 4\n", + "SurveyUSA-Kansas 2\n", + "SurveyUSA-Michigan 1\n", + "SurveyUSA-New Jersey 1\n", + "SurveyUSA-New York 1\n", + "SurveyUSA-North Carolina 2\n", + "SurveyUSA-Oregon 4\n", + "SurveyUSA-Pennsylvania 1\n", + "SurveyUSA-Washington 4\n", "dtype: int64" ] }, @@ -6349,150 +5271,6 @@ " EPIC-MRA\n", " \n", " \n", - " 12\n", - " EPIC-MRA-Michigan\n", - " 2012-09-10\n", - " Michigan\n", - " 8.081481\n", - " EPIC-MRA\n", - " \n", - " \n", - " 13\n", - " Fairleigh-Dickinson (NJ)-New Jersey\n", - " 2012-03-08\n", - " New Jersey\n", - " 11.012846\n", - " Fairleigh-Dickinson (NJ)\n", - " \n", - " \n", - " 14\n", - " Fairleigh-Dickinson (NJ)-New Jersey\n", - " 2012-07-26\n", - " New Jersey\n", - " 11.012846\n", - " Fairleigh-Dickinson (NJ)\n", - " \n", - " \n", - " 15\n", - " Fairleigh-Dickinson (NJ)-New Jersey\n", - " 2012-09-09\n", - " New Jersey\n", - " 12.321317\n", - " Fairleigh-Dickinson (NJ)\n", - " \n", - " \n", - " 16\n", - " Field Poll (CA)-California\n", - " 2011-09-07\n", - " California\n", - " 26.901821\n", - " Field Poll (CA)\n", - " \n", - " \n", - " 17\n", - " Field Poll (CA)-California\n", - " 2011-11-21\n", - " California\n", - " 14.111741\n", - " Field Poll (CA)\n", - " \n", - " \n", - " 18\n", - " Field Poll (CA)-California\n", - " 2012-02-10\n", - " California\n", - " 16.323488\n", - " Field Poll (CA)\n", - " \n", - " \n", - " 19\n", - " Field Poll (CA)-California\n", - " 2012-05-25\n", - " California\n", - " 12.879358\n", - " Field Poll (CA)\n", - " \n", - " \n", - " 20\n", - " Field Poll (CA)-California\n", - " 2012-06-27\n", - " California\n", - " 13.316641\n", - " Field Poll (CA)\n", - " \n", - " \n", - " 21\n", - " Field Poll (CA)-California\n", - " 2012-09-12\n", - " California\n", - " 21.440282\n", - " Field Poll (CA)\n", - " \n", - " \n", - " 22\n", - " Insider Advantage-Georgia\n", - " 2012-05-22\n", - " Georgia\n", - " -8.785026\n", - " Insider Advantage\n", - " \n", - " \n", - " 23\n", - " Insider Advantage-Georgia\n", - " 2012-07-24\n", - " Georgia\n", - " -4.598910\n", - " Insider Advantage\n", - " \n", - " \n", - " 24\n", - " Marist (NY)-New York\n", - " 2011-10-26\n", - " New York\n", - " 18.229994\n", - " Marist (NY)\n", - " \n", - " \n", - " 25\n", - " Marist (NY)-New York\n", - " 2012-01-19\n", - " New York\n", - " 15.323173\n", - " Marist (NY)\n", - " \n", - " \n", - " 26\n", - " Marist (NY)-New York\n", - " 2012-04-11\n", - " New York\n", - " 10.533985\n", - " Marist (NY)\n", - " \n", - " \n", - " 27\n", - " Mason-Dixon-Florida\n", - " 2012-04-06\n", - " Florida\n", - " -4.277919\n", - " Mason-Dixon\n", - " \n", - " \n", - " 28\n", - " Mason-Dixon-Florida\n", - " 2012-07-10\n", - " Florida\n", - " 1.514054\n", - " Mason-Dixon\n", - " \n", - " \n", - " 29\n", - " Mason-Dixon-Florida\n", - " 2012-08-20\n", - " Florida\n", - " -9.210613\n", - " Mason-Dixon\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", @@ -6501,150 +5279,6 @@ " ...\n", " \n", " \n", - " 291\n", - " Rasmussen-Wisconsin\n", - " 2012-02-27\n", - " Wisconsin\n", - " 4.357351\n", - " Rasmussen\n", - " \n", - " \n", - " 292\n", - " Rasmussen-Wisconsin\n", - " 2012-03-27\n", - " Wisconsin\n", - " 10.707061\n", - " Rasmussen\n", - " \n", - " \n", - " 293\n", - " Rasmussen-Wisconsin\n", - " 2012-05-09\n", - " Wisconsin\n", - " 2.821784\n", - " Rasmussen\n", - " \n", - " \n", - " 294\n", - " Rasmussen-Wisconsin\n", - " 2012-06-12\n", - " Wisconsin\n", - " -1.913946\n", - " Rasmussen\n", - " \n", - " \n", - " 295\n", - " Rasmussen-Wisconsin\n", - " 2012-07-25\n", - " Wisconsin\n", - " 1.932597\n", - " Rasmussen\n", - " \n", - " \n", - " 296\n", - " Rasmussen-Wisconsin\n", - " 2012-09-17\n", - " Wisconsin\n", - " 1.932597\n", - " Rasmussen\n", - " \n", - " \n", - " 297\n", - " Suffolk (NH/MA)-Florida\n", - " 2012-04-11\n", - " Florida\n", - " -1.113497\n", - " Suffolk (NH/MA)\n", - " \n", - " \n", - " 298\n", - " Suffolk (NH/MA)-Florida\n", - " 2012-10-28\n", - " Florida\n", - " -0.075990\n", - " Suffolk (NH/MA)\n", - " \n", - " \n", - " 299\n", - " SurveyUSA-California\n", - " 2011-11-10\n", - " California\n", - " 13.393160\n", - " SurveyUSA\n", - " \n", - " \n", - " 300\n", - " SurveyUSA-California\n", - " 2012-02-09\n", - " California\n", - " 25.638748\n", - " SurveyUSA\n", - " \n", - " \n", - " 301\n", - " SurveyUSA-California\n", - " 2012-03-31\n", - " California\n", - " 27.921344\n", - " SurveyUSA\n", - " \n", - " \n", - " 302\n", - " SurveyUSA-California\n", - " 2012-09-10\n", - " California\n", - " 15.256589\n", - " SurveyUSA\n", - " \n", - " \n", - " 303\n", - " SurveyUSA-Florida\n", - " 2012-07-18\n", - " Florida\n", - " 3.754219\n", - " SurveyUSA\n", - " \n", - " \n", - " 304\n", - " SurveyUSA-Florida\n", - " 2012-09-08\n", - " Florida\n", - " 1.921633\n", - " SurveyUSA\n", - " \n", - " \n", - " 305\n", - " SurveyUSA-Georgia\n", - " 2011-12-07\n", - " Georgia\n", - " -5.667851\n", - " SurveyUSA\n", - " \n", - " \n", - " 306\n", - " SurveyUSA-Georgia\n", - " 2012-02-02\n", - " Georgia\n", - " -6.538168\n", - " SurveyUSA\n", - " \n", - " \n", - " 307\n", - " SurveyUSA-Georgia\n", - " 2012-02-25\n", - " Georgia\n", - " -5.667851\n", - " SurveyUSA\n", - " \n", - " \n", - " 308\n", - " SurveyUSA-Georgia\n", - " 2012-07-29\n", - " Georgia\n", - " -6.538168\n", - " SurveyUSA\n", - " \n", - " \n", " 309\n", " SurveyUSA-Kansas\n", " 2011-11-10\n", @@ -6746,131 +5380,32 @@ "" ], "text/plain": [ - " pollster_state poll_date State \\\n", - "0 American Research Group-New Hampshire 2012-03-17 New Hampshire \n", - "1 American Research Group-New Hampshire 2012-06-23 New Hampshire \n", - "2 American Research Group-New Hampshire 2012-09-26 New Hampshire \n", - "3 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio \n", - "4 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio \n", - "5 EPIC-MRA-Michigan 2011-02-15 Michigan \n", - "6 EPIC-MRA-Michigan 2011-07-10 Michigan \n", - "7 EPIC-MRA-Michigan 2011-11-15 Michigan \n", - "8 EPIC-MRA-Michigan 2012-01-23 Michigan \n", - "9 EPIC-MRA-Michigan 2012-04-02 Michigan \n", - "10 EPIC-MRA-Michigan 2012-06-04 Michigan \n", - "11 EPIC-MRA-Michigan 2012-07-28 Michigan \n", - "12 EPIC-MRA-Michigan 2012-09-10 Michigan \n", - "13 Fairleigh-Dickinson (NJ)-New Jersey 2012-03-08 New Jersey \n", - "14 Fairleigh-Dickinson (NJ)-New Jersey 2012-07-26 New Jersey \n", - "15 Fairleigh-Dickinson (NJ)-New Jersey 2012-09-09 New Jersey \n", - "16 Field Poll (CA)-California 2011-09-07 California \n", - "17 Field Poll (CA)-California 2011-11-21 California \n", - "18 Field Poll (CA)-California 2012-02-10 California \n", - "19 Field Poll (CA)-California 2012-05-25 California \n", - "20 Field Poll (CA)-California 2012-06-27 California \n", - "21 Field Poll (CA)-California 2012-09-12 California \n", - "22 Insider Advantage-Georgia 2012-05-22 Georgia \n", - "23 Insider Advantage-Georgia 2012-07-24 Georgia \n", - "24 Marist (NY)-New York 2011-10-26 New York \n", - "25 Marist (NY)-New York 2012-01-19 New York \n", - "26 Marist (NY)-New York 2012-04-11 New York \n", - "27 Mason-Dixon-Florida 2012-04-06 Florida \n", - "28 Mason-Dixon-Florida 2012-07-10 Florida \n", - "29 Mason-Dixon-Florida 2012-08-20 Florida \n", - ".. ... ... ... \n", - "291 Rasmussen-Wisconsin 2012-02-27 Wisconsin \n", - "292 Rasmussen-Wisconsin 2012-03-27 Wisconsin \n", - "293 Rasmussen-Wisconsin 2012-05-09 Wisconsin \n", - "294 Rasmussen-Wisconsin 2012-06-12 Wisconsin \n", - "295 Rasmussen-Wisconsin 2012-07-25 Wisconsin \n", - "296 Rasmussen-Wisconsin 2012-09-17 Wisconsin \n", - "297 Suffolk (NH/MA)-Florida 2012-04-11 Florida \n", - "298 Suffolk (NH/MA)-Florida 2012-10-28 Florida \n", - "299 SurveyUSA-California 2011-11-10 California \n", - "300 SurveyUSA-California 2012-02-09 California \n", - "301 SurveyUSA-California 2012-03-31 California \n", - "302 SurveyUSA-California 2012-09-10 California \n", - "303 SurveyUSA-Florida 2012-07-18 Florida \n", - "304 SurveyUSA-Florida 2012-09-08 Florida \n", - "305 SurveyUSA-Georgia 2011-12-07 Georgia \n", - "306 SurveyUSA-Georgia 2012-02-02 Georgia \n", - "307 SurveyUSA-Georgia 2012-02-25 Georgia \n", - "308 SurveyUSA-Georgia 2012-07-29 Georgia \n", - "309 SurveyUSA-Kansas 2011-11-10 Kansas \n", - "310 SurveyUSA-Kansas 2011-11-20 Kansas \n", - "311 SurveyUSA-North Carolina 2012-04-28 North Carolina \n", - "312 SurveyUSA-North Carolina 2012-09-30 North Carolina \n", - "313 SurveyUSA-Oregon 2011-11-20 Oregon \n", - "314 SurveyUSA-Oregon 2012-03-17 Oregon \n", - "315 SurveyUSA-Oregon 2012-05-09 Oregon \n", - "316 SurveyUSA-Oregon 2012-09-12 Oregon \n", - "317 SurveyUSA-Washington 2011-11-22 Washington \n", - "318 SurveyUSA-Washington 2012-05-09 Washington \n", - "319 SurveyUSA-Washington 2012-08-02 Washington \n", - "320 SurveyUSA-Washington 2012-09-08 Washington \n", - "\n", - " m Pollster \n", - "0 6.436534 American Research Group \n", - "1 0.071010 American Research Group \n", - "2 4.054884 American Research Group \n", - "3 1.875520 Columbus Dispatch (OH) \n", - "4 7.679307 Columbus Dispatch (OH) \n", - "5 -4.201071 EPIC-MRA \n", - "6 -3.096961 EPIC-MRA \n", - "7 -4.201071 EPIC-MRA \n", - "8 6.398112 EPIC-MRA \n", - "9 -0.219418 EPIC-MRA \n", - "10 1.427470 EPIC-MRA \n", - "11 2.361416 EPIC-MRA \n", - "12 8.081481 EPIC-MRA \n", - "13 11.012846 Fairleigh-Dickinson (NJ) \n", - "14 11.012846 Fairleigh-Dickinson (NJ) \n", - "15 12.321317 Fairleigh-Dickinson (NJ) \n", - "16 26.901821 Field Poll (CA) \n", - "17 14.111741 Field Poll (CA) \n", - "18 16.323488 Field Poll (CA) \n", - "19 12.879358 Field Poll (CA) \n", - "20 13.316641 Field Poll (CA) \n", - "21 21.440282 Field Poll (CA) \n", - "22 -8.785026 Insider Advantage \n", - "23 -4.598910 Insider Advantage \n", - "24 18.229994 Marist (NY) \n", - "25 15.323173 Marist (NY) \n", - "26 10.533985 Marist (NY) \n", - "27 -4.277919 Mason-Dixon \n", - "28 1.514054 Mason-Dixon \n", - "29 -9.210613 Mason-Dixon \n", - ".. ... ... \n", - "291 4.357351 Rasmussen \n", - "292 10.707061 Rasmussen \n", - "293 2.821784 Rasmussen \n", - "294 -1.913946 Rasmussen \n", - "295 1.932597 Rasmussen \n", - "296 1.932597 Rasmussen \n", - "297 -1.113497 Suffolk (NH/MA) \n", - "298 -0.075990 Suffolk (NH/MA) \n", - "299 13.393160 SurveyUSA \n", - "300 25.638748 SurveyUSA \n", - "301 27.921344 SurveyUSA \n", - "302 15.256589 SurveyUSA \n", - "303 3.754219 SurveyUSA \n", - "304 1.921633 SurveyUSA \n", - "305 -5.667851 SurveyUSA \n", - "306 -6.538168 SurveyUSA \n", - "307 -5.667851 SurveyUSA \n", - "308 -6.538168 SurveyUSA \n", - "309 -26.128872 SurveyUSA \n", - "310 -6.973400 SurveyUSA \n", - "311 3.435788 SurveyUSA \n", - "312 1.008145 SurveyUSA \n", - "313 7.001203 SurveyUSA \n", - "314 10.204604 SurveyUSA \n", - "315 1.934380 SurveyUSA \n", - "316 8.172477 SurveyUSA \n", - "317 12.315353 SurveyUSA \n", - "318 8.655616 SurveyUSA \n", - "319 14.386038 SurveyUSA \n", - "320 9.553699 SurveyUSA \n", + " pollster_state poll_date State m Pollster\n", + "0 American Research Group-New Hampshire 2012-03-17 New Hampshire 6.436534 American Research Group\n", + "1 American Research Group-New Hampshire 2012-06-23 New Hampshire 0.071010 American Research Group\n", + "2 American Research Group-New Hampshire 2012-09-26 New Hampshire 4.054884 American Research Group\n", + "3 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio 1.875520 Columbus Dispatch (OH)\n", + "4 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio 7.679307 Columbus Dispatch (OH)\n", + "5 EPIC-MRA-Michigan 2011-02-15 Michigan -4.201071 EPIC-MRA\n", + "6 EPIC-MRA-Michigan 2011-07-10 Michigan -3.096961 EPIC-MRA\n", + "7 EPIC-MRA-Michigan 2011-11-15 Michigan -4.201071 EPIC-MRA\n", + "8 EPIC-MRA-Michigan 2012-01-23 Michigan 6.398112 EPIC-MRA\n", + "9 EPIC-MRA-Michigan 2012-04-02 Michigan -0.219418 EPIC-MRA\n", + "10 EPIC-MRA-Michigan 2012-06-04 Michigan 1.427470 EPIC-MRA\n", + "11 EPIC-MRA-Michigan 2012-07-28 Michigan 2.361416 EPIC-MRA\n", + ".. ... ... ... ... ...\n", + "309 SurveyUSA-Kansas 2011-11-10 Kansas -26.128872 SurveyUSA\n", + "310 SurveyUSA-Kansas 2011-11-20 Kansas -6.973400 SurveyUSA\n", + "311 SurveyUSA-North Carolina 2012-04-28 North Carolina 3.435788 SurveyUSA\n", + "312 SurveyUSA-North Carolina 2012-09-30 North Carolina 1.008145 SurveyUSA\n", + "313 SurveyUSA-Oregon 2011-11-20 Oregon 7.001203 SurveyUSA\n", + "314 SurveyUSA-Oregon 2012-03-17 Oregon 10.204604 SurveyUSA\n", + "315 SurveyUSA-Oregon 2012-05-09 Oregon 1.934380 SurveyUSA\n", + "316 SurveyUSA-Oregon 2012-09-12 Oregon 8.172477 SurveyUSA\n", + "317 SurveyUSA-Washington 2011-11-22 Washington 12.315353 SurveyUSA\n", + "318 SurveyUSA-Washington 2012-05-09 Washington 8.655616 SurveyUSA\n", + "319 SurveyUSA-Washington 2012-08-02 Washington 14.386038 SurveyUSA\n", + "320 SurveyUSA-Washington 2012-09-08 Washington 9.553699 SurveyUSA\n", "\n", "[321 rows x 5 columns]" ] @@ -6956,7 +5491,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 0\n", + " 3\n", " \n", " \n", " 1\n", @@ -6980,7 +5515,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 0\n", + " 3\n", " \n", " \n", " 2\n", @@ -7004,7 +5539,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 0\n", + " 3\n", " \n", " \n", " 3\n", @@ -7028,7 +5563,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 0\n", + " 3\n", " \n", " \n", " 4\n", @@ -7052,7 +5587,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 0\n", + " 3\n", " \n", " \n", " 5\n", @@ -7076,7 +5611,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 0\n", + " 3\n", " \n", " \n", " 6\n", @@ -7099,8 +5634,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 7\n", @@ -7123,8 +5658,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 8\n", @@ -7147,8 +5682,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 9\n", @@ -7171,8 +5706,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 10\n", @@ -7195,8 +5730,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 11\n", @@ -7219,896 +5754,32 @@ " -1\n", " 0.377548\n", " 0.427662\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", - " 12\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2011-10-15\n", - " Ohio\n", - " 2.358188\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 13\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2011-11-05\n", - " Ohio\n", - " 6.859215\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 14\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2012-01-29\n", - " Ohio\n", - " 4.969091\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 15\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2012-05-05\n", - " Ohio\n", - " 5.842069\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 16\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2012-06-23\n", - " Ohio\n", - " -1.127675\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 17\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2012-09-08\n", - " Ohio\n", - " 2.512656\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 18\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2012-09-29\n", - " Ohio\n", - " 0.678406\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 19\n", - " Quinnipiac-Ohio\n", - " 2011-07-15\n", - " Ohio\n", - " 3.746496\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 20\n", - " Quinnipiac-Ohio\n", - " 2011-09-23\n", - " Ohio\n", - " 1.612668\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 21\n", - " Quinnipiac-Ohio\n", - " 2011-10-20\n", - " Ohio\n", - " 3.746496\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 22\n", - " Quinnipiac-Ohio\n", - " 2011-11-04\n", - " Ohio\n", - " 2.340016\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 23\n", - " Quinnipiac-Ohio\n", - " 2011-12-02\n", - " Ohio\n", - " -1.942619\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 24\n", - " Quinnipiac-Ohio\n", - " 2012-01-13\n", - " Ohio\n", - " 1.694036\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 25\n", - " Quinnipiac-Ohio\n", - " 2012-02-10\n", - " Ohio\n", - " 1.691996\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 26\n", - " Quinnipiac-Ohio\n", - " 2012-03-23\n", - " Ohio\n", - " 5.796853\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 27\n", - " Quinnipiac-Ohio\n", - " 2012-04-28\n", - " Ohio\n", - " 1.683659\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 28\n", - " Quinnipiac-Ohio\n", - " 2012-05-05\n", - " Ohio\n", - " 0.699432\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 29\n", - " Quinnipiac-Ohio\n", - " 2012-06-22\n", - " Ohio\n", - " 8.821557\n", - " Quinnipiac\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 0\n", - " 3\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 291\n", - " Rasmussen-Virginia\n", - " 2012-08-07\n", - " Virginia\n", - " 1.245810\n", - " Rasmussen\n", - " 19.8\n", - " 8.2\n", - " 64.5\n", - " 86.1\n", - " 33.8\n", - " ...\n", - " 1012075.500\n", - " 0.125\n", - " 0.646\n", - " -3.0\n", - " 14.6\n", - " -2\n", - " 1.000185\n", - " 0.938508\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 292\n", - " Rasmussen-Virginia\n", - " 2012-08-23\n", - " Virginia\n", - " -0.707960\n", - " Rasmussen\n", - " 19.8\n", - " 8.2\n", - " 64.5\n", - " 86.1\n", - " 33.8\n", - " ...\n", - " 1012075.500\n", - " 0.125\n", - " 0.646\n", - " -3.0\n", - " 14.6\n", - " -2\n", - " 1.000185\n", - " 0.938508\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 293\n", - " Rasmussen-Virginia\n", - " 2012-09-13\n", - " Virginia\n", - " -0.132000\n", - " Rasmussen\n", - " 19.8\n", - " 8.2\n", - " 64.5\n", - " 86.1\n", - " 33.8\n", - " ...\n", - " 1012075.500\n", - " 0.125\n", - " 0.646\n", - " -3.0\n", - " 14.6\n", - " -2\n", - " 1.000185\n", - " 0.938508\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 294\n", - " Public Policy Polling (PPP)-Washington\n", - " 2011-05-14\n", - " Washington\n", - " 8.921781\n", - " Public Policy Polling (PPP)\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", - " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 295\n", - " Public Policy Polling (PPP)-Washington\n", - " 2012-02-18\n", - " Washington\n", - " 13.966914\n", - " Public Policy Polling (PPP)\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", - " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 296\n", - " Public Policy Polling (PPP)-Washington\n", - " 2012-06-16\n", - " Washington\n", - " 11.253592\n", - " Public Policy Polling (PPP)\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", - " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 297\n", - " SurveyUSA-Washington\n", - " 2011-11-22\n", - " Washington\n", - " 12.315353\n", - " SurveyUSA\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", - " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 298\n", - " SurveyUSA-Washington\n", - " 2012-05-09\n", - " Washington\n", - " 8.655616\n", - " SurveyUSA\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", - " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 299\n", - " SurveyUSA-Washington\n", - " 2012-08-02\n", - " Washington\n", - " 14.386038\n", - " SurveyUSA\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", - " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 300\n", - " SurveyUSA-Washington\n", - " 2012-09-08\n", - " Washington\n", - " 9.553699\n", - " SurveyUSA\n", - " 3.8\n", - " 11.6\n", - " 72.1\n", - " 89.6\n", - " 31.0\n", " ...\n", - " 867414.826\n", - " 0.127\n", - " 0.641\n", - " 9.8\n", - " 14.8\n", - " 5\n", - " 1.190590\n", - " 0.475625\n", - " 4\n", - " 0\n", - " \n", - " \n", - " 301\n", - " Public Policy Polling (PPP)-West Virginia\n", - " 2012-01-22\n", - " West Virginia\n", - " -7.852240\n", - " Public Policy Polling (PPP)\n", - " 3.5\n", - " 1.3\n", - " 93.0\n", - " 81.9\n", - " 17.3\n", " ...\n", - " 300568.968\n", - " 0.162\n", - " 0.631\n", - " 3.4\n", - " 12.8\n", - " -8\n", - " 0.260437\n", - " 0.321333\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 302\n", - " Public Policy Polling (PPP)-West Virginia\n", - " 2012-09-03\n", - " West Virginia\n", - " -8.389235\n", - " Public Policy Polling (PPP)\n", - " 3.5\n", - " 1.3\n", - " 93.0\n", - " 81.9\n", - " 17.3\n", " ...\n", - " 300568.968\n", - " 0.162\n", - " 0.631\n", - " 3.4\n", - " 12.8\n", - " -8\n", - " 0.260437\n", - " 0.321333\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 303\n", - " Public Policy Polling (PPP)-West Virginia\n", - " 2012-10-01\n", - " West Virginia\n", - " -23.140121\n", - " Public Policy Polling (PPP)\n", - " 3.5\n", - " 1.3\n", - " 93.0\n", - " 81.9\n", - " 17.3\n", " ...\n", - " 300568.968\n", - " 0.162\n", - " 0.631\n", - " 3.4\n", - " 12.8\n", - " -8\n", - " 0.260437\n", - " 0.321333\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 304\n", - " Public Policy Polling (PPP)-Wisconsin\n", - " 2011-02-26\n", - " Wisconsin\n", - " 8.691364\n", - " Public Policy Polling (PPP)\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 305\n", - " Public Policy Polling (PPP)-Wisconsin\n", - " 2011-05-21\n", - " Wisconsin\n", - " 10.899564\n", - " Public Policy Polling (PPP)\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 306\n", - " Public Policy Polling (PPP)-Wisconsin\n", - " 2011-08-20\n", - " Wisconsin\n", - " 3.743916\n", - " Public Policy Polling (PPP)\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 307\n", - " Public Policy Polling (PPP)-Wisconsin\n", - " 2012-02-25\n", - " Wisconsin\n", - " 13.469173\n", - " Public Policy Polling (PPP)\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 308\n", - " Public Policy Polling (PPP)-Wisconsin\n", - " 2012-07-07\n", - " Wisconsin\n", - " 4.195163\n", - " Public Policy Polling (PPP)\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 0\n", - " 3\n", " \n", " \n", " 309\n", @@ -8131,8 +5802,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 310\n", @@ -8155,8 +5826,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 311\n", @@ -8179,8 +5850,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 312\n", @@ -8203,8 +5874,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 313\n", @@ -8227,8 +5898,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 314\n", @@ -8251,8 +5922,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 315\n", @@ -8275,8 +5946,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 316\n", @@ -8299,8 +5970,8 @@ " 2\n", " 0.455410\n", " 0.237802\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 317\n", @@ -8323,8 +5994,8 @@ " -13\n", " 0.335630\n", " 0.351093\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 318\n", @@ -8347,8 +6018,8 @@ " -13\n", " 0.335630\n", " 0.351093\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 319\n", @@ -8371,8 +6042,8 @@ " -12\n", " 0.392000\n", " 0.469934\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", " 320\n", @@ -8395,8 +6066,8 @@ " -12\n", " 0.392000\n", " 0.469934\n", + " 1\n", " 0\n", - " 3\n", " \n", " \n", "\n", @@ -8404,320 +6075,86 @@ "" ], "text/plain": [ - " pollster_state poll_date State \\\n", - "0 American Research Group-New Hampshire 2012-03-17 New Hampshire \n", - "1 American Research Group-New Hampshire 2012-06-23 New Hampshire \n", - "2 American Research Group-New Hampshire 2012-09-26 New Hampshire \n", - "3 Public Policy Polling (PPP)-New Hampshire 2011-04-02 New Hampshire \n", - "4 Public Policy Polling (PPP)-New Hampshire 2011-07-03 New Hampshire \n", - "5 Public Policy Polling (PPP)-New Hampshire 2012-05-12 New Hampshire \n", - "6 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio \n", - "7 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio \n", - "8 Ohio Poll-Ohio 2011-09-16 Ohio \n", - "9 Ohio Poll-Ohio 2012-08-19 Ohio \n", - "10 Public Policy Polling (PPP)-Ohio 2011-03-12 Ohio \n", - "11 Public Policy Polling (PPP)-Ohio 2011-05-21 Ohio \n", - "12 Public Policy Polling (PPP)-Ohio 2011-10-15 Ohio \n", - "13 Public Policy Polling (PPP)-Ohio 2011-11-05 Ohio \n", - "14 Public Policy Polling (PPP)-Ohio 2012-01-29 Ohio \n", - "15 Public Policy Polling (PPP)-Ohio 2012-05-05 Ohio \n", - "16 Public Policy Polling (PPP)-Ohio 2012-06-23 Ohio \n", - "17 Public Policy Polling (PPP)-Ohio 2012-09-08 Ohio \n", - "18 Public Policy Polling (PPP)-Ohio 2012-09-29 Ohio \n", - "19 Quinnipiac-Ohio 2011-07-15 Ohio \n", - "20 Quinnipiac-Ohio 2011-09-23 Ohio \n", - "21 Quinnipiac-Ohio 2011-10-20 Ohio \n", - "22 Quinnipiac-Ohio 2011-11-04 Ohio \n", - "23 Quinnipiac-Ohio 2011-12-02 Ohio \n", - "24 Quinnipiac-Ohio 2012-01-13 Ohio \n", - "25 Quinnipiac-Ohio 2012-02-10 Ohio \n", - "26 Quinnipiac-Ohio 2012-03-23 Ohio \n", - "27 Quinnipiac-Ohio 2012-04-28 Ohio \n", - "28 Quinnipiac-Ohio 2012-05-05 Ohio \n", - "29 Quinnipiac-Ohio 2012-06-22 Ohio \n", - ".. ... ... ... \n", - "291 Rasmussen-Virginia 2012-08-07 Virginia \n", - "292 Rasmussen-Virginia 2012-08-23 Virginia \n", - "293 Rasmussen-Virginia 2012-09-13 Virginia \n", - "294 Public Policy Polling (PPP)-Washington 2011-05-14 Washington \n", - "295 Public Policy Polling (PPP)-Washington 2012-02-18 Washington \n", - "296 Public Policy Polling (PPP)-Washington 2012-06-16 Washington \n", - "297 SurveyUSA-Washington 2011-11-22 Washington \n", - "298 SurveyUSA-Washington 2012-05-09 Washington \n", - "299 SurveyUSA-Washington 2012-08-02 Washington \n", - "300 SurveyUSA-Washington 2012-09-08 Washington \n", - "301 Public Policy Polling (PPP)-West Virginia 2012-01-22 West Virginia \n", - "302 Public Policy Polling (PPP)-West Virginia 2012-09-03 West Virginia \n", - "303 Public Policy Polling (PPP)-West Virginia 2012-10-01 West Virginia \n", - "304 Public Policy Polling (PPP)-Wisconsin 2011-02-26 Wisconsin \n", - "305 Public Policy Polling (PPP)-Wisconsin 2011-05-21 Wisconsin \n", - "306 Public Policy Polling (PPP)-Wisconsin 2011-08-20 Wisconsin \n", - "307 Public Policy Polling (PPP)-Wisconsin 2012-02-25 Wisconsin \n", - "308 Public Policy Polling (PPP)-Wisconsin 2012-07-07 Wisconsin \n", - "309 Public Policy Polling (PPP)-Wisconsin 2012-09-19 Wisconsin \n", - "310 Rasmussen-Wisconsin 2011-10-26 Wisconsin \n", - "311 Rasmussen-Wisconsin 2012-02-27 Wisconsin \n", - "312 Rasmussen-Wisconsin 2012-03-27 Wisconsin \n", - "313 Rasmussen-Wisconsin 2012-05-09 Wisconsin \n", - "314 Rasmussen-Wisconsin 2012-06-12 Wisconsin \n", - "315 Rasmussen-Wisconsin 2012-07-25 Wisconsin \n", - "316 Rasmussen-Wisconsin 2012-09-17 Wisconsin \n", - "317 Rasmussen-Nebraska 2012-03-05 Nebraska \n", - "318 Rasmussen-Nebraska 2012-05-16 Nebraska \n", - "319 SurveyUSA-Kansas 2011-11-10 Kansas \n", - "320 SurveyUSA-Kansas 2011-11-20 Kansas \n", - "\n", - " m Pollster per_black per_hisp per_white \\\n", - "0 6.436534 American Research Group 1.3 2.9 92.2 \n", - "1 0.071010 American Research Group 1.3 2.9 92.2 \n", - "2 4.054884 American Research Group 1.3 2.9 92.2 \n", - "3 3.118546 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", - "4 -0.240062 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", - "5 10.469450 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", - "6 1.875520 Columbus Dispatch (OH) 12.4 3.2 81.0 \n", - "7 7.679307 Columbus Dispatch (OH) 12.4 3.2 81.0 \n", - "8 4.166959 Ohio Poll 12.4 3.2 81.0 \n", - "9 1.501578 Ohio Poll 12.4 3.2 81.0 \n", - "10 4.430867 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "11 2.960337 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "12 2.358188 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "13 6.859215 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "14 4.969091 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "15 5.842069 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "16 -1.127675 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "17 2.512656 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "18 0.678406 Public Policy Polling (PPP) 12.4 3.2 81.0 \n", - "19 3.746496 Quinnipiac 12.4 3.2 81.0 \n", - "20 1.612668 Quinnipiac 12.4 3.2 81.0 \n", - "21 3.746496 Quinnipiac 12.4 3.2 81.0 \n", - "22 2.340016 Quinnipiac 12.4 3.2 81.0 \n", - "23 -1.942619 Quinnipiac 12.4 3.2 81.0 \n", - "24 1.694036 Quinnipiac 12.4 3.2 81.0 \n", - "25 1.691996 Quinnipiac 12.4 3.2 81.0 \n", - "26 5.796853 Quinnipiac 12.4 3.2 81.0 \n", - "27 1.683659 Quinnipiac 12.4 3.2 81.0 \n", - "28 0.699432 Quinnipiac 12.4 3.2 81.0 \n", - "29 8.821557 Quinnipiac 12.4 3.2 81.0 \n", - ".. ... ... ... ... ... \n", - "291 1.245810 Rasmussen 19.8 8.2 64.5 \n", - "292 -0.707960 Rasmussen 19.8 8.2 64.5 \n", - "293 -0.132000 Rasmussen 19.8 8.2 64.5 \n", - "294 8.921781 Public Policy Polling (PPP) 3.8 11.6 72.1 \n", - "295 13.966914 Public Policy Polling (PPP) 3.8 11.6 72.1 \n", - "296 11.253592 Public Policy Polling (PPP) 3.8 11.6 72.1 \n", - "297 12.315353 SurveyUSA 3.8 11.6 72.1 \n", - "298 8.655616 SurveyUSA 3.8 11.6 72.1 \n", - "299 14.386038 SurveyUSA 3.8 11.6 72.1 \n", - "300 9.553699 SurveyUSA 3.8 11.6 72.1 \n", - "301 -7.852240 Public Policy Polling (PPP) 3.5 1.3 93.0 \n", - "302 -8.389235 Public Policy Polling (PPP) 3.5 1.3 93.0 \n", - "303 -23.140121 Public Policy Polling (PPP) 3.5 1.3 93.0 \n", - "304 8.691364 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", - "305 10.899564 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", - "306 3.743916 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", - "307 13.469173 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", - "308 4.195163 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", - "309 6.146157 Public Policy Polling (PPP) 6.5 6.1 83.1 \n", - "310 3.197725 Rasmussen 6.5 6.1 83.1 \n", - "311 4.357351 Rasmussen 6.5 6.1 83.1 \n", - "312 10.707061 Rasmussen 6.5 6.1 83.1 \n", - "313 2.821784 Rasmussen 6.5 6.1 83.1 \n", - "314 -1.913946 Rasmussen 6.5 6.1 83.1 \n", - "315 1.932597 Rasmussen 6.5 6.1 83.1 \n", - "316 1.932597 Rasmussen 6.5 6.1 83.1 \n", - "317 -19.056922 Rasmussen 4.7 9.5 81.8 \n", - "318 -20.181630 Rasmussen 4.7 9.5 81.8 \n", - "319 -26.128872 SurveyUSA 6.1 10.8 77.8 \n", - "320 -6.973400 SurveyUSA 6.1 10.8 77.8 \n", + " pollster_state poll_date State m Pollster \\\n", + "0 American Research Group-New Hampshire 2012-03-17 New Hampshire 6.436534 American Research Group \n", + "1 American Research Group-New Hampshire 2012-06-23 New Hampshire 0.071010 American Research Group \n", + "2 American Research Group-New Hampshire 2012-09-26 New Hampshire 4.054884 American Research Group \n", + "3 Public Policy Polling (PPP)-New Hampshire 2011-04-02 New Hampshire 3.118546 Public Policy Polling (PPP) \n", + "4 Public Policy Polling (PPP)-New Hampshire 2011-07-03 New Hampshire -0.240062 Public Policy Polling (PPP) \n", + "5 Public Policy Polling (PPP)-New Hampshire 2012-05-12 New Hampshire 10.469450 Public Policy Polling (PPP) \n", + "6 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio 1.875520 Columbus Dispatch (OH) \n", + "7 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio 7.679307 Columbus Dispatch (OH) \n", + "8 Ohio Poll-Ohio 2011-09-16 Ohio 4.166959 Ohio Poll \n", + "9 Ohio Poll-Ohio 2012-08-19 Ohio 1.501578 Ohio Poll \n", + "10 Public Policy Polling (PPP)-Ohio 2011-03-12 Ohio 4.430867 Public Policy Polling (PPP) \n", + "11 Public Policy Polling (PPP)-Ohio 2011-05-21 Ohio 2.960337 Public Policy Polling (PPP) \n", + ".. ... ... ... ... ... \n", + "309 Public Policy Polling (PPP)-Wisconsin 2012-09-19 Wisconsin 6.146157 Public Policy Polling (PPP) \n", + "310 Rasmussen-Wisconsin 2011-10-26 Wisconsin 3.197725 Rasmussen \n", + "311 Rasmussen-Wisconsin 2012-02-27 Wisconsin 4.357351 Rasmussen \n", + "312 Rasmussen-Wisconsin 2012-03-27 Wisconsin 10.707061 Rasmussen \n", + "313 Rasmussen-Wisconsin 2012-05-09 Wisconsin 2.821784 Rasmussen \n", + "314 Rasmussen-Wisconsin 2012-06-12 Wisconsin -1.913946 Rasmussen \n", + "315 Rasmussen-Wisconsin 2012-07-25 Wisconsin 1.932597 Rasmussen \n", + "316 Rasmussen-Wisconsin 2012-09-17 Wisconsin 1.932597 Rasmussen \n", + "317 Rasmussen-Nebraska 2012-03-05 Nebraska -19.056922 Rasmussen \n", + "318 Rasmussen-Nebraska 2012-05-16 Nebraska -20.181630 Rasmussen \n", + "319 SurveyUSA-Kansas 2011-11-10 Kansas -26.128872 SurveyUSA \n", + "320 SurveyUSA-Kansas 2011-11-20 Kansas -6.973400 SurveyUSA \n", "\n", - " educ_hs educ_coll ... older_pop per_older per_vote \\\n", - "0 90.9 32.9 ... 184547.160 0.140 0.648 \n", - "1 90.9 32.9 ... 184547.160 0.140 0.648 \n", - "2 90.9 32.9 ... 184547.160 0.140 0.648 \n", - "3 90.9 32.9 ... 184547.160 0.140 0.648 \n", - "4 90.9 32.9 ... 184547.160 0.140 0.648 \n", - "5 90.9 32.9 ... 184547.160 0.140 0.648 \n", - "6 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "7 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "8 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "9 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "10 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "11 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "12 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "13 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "14 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "15 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "16 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "17 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "18 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "19 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "20 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "21 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "22 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "23 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "24 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "25 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "26 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "27 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "28 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - "29 87.4 24.1 ... 1650927.993 0.143 0.624 \n", - ".. ... ... ... ... ... ... \n", - "291 86.1 33.8 ... 1012075.500 0.125 0.646 \n", - "292 86.1 33.8 ... 1012075.500 0.125 0.646 \n", - "293 86.1 33.8 ... 1012075.500 0.125 0.646 \n", - "294 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "295 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "296 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "297 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "298 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "299 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "300 89.6 31.0 ... 867414.826 0.127 0.641 \n", - "301 81.9 17.3 ... 300568.968 0.162 0.631 \n", - "302 81.9 17.3 ... 300568.968 0.162 0.631 \n", - "303 81.9 17.3 ... 300568.968 0.162 0.631 \n", - "304 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "305 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "306 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "307 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "308 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "309 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "310 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "311 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "312 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "313 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "314 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "315 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "316 89.4 25.8 ... 793935.613 0.139 0.629 \n", - "317 90.0 27.7 ... 250599.176 0.136 0.614 \n", - "318 90.0 27.7 ... 250599.176 0.136 0.614 \n", - "319 89.2 29.3 ... 381874.654 0.133 0.615 \n", - "320 89.2 29.3 ... 381874.654 0.133 0.615 \n", + " per_black per_hisp per_white educ_hs educ_coll ... older_pop per_older per_vote dem_adv \\\n", + "0 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", + "1 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", + "2 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", + "3 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", + "4 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", + "5 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", + "6 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", + "7 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", + "8 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", + "9 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", + "10 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", + "11 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", + ".. ... ... ... ... ... ... ... ... ... ... \n", + "309 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "310 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "311 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "312 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "313 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "314 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "315 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "316 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", + "317 4.7 9.5 81.8 90.0 27.7 ... 250599.176 0.136 0.614 -19.0 \n", + "318 4.7 9.5 81.8 90.0 27.7 ... 250599.176 0.136 0.614 -19.0 \n", + "319 6.1 10.8 77.8 89.2 29.3 ... 381874.654 0.133 0.615 -16.9 \n", + "320 6.1 10.8 77.8 89.2 29.3 ... 381874.654 0.133 0.615 -16.9 \n", "\n", - " dem_adv no_party PVI obama_give romney_give kmeans_group \\\n", - "0 -1.5 13.9 2 0.961563 0.733997 4 \n", - "1 -1.5 13.9 2 0.961563 0.733997 4 \n", - "2 -1.5 13.9 2 0.961563 0.733997 4 \n", - "3 -1.5 13.9 2 0.961563 0.733997 4 \n", - "4 -1.5 13.9 2 0.961563 0.733997 4 \n", - "5 -1.5 13.9 2 0.961563 0.733997 4 \n", - "6 3.6 15.4 -1 0.377548 0.427662 0 \n", - "7 3.6 15.4 -1 0.377548 0.427662 0 \n", - "8 3.6 15.4 -1 0.377548 0.427662 0 \n", - "9 3.6 15.4 -1 0.377548 0.427662 0 \n", - "10 3.6 15.4 -1 0.377548 0.427662 0 \n", - "11 3.6 15.4 -1 0.377548 0.427662 0 \n", - "12 3.6 15.4 -1 0.377548 0.427662 0 \n", - "13 3.6 15.4 -1 0.377548 0.427662 0 \n", - "14 3.6 15.4 -1 0.377548 0.427662 0 \n", - "15 3.6 15.4 -1 0.377548 0.427662 0 \n", - "16 3.6 15.4 -1 0.377548 0.427662 0 \n", - "17 3.6 15.4 -1 0.377548 0.427662 0 \n", - "18 3.6 15.4 -1 0.377548 0.427662 0 \n", - "19 3.6 15.4 -1 0.377548 0.427662 0 \n", - "20 3.6 15.4 -1 0.377548 0.427662 0 \n", - "21 3.6 15.4 -1 0.377548 0.427662 0 \n", - "22 3.6 15.4 -1 0.377548 0.427662 0 \n", - "23 3.6 15.4 -1 0.377548 0.427662 0 \n", - "24 3.6 15.4 -1 0.377548 0.427662 0 \n", - "25 3.6 15.4 -1 0.377548 0.427662 0 \n", - "26 3.6 15.4 -1 0.377548 0.427662 0 \n", - "27 3.6 15.4 -1 0.377548 0.427662 0 \n", - "28 3.6 15.4 -1 0.377548 0.427662 0 \n", - "29 3.6 15.4 -1 0.377548 0.427662 0 \n", - ".. ... ... ... ... ... ... \n", - "291 -3.0 14.6 -2 1.000185 0.938508 4 \n", - "292 -3.0 14.6 -2 1.000185 0.938508 4 \n", - "293 -3.0 14.6 -2 1.000185 0.938508 4 \n", - "294 9.8 14.8 5 1.190590 0.475625 4 \n", - "295 9.8 14.8 5 1.190590 0.475625 4 \n", - "296 9.8 14.8 5 1.190590 0.475625 4 \n", - "297 9.8 14.8 5 1.190590 0.475625 4 \n", - "298 9.8 14.8 5 1.190590 0.475625 4 \n", - "299 9.8 14.8 5 1.190590 0.475625 4 \n", - "300 9.8 14.8 5 1.190590 0.475625 4 \n", - "301 3.4 12.8 -8 0.260437 0.321333 2 \n", - "302 3.4 12.8 -8 0.260437 0.321333 2 \n", - "303 3.4 12.8 -8 0.260437 0.321333 2 \n", - "304 2.8 12.8 2 0.455410 0.237802 0 \n", - "305 2.8 12.8 2 0.455410 0.237802 0 \n", - "306 2.8 12.8 2 0.455410 0.237802 0 \n", - "307 2.8 12.8 2 0.455410 0.237802 0 \n", - "308 2.8 12.8 2 0.455410 0.237802 0 \n", - "309 2.8 12.8 2 0.455410 0.237802 0 \n", - "310 2.8 12.8 2 0.455410 0.237802 0 \n", - "311 2.8 12.8 2 0.455410 0.237802 0 \n", - "312 2.8 12.8 2 0.455410 0.237802 0 \n", - "313 2.8 12.8 2 0.455410 0.237802 0 \n", - "314 2.8 12.8 2 0.455410 0.237802 0 \n", - "315 2.8 12.8 2 0.455410 0.237802 0 \n", - "316 2.8 12.8 2 0.455410 0.237802 0 \n", - "317 -19.0 14.8 -13 0.335630 0.351093 0 \n", - "318 -19.0 14.8 -13 0.335630 0.351093 0 \n", - "319 -16.9 14.3 -12 0.392000 0.469934 0 \n", - "320 -16.9 14.3 -12 0.392000 0.469934 0 \n", - "\n", - " kmeans_labels \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 3 \n", - "7 3 \n", - "8 3 \n", - "9 3 \n", - "10 3 \n", - "11 3 \n", - "12 3 \n", - "13 3 \n", - "14 3 \n", - "15 3 \n", - "16 3 \n", - "17 3 \n", - "18 3 \n", - "19 3 \n", - "20 3 \n", - "21 3 \n", - "22 3 \n", - "23 3 \n", - "24 3 \n", - "25 3 \n", - "26 3 \n", - "27 3 \n", - "28 3 \n", - "29 3 \n", - ".. ... \n", - "291 0 \n", - "292 0 \n", - "293 0 \n", - "294 0 \n", - "295 0 \n", - "296 0 \n", - "297 0 \n", - "298 0 \n", - "299 0 \n", - "300 0 \n", - "301 1 \n", - "302 1 \n", - "303 1 \n", - "304 3 \n", - "305 3 \n", - "306 3 \n", - "307 3 \n", - "308 3 \n", - "309 3 \n", - "310 3 \n", - "311 3 \n", - "312 3 \n", - "313 3 \n", - "314 3 \n", - "315 3 \n", - "316 3 \n", - "317 3 \n", - "318 3 \n", - "319 3 \n", - "320 3 \n", + " no_party PVI obama_give romney_give kmeans_group kmeans_labels \n", + "0 13.9 2 0.961563 0.733997 4 3 \n", + "1 13.9 2 0.961563 0.733997 4 3 \n", + "2 13.9 2 0.961563 0.733997 4 3 \n", + "3 13.9 2 0.961563 0.733997 4 3 \n", + "4 13.9 2 0.961563 0.733997 4 3 \n", + "5 13.9 2 0.961563 0.733997 4 3 \n", + "6 15.4 -1 0.377548 0.427662 1 0 \n", + "7 15.4 -1 0.377548 0.427662 1 0 \n", + "8 15.4 -1 0.377548 0.427662 1 0 \n", + "9 15.4 -1 0.377548 0.427662 1 0 \n", + "10 15.4 -1 0.377548 0.427662 1 0 \n", + "11 15.4 -1 0.377548 0.427662 1 0 \n", + ".. ... ... ... ... ... ... \n", + "309 12.8 2 0.455410 0.237802 1 0 \n", + "310 12.8 2 0.455410 0.237802 1 0 \n", + "311 12.8 2 0.455410 0.237802 1 0 \n", + "312 12.8 2 0.455410 0.237802 1 0 \n", + "313 12.8 2 0.455410 0.237802 1 0 \n", + "314 12.8 2 0.455410 0.237802 1 0 \n", + "315 12.8 2 0.455410 0.237802 1 0 \n", + "316 12.8 2 0.455410 0.237802 1 0 \n", + "317 14.8 -13 0.335630 0.351093 1 0 \n", + "318 14.8 -13 0.335630 0.351093 1 0 \n", + "319 14.3 -12 0.392000 0.469934 1 0 \n", + "320 14.3 -12 0.392000 0.469934 1 0 \n", "\n", "[321 rows x 24 columns]" ] @@ -8871,27 +6308,16 @@ "" ], "text/plain": [ - " PVI per_black per_hisp older_pop average_income \\\n", - "PVI 1.000000 -0.294799 0.116418 0.150510 0.595083 \n", - "per_black -0.294799 1.000000 -0.173355 0.278531 -0.064176 \n", - "per_hisp 0.116418 -0.173355 1.000000 0.403386 0.099982 \n", - "older_pop 0.150510 0.278531 0.403386 1.000000 0.023183 \n", - "average_income 0.595083 -0.064176 0.099982 0.023183 1.000000 \n", - "romney_give 0.291997 0.111333 0.289653 0.237119 0.717860 \n", - "obama_give 0.669193 -0.280984 0.307853 -0.036660 0.704609 \n", - "educ_coll 0.494291 -0.110643 0.113554 -0.074438 0.888344 \n", - "educ_hs 0.225624 -0.497133 -0.564734 -0.478205 0.249691 \n", - "\n", - " romney_give obama_give educ_coll educ_hs \n", - "PVI 0.291997 0.669193 0.494291 0.225624 \n", - "per_black 0.111333 -0.280984 -0.110643 -0.497133 \n", - "per_hisp 0.289653 0.307853 0.113554 -0.564734 \n", - "older_pop 0.237119 -0.036660 -0.074438 -0.478205 \n", - "average_income 0.717860 0.704609 0.888344 0.249691 \n", - "romney_give 1.000000 0.554900 0.630611 -0.024673 \n", - "obama_give 0.554900 1.000000 0.835424 0.084808 \n", - "educ_coll 0.630611 0.835424 1.000000 0.272766 \n", - "educ_hs -0.024673 0.084808 0.272766 1.000000 " + " PVI per_black per_hisp older_pop average_income romney_give obama_give educ_coll educ_hs\n", + "PVI 1.000000 -0.294799 0.116418 0.150510 0.595083 0.291997 0.669193 0.494291 0.225624\n", + "per_black -0.294799 1.000000 -0.173355 0.278531 -0.064176 0.111333 -0.280984 -0.110643 -0.497133\n", + "per_hisp 0.116418 -0.173355 1.000000 0.403386 0.099982 0.289653 0.307853 0.113554 -0.564734\n", + "older_pop 0.150510 0.278531 0.403386 1.000000 0.023183 0.237119 -0.036660 -0.074438 -0.478205\n", + "average_income 0.595083 -0.064176 0.099982 0.023183 1.000000 0.717860 0.704609 0.888344 0.249691\n", + "romney_give 0.291997 0.111333 0.289653 0.237119 0.717860 1.000000 0.554900 0.630611 -0.024673\n", + "obama_give 0.669193 -0.280984 0.307853 -0.036660 0.704609 0.554900 1.000000 0.835424 0.084808\n", + "educ_coll 0.494291 -0.110643 0.113554 -0.074438 0.888344 0.630611 0.835424 1.000000 0.272766\n", + "educ_hs 0.225624 -0.497133 -0.564734 -0.478205 0.249691 -0.024673 0.084808 0.272766 1.000000" ] }, "execution_count": 136, @@ -8926,43 +6352,7 @@ "9 44 days\n", "10 570 days\n", "11 500 days\n", - "12 353 days\n", - "13 332 days\n", - "14 247 days\n", - "15 150 days\n", - "16 101 days\n", - "17 24 days\n", - "18 3 days\n", - "19 445 days\n", - "20 375 days\n", - "21 348 days\n", - "22 333 days\n", - "23 305 days\n", - "24 263 days\n", - "25 235 days\n", - "26 193 days\n", - "27 157 days\n", - "28 150 days\n", - "29 102 days\n", " ... \n", - "291 56 days\n", - "292 40 days\n", - "293 19 days\n", - "294 507 days\n", - "295 227 days\n", - "296 108 days\n", - "297 315 days\n", - "298 146 days\n", - "299 61 days\n", - "300 24 days\n", - "301 254 days\n", - "302 29 days\n", - "303 1 days\n", - "304 584 days\n", - "305 500 days\n", - "306 409 days\n", - "307 220 days\n", - "308 87 days\n", "309 13 days\n", "310 342 days\n", "311 218 days\n", @@ -9023,7 +6413,7 @@ " Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", "\n", "\n", - " Time: 10:23:58 Log-Likelihood: -1457.5\n", + " Time: 20:59:22 Log-Likelihood: -1457.5\n", "\n", "\n", " No. Observations: 321 AIC: 2927.\n", @@ -9085,7 +6475,7 @@ "Model: WLS Adj. R-squared: 0.700\n", "Method: Least Squares F-statistic: 150.4\n", "Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", - "Time: 10:23:58 Log-Likelihood: -1457.5\n", + "Time: 20:59:22 Log-Likelihood: -1457.5\n", "No. Observations: 321 AIC: 2927.\n", "Df Residuals: 315 BIC: 2950.\n", "Df Model: 5 \n", @@ -9155,9 +6545,9 @@ "outputs": [ { "data": { - "image/png": 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baGhYwqJFi0rVrJKRlLn8uNEhUw6BTLn5q7/ayfz587CsHmAp0OIsS7GsHubP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\nmf1N0jHAJDP7fCX/cyGE0ANeU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PtBBCCBFgkwe83gMMOe/UEQ4/XtYDXk0kVVTMI4MNhRBCCJFWf38/N920lrff/hlwufPq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\n8+CPf/xjYAz4FnAP03tn73H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spdFmzVqYc2kvk2vLu1EqzO3yY8Xug3bt7Nxqt+dTO1sn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5YQzsVUM9uJkLHcK3eMZZGOnR+6+6fS70Y4Sq9w93kZ9p5EzAPTiBzuX3iG7F3B3t3A0919XSG9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\ntupS50wVCeWRtBGwk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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -9206,12 +6596,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \n", + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " from IPython.kernel.zmq import kernelapp as app\n" + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from ipykernel import kernelapp as app\n" ] } ], @@ -9241,15 +6631,10 @@ { "data": { "text/plain": [ - "array([-15.4217, -21.6313, -5.0082, -10.561 , 16.1516, 1.9687,\n", - " 10.9842, 14.074 , 2.0836, -4.6565, 18.1919, -25.1584,\n", - " 15.7641, -7.8211, 1.3951, -17.1674, -14.4888, -9.6044,\n", - " 6.7223, 17.5189, 18.47 , 8.243 , 2.4987, -8.0447,\n", - " -3.1677, -11.2639, -19.1112, 5.3443, 0.8928, 8.2712,\n", - " 10.5977, 19.1728, -1.8028, -16.6034, -0.1732, -24.4563,\n", - " 6.9845, 4.29 , 18.3768, -7.1117, -14.4658, -11.14 ,\n", - " -8.1462, -29.3198, 18.9368, -0.9413, 7.9092, -12.3842,\n", - " 3.4667, -31.8014])" + "array([-15.4217, -21.6313, -5.0082, -10.561 , 16.1516, 1.9687, 10.9842, 14.074 , 2.0836, -4.6565, 18.1919, -25.1584, 15.7641, -7.8211, 1.3951,\n", + " -17.1674, -14.4888, -9.6044, 6.7223, 17.5189, 18.47 , 8.243 , 2.4987, -8.0447, -3.1677, -11.2639, -19.1112, 5.3443, 0.8928, 8.2712,\n", + " 10.5977, 19.1728, -1.8028, -16.6034, -0.1732, -24.4563, 6.9845, 4.29 , 18.3768, -7.1117, -14.4658, -11.14 , -8.1462, -29.3198, 18.9368,\n", + " -0.9413, 7.9092, -12.3842, 3.4667, -31.8014])" ] }, "execution_count": 146, @@ -9303,9 +6688,9 @@ "outputs": [ { "data": 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c7pRz0jrmlHNS5zn9mNTFU86FEDaFEEJ6/bkkSfwnazxM0jrjZPtS5zn9mFRdS3uaQwh/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\n6g5c5rnR4Hg2evYGcLPdvUiDFDN7H1oR2hY9U+MR931SpOzZaCBeBN2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70xYMOAytzncE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\nI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P80eSVJ9isciVK1eAzpZC+CO3P1RLmu9q5IlijJ+IMf488DVNiUyS+tTytFqV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1.4160660286631208),\n", - " ('Rhode Island', 1.9687713407242649),\n", - " ('South Carolina', 0.96871534442524843),\n", - " ('South Dakota', 0.68017110979691953),\n", - " ('Tennessee', 0.81066155479860558),\n", - " ('Texas', 0.92812466205019228),\n", - " ('Utah', 0.097369058861876509),\n", - " ('Vermont', 1.9907421659896687),\n", - " ('Virginia', 1.2108135939642795),\n", - " ('Washington', 1.558068638876154),\n", - " ('West Virginia', 0.7618468941505212),\n", - " ('Wisconsin', 1.3837649018595277),\n", + " ('North Carolina', 1.1770116288991701),\n", + " ('North Dakota', 0.59630134352743591),\n", + " ('Ohio', 1.2409505020643554),\n", + " ('Oklahoma', 0.28819001562210417),\n", + " ('Oregon', 1.5217875821850471),\n", + " ('Pennsylvania', 1.4160660286631532),\n", + " ('Rhode Island', 1.9687713407242875),\n", + " ('South Carolina', 0.9687153444253096),\n", + " ('South Dakota', 0.68017110979696893),\n", + " ('Tennessee', 0.81066155479866131),\n", + " ('Texas', 0.9281246620502277),\n", + " ('Utah', 0.097369058861922542),\n", + " ('Vermont', 1.9907421659897067),\n", + " ('Virginia', 1.2108135939642581),\n", + " ('Washington', 1.5580686388761602),\n", + " ('West Virginia', 0.76184689415060114),\n", + " ('Wisconsin', 1.3837649018595635),\n", " ('Wyoming', 0.0)]" ] }, @@ -9459,68 +6844,32 @@ { "data": { "text/plain": [ - "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168494\n", - " Rasmussen -10.209446\n", - "CA Field Poll (CA) 23.343924\n", - " Public Policy Polling (PPP) 20.999075\n", - " Rasmussen 22.000000\n", - " SurveyUSA 22.123414\n", - "CO American Research Group 2.000000\n", - " Public Policy Polling (PPP) 5.469907\n", - " Rasmussen -1.573788\n", - "CT Public Policy Polling (PPP) 12.757757\n", - " Quinnipiac 7.293983\n", - " Rasmussen 8.000000\n", - "FL American Research Group 5.000000\n", - " Mason-Dixon -3.543178\n", - " Public Policy Polling (PPP) 3.125154\n", - " Quinnipiac 3.075653\n", - " Rasmussen 0.882884\n", - " Suffolk (NH/MA) -0.003377\n", - " SurveyUSA 4.168952\n", - "GA Insider Advantage -19.174054\n", - " Mason-Dixon -17.000000\n", - " Public Policy Polling (PPP) -3.000000\n", - " SurveyUSA -7.983856\n", - "HI Public Policy Polling (PPP) 27.000000\n", - "IA American Research Group 7.000000\n", - " Mason-Dixon -3.000000\n", - " Public Policy Polling (PPP) 5.878693\n", - " Rasmussen -2.749416\n", - "IL Chicago Trib. / MarketShares 21.000000\n", - "IN Rasmussen -16.000000\n", - " ... \n", - "OH Ohio Poll 3.000406\n", - " Public Policy Polling (PPP) 4.141640\n", - " Quinnipiac 7.729397\n", - " Rasmussen 0.865613\n", - "OR Public Policy Polling (PPP) 9.130153\n", - " SurveyUSA 8.675504\n", - "PA Public Policy Polling (PPP) 6.160027\n", - " Quinnipiac 6.047221\n", - " Rasmussen 10.874768\n", - " SurveyUSA 0.000000\n", - "RI Public Policy Polling (PPP) 17.000000\n", - "SC Public Policy Polling (PPP) -14.558484\n", - "SD Public Policy Polling (PPP) -6.000000\n", - "TN Public Policy Polling (PPP) -7.000000\n", - "TX Public Policy Polling (PPP) -6.998595\n", - "UT Mason-Dixon -51.000000\n", - " Public Policy Polling (PPP) -32.000000\n", - "VA American Research Group 2.000000\n", - " Mason-Dixon 1.000000\n", - " Public Policy Polling (PPP) 5.095802\n", - " Quinnipiac 0.578138\n", - " Rasmussen 0.891780\n", - "VT Public Policy Polling (PPP) 20.000000\n", - "WA Public Policy Polling (PPP) 13.050886\n", - " Rasmussen 11.000000\n", - " SurveyUSA 15.310208\n", - "WI CNN / Opinion Research 4.000000\n", - " Public Policy Polling (PPP) 5.392554\n", - " Rasmussen 2.116005\n", - "WV Public Policy Polling (PPP) -19.756631\n", + "State Pollster \n", + "AZ Public Policy Polling (PPP) -9.168494\n", + " Rasmussen -10.209446\n", + "CA Field Poll (CA) 23.343924\n", + " Public Policy Polling (PPP) 20.999075\n", + " Rasmussen 22.000000\n", + " SurveyUSA 22.123414\n", + "CO American Research Group 2.000000\n", + " Public Policy Polling (PPP) 5.469907\n", + " Rasmussen -1.573788\n", + "CT Public Policy Polling (PPP) 12.757757\n", + " Quinnipiac 7.293983\n", + " Rasmussen 8.000000\n", + " ... \n", + "VA Mason-Dixon 1.000000\n", + " Public Policy Polling (PPP) 5.095802\n", + " Quinnipiac 0.578138\n", + " Rasmussen 0.891780\n", + "VT Public Policy Polling (PPP) 20.000000\n", + "WA Public Policy Polling (PPP) 13.050886\n", + " Rasmussen 11.000000\n", + " SurveyUSA 15.310208\n", + "WI CNN / Opinion Research 4.000000\n", + " Public Policy Polling (PPP) 5.392554\n", + " Rasmussen 2.116005\n", + "WV Public Policy Polling (PPP) -19.756631\n", "Name: poll, dtype: float64" ] }, @@ -9557,47 +6906,31 @@ "data": { "text/plain": [ "State\n", - "Washington 7.047853\n", - "New Hampshire 5.802571\n", - "New Jersey 7.112103\n", - "Nevada 6.592628\n", - "Colorado 5.993525\n", - "Connecticut 7.593603\n", - "Virginia 5.477061\n", - "Massachusetts 8.922189\n", - "Rhode Island 8.905649\n", - "Hawaii 8.872836\n", - "Maryland 8.753382\n", - "Illinois 8.441942\n", - "New Mexico 5.433933\n", - "North Carolina 3.844664\n", - "Arizona 3.433858\n", - "Georgia 3.478935\n", - "West Virginia 2.488544\n", - "South Carolina 3.164272\n", - "Tennessee 2.647995\n", - "Mississippi 3.044697\n", - "Florida 4.503946\n", - "California 6.373831\n", - "New York 6.775401\n", - "Texas 3.144208\n", - "Wisconsin 7.433267\n", - "North Dakota 3.203194\n", - "Nebraska 2.674642\n", - "Ohio 6.666101\n", - "Pennsylvania 7.606781\n", - "Indiana 5.054192\n", - "Iowa 6.996652\n", - "Maine 8.119432\n", - "Missouri 6.034968\n", - "Michigan 8.439942\n", - "Montana 4.328580\n", - "Kansas 3.084341\n", - "Oregon 8.174693\n", - "South Dakota 3.653723\n", - "Vermont 10.693809\n", - "Utah 0.523044\n", - "Minnesota 7.229251\n", + "Wisconsin 7.440163\n", + "North Dakota 3.206165\n", + "Nebraska 2.677124\n", + "Ohio 6.672285\n", + "Pennsylvania 7.613838\n", + "Indiana 5.058881\n", + "Iowa 7.003142\n", + "Arizona 5.652295\n", + "Maine 8.126964\n", + "Missouri 6.040566\n", + "Michigan 8.447772\n", + "Montana 4.332595\n", + " ... \n", + "Hawaii 8.851564\n", + "Vermont 8.983443\n", + "Maryland 8.732396\n", + "Minnesota 6.073006\n", + "Illinois 8.421703\n", + "New Mexico 5.457221\n", + "North Carolina 3.861141\n", + "Georgia 3.493845\n", + "West Virginia 2.499209\n", + "South Carolina 3.177833\n", + "Tennessee 2.659344\n", + "Mississippi 3.057746\n", "Name: poll, dtype: float64" ] }, @@ -9646,297 +6979,188 @@ " \n", " \n", " 0\n", - " Washington\n", - " 7.047853\n", + " Wisconsin\n", + " 7.440163\n", " National\n", " \n", " \n", " 1\n", - " New Hampshire\n", - " 5.802571\n", + " North Dakota\n", + " 3.206165\n", " National\n", " \n", " \n", " 2\n", - " New Jersey\n", - " 7.112103\n", + " Nebraska\n", + " 2.677124\n", " National\n", " \n", " \n", " 3\n", - " Nevada\n", - " 6.592628\n", + " Ohio\n", + " 6.672285\n", " National\n", " \n", " \n", " 4\n", - " Colorado\n", - " 5.993525\n", + " Pennsylvania\n", + " 7.613838\n", " National\n", " \n", " \n", " 5\n", - " Connecticut\n", - " 7.593603\n", + " Indiana\n", + " 5.058881\n", " National\n", " \n", " \n", " 6\n", - " Virginia\n", - " 5.477061\n", + " Iowa\n", + " 7.003142\n", " National\n", " \n", " \n", " 7\n", - " Massachusetts\n", - " 8.922189\n", - " National\n", - " \n", - " \n", - " 8\n", - " Rhode Island\n", - " 8.905649\n", - " National\n", - " \n", - " \n", - " 9\n", - " Hawaii\n", - " 8.872836\n", - " National\n", - " \n", - " \n", - " 10\n", - " Maryland\n", - " 8.753382\n", - " National\n", - " \n", - " \n", - " 11\n", - " Illinois\n", - " 8.441942\n", - " National\n", - " \n", - " \n", - " 12\n", - " New Mexico\n", - " 5.433933\n", - " National\n", - " \n", - " \n", - " 13\n", - " North Carolina\n", - " 3.844664\n", - " National\n", - " \n", - " \n", - " 14\n", - " Arizona\n", - " 3.433858\n", - " National\n", - " \n", - " \n", - " 15\n", - " Georgia\n", - " 3.478935\n", - " National\n", - " \n", - " \n", - " 16\n", - " West Virginia\n", - " 2.488544\n", - " National\n", - " \n", - " \n", - " 17\n", - " South Carolina\n", - " 3.164272\n", - " National\n", - " \n", - " \n", - " 18\n", - " Tennessee\n", - " 2.647995\n", - " National\n", - " \n", - " \n", - " 19\n", - " Mississippi\n", - " 3.044697\n", - " National\n", - " \n", - " \n", - " 20\n", - " Florida\n", - " 4.503946\n", - " National\n", - " \n", - " \n", - " 21\n", - " California\n", - " 6.373831\n", - " National\n", - " \n", - " \n", - " 22\n", - " New York\n", - " 6.775401\n", - " National\n", - " \n", - " \n", - " 23\n", - " Texas\n", - " 3.144208\n", + " Arizona\n", + " 5.652295\n", " National\n", " \n", " \n", - " 24\n", - " Wisconsin\n", - " 7.433267\n", + " 8\n", + " Maine\n", + " 8.126964\n", " National\n", " \n", " \n", - " 25\n", - " North Dakota\n", - " 3.203194\n", + " 9\n", + " Missouri\n", + " 6.040566\n", " National\n", " \n", " \n", - " 26\n", - " Nebraska\n", - " 2.674642\n", + " 10\n", + " Michigan\n", + " 8.447772\n", " National\n", " \n", " \n", - " 27\n", - " Ohio\n", - " 6.666101\n", + " 11\n", + " Montana\n", + " 4.332595\n", " National\n", " \n", " \n", - " 28\n", - " Pennsylvania\n", - " 7.606781\n", - " National\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 29\n", - " Indiana\n", - " 5.054192\n", + " Hawaii\n", + " 8.851564\n", " National\n", " \n", " \n", " 30\n", - " Iowa\n", - " 6.996652\n", + " Vermont\n", + " 8.983443\n", " National\n", " \n", " \n", " 31\n", - " Maine\n", - " 8.119432\n", + " Maryland\n", + " 8.732396\n", " National\n", " \n", " \n", " 32\n", - " Missouri\n", - " 6.034968\n", + " Minnesota\n", + " 6.073006\n", " National\n", " \n", " \n", " 33\n", - " Michigan\n", - " 8.439942\n", + " Illinois\n", + " 8.421703\n", " National\n", " \n", " \n", " 34\n", - " Montana\n", - " 4.328580\n", + " New Mexico\n", + " 5.457221\n", " National\n", " \n", " \n", " 35\n", - " Kansas\n", - " 3.084341\n", + " North Carolina\n", + " 3.861141\n", " National\n", " \n", " \n", " 36\n", - " Oregon\n", - " 8.174693\n", + " Georgia\n", + " 3.493845\n", " National\n", " \n", " \n", " 37\n", - " South Dakota\n", - " 3.653723\n", + " West Virginia\n", + " 2.499209\n", " National\n", " \n", " \n", " 38\n", - " Vermont\n", - " 10.693809\n", + " South Carolina\n", + " 3.177833\n", " National\n", " \n", " \n", " 39\n", - " Utah\n", - " 0.523044\n", + " Tennessee\n", + " 2.659344\n", " National\n", " \n", " \n", " 40\n", - " Minnesota\n", - " 7.229251\n", + " Mississippi\n", + " 3.057746\n", " National\n", " \n", " \n", "\n", + "

41 rows × 3 columns

\n", "" ], "text/plain": [ - " State poll Pollster\n", - "0 Washington 7.047853 National\n", - "1 New Hampshire 5.802571 National\n", - "2 New Jersey 7.112103 National\n", - "3 Nevada 6.592628 National\n", - "4 Colorado 5.993525 National\n", - "5 Connecticut 7.593603 National\n", - "6 Virginia 5.477061 National\n", - "7 Massachusetts 8.922189 National\n", - "8 Rhode Island 8.905649 National\n", - "9 Hawaii 8.872836 National\n", - "10 Maryland 8.753382 National\n", - "11 Illinois 8.441942 National\n", - "12 New Mexico 5.433933 National\n", - "13 North Carolina 3.844664 National\n", - "14 Arizona 3.433858 National\n", - "15 Georgia 3.478935 National\n", - "16 West Virginia 2.488544 National\n", - "17 South Carolina 3.164272 National\n", - "18 Tennessee 2.647995 National\n", - "19 Mississippi 3.044697 National\n", - "20 Florida 4.503946 National\n", - "21 California 6.373831 National\n", - "22 New York 6.775401 National\n", - "23 Texas 3.144208 National\n", - "24 Wisconsin 7.433267 National\n", - "25 North Dakota 3.203194 National\n", - "26 Nebraska 2.674642 National\n", - "27 Ohio 6.666101 National\n", - "28 Pennsylvania 7.606781 National\n", - "29 Indiana 5.054192 National\n", - "30 Iowa 6.996652 National\n", - "31 Maine 8.119432 National\n", - "32 Missouri 6.034968 National\n", - "33 Michigan 8.439942 National\n", - "34 Montana 4.328580 National\n", - "35 Kansas 3.084341 National\n", - "36 Oregon 8.174693 National\n", - "37 South Dakota 3.653723 National\n", - "38 Vermont 10.693809 National\n", - "39 Utah 0.523044 National\n", - "40 Minnesota 7.229251 National" + " State poll Pollster\n", + "0 Wisconsin 7.440163 National\n", + "1 North Dakota 3.206165 National\n", + "2 Nebraska 2.677124 National\n", + "3 Ohio 6.672285 National\n", + "4 Pennsylvania 7.613838 National\n", + "5 Indiana 5.058881 National\n", + "6 Iowa 7.003142 National\n", + "7 Arizona 5.652295 National\n", + "8 Maine 8.126964 National\n", + "9 Missouri 6.040566 National\n", + "10 Michigan 8.447772 National\n", + "11 Montana 4.332595 National\n", + ".. ... ... ...\n", + "29 Hawaii 8.851564 National\n", + "30 Vermont 8.983443 National\n", + "31 Maryland 8.732396 National\n", + "32 Minnesota 6.073006 National\n", + "33 Illinois 8.421703 National\n", + "34 New Mexico 5.457221 National\n", + "35 North Carolina 3.861141 National\n", + "36 Georgia 3.493845 National\n", + "37 West Virginia 2.499209 National\n", + "38 South Carolina 3.177833 National\n", + "39 Tennessee 2.659344 National\n", + "40 Mississippi 3.057746 National\n", + "\n", + "[41 rows x 3 columns]" ] }, "execution_count": 160, @@ -10053,52 +7277,10 @@ " 1.29\n", " \n", " \n", - " 12\n", - " Keystone (PA)\n", - " 0.64\n", - " 1.55\n", - " \n", - " \n", - " 13\n", - " LA Times / Bloomberg\n", - " 0.83\n", - " 1.44\n", - " \n", - " \n", - " 14\n", - " Marist (NY)\n", - " 0.69\n", - " 1.73\n", - " \n", - " \n", - " 15\n", - " Mason-Dixon\n", - " 1.10\n", - " 1.15\n", - " \n", - " \n", - " 16\n", - " Mitchell\n", - " 0.96\n", - " 1.43\n", - " \n", - " \n", - " 17\n", - " Ohio Poll\n", - " 1.24\n", - " 1.05\n", - " \n", - " \n", - " 18\n", - " Public Opinion Strategies\n", - " 0.63\n", - " 1.81\n", - " \n", - " \n", - " 19\n", - " Public Policy Polling (PPP)\n", - " 1.05\n", - " 1.60\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 20\n", @@ -10131,146 +7313,412 @@ " 2.01\n", " \n", " \n", - " 25\n", - " Strategic Vision\n", - " 0.95\n", - " 1.45\n", + " 25\n", + " Strategic Vision\n", + " 0.95\n", + " 1.45\n", + " \n", + " \n", + " 26\n", + " Suffolk (NH/MA)\n", + " 0.77\n", + " 1.37\n", + " \n", + " \n", + " 27\n", + " SurveyUSA\n", + " 1.91\n", + " 0.72\n", + " \n", + " \n", + " 28\n", + " Univ. New Hampshire\n", + " 1.08\n", + " 1.26\n", + " \n", + " \n", + " 29\n", + " USA Today / Gallup\n", + " 0.63\n", + " 2.01\n", + " \n", + " \n", + " 30\n", + " Zogby\n", + " 0.64\n", + " 1.72\n", + " \n", + " \n", + " 31\n", + " Zogby Interactive\n", + " 0.43\n", + " 4.74\n", + " \n", + " \n", + "\n", + "

32 rows × 3 columns

\n", + "" + ], + "text/plain": [ + " Pollster Weight PIE\n", + "0 ABC / Washington Post 0.95 1.41\n", + "1 American Research Group 0.65 1.76\n", + "2 CBS / New York Times 0.66 1.84\n", + "3 Chicago Trib. / MarketShares 1.16 1.13\n", + "4 CNN / Opinion Research 0.77 1.59\n", + "5 Columbus Dispatch (OH) 0.50 6.76\n", + "6 EPIC-MRA 0.75 1.65\n", + "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", + "8 Field Poll (CA) 1.33 0.88\n", + "9 Fox / Opinion Dynamics 0.79 1.60\n", + "10 Franklin Pierce (NH) 0.74 1.60\n", + "11 Insider Advantage 0.95 1.29\n", + ".. ... ... ...\n", + "20 Quinnipiac 0.95 1.34\n", + "21 Rasmussen 1.30 0.88\n", + "22 Research 2000 1.01 1.20\n", + "23 Selzer 1.47 0.92\n", + "24 Star Tribune (MN) 0.81 2.01\n", + "25 Strategic Vision 0.95 1.45\n", + "26 Suffolk (NH/MA) 0.77 1.37\n", + "27 SurveyUSA 1.91 0.72\n", + "28 Univ. New Hampshire 1.08 1.26\n", + "29 USA Today / Gallup 0.63 2.01\n", + "30 Zogby 0.64 1.72\n", + "31 Zogby Interactive 0.43 4.74\n", + "\n", + "[32 rows x 3 columns]" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "natl_weight = pandas.DataFrame([[\"National\", weights.Weight.mean(), weights.PIE.mean()]],\n", + " columns=[\"Pollster\", \"Weight\", \"PIE\"])\n", + "weights = pandas.concat((weights, natl_weight)).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "polls = polls.merge(weights, on=\"Pollster\", how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", + " if __name__ == '__main__':\n" + ] + } + ], + "source": [ + "polls = polls.sort(\"State\")" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def weighted_mean(group):\n", + " return (group[\"poll\"] * group[\"Weight\"] / group[\"Weight\"].sum()).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3045.224651Wisconsin
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30Zogby0.641.72315-1.534117Wisconsin
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" ], "text/plain": [ - " Pollster Weight PIE\n", - "0 ABC / Washington Post 0.95 1.41\n", - "1 American Research Group 0.65 1.76\n", - "2 CBS / New York Times 0.66 1.84\n", - "3 Chicago Trib. / MarketShares 1.16 1.13\n", - "4 CNN / Opinion Research 0.77 1.59\n", - "5 Columbus Dispatch (OH) 0.50 6.76\n", - "6 EPIC-MRA 0.75 1.65\n", - "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", - "8 Field Poll (CA) 1.33 0.88\n", - "9 Fox / Opinion Dynamics 0.79 1.60\n", - "10 Franklin Pierce (NH) 0.74 1.60\n", - "11 Insider Advantage 0.95 1.29\n", - "12 Keystone (PA) 0.64 1.55\n", - "13 LA Times / Bloomberg 0.83 1.44\n", - "14 Marist (NY) 0.69 1.73\n", - "15 Mason-Dixon 1.10 1.15\n", - "16 Mitchell 0.96 1.43\n", - "17 Ohio Poll 1.24 1.05\n", - "18 Public Opinion Strategies 0.63 1.81\n", - "19 Public Policy Polling (PPP) 1.05 1.60\n", - "20 Quinnipiac 0.95 1.34\n", - "21 Rasmussen 1.30 0.88\n", - "22 Research 2000 1.01 1.20\n", - "23 Selzer 1.47 0.92\n", - "24 Star Tribune (MN) 0.81 2.01\n", - "25 Strategic Vision 0.95 1.45\n", - "26 Suffolk (NH/MA) 0.77 1.37\n", - "27 SurveyUSA 1.91 0.72\n", - "28 Univ. New Hampshire 1.08 1.26\n", - "29 USA Today / Gallup 0.63 2.01\n", - "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74" + " resid State\n", + "304 5.224651 Wisconsin\n", + "305 7.432851 Wisconsin\n", + "306 0.277203 Wisconsin\n", + "307 10.002459 Wisconsin\n", + "308 0.728450 Wisconsin\n", + "309 2.679444 Wisconsin\n", + "310 -0.268988 Wisconsin\n", + "311 0.890637 Wisconsin\n", + "312 7.240347 Wisconsin\n", + "313 -0.644929 Wisconsin\n", + "314 -5.380660 Wisconsin\n", + "315 -1.534117 Wisconsin\n", + "316 -1.534117 Wisconsin" ] }, - "execution_count": 162, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "weights" + "group" ] }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "natl_weight = pandas.DataFrame([[\"National\", weights.Weight.mean(), weights.PIE.mean()]],\n", - " columns=[\"Pollster\", \"Weight\", \"PIE\"])\n", - "weights = pandas.concat((weights, natl_weight)).reset_index(drop=True)" + "results = polls.groupby(\"State\").aggregate(weighted_mean)[\"poll\"]" ] }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 169, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "State\n", + "Arizona -5.453958\n", + "California 19.966475\n", + "Colorado 2.667843\n", + "Connecticut 8.936227\n", + "Florida 2.170963\n", + "Georgia -8.811154\n", + "Hawaii 18.584803\n", + "Illinois 15.477867\n", + "Indiana -7.340970\n", + "Iowa 2.038001\n", + "Kansas -9.766281\n", + "Maine 12.222222\n", + " ... \n", + "Pennsylvania 5.436914\n", + "Rhode Island 13.236853\n", + "South Carolina -6.334382\n", + "South Dakota -1.522121\n", + "Tennessee -2.521086\n", + "Texas -2.295507\n", + "Utah -29.179269\n", + "Vermont 14.891764\n", + "Virginia 2.420244\n", + "Washington 12.312505\n", + "West Virginia -9.436884\n", + "Wisconsin 4.530315\n", + "Name: poll, dtype: float64" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "polls = polls.merge(weights, on=\"Pollster\", how=\"left\")" + "results = results.reset_index()\n", + "results[\"obama\"] = 0\n", + "results[\"romney\"] = 0\n", + "results.ix[results[\"poll\"] > 0, [\"obama\"]] = 1\n", + "results.ix[results[\"poll\"] < 0, [\"romney\"]] = 1" ] }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "polls = polls.sort(\"State\")" + "results[[\"State\", \"poll\"]].to_csv(\"./data/2012-predicted.csv\", index=False)" ] }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "def weighted_mean(group):\n", - " return (group[\"poll\"] * group[\"Weight\"] / group[\"Weight\"].sum()).sum()" + "electoral_votes = pandas.read_csv(\"./data/electoral_votes.csv\")" ] }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 173, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", + " if __name__ == '__main__':\n" + ] + } + ], + "source": [ + "electoral_votes.sort(\"State\", inplace=True)\n", + "electoral_votes.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = electoral_votes.merge(results, on=\"State\", how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = results.set_index(\"State\")\n", + "red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Kentucky\", \"Louisiana\",\n", + " \"Oklahoma\", \"Wyoming\"]\n", + "blue_states = [\"Delaware\", \"District of Columbia\"]\n", + "results.ix[red_states, [\"romney\"]] = 1\n", + "results.ix[red_states, [\"obama\"]] = 0\n", + "results.ix[blue_states, [\"obama\"]] = 1\n", + "results.ix[blue_states, [\"romney\"]] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 307, "metadata": { "collapsed": false }, @@ -10283,120 +7731,244 @@ " \n", " \n", " \n", - " resid\n", + " Votes\n", + " poll\n", + " obama\n", + " romney\n", + " \n", + " \n", " State\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " Alabama\n", + " 9\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Alaska\n", + " 3\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Arizona\n", + " 11\n", + " -5.453958\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Arkansas\n", + " 6\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " California\n", + " 55\n", + " 19.966475\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Colorado\n", + " 9\n", + " 2.667843\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Connecticut\n", + " 7\n", + " 8.936227\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Delaware\n", + " 3\n", + " NaN\n", + " 1\n", + " 0\n", " \n", - " \n", - " \n", " \n", - " 304\n", - " 5.224651\n", - " Wisconsin\n", + " District of Columbia\n", + " 3\n", + " NaN\n", + " 1\n", + " 0\n", " \n", " \n", - " 305\n", - " 7.432851\n", - " Wisconsin\n", + " Florida\n", + " 29\n", + " 2.170963\n", + " 1\n", + " 0\n", " \n", " \n", - " 306\n", - " 0.277203\n", - " Wisconsin\n", + " Georgia\n", + " 16\n", + " -8.811154\n", + " 0\n", + " 1\n", " \n", " \n", - " 307\n", - " 10.002459\n", - " Wisconsin\n", + " Hawaii\n", + " 4\n", + " 18.584803\n", + " 1\n", + " 0\n", " \n", " \n", - " 308\n", - " 0.728450\n", - " Wisconsin\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 309\n", - " 2.679444\n", - " Wisconsin\n", + " Rhode Island\n", + " 4\n", + " 13.236853\n", + " 1\n", + " 0\n", " \n", " \n", - " 310\n", - " -0.268988\n", - " Wisconsin\n", + " South Carolina\n", + " 9\n", + " -6.334382\n", + " 0\n", + " 1\n", " \n", " \n", - " 311\n", - " 0.890637\n", - " Wisconsin\n", + " South Dakota\n", + " 3\n", + " -1.522121\n", + " 0\n", + " 1\n", " \n", " \n", - " 312\n", - " 7.240347\n", - " Wisconsin\n", + " Tennessee\n", + " 11\n", + " -2.521086\n", + " 0\n", + " 1\n", " \n", " \n", - " 313\n", - " -0.644929\n", - " Wisconsin\n", + " Texas\n", + " 38\n", + " -2.295507\n", + " 0\n", + " 1\n", " \n", " \n", - " 314\n", - " -5.380660\n", - " Wisconsin\n", + " Utah\n", + " 6\n", + " -29.179269\n", + " 0\n", + " 1\n", " \n", " \n", - " 315\n", - " -1.534117\n", - " Wisconsin\n", + " Vermont\n", + " 3\n", + " 14.891764\n", + " 1\n", + " 0\n", " \n", " \n", - " 316\n", - " -1.534117\n", - " Wisconsin\n", + " Virginia\n", + " 13\n", + " 2.420244\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Washington\n", + " 12\n", + " 12.312505\n", + " 1\n", + " 0\n", + " \n", + " \n", + " West Virginia\n", + " 5\n", + " -9.436884\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Wisconsin\n", + " 10\n", + " 4.530315\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Wyoming\n", + " 3\n", + " NaN\n", + " 0\n", + " 1\n", " \n", " \n", "\n", + "

51 rows × 4 columns

\n", "" ], "text/plain": [ - " resid State\n", - "304 5.224651 Wisconsin\n", - "305 7.432851 Wisconsin\n", - "306 0.277203 Wisconsin\n", - "307 10.002459 Wisconsin\n", - "308 0.728450 Wisconsin\n", - "309 2.679444 Wisconsin\n", - "310 -0.268988 Wisconsin\n", - "311 0.890637 Wisconsin\n", - "312 7.240347 Wisconsin\n", - "313 -0.644929 Wisconsin\n", - "314 -5.380660 Wisconsin\n", - "315 -1.534117 Wisconsin\n", - "316 -1.534117 Wisconsin" + " Votes poll obama romney\n", + "State \n", + "Alabama 9 NaN 0 1\n", + "Alaska 3 NaN 0 1\n", + "Arizona 11 -5.453958 0 1\n", + "Arkansas 6 NaN 0 1\n", + "California 55 19.966475 1 0\n", + "Colorado 9 2.667843 1 0\n", + "Connecticut 7 8.936227 1 0\n", + "Delaware 3 NaN 1 0\n", + "District of Columbia 3 NaN 1 0\n", + "Florida 29 2.170963 1 0\n", + "Georgia 16 -8.811154 0 1\n", + "Hawaii 4 18.584803 1 0\n", + "... ... ... ... ...\n", + "Rhode Island 4 13.236853 1 0\n", + "South Carolina 9 -6.334382 0 1\n", + "South Dakota 3 -1.522121 0 1\n", + "Tennessee 11 -2.521086 0 1\n", + "Texas 38 -2.295507 0 1\n", + "Utah 6 -29.179269 0 1\n", + "Vermont 3 14.891764 1 0\n", + "Virginia 13 2.420244 1 0\n", + "Washington 12 12.312505 1 0\n", + "West Virginia 5 -9.436884 0 1\n", + "Wisconsin 10 4.530315 1 0\n", + "Wyoming 3 NaN 0 1\n", + "\n", + "[51 rows x 4 columns]" ] }, - "execution_count": 167, + "execution_count": 307, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "group" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "results = polls.groupby(\"State\").aggregate(weighted_mean)[\"poll\"]" + "results" ] }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 177, "metadata": { "collapsed": false }, @@ -10404,141 +7976,43 @@ { "data": { "text/plain": [ - "State\n", - "Arizona -6.072142\n", - "California 19.966475\n", - "Colorado 2.671181\n", - "Connecticut 8.940155\n", - "Florida 2.170963\n", - "Georgia -8.813442\n", - "Hawaii 18.594667\n", - "Illinois 15.486753\n", - "Indiana -7.342898\n", - "Iowa 2.036824\n", - "Kansas -9.767203\n", - "Maine 12.220123\n", - "Maryland 16.394026\n", - "Massachusetts 14.180592\n", - "Michigan 8.333980\n", - "Minnesota 7.286051\n", - "Mississippi -8.227142\n", - "Missouri -2.215565\n", - "Montana -7.241413\n", - "Nebraska -8.833230\n", - "Nevada 5.096197\n", - "New Hampshire -1.544893\n", - "New Jersey 10.643486\n", - "New Mexico 9.586405\n", - "New York 23.473550\n", - "North Carolina -0.415848\n", - "North Dakota -9.339133\n", - "Ohio 4.175204\n", - "Oregon 8.681383\n", - "Pennsylvania 5.435867\n", - "Rhode Island 13.246753\n", - "South Carolina -6.340670\n", - "South Dakota -1.523693\n", - "Tennessee -2.526349\n", - "Texas -2.295507\n", - "Utah -29.179413\n", - "Vermont 15.684839\n", - "Virginia 2.422245\n", - "Washington 12.315473\n", - "West Virginia -9.441829\n", - "Wisconsin 4.528761\n", - "Name: poll, dtype: float64" + "328.0" ] }, - "execution_count": 169, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "results = results.reset_index()\n", - "results[\"obama\"] = 0\n", - "results[\"romney\"] = 0\n", - "results.ix[results[\"poll\"] > 0, [\"obama\"]] = 1\n", - "results.ix[results[\"poll\"] < 0, [\"romney\"]] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "results[[\"State\", \"poll\"]].to_csv(\"./data/2012-predicted.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electoral_votes = pandas.read_csv(\"./data/electoral_votes.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "electoral_votes.sort(\"State\", inplace=True)\n", - "electoral_votes.reset_index(drop=True, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "results = electoral_votes.merge(results, on=\"State\", how=\"left\")" + "results[\"Votes\"].mul(results[\"obama\"]).sum()" ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 178, "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "results = results.set_index(\"State\")\n", - "red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Kentucky\", \"Louisiana\",\n", - " \"Oklahoma\", \"Wyoming\"]\n", - "blue_states = [\"Delaware\", \"District of Columbia\"]\n", - "results.ix[red_states, [\"romney\"]] = 1\n", - "results.ix[red_states, [\"obama\"]] = 0\n", - "results.ix[blue_states, [\"obama\"]] = 1\n", - "results.ix[blue_states, [\"romney\"]] = 0" + "outputs": [ + { + "data": { + "text/plain": [ + "210.0" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[\"Votes\"].mul(results[\"romney\"]).sum()" ] }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 179, "metadata": { "collapsed": false }, @@ -10582,7 +8056,7 @@ " \n", " Arizona\n", " 11\n", - " -6.072142\n", + " -5.453958\n", " 0\n", " 1\n", " \n", @@ -10603,14 +8077,14 @@ " \n", " Colorado\n", " 9\n", - " 2.671181\n", + " 2.667843\n", " 1\n", " 0\n", " \n", " \n", " Connecticut\n", " 7\n", - " 8.940155\n", + " 8.936227\n", " 1\n", " 0\n", " \n", @@ -10638,406 +8112,986 @@ " \n", " Georgia\n", " 16\n", - " -8.813442\n", + " -8.811154\n", " 0\n", " 1\n", " \n", " \n", " Hawaii\n", " 4\n", - " 18.594667\n", + " 18.584803\n", " 1\n", " 0\n", " \n", " \n", - " Idaho\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " Rhode Island\n", " 4\n", - " NaN\n", - " 0\n", + " 13.236853\n", " 1\n", + " 0\n", " \n", " \n", - " Illinois\n", - " 20\n", - " 15.486753\n", + " South Carolina\n", + " 9\n", + " -6.334382\n", + " 0\n", " 1\n", + " \n", + " \n", + " South Dakota\n", + " 3\n", + " -1.522121\n", " 0\n", + " 1\n", " \n", " \n", - " Indiana\n", + " Tennessee\n", " 11\n", - " -7.342898\n", + " -2.521086\n", " 0\n", " 1\n", " \n", " \n", - " Iowa\n", + " Texas\n", + " 38\n", + " -2.295507\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Utah\n", " 6\n", - " 2.036824\n", + " -29.179269\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Vermont\n", + " 3\n", + " 14.891764\n", " 1\n", " 0\n", " \n", " \n", - " Kansas\n", - " 6\n", - " -9.767203\n", + " Virginia\n", + " 13\n", + " 2.420244\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Washington\n", + " 12\n", + " 12.312505\n", + " 1\n", + " 0\n", + " \n", + " \n", + " West Virginia\n", + " 5\n", + " -9.436884\n", " 0\n", " 1\n", " \n", " \n", - " Kentucky\n", - " 8\n", + " Wisconsin\n", + " 10\n", + " 4.530315\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Wyoming\n", + " 3\n", " NaN\n", " 0\n", " 1\n", " \n", + " \n", + "\n", + "

51 rows × 4 columns

\n", + "" + ], + "text/plain": [ + " Votes poll obama romney\n", + "State \n", + "Alabama 9 NaN 0 1\n", + "Alaska 3 NaN 0 1\n", + "Arizona 11 -5.453958 0 1\n", + "Arkansas 6 NaN 0 1\n", + "California 55 19.966475 1 0\n", + "Colorado 9 2.667843 1 0\n", + "Connecticut 7 8.936227 1 0\n", + "Delaware 3 NaN 1 0\n", + "District of Columbia 3 NaN 1 0\n", + "Florida 29 2.170963 1 0\n", + "Georgia 16 -8.811154 0 1\n", + "Hawaii 4 18.584803 1 0\n", + "... ... ... ... ...\n", + "Rhode Island 4 13.236853 1 0\n", + "South Carolina 9 -6.334382 0 1\n", + "South Dakota 3 -1.522121 0 1\n", + "Tennessee 11 -2.521086 0 1\n", + "Texas 38 -2.295507 0 1\n", + "Utah 6 -29.179269 0 1\n", + "Vermont 3 14.891764 1 0\n", + "Virginia 13 2.420244 1 0\n", + "Washington 12 12.312505 1 0\n", + "West Virginia 5 -9.436884 0 1\n", + "Wisconsin 10 4.530315 1 0\n", + "Wyoming 3 NaN 0 1\n", + "\n", + "[51 rows x 4 columns]" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Divide undecided voters probabilistically." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do historical adjustments based on how polls changed in the past conditional on \"election environment\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Error analysis\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Error Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Polling errors are from sample variance and other ambiguities inherent to polling\n", + "\n", + "Model the Polling error, based on these factors:\n", + "\n", + "* The error is higher in races with fewer polls\n", + "* The error is higher in races where the polls disagree with one another.\n", + "* The error is higher when there are a larger number of undecided voters.\n", + "* The error is higher when the margin between the two candidates is lopsided.\n", + "* The error is higher the further one is from Election Day.\n", + "\n", + "We don't have the data to model this but we will guestimate so we can show you the full process.\n", + "\n", + "We need error estimates for each state (local error) and for the generic ballot (national error). These are combined to get the total error for each state.\n", + "\n", + "The national error creates correlation between the state results.\n", + "\n", + "$$\n", + "LocalError = \\sqrt{TotalError^{2} + NationalError^{2}}\n", + "$$\n", + "\n", + "\n", + "\n", + "Source: http://fivethirtyeight.blogs.nytimes.com/methodology/?_r=0" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Colorado 1.3\n", + "Florida 2.2\n", + "Iowa 1.3\n", + "New Hampshire 2.5\n", + "Nevada 2.2\n", + "Ohio 1.2\n", + "Virginia 2.2\n", + "Wisconsin 1.5\n", + "dtype: float64" + ] + }, + "execution_count": 282, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "national_margin_of_error = 2.5\n", + "local_margin_of_error = Series(np.array([1.3, 2.2, 1.3, 2.5, 2.2, 1.2, 2.2, 1.5]), index=tossup)\n", + "local_margin_of_error" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "N = 10000\n", + "local_error_sim = DataFrame(np.random.randn(N, len(tossup)), columns=tossup).multiply(local_margin_of_error)\n", + "national_error_sim = Series(np.random.randn(N) * national_margin_of_error)" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Maryland1016.394026102-1.796023-3.503155-1.516988-0.114828-3.993228-0.1981980.411633-0.818125
Massachusetts1114.180592103-2.074291-4.749605-3.405161-5.584828-3.834758-2.608977-4.767058-3.840534
Michigan168.3339801042.3195661.3534382.3402713.1812290.7548083.4941896.8644554.325957
Minnesota107.286051105-1.913171-3.616568-2.6350902.567352-1.552008-2.428179-1.2437711.486598
Mississippi6-8.227142016-0.2764740.925166-0.042459-1.313305-2.946220-0.851892-0.8922480.447254
Missouri10-2.2155650171.2808722.2386180.5614572.403047-1.4694821.6936351.318639-0.256946
Montana3-7.2414130181.0496800.2483491.8857244.2141485.222872-1.261189-2.762444-0.342093
Nebraska5-8.833230019-3.075249-1.355726-2.872972-8.338800-3.956946-2.289728-2.777089-2.140280
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New Mexico59.5864051020.871820-1.3321920.521013-1.6618841.0999023.9778572.8318783.712191
New York2923.4735501030.593552-2.578642-1.367160-7.1318841.2583721.567078-2.3468130.689781
North Carolina15-0.4158480144.9874093.5244014.3782721.6341735.8479387.6702459.2847008.856272
North Dakota3-9.3391330150.754672-1.445605-0.5970891.0202963.5411231.7478761.1764746.016913
Ohio184.1752041062.3913693.0961291.995542-2.8603612.1469103.3241641.5279974.977570
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Oregon78.6813831083.7175232.4193123.9237252.66709210.3160022.914866-0.3422004.188223
Pennsylvania205.435867109-0.4074060.815238-0.834971-9.8858561.1361841.886328-0.3568442.390035
Rhode Island413.24675310
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ColoradoFloridaIowaNew HampshireNevadaOhioVirginiaWisconsin
South Carolina9-6.34067001Colorado1.0000000.6613640.7829670.6322650.6660760.8016570.6636070.762725
South Dakota3-1.52369301Florida0.6613641.0000000.6668010.5173750.5594250.6713660.5580360.643287
Tennessee11-2.52634901Iowa0.7829670.6668011.0000000.6258540.6591620.7992750.6619890.763126
Texas38-2.29550701New Hampshire0.6322650.5173750.6258541.0000000.5362030.6407250.5370080.612874
Utah6-29.17941301Nevada0.6660760.5594250.6591620.5362031.0000000.6749640.5559860.646501
Vermont315.68483910Ohio0.8016570.6713660.7992750.6407250.6749641.0000000.6697900.770683
Virginia132.42224510
Washington1212.31547310
West Virginia5-9.441829010.6636070.5580360.6619890.5370080.5559860.6697901.0000000.641731
Wisconsin104.52876110
Wyoming3NaN010.7627250.6432870.7631260.6128740.6465010.7706830.6417311.000000
\n", "
" ], "text/plain": [ - " Votes poll obama romney\n", - "State \n", - "Alabama 9 NaN 0 1\n", - "Alaska 3 NaN 0 1\n", - "Arizona 11 -6.072142 0 1\n", - "Arkansas 6 NaN 0 1\n", - "California 55 19.966475 1 0\n", - "Colorado 9 2.671181 1 0\n", - "Connecticut 7 8.940155 1 0\n", - "Delaware 3 NaN 1 0\n", - "District of Columbia 3 NaN 1 0\n", - "Florida 29 2.170963 1 0\n", - "Georgia 16 -8.813442 0 1\n", - "Hawaii 4 18.594667 1 0\n", - "Idaho 4 NaN 0 1\n", - "Illinois 20 15.486753 1 0\n", - "Indiana 11 -7.342898 0 1\n", - "Iowa 6 2.036824 1 0\n", - "Kansas 6 -9.767203 0 1\n", - "Kentucky 8 NaN 0 1\n", - "Louisiana 8 NaN 0 1\n", - "Maine 4 12.220123 1 0\n", - "Maryland 10 16.394026 1 0\n", - "Massachusetts 11 14.180592 1 0\n", - "Michigan 16 8.333980 1 0\n", - "Minnesota 10 7.286051 1 0\n", - "Mississippi 6 -8.227142 0 1\n", - "Missouri 10 -2.215565 0 1\n", - "Montana 3 -7.241413 0 1\n", - "Nebraska 5 -8.833230 0 1\n", - "Nevada 6 5.096197 1 0\n", - "New Hampshire 4 -1.544893 0 1\n", - "New Jersey 14 10.643486 1 0\n", - "New Mexico 5 9.586405 1 0\n", - "New York 29 23.473550 1 0\n", - "North Carolina 15 -0.415848 0 1\n", - "North Dakota 3 -9.339133 0 1\n", - "Ohio 18 4.175204 1 0\n", - "Oklahoma 7 NaN 0 1\n", - "Oregon 7 8.681383 1 0\n", - "Pennsylvania 20 5.435867 1 0\n", - "Rhode Island 4 13.246753 1 0\n", - "South Carolina 9 -6.340670 0 1\n", - "South Dakota 3 -1.523693 0 1\n", - "Tennessee 11 -2.526349 0 1\n", - "Texas 38 -2.295507 0 1\n", - "Utah 6 -29.179413 0 1\n", - "Vermont 3 15.684839 1 0\n", - "Virginia 13 2.422245 1 0\n", - "Washington 12 12.315473 1 0\n", - "West Virginia 5 -9.441829 0 1\n", - "Wisconsin 10 4.528761 1 0\n", - "Wyoming 3 NaN 0 1" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", + "Colorado 1.000000 0.661364 0.782967 0.632265 0.666076 0.801657 0.663607 0.762725\n", + "Florida 0.661364 1.000000 0.666801 0.517375 0.559425 0.671366 0.558036 0.643287\n", + "Iowa 0.782967 0.666801 1.000000 0.625854 0.659162 0.799275 0.661989 0.763126\n", + "New Hampshire 0.632265 0.517375 0.625854 1.000000 0.536203 0.640725 0.537008 0.612874\n", + "Nevada 0.666076 0.559425 0.659162 0.536203 1.000000 0.674964 0.555986 0.646501\n", + "Ohio 0.801657 0.671366 0.799275 0.640725 0.674964 1.000000 0.669790 0.770683\n", + "Virginia 0.663607 0.558036 0.661989 0.537008 0.555986 0.669790 1.000000 0.641731\n", + "Wisconsin 0.762725 0.643287 0.763126 0.612874 0.646501 0.770683 0.641731 1.000000" ] }, - "execution_count": 176, + "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results" + "simulated_poll_predictions.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bins = np.arange(-10, 10, 0.2)\n", + "histograms = {s: np.histogram(simulated_poll_predictions[s], bins=bins, density=True)[0] for s in tossup}" ] }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "image/png": 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IEON8MnwauAiYSbbbuTlFjmdTCfgP4Js0tf6e/7j9dr5z+38xb+5ZXPD28ykvL2fIkCEM\nGTKEoUOHMmzYMEaMGMGIESMUyIocI3oKUt9MP6Re162srNz/uqKigoqKijdxWhERkYFn6dKl3HHH\nHTz8p6fZ21hDlFPJci3wTuAMMhrnKQNOAXANWa4BVvDS4v9iyeKXgRacVpw2nHacdqCDcAoeI2LF\nFBeWMmJ4OZOnjmH6jOnMnj2buXPnMnv2bGKxNzOtkcjAUFVVRVVV1Ruu39OY1HlApbsvzK1/CQgO\nkwDpFqAlb0xqr+pqTKqIiAwETU1NlJeXH7RtztvmUV/XTEtTO23tHXSkUqSzKYIgQzxWyKDSEkaN\nCrOgTp8+nVNPPZVzzjmH0047DYBf/epX3PWTn/LS4r/SkW4nyiVk+XtgITDkrb9IkeOaEwar29mX\npdhYR5RVOBvIsgNoJxopIRaNE41EiUajxGMx4vEoiUSMgoI4BYUJBpUVccHb53Pdddcxc+bMfr0q\nkWNBXydOihEmP7oY2Am8RBfJj3L7VgLNeUFqr+oqSBURkRNFfX09a9asYcOGDaxfu5b1r73GhrVr\nWb91K63JJG2ZTKca/w4MzpUhectiwuk8Ds6CGrCVgGqgDYhhDMN4HwEfAM4nHJcnIkdPK+HvZTOQ\nzCvtndbrifFnMrxMLFrI1JPH8c7L3sH111/P7Nmz+6vxIv3maExBcxkHppG5291vM7MbAdz9TjMb\nRZi5t4ww30IzMMvdW7qq28XxFaSKiMhxqb29neeee47HH3qIP//xj6zbupUZhYVMDQKmtLUxNQiY\nCkwBRtHVxCpv9P7XDjTmjioix64M8CpQRZQHybKYaCTB5IljufAd51FWVtZt7dLSUj7xiU8wZowy\nZ8vxrc+D1KNNQaqIiBwvgiDg1Vdf5fFHH+XP99/Pi8uXM7uwkAUtLVwSBMyl+2eZh96ddf8TGViy\nwOvA08R4kvCp6+E59WR5jaHlI3jvBy7l5ptvZtq0aW9FQ0X61NF4krqQA09D7zrMeNTvA5cR9j/6\nmLu/ktu+GWgi/I1Mu/ucLuoqSBURkaOipaWFpUuXUldX1+1+7k4ymaS1tZW2tjZaW1tpbWqitbEx\nLM3NNDU08NLy5Yww45JUigWpFBWE3Yh6S0GqiBy5RsI5YH9Olr9QVjyYy959IV/84hc588wz+7tx\nIr3S12NSo4TjSi8BdhB26z1oXKmZvQv4tLu/y8zmAt9z93m59zYB57j73m7OoSBVRETeNHdn8+bN\nvPDCCzz/1FM8/9RTrNm6ldlFRfSmo1xBEFASBJRkMpSk05S4UwL7SylwJjD+TbRRQaqIvDltwKNE\n+QVZHqWooISzz51BJpWhsaGVlpYk7W0ddKTSpDNpMtk0QZAGjGGDh/G2eafyrsvfxTXXXMPQoUP7\n+2JkAOnrIPU84Ja8DL1fBHD3b+bt82PgKXf/TW59NXChu+/OBannuvthv8JWkCoiIkfK3dm5cydr\n1qxh2csv8/xjj/HCkiWQSjE/FuO85mbOB84mnITiWKEgVUT6Tgp4EniG8Ku0slwZ1MXrDuAlIjwF\nPEXAJgri5UyfPp6LLv4brrnmGubOnRseNZWioaGBxsZGGhoaaGpqoqmpiebmZk466SQWLFhAJKIp\nreTI9HWQ+gHgUne/Ibd+LTDX3T+Tt88fgdvc/fnc+p+Bm9x9mZltJOyjkAXudPefdHEOBakiItKl\nxsZG1q5dy5o1a1i7ejVrX3mFNatXs277dkqjUaYnEpyRTHJ+RwfnARPpKhA8dihIFZFjQxuwFHiO\nGI+RYSlhIJsl/LsUA+IYCSCBUQAU4DTgtDB8yEjmnT+b973/fVx99dUUFxf325XI8eFIg9SeZiPu\n7d3zcCe8wN13mtkI4HEzW+3uz3TeqbKycv/riooKKioqenlaERE5FqRSKXbu3MmOHTvYvn07DQ0N\nRCIRotHo/mV+iUQipFIp6urqqKutpa66mtrqaupqasJtDQ3UNjURBAHTi4qY7s70lhbe7c7ngWmE\nk7XQ3t6/Fy4iclwqBt4OvJ0MXyL8yN8MFBKmfws/2u8LBA4OCLZRW/8cDz34OA89+A2uu+4TlBYN\n5YyzpnH5FZexYMECRowYwYgRIxS8DmBVVVVUVVW94fo9PUmdB1Tmdff9EhDkJ0/Kdfetcvd7c+v7\nu/t2OtYtQMu+eVTztutJqojIcaC2tpYXX3yR5a++yvb169mxaRPbt29nx5491Le2MqqoiHGxGGOD\ngCGZDIEZgRlZIGsWFti/Le7O8HSaYR0dDHNnGOwvw3PLco7tJ6NHSk9SReTE0wC8gPEUER4jy2bC\nrMUpwr96ccxiRCxGNBIjFo1RWJhg6NBBjBk/grFjxzBp0iSmTJnCrFmzOP300yktLe3PC5KjoK+7\n+8YIEyddDOwEXqL7xEnzgDvcfZ6ZFQNRd282sxLgMeBWd3+s0zkUpIqIHGOCIGDlypU8//zzvPDE\nEzz/zDNU19Uxt7CQs1tbGZ/NMg4YC4wDRhKmgJfuKUgVkYHDCQPV1i7KXqAa2EmUTRjbcKoJqMVp\nAuKUlQzhHQvm8k//9E/qZXkCOBpT0FzGgSlo7nb328zsRgB3vzO3zw+BhYT/6z6eG496MnBf7jAx\n4B53v62L4ytIFRHpJ5lMhurqarZu3crWrVtZs3IlLzz+OIuXL2dELMb5QcB5bW2cD5yKAtE3S0Gq\niEhPAmAP8DxRfkuWh4hFI5x1xgw+dv1HWLRoEYlE4g0dOZPJkEql9i9TqRRBEDBmzBglgzrK+jxI\nPdoUpIqIHB2pVIpdu3ZRXV3Nrl272LlzJ1s3bmTrmjVs3bSJrdXV7GpoYERBARPicSYEASe3tTEv\nm2Ue4dNR6VsKUkVEjlQALMG4D+N3BFQzfsw43v93VzBu3Di2bNnCjh072FW9m5rqeuobWmhrS5JK\nJ8l6EsgQ/q3d9/c2QvjX2PJeZ4lFSiktKWXUqCFMmT6BmTNncuaZZ3L++eczefLk/rjwE4qCVBGR\nY8DOnTt5+eWXSaVSxONxEolEl8t4PE4qlSKZTO4v7e3tB60nk0kymQxBEJDNZg8qQSZDNpMhlUyy\ne+tWqrdvZ9fu3VTX1dGcTDKysJDR8Tij3RmdTjOhvZ0JwATCTLhjgDf2fbRsBk4m/PjT2+/fD707\nLwIOSXyfMwm4m3DEjYiIhLYADxLlXqAFYxQBYwkYD5xE+BXrvjICKCLsBxTh8H+tW4BNhH/ZNxFh\nDRFWEbCJgGrAiUaKKYgXUFpSzJAhpZw0diijR49i7NixTJgwgUmTJhEEAclkkra2Njo6Og66n3d0\ndABw3nnncemllw64pFJHo7vvQg50970rP2lS3j7fBy4jzGf9MXd/5QjqKkgV6QdVVVUa49FHWlpa\nePnll1n84ossfuIJXnr5ZdpaWzm3oIASIA2kzMJlbj3tTgrIuJMwo9CMQsK8ioXuFLlTGAQUBgEF\nQUAsCIi6E3En6k40CIhy4LabILwdjwZG5ZbD6H3wJN2bBNRwoLuzAY8C83mzQWp397/JhEHqO3p5\ndBHpWRVQ0c9tkOOLA/XAbsI7wb6yiyjbMHbg7MbZS3g3iGO5KXzC6XvCZVgCsqzB2UMiVs7ok4Zx\n5rkz+Zu/+RuuuOIKZsyYcdCZgyBgy5YtrF69mg0bNrBlyxa2b99OU1MTo0ePZvLkycyYMYOZM2cy\nY8YMYrGeJm7pP32dOClKmDjpEmAHsITuEyfNBb6XS5zUY91cfQWpIv2gsrLyoOmfjgXZbJbW1tbD\nlmQySSqVIp1Od73s6KC9uZnWpqawNDfT2tISlrY2WtvbaU0mCYKAaCQSTo2yr0SjB7ZFoxQWFFBS\nXExJaSklgwaFpbw8LGVlFBYWsnb5chY/+yzrd+zg9KIi5iaTzEmlmAtM4cTKSjvQdRUubqYvnqQq\nSBV5a1Xmikh/agX+CiwnwksYS8iyFiNKceEg0pk06UwSpx0owBhMhGEYJ+GMxSkjwg5gOwG7CagD\n2jErIhEroqSoiEFlxUQjRiQaIRqJEoka0WiEaDQSbotFKCouZPjwYYwaNYoxY8Ywfvx4Jk6cyNSp\nU/t8nG5fz5M6B1jv7ptzB78XuArIDzSvBP4XwN0Xm9lgMxtFeHftqa7IgBMEAa2trTQ1NdHU1ERz\nc/P+162trcRiMQoLCw8pRUVF+1937jbamz8i2WyWdDq9P6Brbm5m06ZNh3Qrze+a0tbWdqB9tbU0\n19fTVF9PU0ND2PaWFtqTSeKxGIlc19X9y0SCeCJBIpHAIhGS7e20t7WFx+7oCEsqRXtHB8l0mvZ0\nmnQ2S3EsRkksRkk0SkkkQokZJUCJO4XuJNxJBAHxfcsgIJHNEg8CCgjnzizpphQTPg0LCKcszy/5\n29o5NBdh277XZtTFYpyeTrMIOAMoSKf75j+IHLd2Ap8AngOGAjcTduaF8CPxX7us9WHgF7nXvwD+\nhfB/2T932u8l4J+A1YRd194PfJfwm3oRETm+lABzgbkE/ENum+NsoTW5ARjCge7KBTjhZ5N8wSHH\n7MB9Nx3pXXSkq9nbVMfhP+Xse91GhGoibAOWEVCH04DTDKQxColGCyhMFDKotIihw8oYNTYMaseP\nH8+kSZOYOnUq5513Xp93X+7pSeoHgEvd/Ybc+rXAXHf/TN4+fwRuc/fnc+t/Jrw3TwIWdlc3t73b\nJ6mPPfYYl1566Ru7OhHpc8VmlJkxyIyySIRCM9LuYVfW/GXeawcKzSja160117U1f33faz19lGPR\nb9rb+ZtEgjHRA/mNm4OA3yaTXFdUhJnxp2SSoZEIc+NxGtx5JJnkooICxkSjLEulWJ7JHPKhwphI\nhPNwGgl4nAgXAkNxXsNZS4SK3Dfnewmfug4F2gh4GmMKEWYgIr0X8DoRTu/vZogcB1K5gLUudw+q\nJfwK/1DnnjOHJUsXd3u0vn6S2tt+uG/qc6WZPpaKHC/a3GlzZxdAtvP3eofXqG79cpx7OJf0orOf\nth+4ae8OAlZlMj3W2cfZQpYt+9cDnjjo/YCnuqn7Clle6fb4InKoLCv6uwkiJ5SlL7/U5/FcT0Hq\nDmB83vp4YHsP+4zL7RPvRd0jiqhFRET6g5ltAq539yfztk0CNhLeS98G/NHdR+a9/wngfe7+TjOr\nBKa6+7V571cCU9z9w2b2Y6DJ3W/Ke38ncK27P2lm0wn7955D2Gs9Bix19wuP0iWLiIj0m54Gsi0F\nppnZJDNLAFcDD3Ta5wHgIwBmNg9ocPfdvawrIiJyItgJDDWz0rxtEzj4y9nuuhPsJO+LXTMrJkzQ\nvM+PgJWEgW458BWUvFlERE5Q3d7g3D0DfJow0/5K4DfuvsrMbjSzG3P7PARsNLP1wJ3AP3ZX96hd\niYiISD9x923A88BtZlZgZrOB64Bf9vIQvweuMLP5uS92v87B9+hSoBloM7NTgE/2XetFRESOLT1O\npuPuDwMPd9p2Z6f1T/e2roiIyAkk/+noB4EfEz4VrQe+ltc92Dn0Ser+be6+wsw+BfyKMO3jd4Ft\neft+Afhv4CbgFeBe4KI+vRIREZFjRLfZfQHMbCFwB+GsDXe5+7c6vf8hwpumEX7L+0l3fy333mag\niTDHcdrd5/T1BYiIiIiIiMiJo6cpaKLAGuASwgRJS4AP5nfbNbPzgJXu3pgLaCvdfV7uvU3AOe6+\n9yheg4iIiIiIiJwgekq6MAdY7+6b3T1N2L3oqvwd3P0Fd2/MrS4mzO6bT9l7RUREREREpFd6ClLH\ncvCYmO25bYdzPfBQ3roDfzazpWZ2wxtrooiIiIiIiAwUPSVO6n7Aah4zu4gwk+H8vM3z3b3azEYA\nj5vZand/plO9Xp9DREREREREjj/u3usetj0FqTvIm7ct93p7551yqfZ/Aix09/q8hlTnlnvM7H7C\n7sPPdK7fU/ImEel7lZWVVFZW9nczRAYk/f6J9A/97on0D7MjGwHaU3ffpcA0M5uUm7ftauCBTiec\nANwHXOvu6/O2F5vZoNzrEuCdwOtH1DoREREREREZULp9kuruGTP7NPAo4RQ0d7v7KjO7Mff+ncDX\ngCHAj3IR8r6pZkYB9+W2xYB73P2xo3YlIiIiIiIictzrqbsv7v4w8HCnbXfmvV4ELOqi3kbgzD5o\no4gcBRXpMIIxAAAgAElEQVQVFf3dBJEBS79/Iv1Dv3six4du50l9Sxpg5v3dBhERERERETk6zOyI\nEif1NCYVM1toZqvNbJ2Z3dzF+x8ys+Vm9pqZPZdLotSruiIiIiIiIiL5un2SamZRYA1wCWGm3yXA\nB919Vd4+5wEr3b3RzBYCle4+rzd1c/X1JFVEREREROQE1ddPUucA6919s7ungXuBq/J3cPcX3L0x\nt7oYGNfbuiIiIgOJmR1URERE5FA9BaljgW1569tz2w7neuChN1hXREREREREBriesvv2uh+umV0E\nXAfMP9K6+ZMqV1RUKPOaiIiIiIjIcaqqqoqqqqo3XL+nManzCMeYLsytfwkI3P1bnfabDdwHLHT3\n9UdYV2NSRURkQOjcxVf3PxERGQj6ekzqUmCamU0yswRwNfBApxNOIAxQr90XoPa2roiIiIiIiEi+\nbrv7unvGzD4NPApEgbvdfZWZ3Zh7/07ga8AQ4Ee5b4jT7j7ncHWP4rWIiIiIiIjIca7b7r5vSQPU\n3VdERAYIdfcVEZGBqK+7+2JmC81stZmtM7Obu3j/FDN7wcySZvb5Tu9tNrPXzOwVM3upt40SERER\nERGRganb7r5mFgV+CFwC7ACWmNkDnbrt1gGfAd7TxSEcqHD3vX3UXhERERERETmB9fQkdQ6w3t03\nu3sauBe4Kn8Hd9/j7kuB9GGOodnKRUREREREpFd6ClLHAtvy1rfntvWWA382s6VmdsORNk5ERERE\nREQGlm67+xIGmW/GfHevNrMRwONmttrdn+m8U2Vl5f7XFRUVVFRUvMnTioiIiIiISH+oqqqiqqrq\nDdfvNruvmc0DKt19YW79S0Dg7t/qYt9bgBZ3/85hjtXl+8ruKyIiA4Wy+4qIyEDU19l9lwLTzGyS\nmSWAq4EHDnfuTg0pNrNBudclwDuB13vbMBERERERERl4ug1S3T0DfBp4FFgJ/MbdV5nZjWZ2I4CZ\njTKzbcDngH8xs61mVgqMAp4xs1eBxcCf3P2xo3kxIiIiIseS2tpaPve5z/GnP/2JIAj6uzkiIseF\nbrv7viUNUHdfEREZINTdt28FQcBf//pXnn32WZYtW8aqFavYtGE3exsacXdi0RgFBQmKChOUlBZS\nVl7M4KFllJWVUV5eTjqdpqGhgcbGJhr3NtPSnKS1rYNkMkUqnSKbzXDKjEl8+7u3cemllx5R23bu\n3Mn1H1/Eo49VEeFUsmwgGgk468wZ3HDj9Vx33XXEYj2lBhEROTEcaXdfBakiIiJvkeMtSA2CgF//\n+te8+OKLXHjhhSxcuJDS0tJe11+6dCk/+9nPePyRKjZu3kE26CARK2Fw2SDGTRzBtOknM2vWLM49\n91zOP/98Bg8evL9ubW0tq1evZt26dWzatIlt27ZRXV3Nrh17qN65l/qmJtKZZqCAKBMwppNhNjAN\nmAIUAE1Ac265rzQQpQ6jHieBM5SAYUA5MAgoyytxIvyGgP+mvLSUT/2/j3PLLbeQSCQOe82bNm3i\nYx+9nr888yJRFpDlX4HTCXNRvozxO4zf4Oxh6smTuPajV/PZz36WsrKyQ37227ZtY/Xq1WzYsIHN\nmzdTW1vL2LFjmTZtGrNmzWLWrFkUFxf3+t+jr6RSKW6++WZWr1rDd//jO8ycOfMtb4OIHF/6PEg1\ns4XAHUAUuKtz0iQzOwX4GXAW8JX8xEg91c3toyBVREQGhOMhSG1ra+NHP/oRP7vrF6xaswH3UqKc\nRsAqAnYTj5Ux+qThnHnOKVxwwQVceeWVzJgxA4DFixfzP//zPzz+yNNs3rqDbJAlxjwyXA5cBIwB\nNgObgI1EWYGxlixbcWoxCjCLEngScIxyIgzFGAmMIcs4nLHAOA4Eo+VvwU8lCfyOCLeBbWPBJRfw\nvR/csf+6AVatWsXHPnI9Ly19hShXkuXrwIzDHhHWA/cT5ZdkWcPQshFkA6c9mSSdTeKeBGIYg4kw\nHOMkYDBQQ0A1AbVAM0YBsWgRxUVFDC4vYdSYoUyYNJ4pU6Ywc+ZMZs+ezWmnndYnT21bWlr41Kc+\nxT2//AMeTMA4lSz/x5RJk/nOHbdx1VVXvelziMiJqU+DVDOLAmuAS4AdwBLgg+6+Km+fEcBE4D1A\n/b4gtTd1c/spSBURkQHhWA1Sa2pquP322/nNPfezded2IkzEuQbn/cAsDuRGbANWAMuJ8BLGErKs\nwYhgFiFwJ8r5ZLkCuBA4jZ5zNO6TJfy4kAJOAkrplJPxGLGEKLeT5QEmjpvAp/7fIu7533tZvmIl\nUa4mSyUw6QiPuRv4C1ACjMyVEUBRD/WywB6gOld2ATuIsh5jMwE7CKgBWolYMQXxYsrKSpgwcSQL\n3/VOFi1axIQJE3ps3a5du/iHRTfyp4eeIOJnkOXfCP99Daghwg8J+D7lpcV84eZ/5Mtf/jKRSG//\n3UVkIOjrIPU84Ja8KWi+CODu3+xi34OmmOltXQWpIiJyIti9ezePPPIIW7dupa66Oiw1NdTW1lLX\n0EBdUxONyeRBdZ555hkuuOCCPjl/bW0tL730EsuXLw+7iK7fyK4ddfR0i21oaGZvUw1RziLLh4Er\ngfFHcGYHthIGsDPofVB6vKvF+G+MnwCXEfAVYGx/N+owOoCdwHZgB8ZKIjxMltdIxAYxa+Yk3vXu\nS1m0aBGTJ0/eX2vDhg1c9/EbeOaZF4lQQZZvAOd0c45fE+EbRKJ1XH31FXz/B99n6NChR/3qROTY\n19dB6geAS939htz6tcBcd/9MF/t2DlJ7VVdBqoiIHK/WrVvH/91/P3/45S9ZsXYtC+JxprW1MTwI\nRznuK8Nzy0M/rpdSlCjk0ssu4Ktf+ypnn312t+fbunUrDz/8MM888wyvL1/Fzu11NLW0kMq0AWmM\nEUQYC5xMllMIu9dGe7iKcmABYVdSGVg6gCUYTxDhIbIsJxErYcaMiZgZr/11JVHek3s63F3X5XwO\nPEWUfyXgRaZPPZl5889lwYIFvPvd7z5k7K2IDAxHGqT2NEDhzUSPva5bWVm5/3VFRQUVFRVv4rQi\nIiJHh7uzdOlS/vC73/GHe++lvraWq9z5ajJJBVDQ0XGER9xLe+oJ/vh/P+UP/3cBpUVlXPnei/nc\n5z7HunXreOqpp1j28nI2rNtJU0sDgaeIMBnjTLJ8EJgMTMiVkThGto+vWU5kBcAFOBeQ5RYgRSqz\nlL+ueApjL/AHskf0VB3CLsDvIMs7gLWsWf8QG9Y/wy/+9ysEfIxErJyxY0Zw7tzTufjii3nve9/L\n0KFDee2111i2bBmvv/4669evZ/OG7eze1UBTawvpTCsA0UghBfFCSkqKGDKklJPGDGXUqJMYN24c\nEydOZP78+Zx99tnqaixyDKiqqqKqquoN1+/pSeo8oDKvy+6XgOAwCZA6P0ntVV09SRURkWNBOp1m\n79691NXVhV106+r2l9rqanZv28aTTz3FoHSa9ySTvCeT4W0cWefWQ79Czr//JYFHiHI3WZ4gwkgi\nzCbDHGA24fjOSUd4RpFjSTvwOrCMKM/hvETAJiAASogwmgiTCJhGwFTClCf7voSJADWdyi6ibMXY\ngbOLLJuBDIOKh3DyyaM5d+5ZXHzxxVx++eV6givSz/q6u2+MMPnRxYSDGV6ii+RHuX0rgea8ILVX\ndRWkiohIf9m2bRv/c9dd/O+dd7K5poahBQUMi8cZFokwzJ3hmQzDOjoYls0yHJgPnPImztd9kCoy\nEKWBDD0nieoNJ0wetRx4lRjPE/AqAdXEo4MYOXwoRUUFdHSkSaUypNMZ0pks2WyWbBAW94BRI4bz\ntx98N5///OcZN25cH7RLRI7GFDSXcWAambvd/TYzuxHA3e80s1GEmXvLCL8KawZmuXtLV3W7OL6C\nVBERecukUikeeOAB7r7jDl56+WWudue6jg7O5ug/o1SQKtIfksBK4DXCcbiFhEFxYV7Ztx4BniXK\nL8iylCGDhvOuKyu46aabmD17dv80X+QE0OdB6tGmIFVEZGDKZDI0NjZSX19PQ0PDIcuGujriiQST\np0xh8uTJTJ48mbFjxxKN9pQIqGsrVqzg7h/9iF/+/OfMAhY1N/M+oLhPr6p7ClJFjidNwMNEuYcs\nf6YoUcrbK87hk//4CYqLi9myZQvbt29nx44d1NTUsGd3LbV7GmlsaCXZkWba9LFcceVlXHfddb2a\n6qezIAioqalh5MiRGmcrx72j8SR1IQeeht51mPGo3wcuI8w//zF3fyW3fTPhb3gWSLv7nC7qKkgV\nERkAtm/fztNPP83TjzzC0088wYZduyhLJBgSjzM4EmGIGYODgCHZLINTKYZkMiTN2FRczKZYjE3p\nNLUdHYwfPpyTJ0xg8imnMHnWLMrLy8Puep1KEARks1nS6TSP33cfWzZu5GPpNNdlMkztp5+BglSR\n41UKqCLKvQQ8CBgRhmAMA0YSMIqAMRzI5V2MsYQID5LlNQrig5h5StdT/QBs2rSJBx98kOeee45X\nX17Btm17aE02EHaFNuLRQZQNKmXsuGFMnT6ZWbNmcfbZZzN//nxGjhz5Fv8sRI5cX49JjRKOK72E\ncIbtJXQaV2pm7wI+7e7vMrO5wPfcfV7uvU3AOe6+t5tzKEgVEelHQRCwevVqFi9ezNYtW5g5axZn\nnHEGU6dOfcNPLSGcLqWqqioMSp98kobGRt4ej1PR3MyFhGmAjvTo7cAWYNO+EovREo8TcSe6rwQB\nUfeDtr0NWEjPKe2PNgWpIgPRvql+nswFreFUP+PHnURtbRNNrQ24Z4hyMnAWWeYQ/oU8DRgJNJD3\nV48oKzHWErCZgBqMBGNHj2Lh5RfxyU9+sseprET6Q18HqecBt+Rl6P0igLt/M2+fHwNPuftvcuur\ngQvdfXcuSD3X3eu6OYeCVBGRt1BNTQ2LFy9m8XPPsfjJJ1ny+usMi8WY687E9nZWlZSwPAjYk05z\n6uTJnPG2tzF77lzOOOMMZs+eTXl5Oe3t7ezatYvq6uoDZccOdm3aRPW2baxat47W5mYujMW4sKWF\nCmAWykurIFVEwqeyLwPLCDMYn8aBDMZHyoGNwBNEuZ8szxCLxpl5ymTe94Er+cQnPsGoUaP6rOUi\nb1RfB6kfAC519xty69cCc939M3n7/BG4zd2fz63/GbjJ3ZeZ2UagkbC7753u/pMuzqEgVUTkDdi9\ne/f+YPO1F18kyGaJRqNEo1EiuWU0Ftu/bGttZemyZTQ0NTGnoIC5LS3MDQLmACO6OH4j4WQRy4Hl\nhYUsTyRY0d5OJBKhI5NhVGEho2MxRrszKpVidDLJaGA0MIUwC26v70YDhIJUETm6ssArGI8S4X6y\nvE5J4RBOmTWBRDzebc0gCOhIpkmnMqTSGdLpLJl0lkwmQyYTkM0GlJUX8XcfvIovfOEL6mYsR+RI\ng9Seej719u55uBNe4O47zWwE8LiZrXb3ZzrvVFlZuf91RUUFFRUVvTytiMjA0N7eziuvvMKLL7zA\n4ieeYPGSJTQ2N+8PNj8eBMQJU6xnc6Xz6wTwDWA6EOno6PGc5cAFuUIyCckkWcJEA4MBa23t8+sU\nEZE3Iwqci3MuWb4CtNKafIaXl71CeCfojhHeKRJAvNMyfF3fvIXvfvuXfPvb32NY+Um8+70Xc9NN\nNzFz5syjd0lyXKqqqqKqquoN1+/pSeo8oDKvu++XgCA/eVKuu2+Vu9+bW9/f3bfTsW4BWvbNo5q3\nXU9SRURyMpkMGzduZNWqVaxcsYJVS5ey4rXXWL11KzOLipjb0REWYBrqPnu80ZNUETkx1AMPEeWX\nZKmipLCMiy6Zy+c+91ne8Y537N8rCALa2tpoaWmhvb2d1tZW2tvbGTlyJOPHj1fW4gGkr7v7xggT\nJ10M7AReovvESfOAO9x9npkVA1F3bzazEuAx4FZ3f6zTORSkisgJL5VK0dTURFNTE83Nzftf19fX\ns27NGlYtXcrKFSvYUF3N6MJCZkUizGxrY2YmwyzgDPpmqnvpXwpSReTEkwSeJMqvyfJAbn1fXx4n\nfLobIezAGcOI4rQDTixaQmlRCSNPGsykKWOYMmUKp512GmeddRaRSIR169axceNGtm3bxs6dO9m1\ns4Y9NU00NbXSnkySiCcYM2YYM0+bwllnncUFF1zA/PnzKSws7K8fhhzG0ZiC5jIOTEFzt7vfZmY3\nArj7nbl9fkiYOLEV+HhuPOrJwH25w8SAe9z9ti6OryBVRI4LqVSKPXv2UFNTQ01NDXv37j0wr2dt\nLQ01NdTv2UPD3r3UNzTQ0NREU1sbTckk2SCgPJFgUDRKWTRKmRllQFk2y9S2NmYGAbMIu+K+lfN2\nyltLQaqInNj2DQrZ10U4xuFHBTYAW/cXYyNRVuNsJmAnAMZQIowARpNlPM5Y4CTCrMfDc8dYQ5TX\ngdcI2IBTTyxSSnlZGZNPHsXEyeMZO3YsEydOZMqUKUyfPp0pU6aQSCSO4s9BOuvzIPVoU5AqIm9W\nOp3m1Vdf5fnnn6d+717KysspKyvbXwYNGnTQejab7fKp5v7S2Ejdjh3U7NhBza5d1NTVsbu+npaO\nDoYXFDAyHmeEGcOCgCGZTDinZzbLYGBIrgzOlXKgDChASYREQaqIyNHXDqwH1gKribCZCDtwduHs\nIaAeaMcoIBYtpLCgkEGlxYwcNZix40cxYcIEJk+ezIwZMzj99NOZOHGiuiX3AQWpInJM6Ojo4NVX\nX2Xx4sVs37yZoSNHMmzYMIYNG8bw4cP3vx42bBjxHjIOdlZbW8sLL7zA83/5C88//jgvr1rFyQUF\nnJ9OMzKZpDmRoCkWoykapTkSoQlocqcpm6UpnSZqRlkstv+J5iB3ytwpy2QoS6cZlMkwnPB72vwy\nBI0BlTdHQaqIyLEgA9QCNcDuXKkmwhYibMbZQUANTh2QIWLFxKIJ4rE4iUScosIEJaWFlJUXM3ho\nGeXl5QwaNIimpibq6uqor22msaGV1tYk7ckO0pk0mSCFe5pYpIiS4hJGjChn4smjmTp1Kqeeeipn\nnXUWZ599NsXFJ2Z/qqPR3XchB7r73pWfNClvn+8DlwFtwMfc/ZUjqKsgVaQfVFVV9VkmbXdnw4YN\n4XQof/kLi59+mr9u3Mi0oiLmplJMSiapj8WoSySoi8WoM6M2CKhLp6lPpShOJBhcUkJJYSElxcVh\nKS0NS1kZJWVllA4ezK4tW3j+2WfZvXcv8woLOb+lhfNzU6iU98mViBy5ScDdhMkbeqIgVaS/VQEV\n/dwGOb60ArsIJ2ZrAppzy32lgSh1ufcHk2UkMJQD/aryl8W5Y20FtuS6OK/F2UTADpxGzAqJR4so\nKiygrKyEESPLGDVmJGPGjGH8+PGcfPLJTJgwgYKCAuLxOIlEgmg0SiKRIB6PE4vFSCQSxGKxHp8A\nFxYWvmXdnvs6cVKUMHHSJcAOYAndJ06aC3wvlzipx7q5+gpSZcBzd5LJJMlkklQqRTqdPuyyo6OD\nvXv3UldXR11dHbU7d1JXXU1dTQ21tbXUNTTQ1NZGcUEBZSUllJWWhl1ey8spGzKEsmHDKBs2jOdf\neIH3ve99lJSUHLbE43EaGxvDMZf7xl42NISva2tp2L2bbRs3svjVVylyZ240ytzmZuYC5wAlvbj2\ngPDPegPhbaAtt+yqDAXOA2YRfvMlciyYTBikvqOnHVGQKtL/KnNF5FiUAaoJn/DuK7sxdhBlG1BN\nwB6cRiCLE8BBxTu97oljFBKLFVJSWMTgIYMYPWYoY8aNZuLEiUydOpWhQ4eyZ8+e8DNmXd3+z4KN\njU001bfQ3NTOtR/9W77+ja93e6a+nid1DrDe3TfnDn4vcBWQH2heCfwvgLsvNrPBZjaK8L7dU115\nk9wds2N7pFs2m6WlpWX/+L9UKkVxcfEhwVBn7k5LSwvV1dUHl23b2LV5M20tLcQTCRIFBeGysJB4\nQcGBZVHR/m+YuluaGdlsdn8JguCQ19FotNtjxONx2traDgrm6vfupWH3bupramjYu5eG+npa29pI\ndnSQTKVIplK0p1IkMxk6MhkKotGwRCLEIxESkQhxMxJm4SxluWUBMCQIGJbJMLyjgwlBwFmE6QOG\n5Uo50NbSQlNd3SHf9+37DrA5EuGVZ5+lNRqlNRKh1SwMBt1pDQJag4B0EFAeizEkGg3HWHYagzmR\nMGPaT4Axb/D/R4QD4zhFjmcO/BtwF+GIqIXADwjHJH+UMENz1zYQ3m7rCKd1+DBhMv0MMB/4MTD2\n6DVcRESOETFgfK4c4IR3hL6XwdlDOlNNQ0tYNm/bSXTxZozlOA/jtGEMIkz3WI4zBGcywf5PnC9y\n3+8e6DFIPVI9BaljgW1569uBub3YZyzhZ9ae6vZo2bJlnHPOOUdaTQaAYjPS7qT7uyE9iACDIxEG\nRyKUmDHIjBFmFJn9/+zde3xV5Z3o/8+zb9m5B8IlISHcVQRERQmIlyigoHXwjlbb0Vpre6qvnvl5\nTjv+nKnUOTPWOTMd7emZSqv2Yu3gtFUHFVCQhosgF0EucikBIiSEhNyTfUn23ut7/lgrYRNDbiQE\nyPf9eq3XXmuv51nr2Wiy813P83wf/Mbg9/lISEj48lxHEXs7jSaXi9LEREp72K5QczNNLhceEdJj\nsS8PlzUG3G67DdGTvxqbXS4q/H4q4oq+08M2KHUhqAwGeSEhgb+3LHZFItzs95NgDIVNTVxhDNcl\nJFAUibA6FoNY7JS6Hu7BohjBj5t7EJoRqjDMAgSLrcB03Mzsl8+m1IUmxh7c7O7vZih1HsjAkPGl\nEUCGJgxluCgDIMYBcPV+h1lnQWpXxyGdUcvO9Z5AdW4KnifDxC2g2rKotqz+bsqX7Iic6yG+UueH\nD8Ph1v0/hUKnnDsUPf3z7yh/ane/o3JKqTMT1UF9SvWqz/f2fjzXWZBayqn9zSOxe0Q7KpPrlPF2\noW63xiYrpZRS5xpjzGHgm9ije58SkeXO+37sadY5IlJmjDmKPUXmdeBm4G3ssb0fAHeJyHZjTBLw\nb8AtnBwFnwJ4NIGDUkqpgaKz1RS2AhOMMaONMT5gIbC0TZmlwNcBjDEzgFoRKe9iXaWUUupCcQw7\n2W+LPOxpROXO8RrgXsArIsec44exg9HPnDJPARcB00UkHbgBe7SSPtBVSik1YHTYkyoiUWPME9hP\ned3AqyKy1xjzuHN+sYgsM8bcaowpwk7A+UhHdfvywyillFL96D+AHxhjlmMvwPdPwBIRaRnrvwb4\nV+BN57gQWAKsieslTcHOu1RnjBkMPHuW2q6UUkqdMzob7oszbGl5m/cWtzl+oqt1lVJKqQuQAK9h\nJw1cC/iBFcCTcWXWYgeha53jj4HEuGOw1xb/PXaQWwr8BHuIsFJKKTVgdLhOKoAxZh72l6YbeEVE\nXmhz/kHg+9hDkRqA74jITudcMfaKFzEgIiLTe/sDKKWUUkoppZS6cHQYpBpj3MB+YA72E90twAPx\nw3aNMTOBPSJS5wS0i0RkhnPuMDBNRKr78DMopZRSSimllLpAdJY4aTpQJCLFIhLBnjuzIL6AiGwU\nkTrncBN2dt94muxBKaWUUkoppVSXdBak5gBH445LnPdO51FgWdyxAKuMMVuNMY/1rIlKKaWUUkop\npQaKzhIndXlNNmPMjcA3gFlxb89y1oYbCqw0xuwTkXVt6um6b0oppZRSSil1ARORLo+w7SxILQVG\nxh2PxO5NPYUx5jLgl8A8EamJa0iZ83rCGPM29vDhdW3r6/rkSp19ixYtYtGiRf3dDKUGJP35U6p/\n6M+eUv3DmO7NAO1suO9WYIIxZrQxxgcsBJa2uWEe8BbwkIgUxb2fZIxJdfaTgZuBXd1qnVJKKaWU\nUkqpAaXDnlQRiRpjngA+wF6C5lUR2WuMedw5vxj4ITAI+LkTIbcsNZMFvOW85wHeEJEP++yTKKWU\nUkoppZQ673U23BcRWQ4sb/Pe4rj9bwLfbKfeIeDyXmijUqoPFBQU9HcTlBqw9OdPqf6hP3tKnR86\nXCf1rDTAGOnvNiillFJKKaWU6hvGmG4lTupsTirGmHnGmH3GmAPGmB+0c/5BY8wOY8xOY8zHThKl\nLtVVSimllFJKKaXiddiTaoxxA/uBOdiZfrcAD4jI3rgyM4E9IlJnjJkHLBKRGV2p69TXnlSllFJK\nKaWUukD1dk/qdKBIRIpFJAIsARbEFxCRjSJS5xxuAnK7WlcppZQaSIwxp2xKKaWU+rLOgtQc4Gjc\ncYnz3uk8CizrYV2llFJKKaWUUgNcZ9l9uzwO1xhzI/ANYFZ368YvqlxQUKCZ15RSSimllFLqPFVY\nWEhhYWGP63c2J3UG9hzTec7x04AlIi+0KXcZ8BYwT0SKullX56QqpZQaENoO8dXvP6WUUgNBb89J\n3QpMMMaMNsb4gIXA0jY3zMMOUB9qCVC7WlcppZRSSimllIrX4XBfEYkaY54APgDcwKsistcY87hz\nfjHwQ2AQ8HPnCXFERKafrm4ffhallFJKKaWUUue5Dof7npUG6HBfpZRSA4QO91VKKTUQ9fZwX4wx\n84wx+4wxB4wxP2jn/CXGmI3GmLAx5qk254qNMTuNMduNMZu72iillFJKKaWUUgNTh8N9jTFu4GfA\nHKAU2GKMWdpm2G4V8CRwRzuXEKBARKp7qb1KKaWUUkoppS5gnfWkTgeKRKRYRCLAEmBBfAEROSEi\nW4HIaa6hq5UrpZRSSimllOqSzoLUHOBo3HGJ815XCbDKGLPVGPNYdxunlFJKKdWb9u7dy3e/+13G\njprIZZOv5Dvf+Q7Lli0jGo32d9OUUko5Ohzuix1knolZIlJmjBkKrDTG7BORdW0LLVq0qHW/oKCA\ngoKCM7ytUkoppfpDZWUl27dvZ9euXRw4cIDi4mKOHD7GiYo6AMZNGMHUKy7jmmuu4eabbyYrK6tP\n2xMMBnnttdd44/X/YPv2/TRFAniYSZTvAgH2fr6GxS8/glBLsn8wF12cy7XXz2TBggXceOONuFyd\npgoSLDsAACAASURBVO8AwLIsamtrKSsr4/jx4xw/fpzKykoqKytpbGykoKCA2267DY+nsz+9lFLq\n/FdYWEhhYWGP63eY3dcYMwNYJCLznOOnAUtEXmin7LNAo4j862mu1e55ze6rlFJqoLjQsvtWVFTw\n4osv8ubv3+ZoSTmRWBCIYRiMi2wMI4kxDmE09kAsC9iDh0+x+ByLUlwmgdSkNPJGD2fK1InccMMN\n3HfffWRkZPSoTdFolJUrV/L666+zasXHnKgpx8VIhDsQvgLMBHzt1KwCtgGb8bCGGNsQGnGZ9sqe\nJAgiUaAZe4CaH0MShhRnSwMSiLEboZb0lEymXnkR8+bdzIMPPkheXl6PPqdSSp1Pupvdt7Mg1QPs\nB2YDx4DNwAPtrXdqjFkENLQEocaYJMAtIg3GmGTgQ+BHIvJhm3oapCqllBoQLoQgdePGjbz00kt8\nsGw9tQ0ncHM5MR4ACoCRwGC6no4iBhQD+4C9uNmKsBWLL0jwZjBmdDbXFczgzjvvZO7cuV/qhbQs\ni8LCQt59913WrfmY/fuO0hiqxpCGi+uJcRcwFxjWw09bBTR2oVwKkEr7wW+8cuATDGtwsZoYe/G6\nkxmVl82Nc2bx1FNPcfHFF/ewrUopde7q1SDVueB84EXADbwqIs8bYx4HEJHFxpgsYAuQhv2ItAG4\nFPsb4S3nMh7gDRF5vp3ra5CqlFJqQDibQWp9fT1r167lpptuIikpqcfXCYfD/O53v+O1V37N1k/3\nEIlGcDOPGPcBNwPpvdbmk4LAdmATHlYTYzNCPWnJmUyeMoZoNMa+vUeoD1RhSHYC5QKE6cA0YEgf\ntKkvNAM7gI24eZ8Ya0lLHsTtd9zEM888w8SJE7t0lWAwyOuvv85/vvkHvjhUyv98+r/z+OOP92nL\nlVKqO3o9SO1rGqQqpZQaKPo6SP3888/5t3/7N959ZxUVVWUY0hHqSPYPYtLkMcy5+SYeeuih0wY/\nlmWxceNG/vSnP1G4eh379x8lGK7BxQjgbizuAvKxn1ufbeXAFgwfA16EGdgB6fB+aEtfCQDLcfNr\nYnxEWlIGty24kWeeeYZJkya1lmpubmbJkiUsWfImG9fvoLahEhcjgXlY5GL4NxL9Mb73N4/x3HPP\n6TxYpVS/0yBVKaWUOkf1dpBqWRZvvfUWP//3l9n48U5CzQ24KSDG/cB87EFNdcAmYB1uVhFjB26X\nl5ys4Vxz/TQmTpzI2jVr+WzbAaprKxE8eLiSGLOdQPBq+qa3VHUsCKxwAtaVpCRlMO3qS9i94yBV\ntRW4GA7cjMWtwA3Yw6xbRIG3cPFDXK4yHnhwAT/96U97PM9XKaXOVF8M953HyeG+r7RNmmSMuQT4\nFXAF8Ex8YqTO6jplNEhVSik1IJxJkBqNRvn0009Zu3Yt27ZtY9vWXRQd/AKRJFzcQYx7gevpfF6k\nnbzIHmL6EVCEcA0W12H3ko5Elzg/14SADzCsQcjHnv/blazIAhTi5odYbGP2Tdfyi1deZsyYMa0l\notEo+/bt49NPP2X37t0cOHCAwwePUlvdiD/RR3KKn5S0RFJSUkhJSSEtLY20tDTS09OZPHkyd955\nZ5czICulBq7eTpzkxk6cNAcoxZ57ekriJGd5mVHAHUBNXOKkTus65TRIVUopNSB0JUitqKjg/fff\nZ9OmTezauZuDB45RXVtHJNoAJOFmDDCJGNOAW4CJaFCpOrcTN88R432yhmQRCDQRbAoQswJAIi6y\ncDEKiwlYTMDumQ1iD0FuxFCHizoM9djpRxqJcQhMI2NH5XH3fbfz5JNPkpub24+fUSl1rurtIHUm\n8GzcEjR/CyAiP26n7ClLzHS1rgapSimlznV1dXX8+le/InfkSObPn9/jRERtg9Q1a9awYsUKNm7Y\nyJ5dxVTV1hCzgrjIw8WlRLkCuAS4CJiAnaNQqTNxFFgNjADysHvOe5pYS4ADwDLcvEmMbaQkDub6\nG6fxrW89xu23397ayxqNRtmzZ09rj21RURGHDx7l+LFqfD4P+bOmMn/+fO655x4dlqzUBai3g9R7\ngFtE5DHn+CEgX0SebKds2yC1S3U1SFVKKXWuqq6u5qV/+Rf+709/ylzL4oTHw5bmZm6ZPZt7H36Y\nW2+9leTk5E6vIyIcOnSI8ePHn/K+YTBuLiXGDIRpwGXYAakmulHnowCwGjdvY/EuxoRJ8qcQCgeJ\nSQBIaqfHdiTQgJs1COuxOEKCN52xY0dwfcFM7rzzTmbPnt2a/Km+vt4OcA8f5ujRo5SWllJWVsaJ\nEyfweDwMHz6c7OxscnNzGTVqFGPGjGHcuHH4fJ0Ng1dK9aXuBqmdfQueSfTY5bqLFi1q3S8oKKCg\noOAMbquUUkqdmcrKSn7ywgss/vd/5w7L4pNwmJbw8gTwzrJl/HLdOr7Z3MzcggLufeQRbrvtNlJS\nUgB7SZCtW7eyccMGNn74IRu3bsUbi33pPkIV0bP3sZTqY8nA7cS4HRBE9tIYOsbJHttELOxZ0W3F\neMTZC9IU2c7e/Z9wYP9qfrH4IYQGXMaHJWGndjIu0jEMxjAEYTgWkzE046IMOIhQiUUNQj32sGUf\nblcCQwYN5sa5M/ja177GvHnzdD6tUn2ksLCQwsLCHtfvrCd1BrAobsju04B1mgRIbXtSu1RXe1KV\nUkqdK8rLy/mXf/onXvvlL7lXhL8NhxndQfkq4B3gj6mpbGhu5rr8fMrLythTXMzkxERmhsNc09zM\nTE6Xjki//5TqXDl2oJkJpNL9OdgxoBb7EdMW3LyHxWogyIisbObcci0PP/ww119/vQatSvWR3h7u\n68FOfjQbOAZspp3kR07ZRUBDXJDapboapCqllDqbRITm5mbC4TChUIhwOEx9fT2/evllfvPrX/Og\nZfH9piZGdvO61cBKIAd79c7EdspokKrUuUKAw8CfcfMuMQpxmRh5uSO45dYbefTRR7n66qv7u5FK\nXTD6Ygma+ZxcRuZVEXneGPM4gIgsNsZkYWfuTcMeg9EAXCoije3Vbef6GqQqpZTqllgsxu7du9m4\ncSNHDh8mUFdnbw0NBBsbCTQ2EggECASDBMJhQk1NhCOR1s3rduN3tkSXC7/LxfymJr7f3Ex2H7Zb\ng1SlzlWC3beyGg9LibIet8vN2NG5zP/KHB577DEmT57c341U6rzV60FqX9MgVSmlVGeqq6v55JNP\n2Lh+PRs+/JAtu3eT4/MxMxZjXDBIMpx2S3I2P3bvZgLQXwP6NEhV6nxhAbuwE0EtJcYmPO4ELhqf\nxy23zsbv91NTU0NNTQ21tbXU1zVQV9NIY0OYUKiJaCzGjbPz+afn/5GLL764vz+MUv2uL3pS53Gy\nN/SV08xH/SkwH3vCwMMist15vxiox54MEBGR6e3U1SBVKaXUKUpKSvjoo49Yu2IFG9ato/TECa72\n+7kmEGBmLMYM7FUczzcapCp1vooC24GP8LDCeS8dIQOLIQiDsAcVtmyCmzeIsZxhg4fx6OMP8nd/\n93c9Xr5KqfNdb89JdWOPfZgDlGIP6z1lXqkx5lbgCRG51RiTD7wkIjOcc4eBaSJS3cE9NEhVSqnz\nwP79+3n+hz9kXWEhV1x+OfmzZzM9P59p06a1ZrXtqbq6OtasWcPK995j1bJlnKiqYrbHQ0FjI9cA\nk7GflJ7vNEhVaqCpB/6Am/+DxV+YMukS/v+//wELFy48pZRlWezZs4fly5ezceNGdm3fT+mxSsKR\nAIm+ZLKzMpk0dQLTpk3jpptuYsaMGa3L8ih1PujtIHUm8Gxcht6/BRCRH8eVeRn4s4i86RzvA24Q\nkXInSL1KRKo6uIcGqUopdQ777LPP+KdnnuHPq1fzZCTCnbEYO4HNPh+b/H52hUKMz8lh+qxZ5BcU\nMH36dC666CJEhFgs1rpZlnXK/uHDh1m5YgWr3nmHXUVFzPT7mdPQwBwRLqf/huT2JQ1SlRrIijC8\nCryC1xPjmllTKT9ezdEjFTSG6gAXbiYAVxDjKuzHc3lAMbAXN58B27AoQgjg86QxNDODSZeN5/4H\nFvLggw/qerDqnNXbQeo9wC0i8phz/BCQLyJPxpV5F3heRDY4x6uA74vINmPMIaAOe7jvYhH5ZTv3\n0CBVKaXOQRs3buQfn36abZs381RTE49bFu31lzYDO4BNwOakJDa5XBwOBnG7XLgAt8uF2xh735jW\nLdvjYU4wyJxolFnYc0YvdBqkKqXs+a6FGN5BGIsdjE4Csuj68jp1wD5gLy42AcuxKGfo4OHMvnkm\n3/jGN5g9e7YuqaPOGd0NUjsbJ9DVb8/T3fBaETlmjBkKrDTG7BORdW0LLVq0qHW/oKCAgoKCLt5W\nKaVUe5qbm9m3bx87d+5kx9at7PzkE744epSROTmMv/RSxk2ezPjx4xk3bhzjxo1rnSclIqxevZp/\nfPppDn3+OT8IhfijSIcBpA+42tkIBk+esKy++4BKKXXecgE3Idx0BtdIB/KBfCwedt4r40T1R/xh\nyVKWLFmIy0QYNTKXryyYy1133UVKSgo+nw+v14vH4/nSfkpKig4hVr2msLCQwsLCHtfvrCd1BrAo\nbrjv04AVnzzJGe5bKCJLnOPW4b5trvUs0Niyjmrc+9qTqpRSZ6CmpoatW7eyY8cOdnz8MTs/+4wD\npaWM9vu5TISpgQCXiTAaOAocBIp8Por8fg6KcDgUYlByMuNHjSIYDhMoLeXpQIAHAG+/frILj/ak\nKqXOjpYldVbi4W1i7ECIYffiWs75tvsesoeO4K/uupmnnnqKCRMm9Ffj1QWot4f7erD/D58NHAM2\n03HipBnAiyIywxiTBLhFpMEYkwx8CPxIRD5scw8NUpVSqouamprYsWMHmzZtYvPq1Wz65BOOV1dz\nZWIil4dCXNbczFTgUuzlVrrCws6MV4Q9dHcOF0aSonORBqlKqXPXcWAZbv6DGOtJ8qdx3fVX8p3/\n9m1uv/32Xhs6bDmjbHQo8sDSF0vQzOfkEjSvisjzxpjHAURksVPmZ8A8IAA84sxHHQu85VzGA7wh\nIs+3c30NUpVSA0o4HObYsWOUlpZSVlZGNBrttPxnmzaxac0adh86xITERPKbm5keDpMPTESDyvOF\nBqlKqfNDGCjExR8R/gtjmph48Vhunn8TY8eOZdy4cVx00UWMGTPmtMFmOBxmzZo1rFmzhm2fbmPv\n54epqKghHKnD4GZo5nCuK5jG/fffzx133KFDjS9wvR6k9jUNUpVS57NIJEJ9fT0NDQ3U19efsl9X\nV8fxsjJKDhygtLiY0tJSSisrqQsGGZGYSI7HQ7Zl4evkd6BHhMsCAfKBK4Hks/LJVF/QIFUpdf4R\nYDeGd3BTiFCBRRVCLdCEIRGPOwF/gp+01CSi0ShVtXVEYw0YBuHmYiyuwOIy7MeqE4FGYA1uVmCx\nGqGWIRnDuOb6y7nvvvu499578fl8NDc3s3PnTnbt2sXevXs5dOgQXxQfpay0mrq6AAk+L6PGDGPS\nlIlMmzaNG2+8kcmTJ2sv7TlIg1SllDoDIkIgEKC8vJzjx49TVlZmv5aUUHb4MMdLSuz3qqqoDQaJ\nWhapXi9pHg+pbjdpxpAKpFkWqbEY2eEwOZZFDpAL5ABDuTCXV1GdO7tB6sPASOAfTnM+FdgFjO7D\nNiilLmxNQCVQEbd5sAPRi4CkLl7nGHbQ+gHCR1icwOBBCAGpuBiOixyEMcQYj/1tOgJoAPY5y/Ps\nIUYx0EyCN43MQemMvziHkXkjyc7OJicnh5EjRzJmzBjGjx9PWlpab/5DqE70xXDfeZwc7vtKfNKk\nuDI/BeYDQeBhEdnejboapCrVDwoLC8/JTNqxWIyKigpKSkrsnsfSUkqPHKHkwAFCgQDJqakkp6WR\nlJ5Ocno6ycnJp2wej+e063K27Dc0NFBVUUHVsWNUlZdTdeIEVTU1VNXVUdXYiAsYnpBAlsdDtgjZ\nzc1khcNkA9nYiwRkAYOxl03p8m9c1atGAyHgMCf/DHoFeAP4cx/f91XsZA0tfu2896X09W2c3SD1\nEewg9bk+vIdS55tCoKCf26A6V4EdAGfR/RR+1cAB4C/Y68sWYyhHqESowaIOe4aiC5fx43H78Hl9\nJPi8JCUlkJKSSFpGEumD0khPTycjI4PMzEyGDx9OdnY2ubm5jBw5khEjRugQ5W7o1SVojDFu4GfY\neTRKgS3GmKXtJE4aLyITjDH5wM+BGV2pq5TqPx0FqeFwmKqqKgKBQOsv6ISEhC5dtyXIbO2BLCuj\nurqaUChEOBA4ZQsFg4SDQcLhMLW1tZQcP055fT2DfT5yfD5yRMhpbiY3HOYm7EAk4GxB57Xc4yHg\n9RJwuwm4XMSweyndIrgBl/PqFml9PyUSITMSYSQwBMhssyUCdDJPVJ0bLOAl4OmzeE/DQHgwYaH9\n/erCVIgGqeeDYWdQdzAty/MAxNotI0ADllTRHK2iOVpHY6iBqrp6oB5owFCNiyoMlcABhFqEOoR6\nhAB2EJ2Ay/jwuL12oJvgJSkxgZTURNLS7UA3LS2NQYMGkZmZSXp6OuFwmEAgQCAQIBgMEgwG7b+R\nwmFCwRCxqEV2ThZjxoxh4sSJTJkyhSlTprQuFTdQdBb+TweKRKQYwBizBFgAxAeafwX8BkBENhlj\nMowxWcCYLtRVqkdaet+N6dmfiiJCMBhsdx7hKfs1NTRUV+Nyu+1eu7S0L/XcJScnk5SURDAYpKqq\nisrKSqoqK6kqK6OqrIzKigqqqqqoqa8nNSmJYUOHMiw7m2EjRzI0J4dhw4a1bpmZmUSj0dZfXu1t\nwWCQUGOjHeg1NhIKBAiHQq3BXigUorm5mQSfD39iIn6/n8TkZPxJSfiTkkhMScGfnMz6jRupLCmh\n6vhxKsvLqaqutnsSGxpojkbJTEgg2e2mPhqlprkZr9tNRlISg1JTyUhLY9DgwWQMHow3IYHjJSV2\nQFpVRVVjI4MTEsj2eskyhuxolMymJhJjMZJFGILd+9iyJTqv6diDd7IBXygEoVDX/mNGoxpQDlAG\n+B/APwP/Dfv/obb2AU8C27CHWf8DcC927+uVQI1T7jFgKdCydtrXgKuA73WjLfF+jN2rW4Hdl/mP\nwB3t1hyE/QfV77CT6f8Q+w+f/w183SnzMPZPySHgE6flvwXynPN/A/weO9HJKGAJdn5nsHsUvgKs\ndd77PTDWOefCzuk81rlHIvCFU3YpcAn2v946IMW5z5Nd+NdQSqlznQHSnG1MuyWE0wW4LaJAPZbU\n0hytoTlaS2OonqraBiizg1070K3GcAI70G3E4MP+fZsIJCEkIwzHIhk764TBRTEuPkN4H4tyhHoM\nfnzeJFKSk8hITyZmCdFIjGg0SixmEY1ZxGIWlmWPHhPA5/XiT/CSlOQnNT2JtIxk0tPTyMjIYNCg\nQaSkpBCL2dewrxNrPY4fiebxeHC5XHg8ntbN7Xa37t91111cffXVvfTfxtbZEjT3ALeIyGPO8UNA\nvog8GVfmXeB5EdngHK8CfoA9ImpeR3Wd9zsc7vv+++/zla98pWefTqlzwCCXi0yXi0FuNwHLoiIW\no9JJv95b3IDfmNbNZwzNIoSdLSTC6e7oBjLdbjJdrtbXFJfrlD+6BQhaFrWWRY3z2rK1fMYst5ts\nt5uhbjfeHj48UKo73g4EmJGQwF8iEdJdLi5PSOBAJEJxNMrcxESiIiwNBpnq8zHW46HGsvgoFOLm\npCTSXS7eDgS4we9nsNvNfwUCCHBjYmLruQK/n0HuL+dNbrlvdtwwr4ORCEWRCLc4T7q/iEYZ5nKR\n6HLxRSTCxqYmFiQl8adg8JRruVmAxV6EoxiycDEFoRKLzbiZh8FDjE8RynAzExiExW6EOjxch0U5\nFntxMwuDF6EB8GLwO/XKnXoZWHwKCG7sPySivIObuRiS4+5xDYbBCDFirMXFCAwTgBAxPsbF5bjO\nqIdDqf4VYy9uJvZ3M5TqFiGKcAKLcuylgsJxZz3O5nVefZzshwxiJ6mK9Gn7Jk+6nF27t3dYprfX\nSb2bTgJNJ0j9sYh87Bx3O0jtamOVUkoppZRSSp1/em1OKvZc0pFxxyOBkk7K5DplvF2o263GKqWU\nUi2MMYeBR0VktTHmd9iPl/cCD4nIjcaY72OP8I3vvvQAvxWR7xpjHsWesvI7YC7wNvZI3w+Au0Xk\nrzq7b9x7fw18U0Suc46/jj0+drRTJAX4loj8yhjzsFO/pex44C8i4oq73lFgoYhsMMb8CjghIt+P\nO18B3CYiW4wxTwJ/jT3W9y3gf4hIg1OvRET+3qlTALwuIiOdYws7p8Qhp2ypiPydc+4+7BxUjXEf\n3Q2sFREd3qSUUqpPdZYVYSswwRgz2hjjAxZiT1SJtxRn4owxZgZQKyLlXayrlFJK9YZnsaeW5sS9\ndwRYIyKD4rZUEfmuc34NcB12FpVCYD0wC7jBOe6O1geuxphRwC+A7wKDRWQQsDu+TA+u3frQ1xiT\ngj2R9RiAiPwfEbkKe9LpRcD/7OF94kc2HQEOt/m3S9MAVSml1NnQYZAqIlHgCeynynuAN0VkrzHm\ncWPM406ZZcAhY0wRsBg7f8Vp6/bZJ1FKKTVgichB4E1OzXX0PnCRMeYhY4zX2a42xlzi1CnCntjz\nEHYw24Cd6+hu7AC2p5KxA75KwGWMeQSYfAbXA7jVGDPLeej7D8BGESk1xlxljMk3xnixe4zDnMz1\n0Z2guG3ZzUCDMeb7xphEY4zbGDPZGHPVGX4OpZRSqlOd5pcXkeUicrGIjBeR5533FovI4rgyTzjn\np4rIto7qKqWUUn3kOeyVigTACTpvBu7HnppSBjyPnVWiRSFQKSKlccdgJwTuDom77x7gX4GN2EOQ\nJ2P30n6pbJv3Orr277F7i6uAK7ADa7BTU/4CO41vMXZg/L+7eJ+2+63HImJhpwW+HDut8AnnPmkd\ntFMppZTqFR0mTgIwxswDXsSei/KKiLzQ5vyDwPexn8I2AN8RkZ3OuWLsxYZiQEREpvf2B1BKKaUu\nZG3nliqllFIXug4TJxlj3MDPgDnYT6G3GGOWthm2ewi4XkTqnID2F8AM55wABSJS3ftNV0oppQYE\nTTColFJqQOlsuO90oEhEikUkgr1C+IL4AiKyUUTqnMNN2Nl94+mXq1JKKdVz7Q3bVUoppS5YnS1B\nkwMcjTsuAfI7KP8osCzuWIBVxpgYsFhEftmjViqllFIDlIg80t9tUEoppc6mzoLULj+5NcbcCHwD\nO31/i1kiUmaMGQqsNMbsE5F1berp02GllFJKKaWUuoCJSJdH2HYWpJYStzabs1/StpAx5jLgl8A8\nEamJa0iZ83rCGPM29vDhdW3rd5a8SSnV+xYtWsSiRYv6uxlKDUj686dU/9CfPaX6hzHdmwHa2ZzU\nrcAEY8xoZ222hcDSNjfMA94CHnLWnGt5P8kYk+rsJ2MvA7CrW61TSimllFJKKTWgdNiTKiJRY8wT\nwAfYS9C8KiJ7jTGPO+cXAz8EBgE/dyLklqVmsoC3nPc8wBsi8mGffRKllFJKKaWUUue9zob7IiLL\ngeVt3lsct/9N4Jvt1DuEvQi4UuocVFBQ0N9NUGrA0p8/pfqH/uwpdX4w/T0f1Bgj/d0GpZRSSiml\nlFJ9wxjTrcRJnc1JxRgzzxizzxhzwBjzg3bOP2iM2WGM2WmM+dhJotSlukoppZRSSimlVLwOe1KN\nMW5gPzAHO9PvFuABEdkbV2YmsEdE6owx84BFIjKjK3Wd+tqTqpRSSimllFIXqN7uSZ0OFIlIsYhE\ngCXAgvgCIrJRROqcw01AblfrKqWUUgOJMeaUTSmllFJf1lmQmgMcjTsucd47nUeBZT2sq5RSSiml\nlFJqgOssu2+Xx+EaY24EvgHM6m7d+EWVCwoKNPOaUkoppZRSSp2nCgsLKSws7HH9zuakzsCeYzrP\nOX4asETkhTblLgPeAuaJSFE36+qcVKWUUgNC2yG++v2nlFJqIOjtOalbgQnGmNHGGB+wEFja5oZ5\n2AHqQy0BalfrKqWUUkoppZRS8Toc7isiUWPME8AHgBt4VUT2GmMed84vBn4IDAJ+7jwhjojI9NPV\n7cPPopRSSimllFLqPNfhcN+z0gAd7quUUmqA0OG+SimlBqLeHu6LMWaeMWafMeaAMeYH7Zy/xBiz\n0RgTNsY81eZcsTFmpzFmuzFmc1cbpZRSSimllFJqYOpwuK8xxg38DJgDlAJbjDFL2wzbrQKeBO5o\n5xICFIhIdS+1VymllFJKKaXUBayzntTpQJGIFItIBFgCLIgvICInRGQrEDnNNXS1cqWUUkoppZRS\nXdJZkJoDHI07LnHe6yoBVhljthpjHutu45RSSimllFJKDSwdDvfFDjLPxCwRKTPGDAVWGmP2ici6\ntoUWLVrUul9QUEBBQcEZ3lYppZRSSimlVH8oLCyksLCwx/U7zO5rjJkBLBKRec7x04AlIi+0U/ZZ\noFFE/vU012r3vGb3VUopNVBodl+llFIDUW9n990KTDDGjDbG+ICFwNLT3btNQ5KMManOfjJwM7Cr\nqw1TSimllFJKKTXwdDjcV0SixpgngA8AN/CqiOw1xjzunF9sjMkCtgBpgGWM+R5wKTAMeMt5auwB\n3hCRD/vuoyillFJKKaWUOt91ONz3rDRAh/sqpVS/Ky0t5ZWXX+aT1auZe9ddLLjjDsaNG9ft6xw8\neJBl77/Pnm3b8Hi9+Px+vH6//erz4fP58Hq9+Hw+Jk6cyE033fSlIbAXMh3uq5RSaiDq7nBfDVKV\nUuo8JyIcO3aM7OxsXK7OZnGcZFkWq1at4uV/+RcK167lAeCGpiZW+f0sNYahw4ZxxwMPsODuu5k2\nbVq7wWRTUxPr1q1j2Tvv8P7bb1NXU8OtwJWhEDHstcmaW16NIeJy0ex202wMaz0ePFlZfP+557jv\nvvvweDrL5Xeq6upq/vCf/0l9QwNTp05l6tSpDB8+vFvXONs0SFVKKTUQ9XqQaoyZB7yIPdz3mUDW\njQAAIABJREFUlbZJk4wxlwC/Aq4AnolPjNRZXaeMBqlKKdUDwWCQJUuW8LPnn+fgkSO43G5mXH45\nM2++mWuuvZbp06eTlpb2pXonTpzg16+9xuIXXyS1sZHvNDbyAJAaV8YCNgHveL28k5BA0OdjwZ13\nsmDhQiZMmMDKlStZtmQJqzds4FKfj1sbG7nVsriCzpMdtBBgOfDPKSkU+/38f888w6OPPUZycvJp\n6zQ1NbFs2TJe//nPWb12LfPcbrKiUXYkJrIjHMbr8zF14kSmzpzJZdOmMXXqVC655BJ8Pl9X/1n7\nlAapSimlBqJeDVKNMW5gPzAHKMWee/qAiOyNKzMUGAXcAdS0BKldqeuU0yBVKXXBiEajlJWVUVpa\nSnl5OZMmTWLcuHG9OqT14MGD/Pyll/j1a68xwxi+29jILcAJ4BNgg8fDhqQktodCjMvJ4ZqCAmbe\neCPDhw/nty+/zPvLl3Ony8W3QyGm0ybr3WnsA95xufivlBQONjcz1+3m1kCAW4AhvfCZNgH/nJTE\nWpeL7zzxBE/8zd8wbNgwwA7kPvnkE377i1/whz/8gckuF19vaOBuID3uGoL9ZbMD2GkMO5KT2WEM\nxaEQo4cPZ/zYsYyfMoXxl17KuHHjGD9+PKNGjcLr9bbbJhEhFApRW1tLTU0NdXV1jBgxgry8vG71\nWMfTIFUppdRA1NtB6kzg2bglaP4WQER+3E7ZU5aY6WpdDVKVUuebxsZGVq5cyf79+yk9eJCSgwcp\nLS2lpLycyoYGhvr95Hi9DBVheySCLzmZubfcwty/+itmz55NZmZmt+8Zi8VYsWIF//eFF9iyZQuP\nxGJ8OxJhbAd1moHPgA3AhpQUjrhc3N/QwF+LMKiHn72v/QX4id/Pm8ADDzzA8NxcfvfLX+JubORr\nwSAPWhaju3nNEHAQKHK2g4mJFPl8FEWjHAuHGTlkCONGj8YYYwekdXXUNjZSGwziMoZBPh8Zbjdp\nLhclkQh1sRiTxoxh8hVXMHn6dCZPnszkyZMZPnx4pw8jzmaQalkWX3zxBTk5OedMT7JSSqmBqbeD\n1HuAW0TkMef4ISBfRJ5sp2zbILVLdTVIVUr1BhHh0KFD1NfXM2zYMIYOHdqrf5jX1NTw3nvv8aff\n/IbV69czIyGBy4NBcqJRcoEcIBfI4tS06QLsAVYBK1NTWdvUxIS8POYuWMDc+fOZNWsWPp+PQCBA\nfX09DQ0N1NfXn7J/pLiYX/37v5MZCvHdhgYWAom99snOTeXAz7xe6l0uHmpq4iq61uPbXc1AMXYQ\nC5ABDHJeMwB/O3VqgM+B3cDuhAR2+/3sCocxHg+TJ0xg2nXXkX/tteTn55OXl3dKYNrXQWowGOTl\nl1/mt796g917iohZYSCKwY/Xk0hyUhKDB6WQlTOY3JG5jBo1imnTpnHXXXd1e06wUkop1VW9HaTe\nDczrYZDapbrGGHn22WdbjwsKCigoKOhq+5VSA1QsFmPHjh2sX7+edcuWsf6TT3BFIgzxeDgRjXIi\nHCYlIYFhGRkMy8xkWFYWw3JyGJaXR/aIEeTk5JCbm0tOTg5Dhgxpd/hmRUUF77zzDm/9+tds+PRT\nbvL5uKuxkduhxz2RzdhDcle53axMTmZ7IEDEskj0eEjzeklzu0l1uUgD0kRIi8XIbG7m/kiE6T3/\n51J9TLAD653AVmPYlJLCpmgUvF7yr7yS/DlzyJ8xgzlz5pxSr6ysjH379lFUVMThw4cpKSmhrKyM\n6qo6cnKzuOSSS7jyyiuZOXMmeXl57d774MGD/OQnP+GdP67gWMUxXIxCuA/hDux0ERGndWXAcee1\nFDdfYDhCjL8AtYwamcud99zG9773PUaNGtVn/1ZKKaUufIWFhRQWFrYe/+hHP+rVIHUGsChuyO7T\ngHWaBEhtg9Qu1dWeVKVUV9TW1vLZZ5+xfu1a1i1bxic7dpDr9XJtJMK14TDXYU+Ob/ntZwG1QEWb\nrRwoS0yk1OejRITS5mYaIhFGDBpEblYWOXl5ZI8Zw7aPP2bH3r3M93i4KxBgPpDSB5+rCbvn1d0H\n11b9S4Aj2PNtN3m9bEpM5OP6+jalvBgycJGJYRhCNhYjETJxUYyLfVgUY1EGGHyeFDLSUsnNG0J6\nRiqbN+0hEKrFzUxifBW4DRjRg9YeBJbjZgkxtpDsH8R1N1zJtx5/jAULFpzyEMeyLI4ePcquXbvY\nv38/Bw8e5OjRowDMnj2be+65h9zc3B60QSml1IWqt3tSPdjJj2YDx4DNtJP8yCm7CGiIC1K7VFeD\nVKUU2MMUi4uLOXz4sL395S8c3rOH4sOHOVxWRjQaZVJiIteFQlwXiXANvZOwB+w5i8ewk+6UOPsX\nA3Npf7inUj315W/nrn7/CVAJHG7dDMcR5mB/zfbmAPAgUIiLtxCWYkyIQemDCQbDNEVCWBIC3BgG\n42I4hhwsRgERDBuI8Rc8riSyhg3l6plTmDt3LnfffXdrIiyllFIDT18sQTOfk8vIvCoizxtjHgcQ\nkcXGmCzszL1p2J0XDcClItLYXt12rq9BqlLnIBGhtLSUcDhMc3MzkUik3Ve/38+ll15KVlZWtzLY\nHjt2jDVr1lC4fDlrPvqI4vJyRiUlMcblYkxTE6PDYcZA65ZJ38xJVOps6nmQ2l8E+3nz58BwIBt7\n5vXplwmCKPZM7K24WYewEYtDeN0ppCSnEItZWFYMy7KwLMESC0ssxNl83gQuuWQU826dy9e//nUm\nTpzY559SKaVU3+r1ILWvaZCq1LkjFAqxevVq3vvjH3lv6VIioRApbjc+lwsv4DMGL9j7zmsQ+Lyp\nCcvlYvL48Uy+6iomX3UVkyZNYvLkyQwePBiAI0eOsGbNGtasWMGa1aupqa3lOq+XgoYGbgCmoENe\n1YXv/AtSe0sTsAt7nELLbw9fm/2W3y5HMazBxQpibMfj8jMqL5ub5l7LV7/6Va6//voeLwHUHR98\n8AEvvfgSGYMymDt3LgsWLGj9faaUUqp7+qIndR4ne0NfOc181J8C87H/Xn1YRLY77xcD9UAMiIjI\nl/J+aJCqVO8SEaqqqkhJScHv73yw6vHjx3nvvfd49/e/p3DDBi5PSOArDQ3cLsLFdL33sgIn2ymw\nOzGR3T4fn4dCJCcm4vN6CTY2coPHww2NjdwATAL6/s9Mpc4tAzdI7akYdjqq9XhYTpQNGKIk+JIQ\nkdYNnH0ALEQgNTmFWddfwde//jXuvvvuLmUv/vjjj/lf//CP/Hn1ZpoiUVzcjSGAsAWLI3jdqWQN\nH8K06ZMoKCjgzjvvPG1CK6WUUif19pxUN/Y4nznY07W20GZeqTHmVuAJEbnVGJMPvCQiM5xzh4Fp\nIlLdwT00SFUKOxnJrl278Pv95OTkkJLStTQ9x44dY8uWLWzdtImthYVs3bWLSHMzwWgUn8dDZmoq\nmenpZGZmkjl0KEOys8kcMQKJxVjxpz9xoLiYWzwebneSA/VmP4Fgz/EMAhehw3WV0iD1TAn2nNwK\n7MdcbmeL33dj/0vvws27WKwAahkxfARz51/Ht7/9bfLz81uv+Nlnn/Hcc8+xYtl6Qk1B3NxFjEeA\n6zl1fEcT9rDnbbjYiOETYhThNn4y0jMYOy6by6+8jFmzZjF37lxGjOhJAiullLow9XaQOhN4Ni5D\n798CiMiP48q8DPxZRN50jvcBN4hIuROkXiUiVR3cQ4NUdV4TEerr60lNTe32EDQRYdu2bSz57W95\n8403SGhqQoDScJgEr5eczExyc3LIGTWK3AkTyBk5ksGDB7P388/tgPSzz2huauJqn4+rAgGuisW4\nipO5PRuBKux0K1VttojbzexYjOuwB9gppfqeBqn95QtgFR7eJsoaPC4348bmcuxYFQ3BOtzcRoxv\nYD+T785vxBj2s/w9wOd4+BSLz7E4ijEJpCalM2r0MKZMncjUqVO56qqrmDFjBklJSX3wGZVS6tzV\n20HqPcAtHa11aox5F3heRDY4x6uA74vINmPMIaAO+7f4YhH5ZTv30CBV9Yuqqir+/Oc/U1VVhd/v\nJzExEb/ff8qWmJhIQkICtbW1lJaWUlJSQumRI5QWFVFSXEzp8eOUVFXhAjxuN1dPmUL+TTeRP2sW\n+fn5p81m+fnnn7Pk9ddZ8pvfQEMD9zc1sTAaZbJzXoAa7F7IloyzpcZQkphIpcfDJcEgV0WjXMWp\ny64opc5tGqSeCyxgB/Bn7LRs8+n9PN4x7MB4L7AXN9uA/VgcRajGZRJJTEhhSGYao8eP4KKLJjBt\n2jQWLlxIRkZGL7dFKaX6X28HqXcD87oQpP5YRD52juOD1BEicswYMxRYCTwpIuva3EOeffbZ1uOC\nggIKCgq62n6luiwcDrNhwwZWLl/Oyv/6Lw588QXX+v3kRiKE3W5CxhBu2bCXJQmLELYs0l0ucoGc\n5mZyw2FygByw38NeP/ME9jpLm1wuNqWksLmpiYzUVPKnTyd/zhwuu+wyNq5fz5LXXqO+spKFkQj3\nRyJciQaZSg0UGqQqiGA/emxZTqgID3uw2IvFF6QmZZI/cxIL71/IQw891KXcAp2xLIsDBw6wbt06\ntm3bRn19Pd/61re4/vrrz/jaSinVnsLCQgoLC1uPf/SjH/VqkDoDWBQ33PdpwIpPnuQM9y0UkSXO\ncetw3zbXehZobFlHNe597UlVfcKyLHbu3MmqlStZ+dZbbNi2jcl+P3MDAebEYszAzifZZ/cHDgCb\ngE0JCXyWkMCVTU3c39TETDRpkFIDkQapqmN1wDpcLAdWYFHCoLRhzLrucr764Fe599578Xg8WJZF\nY2MjNTU11NXVUV9fT319PXV1dVRUVLBjxw727dnP4YNlVNfW0xxtBFy4GImLCQgJxFiF1+3iiisu\n4RvffJhHHnkEn68vvxWVUgNZb/ekerAnW8zGzhu/mY4TJ80AXhSRGcaYJMAtIg3GmGTgQ+BHIvJh\nm3tokKpaiQjhcJhAIHDadTlbXhsaGqioqLC3khIqjh6loqyMispKKqqrqWxsZExSEnMjEeY2NVEA\n6CAqpVR/0iBVdU8lUIibZVisRDjuvB8FPLQs22PwYUgAEjAkY5hAlMuACcB4Z2ubFs8CNmN4C8Mf\nsDjOqJyR3L3wdp566qkeJ36yLIvm5maCwSDBYBCPx8OwYcPOyrJBSqlzV18sQTOfk0vQvCoizxtj\nHgcQkcVOmZ8B84AA8Igz1Hcs8JZzGQ/whog83871NUi9AEQiERoaGlqf5tbX159y3NDQQH1dHfVV\nVTRUV1NfU0N9ba19rrGR+kCA+lCI+nAYr8tFUsvanC6XvTanMSdfsb+WU0QYHosxLBxmWCzGMDhl\nG0Lf9pQqpVR3aZCqzkwD9p9UCfT+eJxi4D3cvEGMbfg8qS1/VDrL+wi0ebX3LYQYdtBrYc/HNZzM\ntGxhB9VejPHhdnnxuD34PF78/gSSknwMHprGqNG5jBkzhokTJzJlyhSmTp3aK0OdlVLnhl4PUvua\nBqlnRywWo6ysjCNHjrRuX+zbx5GiIo4cPUp5dTVet5vEhAT8Pp+dNKglgVBiIv7ERDxeLw11ddTX\n1dmBZWMj9cEg9aEQkViMNJ+PVI+HNLebNGNIA1Iti7RYjLRIhLRIhFQgFUgH+7zz2rKfigaWSqkL\nlwap6vzQgJ30ycXpl/pxYQfMLY+OW169nLp0D9hBaiNQ71y7Pm6/DqjERREuDiOUYFGOUI8hEZ83\nidSUJJITEzBuF26XweV24XK5cLsNbrcbl8vg9rhISUuyl1vLzGTYsGFkZWUxYsQI8vLyGDlyZK/0\n6IbDYbZv386UKVO6vFScUkqD1POKiBAIBKiqqmrdKisr7f3KSmpPnGDw8OHk5OaS62w5OTmkpaVh\nzJf/GwcCAQ4dOkRRUREHDx6kaNcuivbs4WBxMcdqashMSCDP6yXPssgLhRgVjZIH5AFZ2KkcwnFb\nqM1+lFODyvggMxFN/qOUUp3peZBaAHwNeLQXW6PUuSwKHOdknvsG7F7all7bWDvHDbgpx3ACqEao\nRqh3tiAQxZgkEn1JZGSkkTtyCGPHj+biiy9mypQp5Ofnk5ubS21tLevXr2fTpk3s3r2b/XsOcexY\nFY3BBmJWAPuvnwAJ3nTGjM5m5rVXcdttt3Hbbbd12vtbWVnJ5s2b2bFjB0eOHGHChAnMmjWLadOm\n4fF4+uafUqlzQF8M953HyeG+r8QnTYor81PsHO5B4GER2d6NuudMkBqNRmloaCAcDhMOhwmFQq37\n8cdJSSef1GVmZpKent5u0Pj/2Lv3+KjqO/H/r/fM5B4gIYQEwiXc7zcVjJdiULaA/ry1VdeurtbW\n+murbbfbbWt3K3H3+6vtr9vWum0tXW27rW1ltVpt165CMYqAQFRucocESLgFcp1kkpk55/394www\nhFwhIUDez8fjPOacM+dzzudEh5n3+Xw+7w9AfX29FzDu3u0FjycCx/37OVpXhx/ISkoiy+8nS4Qs\n1yUrEmFQczMDVKny+ShPSaEiEKDCdSlvagKfz5s/c8gQsnNzqThwgN379lHd0MDo1FTGAGNDIcZG\nIowBxgDD8ToHGXNCMd7PXmNM2/LxHtKVAidmtnwG+C3eBCZddfZB6jy8IPWBs7iqMcbTDLyMN5v4\nfqAMPzsR9uByAJcjeJ9JRcjGz2iUyThMxfs1NRbvX4UkvJbhDUAJfopxWY9SSWpSJmPGDOWy2TOo\nrq6mdPd+Dh+qprY+SDjaCEQQBuEjDyEXpRyHfUCQgC+d/v36M2x4FhMmjWXGjBnMmTOHGTNmtDml\nnTEXi+5OnOTHS5w0H+8x1nraT5x0JfCjWOKkDsvGyrcbpFZUVPD73/+epKSkM+avbDmXZUJCAgkJ\nCSQmJp72emKpr68/vbvr3r3s376d/aWl7D90iMO1taT6/ST7/aT4/SSLkCxCigjJeLOoJavSKMJx\n4LjjcCwcJhSNMjAtjaz+/cnKzGTgoEEcq6xk9/79BEMhxpwIHBsbGRuNMhYYjdd6mdLZ/1Jx6jj1\nXPEo3hQoY2KvlpbAdFZRbDHGtC0fL9nCV4BHY/ssSDXmYlZE299+itf9OB2vK3NX1QLvA+sJsBaX\nwbiMw+uzNjL2mk3rv9YagL3AHmA3fjYjbMNhH0oV4MPvSyE5MYX+/VLJzskgb3gOw4YNY+jQoYRC\nIRobGwkGg4RCoZPboVAToYZmmpsiOI6D47i4ruI6Lq7j4qjiuoq6LiAkJgZISkogOSWB1LRkUtK8\n39qpqamkpqaSnp5+spEmOzub3NxccnJyGDp0KBkZGZYgy7Spq0FqR5/AOcBuVS2Lnfx54Fa8gQon\n3AL8F4CqrhWRDBHJxZshu6OyHVq+fDm/+Jd/4Qagye+nyeejyefz5rQkrnuqKpHYEnZdwq7rrTsO\nEdcl4rqkJSQwMiWFESKMCIcZEQqxiFP/bOQBCY7TleoBEAaq6us5Vl/P8YoKqoAsvOdtQwCpq+vy\nOdvTH5gcW4wxxvQcAb4K/P/A5/HG08fbDjyC97M0G/g34A68qaduw0uLf+Ib+eVWr5CJ97jy48AP\n8MbzQWxqcbzujvdyonXHswd4ENgUO/sC4Cet1M4Y0zXCuc0DMADvgdI8ol0umwZMiy1e5+VTFKjD\ncQ/R0HSIhqbDHKo8xOYt+/FTBnyI15SSAqSipKIMwiUtdt4UvDHDflofX3xiW/Fam+MHezUiNOCj\nIbZeDmxFqUWpRwnGulKH8LpdJyD44URirdMWN27dj0+SCfgTSUpMIi01mYzMdAblZJCdPYicnBxS\nU1OJRqNEo1Fc1z1j3TmL3+znIiUlhf79+zNgwAAyMjLIzMxk4MCBp/WuDIfDhEIhGhoa4h4UeK9N\nTU2Ew2GysrLIzc0lNzeXIUOGdNhFvKmpiQMHDrB//34qKio4ePAg1dXVJCUlnWzEO9Fgl5qaevKh\nQlpaGv379ycjI4OMjAzS09O79SFCNBolHA4TjUa7/dzQcZCaBxyI2y4HruzEMXl4fSk6KtshESEk\nwvG4fvoJrksCXrDWKp/PWwASvC/8E1/t4roA1Pv9fJiezoddrZAxl4jNzc3sTLJO4Ma0p7KhgdeT\nkkiNRLjG52N6UhJ7IhHKolHuTE7mfxobmZaYyEcDAWpcl3tDIf4rNZX+Ph/BhgZuSEoiN/b99U4o\nBC1+VPm5Bgjh8Ct8rMDHaJQwDivwMQPhWpQVuGzDRzU+/ojSgBJCuBKI4vAXhFn4mXL+/0DGXGQc\nduDnvd6uRjeLn14ohBBCON6Dvev6IfQ7o2eIEkFpwGu+8cWCVV/cupf1WfCjNOFqDeFoDeFoNfWN\nhzh8DG+CeXPRubrgGlateadbz9lRkNrZfkjnlDOnrfGc8crO5QLGmFZtiUR6uwrGXPDebGoCoNxx\n+DDuM/NCQwMAa5ubWdvcfHL//zQ2nlG2LQ7/c3LdZSsuW+O2T/8h7bIJl02tnkepIkppR7dijAGi\nFgkZ061Wv7uqU/FcV3QUpFbg5dw5YThei2h7xwyLHZPQibJd6ptsjDHGnE8iUgp8WlVXiMhzeP1v\ntwH3AK8B/wcvaeAJAeDXqvoFEZkMrAJygLuBB1T1uth5x+P1770cLydTAChR1etE5BvAZap6Z1w9\nVuMlIPyFiOQAPwKuxUsz6gOqVHVkj/0hjDHGmPOoo54AJcA4EckXkUTgLuDVFse8Cvw9gIgUADWq\neqSTZY0xxpiLxWK8waB5se0DwFuqmhm39FPVLwCo6lZgH172+08Cv4s719PAVmCsqg4A/plT38kH\niXvIK97j6fiHvt/GG7I2NVb2XixvnjHGmEtIu19qqhoFHgZex/syXaqq20TkIRF5KHbMa8BeEdkN\nLMHLLdFm2R67E2OMMaYHqeoeYCnwJbzhMH8GxovIPSKSEFtmi8jEuGK/A74MfAR4IW5/Ot7Ej42x\n4z8X995rwBQRuV1EAsAX8RLCx5dtAOpEJA/4p269UWOMMaaXdThPqjHGGNNXxXf3jW0Pw0vtsUZV\nr4/rtjsH78HvBuArqropdvxwvLQKr6nqzXHn/Qjwc7whMh/gzWgzT1Xnxt5fADyF11X4N8BU4Dex\n7r6TgV8DE2J1eQ74sqqO6Mm/hTHGGHO+dBikishC4Em8/NTPqOp3W7z/d8DX8JIn1QOfi/tyLsOb\n1tMBIqo6p7tvwBhjjDHGGGPMpaPdIFVE/MAOYD5egqT1wN3x3XZF5Cpgq6rWxgLaIlUtiL1XClyu\nqlU9eA/GGGOMMcYYYy4RHSVamAPsVtUyVY0AzwO3xh+gqmtUtTa2uRav61I8y95rjDHGGGOMMaZT\nOgpS8/CyF55Qzqmshq35NF7ChxMUWC4iJSLy4NlV0RhjjDHGGGNMX9HRPKmdzqokIvOAB4Br4nZf\no6qHRCQbWCYi21V1ZYtylrnJGGOMMcYYYy5hqtrpHrYdBakVnD4323C81tTTiMh04D+BhapaHVeR\nQ7HXShF5Ga/78MqW5S3DsDHnX1FREUVFRb1dDWP6JPv8GdM77LNnTO/wpvzuvI66+5YA40QkX0QS\ngbuAV1tccATwEnCPqu6O258qIv1i62nAR4HNXaqdMcYYY4wxxpg+pd2WVFWNisjDwOt4U9A8q6rb\nROSh2PtLgMeATODpWIR8YqqZXOCl2L4A8FtVfaPH7sQYY4wxxhhjzEWvo+6+qOpfgL+02Lckbv0z\nwGdaKbcXmNkNdTTG9IDCwsLeroIxfZZ9/ozpHfbZM+bi0O48qeelAiLa23UwxhhjjDHGGNMzRKRL\niZM6GpOKiCwUke0isktEvt7K+38nIhtFZJOIrIolUepUWWOMMcYYY4wxJl67Laki4gd2APPxMv2u\nB+5W1W1xx1wFbFXVWhFZCBSpakFnysbKW0uqMcYYY4wxxlyiursldQ6wW1XLVDUCPA/cGn+Aqq5R\n1drY5lpgWGfLGmOMMX2JiJy2GGOMMeZMHQWpecCBuO3y2L62fBp47SzLGmOMMcYYY4zp4zrK7tvp\nfrgiMg94ALimq2XjJ1UuLCy0zGvGGGOMMcYYc5EqLi6muLj4rMt3NCa1AG+M6cLY9qOAq6rfbXHc\ndOAlYKGq7u5iWRuTaowxpk9o2cXXvv+MMcb0Bd09JrUEGCci+SKSCNwFvNrigiPwAtR7TgSonS1r\njDHGGGOMMcbEa7e7r6pGReRh4HXADzyrqttE5KHY+0uAx4BM4OnYE+KIqs5pq2wP3osxxhhjjDHG\nmItcu919z0sFrLuvMcaYPsK6+xpjjOmLuru7LyKyUES2i8guEfl6K+9PFJE1ItIkIv/Y4r0yEdkk\nIh+IyLrOVsoYY4wxxhhjTN/UbndfEfEDPwbmAxXAehF5tUW33ePAI8BtrZxCgUJVreqm+hpjjDHG\nGGOMuYR11JI6B9itqmWqGgGeB26NP0BVK1W1BIi0cQ6brdwYY4wxxhhjTKd0FKTmAQfitstj+zpL\ngeUiUiIiD3a1csYYY4wxxhhj+pZ2u/viBZnn4hpVPSQi2cAyEdmuqitbHlRUVHRyvbCwkMLCwnO8\nrDHGGGOMMcaY3lBcXExxcfFZl283u6+IFABFqrowtv0o4Krqd1s5djEQVNXvt3GuVt+37L7GGGP6\nCsvua0zvOXjwIHv37iUcDhOJRM5YotEo0WiUYcOGMWnSJIYNG4bP12GOUWNMJ3Q1u29HLaklwDgR\nyQcOAncBd7d17RYVSQX8qlovImnAR4HHO1sxY4wxxhhjzlVJSQmfuf8hNn64FSENb7SbH4m9etun\n1pU6lGrAwScpJCYkk5aawsCB/RgybBBDhuTiOA6NjY2EQiEagyEaG5oIhcI0N0UIh6O4rsukqfnc\netst3H///QwcOLD3/gCd0NjYSElJCVdffTWBQEfhgTE9r8N5UkVkEfAk3if3WVV9QkQeAlDVJSKS\nC6wH+gMuUA9MBgYDL8VOEwB+q6pPtHJ+a0k1xhjTJ1hLqjGnlJeX88Mf/pCqqirmz599dZvJAAAg\nAElEQVTPTTfdREZGRredf9myZXzus19iT1kZfj6Fw6PAsC6cIQgciS2HY8sh/OwH/CjpuPQD0oCU\nFovgYzXCGzjsIjUpk2nTx3DzrTfxqU99iqFDh3bbfZ4L13VZvHgx33niKaIOQIiUxAyGj8jhijnT\nmTdvHrfccguDBw/u7aqai1xXW1I7DFJ7mgWpxhhj+goLUk1bXNft8a6lruvywgsv8Prrr1NYWMgt\nt9zSrUFhZ7z99ts8+eST/PWNd6lrOI6fK4BslA24VJDg78fg7CxmzBrPR+Z+hJtvvpkpU6Z06RpL\nly7lK198lINHKxEeQfkKMKhH7qdzgsC7CCvw8RcctpKU0J+JE0Yw56rLue6661i0aNF5b2197rnn\n+PxD/0iwMQXlaWAhUANsAjbiZzXKe7jsw+9LJXvgQCZPG8XlV1zO3Llzuf7660lNTT2vdTYXLwtS\njTHGmAuUBammpRdffJGv/eO3KN2/B78vjcFZA5k6YyzXXHs1N998MzNnzjyn4NV1XV566SV+8L0f\nsq5kC66bjI85KFtwKSch0J9hQwdzxZXTmD9/Ph/72McYNOjMgM51XaqqqigtLWXfvn2Ul5fT1NRE\ndnY2OTk5DB06lGHDhjFo0KDT6hsOh/nlL3/JL575FR98sJ2I4+JnEQ53AX8DpMddpQnYCmzExzqE\ntThsRxBSk/szoH8aOUMyGZGfx8iRI5kwYQJTp05l+vTpZGRk8NOf/pRvffPbVNU2IHwd5QtAv7P+\n2/WcJrxOiG8TYC0um3Epx+9LY2BGBpOm5HPF7MuZP38+1113HceOHaOsrIwDBw5QUVHBoUOHOHLk\nCMePH+fY0WqcqMOCG+fz+c9/npEjR3Z49bVr13Lnx+9hf0Ul8B3gM7Q/AjAC7AA2InyAn/dw2I5y\njIAvnYEZGYydkMdll8/i2muvZd68edbyas7Q7UGqiCzkVHffZ1omTRKRicAvgVnAP8cnRuqobOwY\nC1KNMcb0CRakGoBoNMq//uu/8uQPfk59QzPCl1E+CxwDNuBjPT7WEmUrEKVf2kDGjh3K5bNnMnHi\nRCZPnsysWbPIzc1t9fyu6/LKK6/w/e/9kLVrN+G4iQifxOUe4HJOpREJAZuB9/GzEmUdLvsI+NPp\nl5ZOc3Mz4WgEx2lGaQYEIR1hAEIGkADU4RJEaQAaAQdIxCcJ+P0JRKMhhByUO1A+Bsyh4xkQ4yne\nbIh78NKjVOBjLz5KUcpxOYJSAwQQMlEeAz4FJHfhGheCMLAL2IywAT/rcPgQpRJIROiH0B8hE2EQ\nSjYuuSiDgQh+/geH90hO6MfMWeO56+47eOCBB+jfv//JK5SXl/Px2+9kXckGfDyCy79wbkF8E7AT\n2AZsIUAJLttwOQgIAV8qKckpZGb0Y+iwLIaNyGP06NGMHz+em266qc3/f82lqVuDVBHx4z06mQ9U\n4D32uVtVt8Udkw2MBG4Dqk8EqZ0pGzvOglRjjDF9ggWpfdvhw4d55JEv8vJLr6NuLi7fAu4EEtso\nocAhYCPwAQHWoezD5TDKcVoGAkPyBgLCe+9vw3H8CHfjci8wmxb5LdsRBrYA5UAGMDC2ZOKNtexM\n+TqgNrZk4f1M7EkuXoA/kI5zgl5sXDof1DfjdSt+DR9/wmEPA9IHcc3cWSQmJPDKK2/g42Yc/p2u\njc3tKsXrNuw9VDix+NmDUIZLBS77GT50OA9/6UG+/OUvk5jY1megdcFgkF/96lfk5eVx6623Whbm\ni0B3B6lXAYvjpqD5BoCqfqeVY0+bYqazZS1INcYY01dYkNo3rVq1ii89/A+8t2Ezfj6Cw7eAa+l8\n4NgaxQsCKzgVDJQjNKDcClx5juc3F78aoBgff0aowOHbeB0fLwSVwG/x8VNUDjLn8uks/tdvsWjR\nojZLrFixgp///Ocsf301x2uO4CPfa0WXBvKHDePm2xfw8MMPM27cuPN3G6bTujtI/QSwQFUfjG3f\nA1ypqo+0cmzLILVTZS1INcYY01dYkHrpa2xs5MUXX+RPf/oTa1Z+wKGjlbgaxcd9uHwVGN3bVTTm\nArMFHz/H5TckJ/q55dYb+PYT3yYlJYWf/OQnvPTCn9i1Zx+OK/j5KA63441nHoT3sGY38L/4eRGH\ntSQn9uPyyydy7333cN9995GcnMzBgwdZv349GzduZOfOnZTuLaN8XyVV1XWEmkNkZWZwy+1/w1e+\n8hUmTZrUq3+NS1V3B6kfBxaeZZDaqbIioosXLz65XVhYSGFhYWfrb4wxxlw0LEi99JSUlLB06VJW\nLH+L7dv209hchY+hCB/BYR5QAEyka+MwjemLosAy/DyNwzLAxc8MHD4OLAKm0XHvgGZgFT7+DLyC\nS0Vsv+IjGx95KGNwGAfkAyOAHKAEP7/DYSUpSf34yNzL+PwXPsfNN99sXYnPUnFxMcXFxSe3H3/8\n8W4NUguAorguu48CbhsJkFoGqZ0qay2pxhhj+goLUi9upaWlLF26lOXLlrPx/V0crzmG4iPALBz+\nBuVqvPGf/Ts6lTGmXfV4D3bSzvE8lXj5WzPpXPf3Jrwu0i+gvIJImCmTxnDPfXdzzz33XDDz216M\nurslNYCX/OgGvAEP62gl+VHs2CKgPi5I7VRZC1KNMcb0FRakXriCwSBHjhzh6NGjVFZWcvz4cSor\nK1mzZg3r12zmcGUljtuMn0koc3G5Bi8gzcfGfhpzKVJgE8If8fEiDjvxSTJZmQOZMi2fq66+iptu\nuomrrrrKWls7oSemoFnEqWlknlXVJ0TkIQBVXSIiuXiZe/vjpSCrByararC1sq2c34JUY4wxfYIF\nqe1bs2YNjY2NzJs3r9t+9JWXl7N69Wo2bNjAtm3b2LOzjIMVVdQ1BIk6YVSjeBlpAZIRkhFSgVSE\nNGAGDnPxAtJJeD9pjDF9j4M3TdBGhBL8rCHKh0CItORMRo8Zwkeuu4o77riDuXPnWuDaQrcHqT3N\nglRjjDF9hQWpp7iuy7Jly/jd735H8fI1lB86jKuCkIASpH9aFpOn5FN4/VzuuOMOLrvssjbPVVpa\nyooVK1i7di0fbt7Knl0VVNfWE44G8cai5eIjH5fxuEzCa/3Mx0u8kh5bujYFhjHGeI7iTRP1PgFW\nEGUdQjMDM7K57IqJLFy0gE9+8pNnzAvrui5btmzh3XffZfPmzWzfvp3SXRUcr6rHcVxUNba4uKqo\ngqoLgE98DM7OZNqscRQUFLBgwQIuv/zyCzow7omW1IWcag19po3xqE/hjWhuBO5X1Q9i+8vwJsty\ngIiqzmmlrAWpxhhj+oRLMUhtbGzk0UcfpXjFSvpnpJORMYCBAweSlZVFdnY22dnZ5ObmMnToUPbu\n3cvSpUt5p/g9jhw7AqTgYy4Oi4C5wHi8rrNHgBJgDQGKibIRQcnKyGLGZeNITEpk+5ZSjhytJtRc\nh+LiYzg+JhFlJl6iorHAKLxA1LrjGmPOF8WbZ/hdfLyN8CYOO0kI9CN3cBb1dY0EGxuIukEgGT95\nCKNxmIQyDm8O20S80KutJQRsw0cJQgkOO4FmUpMyGD58MDMvn0xBQQGzZ89m9uzZXZ6Htid095hU\nP9640vl4E3Ctp8W4UhG5EXhYVW8UkSuBH6lqQey9UuByVa1q5xoWpBpjjOkTLqUg9cMPP+SRL3yR\n4rfXIjoBl7uABnwcQziGUA3UoNThUo8SREjFxzwcFuIFpSPpXACpQBmwHh+rAAeXacAEvMB2SCfP\nY4wxvaEZr7V1M1424fzYkt6N16gEtgCb8bMe2IhLOUodPkklLTmd7OwB5I8dyoQJE5g2bRpz585l\nypQp3ViHtnV3kHoVsDguQ+83AFT1O3HH/Ax4U1WXxra3A9ep6pFYkHqFqh5v5xoWpBpjjOkTejtI\n3b9/PytXrqSkpIQtW7awZ0c5DQ1NzLhsPDfetIi//du/PaNLWkvPPfcc//Lov7GvfD9+PobD14AZ\n5+cGjDHGdFEzsB8oBcoQduFnG8oeHPYh4iOz/0CmzhjD3LnXcuutt3LZZZd1e9fh7g5SPwEsaG+u\nUxH5E/CEqq6ObS8Hvqaq74vIXqAWr7vvElX9z1auYUGqMcaYPqE7gtRoNMpTTz3Fr3/5WyKRKH6/\nD7/fjz/BT8Dvwx/wEUgI4Pf7CTeHKdt7hONVtTSFg4CDjzx8jMFhGspEIB0fqxGKcdhJYqAf+flD\nmVtYwMc+9jEWLFhAU1MTjz76KM8s+T2hZhf4CspDQFZ3/FmMMcb0CsULXt9HWIuPd3DYjKAM6JfJ\n5Cn5zLxsBmPGjGH8+PFMmjSJUaNGnVUA29UgNdCJmnfqum3sv1ZVD4pINrBMRLar6sqWBxUVFZ1c\nLywspLCwsJOXNcYYY/qGNWvW8M1v/DNvrywBHYTLA3iJ9Z02lgje1/xovPGZY4HBuAhui3O7fDK2\n1kw4uoGdu99l7+7lPPvM38e66frwMRGHnwG30PHPB2OMMRc+wfuOGI3yCRwAFKWcmvr3WP3ueta9\n+z7KKpRKXKqBMCIpJAaSSUtJITMznUf+4SG+9KUvnXbm4uJiiouLz75mHbSkFgBFcd19HwXc+ORJ\nse6+xar6fGz7ZHffFudaDARPzKMat99aUo0xxvQJXW1Jramp4bHHHuPXv3yR2mAdPu7C5fPAZZy/\nMZiH8GaXG3+ermeMMebC1YSX3O5w7PV5pkzYypbtG9ot1d0tqSXAOBHJBw4CdwF3tzjmVeBh4PlY\nUFsTG4+aCvhVtV5E0oCPAo93tmLGGGNMX+C6LjU1NVRXV1NdXU1tbS07d+7kqR8+zfZdu/AzE4cf\nALfhktwLNRwSW4wxxphkvKR3I2PbZcDWbr9Ku0GqqkZF5GHgdbx8x8+q6jYReSj2/hJVfU1EbhSR\n3UAD8KlY8VzgpdhT4wDwW1V9o9vvwBhjjDmPamtrKS0tpayszHvdsYPSbdsoLStj/9GjRBwHv8+H\nX8R7PbH4/WecSyQBiOJ9TSYAiQhJCP3xngl/GufkDwFjjDGmb+hwntQer4B19zXGGBNTU1PD22+/\nzZuvv86bf/kLjaEQ06dPZ+a11zJj5kxmzpzJsGHDzug221mhUIiysrJTAebu3ZR++CFlpaWEmpra\nLeu6LoeqqohEo4xKSWEUkN/UxKhwmHy8GTlH4s1ud2JUaJTTR4meGW424D2VvnAnYDfGGGPa9hRT\nJvzivHf3NcYYY9oVjUY5cuQIFRUVVFRUcPDgQRobG8nJySEnJ4fc3Fxyc3MZNGjQGa2J9fX1rFy5\nkjffeIM3X3uNHfv2UZCczLz6ep5WpT+w8fBhNq5YwU9SU9kQiRAWYcaECcwoKGDmnDlkZmbS0NBA\nMBg8tdTUEKyuJlhbS7CujkMHD1JaUUFNQwMjUlPJF2FUJMKoUIjL6Pxsdbl4+WwlEun036cI2AP8\nptV3U4GpwE/x5g01xhhjTIctqSKyEHgSr7vvM/FJk+KOeQpYBDQC96vqB10oay2pxvSC4uJiy6Rt\nUFVc18VxHBzHIRKJUFtbS21tLXV1dSfXT+6rqeH4wYMcLCvzAtLKSirr6xmUlEReQgJDgbxwmBTH\n4WhSEof9fo6ocjgcpiYcZlB6OjlZWeTm5FBTW8uW3bu5IiWF64NB5rkuc4CkDup8GG9K9I3AxvR0\n6n0+0lyXdMfxlkiEdFXSgbTYMgQvEB1Kz7RZ/gr4PrAXL9/u7cATwAC8ZAy78YLUMx8h2/efMedX\nMVDYy3Uw5lLSCy2pIuIHfgzMByqA9SLyqqpuizvmRmCsqo4TkSuBp4GCzpQ1xvQeC1IvbKpKMBik\nsrKSY8eOUVNTc3rQWFND3fHj1B47Rm1VFfW1tTQ3NxMOhwlHIieXSDTqrUejhKNRHFUc1/WW2Dpw\ncvxkos9H/4QEBvj9DPD5GAD0d10GRKMMiEToH40yBfgbIC+25AAJoRCEQqffRIvWxghQWVfH4bo6\njpSWkgwUACldaJUErzUzF1gAEAx29U/b7b4PfA/4NXADUA58Hu9vtAoLQ425sBRjQaoxF76OuvvO\nAXarahmAiDwP3ArEB5q3AP8FoKprRSRDRHLxhud0VNYYcwFTVRoaGlpvUYvta2pqIjk5mZSUFFJS\nUk5bP7EkJSXh9/vbXVzXPb27ZmyJ78YZjUZJTExsd4lGo2eWr672un/W1BCsr6e5qclrOYxGT2tF\ndOLWExISSElOJiU1lZTUVJJTUkhJTz+19OtHOBQ6de66Om8JBgk2NBBsbKShqYmkhATSU1JIT0sj\nPT2d9H79SO/fn7QBA0jPyCClXz9qKys5dvAglYcPU3nsGJU1NRyrr8cPZCclMSgQIAMYoMoAx2FA\nJMKAcJgReK12A2KvSXjjIeOXhBbr/lYWn/cfGxzHW7oYNHZWAl5L5tAeOXvvqMPrzvtLvBT24I07\n/W+8L8Hn8FpPw8B9bZ4lH3gWL8RtBr4OvBB7707gu3j/BY0xxpi+oaMgNQ84ELddDlzZiWPy8H6H\ndFS2Q6+++iq33nprV4sZYzrh8cf73qxQfiBJxGs5xGtB9MX2x++LqBJSJeS6NKniduLc6T4f6SKk\n+3yk+XwM8vmINDURrK3lgOsSjC3RNsr39/nI9vsZ7vczKymJVF+sY6p76uoNPh8NSUkcTOqoU6w5\nHw5FozSEQvx3ejovtEjmlBYKUQT08/nYHg5zdUrKGa3NAW4kylF8fBMfWTjsRDmOn8sBcPgVwp/w\n2xylxnQLh134Wdfb1TDmkuFQhvi6/zdJR0FqZ3spndOM4mebpdEYY7rKARpVvZbDbhZ0XYLgtUae\nhTrXpc512dNDLZmm57zQiW7HK1t2hwai/AUAl3WnPQhx+OvJdaWGKHvOuY7GGE+U3b1dBWMuKVu2\ndX8811GQWgEMj9sejtci2t4xw2LHJHSibJcG0BpjjDEXkliCwD8BSarqtnjvv/C+C3fg5W64N7Y/\nHy/HUkBVXREpBT6tqitEpBG4/ET+BhGZCGxUVWs6N8YY02d0lOSwBBgnIvkikgjcBbza4phXgb8H\nEJECoEZVj3SyrDHGGHMxW4M3kPTj8TtFJB1YCCzv4vkO4g1SPWFEbJ8xxhjTZ7QbpKpqFHgYeB3Y\nCixV1W0i8pCIPBQ75jVgr4jsBpbgJTVss2yP3YkxxhhznqlqLd4sM/8hIgtEJCHWUvrfeHkZTuRO\n6qzfA/8iIoNEZBDwGG1NsWqMMcZcojrq7ouq/gVig2ZO7VvSYvvhzpY1xhhjLiWq+j0ROQ78OzAG\nL+nvy8DdqhoWEeXMHA9tDYr+P3jJmjfFtv87ts8YY4zpM0Q7SB4SG2/zJF7yy2dU9bst3v874Gt4\nT4rrgc+p6qbYe2V4X9YOEFHVOd19A8YYY4wxxhhjLh3tBqki4sdL+DAfL0HSerwnw9vijrkK2Kqq\ntbGAtkhVC2LvleIlgKjqwXswxhhjjDHGGHOJ6Chx0hxgt6qWqWoEeB44bdJSVV0TG5MDsBYvu288\ny95rjDHGGGOMMaZTOgpS8/ASP5xQHtvXlk8Dr8VtK7BcREpE5MGzq6IxxhhjjDHGmL6io8RJnZ7t\nXkTmAQ8A18TtvkZVD4lINrBMRLar6soW5Tp9DWOMMcYYY4wxFx9V7XQP246C1ApgeNz2cLzW1NOI\nyHTgP4GFqlodV5FDsddKEXkZr/vwypblO0reZIzpfkVFRRQVFfV2NYzpk+zzZ0zvsM+eMb1DpGsj\nQDvq7lsCjBORfBFJBO4CXm1xwRHAS8A9qro7bn+qiPSLracBHwU2d6l2xhhjjDHGGGP6lHZbUlU1\nKiIPA6/jTUHzrKpuE5GHYu8vwZtoPBN4OhYhn5hqJhd4KbYvAPxWVd/osTsxxhhjjDHGGHPR66i7\nL6r6F+AvLfYtiVv/DPCZVsrtBWZ2Qx2NMT2gsLCwt6tgTJ9lnz9jeod99oy5OLQ7T+p5qYCI9nYd\njDHGGGOMMcb0DBHpUuKkjsakIiILRWS7iOwSka+38v7fichGEdkkIqtiSZQ6VdYYY4wxxhhjjInX\nbkuqiPiBHcB8vEy/64G7VXVb3DFXAVtVtVZEFgJFqlrQmbKx8taSaowxxhhjjDGXqO5uSZ0D7FbV\nMlWNAM8Dt8YfoKprVLU2trkWGNbZssYYY0xfIiKnLcYYY4w5U0dBah5wIG67PLavLZ8GXjvLssYY\nY4wxxhhj+riOsvt2uh+uiMwDHgCu6WrZ+EmVCwsLLfOaMcYYY4wxxlykiouLKS4uPuvyHY1JLcAb\nY7owtv0o4Krqd1scNx14CVioqru7WNbGpBpjjOkTWnbxte8/Y4wxfUF3j0ktAcaJSL6IJAJ3Aa+2\nuOAIvAD1nhMBamfLGmOMMcYYY4wx8drt7quqURF5GHgd8APPquo2EXko9v4S4DEgE3g69oQ4oqpz\n2irbg/dijDHGGGOMMeYi12533/NSAevua4wxpo+w7r7GGGP6ou7u7ouILBSR7SKyS0S+3sr7E0Vk\njYg0icg/tnivTEQ2icgHIrKus5UyxhhjjDHGGNM3tdvdV0T8wI+B+UAFsF5EXm3Rbfc48AhwWyun\nUKBQVau6qb7GGGOMMeYSE41GaWxsJBgMEgqFaGhoOPna1NREQUEBAwcO7O1qGmPOk46moJkD7FbV\nMgAReR64FTgZpKpqJVApIje1cQ6brdwYY4wxpo9zXZcNGzbwxz/+kbfffoctG/dQXVuFqw147Rr+\n2BIAAkjsFfwgtdz5iZv5xa9+QWpqai/ehTHmfOgoSM0DDsRtlwNXduH8CiwXEQdYoqr/2cX6GWOM\nMcaYi0xdXR2rVq3izTff5J23V7Ft635q66tQfPiZisu1KJ8DLgNG4P0kbTFm+7SNEl584Yu8+Idc\nPv+F+/nBD35AINDRz1hjzMWqo0/3uWZ0uEZVD4lINrBMRLar6sqWBxUVFZ1cLywspLCw8Bwva4wx\nxhhz8aurq+PNN99k1apV7Nixg2g02u7xruvS1NREY7CJUGMTTU0Rwk0RmsMRIhGHSNQBIDMznWEj\nshmZP4IxY8YwZcoUZs6cybhx4/D5OkxZAkBVVRUrV65k7dq1bN68mZ3byjh0qIqGUD2uhhAG4Wc8\nUa4HZuMFpHk4Z9XJ7gocVoP7V37yH4+w5GfZfOuxf+Sb3/xmu/UtLy/nJz/5CX/471fZW1ZO5oAB\n/NM3HuarX/1qp+/TGNN1xcXFFBcXn3X5drP7ikgBUKSqC2PbjwKuqn63lWMXA0FV/X4b52r1fcvu\na4wxpq+w7L6mLZs2bWLZsmWUlJTw4eYdHNh3lLqGOlwN4WMwPsbjMhElqYMzCUo/IAVIjnuNX3eB\nQ8B+/OxGKMOlApdKoBmfpJIQSELVxVUXjV9wASd2DmJ1G4vLFFymAGNjywggodv/Th4FXkb4Mmmp\nzXz/h//GZz/7WcAL0l988UWefeYXrFq5gYamavzMwuEOvBQra/DxXXz+am67/aP86EdPMnTo0B6q\npzHmhK5m9+0oSA0AO4AbgIPAOuDu1uY7FZEioP5EECoiqYBfVetFJA14A3hcVd9oUc6CVGOMMX2C\nBakmXjAY5Fvf+hbP/vz31DfW4Wc8MAWHWcCE2DKKngv2WtOIlyuzKnbdxNiS0OI1EUjCG0PaW6LA\nrxC+QVZmCunpqew7UA70x8ctONwKXAektSinwNv4+S4ObzJ5wgT+/YdPsGjRojOu4Lou7733Hn/8\n4x95Z+UqtmzaS01dDSI+EgNJpKQk069fClmD+pGdk0VOTg65ubmMHDmSgoICZs6caS22xtDNQWrs\nhIuAJ/H+FXpWVZ8QkYcAVHWJiOQC64H+eI/V6oHJwGDgpdhpAsBvVfWJVs5vQaoxxpg+wYJUA7Bi\nxQq+/k//zHvvb8THJBy+CnwMOmwlNa1rQvg5ig9YiNeS21nlCD9GeZoB6al89nP3oqqsfGsV27ft\npy5YhRKIG0c7B5iO15p8HDgWez2OcBgfBxGOoBzFYT8QJimhP4MGZjBu4jCmz5hOQUEBN9xwA4MH\nD+7uP4QxF6xuD1J7mgWpxhhj+goLUvuuYDDI4sWLeWbJ76hrCOLjPlwewWstNb2vGXgBP08ipBPl\nI8SPoz37ySqqgJ3ADoQP8bMBlx24HERIRCRw8t8FQbx1EQQfIpAQCHDVNTP48j98kRtvvLEb7tOY\n3mFBqjHGGHOBsiD10hAMBnn22WdZ+vsX2LhhF66r+P1+An4/gYCfxMQAiYkJJCUlkJyaSCQSYfuO\n3fiYGNdqmtzbt2F6lYM3ki7MqTG+TovFBY7j40VcXibgc7ls1iQ++7nPcN9991l2Y3NR6Ynuvgs5\n1d33mZZJk0RkIvBLYBbwz/GJkToqGzvGglRjjDF9ggWpFyfXdXnttdf4xS9+wVt/XU9VXSU+RqLc\ngrIA6AeEgKbY0nLdAf4fYFIv3YG5+LlACcILwPMgVUwYO5r7Hvg7Hn74YdLT03u7gsa0q7sTJ/nx\nEifNxxtFv54WiZNi08uMBG4DquMSJ3VYNnacBanGGGP6BAtSe144HOb555+nsrKSoUOHkpeXx8iR\nI8nLy2u35SkajVJaWsrevXvZt28fBw4c4MCBA7y94l3KystBU/CxAIdb8PJJZp23ezLmTNsR/oCP\n3+Gwm9SkTEaPHkLBNVewcOFCbrrpJpKTrbXeXDi6O0i9ClgcNwXNNwBU9TutHHvaFDOdLWtBqjHG\nmL7CgtSe8c477/Czn/2M5f+7iiPHD+NjMEIWSg1KHUoQr1tlIj5JIuBPIDEhEcdxCEebcdxmvDGJ\nyQgD8JGBMAjIIUoh8FFgHGc/LtGYnlQNfAC8R4CVuLyHSyXJCQMYMTKXK6+axYIFC/j4xz9ugavp\nNV0NUjvqzJ4HHIjbLgeu7OS5z6WsMcYYY0yr9u/fz9NPP80f//Bndu3Zj+OCn+txeByYj0tr815G\ngVpcrSYcrSYcrcGbNzQLGARkAgEUr3OuMRePTOB64Hqi/FNsXz1NkY3s3P0+e2x2yvIAACAASURB\nVHev5Le/+Qb33PMA6SkDmTZ9NAtvXMC9997LqFGjOjy767qUlpaybt066urqmDBhAtOnT2fgwIFd\nqmVdXR2bN29m+/btzJgxgyuuuKLLd2r6jo6C1HN5xNvpskVFRSfXCwsLKSwsPIfLGmOMMeZSUVVV\nxSuvvMKyZctY/+5GDpQfoTlSj5/LcLgXb8qRaTgdtnIG8AJS66Zr+oJ+wLXAtUT5YmxfNcHQu7y7\n9i3WrX2VxYu/TcCfwsjhQyi84WqmTZvGjh072LNnD2W7Kzh6tIZgqJGoEwQCsR4KKbgcQ6kB/AR8\nKSQnpTCgfxqDczPIG55LIBCg/MBBjhyspqaugVBTiKgTAiII/RGycSlnyODBLP7Xb/Dggw/aXLKX\noOLiYoqLi8+6fEfdfQuAorguu48CbhsJkFp29+1UWevua4wxpq/oy919Xdfl2LFjhMNhr5ttOHza\neiQSIRKJsGXLFv7617/y3totHDxcScQJ4mM4whwcrsWbEmQmXiuoMebsRYFNwCoCvIFSijASh4ko\no/FSzowERgADWpRVvG7Gh05bfOwDwriMAoa0WAYBJ4LROoRngO+RlBDm/k/fyfe+9z1LAHUJ6+4x\nqQG85Ec34OXJXkcryY9ixxYB9XFBaqfKWpBqjDGmr+grQequXbt47bXXeOedd9j0/jYOVFQSaq7B\ny1Dqx/uhKrFXb11i+30MAq7E4Rq8gHQqkNQ7N2KM6WEO8Bp+/j9cNjP3I3P48U//g6lTp/Z2xUw3\n64kpaBZxahqZZ1X1CRF5CEBVl4hILl7m3v543z71wGRVDbZWtpXzW5BqjDGmT7iUgtTy8nLWrl17\ncozZ9g93UVp6mPqGGhQXP2OBWTjMwQs0p+K1pBhjTGu24Oe7OLzIyGEj+MxD93L33XczZsyY3q6Y\n6QbdHqT2NAtSjTHG9BUXW5Dqui5Lly7lueeeo3R3OUcOV1Pf2EAk2gAoPgbjYxjKOBwmA9Niy3As\nE64x5uwcR1iCjz/g8CF+XzJ5Q3K46tpZ3Hzzzdx+++2kpqb2diVNF/VES+pCTrWGPtPGeNSngEVA\nI3C/qn4Q218G1OG15UdUdU4rZS1INcYY0ydcLEHqO++8Q9Hix3nrrRIcJwHhdlwmc2p82khgIBaI\nGmN6lgN8CKzFzwqU1bgcIjVpIBMmDmf8xDFkZWWRk5Nzcl7k4cOHk5+fb+NbLzDdPSbVjzeudD5Q\ngdet97RxpSJyI/Cwqt4oIlcCP1LVgth7pcDlqlrVzjUsSDXGGNMnnK8g9ejRo7z88sts3LiR8ePH\nM3v2bGbPnk1iYmKbZfbs2cNjjz3GKy8tp6GpAT+fwOFB4GosGDXGXDjqgPUIa/CxG6ESqEKpxj05\nL3ID4EckkaRAKpkZ/Rg+cjATJo1j6tSpXHHFFRQUFFiL7HnU3UHqVcDiuAy93wBQ1e/EHfMz4E1V\nXRrb3g5cp6pHYkHqFap6vJ1rWJBqjDGmT+hskNrY2EhycnKnpmXYt28ff/jDH3jrrbd4f91WDh89\nRtRtwMcofIxBKcflAEo9fkkjNTWdnOwMxkwYzoQJE1BVnn/uFSqrj+DnBhwewpvWpe2A1hhjLmyK\n18GzCigH9gJ78fMhsDP2b2IVPkklNTmdcePymL/geu655x6mT5/emxW/ZHV3kPoJYIGqPhjbvge4\nUlUfiTvmT8ATqro6tr0c+Jqqvi8ie4FavLb6Jar6n61cw4JUY4wxfULLILW0tJQ33niD1atXs/GD\nLZSVHqYuWIer3phPCCAkIBLA7wvg9/tJCARITEhABKpqa3C1CT/jUQpwuRovI+5kIKHF1ZuAfUAp\nsBdhJ362oTThcD/wMc6cZsIYYy5VEWA/sAdhLX5eJ8r7+CSBobmDufa6K7j99tu57bbb2u2FYjqn\nq0FqoIP3Oxs9tnXBa1X1oIhkA8tEZLuqrmx5UFFR0cn1wsJCCgsLO3lZY4wx5sLmui7l5eXs2LHj\njPdGjZqIn3yEqUT5BDAFmASMih3RgNKAagOu00DEaaAp3AAE8Z7/TgHG4+DvRE2SgQmxxfuCj57r\nzRljzEUrARgDjEH5KFG+Bbi4up3yQ6t44fnlLH3+H1DupV9qFgmBAFHHxXVdXNfBdRVXXVx1UXVB\nlcSEZPr3SyNnSAYj8vMYPXo0EydOZNq0acycObNPjZMtLi6muLj4rMt31JJaABTFdfd9FHDjkyfF\nuvsWq+rzse2T3X1bnGsxEDwxj2rcfmtJNcYYc9FzXZdNmzaxZcsWdmzdyo4PPmDH9u3srqggIyGB\nCQkJvFlb27IUNt7TGGMuZJVACV7LayJecJvYYj0Bb87no3jdiyvwsRcfe1D243IEpQYhiYRACqnJ\nKWRmpjMkbyB5w/PIz89n7NixTJ48malTp5KRkXFONd61axebNm1i0aJFF8y42+7u7hvAS5x0A3AQ\nWEf7iZMKgCdVtUBEUgG/qtaLSBrwBvC4qr7R4hoWpBpjjOk19fX1rF69mtTUVKZPn86AAZ3v8lpZ\nWckbb7zB//7hD7y+fDmZqswCJjQ0MEGVCcB4oF/s+DO/ne37zxhj+gYHL4g9FLccxE8pwn6Ug7hU\notQS8KWTO3gQl82ZzLx587j99tsZOXJkq2cNBoO89NJL/PnPf2bNOx9w6EgljtuEMBDlGKlJmUyc\nNJLr51/HnXfeyezZs8/fLcfpiSloFnFqCppnVfUJEXkIQFWXxI75MV6WhQbgU7HxqKOBl2KnCQC/\nVdUnWjm/BanGGGPOm+bmZtasWcOKN97gr6++ysadO7k8JYVmYEsoRPaAAcyYNo0Z11zDjFmzmDlz\nJvn5+fh8PqLRKOvWreN///xn/vell9hRWsq8pCQW1tezgFOddNtiQaoxxpj2hYGtwPv4WI3wLg67\n8Esy2VlZzLhsPKNGj+LdVevZsfMAoeZqfAxFuAaHeUAB3rARP15qIC8Tsp/lRPkAEZdBmYOYfeUU\nFi5ayB133EFubm6P31W3B6k9zYJUY4wxXdHc3MzRo0dPLpFIhOTkZJKTk0lJSWl1fdu2bfx12TL+\n+sorvLthA5OTk7m+sZEbolGuBk50hnKA3cBGYKPfz8a0NDZGo9Q6DpNGjWLX/v0M9/tZFAqxMFa2\nK+k0LEg1xhjTdQ6wEy9wfRcfu4lyLXAVcAXQv5PnUbzkeWvx8RbCShx2EfCnkZc7mDlXz2DRokXc\nfvvt59zluCULUo0xxlzQ6urqKC0t5ejRo4RCIZqammhqajq5fnJfMEhNZSVHKyo4evgwR48f50hN\nDaFwmOzkZHICAbJFSFKlSYQQXv7aJlVCrktTbAlFo4xKSeGG5mZuCIeZC3T1q/c43nTyY4GhsX1T\ngZ8Cc7twnjO/nTszJnU/XoKkuk4ca4wxxnRFBNgCrMfPSpTVuOwnMTCA/BG5TJ4+jszMTLKyssjK\nyiI7O5ucnBxyc3NPLoFAR7l4e6a770JOdfd9Jj5pUtwxTwGL8CYkul9VP+hCWQtSjekFxcXFlknb\ndDvHcaivr6eyspLS0lL27t1L6a5dlH74IaV797K3ooKmcJjRqankipCiSrIqya5LsuuS4jgkx5YU\nvLGcObFlcGzJ4PyEaguBK4HHW+x/Bfh/gQq8NBldYS2pxvS2YqCwl+tgzIUuBGwASvCzGaEaqAbq\nUOpQgl7meUJAmHnXXc+K4uXtnrFbp6ARET/wY2A+3vfxehF5tZXESWNVdZyIXAk8DRR0pqwxpvdY\nkNpzTgRq9fX1+P3+k11Pk5KS8PnaD2scx6GxsZFgMEhDQwMNDQ0Eg0EqKytPdXE9cIAj+/d7rYvH\njnG0poaqhgYA/D4fPhH8IqfWfT5v3efrMLgTEZITE/8ve3ceHkd1Jfz/e7q6pdbmTZb3fcFgjGO8\nyjGLHJxgCBOWTAaYkIQJISQZ8oa8SSYhmXcQmYRlfk8WMiSMEwiZJAQcBgcIgcFAEIuRt5jN+yrb\nsmRrX1pSq5c6vz+qbLeFrMWWLNs6n+epp6uq61bdaj9y96l777lkpKcT9peMjAzCGRlkZGYSzsgg\n4Dg01tXRUF9PQ2MjDZEIDU1NNLS00BKPkx0KkZuWxiTHYWIsxsSWFq7DG685EcgDpKGhBz7p3nUz\n8D0+GKT+DriJjgPUJHRpUhhjzKlWhAWpxnQmA68r8UKSnR77ABUHH+3xGnTWNjsf2KmqJQAi8gRw\nNZAaaH4C+G8AVV0jIoNEZATeb5HOyhpjzlCu6xKNRkkmk2RmZuI4vfeTPJFIEI1GSSQ6ntVRVY8c\n21430sNLMtnxf7mqSjweP/Yczc1Em5qIRiJEm5tpaWoiUl/fbqAWTSTICYXIDgZxgZZkkmgySWsi\nQchxyAiFCKelEQ6FSE9LIxqL0RSN0tTaSmsiQWYoRFYwSJbjkBUIkC1CnirDEgmGRaNMdl0WcrRl\ncTgwxK+7m0ySxAuSXP81db0zLn6XWX9pafMa9c8z4DhLJhCIxSAW68LVTm9X47WYvgFc7O+rBf4C\nrAEmAL8GPgIU4nWWygCeBX4CLAY+h/csegFelt8PcvHC3QK8jsN/Bd7D+3HwByAXKAEm4c1qGgAe\nBf4/vGkO8oBvA188+Rs2xhhjuq13+jZ1FqSOBvanbJfifdd2dsxovGE7nZXt1J/+9Ceuu+667hYz\nxnTB3Xe3bSMy3SHAgECAAY7DgECAiYEAOenpZGVkHPtftj9WQ4FW1yWqSjSRIBqLEY1EGCrC+ECA\nrHCYjPZaO/0hEbXBILXZ2Wzr/VszvsEtLXwWmJ2RAcDuWIz0WIy7s7OpbGzkrowMfh4MsjkaZVss\nxoKMDJaEQjyryream8l1HC5LT6c2meSXzc0fOL/DxxCEBO8Ca3G4EJiLyzpgLg5TUFpIokeOdalC\nGIIwGqWWJF/G4RGky4kzjOm/kuzCYVVfV8OYs0aSfYjT83OxdhakdnWwzEmF0CKWCMIYc+ZRoN51\nqXfdvq6K6WUl8fgx2yv87spvtgk8V7e0QEvLke2aZJIdHbQqJ3mlzfZbKVu1JCg57rHHllt73PeM\nMcdKsKevq2DMWWXj5p6P5zoLUg8AY1O2x+K1iHZ0zBj/mFAXynZrAK0xxhhzqonIDuBfgfV4Q1ZG\nq2qliOwBblHVv4pIIV5+hpv8MvnAM6o6POU89wBjVfUzIjIB2A0EVdUVkVeB36nqr/1jb/bPfXE7\nx14B3AVMxev/mwncp6p39fJHYYwxxpwSnSUmXA9MFZEJIpIGXI833CbVs8Bn4ciXcp2qHupiWWOM\nMeZ091u877mbgP9V1crjHJfa+6gcGCIiGSn7xp1sRUQkHXgK+A9gmKoOBp7H5qYxxhhzFukwSFXV\nBHA78CKwGViuqltE5DYRuc0/5nlgt4jsBJYBX+mobK/diTHGGNM7fgt8FPgCfqLAzqjqXryHtYUi\nEhKRhcBVdDyMpiuBZpq/VAGHW1U/1pU6GWOMMWeKTmdeVdUXgBfa7FvWZvv2rpY1xhhjziSquldE\nVgEzOX6PIOWDAeingd8A1cBaYDnHzkzT9nhts952G1VtFJH/A/wRSAf+jDd1qzHGGHPWENWOcyOJ\nyFLgp3hfrA+r6v1t3v808C94T4AbgS+r6nv+eyVAA96MBXFVnd/TN2CMMcacCURkObBZVS2ttjHG\nGNOBDoNUEXGAbcASvARJ64AbU7vt+l2YNqtqvR/QFqpqvv/eHmCOqtb04j0YY4wxpx0RmYs3teoe\n4HJgBZCvqu/2acWMMcaY01xn3X3nAztVtQRARJ7Am9/8SJCqqsUpx6/By+6bypI5GGOM6Y9G4AWm\nuXjzhn/JAlRjjDGmc50FqaPxvlgPKwUWdHD8LXhZBg9T4GURSQLLVPVXJ1RLY4wx5gyjqs8Bz/V1\nPYwxxpgzTWdBascDVlOIyGLg88CilN2LVLVcRPKAl0Rkq6q+0aZcl69hjDHGGGOMMebMo6pd7mHb\nWZB6ABibsj0WrzX1GCIyE/gVsFRVa1MqUu6/VorIn/C6D7/RtnxnyZuMMT2vsLCQwsLCvq6GMf2S\n/f0Z0zfsb8+YviHSvRGgHc6TijfH21QRmSAiacD1tEm/LyLj8Mbc3KSqO1P2Z4pIjr+ehTeP2/vd\nqp0xxhhjjDHGmH6lw5ZUVU2IyO3Ai3hT0DyiqltE5Db//WXAvwGDgYf8CPnwVDMjgBX+viDwmKqu\n7LU7McYYY4wxxhhzxuusuy+q+gLwQpt9y1LWvwB8oZ1yu4FZPVBHY0wvKCgo6OsqGNNv2d+fMX3D\n/vaMOTN0OE/qKamAiPZ1HYwxxhhjjDHG9A4R6VbipM7GpCIiS0Vkq4jsEJFvt/P+p0XkXRF5T0RW\n+UmUulTWGGOMMcYYY4xJ1WFLqog4wDZgCV6m33XAjaq6JeWYhcBmVa0XkaVAoarmd6WsX95aUo0x\nxhhjjDHmLNXTLanzgZ2qWqKqceAJ4OrUA1S1WFXr/c01wJiuljXGGGP6ExE5ZjHGGGPMB3UWpI4G\n9qdsl/r7jucW4PkTLGuMMcYYY4wxpp/rLLtvl/vhishi4PPAou6WTZ1UuaCgwDKvGWOMMcYYY8wZ\nqqioiKKiohMu39mY1Hy8MaZL/e07AVdV729z3ExgBbBUVXd2s6yNSTXGGNMvtO3ia99/xhhj+oOe\nHpO6HpgqIhNEJA24Hni2zQXH4QWoNx0OULta1hhjjDHGGGOMSdVhd19VTYjI7cCLgAM8oqpbROQ2\n//1lwL8Bg4GH/CfEcVWdf7yyvXgvxhhjjDHGGGPOcB129z0lFbDuvsYYY/oJ6+5rjDGmP+rp7r6I\nyFIR2SoiO0Tk2+28f66IFItIVES+0ea9EhF5T0TeFpG1Xa2UMcYYY4wxxpj+qcPuviLiAA8CS4AD\nwDoRebZNt91q4KvANe2cQoECVa3pofoaY4wxxhhjjDmLddaSOh/YqaolqhoHngCuTj1AVStVdT0Q\nP845bLZyY4wxxhhjjDFd0lmQOhrYn7Jd6u/rKgVeFpH1InJrdytnjDHGGGOMMaZ/6bC7L16QeTIW\nqWq5iOQBL4nIVlV9o+1BhYWFR9YLCgooKCg4ycsaY4wxxhhjjOkLRUVFFBUVnXD5DrP7ikg+UKiq\nS/3tOwFXVe9v59i7gIiq/ug452r3fcvua4wxpr+w7L7GGGP6o57O7rsemCoiE0QkDbgeePZ4125T\nkUwRyfHXs4CPAe93tWLGGGOMMcYYY/qfDoNUVU0AtwMvApuB5aq6RURuE5HbAERkhIjsB74O/KuI\n7BORbGAE8IaIvAOsAZ5T1ZW9eTPGGGOMObs9++yzfPe73+WRRx6huLiYSCTSK9dxXZeysjJisVi3\ny5aWlvLwww/zuc99jpkzZpM7cByXXFzAo48+SiKR6IXaep555hnmzJ7PU0891WvXMMaYU6HD7r6n\npALW3dcYY0w/Yd19T1xVVRVLP3YVf3t7E0Fm43IQpRKlAQgRcjIJp4cZNDCL4aMGM2BgNoFAAMdx\ncByHYDB4ZPvwekNDAxWHqqipbKChoZmm5iixRIxkshUlipe6I4EQJhTMIDOcweDB2YwYNYTRY0cx\nYcIExo0bx+bNm/nburfZuaOM+sY6XI0SYCIBPkSCBcAEArwBrECpYuzoMVzzySu44447mDhx4kl/\nNq7r8pmbPssfHv8TwmdQniB3UA733P//+OIXv3jS5zfGmJPV3e6+FqQaY4wxp8jpFKTu2LGDH/zg\nB6xd/TazZp/PZZddxjXXXMPQoUP7rE7Hc9999/Gv3/shuB8hyS+B4SnvunhTtpf7y0H/tRFIIiTw\nAs0EQhzFPbJPGYTLKCA3ZRmasp4GJICKlPN7i8MehH0oFQjj/WB0JjADmAA4x7mbEuAFHJ4gyRqy\nwgNZdPGFfOnLt3Httdd2+7PZu3cv+fMupaIyiMtzwLlAC/AIwr+TlQnf+e7/4c477yQQ6GyUV/ua\nm5vZvXs3u3btYt++fZSWllJeXk5OTg63334755133gmd1xjTf/R4kCoiS4Gf4v1v+3DbpEkici7w\nKHAh8L3UxEidlfWPsSDVGGNMv9DXQerhwPSZFS9RH6nFoYAkS3D4G8o6XPYSdLIYMWwos+dNZ/Hi\nxVx77bWMHz+euro6Nm7cyObNm9m5cyclJSUcKC2jvLSa2roIsVgcEUEE/zWACAQCAQIiBALCyFFD\n+NI/38qtt95KMNjZBAOwbds2PnbZVew/UI/yG+DKXv+MTq0W4DUCPIWygswMh699/VbuvvvuLn0+\nv/zlL/nyl74BegMu/wmE2xwRB5YT4F8JhRr4yu03c99995GWlnbMUXv37uXll19m3bp1vP/eRnbt\nKKOuvpF4IoarUbxAPYsAAxEGIwwFhgFVJFhFOC2bBfkz+MKtt3DDDTd0qe7GmP6lR4NUEXGAbcAS\n4ACwDrhRVbekHJMHjAeuAWoPB6ldKesfZ0GqMcaYfqEvgtT2A9PP4wV8WW2OjgGbgLcJ8BbCGpJs\n999LIgwiQB7CSJSxJJkIjAJGAgPxZq5LpizuMevCOwi/Q6WSGedN5cu3f7HdgNV1Xb7y5a/wy1/+\njgA3k+R+ILtXPp/TRwJ4igCFSKCM66+/iv988D8ZMmTIB46MxWJccfnH+WvRGuB3wNWdnNsFniPA\nd5HAfhZ+eCYH9lVx6FAtLa0NKC4BxhFgGgkuBM4DJgJ5eC3KAzh+GpMY8AYBnkZ5GqhhwtixXPup\nj/O1r32NcePGHa2F61JTU0NlZSXV1dVUV1dTU1PDuHHjWLx48Qm39BpjTn89HaQuBO5KmYLmOwCq\nel87xx4zxUxXy1qQaowxpr84FUFqNBpl+fLlLF/+R1a9/jYNTbU4LCbJP9F+YNqZJNAADKJNIv+T\nsAXh8XYD1jfffJNrP3EjDY3ZuPwBmNdD1zxTKPAGDoW4rOaiRfN5aNnPOf/88wHYsGEDiy+9kkhk\nNC5PA2O7ee4ihOdRpgDTgHPwHjL01L/tbuB5vzvzegKShmoCJYHXshsE0hHCCBkIGbjUoETIyRzC\nueeN4+JLF3HdddexcOFCC1yNOUv0dJD698Dlqnqrv30TsEBVv9rOsW2D1C6VtSDVGGNMf9FekOq6\nLsXFxTz33HO88fqbbNm4l/pIA5nhTMaMzWPGzGnk5+ezZMkSZsyY8YEf7c3NzfzhD3/gyT/+D2tX\nb6SusZIAI4EluFwJXE73A9NT6diAVVUR/hXlm0CoryvXx7bicA9JnmTqpElc+pEP88jDf0C4A5e7\n8QK+01kz3hjeLH/J5PhjdSuAvyGsxuE1ErwDtDIoO5fpF0xk9pxZTJ8+ndmzZ3PhhRd+oMuyMeb0\n1tNB6ieBpScYpHaprIjoXXfddWS7oKCAgoKCrtbfGGOMOWO0DVJzskbR2FQDpBPkApIsQpmP18JV\nCmzBYQPwLkl2AQky0gYwckQuI0YPYdP7JdRHqggwBmEJSS4HLsFL/nMm2ooXzHSndbA/qEB4AOHP\nuDyI92/cH5QB6xGKcXgXpYQkZUAjAckinJ7JkMEDGDsuj4mTJ3DBBReQn59Pfn4+4XDb8bnHV1VV\nxUsvvcQbb7zBxvc3gcCAATkMGjSIIUOGkJubS15eHkOHDmXEiBGMHTu2R7IyG3M2Kyoqoqio6Mj2\n3Xff3aNBaj5QmNJl907APU4CpLZBapfKWkuqMcaYs5nruqxdu5ann3yS+3/84zbv/gUv7+DILp6t\nEtiCN3V5KV5X2IuBD45bNObs1YqX7mQfsBcowWEbsB2XEpQ6nEA2OZk5jB6TyznnTWLmzJnMnTuX\nAwcOUFxczHvvbGLP7nIaIg242kKA0QSYToJZQIgA1Qi1CLVAPUo9SgSlCaWRzPAAPv2Za7jnnntO\ny4zYxpxuerolNYiX/OgyvMdZa2kn+ZF/bCHQmBKkdqmsBanGGGPONq2trbz66qs8/cQTPPP00wxJ\nJrkmGuWeRKLNkfb9Z0zPiwJ7gJ3ATgJsJMBmkpT4GYpnkGAOMN1fJtK9rtNx4C84/Jgk65g+bRrf\n/+H/45Of/GSP34kxZ4vemILmCo5OI/OIqt4rIrcBqOoyERmBl7l3AF76uEZguqpG2ivbzvktSDXG\nGNPjVJWVK1fy03//d0r27CEjHCacnk44HCackUFGRgbhzExvycoid+RIRo4ezciRI48sw4cPJz09\n/bjnr6+vp6qq6kim0oMHD7JyxQr+95VXmJGWxjWNjVytylS/zAe/ne37z5gz216EZSjLyEgPcP2N\nV3H//fczbNiwkz6z67pUVVX1yLmM6Ws9HqT2NgtSjTHG9KTW1lYef/xxfnT33UhVFd+IRJiH17YS\nxZuZsu16C1AFHMzIoDwUolyE8nicimiUnHCYkbm5jBg2jNbWVqpqaqhuaKC2uZnMYJDctDSGOg65\nQF4iwaVNTVwFDG+nbhakGnO2SgAv+K2rq5k6aRLz8mcxY8YM5s6dy8KFC8nOPv40SolEgldffZXn\nn3+eVW8Us33rfuqbaoA46aFB5C+8gNu+9EWuv/56y3hszki90ZK6lKOtoQ8fZzzqz4Ar8NK43ayq\nb/v7S/Dy1ieBuKrOb6esBanGGGNOWm1tLf/185/z4I9+xIxEgm9GIizh5CbWcIFqvPykB4Ew3qyR\nQ/FGgXY396wFqcb0B6XA4zi8jTdOdh9KDSIZZKZnMzR3ABOmjGLUqJFsfG8ru3eV0xStQRiAw0yS\nXIwyD2+8eh7wJgH+B2UFSCPnTJ7AjTd9iq997WsMGjSoT+/UmK7q6TGpDt640iV4I9TX0WZcqYhc\nCdyuqleKyALgAVXN99/bA8xR1ZoOrmFBqjHG9GPxeJzdu3ezfft2tm3bxvZ33mHbxo1s37OHhOsy\nYeRIJk6ezMTp05k4dSoTJ05k4sSJjB8/nvT0dHbv3s1P77+f3//ud3wC+L8tLczs65s6DgtSjemv\nEsB+vLGy3nhZh70kmQXMxgtIczs5hwLbEJ4mwOMk2cbQwcP4+Cc+wh132p18bwAAIABJREFU3MGs\nWbN6rfaRSIQDBw6Qk5PDqFGjeu065uzV00HqQuCulAy93wFQ1ftSjvkv4FVVXe5vbwUuVdVDfpA6\nV1WrO7iGBanGGHOGc12XLVu2sGbNGtYUFVGyfTuBQIBAIIDjON4SDBI4vO441NfVsW37dvZVVTE6\nHGZaIMA50SjTYjHOwZuEJcTRn3R7gD0ZGexJS2NPMklpSwtDc3JojUa5NZnkq/E4p/tPJwtSjTE9\npwqvi/HjJCkiLZjB3LnT+dw/fYabb765S3PJbtq0iSeffJJXX32NnVv30xKNEYvFiSfiJJJxXI0D\nMf/oMBAn5GQxcfwoLr3sw1x//fUsXrzYuiCbTvV0kPr3wOUdzXUqIn8G7lXVt/ztl4F/UdUNIrIb\nqMfr7rtMVX/VzjUsSDXGmDPMwYMHvYB01SrW/PWvrN+0ibxgkAWqLGhqOpIoKNnO4vqv2XiB6GSg\n/dREHUvgdfHJ9c91JrAg1RjTO+JAMQGeAVbgcogxI0fxiWsv54477mDq1Kns2rWLJ598kr++8irv\n/G0b1XXVuJrE4QJcLkGZCQzCy4WauuTg/S8t/nXeBd4iyEqSrEZpIndQHnMXnM9VV32cSy65hFAo\nRFpaGsFg8Jj1tLQ00tLSjjzENP1HTwepnwSWdiFIvU9VV/nbqUHqKFUtE5E84CXgq6r6Rptr6F13\n3XVku6CggIKCgq7W3xhjTC+JxWLs2bOHXbt2sXPnTnZu2sTOjRvZtG0bDZEI89PTyY9EWOC6zMcb\np2k6ZkGqMebU2A88T5DlJChGxEE1gcN5KBfj8mFgLt5jwpMZuX/4WsUEeBV4FZcyvMeR6r+mrh9+\nFSCEECIQCOIEggQdh2AwRHpakPRwGhd8aCrf/d6dXHTRRSdZP9MXioqKKCoqOrJ9991392iQmg8U\npnT3vRNwU5Mn+d19i1T1CX/7SHffNue6C4gcnkc1Zb+1pBpjzCmiqkQikSNTplRXVx+dQqWqioMl\nJezavJmdJSWU1dYyNiODyYEAU1pbvQWv9XMKYM/Au8+CVGPMqdeKF0hOxMuDejqIAU0dLBEcXibJ\n04RD6Sy+bEG3A9ZEIsHq1auZPn06Q4YM6YV7MN3R0y2pQbzESZcBZcBaOk6clA/8VFXzRSQTcFS1\nUUSygJXA3aq6ss01LEg1xpzVWltbaWhooKGhgcbGxiPrDQ0NNDU1MXDgQIYNG3ZkGTJkyHG7QUWj\nUUpKStizZ4+3bN/Ons2bKd2/n1gsRtJ1cV2XZDJJMuXVVSUWj1PT1ERQhKHp6eQ6DrkiDHVdcmMx\ncltbGYYXgE4GxtP97LWmYxakGmNMdySAV3H47XEDVtd1eeedd3jxxRdZvXo17729nfKD1bTG64As\noJmB2UMpuGweX/jCF7jyyiutq3Ef6I0paK7g6BQ0j6jqvSJyG4CqLvOPeRBYivfo45/8rr6TgBX+\naYLAY6p6bzvntyDVGHNWaG1tZcOGDby1ahVvrVzJ2vXrqWhoQFUZkJbGgGCQAYEAA0TIAQa4LlnJ\nJPWOQ0UgQIXrcigWozEeZ2h2NsMGD2ZYXh6DhgyhrLSUPQcOUBOJMC4zk4mBABNjMSa2tDARGIs3\nYsjBa+F02lkP4U2bEu6TT8eABanGGHPijg1Y00NhnIBDc2sdEMJhGsocXOYAFwDTgYF46XFeweFp\nXF5AJMqkCeO49u8/zu233864ceOOXOHgwYOsWrWKDRs2sGXLFnZuL6HsQDWNkQggOAEv8V/QcQiF\nHEJph7snh8jKyWDK1InMmTOHSy65hDlz5hAMBvvgczo99XiQ2tssSDXG9KZ4PE59fT21tbXU1dVR\nW1tLY2MjTU1Nxy51dTTV1xOpr6c5EiErJ4dh48YxbNQohg8ffkxLZ15eHmlpaRw8eJC33nqLt157\njeJXXuGd7ds5NyODha2tfLi1lXxgNN1PChTDy9lY4S+1wCi8jlqjsG62ZzILUo0xpickgNf99RnA\nsC6WU2Ar8L84/A9J1pORPhDXVVrjTUCCAMMJMAGXabici/ftOx7v27cFiKYsqdsRHDYBG0myB2gm\nFMwhd9BAJp8zmgtmzmDhwoVcddVV/bL7sQWpxpizQjKZpLKykvLycsrLyzl48CC1tbVEo1GiLS20\nNDYSbWryluZmWpqbiba00NjQQF19PbWNjdQ1N9MSizEoPZ1BwSCDHYdBQI4qWcmktyQS3oKXITYL\nyMTrFlIBVIRCVKSnU+E4VKhSkUhQGY2SHgoRAhamp/PhxkY+rN7U62dKllnTfW8At+L9vDlRvROk\nXgncCHymB85ljDH9SQtQDGTgBaPDOflEUofVAzuA7cBmgryDy2ZcSgk52Ywcnsfs+dP5yEc+wrXX\nXsuYMWPaPUtzczNr165l3bp1bNy4kR3bdnKwrIaAEyAtFCSU5pCWHiItPUR6OO1INuWcnBzOOecc\nPvShDzFv3rzjnv9U6Y3uvks52t334dSkSSnH/Ay4AmgGblbVt7tR1oJUY/pAUVHRMZm0VZVoNHpM\ni2NzczOZmZlkZWV9YOluFxbXdamtrT02Uc/h9YoKKktLKd+3zwtKq6qoikQYkp7OiFCIkSKMTCQY\nHI+TkUgQdl3CeF8p4TZLDl4C/cH+aw4993VzmOJ99QzAWjXPdPfiBZ/Pp+yb6i9t9/0Q+IeTvJ61\npBrT14qAgj6ug+nfWoFNwAYCFCOsJslOnECYvCG5TJwykvID1VRV1dMcbcLVZoRBBBgHTCHJdLx+\nWi7elEAx/zUORBFaEaIEqAN247Ifl0OAEHKyyMnKYtjwQUyYPJphw4bR0tJydGluoaWplZaWGK3R\nOK2xBOH0IBdcOI38/HyuvPJKZsyYcUJjens6cZKDlzhpCd50dOvoOHHSAuABP3FSp2X98hakGtNL\nVJWamhpKS0s5cOCA97p/P6U7dvD6qlXkZmdT19BAXSRCbVMTAIPT0hgcDDIoECAT7xljkypNrktT\nMukt8TjBQICs9HTCoRAiHf+fE43FqI9GGRAKkRsKkes4DAVyEwkvYU88Th4wMmUZhiXtMb3vLbx2\nyFq8ALIc+DDeT4hSvIcQ5Xg/B8qAESd5PQtSjelrhf5izOkkiRc2vY3X+joar2X3cNaJtJM8v+J9\n0+07sgg7cTiIkomShUs2Xl+ytk0AjQT4G8IGkmwHkmSFBzFu3DBmzTmfiy66iBtuuKHTLszdDVI7\nawqZD+xU1RL/5E8AVwOpgeYngP8GUNU1IjJIREbgfaqdlTVnsEQiQUtLi9f90l9aW1u9bKL+ciTL\naMp6Vx5KiAhpaUe7LLT3Chxz/bZ1aWlpwXVdAoEAjuMcs6TuU9Vj6tle3SORiNe6WFlJXUWF91pT\nQ21dnRfkNTWhqqQFg4SCwaOvoZBXZ/+1sydPx9Sl7Wfnbx+5J38i7GPuzd+nqpRXVHCgpoaw4zA6\nPZ0xIoyOxxnT0sICVVqA2zm21THD+1A7/fdRoDWZpKm5mWinR3v/tQ4Ggq2t0NrahRLGnBpz8Z49\nvwNciNequhjY7e+b7e+bjPfzYR7eRA4A9wP/CTTgjRX+BfARvJ8a9wO/xusyfg7wNNB+R6tiYKG/\nXgBcAvwVeM/f/wcgF2+80xeA//WvMBX4C5Dnl/sMcAvwG+Bhv+wjeH/Zv8DLbWiMMeb05OAlepre\nS+cXvNSJQ4BZgPdbLtHF0u6RNQUO0RTdyJbtm9i+fS1PPH4PD//yUTa8s65Ha9xZkDqao9/H4D1Y\nXtCFY0bjfWd3VrZTK1as4JOf/GR3ixnT68Ii3hhHx2Gq4yBAPBYj1tpKXJWYKs3+6+HtrrSZOCJe\nRlYRLyur/xo4/Ir3o9oFkqokU15d/1WAEcEgF4XDZB0OjFWpDgapzsnhXWBrNMp/hC3PqzGZTU18\nNhhkcno677W0MNBxaHZdPidyZF8S+F4oRFVLC9fk5NCYTFLc3MwlWVmEAwGaXZd7gZ8FAuxobaU0\nHmdeRgYzHIeGZJIvHv47bGxsc/VLcMhHCJHgXWAtDjOBC3FZC8zGYTIuB1CqCXA+3v8CEeCTCEGS\nvItwiACP4VKOyzYCVCF8CKUMl6sJsuiUfZ7GnM6S7ME5knDHGHPyMojHuxrudl1nQWpX+yGd1JCv\nzroKGnM6iqpSnkhQnuj5P8yeUJtMsqWTVsttsdgpqo0xp7eqZJKNh/9e4vEj+zem/A3t8/c/09Bw\nZN+Lkchxz/mK34W+YwmSvHnMniRrUrbqSLAv5b32f1wrdbgpKZ1ctuG1/R6+yqtdqIsx/UOCkr6u\ngjFnlY2bez6e6yxIPYDXEfqwsXgtoh0dM8Y/JtSFst3qm2yMMcb0NBFZDCwHpgEbVXW0iAzAS8l4\nHlCJ1+N3IvA7VR3rl7sR+ApwPvAi8H9VtVxEmoB5qrq5zXW+DcxR1X9I2fc48J4/B/mr/vl/7b93\nM3CLql4sIkHgu8D1eH14fw98T1UTqeVSy6RcwwWmqOrunvzcjDHGmN7SWWqm9cBUEZkgIml4X47P\ntjnmWeCzACKSD9Sp6qEuljXGGGP62mq8Gd9vBVYBqGoDXq6kLwIHVHVv20Kq+rgfDI7H63l0OIP9\nfmBKO9c54B+bary/v0OqmlDV76vq+Xi5na7C/+41xhhjzjYdBqmqmsDLrfIisBlYrqpbROQ2EbnN\nP+Z5YLeI7ASW4T1VPm7ZXrsTY4wx5gSoagveg9X/C8f0p32znX0AiMg5IvIREUnHSwYcxctoBF7m\non8XkSnimSkiQ/BmtTlHRG4UkaCIXA+cCzyXeur26igii0XkAj9zfiPe0PRke8caY4wxZ7pOJzpU\n1ReAF9rsW9Zm+/auljXGGGNOQ68B+XDMANE3gH/m2CD1cK6GdLxpVs/DCxhX4bW6AvzYf38lMBQv\nq/21qlomIlcBDwAP4c0zcJWq1rRz/sPrh7eH+2XG4GVNegL4XTv3kVqmvXMaY4wxp70O50kFEJGl\nwE/xciM/rKr3t3n/08C/4D39bQS+rKrv+e+V4GXnTwJxVZ3f0zdgjDHGGGOMMebs0WGQ6ncr2gYs\nwRszsw64MbXbrogsBDarar0f0Baqar7/3h68JBE1Hzy7McYYY4wxxhhzrM4SJ80HdqpqiarG8boX\nXZ16gKoWq2q9v7mGD85Xbtl7jTHGGGOMMcZ0SWdB6mi8LIWHlfr7jucWvMQQhynwsoisF5FbT6yK\nxhhjjDHGGGP6i84SJ3U52YI/z9zngUUpuxf5c8blAS+JyFZVfaNNOUvoYIwxxhhjjDFnMVXtcg/b\nzoLUA8DYlO2xeK2pxxCRmcCvgKWqWptSkXL/tVJE/oTXffiNtuU7S95kjOl5hYWFFBYW9nU1jOmX\n7O/PmL5hf3vG9A2R7o0A7ay773pgqohMEJE04Hrg2TYXHAesAG5S1Z0p+zNFJMdfzwI+BrzfrdoZ\nY4wxxhhjjOlXOmxJVdWEiNwOvIg3Bc0jqrpFRG7z318G/BswGHjIj5APTzUzAljh7wsCj6nqyl67\nE2OMMcYYY4wxZ7zOuvuiqi8AL7TZtyxl/QvAF9optxuY1QN1NMb0goKCgr6ugjH9lv39GdM37G/P\nmDNDh/OknpIKiGhf18EYY4wxxhhjTO8QkW4lTupsTCoislREtorIDhH5djvvf1pE3hWR90RklZ9E\nqUtljTHGGGOMMcaYVB22pIqIA2wDluBl+l0H3KiqW1KOWQhsVtV6EVkKFKpqflfK+uWtJdUYY4wx\nxhhjzlI93ZI6H9ipqiWqGgeeAK5OPUBVi1W13t9cA4zpalljjDGmPxGRYxZjjDHGfFBnQepoYH/K\ndqm/73huAZ4/wbLGGGOMMcYYY/q5zrL7drkfrogsBj4PLOpu2dRJlQsKCizzmjHGGGOMMcacoYqK\niigqKjrh8p2NSc3HG2O61N++E3BV9f42x80EVgBLVXVnN8vamFRjjDH9Qtsuvvb9Z4wxpj/o6TGp\n64GpIjJBRNKA64Fn21xwHF6AetPhALWrZY0xxhhjjDHGmFQddvdV1YSI3A68CDjAI6q6RURu899f\nBvwbMBh4yH9CHFfV+ccr24v3YowxxhhjjDHmDNdhd99TUgHr7muMMaafsO6+xhhj+qOe7u6LiCwV\nka0iskNEvt3O++eKSLGIREXkG23eKxGR90TkbRFZ29VKGWOMMcYYY4zpnzrs7isiDvAgsAQ4AKwT\nkWfbdNutBr4KXNPOKRQoUNWaHqqvMcYYY4wxxpizWGctqfOBnapaoqpx4Ang6tQDVLVSVdcD8eOc\nw2YrN8YYY4wxxhjTJZ3Nkzoa2J+yXQos6Mb5FXhZRJLAMlX9VTfrZ4wxxhhjzlKxWIw9e/awb98+\nSktLKSsro6KigsrKSqqrq6muqqOuuhHXVdLDITIy0sjISicjM4OMDG/JysoiMzOTT33qUxQUFPT1\nLRljekBnQerJZnRYpKrlIpIHvCQiW1X1jbYHFRYWHlkvKCiw/2CMMcYYY3pBcXExzz77LDNnzmTx\n4sWMGDGi1661Y8cOiouL2bFjB7t376a09ABl+yupqW6kqaWFWLwFpQUII2QTYCDCICAXZSguF6Dk\nAYPwfrK2AFF/iRCgCSGC0Awc4he/+DsGZufw+Vtv4Pvf/z7Z2dm9dm/GmI4VFRVRVFR0wuU7zO4r\nIvlAoaou9bfvBFxVvb+dY+8CIqr6o+Ocq933LbuvMcaY/sKy+5q+8Oabb3LvPffx11fWEI21EGQW\nLqW4lCGEyAjnMCxvEOecO54ZF8xgwYIFzJkzh9zcXAYMGEAgcPzRYdFolNdee40333yTDRs2sHXj\nbg4eqqG5tQGAAKMIMAplHEkmAmOAkf4yChgOhHroTqPAn3D4MS6bmD1rBvf+xw/56Ec/2kPnP7W2\nbNnC2rVrufHGG0lLS+vr6hhzUrqb3bezIDUIbAMuA8qAtcCN7c13KiKFQOPhIFREMgFHVRtFJAtY\nCdytqivblLMg1RhjTL9gQao5VYqKirj/vv/g1b+upTXeisM1JPksUMDRoNDF+3m3A9hBgC0EeA+X\nnbhU4qUbSeC1YjoIQUQcAhIgEAjiugkSbiNCLg5TcfkQLjOBacC5eAFoX6Um2UGAX+Dya3IyM/jc\n5z/FD3/4QwYMGNCts+zdu5c1a9bw7rvvsm3bNnbvLOFgWR1pIYeBg7MYkjeQIUOGkJeXx7Bhwxg5\nciSjR49mwoQJTJkyhczMzC5dJxqN8tRTT/HnP/+ZVa//jfJDFSTdOEIuSC3582dxz30/sN6G5ozV\no0Gqf8IrgJ8CDvCIqt4rIrcBqOoyERkBrAMG4P1v1whMB4YBK/zTBIHHVPXeds5vQaoxxph+wYJU\n01tc1+X555/nZw88yOuvrac1Hk8JTC+l8xFexz0z0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"text/plain": [ - "328.0" + "" ] }, - "execution_count": 177, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "results[\"Votes\"].mul(results[\"obama\"]).sum()" + "fig, axes = plt.subplots(len(tossup), 1, sharex=True, figsize=(16, 8))\n", + "\n", + "for state, ax in zip(tossup, axes):\n", + " ax.fill_between(bins[1:], 0, histograms[state], facecolor='red')\n", + " ax.fill_between(bins[50:], 0, histograms[state][49:], facecolor='blue')\n", + " ax.set_title(state)\n", + " ax.plot([0.0, 0.0], [0, 0.2], color='k', linewidth=4) " ] }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 308, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "text/plain": [ - "210.0" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Solid Obama Electorial College Votes 237.0\n", + "Solid Romney Electorial College Votes 206.0\n" + ] } ], "source": [ - "results[\"Votes\"].mul(results[\"romney\"]).sum()" + "# electorial votes for the non-tossup states\n", + "solid = set(results.index).difference(tossup)\n", + "solid_obama = results[\"Votes\"].mul(results[\"obama\"])[solid].sum()\n", + "solid_romney = results[\"Votes\"].mul(results[\"romney\"])[solid].sum()\n", + "\n", + "print \"Solid Obama Electorial College Votes\", solid_obama\n", + "print \"Solid Romney Electorial College Votes\", solid_romney" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 312, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# electorial college votes for the tossup states, from the simulation\n", + "tossup_electoral_votes = electoral_votes.set_index('State').ix[tossup, 'Votes']\n", + "\n", + "obama_simulated_electorial_college = (simulated_poll_predictions > 0).multiply(tossup_electoral_votes)\n", + "obama_simulated_electorial_college['Solid States'] = solid_obama\n", + "obama_simulated_electorial_college['Total'] = obama_simulated_electorial_college.sum(axis=1)\n", + "\n", + "romney_simulated_electorial_college = (simulated_poll_predictions < 0).multiply(tossup_electoral_votes)\n", + "romney_simulated_electorial_college['Solid States'] = solid_romney\n", + "romney_simulated_electorial_college['Total'] = romney_simulated_electorial_college.sum(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 313, "metadata": { "collapsed": false }, @@ -11050,472 +9104,445 @@ " \n", " \n", " \n", - " Votes\n", - " poll\n", - " obama\n", - " romney\n", - " \n", - " \n", - " State\n", - " \n", - " \n", - " \n", - " \n", + " Colorado\n", + " Florida\n", + " Iowa\n", + " New Hampshire\n", + " Nevada\n", + " Ohio\n", + " Virginia\n", + " Wisconsin\n", + " Solid States\n", + " Total\n", " \n", " \n", " \n", " \n", - " Alabama\n", + " 0\n", " 9\n", - " NaN\n", + " 29\n", + " 6\n", " 0\n", - " 1\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 328\n", " \n", " \n", - " Alaska\n", - " 3\n", - " NaN\n", + " 1\n", + " 9\n", + " 29\n", + " 6\n", " 0\n", - " 1\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 328\n", " \n", " \n", - " Arizona\n", - " 11\n", - " -6.072142\n", + " 2\n", + " 9\n", " 0\n", - " 1\n", - " \n", - " \n", - " Arkansas\n", " 6\n", - " NaN\n", - " 0\n", - " 1\n", - " \n", - " \n", - " California\n", - " 55\n", - " 19.966475\n", - " 1\n", " 0\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 299\n", " \n", " \n", - " Colorado\n", + " 3\n", " 9\n", - " 2.671181\n", - " 1\n", " 0\n", - " \n", - " \n", - " Connecticut\n", - " 7\n", - " 8.940155\n", - " 1\n", " 0\n", - " \n", - " \n", - " Delaware\n", - " 3\n", - " NaN\n", - " 1\n", " 0\n", - " \n", - " \n", - " District of Columbia\n", - " 3\n", - " NaN\n", - " 1\n", + " 6\n", + " 18\n", " 0\n", + " 10\n", + " 237\n", + " 280\n", " \n", " \n", - " Florida\n", + " 4\n", + " 9\n", " 29\n", - " 2.170963\n", - " 1\n", - " 0\n", + " 6\n", + " 4\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 332\n", " \n", " \n", - " Georgia\n", - " 16\n", - " -8.813442\n", + " 5\n", + " 9\n", " 0\n", - " 1\n", + " 0\n", + " 4\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 297\n", " \n", " \n", - " Hawaii\n", - " 4\n", - " 18.594667\n", - " 1\n", + " 6\n", + " 9\n", + " 29\n", + " 6\n", " 0\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 328\n", " \n", " \n", - " Idaho\n", + " 7\n", + " 9\n", + " 29\n", + " 6\n", " 4\n", - " NaN\n", - " 0\n", - " 1\n", + " 6\n", + " 18\n", + " 13\n", + " 10\n", + " 237\n", + " 332\n", " \n", " \n", - " Illinois\n", - " 20\n", - " 15.486753\n", - " 1\n", + " 8\n", + " 9\n", + " 29\n", + " 6\n", + " 4\n", + " 6\n", + " 18\n", " 0\n", + " 10\n", + " 237\n", + " 319\n", " \n", " \n", - " Indiana\n", - " 11\n", - " -7.342898\n", + " 9\n", + " 0\n", + " 29\n", + " 0\n", " 0\n", - " 1\n", - " \n", - " \n", - " Iowa\n", " 6\n", - " 2.036824\n", - " 1\n", + " 18\n", " 0\n", + " 10\n", + " 237\n", + " 300\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin Solid States Total\n", + "0 9 29 6 0 6 18 13 10 237 328\n", + "1 9 29 6 0 6 18 13 10 237 328\n", + "2 9 0 6 0 6 18 13 10 237 299\n", + "3 9 0 0 0 6 18 0 10 237 280\n", + "4 9 29 6 4 6 18 13 10 237 332\n", + "5 9 0 0 4 6 18 13 10 237 297\n", + "6 9 29 6 0 6 18 13 10 237 328\n", + "7 9 29 6 4 6 18 13 10 237 332\n", + "8 9 29 6 4 6 18 0 10 237 319\n", + "9 0 29 0 0 6 18 0 10 237 300" + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obama_simulated_electorial_college.iloc[:10,]" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ColoradoFloridaIowaNew HampshireNevadaOhioVirginiaWisconsinSolid StatesTotal
Kansas6-9.7672030010040000206210
Kentucky8NaN1000400001206210
Louisiana8NaN202901
Maine412.22012310
Maryland1016.39402610
Massachusetts1114.18059210
Michigan168.33398010206239
Minnesota107.286051130
Mississippi296-8.227142401
Missouri10-2.21556501
Montana3-7.2414131301206258
Nebraska5-8.833230401
Nevada65.09619710
New Hampshire4-1.54489301
New Jersey1410.64348610
New Mexico59.58640510000206206
New York502923.473550160
North Carolina15-0.41584801
North Dakota3-9.33913301
Ohio184.17520410
Oklahoma7NaN01206241
Oregon78.6813831600
Pennsylvania205.43586710
Rhode Island413.24675310
South Carolina9-6.34067001
South Dakota3-1.52369301
Tennessee11-2.52634901206210
Texas38-2.2955077000001
Utah6-29.17941301
Vermont315.684839100206206
Virginia8000000132.42224510206219
Washington1212.3154731990
West Virginia5-9.4418296401
Wisconsin104.52876110
Wyoming3NaN1301206238
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Jersey 14 10.643486 1 0\n", - "New Mexico 5 9.586405 1 0\n", - "New York 29 23.473550 1 0\n", - "North Carolina 15 -0.415848 0 1\n", - "North Dakota 3 -9.339133 0 1\n", - "Ohio 18 4.175204 1 0\n", - "Oklahoma 7 NaN 0 1\n", - "Oregon 7 8.681383 1 0\n", - "Pennsylvania 20 5.435867 1 0\n", - "Rhode Island 4 13.246753 1 0\n", - "South Carolina 9 -6.340670 0 1\n", - "South Dakota 3 -1.523693 0 1\n", - "Tennessee 11 -2.526349 0 1\n", - "Texas 38 -2.295507 0 1\n", - "Utah 6 -29.179413 0 1\n", - "Vermont 3 15.684839 1 0\n", - "Virginia 13 2.422245 1 0\n", - "Washington 12 12.315473 1 0\n", - "West Virginia 5 -9.441829 0 1\n", - "Wisconsin 10 4.528761 1 0\n", - "Wyoming 3 NaN 0 1" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin Solid States Total\n", + "0 0 0 0 4 0 0 0 0 206 210\n", + "1 0 0 0 4 0 0 0 0 206 210\n", + "2 0 29 0 4 0 0 0 0 206 239\n", + "3 0 29 6 4 0 0 13 0 206 258\n", + "4 0 0 0 0 0 0 0 0 206 206\n", + "5 0 29 6 0 0 0 0 0 206 241\n", + "6 0 0 0 4 0 0 0 0 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Do historical adjustments based on how polls changed in the past conditional on \"election environment\"" + "romney_simulated_electorial_college['Total'].hist(bins=300, figsize=(16, 4))" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 321, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.93440000000000001" + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\"Error analysis\"" + "sum(obama_simulated_electorial_college['Total'] > romney_simulated_electorial_college['Total']) / float(N)" ] } ], @@ -11535,7 +9562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, From 5c611cc904ecd13410a4c3689ff39f7fcd9d0dc9 Mon Sep 17 00:00:00 2001 From: Jim Date: Tue, 24 May 2016 22:40:50 -0400 Subject: [PATCH 08/11] change error estimates --- silver_model.ipynb | 1460 +++++++++++++++++++++++--------------------- 1 file changed, 774 insertions(+), 686 deletions(-) diff --git a/silver_model.ipynb b/silver_model.ipynb index c66ffc3..b69596e 100644 --- a/silver_model.ipynb +++ b/silver_model.ipynb @@ -23,6 +23,7 @@ "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import pandas\n", + "from pandas import DataFrame, Series\n", "from scipy import stats\n", "np.set_printoptions(precision=4, suppress=True)\n", "%matplotlib inline\n", @@ -1365,7 +1366,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4278,7 +4279,7 @@ { "data": { "text/plain": [ - "array([2, 4, 1, 2, 3, 4, 4, 4, 0, 3, 2, 4, 1, 4, 1, 1, 1, 2, 2, 1, 4, 4, 1, 4, 2, 1, 1, 1, 1, 4, 4, 1, 3, 2, 1, 1, 1, 1, 1, 4, 2, 1, 2, 3, 1, 4, 4, 4, 2, 1, 1])" + "array([3, 4, 3, 3, 0, 4, 4, 4, 1, 0, 3, 4, 3, 4, 3, 2, 2, 3, 3, 2, 4, 4, 2, 2, 3, 2, 2, 2, 3, 4, 4, 3, 0, 3, 2, 2, 3, 2, 2, 4, 3, 2, 3, 0, 3, 2, 4, 4, 3, 2, 2])" ] }, "execution_count": 94, @@ -4331,13 +4332,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "['District of Columbia']\n", - "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri' 'Montana' 'Nebraska' 'Nevada' 'New Mexico' 'North Dakota' 'Ohio' 'Oklahoma' 'Oregon'\n", - " 'Pennsylvania' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", - "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi' 'North Carolina' 'South Carolina' 'Tennessee' 'West Virginia']\n", "['California' 'Florida' 'New York' 'Texas']\n", - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey' 'Rhode Island' 'Vermont'\n", - " 'Virginia' 'Washington']\n" + "['District of Columbia']\n", + "['Iowa' 'Kansas' 'Maine' 'Michigan' 'Minnesota' 'Missouri' 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania' 'South Dakota' 'Vermont'\n", + " 'Wisconsin' 'Wyoming']\n", + "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Idaho' 'Indiana' 'Kentucky' 'Louisiana' 'Mississippi' 'Nevada' 'New Mexico' 'North Carolina' 'Oklahoma'\n", + " 'South Carolina' 'Tennessee' 'Utah' 'West Virginia']\n", + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'New Hampshire' 'New Jersey' 'Rhode Island' 'Virginia' 'Washington']\n" ] } ], @@ -4358,7 +4359,7 @@ { "data": { "text/plain": [ - "array([4, 3, 0, 4, 2, 3, 3, 3, 1, 2, 4, 3, 0, 3, 0, 0, 0, 4, 4, 0, 3, 3, 0, 3, 4, 0, 0, 0, 3, 3, 3, 4, 2, 4, 0, 0, 4, 0, 0, 3, 4, 0, 4, 2, 0, 3, 3, 3, 4, 0, 0], dtype=int32)" + "array([2, 1, 2, 2, 3, 1, 1, 1, 4, 3, 2, 1, 0, 1, 2, 0, 0, 2, 2, 0, 1, 1, 2, 1, 2, 2, 0, 0, 2, 1, 1, 2, 3, 2, 0, 2, 2, 0, 2, 1, 2, 0, 2, 3, 0, 1, 1, 1, 2, 0, 0], dtype=int32)" ] }, "execution_count": 98, @@ -4381,13 +4382,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri' 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania' 'South Dakota'\n", - " 'Utah' 'Wisconsin' 'Wyoming']\n", - "['District of Columbia']\n", + "['Idaho' 'Iowa' 'Kansas' 'Maine' 'Montana' 'Nebraska' 'North Dakota' 'Oregon' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey' 'Rhode Island' 'Vermont'\n", + " 'Virginia' 'Washington']\n", + "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Indiana' 'Kentucky' 'Louisiana' 'Michigan' 'Mississippi' 'Missouri' 'Nevada' 'New Mexico' 'North Carolina' 'Ohio'\n", + " 'Oklahoma' 'Pennsylvania' 'South Carolina' 'Tennessee' 'West Virginia']\n", "['California' 'Florida' 'New York' 'Texas']\n", - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'Minnesota' 'Nevada' 'New Hampshire' 'New Jersey' 'Rhode Island'\n", - " 'Vermont' 'Virginia' 'Washington']\n", - "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi' 'New Mexico' 'North Carolina' 'Oklahoma' 'South Carolina' 'Tennessee' 'West Virginia']\n" + "['District of Columbia']\n" ] } ], @@ -4464,8 +4465,8 @@ { "data": { "text/plain": [ - "array(['Washington', 'New Hampshire', 'New Jersey', 'Nevada', 'Colorado', 'Connecticut', 'Virginia', 'Massachusetts', 'Rhode Island', 'Hawaii', 'Vermont',\n", - " 'Maryland', 'Minnesota', 'Illinois'], dtype=object)" + "array(['New Mexico', 'North Carolina', 'Nevada', 'Ohio', 'Pennsylvania', 'Indiana', 'Arizona', 'Missouri', 'Michigan', 'Georgia', 'West Virginia',\n", + " 'South Carolina', 'Tennessee', 'Mississippi'], dtype=object)" ] }, "execution_count": 105, @@ -4509,9 +4510,9 @@ }, { "data": { - "image/png": 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Y8Wk0mBwoWRcREZGoKecqnYlK0Y5s9lns+DQajIiIiIhImVEFUxEREZEy0luqdJaiHdns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\nAACAQWDMOgAAAFAzFOsAAABApijWAQAAgExRrAMAAACZolg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CdUlo0yZ/p/3mm/2yaZMP2o8+Gj75SaisLHYLRUREpFieeMJXV1+61Ce4SKIg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\nSJIkSUPGFUwlSVKvueqoVjPTYKZhGowkSf3gqqNa6cxZnwO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zl16K//hFF9kn+++vspa096eyFpW1qKwlrycl563Mhx+a/eQnZueea7ZuXdDR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\ne6UoSZUSskCEBkWrcBkVyQIVBaVsJzPVVhAKtfs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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4651,47 +4652,47 @@ { "data": { "text/plain": [ - "[['Wisconsin', 5.3767534315390426],\n", - " ['North Dakota', 5.3767534315390426],\n", - " ['Nebraska', 5.3767534315390426],\n", - " ['Ohio', 5.3767534315390426],\n", - " ['Pennsylvania', 5.3767534315390426],\n", - " ['Indiana', 5.3767534315390426],\n", - " ['Iowa', 5.3767534315390426],\n", - " ['Arizona', 5.3767534315390426],\n", - " ['Maine', 5.3767534315390426],\n", - " ['Missouri', 5.3767534315390426],\n", - " ['Michigan', 5.3767534315390426],\n", - " ['Montana', 5.3767534315390426],\n", - " ['Kansas', 5.3767534315390426],\n", - " ['Oregon', 5.3767534315390426],\n", - " ['South Dakota', 5.3767534315390426],\n", - " ['Utah', 5.3767534315390426],\n", + "[['Wisconsin', 4.5081131780406549],\n", + " ['North Dakota', 4.5081131780406549],\n", + " ['Nebraska', 4.5081131780406549],\n", + " ['Iowa', 4.5081131780406549],\n", + " ['Maine', 4.5081131780406549],\n", + " ['Montana', 4.5081131780406549],\n", + " ['Kansas', 4.5081131780406549],\n", + " ['Oregon', 4.5081131780406549],\n", + " ['South Dakota', 4.5081131780406549],\n", + " ['Utah', 4.5081131780406549],\n", + " ['Washington', 4.636017894647817],\n", + " ['New Hampshire', 4.636017894647817],\n", + " ['New Jersey', 4.636017894647817],\n", + " ['Colorado', 4.636017894647817],\n", + " ['Connecticut', 4.636017894647817],\n", + " ['Virginia', 4.636017894647817],\n", + " ['Massachusetts', 4.636017894647817],\n", + " ['Rhode Island', 4.636017894647817],\n", + " ['Hawaii', 4.636017894647817],\n", + " ['Vermont', 4.636017894647817],\n", + " ['Maryland', 4.636017894647817],\n", + " ['Minnesota', 4.636017894647817],\n", + " ['Illinois', 4.636017894647817],\n", + " ['New Mexico', 4.3030153035887064],\n", + " ['North Carolina', 4.3030153035887064],\n", + " ['Nevada', 4.3030153035887064],\n", + " ['Ohio', 4.3030153035887064],\n", + " ['Pennsylvania', 4.3030153035887064],\n", + " ['Indiana', 4.3030153035887064],\n", + " ['Arizona', 4.3030153035887064],\n", + " ['Missouri', 4.3030153035887064],\n", + " ['Michigan', 4.3030153035887064],\n", + " ['Georgia', 4.3030153035887064],\n", + " ['West Virginia', 4.3030153035887064],\n", + " ['South Carolina', 4.3030153035887064],\n", + " ['Tennessee', 4.3030153035887064],\n", + " ['Mississippi', 4.3030153035887064],\n", " ['Florida', 3.3877002862540975],\n", " ['California', 3.3877002862540975],\n", " ['New York', 3.3877002862540975],\n", - " ['Texas', 3.3877002862540975],\n", - " ['Washington', 4.5126101123209397],\n", - " ['New Hampshire', 4.5126101123209397],\n", - " ['New Jersey', 4.5126101123209397],\n", - " ['Nevada', 4.5126101123209397],\n", - " ['Colorado', 4.5126101123209397],\n", - " ['Connecticut', 4.5126101123209397],\n", - " ['Virginia', 4.5126101123209397],\n", - " ['Massachusetts', 4.5126101123209397],\n", - " ['Rhode Island', 4.5126101123209397],\n", - " ['Hawaii', 4.5126101123209397],\n", - " ['Vermont', 4.5126101123209397],\n", - " ['Maryland', 4.5126101123209397],\n", - " ['Minnesota', 4.5126101123209397],\n", - " ['Illinois', 4.5126101123209397],\n", - " ['New Mexico', 3.2804611900008411],\n", - " ['North Carolina', 3.2804611900008411],\n", - " ['Georgia', 3.2804611900008411],\n", - " ['West Virginia', 3.2804611900008411],\n", - " ['South Carolina', 3.2804611900008411],\n", - " ['Tennessee', 3.2804611900008411],\n", - " ['Mississippi', 3.2804611900008411]]" + " ['Texas', 3.3877002862540975]]" ] }, "execution_count": 111, @@ -4836,7 +4837,7 @@ "ESS 173.017\n", "MESS 173.017\n", "time_weight 0.615572\n", - "kmeans_labels 3\n", + "kmeans_labels 1\n", "pollster_state American Research Group-Colorado\n", "Name: 168, dtype: object" ] @@ -5491,7 +5492,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 3\n", + " 1\n", " \n", " \n", " 1\n", @@ -5515,7 +5516,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 3\n", + " 1\n", " \n", " \n", " 2\n", @@ -5539,7 +5540,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 3\n", + " 1\n", " \n", " \n", " 3\n", @@ -5563,7 +5564,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 3\n", + " 1\n", " \n", " \n", " 4\n", @@ -5587,7 +5588,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 3\n", + " 1\n", " \n", " \n", " 5\n", @@ -5611,7 +5612,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 3\n", + " 1\n", " \n", " \n", " 6\n", @@ -5634,8 +5635,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 1\n", - " 0\n", + " 2\n", + " 2\n", " \n", " \n", " 7\n", @@ -5658,8 +5659,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 1\n", - " 0\n", + " 2\n", + " 2\n", " \n", " \n", " 8\n", @@ -5682,8 +5683,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 1\n", - " 0\n", + " 2\n", + " 2\n", " \n", " \n", " 9\n", @@ -5706,8 +5707,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 1\n", - " 0\n", + " 2\n", + " 2\n", " \n", " \n", " 10\n", @@ -5730,8 +5731,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 1\n", - " 0\n", + " 2\n", + " 2\n", " \n", " \n", " 11\n", @@ -5754,8 +5755,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 1\n", - " 0\n", + " 2\n", + " 2\n", " \n", " \n", " ...\n", @@ -5802,7 +5803,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5826,7 +5827,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5850,7 +5851,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5874,7 +5875,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5898,7 +5899,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5922,7 +5923,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5946,7 +5947,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5970,7 +5971,7 @@ " 2\n", " 0.455410\n", " 0.237802\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -5994,7 +5995,7 @@ " -13\n", " 0.335630\n", " 0.351093\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -6018,7 +6019,7 @@ " -13\n", " 0.335630\n", " 0.351093\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -6042,7 +6043,7 @@ " -12\n", " 0.392000\n", " 0.469934\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -6066,7 +6067,7 @@ " -12\n", " 0.392000\n", " 0.469934\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", @@ -6130,31 +6131,31 @@ "320 6.1 10.8 77.8 89.2 29.3 ... 381874.654 0.133 0.615 -16.9 \n", "\n", " no_party PVI obama_give romney_give kmeans_group kmeans_labels \n", - "0 13.9 2 0.961563 0.733997 4 3 \n", - "1 13.9 2 0.961563 0.733997 4 3 \n", - "2 13.9 2 0.961563 0.733997 4 3 \n", - "3 13.9 2 0.961563 0.733997 4 3 \n", - "4 13.9 2 0.961563 0.733997 4 3 \n", - "5 13.9 2 0.961563 0.733997 4 3 \n", - "6 15.4 -1 0.377548 0.427662 1 0 \n", - "7 15.4 -1 0.377548 0.427662 1 0 \n", - "8 15.4 -1 0.377548 0.427662 1 0 \n", - "9 15.4 -1 0.377548 0.427662 1 0 \n", - "10 15.4 -1 0.377548 0.427662 1 0 \n", - "11 15.4 -1 0.377548 0.427662 1 0 \n", + "0 13.9 2 0.961563 0.733997 4 1 \n", + "1 13.9 2 0.961563 0.733997 4 1 \n", + "2 13.9 2 0.961563 0.733997 4 1 \n", + "3 13.9 2 0.961563 0.733997 4 1 \n", + "4 13.9 2 0.961563 0.733997 4 1 \n", + "5 13.9 2 0.961563 0.733997 4 1 \n", + "6 15.4 -1 0.377548 0.427662 2 2 \n", + "7 15.4 -1 0.377548 0.427662 2 2 \n", + "8 15.4 -1 0.377548 0.427662 2 2 \n", + "9 15.4 -1 0.377548 0.427662 2 2 \n", + "10 15.4 -1 0.377548 0.427662 2 2 \n", + "11 15.4 -1 0.377548 0.427662 2 2 \n", ".. ... ... ... ... ... ... \n", - "309 12.8 2 0.455410 0.237802 1 0 \n", - "310 12.8 2 0.455410 0.237802 1 0 \n", - "311 12.8 2 0.455410 0.237802 1 0 \n", - "312 12.8 2 0.455410 0.237802 1 0 \n", - "313 12.8 2 0.455410 0.237802 1 0 \n", - "314 12.8 2 0.455410 0.237802 1 0 \n", - "315 12.8 2 0.455410 0.237802 1 0 \n", - "316 12.8 2 0.455410 0.237802 1 0 \n", - "317 14.8 -13 0.335630 0.351093 1 0 \n", - "318 14.8 -13 0.335630 0.351093 1 0 \n", - "319 14.3 -12 0.392000 0.469934 1 0 \n", - "320 14.3 -12 0.392000 0.469934 1 0 \n", + "309 12.8 2 0.455410 0.237802 2 0 \n", + "310 12.8 2 0.455410 0.237802 2 0 \n", + "311 12.8 2 0.455410 0.237802 2 0 \n", + "312 12.8 2 0.455410 0.237802 2 0 \n", + "313 12.8 2 0.455410 0.237802 2 0 \n", + "314 12.8 2 0.455410 0.237802 2 0 \n", + "315 12.8 2 0.455410 0.237802 2 0 \n", + "316 12.8 2 0.455410 0.237802 2 0 \n", + "317 14.8 -13 0.335630 0.351093 2 0 \n", + "318 14.8 -13 0.335630 0.351093 2 0 \n", + "319 14.3 -12 0.392000 0.469934 2 0 \n", + "320 14.3 -12 0.392000 0.469934 2 0 \n", "\n", "[321 rows x 24 columns]" ] @@ -6413,7 +6414,7 @@ " Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", "\n", "\n", - " Time: 20:59:22 Log-Likelihood: -1457.5\n", + " Time: 22:40:17 Log-Likelihood: -1457.5\n", "\n", "\n", " No. Observations: 321 AIC: 2927.\n", @@ -6475,7 +6476,7 @@ "Model: WLS Adj. R-squared: 0.700\n", "Method: Least Squares F-statistic: 150.4\n", "Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", - "Time: 20:59:22 Log-Likelihood: -1457.5\n", + "Time: 22:40:17 Log-Likelihood: -1457.5\n", "No. Observations: 321 AIC: 2927.\n", "Df Residuals: 315 BIC: 2950.\n", "Df Model: 5 \n", @@ -6547,7 +6548,7 @@ "data": { "image/png": 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PtBBCCBFgkwe83gMMOe/UEQ4/XtYDXk0kVVTMI4MNhRBCCJFWf38/N920lrff/hlwufPq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\n8+CPf/xjYAz4FnAP03tn73H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spdFmzVqYc2kvk2vLu1EqzO3yY8Xug3bt7Nxqt+dTO1sn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5YQzsVUM9uJkLHcK3eMZZGOnR+6+6fS70Y4Sq9w93kZ9p5EzAPTiBzuX3iG7F3B3t3A0919XSG9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\ntupS50wVCeWRtBGwk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P80eSVJ9isciVK1eAzpZC+CO3P1RLmu9q5IlijJ+IMf488DVNiUyS+tTytFqV\nE2YonVZL6lfZbJY9e/awZ8+ejiXMhUKBs2fPpD3M5S0tTXPmzBMUi8U2RqZ2aihpBgghZIC7WxCL\n1NduHzFeiSPGJand/JErqDLlXAhhP0kdc2kX9ZcAE8CZFscl9Z2hoSH27x9nZuZYlcN/xxkfP+Ch\n+nXAabUkqbdUGwj4Gu4cCPhx4EKM8Y9aH9ptsVjTrL7gQJP+Yo2k1Bscg9A/nHJO6iHdNmJcreOP\nJKl3+CO3P5g0Sz2oW0aMq7X8kST1Bn/k9geTZknqcv5IkrqfP3LXP5NmSZKkJvFH7vq1pqQ5hDAC\nvAq4P8b4ohDCNpKTm/z35odaMQaTZqkHFQoFcrkc4I5FktT91npyk9cAfwJ8Rfr/e4EjzQlN0nqU\nz+eZnDzE6OgWxsamGRubZmRkMwcPPkQ+n+90eJIkNayepHk4xnga+DxAjHEJ+FxLo5LUs5YHy8zM\nbGRh4RrF4iWKxUssLFzj9Ol72blzt4mzJKnn1JM0fzqE8KXL/4QQngd4jkhJZU1NTaejy09y+3ym\nm1haOsnc3D6OHKl8KlpJkrpRPTXNzwF+EXgW8LfAlwHjMca2nSfSmmapN3gCAElSL1tTTXOM8W3A\nC4DnAw8B29qZMEvqHblcLp2GqVLCDDDCwMC2myPPJUnqBRtqNQghbABeAmxO2+9Le35/vsWxSZIk\nSV2hZtIMvAGYB94JfKG14agap+9St9u+fTuLi1eBG1Qrz1hcvMqOHTvaGJkkSWtTT9J8f4xxe8sj\nUUX5fJ6pqWnOnj3jGYjU1YaGhti/f5yZmWPpQMA7ZTLHGR8/4I8+SVJPqWcg4M8BfxZjPN+ekMrG\n0LcDAT3XvXqN66wkqVet9eQmfwn8fghhIYTwqfTyyeaGqEqcvku9Znh4mMuXLzIxMc/g4Fay2V1k\ns7sYHNzKxMS8CbMkqSfV09N8HRgD3hVj7EhNc7/2NDt9l3pdsVi8OUuGdfiSpG5Xrae5nprmfwD+\ntlMJcz9bnr5rYaG+6bv27NnTttikemSzWddLSV3PgfaqRz1J8weAPw8h/DHwv9LbnHJOknqMiYF0\nOwfaqxH11DR/AHgzcA9wH/CU9KIWu336rkqcvktSdfl8nsnJQ4yObmFsbJqxsWlGRjZz8OBD5PP5\nTocndcTyoOWZmY0sLFyjWLxEsXiJhYVrnD59Lzt37vb7odvUrGnuBv1a0wwwOXmImZmNVabvOszE\nxDynTj1M2CdKAAAgAElEQVTe5sgk9QJnM5HKc/+qcqrVNFdMmkMIj8UYXx5CeEOZu2OMcayZQVbT\nz0mzOzxJa2FiIN3JgfaqZLVJ86dijE8JIewtc3eMMb6liTFW1c9JMySJ85EjRzlz5glrriTVzcRA\nKm92dpaxsWmKxUtV22Wzuzh37rgDmvvIamfPeB9AjPFCK4JS/YaHhzl16nEee+zVTt8lqW7OwCNJ\nzVMtaf6yEMKPA+WybWfP6ACn75Ikae1uH2hf+SiMA+1VqtrsGXeTzJJxX5mLs2dIUpdzBh6pvKGh\nIfbvHyeTOVaxTSZznPHxAx7V1U3VaprfHmN8dpvjKavfa5olabUcCCiV50B7lVOtprmeeZolST3q\n0UePMzJynkzmMLf3ON8gkznMyMh5Tpyo3NsmrVfDw8NcvnyRiYl5Bge3ks3uIpvdxeDgViYm5k2Y\nyygUCszOzjI7O0uxWOx0OG1Xraf5S2OMH29zPGXZ0yxJq+cMPFJ1xWLRgfZV9NOZE1c15Vw3MWmW\npLUzMZDUqH4rYzFpliRJUsP6bVyESbMkSZIa0o8nSHIgoCRJkhqyfIKkygkzlJ4gab0zaZYkSZJq\nsDxDkiRJd7A843b2NEuSJOkOnjnxdvY0S5IkqSynnLvFnmZJkiSV5ZkTb7GnWZIkSTX1wwmSnKdZ\nkiRJqsHyDEmSJGkNTJolSZKkGkyaJUmSpBo2dDoASb2jUCiQy+WA9TsIRFJvczulVrGnWVJN+Xye\nyclDjI5uYWxsmrGxaUZGNnPw4EPk8/lOhydJbqfUcs6eIamqfpvYXlLvcTulZnHKOUmrNjl5iJmZ\njSwtnSx7fyZzmImJeU6derzNkUlSwu2UmsWkWdKqFAoFRke3sLBwjVs9NyvNMTj4IHNz160dlNR2\nbqfUTM7TLGlVcrkcAwPbqLwjAhhhYGDbzbNESVI7uZ1Su5g0S5IkSTVYniGpIg97Sup2bqfUTJZn\nNKhQKDA7O8vs7CzFYrHT4UgdMzQ0xP7942Qyxyq2yWSOMz5+wB2RpI5wO6V2sae5RD6fZ2pqmrNn\nz6T1UbC4eJXx8QOcOHHMqWrUl5zKSVK3czulZrGnuQ7LX7iZmY0sLFyjWLxEsXiJhYVrnD59Lzt3\n7nZydPWl4eFhLl++yMTEPIODW8lmd5HN7mJwcCsTE/PuiCR1nNsptYM9zSnneJRqKxaLN0efe3pa\nSd3I7ZTWwnmaa3AQgSRJkizPqME5HiVJklSNSbMkSZJUg+UZWJ4hSZIkyzNqco5HSZIkVWNPc8o5\nHiVJ7VIoFMjlcoAzPEjdxJ7mOjjHoySp1fL5PJOThxgd3cLY2DRjY9OMjGzm4MGHPBeA1OXsaS7D\nOR4lSc3mEU2p+zlPsyRJHeZJtKTuZ9IsqSzrKqX2cJYmqTdY0yzpNtZVSu3lSbSk3mfSLPWZ5brK\nmZmNLCxco1i8RLF4iYWFa5w+fS87d+42cZYkaQXLM6Q+Y12l1H6WZ0i9wZpmSYA7bqmT/MEqdT9r\nmiUB1lVKnfToo8cZGTlPJnMYuFFyzw0ymcOMjJznxInKZ6aV1FkmzZIktYEn0ZJ6m+UZUh+xPEPq\nDp5ES+pO1jRLusm6SkmSyjNplnSTp/KVJKk8BwJKusm6SkmSGmdPs9THrKuUJOkWyzMkSZKkGizP\nkCRJktbApFmSJEmqwaRZkiRJqmFDpwOQJHWnQqFALpcDHCgqSS3taQ4hvCiEcC2E8N4Qwk+VuX9v\nCKEYQnh7enlFK+ORJNWWz+eZnDzE6OgWxsamGRubZmRkMwcPPkQ+n+90eJLUES2bPSOEcDfwHuBf\nAh8G/ifwPTHGd5e02Qv8eIxxrMZzOXuGJLWBJ7+R1M86NXvGc4H3xRivxxiXgNcBLy0XXwtjkCQ1\nYGpqOk2YT3IrYQbYxNLSSebm9nHkyNFOhSdJHdPKpPl+4B9L/v9QelupCDw/hHAlhPDGEMK2FsYj\nSaqiUChw9uyZtIe5vKWlac6ceYJisdjGyCSp81o5ELCeeorLwAMxxs+GEF4M/AHwjHINH3744ZvX\n9+7dy969e5sQoiRpWS6XY2BgGwsLm6q0GmFgYBtXrlxhz549bYtNklrhwoULXLhwoa62rUyaPww8\nUPL/AyS9zTfFGD9Vcv2PQwi/HEJ4aozxEyufrDRpliRJktZqZUfsI488UrFtK8sz/gb42hDC5hDC\nPcAEcK60QQhhUwghpNefSzIw8Y6EWZLUetu3b2dx8Spwo0qrORYXr7Jjx452hSVJXaFlSXOM8XPA\ny4HzwFXgdIzx3SGEl4UQXpY2GwfeGUJ4B/Ao8N2tikeSVN3Q0BD794+TyRyr2CaTOc74+AHnbJbU\nd1o25VwzOeWcJLWHU85J6medmnJOktRjhoeHuXz5IhMT8wwObiWb3UU2u4vBwa1MTMybMEvqW/Y0\nS5LKKhaLXLlyBfA02pL6Q7WeZpNmSZIkCcszJEmSpDUxaZYkSZJqMGmWJEmSajBpliRJkmowaZYk\nSZJqMGmWJEmSajBpliRJkmrY0OkA1otCoUAulwM8CYAkSdJ6Y0/zGuXzeSYnDzE6uoWxsWnGxqYZ\nGdnMwYMPkc/nOx2eJEmSmsAzAq5BPp9n587dzM3tY2npKLApvecGmcwxRkbOc/nyRYaHhzsZpiRJ\nkurgabRbZHLyEDMzG1laOln2/kzmMBMT85w69XibI5MkSVKjTJpboFAoMDq6hYWFa9zqYV5pjsHB\nB5mbu26NsyRJUperljRb07xKuVyOgYFtVE6YAUYYGNjGlStX2hWWJEmSWsCkWZIkSarB8oxVsjxD\nkiRpfbE8owWGhobYv3+cTOZYxTaZzHHGxw+YMEuSJPU4e5rXwCnnJEmS1g97mltkeHiYy5cvMjEx\nz+DgVrLZXWSzuxgc3MrExLwJsyRJ0jphT3OTFIvFm7NkeBptSZKk3uM8zZIkSVINlmdIkiRJa2DS\nLEmSJNVg0ixJkiTVYNIsSZIk1WDSLEmSJNVg0ixJkiTVYNIsSZIk1WDSLEmSJNVg0ixJkiTVsKHT\nAUiSpOYrFArkcjkAduzYQTab7XBEUm+zp1mSpHUkn88zOXmI0dEtjI1NMzY2zcjIZg4efIh8Pt/p\n8KSeFWKMnY6hphBC7IU4JUnqpHw+z86du5mb28fS0lFgU3rPDTKZY4yMnOfy5YsMDw93Mkypa4UQ\niDGGsvf1QjJq0ixJUm2Tk4eYmdnI0tLJsvdnMoeZmJjn1KnH2xyZ1BtMmiVJWucKhQKjo1tYWLjG\nrR7mleYYHHyQubnr1jhLZVRLmq1pllahUCgwOzvL7OwsxWKx0+FIErlcjoGBbVROmAFGGBjYxpUr\nV9oVlrRumDRLDXCAjSRJ/cnyDKlODrCR1M0sz5DWzvIMqQmmpqbThPkkt++QNrG0dJK5uX0cOXK0\nU+FJ6nNDQ0Ps3z9OJnOsYptM5jjj4wdMmKVVsKdZqoM9OJJ6gUfEpLWxp1laIwfYSOoFw8PDXL58\nkYmJeQYHt5LN7iKb3cXg4FYmJuZNmKU18DTakiStI8PDw5w69TiPPfbqmz/iPY22tHaWZ0h1sDxD\nkqT1z/IMaY0cYCNJUn+zp1mqkwNsJEla3+xplprAATaSJPUve5qlVSgWiw6wkSRpnanW02zSLEmS\nJGF5hiRJkrQmJs2SJElSDSbNkiRJUg0mzZIkSVINJs2SJElSDSbNkiRJUg0mzZIkSVINJs2SJElS\nDSbNkiRJUg0mzZIkSVINJs2SJElSDSbNkiRJUg0mzZIkSVINJs2SJElSDSbNkiRJUg0mzZIkSVIN\nJs2SJElSDSbNkiRJUg0mzZIkSVINJs2SJElSDRs6HYAkSdJ6VigUyOVyAOzYsYNsNtvhiLQa9jRL\nkiS1QD6fZ3LyEKOjWxgbm2ZsbJqRkc0cPPgQ+Xy+0+GpQSHG2OkYagohxF6IU5IkCZKEeefO3czN\n7WNp6SiwKb3nBpnMMUZGznP58kWGh4c7GaZWCCEQYwxl7+uFZNSkWZIk9ZLJyUPMzGxkaelk2fsz\nmcNMTMxz6tTjbY5M1Zg0S5IktUmhUGB0dAsLC9e41cO80hyDgw8yN3fdGucuUi1ptqZZkiSpiXK5\nHAMD26icMAOMMDCwjStXrrQrLK2RSbMkSZJUg+UZkiRJTWR5Ru+yPEOSJKlNhoaG2L9/nEzmWMU2\nmcxxxscPmDD3EHuaJUmSmswp53qTPc2SJEltNDw8zOXLF5mYmGdwcCvZ7C6y2V0MDm5lYmLehLkH\n2dMsSZLUQsVi8eYsGZ5Gu7s5T7MkSZJUg+UZkiRJ0hqYNEuSJEk1mDRLkiRJNZg0S5IkSTWYNEuS\nJEk1mDRLkiRJNZg0S5IkSTVs6HQAkiRJEkChUCCXywHddyKYlvY0hxBeFEK4FkJ4bwjhpyq0+YX0\n/ishhGe3Mh5JkiR1n3w+z+TkIUZHtzA2Ns3Y2DQjI5s5ePAh8vl8p8MDWpg0hxDuBh4DXgRsA74n\nhPDgijYvAb4mxvi1wEPAr7QqHkmSJHWffD7Pzp27mZnZyMLCNYrFSxSLl1hYuMbp0/eyc+furkic\nW9nT/FzgfTHG6zHGJeB1wEtXtBkDfgsgxvjXwFAIYVMLY5IkSVIXmZqaZm5uH0tLJ4HSNHATS0sn\nmZvbx5EjRzsV3k2tTJrvB/6x5P8PpbfVavO0FsYkSZKkLlEoFDh79gxLS5WT4qWlac6ceYJisdjG\nyO7UyqQ51tkurPJxkiRJ6mG5XI6BgW3c3sO80ggDA9u4cuVKu8Iqq5WzZ3wYeKDk/wdIepKrtXla\netsdHn744ZvX9+7dy969e5sRoyRJkvrUhQsXuHDhQl1tQ4yt6dgNIWwA3gO8EPgI8Fbge2KM7y5p\n8xLg5THGl4QQngc8GmN8Xpnniq2KU5IkSZ1RKBQYHd3CwsI1Kvc2zzE4+CBzc9dbPgVdCIEY48oq\nCKCF5Rkxxs8BLwfOA1eB0zHGd4cQXhZCeFna5o3A+0MI7wN+DfiRVsUjSZKk7jI0NMT+/eNkMscq\ntslkjjM+fqDjcza3rKe5mexpliRJWp+Wp5xLZtA4yq0e5xtkMscYGTnP5csXGR4ebnksHelpliRJ\nkmoZHh7m8uWLTEzMMzi4lWx2F9nsLgYHtzIxMd+2hLkWe5olSZLUFYrF4s1ZMjpxGu1qPc0mzZIk\nSRKWZ0iSJElrYtIsSZIk1WDSLEmSJNVg0ixJkiTVYNIsSZIk1WDSLEmSJNVg0ixJkiTVYNIsSZIk\n1WDSLEmSJNVg0ixJkiTVYNIsSZIk1WDSXMWFCxda/phuXEY3xrReltGNMbVjGd0Y03pZRjfGtF6W\n0Y0xtWMZ3RjTellGN8bUjmW0I6Z2MGmuohtXvHYsoxtjWi/L6MaY2rGMboxpvSyjG2NaL8voxpja\nsYxujGm9LKMbY2rHMkyaJUmSpD5h0ixJkiTVEGKMnY6hphBC9wcpSZKknhdjDOVu74mkWZIkSeok\nyzMkSZKkGkyaJUmSpBpMmiVJkqQaNnQ6ALVGCOEe4JnAMHCzoD3G+OYmPPdPxBj/S5nbfzzG+PNr\nff5mCCEEbn/dX+hgOG3Tr6+7G/lZ1BZC+Fbgu4EvjzF+ewjhG4AvLredCiG8FPijGOPn2h1nPRr9\nvEMIT+HO7fP7mxjPqvYBIYTbOtPWst6GEP5xxU2xNJZbi4hfWeHxDb+GEMII8FzgS1c85jeqPOYB\n4P4Y419VapO2+zfAO2KMV0MIzwR+Hfg88MMxxmtVHrcP+HrgvpKbY4zxP1ZbXrdqZF0PIXw5t7/u\nsut5COGFJOvHSovAh2KMH1x1wE3kQMAVGv3CpRvyF6Tt7yL90GOM31djOXVtMEMI3wJcjzG+P4Qw\nCvxnki/pdIxxrsJz7waeAAaALFAEvhj4hxjj08u0vwd4BXAQ+ArgI8Ap4GdjjP+rTPtPxRifUub2\nJ2OMX1LlNd8DPA8YjTGeDiHcl77uT5dpmwF+hNvf27R53FPh+e8HHksfk+XWextjjHenbV4RY/zZ\n9PrPUHkjfsfGLN1Q/ADJ+3Q/8CHgtcBvxgpfpBDCs4CPxxjn0s/8J0k+v1fHGD9b4TENrVP1vO5q\nQghPB74QY7xepc2OGOOVWs9V0j4LPEz5z6/SDnIYeAkwEmP8ufR13RVj/MeSNqveCa/yu/R04FWU\n3+GVW0Y96+DBGOOp9PoPcueOIqTtb25z1rLepo9pdLvW0Oe3ys/7R4Ep4L+RfAZfHEL4Z8DjMcbn\nl2mfI9k+vQ44FWP863LPW9K+oe1a+piJGOPpMrc/EmN8ZZnbG/7uhRC2Ab8D7OD2z7HsY1b5Ohrd\nBzwnfR07gMGSu6q9jpoJbQhhb8lD/jnwfwIngX8AvhL4UeC3K3TCNPQa0sd8J8k2+b3APwPelf69\nGGP8F2XafyXweyTfb2KMXxRCOADsizH+uzLt3w98U4zxRgjhfwDXgM8A3xxj/JYKMT0G/Gvgz4Hl\nbf7yd/wHyrT/hRjjj5W5/dEY41S5ZaT3Pxv4Zu78jpfblzW0XUsf09C6HkJ4EfDfgdEVd1Vqf51k\n/Y7Ax0tex8eATUAO+O4Y43vLxdc2MUYv6QX4TuDTwNuBpZK/f16h/SuBOeBRYB44AdwAfqHKMral\nz/sFkh32F5avV2h/DfjK9PrvAb8L/AZwrsoy/gb48fT6k+nf/wj8ZIX2J4BLwLcCW9O/F4FHV7T7\nFuCFJF/8b1lxOQR8sEpMXwd8IH09n05v+zbgdIX2vwhcJdmpfib9+x7gkSrLeAMwQ7IhKKZ/Xw88\nVNLmV0quvwb4zRWX15AkweWe/6fTGB4CXpT+fTfwiiox5YBnptd/jWTD+cckO/1mrVM1X/eK9q8D\nnp9e/4F0OZ8F/l2VZeSBK8BPkPzoqfVdei3wFpLv1KfSv5eW18sy7V+QLuNNwKfS2/YCb1jRbm/J\n5SdJdoqHgH3p3xzwE038Lv1V+lpevGLZe9ewDr6x5PqFdJ2447Liedey3ja0XVvl59dQ+/Qx7we2\npNeXt1N3A5+o8pgdwH8h+cH6dyTJ5OYKbevarpWJ6SUrbjtO0ru45u9e+pi3pLENAU+mf38ZONjE\n19HoPuBdwDGS/dPm0kuF9ruBjwKfINmPfQL4HPD+KjH9LfC0Fbc9DXhXM15DyTL+9YrH/ADwXyu0\nfxPJdv2ukvZZksS8XPtPpn/vTT+7gdLHVnjMk8ADle4v0/5TFW6v9r14iGQ/+XqSntnXp///boX2\nDW3XVrOuk3yXfgjYWOfrfgXwauDekvf4P6e33wf8KvD/1fs+turS0YV322UVX7h/AL4uvV5I/z6X\nFTv6FY9pdIO5/CXNpBumpwD3kPReVlpGkaSHrjSue4CPVGj/YWB4xW3DK9sD10kS38+nf5cv7wf+\nEhirEtMl4PtWvLdfVCWmjwBftfx60r9bgdkqy/gEcN+KxzwVuNak9eP6ckwlt30VFTawK+K4C/gn\n4MvS1/1PTVynGnrdaRz3pNffBewCngW8r8oyMsBLgTMkCdifAJNU2CCmyxheEdP9wOUK7d8B/MsV\n68cg8LEqMTW6E17Nd+mTwN0NrCOtXgfvIvnhOtDAYxrarq3y82uofXr/x4ANK+K6F/hoHa8pAP+K\n5IfcF4DZdH28q6RNXdu1Ffc/CHwQ2JP+//MkyduXNOvzBgpAZsVjvgj4QIX2q3kdje4DPkl65LnO\ndWo1Ce0ngKEVtw1RIeFs9DUsv46S60+m68ndVN7efqJkGU+WLrtC+78Hvhb4LuBPSj67QpWY/o6k\n5KjWe/qD6WUe+Lfp9X+bXl4FvKfKY/++ZJ1d/jxeTNKLX+nzrnu7tpp1PW3fyDqVX/5elNx2D5Cv\n531u16WjC++2yyq+cMWS6x/jViLyySrLaHSD+SFghGRH+RfpbQM1lvEPpBt5kt7aZ5Ec3qi0IWho\no0yFXtIa7+2Ty1+gki91qLLBfLJkY/bR9D0KVPgVXvIZDKbXrwNfnr5Xnypps7nk+tMrXao8/xet\nuO0+qid2N0gOKX4j8DfpbZlKr2OV61TN171yHUz/3g98uOT2iu/tiscPkfTqvpOkV/G3gd0r2tzc\nAKbr8BBJwlcppidXXif57lVLaBvdCa/mu/Q/gG9oYD1v6LMoiXmSpOf8e6mQoJW0/3S98axcd6hj\nu7bKz6+h9mm7s6RHaUo+839Phd6xksd9NUkpyHtJjvz8NPB9JL1nry9p13CymbbZCfwjyRGZvwSy\nTf68P0q6HQHeR/LD+ymV1sPVvA4a3wf8FvCiBtap1SS0ryHpNPpWkh8n+0iOtPxWM15Dyfs5kl5/\nO/B84BlU2I6kz7t8JHB5HdwG5Cq0//70tT8JfGt620uBCyvale5PXgacS2OpuJ/h1lGnz3H7Uac3\nkxwZe16V1136Hf84yfe72v61oe3aatZ1kl7jH2zg+T9IegS05LZvIj2CDWys9HraeXEg4O0+FkIY\niUl943WSDyxP5VlG3h9CeFaM8W9JenN+OITwJMnOvJJ5ko3LEvBPIYSvStt/aYX2vwi8lWTlXK5n\n2kVSFlDJ60lqQ3+H5PDzm0m+iGcqtH8COBdC+E8kK+5mkkMiT5RrHGM8WGXZlXwQ+Abgf5bc9s9J\ndnzlXEvbvxV4G0nZwqdIdsiVvJXk1/XrgfPAaZL3+29K2ryTZAcFyQa2nEiy0VnpTcBrQwjT3Hqf\nXpUuq5LfJXn/n0JSDwbJTrnSgJ/VrFP1vO5SV9LXsBn4I4AQwtNIdgZVpXXo3wlMkCTdryNJMF4b\nQnhjjPFH0qY5YA/wZySHkn+J5HDheyo89btDCC+KMb6p5LYXknxelZwD/jCE8Ko0hq8EptPby1nN\nd+mDwJtCCL9P8gNoWYzl64cb+izSOuvfJ3lfPkiSQP1yCGF/jPFPK8Q0G0L4phjjX1aJu1Sj2zVo\n/PNrtD0k9axvCCEcAu4LIfwdyXf828s1DiG8nOTHxTNI3teDsWTgVgjhDMmOfVld27UKA5B+gyTZ\neRnwnBACsfzgs0a/e5C8PwdIksgzJOVaiyTbiXIa2j6nGt0H3Au8PoTwF9y5npcbS1EkKWN4EvhI\nOnYjT9K5UckPk2zHf4WkdvWjJIf7H2nSa4CkPn532uZE+pgI/NcK7f8L8D9CCMeBDSGE7wGOkpQF\n3CHG+JoQwhPp9c+kN/8lsLK+vty+ZeV6fdt+Jsa4FyCE8KoY409XiLeSD4UQtsQYP0CyT30pyeex\nWKF9o9s1aHxd/ybgcAjh/yEpOSxdRrlxSf8BOB9COEeyn38a8B0k2wlI9gfVPvu2cCBgifTDfV+M\n8UwI4fuAx0m/cDHGV5Rp/20kvT5vCSF8I0mCdB/wIzHGsxWW8QTJCPDXhBD+X2CMZMX+YIzxOys8\n5pkkNc/vS/9/Bsnh2WoJRenjv5kkaXtTLDPKNYQwQNJb82+4NdDk90gGmtzxpSszGGtZjJUHEXw7\nyaCAXwP+b5Jk84eAQzHGO5LOEMJzgc/FGC+nr/dXSN7bn4gx/kWFZXwJyTr9iRDCxnQ595HU/n20\nQsx1Swc7/SJJwpgh+eEzA/xojLFQ4TGBpGdlaXmnG6rPELCadaqh1x1C+BrgZ4D/Bfz7mAxqOUDS\n8/BTFZbx7SQJy0tISm1+C/iDGONCev9TScpU7kv//2qAGOPfhxA2kdRL3kdSk361zPM/j6T3440k\nCcUpkg3mS2OMb60Q070kO+EDrNgJxxjnKzymoe9SCOE16dXSDWW1QTxDJD1w9X4W7wZeGWOcKbnt\nAPAzMcatFWL6FeB7gD8g+bGwrOwOr9HtWvqYRj+/RtvfRVJD+ZfAdtIyJ+Ct5bZR6WP+iCTRfMPy\nelemzb7l7Um927V0AFK5HWEovT3GuKXM8hr6vMs8/i6Sowv3kRxK/0yZNg1tnyssp9Y+4OEKD40x\nxjuS2hDCSZLP6ndCCD9BcpTkc+nz/2A9MTWq1muo8JivIunVv2MdLGnzUpJ90fI6+Ksxxj+o47lb\nOjtOqHPWibTtDwA3YoxvDCG8mOQozj3Aj8UYf7lM+9eUPu3yzVTYrqWPaXQ/8/0VXlqMMf5WhWVs\nA8a5tZ6fTTuQuoZJcxW1vnAhhC+OMX6y3ONiHdOjhBDuJtkQVtxgdqNw+4hoSA55TwGvizE+WuVx\nzyYZsLC8cfr1GOPbWhVnq6Sf2zBJrdXnOx1PO4QQ3klShvE7McaPVGhzKMb462tYxv0kifny+vHa\nGGO1IwuNPv8fxhhfWub2348xfleTlnEgxnhHD2AIYTzGeEcvSQihAHxp6XoUkplj/inGOFRhGa8p\n+beuHd6Kx9eTSGyrkOzeTErXKoTw6eUfWQ0+7i5gUzN+CJd57rtb/Z0OPTBlZ6PqTWjTH607uDMZ\nrDgdXINxtPQ7HtY4U1Gdy2ho1okKzzFAUtr3qWbEpFtMmtcgPZT1r0p7PUIylcubY4ybm7SMuqZy\nCiGcjzHuK4mrnJuHRSockiz3gLrmdQ7JlFZvijF+fZn7NpAcpt1WrWdkNTGF6lNxLV+v1AO3mmnt\nsiTTLK3c6JfG1NBnsZplrGj/cZKBUG9JL++INb7YaW/gc7lzuqhKU5B9d4zxdWVuLzsVV3pf6dyk\npT12TZubtJGdcFjlVIlpm3qniGxoGSGEXyTpBT5ZctuPAV8bY/zRle3XKtQ5B28I4QPAC0tfYwjh\nO0h+6I6k/5dOl3dbr+yKZVRap95I0qNeV5lJ2tP1SyQ9UZ+LMW4MIYwBzy3XYx6SEqQ/Kz1SEZIj\nWHtjjD9Xpv0GkvKQoQZ6cAdI6lzLTd1VaYrImutICGFPjHE2vV5xu1hlm/BVJEdhnl0mrmdUeMy/\nIKkNvzmdZr3b/3qEEI6SDBa8wq2p15aDKjcd3GqmRWv0+3cX8O9I5gr/shjj14UQ9pDURc+Uaf8G\nkpKEYyTb2heQvM9/HGN8vEJMDe1nQjKt3c+RdKSVnZY0bReWt/Mrv9elqnzHn0FyxOorSOrmXxdj\n/OXnBjcAACAASURBVLsVbRrav4ba02kux1Ru+/ylJLMzlfu8y+4rO8Ga5hIhhK8nGS1dbkNzT5mH\n/BVJHdh3xBg/l66Efwr8pyrLaHTF+CXggfQ5T5HM1fmTJIdfSv12yfX/XmHxcUWb0v+fRjIKfXl+\nxLtIDv2WnQ+zjEXgjsOXAOl78wWSurlqO6PVxHR/yfUHuPNLWnFnTvJZv5DkcPWrSA6B/jBJne4d\n0sNNv0Qye8TKjVnpa2/0s1jNMko9l2SD/ALgMPAlabI+G2N8dZllVJzLlKR2sJxjIYRPxhjfWPI8\nx0lq3O5ImkOVuUnLPflqNpjVdsKlryPd2APcE5K60NL5jZ9OUudbVrh9Tt1St9Ujpjv4kFwNK78z\nX02yoy1nJ/BDIYR/T7Ljup9kgM1fl/zguu09KPP8t4Iqn8hXnIOX8rX7kHwW50MIL4gxfiSE8F0k\n6+W3lbQ5yP9u77zDJimqtv87C0sOkjO7uEgQUeFFouCKSlAE+RQFJCwqKBiQoBgACZKUpICogMKS\nERQVUQkvy0oSeQkKCpKW4JIWlgySzvfHXbPTT091T3fPPGF3676uuZ55eqq6q1PVqVP3uc9Ao3kj\nxF18BL2LS1P+TD0E/NHMLkVGWmtf0UkukpyajlYiWl7wG9F7HKOZ7I3oVFn8C/gtMkoGIPRT96LJ\n0X8K2pzHWYhe8nty3NB8QRN/3YA5wvcsxiFFgxZ+gt5J6OwXsyjqE36FzvUgIEplybXtC8gQPB3x\nc1cEzjOzg1vGYK/OAGAfNMH5e7f2BJyHuMH7Uvz+tNrf9B0/FNHnTkTPF+jen4ioXnlshGQrXzTx\n3G8PxuENaByJodY4g4Jof9bN8YGel9YEoSjhT/QdDxPgcxEl7iGkTHVLMHp/mylad3zdAdkpMLB/\nyCPWJ5yHKCUXMfB+jyjPbvI0Z2DiF15M503DAwcyUuc09JAfhgI6vuPuE2NlQ/k/U/BgeITnY2ZP\nAau7+zQze87dFw5LRL9397VrnWBxm76DjNKD3P1lE1/pMKQLeWSkfH7GOR/iut7h7tsXHGMvFJxw\nFBpUs57H2EBfq01NYGZTkVD9Q5lruxpKrhDzAExF0cB/7MfxS9rU+Bhh4jYB+ArSuxwdKXMX4ppe\n1PLAmDhx73L3/Qr2uzoKhNzZ3Seb2fEo8Osj7j49Un468G7PJCbp0u5a70Wo8xTyhpYOwtamM+yI\nBooZ+0aGzhkl7/e1wK1ocH0QGSlHAje2PCqhXBmf8QngEHf/WWT/E8ra3mpn9hqUHMs9njTgThQc\neQ6dHr4pRQcNz8T+yFg+EKkrRK918Jjf74GeZWYGfA1YuchjbjVpJmY2DWmEv57zyj7v7gtFyj8d\nyr+W2TY3krRbtKBN30Rexx/T2U/FYhCeBVaKvQORslPC/lZE9KMZu0bPyFHuXhTEWgtm9hywqFek\nmoTJwqc8k8DIzN4N/NrdVw7/f9bdzw3fJxTsqux9fQhYpYYX/3mkntH1HCLveMuYK33HzexRYC13\nfyrTF45C40wHPcrMnkRG86vhfq6LgiKnxTzcoU7dceaHSMatyOHSKreiuz8cvo8tKhd7x0Of8FV3\nvyazbTxwsru/K19+KBDu95JeEK8wUpCM5gzM7BnEL6x8UcLgcAEKWvqcR5awc+VrPRi5geJR5H14\nHsnuRF/SUK+1NN6KZi6jKUwDls0NLi35oMUj5c9k4OzvJaSze3ZRh9hgoK/VpvB7kQfuv2igfCtX\nfjq632+Z2WPAysioeD52bc3sidCmypzH8HxUziLY8Bh7IQN2IxQ8cS2SL7re3TsUMbJGRrgGiyIv\n/uPuvkTJcdZGXrrrkbdvi9j+Q9l/o8DCDs5/QfnaHWaDQbg25zoYRUuE96814M2PtKBjgWGTSzxt\ngwITNeoQJKN3buT355FsWjfKTn6J15B3cD/kjbsL4su9Fudmz4mMiSg3uy7M7D6kRTs1Y+CsiPRy\nO4ImzexKlEjmhMy2vYGPu/uHC44xJXztuFYF9/sOlD0umlGy4BhnezMFoo57FLsXody5yFCsSq8r\nmmBMdfciZadaMAWhboQmoAOuV8EzdRmabJYpkeTr7OEFNImC8lOBce7+SuaZWhD4p7uvUNCmM9z9\nN2b2M6Tk8gpyUHRQTEKduuPMdcgYf4hqqhO1Edq0hGdS0luXWIpQpjJ10MyWAF519xdCX7ALyvFw\ndsH9vg6YUOTAGClI9IyBmIgimc8pKmDxZanR6CX4spl9mfKH+++IdlD1wagt5WQ1l8bD/tYN+2/h\nfWF7B9x9QsW2Z+uUyVv13KaAsmv6lknKZk93by2j1pW1OwY4yMwOKxqsIvgO6iyOo5069huIR/b9\nPh3jZCRhdzhwmRcE6mVQSYLMepPiOg7J0B1N5wAZiwCv+16Alp5/bGaVBmG0JN9hNJvZk+6+ZMEx\naklEejtmYEXaOtgPx8qGcoaSF2S5hReigbnS5N2Vor2VMbPDaEYSUZujlYIyFC3xgigwUEzpeByt\nJP06s+3jDKQsYGZjW56vkklu0TNyOnCxmR0IjDKzDZDXv8ODH/B14Coz2wm9H29HwVUfKTnu2KLf\nCjARuNTMfkznMxg1WN1952BEbEh4RoAbsgZMFtaMXvM14IYw0cjK8Lm7fy5S/nrgeDM7wN1fMklL\nHoVoB1HUccwEnBn+7p7bXnQetWXR3P3nVoGrm8Ef0XnvE85pFOpHf19QfmfaK6ytCeUCiM5RhLrj\nzOnhk0dhf2D16W2t7K5Hh/qGaDC3lxxjAvWog39A48RtiJayFepH16It+ZnF/yK61i9pv0utZ6ov\ngaL9QPI0Z2AKjroRDZT5jmbTUGZChV25Fy9RHYZe6EoPhtWUcgp16i6N74w4dL9HL/IK6AH/skeo\nJiYJq6vd/W+ZbYUBNk1Qt02hzheQjNX3MnUOQvf0WmSQvu7un8y0uVTWzjrl9ZZGL/7TmW3uxYEp\nU4APeEZNJRhef4nVCasJS9U8xnKIz7xx+IxGgYGTPUMhyJSvJEFmvUlx1V1ZqPVeNDxGR5BQ8K48\nXuRNs5oSkWa2DFp52oA2F/8mYPvYZMbMfoCMzRNpT6q+hiY/3yg4v1g73wNcFVspMLOLkAFbqsFr\nJUu8WXh8ufcjKM7iTtrv3hrAdp5R28jegwb3r0X5+CLSKn4Y8VB/VDTBCF7DrUJ7Hkb3slRRoKZB\nO4XiYKco39i0NP97FOPR4n+/ijzgHZrh1oBeExwE45BR+CoDg7YOipRfFj23G6JJ4aLIYN7B3Tv4\n3WWOGS+WLBsb2150HlZT7jHUyXN1x6D7n+fqtsovjIz5LVG/+V+U7XQXr7hKFvYz2t1fL/ittnxq\nXVh92ufq6Bmcn/Yz+DJ6BovsilrUwWCHLOrubmb/Qc/WC8iLv3Sk/KRWm/O/FXnxhwPJaM4gLA+0\n8rZnl4ndu/CLahxjUmuf+d/69WBYzaXxUKeyPqKZPY64ii9mti0I/Nvd8zI5rd+bKFXU0mwMBuc7\nPKPRa+JC/9vdlzdF399XZCAV7HN8lXLuPqmg/pPASp6REwxenAdi3s2y4xUdI1PXkKdhO8RpXqCK\nh98qSJANNpq8F1UH4czq0AZoApXF8sBd7h5NqJE7XleJSDP7LRqsvx08dvOjie5K7r51pPxTwNrZ\nCa6ZrQDc5sU0pPxAOx8yUA/zeAzCIQWn5B7R4C045rzAW16ufrM4im1ova+Xu/u0KvsfKahr0DY8\nxjVIj/zYYFAY8lh+LPasW0V6Ta7OC8BydcaAUG8Fwv0rc7rUdcwMFawhVzc4pMYgrflCqo2ZXYUM\n6qmZbe9BlIN3F9RZOrbPku21KH2hThN622hgfdrv601Fhn8oX4s6aKJYLo/Sjl/g7muEPvQ5byA1\nOWLgw5yScCR90Cxo7hrld0QyaiCez2Q0816tj22qfQwqpu3soU1PE9I7Z7bNjYIniuqchCLev44o\nFq3l5EP7eK2moqDJ7LbVEJ+51cZnM7/9BkXYv3cQn6mJ4TiroYF4dbSEXTsVeckx9g33+xlCdj60\nBLpqxfofRN7wsjLvRQEw2W0rAu8ZrGvXx+szIXxeAXbN/L8rsAUh/XOfjlX0bhSl8b2feCrw+yuc\nT+uzHeJ29/OaHQesF75/LFy7l4Gth/E+btrqwxDNYiJamVg6U+bPme9/KfhMLjnGNYg+1XIoGVrG\nvqaP5zEdmCO3bXS2b8r9dhY1UlyHOtejiVrV8qNin5Ly/0YJmuqe+zZITeIspLIwEU1AW7+PzXx/\ne9Gny7Wds8a1XRJYMHyfE1Gldi06d7Ra+RRKcDUK+Bbqd79U0qai9OjR8RKpa9yDchpsEf7+i5By\nvqDOdciRVfU+/LZg+69L6uyLYicKn4tc+XPQuHQ9cHDYtiYKcmyVsW7PYNXjDdUneZozMOmGftfd\nb6tY/gEUFfuEKUDgbmQQbuyBzlFQbxG0zNviXF3m7tE0yU2OUXfZM9TZhrYXOKtF26E1as0CbLpG\nEJvZae6+e/jeQSvInEOR/uk3Ec/sF7S9RLsBP3b3o81sW2APd98ylP98OOdNkFD9XxCNY7JnqCe5\nY6yFKBD56xTl2FmzLIJ1j3EWCvyb7O73x8rkyk9GntDrzewA1Bm+CZzi7kcU1LkLGUz3Z7atjDrZ\nDg9LlZUFs950RkO97HM7iuCpLnhuV/ea3sLwHGaVYlod5mvoGbvUByoO3IsoCbdntr0HrZKsHNn/\nV1Fa8mNopwLfHwVczpD384JMYDXOYy406c5rTRfp/D6OjJOXzezm0L7ngBPcfc1I+SYrSbV0eM3s\nbmAzd3/YzM5H9+JVYHEPXnzrXeFhethfYbIZM7vbQ+ChNcuOehfK1HZ1ZtumwEnuvkb4P9v/zYXG\ni6oprjEpHH0GTSpadcpogG8x8Dkn/P8mckb8Ghk+L4byX0STqaoxC5jZ92hLrX0RUWt2BC5096+F\nMo3pO6HOJJQvIMvV/SawpYc01bnyNwNfdPfbzOwY2rzbSe4e491iSuTSuj9Tkee5MBbD4rSwhdBq\nYyzQfgo1KH3h97q0z9qa9VaTOmhm86AJyGvISfSGSQt8KQ+CCb3e7+FAMpozMLOfIK/Nr+nkNMdU\nJ55394XC0uVUMnzXkgdvA0SQv5s252o1YCt37wi6qHuM0Em8HS0zFS615Op07cxy5ddAetRTyQXY\neDGlo2sEsZl9292PCt8PobMThy5LyqZsSp8O7XkMuMjduwVAtTqlPWjTGmKcyj2AExDn7aPIqNkM\nzdp37LL/SlkEezzGKNSpPdHF0HwaLeW9aWb3owH5ecTd7IgYD3U6ZL3Cs1YUAX4SBdqkHpKh9GGA\nrPvc1kpkEOqcgrIU/o62UbsVCtZ7G7p2X2oZYWa2O6JjnIHe77Fo4naQxyXnqgR7DrgGwQA+EC3f\ntpZWz0ZplV/LVzaz9yPd3rnR5PA5YCHUR0SD8TIT28WBf3ngSpcMtl3vd6TOTSjw81w6JT4nRcq3\n+sLRyBAcQ1sZp18KD1UM2o29HfMwvmhfsXMIdbZGmrSXIZ71GGSA7uQhfXOu/yuMKyjqC60m3cnM\nvoImb0fR5qQfgMaqe1CcyF0eUmQ3fF8fRhSUf5jZs+7+NhPf9yB3/3jB/mrBanJ1rSbvNtTZFfXR\nD6AJzWfd/R+Rcq0JVesdzWIx4HyPpBy3mpS+8Puk8LX0fltbz/qbaCKc17N+p7uvVXCM8bHt4RiT\nin4rg/Ugmzds8BHg7h4pHxQQcCaarbU+ZyIuUaz8/Yiv8/+Q7BHoZY0uBYXfb0ZBQdltnwH+1o9j\noJfgJWosaaCOe83w/dnwd12kBV1UZ0E0s23pmi7Y5Rg3ImF70GDxAxSk969I2TmAzwPz1DiHOcO1\nqkOvWR34EnB+uAY3h3Z9rORebBK+Tw9/tySzvFhQb+FwPTfNfvp1DGQATUQz+rfC34mIBxkrPz1c\n43EEGkB4bl4sOcY/gf/JbVubzFJb7repwJjw/bnwdzUyS+Nk6B7IuIx++vXcoqj4v4bnttWmccCt\nJce4Etgot20DFHTXujd3537fFBnNl6Mo+A9VfSYrPrcnoCXPzcI13Qwtz55YUP4WYN/cM3Uw8I2S\nY9yClIQOAc4L25ZAE7JG9ztS53lyNIUu5/0ochp8CHndQBOBouXv0xFXP7ttWeSJzJddKvzdGqkD\nXID6ggvD/5/o8z1cBfV/P0EToFWhvVSdKTcaTbrOQxPp8xCFYK4+t+cBSmhCiFsbvfc1jvFc5vuT\nrXMoun8NjzE6fDZG4+rGyLB9W0H5aUiRZE00KQD1jdG+EOVxuIf2WPZlRM/4ZqTs+PB5Ba3AtP7/\nAOX0ykGj9NG2cV5joJ3zCzRhqkzxqHCsxcI+/0hFetTM8Bn2BszMH8QnfA4ZIZuFbdugpZ2iOs+S\nM2iRwVdkBDc5xvXkuL1dzmMoOrN1UcATaMC4GhkwG3drU41j3FvUORaUfyvU+RxdjP789UDLU3Mg\nY3N6l2fkJeQZezD76eMxzkLKBaugAWAV1LlHDW00aTkVuBQFI4E8/9E2hd93R0bLV5EH/GvIaP1i\nQfnpreccefznD+fxQh+fqVrPbWj/Eq32hb+jit691r6IcySfz9R/qV/nVPG8/4MoBNlti6Pgreh1\nytyL1uRirqLy4fd1aavOrBy27UTBwN3kfofncJ0a531AeOaeQKoOoAnKXwvKX4A82RuG/7dHfNSj\nI2WnoWV26DRoV8mVPRwlWjo88mltP6zkPPaLbDPgF7ltC6OJ/JPI0DkKvetPoElNdFKcqb8IWh7/\nNpK9XLSk7FMoyCu7bVm0MgYao3oaD5D02Brh+zUonmQXYEqmTBEPvSon/WJykw9kvN1WUL4r7zZX\n/lSkyZzdtkrRMxh+n6/mdVoYGc7/RWPUf9FKUn5S05gPjGiKVdpyYOZ7/rkvfdaBP4f7vCcDYzB2\nLSj/NjSZ/w1yVrQ+V/Ty3PX7k3SaIzApQeS5fx08LZcM1a/C99ZSyo3IGCzCvcjTldVT3Y4CfdqG\nx7gG6R2eSTurVZl81wNmtoaLWnEXsGdYtprBs7YeU6i6+82Z7/9G3qIy/M7MtvZ6GbJOAC40pXfO\nZ/OK8ex2RrP+/YEDTNnfWpzmGFfxUTNbyd0fRPdxGzTYliXXOBJl2qqa4a/JMbZA/NPW8/HvwOcs\n4sFOQNH6TwKtNNurAT8qOoC7n2ZKYPEFFBH9CPJeXlxQpa42aS1+ckDX5zaHUchrmMX8oV1FuB2l\nED/YlQVsXuR9bXGWVyLD7zOz/YD/dXEk10f89TfREu4NoUyv6Yjr4jk0EE8HpgZ61TTa+rqxg9+M\nPOrZbedQrGFf6X5bO5soSCO8jg7vsWii96a3OaSPomcydg7bm9lngd+a+NDLANu6+3WR4p8ETjOz\nzyCD4vBImRZi6YSzKNPEB9g10F9Ohxm0oYmIWpXFUciY/aB3LtVfhPjEe0Yb0EkD/DhwoplFaYDh\n+Fea2Ym0aQ17h+2g1Yy7M/uvzWFHE5AWh/dbyGu+QNhPC1VUqsqu7ethH58L7VwSOWguLSj/BTK8\n27BtcfSOdx7Yfc+w31FodeIxd/+3mW2YL2ttXnpLIaXVvinAxV6gVuRKGLWLKSNnGaWvcRpt4GqL\n66TnE4HVTaOdxQbUU/T4FXqOOtTLKtYfEiROcwYmibNzkYh8Fu5diOjhpcga2UWZmjakzRNr8dlW\nQZzm60v2vySdWXiKAi4mtYrkf/M4n+1jaDnqWjNbj0xn5u6XhDK9Btj8BgWrXeuZIKkimNnFaKn0\nBjQwts7Fi4yoXgIJTFnVvko5p3k3tER5uZltiby7cyEO5E8K9ltXpqfJMaYgjewpmW1jkfEfDRwZ\nbFhNbdK6/ORQp+tzmyt/Bhoc90He0MVQJP9c7r5Xvnyos1LY7zq09WtvQUbwA2a2DuJEXxbKP4q8\nac+F9/BSZDzu4e7rhTK9vksnIk/wYbR50wcCt7j73pHyPwJudvdzzWx/pA7xBqIpdHAqM/UqBw9W\nvd/WmU00P+hGdXhN2skvIG9bpQyQod4HkeE3B6IY7ezujxWUnQcZ+7ujrHUD4jNi590EJi3vScir\ndgl6vuYH/l/23EyxH+t7JiAs89tYJBFWxLu9GTjeMxlqw4Rgf3d/X6T8KBTTMSAeBDjNFfswD7IX\nXgnla3PYhwLBmL8UORyOQQbzee4eSyQVq18qrWgK4j8FSaG+4e7zBY76up7RuA9lD6EzLmc0mmhv\nBezY6jcix5kPrf7lx/wbMmV6SaNdFkvxFvK+ZxOB1YbVzPBnks1bos77PRxIRnMGwdN4K+owH0QP\n95HAjR5PErEcytT0AeTJmRFhX2akmdmiKPCjpZ5xuRerZ2yBZs55/eOuhmAVhM5yPEq5XDUd8fru\nflNk+3ruHvWAW02lCuuDtmw3mNJCj6edGOTl0KZrvUIqVlOa2bm8JFmCme2LOMd1MvzVPcaByFty\nHG0jah+0lN7hMQv7nEBctaBoQvJ5ipM49JytyYYmSKhxIgNThr9lkRemw4jJlGsFqy2EPEpLBKPj\nOXdfuE/nMTcyUnakHWR0PgoE7PoOm6L/F0RGc9Hkvnbw4GDDzP6OVBA6km0UlD8WrSbtiaggR6Dn\n/steHPg5H5q4fRB542fAi5OV1MlA16qzEsqA9ihaqdjOc4HbZvYSknXrmHAH4/A5d5+vYP/PIjrG\nW5ltfUtrbhXUkEK5Ss9KzAEUHFG19IpDvXkQNeBdiIrzw5Kyx6GJ+c1hEn4x6ue298gqp5ldiFZs\nDkXBgouY0kXf6BF1nJLjfji0bZ3Ib7sgu+I1OgNko4HadWE1E4GFOkX3Mu+dbpWvq+jxR+BbnlEj\nGolIRnMGoaNZwt1fz3QE8wN3xjpMM/s9eqiPRA/aB9BD+Mcioyu8EFfnO34z28Hdz4+UfwAFpEx0\n93zqyrJzqSNr96LXEBu3iJJC2P6Muy9aof4YuihV9AKrnsL4IYL3GxnvHTNiK5FCy6LE+Ogq02PW\nm/RaZnDJG1G/iA0uZnYB8G4UZf4KA+k7ZdH42X0tjYLorvewemFmm7j75PC9UHKxwFM5w6g0RY8v\n7+6v5Z+1XgbhzD5KExn0cj9M6gt7oGQjH3X3TwRj/cHYu2FmOwK3u/s/zWxVlOb7TeTluTtfvgnM\n7MceVxM50YtltW5BHrrjLchQmdnBwCsxI8TM7kCrdOd79Uyk70RatY+bKHHfQOf+w1hfZ5KU3B74\nMZ30q9gz9Qfgc1lvmUkt5ayC/vzDyHN6K1qteDJfJlKnUga6gknnGijY8nuofxgwATWzfyD+8xWR\n426O4hE65P/C739DgaHnZrbtEPYXM9RqKctYBTWkUK62Okym7ncR5/k42tky9wHO9Yzn2OLypAuh\nxB2taxd1CFh9acVpwDLBRpghz1Y0JhYhXO9nYhMY0+rkTu5+ZY395aUxCf8XSWPWTgTW5V52eKet\n/or3Uih4+iZE18o6IQ8rOfaQIhnNGbReflcmr/vQ8tMzyPiKGolIAeDFjJG9KJLuWq3gGG+ipa9P\n+8ClliIpp2dQ51T5Rll9WbvLgcPdPZ8tLV9uFHqQn0XepyxaBlSRJM7qaFLxAWAjNPOchLy6fyio\n80HUac7wMsQGx0z5WimMqyDSUeQ7JiiXWRpftG8PMj3WUHrNzD5E59J2vk7MmHgWWMndpxe1rQrM\n7HNIomj/8P+dHrJuWc30wmZ2Gxoo7jJlTLsUeXQOc/exmXK9DMJLIqPvheB12wUZaWf7QI9cL6me\nP4pWhl4DPunut5h4tTt50AfPlW+iw74pCp56IDzzx4Tz+HbBJKCwbyma5JrZc8AiwShqef7nCsdd\nNlJ+WzRp2xJxms8DflU0UQ91/o48rPeY2c8QTe1V5A3dOVJ+SvgaG4SjXuCC4y7ouZUbM/tFaPtX\nvZinH9tXpQx0kUnnjJ8YaPxn5cEmIN7yV5Ae+luhD/4k0n7/jhenmK9FAzRxzTdD6dx/Gsaycege\nrh0pfyOwt8tDexmivryAKAerx9pUF1ZRr9jiNIgZjoDW35hDwOpLK96H1I2mZiaTK6JgteiYX3Bu\nywHXFfSFDwPjvKJkbKhTVxpzKlL1+VdmH6uhJD7LmFaznsga9dbAO10HZnY64t7/hU4Pe0d/MGzw\nERCNOFI+aDlyQvh+NOoIbkOztFj5JwmyaGgpdkm0nFkWMf4C6pyfAL6Q3V5Q/oco33ud86gra3cq\nMoTPJBcJniv3VsnnDUqy+1FfqeIL4foeiWThjkSGdmHUL0oI8WOCzBTiCf4I+F1JnaVQh7JbaNvn\nkHeq9fvYzOfLKMjyI4jr+RHEmdurx+eukfRaeOYeLPsUHO8OMlnUemj3HJSoetTc18cIWQmB9ZD0\n3hPI8OzX+30zsFb4fgzirN5OTqqt6f0oOe5oCrIO0lbhmBdNEuZGwTBlail3t9qIVhTOQ5JRv8uV\n+3z4vBKe689nnvEjgHtKjvEwMppB/eAa4V0pVbVBtI9d0fL4y5TLVrak6UahgLcl0Dv7VJ/u9zyo\n33ggc503A74SKXsOJeoSJceolYGuwf73Q2PG68jZ8joKZi2UC8zUXRRRGw5ABtViJWVrKctQUw2p\nYB9vL3uPUP+flwxcAHiyH9c27K+StCJttZZvoTibTZFHegM0JuxTcH75z6oocPs64AcFbZqAJkVL\n1DiPWtKYSCr2sdAPfCn8nYroEQDbohXz/DOSVw6ZD3g0fF+EXOZT6im4vEhOwWUkfoa9ASP1gwyC\nFh9u/oIyl6FobICfhZfnckpSrhKM49DR/Atxl+ak2Gi+Dnmt7qW67E5dWbsz6dSoPpOcPjVtg6Hl\nuWj9P4YusjqoY/o5GoDvCd8/C6xQUP5ecimaEaXgvpJj1E1h/Inwot6GBqPW3+j9Q4bcIrlti5BL\neUwPMj1D9GzvhzzwO1JBOzrUyUsZLRDejQcqHvODRFJ1I49I7DMmfFYs2ec25AyWLm2YTnt17VCj\njAAAIABJREFU7T9h/4sCj/f5+q6GgrxOyfz/7pJnqq7We8sAHI1WwhZEwaL5AWsS6pPeCH9bn/9F\nxvb6Jcf4EQp2BKnLPBGu2RkVzn+ucG+uQUoXReWeQMvo66EgxtY5lTkdZmjwhv8XoLh/PhV53jag\nLbW3HOKi9uteTyIYGuF/Q0bqpIr1je7yYAshQ2un8LdUaq7heUwlGES0jeYFgUf6eIwLaMv/7UY7\nNfsXCso30isOz9+aqM/ppotfSVqR9rg9CqmK/DO0/W7g6xDV2I45l15H7/yRFOQhCM/rw5G6Ze9S\nbWnM8Cz9Auko/4IuqdrDM7J6bttqiM8MGmufzZ3HM2iScX74+0zrGYjs/w5qTBSG6zPsDRiJn9CR\nLRF7EXLlFiHMnNCM6yDkwVqmpM4Lme8LoyW0a4CXC8pPKPjsWnKMvxEGvMy2HQgDUx+uzzx0Gqdz\nUTGxCOLDHoFm6tGOgJoGcPj9XuC9uW3vocDQRp7GT4fvrYFiN+C4gvJPAcvlti1H0DLNbDs18/1M\nBorId0xIkNRR9jMx930iXRKo1Lx/U6jhmQ51YgPAIxR0tMBkgucDGRFPoE73u5H9vlmw/24Dxd9R\nsNbJwHoVzrtWIoPwey2BfiQf+RSaRLcG2vcRPD6R8hOor8NeN8nHEX14ZjZG+txFhp0BH0bUlOnI\ng/9NCibFoc4JyNN3D6I4gAzoOwrKrxme07tb9wytUFxYUP5xFDMBGc89DTTgS85hdSQX+hhayXgM\nGUXvLKmzHDIGn8k9+4XPeoN2vR0ZKv8K72nr83BB+TPQJGOecP9GIarGTwrK39Ht/kbqPEVbS/1O\nRNNbg+L+uZW0qVSvOFfn/eEetK7tM2jSWGlyX7LfvunLVzjW/cixsibiis/4lNSZjGKfWivf8yJb\nZHL4f1zRva/RrlreaeqveO+PDOsdqOjMGY5P4jRnEMjvP0ayO6PRrPBXSO6rkJtX8xi/94waQOCo\nHYEersq8vC7HqC1rZzUiwM1sMsqAdFNm2wbAUe4+vqBOLaUKM/tdaPsBLo75Ash4GesFagpWP4Xx\njOCNENiyKBosHvfAbcuVPxYN0ifQ5o19Dfizu+8ba1MVZDh5IHmvXVGQXjaw6CwvkF4bClinpNFL\n7v5USflKqboDl3leNDieg569Adxsdy/SIMXM3oNWhLZHz9RExH2fEil7DhqIF0P37DAzWxPxNoti\nEP6MJoQXMZBn5x6RhDPpAW/v7rdnOI+jkTdm8Xz5UGf+sMOXwv9LIuO0g58cfj8AUYXmBr7u7ucH\nnvNRHmTtCupVkq0MfO97kOFXVVHnMcTFPh8FA0Y1aCP1Ngde88AJNkn4LeRxLv71wM/cfWLm2s4P\n3OtxnvVDaLXq2Uz5JZBU27gq7evS9pby0N+QEk0rCPcmL+GjWoMA8gZtq5uivJayTEMOe4sbvxyS\nQFwubO/gD5vZHOiaHInG4TK94my9WgGsoc5mqP9Y0t23ij2DZvYy6ocLEXtmm8Ayqb1r1KkrjZlV\nUGrptbe430W6+JjUvAbIErr7nwrK1lJwqRsHM1xIRnMGZnYpmpUeRDta9zA0O94mlGmJ8+eDrmBg\n0EFMnL9pu5ZCHpjFssf1gZHWi3gmsMvqydpVigDPlI+9DHMgL3BUzsgqKFXkyi9LWM6j3QncgLhl\nhZJTwXj4LHqpp6IB/OqCsvcB73dF79+GDJFpSD5osUj5lpbpdgT5MTJapplyvcgsXYGCMrPatu9H\n2ao2q7LfwYDVVGAIHf/iaOJyhbuPMzNDHpsFcmXXRBOFz6Blz4ko8OkVKiLs+8MoAcaaiNb0czSA\nvhXKzEMmkYG7vxGCtpb2jJ5tbr/PU0OgP0wWlnAFbWWN5v94JEjWKgYnRuqtSibJR5j0zu3u/4iU\nrS1baWb3Au9z92crnneh3GSFussRDM4u7/YMYyJzbQ2pECwSKX8s8tDtiwy7dyLv6X3u/t0mbY0c\no5byUKhTO4C8QbueR1Syrvrwoe/eFRldC6P+/xEv0LPO1V0QUYt2RM6Qq0ucGtcCf0J9grn7Hma2\nPJpkLB8pPw29e5WlOq1+AOtXEb3idBRIu5CZvQtJ522YKfcWGhsL0Uen1/FotSWq0x4pn71/S1JN\nGjOvoNSCezxgsslEupaCy0wDHwHu7pHyQcuk8+W2zcfAdL1n0rnc/svc9om5fZRxXKNBd5nylXi3\nDEy/HF0KLjnvO1HWqey28UhqL1Z+CjkKChqQHx2Ee7ICmjBUXgasse9voWx9IGPlv6gD+X6u3Ido\nLxV9KPLZNFe+LGCydCkWeWNH57aV8jwbnPfCyFt+KxoISpduQ50izv0zBdubpOqeA/Hszke8/LUr\nns84FMhzL+rYW1JVNwG/6fFaXUfJsmik/JUE6hRtys9OSPIxVr5ScGKXY25KhC+e+f0BtJxaOZ0v\nytL259APjCMTzFRSpzKXO/y+IqK6vIGCvt4I/48pKH87MuSz13Zd5LWMlZ87POcvhnfuJWQ0F9LI\nEF/1PUW/R8pfjtRP6jxTtQPIGzy3dVOUN6asUJ3DvnJ4t89C2fRADohjCsofjzS167SlVgBreDdW\nyj1Tc5Dr1/p5byqcw/VojP831eOYat0/1L8uUrPOvZRQYyLlN0RUn5uQc+mv4f+NSurMiXI57BD+\nVo5ZGapP8jRnYGZ/BXbzzNKiSSrtTC9Z9syUfTcarD/r7stktp/q7fSbZ1KQitJzWbBC+buQKsVF\nGe/KbsC73H2/TLknkAH3L+KScEChtux05B17I7NtNIpijy2jHAeshQaY+1FneDzwD3ffJ3bcUK/U\nY269ayLPgwbt7YHFXV6DzYBV3P3kbvs1SQfN7xkZnrB9Cl1SeXr/vAzXouXeg9z9FZN25qGIs7tJ\nee3KxzgHTUZOQBzBnZFG7iXufnyubCtj3MnIE5+VyBqHJh2rRo6xOAo4fA3p7r5oZlshA/TEgnat\nRnh/0GD2eS/XW/4KMkhXQdJKZ/lAytB8KNJ+gfB/kZYpnlmStIGaumORF+0XVBPoXw0Zzg+iZ/3a\n0L7NPEJ3ynlP/4MGmhdQsFpRtrfJyCt2vYmqsS/yTp/i7kdEyjeRrawrtbcd8BMUqLWjuy9oZu9D\nlJEPFxxjEjKEv+ttCtbhaBIxPlJ+K+Qx/xl6tlrcyt3d/c8l59KKUZlW1Hdkyp6NBuuFkKEyCd3D\nW2PXz8xORQP8pXRmLo2uNpok2s5w999YW2rvFRSI16FfWxU2MEX5oqgfrJSiPJz3rzyS0KPgWIbG\nmx2Qp/kh5OmsrNNd4RjXo0nRVAbqcntRX2g1s1+aNOGXda06tcbXeREHOjuGRyXoBgNmtisFK9le\nnCW07v27A9jcCyhgBXX2QpOjo+jUSY/20zVXvFdDnu95aadyfxX4eH5MHk7M9kZzboBcGQ3aE2lz\nVndCnuPocp6JI/dZtDzybuSZOtndfxUpOwpF9F7n1Zc4KvFuzWxPJAI/T8nuiga8SahTOTr8b4j0\nv2XB4DUvWgbfLRzvVWRU7O8Fy9hm9gnEWb0XZWq6M/y9ztvJMRpr8Ib6p6Igm6MQP7DFn7vS3d+Z\nKfeXXNUOQ6pfBmpd2EBu2nQUbHoLMkQe7NMxnkJR0NMyy8PLIXmwtXNlJ6HrszEyIlpwNBj/yCPZ\nIWu0ZTE08O6CDJWzETWhMClNpu5lyGv1+5LnbvOWQWUD9VwdrY58EiVL+HqmziTKUz0DcYH+UH9+\nRG8agzxff/CCjI5hCXp5pKBxgbuvEZZbn/OCZX+ryBfPlP8hkps6I7a/fsCacbmfR5Pb1zLbWiog\nUQPFzNZCFKnWtT3N3f8vV2bFWN0suj1f4T3cBPGNPxnqdDgighNkxm5bmylwgoQ6i6Cx95kwsdsP\ncc1P9AqUiJI2n0n5c1vmnLkYPUc30Gn8x5KCVOawW6eefAc8zmGfUFy8MnWhNPulmV0C3Obu3888\nt99EAeU7ZsrVpuE0QaBBXI0M2kqUsFCv7v3bD3n5f0zbGdCqEOVmN5hIL48EDp7JbFsUrbJ05E0w\n6fNfjlYmPdgh+6FMsY0nk/1GMpq7D5CtjiYrOj8XekB3BTZH3tbzETdqdS/J11735bMavNswSC2N\nosvfSWS26vEAqdXRDG9+2jO8l9EMr6wzHIW8xk9X8OB09Zibgs1aAQYzmpw/j9g5hPqPI0/mizYw\nW9OAFMaRzvgUtBydzUBUqVMuaEfeKI+h1DC3immbm8AGZrV6FE1enkeGWpGxckTRxDFT5kAPmbpy\nXq/8hOTgTJ3/Iq/yOWgZD3IDbMGA2mhwiexnHeAQdy8N8hksWLPgxMp88VD+OuSxe4iBA2TXyaFV\n5xvX4nKHOlcgWtp1mW0bAd/zHvj7YXAvijuBkol3qL8aAxMxPYnUTL7RtE0jHTYwGHnGZoo5rpU5\n7DaEK3XhePOihB7Pepe4CFPszO/R+7QsWiF6AQXNN57A9AJT/M9q3dqeq3NIwU9F928Kgxx0ZwrK\n3M0zcRam1fjTPLJy3+rXfGB8UOGK93Bhtjeam8C03PkW8nKd6+63hu2PIT5cYfpVq5h9L1P+Wyhw\n5WJTCu6fo4f9OHc/sKDOO9z93prnNBqlHW1FgP816wGKlF8dzVSXcvcvh4FmLnf/e0H5qh7zfNrk\nX7v7/6t4Do2i5bMGdj8QjPKyQRu6GOZWUemgYfv+F8mQXW0KCHkTeY3W9i4BGmXtsgY0pF4G1CaD\nS2QfcyIuY9FkYTPgIXe/J7NtVRTI1ZHm1hQEegSKSs9eJ/dMFrNM+SbBiZehye0yqG/Y38xWRisq\nHdeqiccuTNrOpa21uijSs90pNokzsyuRaslZmXdvJ+R9jk5IzOyniPpyGe0MYx9FKy3TWm1Exn7Z\nM+JIovI2d3/EelBkMdHcXgAuRrSM67xglSBXb0FkeGVpZ7FA36WR92xjdE2fRis4x3uNpfKqqNKu\n0PfvhJI1LY6u/VXofmZXAXpOYV8XViEIPlN2UxQXsDZt59f/ocyJV5UcYxSShRyD3qubvUIA5WDB\nlGl1ExSnkadBVA6KHG7kx/KwzZBzJpZh+S6kVHZ1ZtumwEnuvsagN7giktFcgHBzsy9pViliEur0\nbkSd8kWupbYqRnOWA5flfg3wwJXUH4M8wvd7huJR4OXLG22VjlGhDU34i5U85pbjjtUxaK1htHy/\njeawz7IlyZbxGPOg1lY6qNGmjd39L6b0uLj7/WFQOhIZeHN6QQrUwWxXU9QdXCL3ZH7E+xzn7usX\nHGNG2tzMtuWQ5/EdkfK1pL6awBrwxRscYxL1+Ma1uNyhzpmZf7P91QCaA7ASXSZWyFu/OpLCPNka\nKrKY2WnomXKkfTsJyWJGvexm9k50r9+T+6njvQgG8/8hveLfohW15VDq4CWB/+mXd7Nqu0xSc1ei\nVYvLQ5uWRQG5j6BUy8+Fsj3R5+rCKlD6MmXXQZOP05EG9lR0bbdFmTA/4O43dznegJia4TJQS65z\n/t5t4u6Tw/dNi/YXG2dqtOXP7r55+F60guoeWbEKfeeWWQdemNxf4e4dEzAz2xpNmC+jLZX7MTRR\nv7TpOfQbyWjOIAyGJ6NluYVhwHJ9vgMci3iYuyDu8xWh3uru/mjJMc7MbWrdgDk8zj3qkPQK3qlL\n3X2LzLZaXj4zqxKsUeQda8JfrOQx79FonhulP98dqZ68ApyGtJ4LOeSDZDRPoYEH1cweQCL1E939\n5T636Rngox7hIJtkjj4Vu99N2lXmleqjx7wux24KA+/JS8gwPMgL+OKWo/aEba30wjFvSVepLzM7\nzd13D99jwYmtcyjUS62D4ADYDQV8Loe8uuegBDvRZ9Sa8Y3no83lfgQphrzYj3OoAjNbA1Fcls9s\nmwN5UHdFmsKbelgZ7LKvpZFjZDzywk7zyGqVKXD3VhSs+yAy8I9EzoCzc2VPQvS5z+ScMKMQve8p\nd/9KnXMuaX+ldpnZT9D9+rQHnfCwfQGkePBQa1wZaljFIPhQ9kLE2/9eZD/fA9Zw909HfvsfNOa/\nh4HxQMPpDBhb9JtnqIlmdidSp3mrbLxpjTNmdrcHylfJ+D9gzDezz3qQjLOaK1Zm9h3klPgubcGA\nw5GTsSNgOdRZBU10W5KxFxVNuocLyWjOwBqKzpt0dHdFot9vAL/wivw3K1DcyPx+Dcr6dXD4fz7E\nwXrU3XetcXr5/Y6vUi7mHbMG/MXIPsYgpYp/5rZnReQNeeS3ybUp5qGdqzXAm9kmSGZoGqLR3OCZ\nZAM5j2PlYwwFrIHSQY19b48GiM2yhkMYOLdABsWUfrSrrkHbBFUHlx6PcTvSFc0vGZ7g7nkvHibq\nxCHufkvJPr/t7keF74dQbDQfmqlTmy+eqduS4DuOtv78Poha9v2CNlbiG4f+qQgtZZJCL1joz1am\nk/JzQ8l+C2Fmx7j7AZn/aymyhDprIWN5PDKcX0UrCztGyj6L+sLXrR1UOz+S61wpV/YeYNt8nxd+\nWx34rbuvUuuEi8+hUrtMq6Pre5xyMxZR2zpUXMxsGxTgWph4qA/nUDn5VDAC1/fIioDlkqnkfrsT\npVo/B8XxzEC/+pCmCJOppbxk9SHcv3MQtStKjcyU3diD/n/Z+J8f802qGed7Jg9EFYQJ677I078C\nmkifjqhIsZXA97j7HXWOMRxIRnMG1qPovCkA4RMoi9KWJeXqKG4siAKeLkRSS5cjLdo9sgZMmWcv\ni354+awBf7HGvqfQRbkgMhjtibQfdwr/v4y4gqAl+G+6++m9HGOoYIOsdGCSMzoWafveiTqxjdEy\nbOHqQ6/tCp67Q9AE8NwuxQcVVoMvHoyDsxA1peUt2Q0FuHQsGZrZKchTUknqq0abe5GtnIKWpx/K\nbBuD7kXWq5SldS2O+qg83/hcd98rU+cLkeY68mjvjbSh5y04p13QJO41OqksHSogVWG9KbJMR0Gx\n19LOWFqYiCkYLSu7KCz3IRm2Z5ADIc/nLFyFMHHrn/U+KTRUbZeZvYSy38XaNBrxT+eL/PZ3ROO4\nAF3bRoltupxDnSD4Qkk4s9Ig2eeBhQfDSdEUJnWVU4BPAW+4+3wm6sK6notjCv3TzojG8C/UV53n\nJdlaG7TnNkR9+kPY/x9iz0sfjjMNxR+cjfqZYQnE7IZkNGdg0mxc0d1fDQPNuijhybSiF7LGvntR\n3FgMcevmQcuPHUt4JZ69LKJevtC2A9HL1woEPBsl+egIBrSK/MWSJaB8m7pKRJXBxCP9krvfHv7P\nKme8F/ipF3BWRxqsB6WDGsf4Ilq2vRFYFRnMpR1UP9plohXd4+5jGjU8vs9t0IrQYsgL1aG7nCnb\niJdtZusib8nyyFtyhrv/raDsmdn9tjZTbNBuirKVPWBmy6BApjeRDvPjubKt96QwwLTAY/gksJJ3\nLr8/4JmVoYgxHuUYx84js4/FUdKg3dFE/zAvoKuZgu528khAZS+whoosoe5KXkPa0cx+hYyIM83s\naNTH/xfRGj6RK9sRGJX7vW9awFXbZWb/QCspV0T2sTmS/1qz4BiVU9hn6iyOJl9Lu/sPghd4VGzC\nbjWC4Ltdu6Lfzews5EWNpoIeDpioJtNRH/1Pbwe03+juKxfUWQStdO+Cghr/hAzc33kkpbsNTKOd\nD1iO9Z3vCvveASXiOR/p4hdSner0baH8aPRs7IxWPm+gHYvQV6piL0hGcwY2SKLzYd+VFTesM5Cv\n5bn5GOLoQp+C+sLxTkAG0aG0l28PBm7xSIrkUKerFm3ZElAW3mOAlJk94e5LZf6/wUMK1LDE9bhX\npI0MN6wP2qQl+27RUgxJ7H0YJYeYMWErMSZ6blcYZK/y3NJqU5i4insib9cXgZYaw4UeT/k9aHzx\npjDFB2zm7g+b2fno/ryK+MRb58o2klIzs4lIq/bbaNIzFil8vOTuO/fpPBYG9ge+irzT33P3+7vU\neRgFYXYM6j22ZQo9SJwFp8CnqagMlKk3CnnnF0DP2Eu5319HxkbR/fuMu89VdowmMC2T7xhrV3iv\njwa+goyTt8J5fBI4CSlPdChV5PZvdElhH8p9ALgE6c5v5AogH4+M9mjq7dxxopS+8NtbyEtZhGUL\n3o2LUCDmX+hcGepLTEFd2EBJ0KwDqHTSlak/DvHwv4BWehaLlKmVRjtTbxRatdgZBVlOQc/UDyNl\nK/dtkbpvQ+pcX0P91W9QavPryuoNBZLRnIENkuh82PckKipuRDw+M36iPWiWenxqtu0/oQ3TMtsW\nB/7u7sv24xiDCTN7EQ1wL0V+WxAZzfMPfcuqI2fQFi2998SzjhgTg0ZLsc5I6/lQStvD3P3IPh3j\nYSR8/w8ze9aVzGZdFNjXMQhbRV62FfOHZxShZNIaDMhV6aSAxLj4z7syV45Gg/YY5BF8LD/YWUMp\ntdCekxBtZDRK0XsR8FV3fzZ2DqFeV1nJ0E/ujQzmScDB7n5X0T5z+58A/A96JrouJ5vZ3ohbPGi8\nR6uoDBSMhwU9KEvk9rEwogO8ldt+CAX8ddrPVKHBMlgwJbo4BK1kTkPUnP+iILwOYyhXdxwyoD6L\nnEKtxGB7oWd420zZ21ECrKusTeubB3jYB654LIaoCH+MHG9LxLOents+vstpurtfG9nfISXlh/xe\nAC1ayibuPjVznVZEqhOlNFHTqvG2yCv8EeD6mMPPxHlfKX8da7bzg8Av0ep8RzbfOn1brt4CiJqy\nE5IPvAQ9U59DGQX3Kqo7FEhG8xDCaipuBA/BeGpkEAz1RqNOK7tsDcXSMLWMZqupRRvq9JTiugym\n9OfHuPuvI799EnGau6ZBH0706h0bbAQj4Qvo/i3h7muaAi6XdveLIuUn5Da9BNzhfYyEtoyyhYmC\nsLy7v1bkkbGKvGzrjT88AfERX6QzsCimlvIoyv64Bgog3DgsnT5VcA6VpdRsYGY8A+agrcP7ZjiH\nKM+3hvH4BOpfjkUexI5nuGiyZ2YbIArH8rmfijzmtVJcN4FVVAYys32QRNxOBe38m7v/uB9tqopw\n3G6IelDNbCGUwr31fNwYmxBkytdKYR+2Zb2mrWs7RyiXlR09AVEiYynhv4PSzu9f4VxnKpjZKJen\n/1soMP27yMO6BRIn+J27n1BQd2NkV3wK3b+JyAPcQdcK5Wun0Q71lkeTpJ3Re3sxuvexCUndvm0r\n9Ex9FLgercpf6iF5lSm+7GEfgsyMZZjtjWYr1jSGgR6AvlAhMsetpLhhDdJ3mqSNPoSWyI5AL9+e\nKE1vTJLnRETPOIz28u2BiJ6xd6R8bS1aq5jiuglMqhAnonP8rbeXGD+BBv593f28Xo4xuyO8J5uh\n6/xTV5DsOJS5bu3y2oPWptsQJ/Yuk4rDpYgLeJi7jw1l8h7v9ajAy7bm/OGpSKGhw0tWcA4HoACn\nuYGvu/v5Ji7gUWUTPasgpWY9ZMarYTxOae2rqK1Fkz1TCvDzkNc734+UBd+tRIUU101gFZWBgtGx\nXWwSaNKivdjd39uPNlVFiSc7i754UM3sD8CZVExhH/6/Ab2bf8pc280QBWR8ptx9wAYeWX0IXui/\negG3t+G5rIok5/IrQ6W0lH7D2koYE4EPIurcWER//Cnwo/zk0MwORYbmYug9Osvdry/Yf1Y1ai0q\nptE2UTE/hYzyTdBk9SzgNx5Z3c3Uq9S3mdmn0ST4Ctq8+OjKvpnt7u6nxX4bKiSjuTsVAgo0lPt0\n/FLFDauZQTDUmYo6nYesrQKyGuIExTzNcyPDekfagYDno0DADg+3VdCijdSplOK6KUxLjIeiF7TW\nEmNCdwSvwVru/lRmwBsFPOORFKdWM7i0YZs+Brzo7tea2XrIAFsA2MvdLwllJlTYlXuOl93U4Aye\n12VrvhurAm+2DEWTVuncnkk/G6nTVUrNesuM17OsZDdYkBGr4yW2QU5xbRWVgSzQgUr2U/r7YMPM\nRns8AGwR72FJvuR48wJvxcaLTJn1Ed/9cmSwnY34xNt4JvGIlSthjEKKHv0KmPwOWgG9g86VoZ7i\nmBq0JauEcTdtJYyyZGl/QpOX33r3xD1T6ELPg6g61YtIQecs9G5UCfBv1e3at5nZvcA4JI7QSig0\nuchLPuxw9/Qp+CCi/LHIuzJcbTgVeBa9GIdnPoeV1JmOIpJBGZ7mRy/IC7lyGyFaQ2wfxyDdy9hv\nlwHr1DyPh4C3tdoX/i6BMhv261otjJaydkLet7cN9zM0q3yQ0Ttv7v4tCDxSUP4EtMS2GbBa+Hsd\nig/otS0rFnzGhM+KfTjGbWjg+k7Y9xzAnNlPQb19ET90VMPjbork4WK/LYYCtm4Obftut3NFgVnH\nIk7gn5GRPW+FdlwJ7Jq73zuhhCX9eqaObx2jYvkn0ArX0eH9XrBfbckcY7VwrSajSfcVKNhplVy5\npxDXO7aPpRC9oK9tq3kevyI4xXLPz6192v+xiHcMMvJeQUbn1l3qLQccgFYAv4UoVfkyDwGrFdRf\nFS3R9+s6PYUShAzbvYq0aREU2Hw9kmP8HVpRGT1M7YnaAX0+xjJo1f1kNIF5A3nYzwZ2H+57kv3M\n9p7mPKyGhvIQtefM3Kau3m8zuxHY291vNimC/BN4AXETV8+Uuxw4xd3/ENnHlshjFwuoqq1Faw1T\nXCeMDJjZGagD3wdNxBZDRs9cHgnMsEEMLu3BC3wSkpe6IbNtQ5QNrUMlxhqkYg4e+aVQsN3TmZ/c\n49k1JyMJpuvDcua+iG98iuc4ndablFqtzHjWIC12XZjZ9YgW9iCd/UhsRaxuiutGgYNWTRnoV0i+\nrYNba2Y/AMZ6JAPdUMGkVvCKu38u/L8k0vu/1N0P6sP+Hwfe7u4vm9nNyMnyHEr6E5Woq7HvE5Eu\n8LaeUbkx8aQvQXEJ+/RyjMw+H0ITosoxQ0MJq6CE0afjfBCtFHRwk2vup3bWwYL9LALsgfrDxWP9\n+XAhGc3MWEpupKE81LAuGQRDmXWRKPqtYTnkVLRsvb+HjECh3FRgBS8Wtn84dgyrqUUQQnb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GwqsEKs0wtLYA+7+zJD37KEhGoInmXc/aXw/5LAKHd/vLRiwrDBaqa4bho42KBdjeTjrGZmvFkF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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -6906,31 +6907,31 @@ "data": { "text/plain": [ "State\n", - "Wisconsin 7.440163\n", - "North Dakota 3.206165\n", - "Nebraska 2.677124\n", - "Ohio 6.672285\n", - "Pennsylvania 7.613838\n", - "Indiana 5.058881\n", - "Iowa 7.003142\n", - "Arizona 5.652295\n", - "Maine 8.126964\n", - "Missouri 6.040566\n", - "Michigan 8.447772\n", - "Montana 4.332595\n", + "Wisconsin 6.238169\n", + "North Dakota 2.688194\n", + "Nebraska 2.244621\n", + "Iowa 5.871751\n", + "Maine 6.814014\n", + "Montana 3.632644\n", + "Kansas 2.588450\n", + "Oregon 6.860391\n", + "South Dakota 3.066288\n", + "Utah 0.438951\n", + "Washington 7.223234\n", + "New Hampshire 5.946964\n", " ... \n", - "Hawaii 8.851564\n", - "Vermont 8.983443\n", - "Maryland 8.732396\n", - "Minnesota 6.073006\n", - "Illinois 8.421703\n", - "New Mexico 5.457221\n", - "North Carolina 3.861141\n", - "Georgia 3.493845\n", - "West Virginia 2.499209\n", - "South Carolina 3.177833\n", - "Tennessee 2.659344\n", - "Mississippi 3.057746\n", + "Arizona 4.523532\n", + "Missouri 4.834265\n", + "Michigan 6.760751\n", + "Georgia 4.582913\n", + "West Virginia 3.278239\n", + "South Carolina 4.168397\n", + "Tennessee 3.488289\n", + "Mississippi 4.010877\n", + "Florida 4.503946\n", + "California 6.373831\n", + "New York 6.775401\n", + "Texas 3.144208\n", "Name: poll, dtype: float64" ] }, @@ -6980,73 +6981,73 @@ " \n", " 0\n", " Wisconsin\n", - " 7.440163\n", + " 6.238169\n", " National\n", " \n", " \n", " 1\n", " North Dakota\n", - " 3.206165\n", + " 2.688194\n", " National\n", " \n", " \n", " 2\n", " Nebraska\n", - " 2.677124\n", + " 2.244621\n", " National\n", " \n", " \n", " 3\n", - " Ohio\n", - " 6.672285\n", + " Iowa\n", + " 5.871751\n", " National\n", " \n", " \n", " 4\n", - " Pennsylvania\n", - " 7.613838\n", + " Maine\n", + " 6.814014\n", " National\n", " \n", " \n", " 5\n", - " Indiana\n", - " 5.058881\n", + " Montana\n", + " 3.632644\n", " National\n", " \n", " \n", " 6\n", - " Iowa\n", - " 7.003142\n", + " Kansas\n", + " 2.588450\n", " National\n", " \n", " \n", " 7\n", - " Arizona\n", - " 5.652295\n", + " Oregon\n", + " 6.860391\n", " National\n", " \n", " \n", " 8\n", - " Maine\n", - " 8.126964\n", + " South Dakota\n", + " 3.066288\n", " National\n", " \n", " \n", " 9\n", - " Missouri\n", - " 6.040566\n", + " Utah\n", + " 0.438951\n", " National\n", " \n", " \n", " 10\n", - " Michigan\n", - " 8.447772\n", + " Washington\n", + " 7.223234\n", " National\n", " \n", " \n", " 11\n", - " Montana\n", - " 4.332595\n", + " New Hampshire\n", + " 5.946964\n", " National\n", " \n", " \n", @@ -7057,74 +7058,74 @@ " \n", " \n", " 29\n", - " Hawaii\n", - " 8.851564\n", + " Arizona\n", + " 4.523532\n", " National\n", " \n", " \n", " 30\n", - " Vermont\n", - " 8.983443\n", + " Missouri\n", + " 4.834265\n", " National\n", " \n", " \n", " 31\n", - " Maryland\n", - " 8.732396\n", + " Michigan\n", + " 6.760751\n", " National\n", " \n", " \n", " 32\n", - " Minnesota\n", - " 6.073006\n", + " Georgia\n", + " 4.582913\n", " National\n", " \n", " \n", " 33\n", - " Illinois\n", - " 8.421703\n", + " West Virginia\n", + " 3.278239\n", " National\n", " \n", " \n", " 34\n", - " New Mexico\n", - " 5.457221\n", + " South Carolina\n", + " 4.168397\n", " National\n", " \n", " \n", " 35\n", - " North Carolina\n", - " 3.861141\n", + " Tennessee\n", + " 3.488289\n", " National\n", " \n", " \n", " 36\n", - " Georgia\n", - " 3.493845\n", + " Mississippi\n", + " 4.010877\n", " National\n", " \n", " \n", " 37\n", - " West Virginia\n", - " 2.499209\n", + " Florida\n", + " 4.503946\n", " National\n", " \n", " \n", " 38\n", - " South Carolina\n", - " 3.177833\n", + " California\n", + " 6.373831\n", " National\n", " \n", " \n", " 39\n", - " Tennessee\n", - " 2.659344\n", + " New York\n", + " 6.775401\n", " National\n", " \n", " \n", " 40\n", - " Mississippi\n", - " 3.057746\n", + " Texas\n", + " 3.144208\n", " National\n", " \n", " \n", @@ -7134,31 +7135,31 @@ ], "text/plain": [ " State poll Pollster\n", - "0 Wisconsin 7.440163 National\n", - "1 North Dakota 3.206165 National\n", - "2 Nebraska 2.677124 National\n", - "3 Ohio 6.672285 National\n", - "4 Pennsylvania 7.613838 National\n", - "5 Indiana 5.058881 National\n", - "6 Iowa 7.003142 National\n", - "7 Arizona 5.652295 National\n", - "8 Maine 8.126964 National\n", - "9 Missouri 6.040566 National\n", - "10 Michigan 8.447772 National\n", - "11 Montana 4.332595 National\n", + "0 Wisconsin 6.238169 National\n", + "1 North Dakota 2.688194 National\n", + "2 Nebraska 2.244621 National\n", + "3 Iowa 5.871751 National\n", + "4 Maine 6.814014 National\n", + "5 Montana 3.632644 National\n", + "6 Kansas 2.588450 National\n", + "7 Oregon 6.860391 National\n", + "8 South Dakota 3.066288 National\n", + "9 Utah 0.438951 National\n", + "10 Washington 7.223234 National\n", + "11 New Hampshire 5.946964 National\n", ".. ... ... ...\n", - "29 Hawaii 8.851564 National\n", - "30 Vermont 8.983443 National\n", - "31 Maryland 8.732396 National\n", - "32 Minnesota 6.073006 National\n", - "33 Illinois 8.421703 National\n", - "34 New Mexico 5.457221 National\n", - "35 North Carolina 3.861141 National\n", - "36 Georgia 3.493845 National\n", - "37 West Virginia 2.499209 National\n", - "38 South Carolina 3.177833 National\n", - "39 Tennessee 2.659344 National\n", - "40 Mississippi 3.057746 National\n", + "29 Arizona 4.523532 National\n", + "30 Missouri 4.834265 National\n", + "31 Michigan 6.760751 National\n", + "32 Georgia 4.582913 National\n", + "33 West Virginia 3.278239 National\n", + "34 South Carolina 4.168397 National\n", + "35 Tennessee 3.488289 National\n", + "36 Mississippi 4.010877 National\n", + "37 Florida 4.503946 National\n", + "38 California 6.373831 National\n", + "39 New York 6.775401 National\n", + "40 Texas 3.144208 National\n", "\n", "[41 rows x 3 columns]" ] @@ -7592,31 +7593,31 @@ "data": { "text/plain": [ "State\n", - "Arizona -5.453958\n", + "Arizona -5.768496\n", "California 19.966475\n", - "Colorado 2.667843\n", - "Connecticut 8.936227\n", + "Colorado 2.705829\n", + "Connecticut 8.980923\n", "Florida 2.170963\n", - "Georgia -8.811154\n", - "Hawaii 18.584803\n", - "Illinois 15.477867\n", - "Indiana -7.340970\n", - "Iowa 2.038001\n", - "Kansas -9.766281\n", - "Maine 12.222222\n", + "Georgia -8.644088\n", + "Hawaii 18.697047\n", + "Illinois 15.578978\n", + "Indiana -7.756370\n", + "Iowa 1.832903\n", + "Kansas -9.926964\n", + "Maine 11.856359\n", " ... \n", - "Pennsylvania 5.436914\n", - "Rhode Island 13.236853\n", - "South Carolina -6.334382\n", - "South Dakota -1.522121\n", - "Tennessee -2.521086\n", + "Pennsylvania 5.211292\n", + "Rhode Island 13.349511\n", + "South Carolina -5.875070\n", + "South Dakota -1.796079\n", + "Tennessee -2.136715\n", "Texas -2.295507\n", - "Utah -29.179269\n", - "Vermont 14.891764\n", - "Virginia 2.420244\n", - "Washington 12.312505\n", - "West Virginia -9.436884\n", - "Wisconsin 4.530315\n", + "Utah -29.204379\n", + "Vermont 15.005680\n", + "Virginia 2.443012\n", + "Washington 12.346282\n", + "West Virginia -9.075658\n", + "Wisconsin 4.259403\n", "Name: poll, dtype: float64" ] }, @@ -7718,7 +7719,7 @@ }, { "cell_type": "code", - "execution_count": 307, + "execution_count": 176, "metadata": { "collapsed": false }, @@ -7762,7 +7763,7 @@ " \n", " Arizona\n", " 11\n", - " -5.453958\n", + " -5.768496\n", " 0\n", " 1\n", " \n", @@ -7783,14 +7784,14 @@ " \n", " Colorado\n", " 9\n", - " 2.667843\n", + " 2.705829\n", " 1\n", " 0\n", " \n", " \n", " Connecticut\n", " 7\n", - " 8.936227\n", + " 8.980923\n", " 1\n", " 0\n", " \n", @@ -7818,14 +7819,14 @@ " \n", " Georgia\n", " 16\n", - " -8.811154\n", + " -8.644088\n", " 0\n", " 1\n", " \n", " \n", " Hawaii\n", " 4\n", - " 18.584803\n", + " 18.697047\n", " 1\n", " 0\n", " \n", @@ -7839,28 +7840,28 @@ " \n", " Rhode Island\n", " 4\n", - " 13.236853\n", + " 13.349511\n", " 1\n", " 0\n", " \n", " \n", " South Carolina\n", " 9\n", - " -6.334382\n", + " -5.875070\n", " 0\n", " 1\n", " \n", " \n", " South Dakota\n", " 3\n", - " -1.522121\n", + " -1.796079\n", " 0\n", " 1\n", " \n", " \n", " Tennessee\n", " 11\n", - " -2.521086\n", + " -2.136715\n", " 0\n", " 1\n", " \n", @@ -7874,42 +7875,42 @@ " \n", " Utah\n", " 6\n", - " -29.179269\n", + " -29.204379\n", " 0\n", " 1\n", " \n", " \n", " Vermont\n", " 3\n", - " 14.891764\n", + " 15.005680\n", " 1\n", " 0\n", " \n", " \n", " Virginia\n", " 13\n", - " 2.420244\n", + " 2.443012\n", " 1\n", " 0\n", " \n", " \n", " Washington\n", " 12\n", - " 12.312505\n", + " 12.346282\n", " 1\n", " 0\n", " \n", " \n", " West Virginia\n", " 5\n", - " -9.436884\n", + " -9.075658\n", " 0\n", " 1\n", " \n", " \n", " Wisconsin\n", " 10\n", - " 4.530315\n", + " 4.259403\n", " 1\n", " 0\n", " \n", @@ -7930,34 +7931,34 @@ "State \n", "Alabama 9 NaN 0 1\n", "Alaska 3 NaN 0 1\n", - "Arizona 11 -5.453958 0 1\n", + "Arizona 11 -5.768496 0 1\n", "Arkansas 6 NaN 0 1\n", "California 55 19.966475 1 0\n", - "Colorado 9 2.667843 1 0\n", - "Connecticut 7 8.936227 1 0\n", + "Colorado 9 2.705829 1 0\n", + "Connecticut 7 8.980923 1 0\n", "Delaware 3 NaN 1 0\n", "District of Columbia 3 NaN 1 0\n", "Florida 29 2.170963 1 0\n", - "Georgia 16 -8.811154 0 1\n", - "Hawaii 4 18.584803 1 0\n", + "Georgia 16 -8.644088 0 1\n", + "Hawaii 4 18.697047 1 0\n", "... ... ... ... ...\n", - "Rhode Island 4 13.236853 1 0\n", - "South Carolina 9 -6.334382 0 1\n", - "South Dakota 3 -1.522121 0 1\n", - "Tennessee 11 -2.521086 0 1\n", + "Rhode Island 4 13.349511 1 0\n", + "South Carolina 9 -5.875070 0 1\n", + "South Dakota 3 -1.796079 0 1\n", + "Tennessee 11 -2.136715 0 1\n", "Texas 38 -2.295507 0 1\n", - "Utah 6 -29.179269 0 1\n", - "Vermont 3 14.891764 1 0\n", - "Virginia 13 2.420244 1 0\n", - "Washington 12 12.312505 1 0\n", - "West Virginia 5 -9.436884 0 1\n", - "Wisconsin 10 4.530315 1 0\n", + "Utah 6 -29.204379 0 1\n", + "Vermont 3 15.005680 1 0\n", + "Virginia 13 2.443012 1 0\n", + "Washington 12 12.346282 1 0\n", + "West Virginia 5 -9.075658 0 1\n", + "Wisconsin 10 4.259403 1 0\n", "Wyoming 3 NaN 0 1\n", "\n", "[51 rows x 4 columns]" ] }, - "execution_count": 307, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -8056,7 +8057,7 @@ " \n", " Arizona\n", " 11\n", - " -5.453958\n", + " -5.768496\n", " 0\n", " 1\n", " \n", @@ -8077,14 +8078,14 @@ " \n", " Colorado\n", " 9\n", - " 2.667843\n", + " 2.705829\n", " 1\n", " 0\n", " \n", " \n", " Connecticut\n", " 7\n", - " 8.936227\n", + " 8.980923\n", " 1\n", " 0\n", " \n", @@ -8112,14 +8113,14 @@ " \n", " Georgia\n", " 16\n", - " -8.811154\n", + " -8.644088\n", " 0\n", " 1\n", " \n", " \n", " Hawaii\n", " 4\n", - " 18.584803\n", + " 18.697047\n", " 1\n", " 0\n", " \n", @@ -8133,28 +8134,28 @@ " \n", " Rhode Island\n", " 4\n", - " 13.236853\n", + " 13.349511\n", " 1\n", " 0\n", " \n", " \n", " South Carolina\n", " 9\n", - " -6.334382\n", + " -5.875070\n", " 0\n", " 1\n", " \n", " \n", " South Dakota\n", " 3\n", - " -1.522121\n", + " -1.796079\n", " 0\n", " 1\n", " \n", " \n", " Tennessee\n", " 11\n", - " -2.521086\n", + " -2.136715\n", " 0\n", " 1\n", " \n", @@ -8168,42 +8169,42 @@ " \n", " Utah\n", " 6\n", - " -29.179269\n", + " -29.204379\n", " 0\n", " 1\n", " \n", " \n", " Vermont\n", " 3\n", - " 14.891764\n", + " 15.005680\n", " 1\n", " 0\n", " \n", " \n", " Virginia\n", " 13\n", - " 2.420244\n", + " 2.443012\n", " 1\n", " 0\n", " \n", " \n", " Washington\n", " 12\n", - " 12.312505\n", + " 12.346282\n", " 1\n", " 0\n", " \n", " \n", " West Virginia\n", " 5\n", - " -9.436884\n", + " -9.075658\n", " 0\n", " 1\n", " \n", " \n", " Wisconsin\n", " 10\n", - " 4.530315\n", + " 4.259403\n", " 1\n", " 0\n", " \n", @@ -8224,28 +8225,28 @@ "State \n", "Alabama 9 NaN 0 1\n", "Alaska 3 NaN 0 1\n", - "Arizona 11 -5.453958 0 1\n", + "Arizona 11 -5.768496 0 1\n", "Arkansas 6 NaN 0 1\n", "California 55 19.966475 1 0\n", - "Colorado 9 2.667843 1 0\n", - "Connecticut 7 8.936227 1 0\n", + "Colorado 9 2.705829 1 0\n", + "Connecticut 7 8.980923 1 0\n", "Delaware 3 NaN 1 0\n", "District of Columbia 3 NaN 1 0\n", "Florida 29 2.170963 1 0\n", - "Georgia 16 -8.811154 0 1\n", - "Hawaii 4 18.584803 1 0\n", + "Georgia 16 -8.644088 0 1\n", + "Hawaii 4 18.697047 1 0\n", "... ... ... ... ...\n", - "Rhode Island 4 13.236853 1 0\n", - "South Carolina 9 -6.334382 0 1\n", - "South Dakota 3 -1.522121 0 1\n", - "Tennessee 11 -2.521086 0 1\n", + "Rhode Island 4 13.349511 1 0\n", + "South Carolina 9 -5.875070 0 1\n", + "South Dakota 3 -1.796079 0 1\n", + "Tennessee 11 -2.136715 0 1\n", "Texas 38 -2.295507 0 1\n", - "Utah 6 -29.179269 0 1\n", - "Vermont 3 14.891764 1 0\n", - "Virginia 13 2.420244 1 0\n", - "Washington 12 12.312505 1 0\n", - "West Virginia 5 -9.436884 0 1\n", - "Wisconsin 10 4.530315 1 0\n", + "Utah 6 -29.204379 0 1\n", + "Vermont 3 15.005680 1 0\n", + "Virginia 13 2.443012 1 0\n", + "Washington 12 12.346282 1 0\n", + "West Virginia 5 -9.075658 0 1\n", + "Wisconsin 10 4.259403 1 0\n", "Wyoming 3 NaN 0 1\n", "\n", "[51 rows x 4 columns]" @@ -8326,7 +8327,7 @@ }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 180, "metadata": { "collapsed": false }, @@ -8334,31 +8335,31 @@ { "data": { "text/plain": [ - "Colorado 1.3\n", - "Florida 2.2\n", - "Iowa 1.3\n", - "New Hampshire 2.5\n", - "Nevada 2.2\n", - "Ohio 1.2\n", - "Virginia 2.2\n", - "Wisconsin 1.5\n", + "Colorado 3\n", + "Florida 3\n", + "Iowa 3\n", + "New Hampshire 3\n", + "Nevada 3\n", + "Ohio 3\n", + "Virginia 3\n", + "Wisconsin 3\n", "dtype: float64" ] }, - "execution_count": 282, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "national_margin_of_error = 2.5\n", - "local_margin_of_error = Series(np.array([1.3, 2.2, 1.3, 2.5, 2.2, 1.2, 2.2, 1.5]), index=tossup)\n", + "national_margin_of_error = 3.0\n", + "local_margin_of_error = Series(np.array([3.0] * 8), index=tossup)\n", "local_margin_of_error" ] }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 181, "metadata": { "collapsed": true }, @@ -8372,7 +8373,7 @@ }, { "cell_type": "code", - "execution_count": 284, + "execution_count": 182, "metadata": { "collapsed": false }, @@ -8398,113 +8399,113 @@ " \n", " \n", " 0\n", - " 2.111649\n", - " -1.345864\n", - " -0.686623\n", - " -2.682422\n", - " 1.903897\n", - " -2.761846\n", - " 3.838586\n", - " -1.141810\n", + " 4.873036\n", + " -1.835269\n", + " -1.584515\n", + " -3.218906\n", + " 2.596223\n", + " -6.904616\n", + " 5.234435\n", + " -2.283621\n", " \n", " \n", " 1\n", - " 0.414751\n", - " -0.548615\n", - " 1.900740\n", - " -5.150352\n", - " -0.709318\n", - " -0.460865\n", - " 2.494293\n", - " -1.649837\n", + " 0.957117\n", + " -0.748111\n", + " 4.386324\n", + " -6.180422\n", + " -0.967252\n", + " -1.152163\n", + " 3.401308\n", + " -3.299674\n", " \n", " \n", " 2\n", - " -0.224157\n", - " -1.931289\n", - " 0.054878\n", - " 1.457038\n", - " -2.421362\n", - " 1.373668\n", - " 1.983500\n", - " 0.753742\n", + " 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5.847938 7.670245 9.284700 8.856272\n", - "5 0.754672 -1.445605 -0.597089 1.020296 3.541123 1.747876 1.176474 6.016913\n", - "6 2.391369 3.096129 1.995542 -2.860361 2.146910 3.324164 1.527997 4.977570\n", - "7 3.948715 4.409581 2.599458 0.855990 3.623648 5.869691 3.738883 4.273370\n", - "8 3.717523 2.419312 3.923725 2.667092 10.316002 2.914866 -0.342200 4.188223\n", - "9 -0.407406 0.815238 -0.834971 -9.885856 1.136184 1.886328 -0.356844 2.390035" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", + "0 11.415885 4.172714 4.085408 -0.904325 11.467091 0.925123 11.514468 5.812802\n", + "1 3.178078 0.937984 5.734359 -8.187729 3.581729 2.355688 5.359453 0.474861\n", + "2 0.302305 -2.348852 0.073305 -1.660233 -0.154249 5.540650 3.261545 3.880646\n", + "3 1.513912 -3.774705 -2.430252 -8.224232 0.335699 1.689300 -3.526455 -0.825343\n", + "4 4.499779 3.490815 3.674634 2.295036 5.537386 8.551434 11.277888 10.341004\n", + "5 0.133780 -2.488465 -2.405113 1.558383 3.189730 0.085190 1.019217 8.563627\n", + "6 2.547089 3.503356 2.214198 -3.098405 1.087077 2.425474 1.297113 5.500056\n", + "7 5.451011 5.192502 2.917898 1.361217 2.998887 7.979555 4.210034 3.593357\n", + "8 4.668911 2.441775 5.725319 3.534539 12.088102 0.300757 -1.391801 3.243533\n", + "9 -0.754017 0.859513 -1.160148 -11.528999 0.175280 2.536686 -0.806660 2.605480" ] }, - "execution_count": 286, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -8866,7 +8867,7 @@ }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 185, "metadata": { "collapsed": false }, @@ -8893,89 +8894,89 @@ " \n", " Colorado\n", " 1.000000\n", - " 0.661364\n", - " 0.782967\n", - " 0.632265\n", - " 0.666076\n", - " 0.801657\n", - " 0.663607\n", - " 0.762725\n", + " 0.495928\n", + " 0.493505\n", + " 0.503438\n", + " 0.500832\n", + " 0.505176\n", + " 0.499251\n", + " 0.502516\n", " \n", " \n", " Florida\n", - " 0.661364\n", + " 0.495928\n", " 1.000000\n", - " 0.666801\n", - " 0.517375\n", - " 0.559425\n", - " 0.671366\n", - " 0.558036\n", - " 0.643287\n", + " 0.504859\n", + " 0.484577\n", + " 0.495763\n", + " 0.494672\n", + " 0.493993\n", + " 0.501656\n", " \n", " \n", " Iowa\n", - " 0.782967\n", - " 0.666801\n", + " 0.493505\n", + " 0.504859\n", " 1.000000\n", - " 0.625854\n", - " 0.659162\n", - " 0.799275\n", - " 0.661989\n", - " 0.763126\n", + " 0.493213\n", + " 0.489625\n", + " 0.502977\n", + " 0.496665\n", + " 0.505508\n", " \n", " \n", " New Hampshire\n", - " 0.632265\n", - " 0.517375\n", - " 0.625854\n", + " 0.503438\n", + " 0.484577\n", + " 0.493213\n", " 1.000000\n", - " 0.536203\n", - " 0.640725\n", - " 0.537008\n", - " 0.612874\n", + " 0.505407\n", + " 0.500251\n", + " 0.505616\n", + " 0.506102\n", " \n", " \n", " Nevada\n", - " 0.666076\n", - " 0.559425\n", - " 0.659162\n", - " 0.536203\n", + " 0.500832\n", + " 0.495763\n", + " 0.489625\n", + " 0.505407\n", " 1.000000\n", - " 0.674964\n", - " 0.555986\n", - " 0.646501\n", + " 0.497530\n", + " 0.491802\n", + " 0.504529\n", " \n", " \n", " Ohio\n", - " 0.801657\n", - " 0.671366\n", - " 0.799275\n", - " 0.640725\n", - " 0.674964\n", + " 0.505176\n", + " 0.494672\n", + " 0.502977\n", + " 0.500251\n", + " 0.497530\n", " 1.000000\n", - " 0.669790\n", - " 0.770683\n", + " 0.491498\n", + " 0.493094\n", " \n", " \n", " Virginia\n", - " 0.663607\n", - " 0.558036\n", - " 0.661989\n", - " 0.537008\n", - " 0.555986\n", - " 0.669790\n", + " 0.499251\n", + " 0.493993\n", + " 0.496665\n", + " 0.505616\n", + " 0.491802\n", + " 0.491498\n", " 1.000000\n", - " 0.641731\n", + " 0.499111\n", " \n", " \n", " Wisconsin\n", - " 0.762725\n", - " 0.643287\n", - " 0.763126\n", - " 0.612874\n", - " 0.646501\n", - " 0.770683\n", - " 0.641731\n", + " 0.502516\n", + " 0.501656\n", + " 0.505508\n", + " 0.506102\n", + " 0.504529\n", + " 0.493094\n", + " 0.499111\n", " 1.000000\n", " \n", " \n", @@ -8984,17 +8985,17 @@ ], "text/plain": [ " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", - "Colorado 1.000000 0.661364 0.782967 0.632265 0.666076 0.801657 0.663607 0.762725\n", - "Florida 0.661364 1.000000 0.666801 0.517375 0.559425 0.671366 0.558036 0.643287\n", - "Iowa 0.782967 0.666801 1.000000 0.625854 0.659162 0.799275 0.661989 0.763126\n", - "New Hampshire 0.632265 0.517375 0.625854 1.000000 0.536203 0.640725 0.537008 0.612874\n", - "Nevada 0.666076 0.559425 0.659162 0.536203 1.000000 0.674964 0.555986 0.646501\n", - "Ohio 0.801657 0.671366 0.799275 0.640725 0.674964 1.000000 0.669790 0.770683\n", - "Virginia 0.663607 0.558036 0.661989 0.537008 0.555986 0.669790 1.000000 0.641731\n", - "Wisconsin 0.762725 0.643287 0.763126 0.612874 0.646501 0.770683 0.641731 1.000000" + "Colorado 1.000000 0.495928 0.493505 0.503438 0.500832 0.505176 0.499251 0.502516\n", + "Florida 0.495928 1.000000 0.504859 0.484577 0.495763 0.494672 0.493993 0.501656\n", + "Iowa 0.493505 0.504859 1.000000 0.493213 0.489625 0.502977 0.496665 0.505508\n", + "New Hampshire 0.503438 0.484577 0.493213 1.000000 0.505407 0.500251 0.505616 0.506102\n", + "Nevada 0.500832 0.495763 0.489625 0.505407 1.000000 0.497530 0.491802 0.504529\n", + "Ohio 0.505176 0.494672 0.502977 0.500251 0.497530 1.000000 0.491498 0.493094\n", + "Virginia 0.499251 0.493993 0.496665 0.505616 0.491802 0.491498 1.000000 0.499111\n", + "Wisconsin 0.502516 0.501656 0.505508 0.506102 0.504529 0.493094 0.499111 1.000000" ] }, - "execution_count": 287, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" } @@ -9005,7 +9006,94 @@ }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 186, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DemWinPctRepWinPct
Colorado0.74680.2532
Florida0.69370.3063
Iowa0.67040.3296
New Hampshire0.35610.6439
Nevada0.88450.1155
Ohio0.82330.1767
Virginia0.72500.2750
Wisconsin0.85080.1492
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" + ], + "text/plain": [ + " DemWinPct RepWinPct\n", + "Colorado 0.7468 0.2532\n", + "Florida 0.6937 0.3063\n", + "Iowa 0.6704 0.3296\n", + "New Hampshire 0.3561 0.6439\n", + "Nevada 0.8845 0.1155\n", + "Ohio 0.8233 0.1767\n", + "Virginia 0.7250 0.2750\n", + "Wisconsin 0.8508 0.1492" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wins = DataFrame([sum(simulated_poll_predictions[s] > 0) / float(N) for s in tossup], index=tossup, columns=[\"DemWinPct\"])\n", + "wins['RepWinPct'] = 1 - wins['DemWinPct']\n", + "wins" + ] + }, + { + "cell_type": "code", + "execution_count": 187, "metadata": { "collapsed": false }, @@ -9017,16 +9105,16 @@ }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 188, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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IEON8MnwauAiYSbbbuTlFjmdTCfgP4Js0tf6e/7j9dr5z+38xb+5ZXPD28ykvL2fIkCEM\nGTKEoUOHMmzYMEaMGMGIESMUyIocI3oKUt9MP6Re162srNz/uqKigoqKijdxWhERkYFn6dKl3HHH\nHTz8p6fZ21hDlFPJci3wTuAMMhrnKQNOAXANWa4BVvDS4v9iyeKXgRacVpw2nHacdqCDcAoeI2LF\nFBeWMmJ4OZOnjmH6jOnMnj2buXPnMnv2bGKxNzOtkcjAUFVVRVVV1Ruu39OY1HlApbsvzK1/CQgO\nkwDpFqAlb0xqr+pqTKqIiAwETU1NlJeXH7RtztvmUV/XTEtTO23tHXSkUqSzKYIgQzxWyKDSEkaN\nCrOgTp8+nVNPPZVzzjmH0047DYBf/epX3PWTn/LS4r/SkW4nyiVk+XtgITDkrb9IkeOaEwar29mX\npdhYR5RVOBvIsgNoJxopIRaNE41EiUajxGMx4vEoiUSMgoI4BYUJBpUVccHb53Pdddcxc+bMfr0q\nkWNBXydOihEmP7oY2Am8RBfJj3L7VgLNeUFqr+oqSBURkRNFfX09a9asYcOGDaxfu5b1r73GhrVr\nWb91K63JJG2ZTKca/w4MzpUhectiwuk8Ds6CGrCVgGqgDYhhDMN4HwEfAM4nHJcnIkdPK+HvZTOQ\nzCvtndbrifFnMrxMLFrI1JPH8c7L3sH111/P7Nmz+6vxIv3maExBcxkHppG5291vM7MbAdz9TjMb\nRZi5t4ww30IzMMvdW7qq28XxFaSKiMhxqb29neeee47HH3qIP//xj6zbupUZhYVMDQKmtLUxNQiY\nCkwBRtHVxCpv9P7XDjTmjioix64M8CpQRZQHybKYaCTB5IljufAd51FWVtZt7dLSUj7xiU8wZowy\nZ8vxrc+D1KNNQaqIiBwvgiDg1Vdf5fFHH+XP99/Pi8uXM7uwkAUtLVwSBMyl+2eZh96ddf8TGViy\nwOvA08R4kvCp6+E59WR5jaHlI3jvBy7l5ptvZtq0aW9FQ0X61NF4krqQA09D7zrMeNTvA5cR9j/6\nmLu/ktu+GWgi/I1Mu/ucLuoqSBURkaOipaWFpUuXUldX1+1+7k4ymaS1tZW2tjZaW1tpbWqitbEx\nLM3NNDU08NLy5Yww45JUigWpFBWE3Yh6S0GqiBy5RsI5YH9Olr9QVjyYy959IV/84hc588wz+7tx\nIr3S12NSo4TjSi8BdhB26z1oXKmZvQv4tLu/y8zmAt9z93m59zYB57j73m7OoSBVRETeNHdn8+bN\nvPDCCzz/1FM8/9RTrNm6ldlFRfSmo1xBEFASBJRkMpSk05S4UwL7SylwJjD+TbRRQaqIvDltwKNE\n+QVZHqWooISzz51BJpWhsaGVlpYk7W0ddKTSpDNpMtk0QZAGjGGDh/G2eafyrsvfxTXXXMPQoUP7\n+2JkAOnrIPU84Ja8DL1fBHD3b+bt82PgKXf/TW59NXChu+/OBannuvthv8JWkCoiIkfK3dm5cydr\n1qxh2csv8/xjj/HCkiWQSjE/FuO85mbOB84mnITiWKEgVUT6Tgp4EniG8Ku0slwZ1MXrDuAlIjwF\nPEXAJgri5UyfPp6LLv4brrnmGubOnRseNZWioaGBxsZGGhoaaGpqoqmpiebmZk466SQWLFhAJKIp\nreTI9HWQ+gHgUne/Ibd+LTDX3T+Tt88fgdvc/fnc+p+Bm9x9mZltJOyjkAXudPefdHEOBakiItKl\nxsZG1q5dy5o1a1i7ejVrX3mFNatXs277dkqjUaYnEpyRTHJ+RwfnARPpKhA8dihIFZFjQxuwFHiO\nGI+RYSlhIJsl/LsUA+IYCSCBUQAU4DTgtDB8yEjmnT+b973/fVx99dUUFxf325XI8eFIg9SeZiPu\n7d3zcCe8wN13mtkI4HEzW+3uz3TeqbKycv/riooKKioqenlaERE5FqRSKXbu3MmOHTvYvn07DQ0N\nRCIRotHo/mV+iUQipFIp6urqqKutpa66mtrqaupqasJtDQ3UNjURBAHTi4qY7s70lhbe7c7ngWmE\nk7XQ3t6/Fy4iclwqBt4OvJ0MXyL8yN8MFBKmfws/2u8LBA4OCLZRW/8cDz34OA89+A2uu+4TlBYN\n5YyzpnH5FZexYMECRowYwYgRIxS8DmBVVVVUVVW94fo9PUmdB1Tmdff9EhDkJ0/Kdfetcvd7c+v7\nu/t2OtYtQMu+eVTztutJqojIcaC2tpYXX3yR5a++yvb169mxaRPbt29nx5491Le2MqqoiHGxGGOD\ngCGZDIEZgRlZIGsWFti/Le7O8HSaYR0dDHNnGOwvw3PLco7tJ6NHSk9SReTE0wC8gPEUER4jy2bC\nrMUpwr96ccxiRCxGNBIjFo1RWJhg6NBBjBk/grFjxzBp0iSmTJnCrFmzOP300yktLe3PC5KjoK+7\n+8YIEyddDOwEXqL7xEnzgDvcfZ6ZFQNRd282sxLgMeBWd3+s0zkUpIqIHGOCIGDlypU8//zzvPDE\nEzz/zDNU19Uxt7CQs1tbGZ/NMg4YC4wDRhKmgJfuKUgVkYHDCQPV1i7KXqAa2EmUTRjbcKoJqMVp\nAuKUlQzhHQvm8k//9E/qZXkCOBpT0FzGgSlo7nb328zsRgB3vzO3zw+BhYT/6z6eG496MnBf7jAx\n4B53v62L4ytIFRHpJ5lMhurqarZu3crWrVtZs3IlLzz+OIuXL2dELMb5QcB5bW2cD5yKAtE3S0Gq\niEhPAmAP8DxRfkuWh4hFI5x1xgw+dv1HWLRoEYlE4g0dOZPJkEql9i9TqRRBEDBmzBglgzrK+jxI\nPdoUpIqIHB2pVIpdu3ZRXV3Nrl272LlzJ1s3bmTrmjVs3bSJrdXV7GpoYERBARPicSYEASe3tTEv\nm2Ue4dNR6VsKUkVEjlQALMG4D+N3BFQzfsw43v93VzBu3Di2bNnCjh072FW9m5rqeuobWmhrS5JK\nJ8l6EsgQ/q3d9/c2QvjX2PJeZ4lFSiktKWXUqCFMmT6BmTNncuaZZ3L++eczefLk/rjwE4qCVBGR\nY8DOnTt5+eWXSaVSxONxEolEl8t4PE4qlSKZTO4v7e3tB60nk0kymQxBEJDNZg8qQSZDNpMhlUyy\ne+tWqrdvZ9fu3VTX1dGcTDKysJDR8Tij3RmdTjOhvZ0JwATCTLhjgDf2fbRsBk4m/PjT2+/fD707\nLwIOSXyfMwm4m3DEjYiIhLYADxLlXqAFYxQBYwkYD5xE+BXrvjICKCLsBxTh8H+tW4BNhH/ZNxFh\nDRFWEbCJgGrAiUaKKYgXUFpSzJAhpZw0diijR49i7NixTJgwgUmTJhEEAclkkra2Njo6Og66n3d0\ndABw3nnncemllw64pFJHo7vvQg50970rP2lS3j7fBy4jzGf9MXd/5QjqKkgV6QdVVVUa49FHWlpa\nePnll1n84ossfuIJXnr5ZdpaWzm3oIASIA2kzMJlbj3tTgrIuJMwo9CMQsK8ioXuFLlTGAQUBgEF\nQUAsCIi6E3En6k40CIhy4LabILwdjwZG5ZbD6H3wJN2bBNRwoLuzAY8C83mzQWp397/JhEHqO3p5\ndBHpWRVQ0c9tkOOLA/XAbsI7wb6yiyjbMHbg7MbZS3g3iGO5KXzC6XvCZVgCsqzB2UMiVs7ok4Zx\n5rkz+Zu/+RuuuOIKZsyYcdCZgyBgy5YtrF69mg0bNrBlyxa2b99OU1MTo0ePZvLkycyYMYOZM2cy\nY8YMYrGeJm7pP32dOClKmDjpEmAHsITuEyfNBb6XS5zUY91cfQWpIv2gsrLyoOmfjgXZbJbW1tbD\nlmQySSqVIp1Od73s6KC9uZnWpqawNDfT2tISlrY2WtvbaU0mCYKAaCQSTo2yr0SjB7ZFoxQWFFBS\nXExJaSklgwaFpbw8LGVlFBYWsnb5chY/+yzrd+zg9KIi5iaTzEmlmAtM4cTKSjvQdRUubqYvnqQq\nSBV5a1Xmikh/agX+CiwnwksYS8iyFiNKceEg0pk06UwSpx0owBhMhGEYJ+GMxSkjwg5gOwG7CagD\n2jErIhEroqSoiEFlxUQjRiQaIRqJEoka0WiEaDQSbotFKCouZPjwYYwaNYoxY8Ywfvx4Jk6cyNSp\nU/t8nG5fz5M6B1jv7ptzB78XuArIDzSvBP4XwN0Xm9lgMxtFeHftqa7IgBMEAa2trTQ1NdHU1ERz\nc/P+162trcRiMQoLCw8pRUVF+1937jbamz8i2WyWdDq9P6Brbm5m06ZNh3Qrze+a0tbWdqB9tbU0\n19fTVF9PU0ND2PaWFtqTSeKxGIlc19X9y0SCeCJBIpHAIhGS7e20t7WFx+7oCEsqRXtHB8l0mvZ0\nmnQ2S3EsRkksRkk0SkkkQokZJUCJO4XuJNxJBAHxfcsgIJHNEg8CCgjnzizpphQTPg0LCKcszy/5\n29o5NBdh277XZtTFYpyeTrMIOAMoSKf75j+IHLd2Ap8AngOGAjcTduaF8CPxX7us9WHgF7nXvwD+\nhfB/2T932u8l4J+A1YRd194PfJfwm3oRETm+lABzgbkE/ENum+NsoTW5ARjCge7KBTjhZ5N8wSHH\n7MB9Nx3pXXSkq9nbVMfhP+Xse91GhGoibAOWEVCH04DTDKQxColGCyhMFDKotIihw8oYNTYMaseP\nH8+kSZOYOnUq5513Xp93X+7pSeoHgEvd/Ybc+rXAXHf/TN4+fwRuc/fnc+t/Jrw3TwIWdlc3t73b\nJ6mPPfYYl1566Ru7OhHpc8VmlJkxyIyySIRCM9LuYVfW/GXeawcKzSja160117U1f33faz19lGPR\nb9rb+ZtEgjHRA/mNm4OA3yaTXFdUhJnxp2SSoZEIc+NxGtx5JJnkooICxkSjLEulWJ7JHPKhwphI\nhPNwGgl4nAgXAkNxXsNZS4SK3Dfnewmfug4F2gh4GmMKEWYgIr0X8DoRTu/vZogcB1K5gLUudw+q\nJfwK/1DnnjOHJUsXd3u0vn6S2tt+uG/qc6WZPpaKHC/a3GlzZxdAtvP3eofXqG79cpx7OJf0orOf\nth+4ae8OAlZlMj3W2cfZQpYt+9cDnjjo/YCnuqn7Clle6fb4InKoLCv6uwkiJ5SlL7/U5/FcT0Hq\nDmB83vp4YHsP+4zL7RPvRd0jiqhFRET6g5ltAq539yfztk0CNhLeS98G/NHdR+a9/wngfe7+TjOr\nBKa6+7V571cCU9z9w2b2Y6DJ3W/Ke38ncK27P2lm0wn7955D2Gs9Bix19wuP0iWLiIj0m54Gsi0F\nppnZJDNLAFcDD3Ta5wHgIwBmNg9ocPfdvawrIiJyItgJDDWz0rxtEzj4y9nuuhPsJO+LXTMrJkzQ\nvM+PgJWEgW458BWUvFlERE5Q3d7g3D0DfJow0/5K4DfuvsrMbjSzG3P7PARsNLP1wJ3AP3ZX96hd\niYiISD9x923A88BtZlZgZrOB64Bf9vIQvweuMLP5uS92v87B9+hSoBloM7NTgE/2XetFRESOLT1O\npuPuDwMPd9p2Z6f1T/e2roiIyAkk/+noB4EfEz4VrQe+ltc92Dn0Ser+be6+wsw+BfyKMO3jd4Ft\neft+Afhv4CbgFeBe4KI+vRIREZFjRLfZfQHMbCFwB+GsDXe5+7c6vf8hwpumEX7L+0l3fy333mag\niTDHcdrd5/T1BYiIiIiIiMiJo6cpaKLAGuASwgRJS4AP5nfbNbPzgJXu3pgLaCvdfV7uvU3AOe6+\n9yheg4iIiIiIiJwgekq6MAdY7+6b3T1N2L3oqvwd3P0Fd2/MrS4mzO6bT9l7RUREREREpFd6ClLH\ncvCYmO25bYdzPfBQ3roDfzazpWZ2wxtrooiIiIiIiAwUPSVO6n7Aah4zu4gwk+H8vM3z3b3azEYA\nj5vZand/plO9Xp9DREREREREjj/u3usetj0FqTvIm7ct93p7551yqfZ/Aix09/q8hlTnlnvM7H7C\n7sPPdK7fU/ImEel7lZWVVFZW9nczRAYk/f6J9A/97on0D7MjGwHaU3ffpcA0M5uUm7ftauCBTiec\nANwHXOvu6/O2F5vZoNzrEuCdwOtH1DoREREREREZULp9kuruGTP7NPAo4RQ0d7v7KjO7Mff+ncDX\ngCHAj3IR8r6pZkYB9+W2xYB73P2xo3YlIiIiIiIictzrqbsv7v4w8HCnbXfmvV4ELOqi3kbgzD5o\no4gcBRXpMIIxAAAgAElEQVQVFf3dBJEBS79/Iv1Dv3six4du50l9Sxpg5v3dBhERERERETk6zOyI\nEif1NCYVM1toZqvNbJ2Z3dzF+x8ys+Vm9pqZPZdLotSruiIiIiIiIiL5un2SamZRYA1wCWGm3yXA\nB919Vd4+5wEr3b3RzBYCle4+rzd1c/X1JFVEREREROQE1ddPUucA6919s7ungXuBq/J3cPcX3L0x\nt7oYGNfbuiIiIgOJmR1URERE5FA9BaljgW1569tz2w7neuChN1hXREREREREBriesvv2uh+umV0E\nXAfMP9K6+ZMqV1RUKPOaiIiIiIjIcaqqqoqqqqo3XL+nManzCMeYLsytfwkI3P1bnfabDdwHLHT3\n9UdYV2NSRURkQOjcxVf3PxERGQj6ekzqUmCamU0yswRwNfBApxNOIAxQr90XoPa2roiIiIiIiEi+\nbrv7unvGzD4NPApEgbvdfZWZ3Zh7/07ga8AQ4Ee5b4jT7j7ncHWP4rWIiIiIiIjIca7b7r5vSQPU\n3VdERAYIdfcVEZGBqK+7+2JmC81stZmtM7Obu3j/FDN7wcySZvb5Tu9tNrPXzOwVM3upt40SERER\nERGRganb7r5mFgV+CFwC7ACWmNkDnbrt1gGfAd7TxSEcqHD3vX3UXhERERERETmB9fQkdQ6w3t03\nu3sauBe4Kn8Hd9/j7kuB9GGOodnKRUREREREpFd6ClLHAtvy1rfntvWWA382s6VmdsORNk5ERERE\nREQGlm67+xIGmW/GfHevNrMRwONmttrdn+m8U2Vl5f7XFRUVVFRUvMnTioiIiIiISH+oqqqiqqrq\nDdfvNruvmc0DKt19YW79S0Dg7t/qYt9bgBZ3/85hjtXl+8ruKyIiA4Wy+4qIyEDU19l9lwLTzGyS\nmSWAq4EHDnfuTg0pNrNBudclwDuB13vbMBERERERERl4ug1S3T0DfBp4FFgJ/MbdV5nZjWZ2I4CZ\njTKzbcDngH8xs61mVgqMAp4xs1eBxcCf3P2xo3kxIiIiIseS2tpaPve5z/GnP/2JIAj6uzkiIseF\nbrv7viUNUHdfEREZINTdt28FQcBf//pXnn32WZYtW8aqFavYtGE3exsacXdi0RgFBQmKChOUlBZS\nVl7M4KFllJWVUV5eTjqdpqGhgcbGJhr3NtPSnKS1rYNkMkUqnSKbzXDKjEl8+7u3cemllx5R23bu\n3Mn1H1/Eo49VEeFUsmwgGgk468wZ3HDj9Vx33XXEYj2lBhEROTEcaXdfBakiIiJvkeMtSA2CgF//\n+te8+OKLXHjhhSxcuJDS0tJe11+6dCk/+9nPePyRKjZu3kE26CARK2Fw2SDGTRzBtOknM2vWLM49\n91zOP/98Bg8evL9ubW0tq1evZt26dWzatIlt27ZRXV3Nrh17qN65l/qmJtKZZqCAKBMwppNhNjAN\nmAIUAE1Ac265rzQQpQ6jHieBM5SAYUA5MAgoyytxIvyGgP+mvLSUT/2/j3PLLbeQSCQOe82bNm3i\nYx+9nr888yJRFpDlX4HTCXNRvozxO4zf4Oxh6smTuPajV/PZz36WsrKyQ37227ZtY/Xq1WzYsIHN\nmzdTW1vL2LFjmTZtGrNmzWLWrFkUFxf3+t+jr6RSKW6++WZWr1rDd//jO8ycOfMtb4OIHF/6PEg1\ns4XAHUAUuKtz0iQzOwX4GXAW8JX8xEg91c3toyBVREQGhOMhSG1ra+NHP/oRP7vrF6xaswH3UqKc\nRsAqAnYTj5Ux+qThnHnOKVxwwQVceeWVzJgxA4DFixfzP//zPzz+yNNs3rqDbJAlxjwyXA5cBIwB\nNgObgI1EWYGxlixbcWoxCjCLEngScIxyIgzFGAmMIcs4nLHAOA4Eo+VvwU8lCfyOCLeBbWPBJRfw\nvR/csf+6AVatWsXHPnI9Ly19hShXkuXrwIzDHhHWA/cT5ZdkWcPQshFkA6c9mSSdTeKeBGIYg4kw\nHOMkYDBQQ0A1AbVAM0YBsWgRxUVFDC4vYdSYoUyYNJ4pU6Ywc+ZMZs+ezWmnndYnT21bWlr41Kc+\nxT2//AMeTMA4lSz/x5RJk/nOHbdx1VVXvelziMiJqU+DVDOLAmuAS4AdwBLgg+6+Km+fEcBE4D1A\n/b4gtTd1c/spSBURkQHhWA1Sa2pquP322/nNPfezded2IkzEuQbn/cAsDuRGbANWAMuJ8BLGErKs\nwYhgFiFwJ8r5ZLkCuBA4jZ5zNO6TJfy4kAJOAkrplJPxGLGEKLeT5QEmjpvAp/7fIu7533tZvmIl\nUa4mSyUw6QiPuRv4C1ACjMyVEUBRD/WywB6gOld2ATuIsh5jMwE7CKgBWolYMQXxYsrKSpgwcSQL\n3/VOFi1axIQJE3ps3a5du/iHRTfyp4eeIOJnkOXfCP99Daghwg8J+D7lpcV84eZ/5Mtf/jKRSG//\n3UVkIOjrIPU84Ja8KWi+CODu3+xi34OmmOltXQWpIiJyIti9ezePPPIIW7dupa66Oiw1NdTW1lLX\n0EBdUxONyeRBdZ555hkuuOCCPjl/bW0tL730EsuXLw+7iK7fyK4ddfR0i21oaGZvUw1RziLLh4Er\ngfFHcGYHthIGsDPofVB6vKvF+G+MnwCXEfAVYGx/N+owOoCdwHZgB8ZKIjxMltdIxAYxa+Yk3vXu\nS1m0aBGTJ0/eX2vDhg1c9/EbeOaZF4lQQZZvAOd0c45fE+EbRKJ1XH31FXz/B99n6NChR/3qROTY\n19dB6geAS939htz6tcBcd/9MF/t2DlJ7VVdBqoiIHK/WrVvH/91/P3/45S9ZsXYtC+JxprW1MTwI\nRznuK8Nzy0M/rpdSlCjk0ssu4Ktf+ypnn312t+fbunUrDz/8MM888wyvL1/Fzu11NLW0kMq0AWmM\nEUQYC5xMllMIu9dGe7iKcmABYVdSGVg6gCUYTxDhIbIsJxErYcaMiZgZr/11JVHek3s63F3X5XwO\nPEWUfyXgRaZPPZl5889lwYIFvPvd7z5k7K2IDAxHGqT2NEDhzUSPva5bWVm5/3VFRQUVFRVv4rQi\nIiJHh7uzdOlS/vC73/GHe++lvraWq9z5ajJJBVDQ0XGER9xLe+oJ/vh/P+UP/3cBpUVlXPnei/nc\n5z7HunXreOqpp1j28nI2rNtJU0sDgaeIMBnjTLJ8EJgMTMiVkThGto+vWU5kBcAFOBeQ5RYgRSqz\nlL+ueApjL/AHskf0VB3CLsDvIMs7gLWsWf8QG9Y/wy/+9ysEfIxErJyxY0Zw7tzTufjii3nve9/L\n0KFDee2111i2bBmvv/4669evZ/OG7eze1UBTawvpTCsA0UghBfFCSkqKGDKklJPGDGXUqJMYN24c\nEydOZP78+Zx99tnqaixyDKiqqqKqquoN1+/pSeo8oDKvy+6XgOAwCZA6P0ntVV09SRURkWNBOp1m\n79691NXVhV106+r2l9rqanZv28aTTz3FoHSa9ySTvCeT4W0cWefWQ79Czr//JYFHiHI3WZ4gwkgi\nzCbDHGA24fjOSUd4RpFjSTvwOrCMKM/hvETAJiAASogwmgiTCJhGwFTClCf7voSJADWdyi6ibMXY\ngbOLLJuBDIOKh3DyyaM5d+5ZXHzxxVx++eV6givSz/q6u2+MMPnRxYSDGV6ii+RHuX0rgea8ILVX\ndRWkiohIf9m2bRv/c9dd/O+dd7K5poahBQUMi8cZFokwzJ3hmQzDOjoYls0yHJgPnPImztd9kCoy\nEKWBDD0nieoNJ0wetRx4lRjPE/AqAdXEo4MYOXwoRUUFdHSkSaUypNMZ0pks2WyWbBAW94BRI4bz\ntx98N5///OcZN25cH7RLRI7GFDSXcWAambvd/TYzuxHA3e80s1GEmXvLCL8KawZmuXtLV3W7OL6C\nVBERecukUikeeOAB7r7jDl56+WWudue6jg7O5ug/o1SQKtIfksBK4DXCcbiFhEFxYV7Ztx4BniXK\nL8iylCGDhvOuKyu46aabmD17dv80X+QE0OdB6tGmIFVEZGDKZDI0NjZSX19PQ0PDIcuGujriiQST\np0xh8uTJTJ48mbFjxxKN9pQIqGsrVqzg7h/9iF/+/OfMAhY1N/M+oLhPr6p7ClJFjidNwMNEuYcs\nf6YoUcrbK87hk//4CYqLi9myZQvbt29nx44d1NTUsGd3LbV7GmlsaCXZkWba9LFcceVlXHfddb2a\n6qezIAioqalh5MiRGmcrx72j8SR1IQeeht51mPGo3wcuI8w//zF3fyW3fTPhb3gWSLv7nC7qKkgV\nERkAtm/fztNPP83TjzzC0088wYZduyhLJBgSjzM4EmGIGYODgCHZLINTKYZkMiTN2FRczKZYjE3p\nNLUdHYwfPpyTJ0xg8imnMHnWLMrLy8Puep1KEARks1nS6TSP33cfWzZu5GPpNNdlMkztp5+BglSR\n41UKqCLKvQQ8CBgRhmAMA0YSMIqAMRzI5V2MsYQID5LlNQrig5h5StdT/QBs2rSJBx98kOeee45X\nX17Btm17aE02EHaFNuLRQZQNKmXsuGFMnT6ZWbNmcfbZZzN//nxGjhz5Fv8sRI5cX49JjRKOK72E\ncIbtJXQaV2pm7wI+7e7vMrO5wPfcfV7uvU3AOe6+t5tzKEgVEelHQRCwevVqFi9ezNYtW5g5axZn\nnHEGU6dOfcNPLSGcLqWqqioMSp98kobGRt4ej1PR3MyFhGmAjvTo7cAWYNO+EovREo8TcSe6rwQB\nUfeDtr0NWEjPKe2PNgWpIgPRvql+nswFreFUP+PHnURtbRNNrQ24Z4hyMnAWWeYQ/oU8DRgJNJD3\nV48oKzHWErCZgBqMBGNHj2Lh5RfxyU9+sseprET6Q18HqecBt+Rl6P0igLt/M2+fHwNPuftvcuur\ngQvdfXcuSD3X3eu6OYeCVBGRt1BNTQ2LFy9m8XPPsfjJJ1ny+usMi8WY687E9nZWlZSwPAjYk05z\n6uTJnPG2tzF77lzOOOMMZs+eTXl5Oe3t7ezatYvq6uoDZccOdm3aRPW2baxat47W5mYujMW4sKWF\nCmAWykurIFVEwqeyLwPLCDMYn8aBDMZHyoGNwBNEuZ8szxCLxpl5ymTe94Er+cQnPsGoUaP6rOUi\nb1RfB6kfAC519xty69cCc939M3n7/BG4zd2fz63/GbjJ3ZeZ2UagkbC7753u/pMuzqEgVUTkDdi9\ne/f+YPO1F18kyGaJRqNEo1EiuWU0Ftu/bGttZemyZTQ0NTGnoIC5LS3MDQLmACO6OH4j4WQRy4Hl\nhYUsTyRY0d5OJBKhI5NhVGEho2MxRrszKpVidDLJaGA0MIUwC26v70YDhIJUETm6ssArGI8S4X6y\nvE5J4RBOmTWBRDzebc0gCOhIpkmnMqTSGdLpLJl0lkwmQyYTkM0GlJUX8XcfvIovfOEL6mYsR+RI\ng9Seej719u55uBNe4O47zWwE8LiZrXb3ZzrvVFlZuf91RUUFFRUVvTytiMjA0N7eziuvvMKLL7zA\n4ieeYPGSJTQ2N+8PNj8eBMQJU6xnc6Xz6wTwDWA6EOno6PGc5cAFuUIyCckkWcJEA4MBa23t8+sU\nEZE3Iwqci3MuWb4CtNKafIaXl71CeCfojhHeKRJAvNMyfF3fvIXvfvuXfPvb32NY+Um8+70Xc9NN\nNzFz5syjd0lyXKqqqqKqquoN1+/pSeo8oDKvu++XgCA/eVKuu2+Vu9+bW9/f3bfTsW4BWvbNo5q3\nXU9SRURyMpkMGzduZNWqVaxcsYJVS5ey4rXXWL11KzOLipjb0REWYBrqPnu80ZNUETkx1AMPEeWX\nZKmipLCMiy6Zy+c+91ne8Y537N8rCALa2tpoaWmhvb2d1tZW2tvbGTlyJOPHj1fW4gGkr7v7xggT\nJ10M7AReovvESfOAO9x9npkVA1F3bzazEuAx4FZ3f6zTORSkisgJL5VK0dTURFNTE83Nzftf19fX\ns27NGlYtXcrKFSvYUF3N6MJCZkUizGxrY2YmwyzgDPpmqnvpXwpSReTEkwSeJMqvyfJAbn1fXx4n\nfLobIezAGcOI4rQDTixaQmlRCSNPGsykKWOYMmUKp512GmeddRaRSIR169axceNGtm3bxs6dO9m1\ns4Y9NU00NbXSnkySiCcYM2YYM0+bwllnncUFF1zA/PnzKSws7K8fhhzG0ZiC5jIOTEFzt7vfZmY3\nArj7nbl9fkiYOLEV+HhuPOrJwH25w8SAe9z9ti6OryBVRI4LqVSKPXv2UFNTQ01NDXv37j0wr2dt\nLQ01NdTv2UPD3r3UNzTQ0NREU1sbTckk2SCgPJFgUDRKWTRKmRllQFk2y9S2NmYGAbMIu+K+lfN2\nyltLQaqInNj2DQrZ10U4xuFHBTYAW/cXYyNRVuNsJmAnAMZQIowARpNlPM5Y4CTCrMfDc8dYQ5TX\ngdcI2IBTTyxSSnlZGZNPHsXEyeMZO3YsEydOZMqUKUyfPp0pU6aQSCSO4s9BOuvzIPVoU5AqIm9W\nOp3m1Vdf5fnnn6d+717KysspKyvbXwYNGnTQejab7fKp5v7S2Ejdjh3U7NhBza5d1NTVsbu+npaO\nDoYXFDAyHmeEGcOCgCGZTDinZzbLYGBIrgzOlXKgDChASYREQaqIyNHXDqwH1gKribCZCDtwduHs\nIaAeaMcoIBYtpLCgkEGlxYwcNZix40cxYcIEJk+ezIwZMzj99NOZOHGiuiX3AQWpInJM6Ojo4NVX\nX2Xx4sVs37yZoSNHMmzYMIYNG8bw4cP3vx42bBjxHjIOdlZbW8sLL7zA83/5C88//jgvr1rFyQUF\nnJ9OMzKZpDmRoCkWoykapTkSoQlocqcpm6UpnSZqRlkstv+J5iB3ytwpy2QoS6cZlMkwnPB72vwy\nBI0BlTdHQaqIyLEgA9QCNcDuXKkmwhYibMbZQUANTh2QIWLFxKIJ4rE4iUScosIEJaWFlJUXM3ho\nGeXl5QwaNIimpibq6uqor22msaGV1tYk7ckO0pk0mSCFe5pYpIiS4hJGjChn4smjmTp1Kqeeeipn\nnXUWZ599NsXFJ2Z/qqPR3XchB7r73pWfNClvn+8DlwFtwMfc/ZUjqKsgVaQfVFVV9VkmbXdnw4YN\n4XQof/kLi59+mr9u3Mi0oiLmplJMSiapj8WoSySoi8WoM6M2CKhLp6lPpShOJBhcUkJJYSElxcVh\nKS0NS1kZJWVllA4ezK4tW3j+2WfZvXcv8woLOb+lhfNzU6iU98mViBy5ScDdhMkbeqIgVaS/VQEV\n/dwGOb60ArsIJ2ZrAppzy32lgSh1ufcHk2UkMJQD/aryl8W5Y20FtuS6OK/F2UTADpxGzAqJR4so\nKiygrKyEESPLGDVmJGPGjGH8+PGcfPLJTJgwgYKCAuLxOIlEgmg0SiKRIB6PE4vFSCQSxGKxHp8A\nFxYWvmXdnvs6cVKUMHHSJcAOYAndJ06aC3wvlzipx7q5+gpSZcBzd5LJJMlkklQqRTqdPuyyo6OD\nvXv3UldXR11dHbU7d1JXXU1dTQ21tbXUNTTQ1NZGcUEBZSUllJWWhl1ey8spGzKEsmHDKBs2jOdf\neIH3ve99lJSUHLbE43EaGxvDMZf7xl42NISva2tp2L2bbRs3svjVVylyZ240ytzmZuYC5wAlvbj2\ngPDPegPhbaAtt+yqDAXOA2YRfvMlciyYTBikvqOnHVGQKtL/KnNF5FiUAaoJn/DuK7sxdhBlG1BN\nwB6cRiCLE8BBxTu97oljFBKLFVJSWMTgIYMYPWYoY8aNZuLEiUydOpWhQ4eyZ8+e8DNmXd3+z4KN\njU001bfQ3NTOtR/9W77+ja93e6a+nid1DrDe3TfnDn4vcBWQH2heCfwvgLsvNrPBZjaK8L7dU115\nk9wds2N7pFs2m6WlpWX/+L9UKkVxcfEhwVBn7k5LSwvV1dUHl23b2LV5M20tLcQTCRIFBeGysJB4\nQcGBZVHR/m+YuluaGdlsdn8JguCQ19FotNtjxONx2traDgrm6vfupWH3bupramjYu5eG+npa29pI\ndnSQTKVIplK0p1IkMxk6MhkKotGwRCLEIxESkQhxMxJm4SxluWUBMCQIGJbJMLyjgwlBwFmE6QOG\n5Uo50NbSQlNd3SHf9+37DrA5EuGVZ5+lNRqlNRKh1SwMBt1pDQJag4B0EFAeizEkGg3HWHYagzmR\nMGPaT4Axb/D/R4QD4zhFjmcO/BtwF+GIqIXADwjHJH+UMENz1zYQ3m7rCKd1+DBhMv0MMB/4MTD2\n6DVcRESOETFgfK4c4IR3hL6XwdlDOlNNQ0tYNm/bSXTxZozlOA/jtGEMIkz3WI4zBGcywf5PnC9y\n3+8e6DFIPVI9BaljgW1569uBub3YZyzhZ9ae6vZo2bJlnHPOOUdaTQaAYjPS7qT7uyE9iACDIxEG\nRyKUmDHIjBFmFJn9/+zde3xV5Z3o/8+zb9m5B8IlISHcVQRERQmIlyigoHXwjlbb0Vpre6qvnvl5\nTjv+nKnUOTPWOTMd7emZSqv2Yu3gtFUHFVCQhosgF0EucikBIiSEhNyTfUn23ut7/lgrYRNDbiQE\nyPf9eq3XXmuv51nr2Wiy813P83wf/Mbg9/lISEj48lxHEXs7jSaXi9LEREp72K5QczNNLhceEdJj\nsS8PlzUG3G67DdGTvxqbXS4q/H4q4oq+08M2KHUhqAwGeSEhgb+3LHZFItzs95NgDIVNTVxhDNcl\nJFAUibA6FoNY7JS6Hu7BohjBj5t7EJoRqjDMAgSLrcB03Mzsl8+m1IUmxh7c7O7vZih1HsjAkPGl\nEUCGJgxluCgDIMYBcPV+h1lnQWpXxyGdUcvO9Z5AdW4KnifDxC2g2rKotqz+bsqX7Iic6yG+UueH\nD8Ph1v0/hUKnnDsUPf3z7yh/ane/o3JKqTMT1UF9SvWqz/f2fjzXWZBayqn9zSOxe0Q7KpPrlPF2\noW63xiYrpZRS5xpjzGHgm9ije58SkeXO+37sadY5IlJmjDmKPUXmdeBm4G3ssb0fAHeJyHZjTBLw\nb8AtnBwFnwJ4NIGDUkqpgaKz1RS2AhOMMaONMT5gIbC0TZmlwNcBjDEzgFoRKe9iXaWUUupCcQw7\n2W+LPOxpROXO8RrgXsArIsec44exg9HPnDJPARcB00UkHbgBe7SSPtBVSik1YHTYkyoiUWPME9hP\ned3AqyKy1xjzuHN+sYgsM8bcaowpwk7A+UhHdfvywyillFL96D+AHxhjlmMvwPdPwBIRaRnrvwb4\nV+BN57gQWAKsieslTcHOu1RnjBkMPHuW2q6UUkqdMzob7oszbGl5m/cWtzl+oqt1lVJKqQuQAK9h\nJw1cC/iBFcCTcWXWYgeha53jj4HEuGOw1xb/PXaQWwr8BHuIsFJKKTVgdLhOKoAxZh72l6YbeEVE\nXmhz/kHg+9hDkRqA74jITudcMfaKFzEgIiLTe/sDKKWUUkoppZS6cHQYpBpj3MB+YA72E90twAPx\nw3aNMTOBPSJS5wS0i0RkhnPuMDBNRKr78DMopZRSSimllLpAdJY4aTpQJCLFIhLBnjuzIL6AiGwU\nkTrncBN2dt94muxBKaWUUkoppVSXdBak5gBH445LnPdO51FgWdyxAKuMMVuNMY/1rIlKKaWUUkop\npQaKzhIndXlNNmPMjcA3gFlxb89y1oYbCqw0xuwTkXVt6um6b0oppZRSSil1ARORLo+w7SxILQVG\nxh2PxO5NPYUx5jLgl8A8EamJa0iZ83rCGPM29vDhdW3r6/rkSp19ixYtYtGiRf3dDKUGJP35U6p/\n6M+eUv3DmO7NAO1suO9WYIIxZrQxxgcsBJa2uWEe8BbwkIgUxb2fZIxJdfaTgZuBXd1qnVJKKaWU\nUkqpAaXDnlQRiRpjngA+wF6C5lUR2WuMedw5vxj4ITAI+LkTIbcsNZMFvOW85wHeEJEP++yTKKWU\nUkoppZQ673U23BcRWQ4sb/Pe4rj9bwLfbKfeIeDyXmijUqoPFBQU9HcTlBqw9OdPqf6hP3tKnR86\nXCf1rDTAGOnvNiillFJKKaWU6hvGmG4lTupsTirGmHnGmH3GmAPGmB+0c/5BY8wOY8xOY8zHThKl\nLtVVSimllFJKKaXiddiTaoxxA/uBOdiZfrcAD4jI3rgyM4E9IlJnjJkHLBKRGV2p69TXnlSllFJK\nKaWUukD1dk/qdKBIRIpFJAIsARbEFxCRjSJS5xxuAnK7WlcppZQaSIwxp2xKKaWU+rLOgtQc4Gjc\ncYnz3uk8CizrYV2llFJKKaWUUgNcZ9l9uzwO1xhzI/ANYFZ368YvqlxQUKCZ15RSSimllFLqPFVY\nWEhhYWGP63c2J3UG9hzTec7x04AlIi+0KXcZ8BYwT0SKullX56QqpZQaENoO8dXvP6WUUgNBb89J\n3QpMMMaMNsb4gIXA0jY3zMMOUB9qCVC7WlcppZRSSimllIrX4XBfEYkaY54APgDcwKsistcY87hz\nfjHwQ2AQ8HPnCXFERKafrm4ffhallFJKKaWUUue5Dof7npUG6HBfpZRSA4QO91VKKTUQ9fZwX4wx\n84wx+4wxB4wxP2jn/CXGmI3GmLAx5qk254qNMTuNMduNMZu72iillFJKKaWUUgNTh8N9jTFu4GfA\nHKAU2GKMWdpm2G4V8CRwRzuXEKBARKp7qb1KKaWUUkoppS5gnfWkTgeKRKRYRCLAEmBBfAEROSEi\nW4HIaa6hq5UrpZRSSimllOqSzoLUHOBo3HGJ815XCbDKGLPVGPNYdxunlFJKKdWb9u7dy3e/+13G\njprIZZOv5Dvf+Q7Lli0jGo32d9OUUko5Ohzuix1knolZIlJmjBkKrDTG7BORdW0LLVq0qHW/oKCA\ngoKCM7ytUkoppfpDZWUl27dvZ9euXRw4cIDi4mKOHD7GiYo6AMZNGMHUKy7jmmuu4eabbyYrK6tP\n2xMMBnnttdd44/X/YPv2/TRFAniYSZTvAgH2fr6GxS8/glBLsn8wF12cy7XXz2TBggXceOONuFyd\npgoSLDsAACAASURBVO8AwLIsamtrKSsr4/jx4xw/fpzKykoqKytpbGykoKCA2267DY+nsz+9lFLq\n/FdYWEhhYWGP63eY3dcYMwNYJCLznOOnAUtEXmin7LNAo4j862mu1e55ze6rlFJqoLjQsvtWVFTw\n4osv8ubv3+ZoSTmRWBCIYRiMi2wMI4kxDmE09kAsC9iDh0+x+ByLUlwmgdSkNPJGD2fK1InccMMN\n3HfffWRkZPSoTdFolJUrV/L666+zasXHnKgpx8VIhDsQvgLMBHzt1KwCtgGb8bCGGNsQGnGZ9sqe\nJAgiUaAZe4CaH0MShhRnSwMSiLEboZb0lEymXnkR8+bdzIMPPkheXl6PPqdSSp1Pupvdt7Mg1QPs\nB2YDx4DNwAPtrXdqjFkENLQEocaYJMAtIg3GmGTgQ+BHIvJhm3oapCqllBoQLoQgdePGjbz00kt8\nsGw9tQ0ncHM5MR4ACoCRwGC6no4iBhQD+4C9uNmKsBWLL0jwZjBmdDbXFczgzjvvZO7cuV/qhbQs\ni8LCQt59913WrfmY/fuO0hiqxpCGi+uJcRcwFxjWw09bBTR2oVwKkEr7wW+8cuATDGtwsZoYe/G6\nkxmVl82Nc2bx1FNPcfHFF/ewrUopde7q1SDVueB84EXADbwqIs8bYx4HEJHFxpgsYAuQhv2ItAG4\nFPsb4S3nMh7gDRF5vp3ra5CqlFJqQDibQWp9fT1r167lpptuIikpqcfXCYfD/O53v+O1V37N1k/3\nEIlGcDOPGPcBNwPpvdbmk4LAdmATHlYTYzNCPWnJmUyeMoZoNMa+vUeoD1RhSHYC5QKE6cA0YEgf\ntKkvNAM7gI24eZ8Ya0lLHsTtd9zEM888w8SJE7t0lWAwyOuvv85/vvkHvjhUyv98+r/z+OOP92nL\nlVKqO3o9SO1rGqQqpZQaKPo6SP3888/5t3/7N959ZxUVVWUY0hHqSPYPYtLkMcy5+SYeeuih0wY/\nlmWxceNG/vSnP1G4eh379x8lGK7BxQjgbizuAvKxn1ufbeXAFgwfA16EGdgB6fB+aEtfCQDLcfNr\nYnxEWlIGty24kWeeeYZJkya1lmpubmbJkiUsWfImG9fvoLahEhcjgXlY5GL4NxL9Mb73N4/x3HPP\n6TxYpVS/0yBVKaWUOkf1dpBqWRZvvfUWP//3l9n48U5CzQ24KSDG/cB87EFNdcAmYB1uVhFjB26X\nl5ys4Vxz/TQmTpzI2jVr+WzbAaprKxE8eLiSGLOdQPBq+qa3VHUsCKxwAtaVpCRlMO3qS9i94yBV\ntRW4GA7cjMWtwA3Yw6xbRIG3cPFDXK4yHnhwAT/96U97PM9XKaXOVF8M953HyeG+r7RNmmSMuQT4\nFXAF8Ex8YqTO6jplNEhVSik1IJxJkBqNRvn0009Zu3Yt27ZtY9vWXRQd/AKRJFzcQYx7gevpfF6k\nnbzIHmL6EVCEcA0W12H3ko5Elzg/14SADzCsQcjHnv/blazIAhTi5odYbGP2Tdfyi1deZsyYMa0l\notEo+/bt49NPP2X37t0cOHCAwwePUlvdiD/RR3KKn5S0RFJSUkhJSSEtLY20tDTS09OZPHkyd955\nZ5czICulBq7eTpzkxk6cNAcoxZ57ekriJGd5mVHAHUBNXOKkTus65TRIVUopNSB0JUitqKjg/fff\nZ9OmTezauZuDB45RXVtHJNoAJOFmDDCJGNOAW4CJaFCpOrcTN88R432yhmQRCDQRbAoQswJAIi6y\ncDEKiwlYTMDumQ1iD0FuxFCHizoM9djpRxqJcQhMI2NH5XH3fbfz5JNPkpub24+fUSl1rurtIHUm\n8GzcEjR/CyAiP26n7ClLzHS1rgapSimlznV1dXX8+le/InfkSObPn9/jRERtg9Q1a9awYsUKNm7Y\nyJ5dxVTV1hCzgrjIw8WlRLkCuAS4CJiAnaNQqTNxFFgNjADysHvOe5pYS4ADwDLcvEmMbaQkDub6\nG6fxrW89xu23397ayxqNRtmzZ09rj21RURGHDx7l+LFqfD4P+bOmMn/+fO655x4dlqzUBai3g9R7\ngFtE5DHn+CEgX0SebKds2yC1S3U1SFVKKXWuqq6u5qV/+Rf+709/ylzL4oTHw5bmZm6ZPZt7H36Y\nW2+9leTk5E6vIyIcOnSI8ePHn/K+YTBuLiXGDIRpwGXYAakmulHnowCwGjdvY/EuxoRJ8qcQCgeJ\nSQBIaqfHdiTQgJs1COuxOEKCN52xY0dwfcFM7rzzTmbPnt2a/Km+vt4OcA8f5ujRo5SWllJWVsaJ\nEyfweDwMHz6c7OxscnNzGTVqFGPGjGHcuHH4fJ0Ng1dK9aXuBqmdfQueSfTY5bqLFi1q3S8oKKCg\noOAMbquUUkqdmcrKSn7ywgss/vd/5w7L4pNwmJbw8gTwzrJl/HLdOr7Z3MzcggLufeQRbrvtNlJS\nUgB7SZCtW7eyccMGNn74IRu3bsUbi33pPkIV0bP3sZTqY8nA7cS4HRBE9tIYOsbJHttELOxZ0W3F\neMTZC9IU2c7e/Z9wYP9qfrH4IYQGXMaHJWGndjIu0jEMxjAEYTgWkzE046IMOIhQiUUNQj32sGUf\nblcCQwYN5sa5M/ja177GvHnzdD6tUn2ksLCQwsLCHtfvrCd1BrAobsju04B1mgRIbXtSu1RXe1KV\nUkqdK8rLy/mXf/onXvvlL7lXhL8NhxndQfkq4B3gj6mpbGhu5rr8fMrLythTXMzkxERmhsNc09zM\nTE6Xjki//5TqXDl2oJkJpNL9OdgxoBb7EdMW3LyHxWogyIisbObcci0PP/ww119/vQatSvWR3h7u\n68FOfjQbOAZspp3kR07ZRUBDXJDapboapCqllDqbRITm5mbC4TChUIhwOEx9fT2/evllfvPrX/Og\nZfH9piZGdvO61cBKIAd79c7EdspokKrUuUKAw8CfcfMuMQpxmRh5uSO45dYbefTRR7n66qv7u5FK\nXTD6Ygma+ZxcRuZVEXneGPM4gIgsNsZkYWfuTcMeg9EAXCoije3Vbef6GqQqpZTqllgsxu7du9m4\ncSNHDh8mUFdnbw0NBBsbCTQ2EggECASDBMJhQk1NhCOR1s3rduN3tkSXC7/LxfymJr7f3Ex2H7Zb\ng1SlzlWC3beyGg9LibIet8vN2NG5zP/KHB577DEmT57c341U6rzV60FqX9MgVSmlVGeqq6v55JNP\n2Lh+PRs+/JAtu3eT4/MxMxZjXDBIMpx2S3I2P3bvZgLQXwP6NEhV6nxhAbuwE0EtJcYmPO4ELhqf\nxy23zsbv91NTU0NNTQ21tbXU1zVQV9NIY0OYUKiJaCzGjbPz+afn/5GLL764vz+MUv2uL3pS53Gy\nN/SV08xH/SkwH3vCwMMist15vxiox54MEBGR6e3U1SBVKaXUKUpKSvjoo49Yu2IFG9ato/TECa72\n+7kmEGBmLMYM7FUczzcapCp1vooC24GP8LDCeS8dIQOLIQiDsAcVtmyCmzeIsZxhg4fx6OMP8nd/\n93c9Xr5KqfNdb89JdWOPfZgDlGIP6z1lXqkx5lbgCRG51RiTD7wkIjOcc4eBaSJS3cE9NEhVSqnz\nwP79+3n+hz9kXWEhV1x+OfmzZzM9P59p06a1ZrXtqbq6OtasWcPK995j1bJlnKiqYrbHQ0FjI9cA\nk7GflJ7vNEhVaqCpB/6Am/+DxV+YMukS/v+//wELFy48pZRlWezZs4fly5ezceNGdm3fT+mxSsKR\nAIm+ZLKzMpk0dQLTpk3jpptuYsaMGa3L8ih1PujtIHUm8Gxcht6/BRCRH8eVeRn4s4i86RzvA24Q\nkXInSL1KRKo6uIcGqUopdQ777LPP+KdnnuHPq1fzZCTCnbEYO4HNPh+b/H52hUKMz8lh+qxZ5BcU\nMH36dC666CJEhFgs1rpZlnXK/uHDh1m5YgWr3nmHXUVFzPT7mdPQwBwRLqf/huT2JQ1SlRrIijC8\nCryC1xPjmllTKT9ezdEjFTSG6gAXbiYAVxDjKuzHc3lAMbAXN58B27AoQgjg86QxNDODSZeN5/4H\nFvLggw/qerDqnNXbQeo9wC0i8phz/BCQLyJPxpV5F3heRDY4x6uA74vINmPMIaAOe7jvYhH5ZTv3\n0CBVKaXOQRs3buQfn36abZs381RTE49bFu31lzYDO4BNwOakJDa5XBwOBnG7XLgAt8uF2xh735jW\nLdvjYU4wyJxolFnYc0YvdBqkKqXs+a6FGN5BGIsdjE4Csuj68jp1wD5gLy42AcuxKGfo4OHMvnkm\n3/jGN5g9e7YuqaPOGd0NUjsbJ9DVb8/T3fBaETlmjBkKrDTG7BORdW0LLVq0qHW/oKCAgoKCLt5W\nKaVUe5qbm9m3bx87d+5kx9at7PzkE744epSROTmMv/RSxk2ezPjx4xk3bhzjxo1rnSclIqxevZp/\nfPppDn3+OT8IhfijSIcBpA+42tkIBk+esKy++4BKKXXecgE3Idx0BtdIB/KBfCwedt4r40T1R/xh\nyVKWLFmIy0QYNTKXryyYy1133UVKSgo+nw+v14vH4/nSfkpKig4hVr2msLCQwsLCHtfvrCd1BrAo\nbrjv04AVnzzJGe5bKCJLnOPW4b5trvUs0Niyjmrc+9qTqpRSZ6CmpoatW7eyY8cOdnz8MTs/+4wD\npaWM9vu5TISpgQCXiTAaOAocBIp8Por8fg6KcDgUYlByMuNHjSIYDhMoLeXpQIAHAG+/frILj/ak\nKqXOjpYldVbi4W1i7ECIYffiWs75tvsesoeO4K/uupmnnnqKCRMm9Ffj1QWot4f7erD/D58NHAM2\n03HipBnAiyIywxiTBLhFpMEYkwx8CPxIRD5scw8NUpVSqouamprYsWMHmzZtYvPq1Wz65BOOV1dz\nZWIil4dCXNbczFTgUuzlVrrCws6MV4Q9dHcOF0aSonORBqlKqXPXcWAZbv6DGOtJ8qdx3fVX8p3/\n9m1uv/32Xhs6bDmjbHQo8sDSF0vQzOfkEjSvisjzxpjHAURksVPmZ8A8IAA84sxHHQu85VzGA7wh\nIs+3c30NUpVSA0o4HObYsWOUlpZSVlZGNBrttPxnmzaxac0adh86xITERPKbm5keDpMPTESDyvOF\nBqlKqfNDGCjExR8R/gtjmph48Vhunn8TY8eOZdy4cVx00UWMGTPmtMFmOBxmzZo1rFmzhm2fbmPv\n54epqKghHKnD4GZo5nCuK5jG/fffzx133KFDjS9wvR6k9jUNUpVS57NIJEJ9fT0NDQ3U19efsl9X\nV8fxsjJKDhygtLiY0tJSSisrqQsGGZGYSI7HQ7Zl4evkd6BHhMsCAfKBK4Hks/LJVF/QIFUpdf4R\nYDeGd3BTiFCBRRVCLdCEIRGPOwF/gp+01CSi0ShVtXVEYw0YBuHmYiyuwOIy7MeqE4FGYA1uVmCx\nGqGWIRnDuOb6y7nvvvu499578fl8NDc3s3PnTnbt2sXevXs5dOgQXxQfpay0mrq6AAk+L6PGDGPS\nlIlMmzaNG2+8kcmTJ2sv7TlIg1SllDoDIkIgEKC8vJzjx49TVlZmv5aUUHb4MMdLSuz3qqqoDQaJ\nWhapXi9pHg+pbjdpxpAKpFkWqbEY2eEwOZZFDpAL5ABDuTCXV1GdO7tB6sPASOAfTnM+FdgFjO7D\nNiilLmxNQCVQEbd5sAPRi4CkLl7nGHbQ+gHCR1icwOBBCAGpuBiOixyEMcQYj/1tOgJoAPY5y/Ps\nIUYx0EyCN43MQemMvziHkXkjyc7OJicnh5EjRzJmzBjGjx9PWlpab/5DqE70xXDfeZwc7vtKfNKk\nuDI/BeYDQeBhEdnejboapCrVDwoLC8/JTNqxWIyKigpKSkrsnsfSUkqPHKHkwAFCgQDJqakkp6WR\nlJ5Ocno6ycnJp2wej+e063K27Dc0NFBVUUHVsWNUlZdTdeIEVTU1VNXVUdXYiAsYnpBAlsdDtgjZ\nzc1khcNkA9nYiwRkAYOxl03p8m9c1atGAyHgMCf/DHoFeAP4cx/f91XsZA0tfu2896X09W2c3SD1\nEewg9bk+vIdS55tCoKCf26A6V4EdAGfR/RR+1cAB4C/Y68sWYyhHqESowaIOe4aiC5fx43H78Hl9\nJPi8JCUlkJKSSFpGEumD0khPTycjI4PMzEyGDx9OdnY2ubm5jBw5khEjRugQ5W7o1SVojDFu4GfY\neTRKgS3GmKXtJE4aLyITjDH5wM+BGV2pq5TqPx0FqeFwmKqqKgKBQOsv6ISEhC5dtyXIbO2BLCuj\nurqaUChEOBA4ZQsFg4SDQcLhMLW1tZQcP055fT2DfT5yfD5yRMhpbiY3HOYm7EAk4GxB57Xc4yHg\n9RJwuwm4XMSweyndIrgBl/PqFml9PyUSITMSYSQwBMhssyUCdDJPVJ0bLOAl4OmzeE/DQHgwYaH9\n/erCVIgGqeeDYWdQdzAty/MAxNotI0ADllTRHK2iOVpHY6iBqrp6oB5owFCNiyoMlcABhFqEOoR6\nhAB2EJ2Ay/jwuL12oJvgJSkxgZTURNLS7UA3LS2NQYMGkZmZSXp6OuFwmEAgQCAQIBgMEgwG7b+R\nwmFCwRCxqEV2ThZjxoxh4sSJTJkyhSlTprQuFTdQdBb+TweKRKQYwBizBFgAxAeafwX8BkBENhlj\nMowxWcCYLtRVqkdaet+N6dmfiiJCMBhsdx7hKfs1NTRUV+Nyu+1eu7S0L/XcJScnk5SURDAYpKqq\nisrKSqoqK6kqK6OqrIzKigqqqqqoqa8nNSmJYUOHMiw7m2EjRzI0J4dhw4a1bpmZmUSj0dZfXu1t\nwWCQUGOjHeg1NhIKBAiHQq3BXigUorm5mQSfD39iIn6/n8TkZPxJSfiTkkhMScGfnMz6jRupLCmh\n6vhxKsvLqaqutnsSGxpojkbJTEgg2e2mPhqlprkZr9tNRlISg1JTyUhLY9DgwWQMHow3IYHjJSV2\nQFpVRVVjI4MTEsj2eskyhuxolMymJhJjMZJFGILd+9iyJTqv6diDd7IBXygEoVDX/mNGoxpQDlAG\n+B/APwP/Dfv/obb2AU8C27CHWf8DcC927+uVQI1T7jFgKdCydtrXgKuA73WjLfF+jN2rW4Hdl/mP\nwB3t1hyE/QfV77CT6f8Q+w+f/w183SnzMPZPySHgE6flvwXynPN/A/weO9HJKGAJdn5nsHsUvgKs\ndd77PTDWOefCzuk81rlHIvCFU3YpcAn2v946IMW5z5Nd+NdQSqlznQHSnG1MuyWE0wW4LaJAPZbU\n0hytoTlaS2OonqraBiizg1070K3GcAI70G3E4MP+fZsIJCEkIwzHIhk764TBRTEuPkN4H4tyhHoM\nfnzeJFKSk8hITyZmCdFIjGg0SixmEY1ZxGIWlmWPHhPA5/XiT/CSlOQnNT2JtIxk0tPTyMjIYNCg\nQaSkpBCL2dewrxNrPY4fiebxeHC5XHg8ntbN7Xa37t91111cffXVvfTfxtbZEjT3ALeIyGPO8UNA\nvog8GVfmXeB5EdngHK8CfoA9ImpeR3Wd9zsc7vv+++/zla98pWefTqlzwCCXi0yXi0FuNwHLoiIW\no9JJv95b3IDfmNbNZwzNIoSdLSTC6e7oBjLdbjJdrtbXFJfrlD+6BQhaFrWWRY3z2rK1fMYst5ts\nt5uhbjfeHj48UKo73g4EmJGQwF8iEdJdLi5PSOBAJEJxNMrcxESiIiwNBpnq8zHW46HGsvgoFOLm\npCTSXS7eDgS4we9nsNvNfwUCCHBjYmLruQK/n0HuL+dNbrlvdtwwr4ORCEWRCLc4T7q/iEYZ5nKR\n6HLxRSTCxqYmFiQl8adg8JRruVmAxV6EoxiycDEFoRKLzbiZh8FDjE8RynAzExiExW6EOjxch0U5\nFntxMwuDF6EB8GLwO/XKnXoZWHwKCG7sPySivIObuRiS4+5xDYbBCDFirMXFCAwTgBAxPsbF5bjO\nqIdDqf4VYy9uJvZ3M5TqFiGKcAKLcuylgsJxZz3O5nVefZzshwxiJ6mK9Gn7Jk+6nF27t3dYprfX\nSb2bTgJNJ0j9sYh87Bx3O0jtamOVUkoppZRSSp1/em1OKvZc0pFxxyOBkk7K5DplvF2o263GKqWU\nUi2MMYeBR0VktTHmd9iPl/cCD4nIjcaY72OP8I3vvvQAvxWR7xpjHsWesvI7YC7wNvZI3w+Au0Xk\nrzq7b9x7fw18U0Suc46/jj0+drRTJAX4loj8yhjzsFO/pex44C8i4oq73lFgoYhsMMb8CjghIt+P\nO18B3CYiW4wxTwJ/jT3W9y3gf4hIg1OvRET+3qlTALwuIiOdYws7p8Qhp2ypiPydc+4+7BxUjXEf\n3Q2sFREd3qSUUqpPdZYVYSswwRgz2hjjAxZiT1SJtxRn4owxZgZQKyLlXayrlFJK9YZnsaeW5sS9\ndwRYIyKD4rZUEfmuc34NcB12FpVCYD0wC7jBOe6O1geuxphRwC+A7wKDRWQQsDu+TA+u3frQ1xiT\ngj2R9RiAiPwfEbkKe9LpRcD/7OF94kc2HQEOt/m3S9MAVSml1NnQYZAqIlHgCeynynuAN0VkrzHm\ncWPM406ZZcAhY0wRsBg7f8Vp6/bZJ1FKKTVgichB4E1OzXX0PnCRMeYhY4zX2a42xlzi1CnCntjz\nEHYw24Cd6+hu7AC2p5KxA75KwGWMeQSYfAbXA7jVGDPLeej7D8BGESk1xlxljMk3xnixe4zDnMz1\n0Z2guG3ZzUCDMeb7xphEY4zbGDPZGHPVGX4OpZRSqlOd5pcXkeUicrGIjBeR5533FovI4rgyTzjn\np4rIto7qKqWUUn3kOeyVigTACTpvBu7HnppSBjyPnVWiRSFQKSKlccdgJwTuDom77x7gX4GN2EOQ\nJ2P30n6pbJv3Orr277F7i6uAK7ADa7BTU/4CO41vMXZg/L+7eJ+2+63HImJhpwW+HDut8AnnPmkd\ntFMppZTqFR0mTgIwxswDXsSei/KKiLzQ5vyDwPexn8I2AN8RkZ3OuWLsxYZiQEREpvf2B1BKKaUu\nZG3nliqllFIXug4TJxlj3MDPgDnYT6G3GGOWthm2ewi4XkTqnID2F8AM55wABSJS3ftNV0oppQYE\nTTColFJqQOlsuO90oEhEikUkgr1C+IL4AiKyUUTqnMNN2Nl94+mXq1JKKdVz7Q3bVUoppS5YnS1B\nkwMcjTsuAfI7KP8osCzuWIBVxpgYsFhEftmjViqllFIDlIg80t9tUEoppc6mzoLULj+5NcbcCHwD\nO31/i1kiUmaMGQqsNMbsE5F1berp02GllFJKKaWUuoCJSJdH2HYWpJYStzabs1/StpAx5jLgl8A8\nEamJa0iZ83rCGPM29vDhdW3rd5a8SSnV+xYtWsSiRYv6uxlKDUj686dU/9CfPaX6hzHdmwHa2ZzU\nrcAEY8xoZ222hcDSNjfMA94CHnLWnGt5P8kYk+rsJ2MvA7CrW61TSimllFJKKTWgdNiTKiJRY8wT\nwAfYS9C8KiJ7jTGPO+cXAz8EBgE/dyLklqVmsoC3nPc8wBsi8mGffRKllFJKKaWUUue9zob7IiLL\ngeVt3lsct/9N4Jvt1DuEvQi4UuocVFBQ0N9NUGrA0p8/pfqH/uwpdX4w/T0f1Bgj/d0GpZRSSiml\nlFJ9wxjTrcRJnc1JxRgzzxizzxhzwBjzg3bOP2iM2WGM2WmM+dhJotSlukoppZRSSimlVLwOe1KN\nMW5gPzAHO9PvFuABEdkbV2YmsEdE6owx84BFIjKjK3Wd+tqTqpRSSimllFIXqN7uSZ0OFIlIsYhE\ngCXAgvgCIrJRROqcw01AblfrKqWUUgOJMeaUTSmllFJf1lmQmgMcjTsucd47nUeBZT2sq5RSSiml\nlFJqgOssu2+Xx+EaY24EvgHM6m7d+EWVCwoKNPOaUkoppZRSSp2nCgsLKSws7HH9zuakzsCeYzrP\nOX4asETkhTblLgPeAuaJSFE36+qcVKWUUgNC2yG++v2nlFJqIOjtOalbgQnGmNHGGB+wEFja5oZ5\n2AHqQy0BalfrKqWUUkoppZRS8Toc7isiUWPME8AHgBt4VUT2GmMed84vBn4IDAJ+7jwhjojI9NPV\n7cPPopRSSimllFLqPNfhcN+z0gAd7quUUmqA0OG+SimlBqLeHu6LMWaeMWafMeaAMeYH7Zy/xBiz\n0RgTNsY81eZcsTFmpzFmuzFmc1cbpZRSSimllFJqYOpwuK8xxg38DJgDlAJbjDFL2wzbrQKeBO5o\n5xICFIhIdS+1VymllFJKKaXUBayzntTpQJGIFItIBFgCLIgvICInRGQrEDnNNXS1cqWUUkoppZRS\nXdJZkJoDHI07LnHe6yoBVhljthpjHutu45RSSimllFJKDSwdDvfFDjLPxCwRKTPGDAVWGmP2ici6\ntoUWLVrUul9QUEBBQcEZ3lYppZRSSimlVH8oLCyksLCwx/U7zO5rjJkBLBKRec7x04AlIi+0U/ZZ\noFFE/vU012r3vGb3VUopNVBodl+llFIDUW9n990KTDDGjDbG+ICFwNLT3btNQ5KMManOfjJwM7Cr\nqw1TSimllFJKKTXwdDjcV0SixpgngA8AN/CqiOw1xjzunF9sjMkCtgBpgGWM+R5wKTAMeMt5auwB\n3hCRD/vuoyillFJKKaWUOt91ONz3rDRAh/sqpVS/Ky0t5ZWXX+aT1auZe9ddLLjjDsaNG9ft6xw8\neJBl77/Pnm3b8Hi9+Px+vH6//erz4fP58Hq9+Hw+Jk6cyE033fSlIbAXMh3uq5RSaiDq7nBfDVKV\nUuo8JyIcO3aM7OxsXK7OZnGcZFkWq1at4uV/+RcK167lAeCGpiZW+f0sNYahw4ZxxwMPsODuu5k2\nbVq7wWRTUxPr1q1j2Tvv8P7bb1NXU8OtwJWhEDHstcmaW16NIeJy0ex202wMaz0ePFlZfP+557jv\nvvvweDrL5Xeq6upq/vCf/0l9QwNTp05l6tSpDB8+vFvXONs0SFVKKTUQ9XqQaoyZB7yIPdz3mUDW\njQAAIABJREFUlbZJk4wxlwC/Aq4AnolPjNRZXaeMBqlKKdUDwWCQJUuW8LPnn+fgkSO43G5mXH45\nM2++mWuuvZbp06eTlpb2pXonTpzg16+9xuIXXyS1sZHvNDbyAJAaV8YCNgHveL28k5BA0OdjwZ13\nsmDhQiZMmMDKlStZtmQJqzds4FKfj1sbG7nVsriCzpMdtBBgOfDPKSkU+/38f888w6OPPUZycvJp\n6zQ1NbFs2TJe//nPWb12LfPcbrKiUXYkJrIjHMbr8zF14kSmzpzJZdOmMXXqVC655BJ8Pl9X/1n7\nlAapSimlBqJeDVKNMW5gPzAHKMWee/qAiOyNKzMUGAXcAdS0BKldqeuU0yBVKXXBiEajlJWVUVpa\nSnl5OZMmTWLcuHG9OqT14MGD/Pyll/j1a68xwxi+29jILcAJ4BNgg8fDhqQktodCjMvJ4ZqCAmbe\neCPDhw/nty+/zPvLl3Ony8W3QyGm0ybr3WnsA95xufivlBQONjcz1+3m1kCAW4AhvfCZNgH/nJTE\nWpeL7zzxBE/8zd8wbNgwwA7kPvnkE377i1/whz/8gckuF19vaOBuID3uGoL9ZbMD2GkMO5KT2WEM\nxaEQo4cPZ/zYsYyfMoXxl17KuHHjGD9+PKNGjcLr9bbbJhEhFApRW1tLTU0NdXV1jBgxgry8vG71\nWMfTIFUppdRA1NtB6kzg2bglaP4WQER+3E7ZU5aY6WpdDVKVUuebxsZGVq5cyf79+yk9eJCSgwcp\nLS2lpLycyoYGhvr95Hi9DBVheySCLzmZubfcwty/+itmz55NZmZmt+8Zi8VYsWIF//eFF9iyZQuP\nxGJ8OxJhbAd1moHPgA3AhpQUjrhc3N/QwF+LMKiHn72v/QX4id/Pm8ADDzzA8NxcfvfLX+JubORr\nwSAPWhaju3nNEHAQKHK2g4mJFPl8FEWjHAuHGTlkCONGj8YYYwekdXXUNjZSGwziMoZBPh8Zbjdp\nLhclkQh1sRiTxoxh8hVXMHn6dCZPnszkyZMZPnx4pw8jzmaQalkWX3zxBTk5OedMT7JSSqmBqbeD\n1HuAW0TkMef4ISBfRJ5sp2zbILVLdTVIVUr1BhHh0KFD1NfXM2zYMIYOHdqrf5jX1NTw3nvv8aff\n/IbV69czIyGBy4NBcqJRcoEcIBfI4tS06QLsAVYBK1NTWdvUxIS8POYuWMDc+fOZNWsWPp+PQCBA\nfX09DQ0N1NfXn7J/pLiYX/37v5MZCvHdhgYWAom99snOTeXAz7xe6l0uHmpq4iq61uPbXc1AMXYQ\nC5ABDHJeMwB/O3VqgM+B3cDuhAR2+/3sCocxHg+TJ0xg2nXXkX/tteTn55OXl3dKYNrXQWowGOTl\nl1/mt796g917iohZYSCKwY/Xk0hyUhKDB6WQlTOY3JG5jBo1imnTpnHXXXd1e06wUkop1VW9HaTe\nDczrYZDapbrGGHn22WdbjwsKCigoKOhq+5VSA1QsFmPHjh2sX7+edcuWsf6TT3BFIgzxeDgRjXIi\nHCYlIYFhGRkMy8xkWFYWw3JyGJaXR/aIEeTk5JCbm0tOTg5Dhgxpd/hmRUUF77zzDm/9+tds+PRT\nbvL5uKuxkduhxz2RzdhDcle53axMTmZ7IEDEskj0eEjzeklzu0l1uUgD0kRIi8XIbG7m/kiE6T3/\n51J9TLAD653AVmPYlJLCpmgUvF7yr7yS/DlzyJ8xgzlz5pxSr6ysjH379lFUVMThw4cpKSmhrKyM\n6qo6cnKzuOSSS7jyyiuZOXMmeXl57d774MGD/OQnP+GdP67gWMUxXIxCuA/hDux0ERGndWXAcee1\nFDdfYDhCjL8AtYwamcud99zG9773PUaNGtVn/1ZKKaUufIWFhRQWFrYe/+hHP+rVIHUGsChuyO7T\ngHWaBEhtg9Qu1dWeVKVUV9TW1vLZZ5+xfu1a1i1bxic7dpDr9XJtJMK14TDXYU+Ob/ntZwG1QEWb\nrRwoS0yk1OejRITS5mYaIhFGDBpEblYWOXl5ZI8Zw7aPP2bH3r3M93i4KxBgPpDSB5+rCbvn1d0H\n11b9S4Aj2PNtN3m9bEpM5OP6+jalvBgycJGJYRhCNhYjETJxUYyLfVgUY1EGGHyeFDLSUsnNG0J6\nRiqbN+0hEKrFzUxifBW4DRjRg9YeBJbjZgkxtpDsH8R1N1zJtx5/jAULFpzyEMeyLI4ePcquXbvY\nv38/Bw8e5OjRowDMnj2be+65h9zc3B60QSml1IWqt3tSPdjJj2YDx4DNtJP8yCm7CGiIC1K7VFeD\nVKUU2MMUi4uLOXz4sL395S8c3rOH4sOHOVxWRjQaZVJiIteFQlwXiXANvZOwB+w5i8ewk+6UOPsX\nA3Npf7inUj315W/nrn7/CVAJHG7dDMcR5mB/zfbmAPAgUIiLtxCWYkyIQemDCQbDNEVCWBIC3BgG\n42I4hhwsRgERDBuI8Rc8riSyhg3l6plTmDt3LnfffXdrIiyllFIDT18sQTOfk8vIvCoizxtjHgcQ\nkcXGmCzszL1p2J0XDcClItLYXt12rq9BqlLnIBGhtLSUcDhMc3MzkUik3Ve/38+ll15KVlZWtzLY\nHjt2jDVr1lC4fDlrPvqI4vJyRiUlMcblYkxTE6PDYcZA65ZJ38xJVOps6nmQ2l8E+3nz58BwIBt7\n5vXplwmCKPZM7K24WYewEYtDeN0ppCSnEItZWFYMy7KwLMESC0ssxNl83gQuuWQU826dy9e//nUm\nTpzY559SKaVU3+r1ILWvaZCq1LkjFAqxevVq3vvjH3lv6VIioRApbjc+lwsv4DMGL9j7zmsQ+Lyp\nCcvlYvL48Uy+6iomX3UVkyZNYvLkyQwePBiAI0eOsGbNGtasWMGa1aupqa3lOq+XgoYGbgCmoENe\n1YXv/AtSe0sTsAt7nELLbw9fm/2W3y5HMazBxQpibMfj8jMqL5ub5l7LV7/6Va6//voeLwHUHR98\n8AEvvfgSGYMymDt3LgsWLGj9faaUUqp7+qIndR4ne0NfOc181J8C87H/Xn1YRLY77xcD9UAMiIjI\nl/J+aJCqVO8SEaqqqkhJScHv73yw6vHjx3nvvfd49/e/p3DDBi5PSOArDQ3cLsLFdL33sgIn2ymw\nOzGR3T4fn4dCJCcm4vN6CTY2coPHww2NjdwATAL6/s9Mpc4tAzdI7akYdjqq9XhYTpQNGKIk+JIQ\nkdYNnH0ALEQgNTmFWddfwde//jXuvvvuLmUv/vjjj/lf//CP/Hn1ZpoiUVzcjSGAsAWLI3jdqWQN\nH8K06ZMoKCjgzjvvPG1CK6WUUif19pxUN/Y4nznY07W20GZeqTHmVuAJEbnVGJMPvCQiM5xzh4Fp\nIlLdwT00SFUKOxnJrl278Pv95OTkkJLStTQ9x44dY8uWLWzdtImthYVs3bWLSHMzwWgUn8dDZmoq\nmenpZGZmkjl0KEOys8kcMQKJxVjxpz9xoLiYWzwebneSA/VmP4Fgz/EMAhehw3WV0iD1TAn2nNwK\n7MdcbmeL33dj/0vvws27WKwAahkxfARz51/Ht7/9bfLz81uv+Nlnn/Hcc8+xYtl6Qk1B3NxFjEeA\n6zl1fEcT9rDnbbjYiOETYhThNn4y0jMYOy6by6+8jFmzZjF37lxGjOhJAiullLow9XaQOhN4Ni5D\n798CiMiP48q8DPxZRN50jvcBN4hIuROkXiUiVR3cQ4NUdV4TEerr60lNTe32EDQRYdu2bSz57W95\n8403SGhqQoDScJgEr5eczExyc3LIGTWK3AkTyBk5ksGDB7P388/tgPSzz2huauJqn4+rAgGuisW4\nipO5PRuBKux0K1VttojbzexYjOuwB9gppfqeBqn95QtgFR7eJsoaPC4348bmcuxYFQ3BOtzcRoxv\nYD+T785vxBj2s/w9wOd4+BSLz7E4ijEJpCalM2r0MKZMncjUqVO56qqrmDFjBklJSX3wGZVS6tzV\n20HqPcAtHa11aox5F3heRDY4x6uA74vINmPMIaAO+7f4YhH5ZTv30CBV9Yuqqir+/Oc/U1VVhd/v\nJzExEb/ff8qWmJhIQkICtbW1lJaWUlJSQumRI5QWFVFSXEzp8eOUVFXhAjxuN1dPmUL+TTeRP2sW\n+fn5p81m+fnnn7Pk9ddZ8pvfQEMD9zc1sTAaZbJzXoAa7F7IloyzpcZQkphIpcfDJcEgV0WjXMWp\ny64opc5tGqSeCyxgB/Bn7LRs8+n9PN4x7MB4L7AXN9uA/VgcRajGZRJJTEhhSGYao8eP4KKLJjBt\n2jQWLlxIRkZGL7dFKaX6X28HqXcD87oQpP5YRD52juOD1BEicswYMxRYCTwpIuva3EOeffbZ1uOC\nggIKCgq62n6luiwcDrNhwwZWLl/Oyv/6Lw588QXX+v3kRiKE3W5CxhBu2bCXJQmLELYs0l0ucoGc\n5mZyw2FygByw38NeP/ME9jpLm1wuNqWksLmpiYzUVPKnTyd/zhwuu+wyNq5fz5LXXqO+spKFkQj3\nRyJciQaZSg0UGqQqiGA/emxZTqgID3uw2IvFF6QmZZI/cxIL71/IQw891KXcAp2xLIsDBw6wbt06\ntm3bRn19Pd/61re4/vrrz/jaSinVnsLCQgoLC1uPf/SjH/VqkDoDWBQ33PdpwIpPnuQM9y0UkSXO\ncetw3zbXehZobFlHNe597UlVfcKyLHbu3MmqlStZ+dZbbNi2jcl+P3MDAebEYszAzifZZ/cHDgCb\ngE0JCXyWkMCVTU3c39TETDRpkFIDkQapqmN1wDpcLAdWYFHCoLRhzLrucr764Fe599578Xg8WJZF\nY2MjNTU11NXVUV9fT319PXV1dVRUVLBjxw727dnP4YNlVNfW0xxtBFy4GImLCQgJxFiF1+3iiisu\n4RvffJhHHnkEn68vvxWVUgNZb/ekerAnW8zGzhu/mY4TJ80AXhSRGcaYJMAtIg3GmGTgQ+BHIvJh\nm3tokKpaiQjhcJhAIHDadTlbXhsaGqioqLC3khIqjh6loqyMispKKqqrqWxsZExSEnMjEeY2NVEA\n6CAqpVR/0iBVdU8lUIibZVisRDjuvB8FPLQs22PwYUgAEjAkY5hAlMuACcB4Z2ubFs8CNmN4C8Mf\nsDjOqJyR3L3wdp566qkeJ36yLIvm5maCwSDBYBCPx8OwYcPOyrJBSqlzV18sQTOfk0vQvCoizxtj\nHgcQkcVOmZ8B84AA8Igz1Hcs8JZzGQ/whog83871NUi9AEQiERoaGlqf5tbX159y3NDQQH1dHfVV\nVTRUV1NfU0N9ba19rrGR+kCA+lCI+nAYr8tFUsvanC6XvTanMSdfsb+WU0QYHosxLBxmWCzGMDhl\nG0Lf9pQqpVR3aZCqzkwD9p9UCfT+eJxi4D3cvEGMbfg8qS1/VDrL+wi0ebX3LYQYdtBrYc/HNZzM\ntGxhB9VejPHhdnnxuD34PF78/gSSknwMHprGqNG5jBkzhokTJzJlyhSmTp3aK0OdlVLnhl4PUvua\nBqlnRywWo6ysjCNHjrRuX+zbx5GiIo4cPUp5dTVet5vEhAT8Pp+dNKglgVBiIv7ERDxeLw11ddTX\n1dmBZWMj9cEg9aEQkViMNJ+PVI+HNLebNGNIA1Iti7RYjLRIhLRIhFQgFUgH+7zz2rKfigaWSqkL\nlwap6vzQgJ30ycXpl/pxYQfMLY+OW169nLp0D9hBaiNQ71y7Pm6/DqjERREuDiOUYFGOUI8hEZ83\nidSUJJITEzBuF26XweV24XK5cLsNbrcbl8vg9rhISUuyl1vLzGTYsGFkZWUxYsQI8vLyGDlyZK/0\n6IbDYbZv386UKVO6vFScUkqD1POKiBAIBKiqqmrdKisr7f3KSmpPnGDw8OHk5OaS62w5OTmkpaVh\nzJf/GwcCAQ4dOkRRUREHDx6kaNcuivbs4WBxMcdqashMSCDP6yXPssgLhRgVjZIH5AFZ2KkcwnFb\nqM1+lFODyvggMxFN/qOUUp3peZBaAHwNeLQXW6PUuSwKHOdknvsG7F7all7bWDvHDbgpx3ACqEao\nRqh3tiAQxZgkEn1JZGSkkTtyCGPHj+biiy9mypQp5Ofnk5ubS21tLevXr2fTpk3s3r2b/XsOcexY\nFY3BBmJWAPuvnwAJ3nTGjM5m5rVXcdttt3Hbbbd12vtbWVnJ5s2b2bFjB0eOHGHChAnMmjWLadOm\n4fF4+uafUqlzQF8M953HyeG+r8QnTYor81PsHO5B4GER2d6NuudMkBqNRmloaCAcDhMOhwmFQq37\n8cdJSSef1GVmZpKent5u0Pj/2Lv3+KjqO/H/r/fM5B4gIYQEwiXc7zcVjJdiULaA/ry1VdeurtbW\n+murbbfbbWt3K3H3+6vtr9vWum0tXW27rW1ltVpt165CMYqAQFRucocESLgFcp1kkpk55/394www\nhFwhIUDez8fjPOacM+dzzudEh5n3+Xw+7w9AfX29FzDu3u0FjycCx/37OVpXhx/ISkoiy+8nS4Qs\n1yUrEmFQczMDVKny+ShPSaEiEKDCdSlvagKfz5s/c8gQsnNzqThwgN379lHd0MDo1FTGAGNDIcZG\nIowBxgDD8ToHGXNCMd7PXmNM2/LxHtKVAidmtnwG+C3eBCZddfZB6jy8IPWBs7iqMcbTDLyMN5v4\nfqAMPzsR9uByAJcjeJ9JRcjGz2iUyThMxfs1NRbvX4UkvJbhDUAJfopxWY9SSWpSJmPGDOWy2TOo\nrq6mdPd+Dh+qprY+SDjaCEQQBuEjDyEXpRyHfUCQgC+d/v36M2x4FhMmjWXGjBnMmTOHGTNmtDml\nnTEXi+5OnOTHS5w0H+8x1nraT5x0JfCjWOKkDsvGyrcbpFZUVPD73/+epKSkM+avbDmXZUJCAgkJ\nCSQmJp72emKpr68/vbvr3r3s376d/aWl7D90iMO1taT6/ST7/aT4/SSLkCxCigjJeLOoJavSKMJx\n4LjjcCwcJhSNMjAtjaz+/cnKzGTgoEEcq6xk9/79BEMhxpwIHBsbGRuNMhYYjdd6mdLZ/1Jx6jj1\nXPEo3hQoY2KvlpbAdFZRbDHGtC0fL9nCV4BHY/ssSDXmYlZE299+itf9OB2vK3NX1QLvA+sJsBaX\nwbiMw+uzNjL2mk3rv9YagL3AHmA3fjYjbMNhH0oV4MPvSyE5MYX+/VLJzskgb3gOw4YNY+jQoYRC\nIRobGwkGg4RCoZPboVAToYZmmpsiOI6D47i4ruI6Lq7j4qjiuoq6LiAkJgZISkogOSWB1LRkUtK8\n39qpqamkpqaSnp5+spEmOzub3NxccnJyGDp0KBkZGZYgy7Spq0FqR5/AOcBuVS2Lnfx54Fa8gQon\n3AL8F4CqrhWRDBHJxZshu6OyHVq+fDm/+Jd/4Qagye+nyeejyefz5rQkrnuqKpHYEnZdwq7rrTsO\nEdcl4rqkJSQwMiWFESKMCIcZEQqxiFP/bOQBCY7TleoBEAaq6us5Vl/P8YoKqoAsvOdtQwCpq+vy\nOdvTH5gcW4wxxvQcAb4K/P/A5/HG08fbDjyC97M0G/g34A68qaduw0uLf+Ib+eVWr5CJ97jy48AP\n8MbzQWxqcbzujvdyonXHswd4ENgUO/sC4Cet1M4Y0zXCuc0DMADvgdI8ol0umwZMiy1e5+VTFKjD\ncQ/R0HSIhqbDHKo8xOYt+/FTBnyI15SSAqSipKIMwiUtdt4UvDHDflofX3xiW/Fam+MHezUiNOCj\nIbZeDmxFqUWpRwnGulKH8LpdJyD44URirdMWN27dj0+SCfgTSUpMIi01mYzMdAblZJCdPYicnBxS\nU1OJRqNEo1Fc1z1j3TmL3+znIiUlhf79+zNgwAAyMjLIzMxk4MCBp/WuDIfDhEIhGhoa4h4UeK9N\nTU2Ew2GysrLIzc0lNzeXIUOGdNhFvKmpiQMHDrB//34qKio4ePAg1dXVJCUlnWzEO9Fgl5qaevKh\nQlpaGv379ycjI4OMjAzS09O79SFCNBolHA4TjUa7/dzQcZCaBxyI2y4HruzEMXl4fSk6KtshESEk\nwvG4fvoJrksCXrDWKp/PWwASvC/8E1/t4roA1Pv9fJiezoddrZAxl4jNzc3sTLJO4Ma0p7KhgdeT\nkkiNRLjG52N6UhJ7IhHKolHuTE7mfxobmZaYyEcDAWpcl3tDIf4rNZX+Ph/BhgZuSEoiN/b99U4o\nBC1+VPm5Bgjh8Ct8rMDHaJQwDivwMQPhWpQVuGzDRzU+/ojSgBJCuBKI4vAXhFn4mXL+/0DGXGQc\nduDnvd6uRjeLn14ohBBCON6Dvev6IfQ7o2eIEkFpwGu+8cWCVV/cupf1WfCjNOFqDeFoDeFoNfWN\nhzh8DG+CeXPRubrgGlateadbz9lRkNrZfkjnlDOnrfGc8crO5QLGmFZtiUR6uwrGXPDebGoCoNxx\n+DDuM/NCQwMAa5ubWdvcfHL//zQ2nlG2LQ7/c3LdZSsuW+O2T/8h7bIJl02tnkepIkppR7dijAGi\nFgkZ061Wv7uqU/FcV3QUpFbg5dw5YThei2h7xwyLHZPQibJd6ptsjDHGnE8iUgp8WlVXiMhzeP1v\ntwH3AK8B/wcvaeAJAeDXqvoFEZkMrAJygLuBB1T1uth5x+P1770cLydTAChR1etE5BvAZap6Z1w9\nVuMlIPyFiOQAPwKuxUsz6gOqVHVkj/0hjDHGmPOoo54AJcA4EckXkUTgLuDVFse8Cvw9gIgUADWq\neqSTZY0xxpiLxWK8waB5se0DwFuqmhm39FPVLwCo6lZgH172+08Cv4s719PAVmCsqg4A/plT38kH\niXvIK97j6fiHvt/GG7I2NVb2XixvnjHGmEtIu19qqhoFHgZex/syXaqq20TkIRF5KHbMa8BeEdkN\nLMHLLdFm2R67E2OMMaYHqeoeYCnwJbzhMH8GxovIPSKSEFtmi8jEuGK/A74MfAR4IW5/Ot7Ej42x\n4z8X995rwBQRuV1EAsAX8RLCx5dtAOpEJA/4p269UWOMMaaXdThPqjHGGNNXxXf3jW0Pw0vtsUZV\nr4/rtjsH78HvBuArqropdvxwvLQKr6nqzXHn/Qjwc7whMh/gzWgzT1Xnxt5fADyF11X4N8BU4Dex\n7r6TgV8DE2J1eQ74sqqO6Mm/hTHGGHO+dBikishC4Em8/NTPqOp3W7z/d8DX8JIn1QOfi/tyLsOb\n1tMBIqo6p7tvwBhjjDHGGGPMpaPdIFVE/MAOYD5egqT1wN3x3XZF5Cpgq6rWxgLaIlUtiL1XClyu\nqlU9eA/GGGOMMcYYYy4RHSVamAPsVtUyVY0AzwO3xh+gqmtUtTa2uRav61I8y95rjDHGGGOMMaZT\nOgpS8/CyF55Qzqmshq35NF7ChxMUWC4iJSLy4NlV0RhjjDHGGGNMX9HRPKmdzqokIvOAB4Br4nZf\no6qHRCQbWCYi21V1ZYtylrnJGGOMMcYYYy5hqtrpHrYdBakVnD4323C81tTTiMh04D+BhapaHVeR\nQ7HXShF5Ga/78MqW5S3DsDHnX1FREUVFRb1dDWP6JPv8GdM77LNnTO/wpvzuvI66+5YA40QkX0QS\ngbuAV1tccATwEnCPqu6O258qIv1i62nAR4HNXaqdMcYYY4wxxpg+pd2WVFWNisjDwOt4U9A8q6rb\nROSh2PtLgMeATODpWIR8YqqZXOCl2L4A8FtVfaPH7sQYY4wxxhhjzEWvo+6+qOpfgL+02Lckbv0z\nwGdaKbcXmNkNdTTG9IDCwsLeroIxfZZ9/ozpHfbZM+bi0O48qeelAiLa23UwxhhjjDHGGNMzRKRL\niZM6GpOKiCwUke0isktEvt7K+38nIhtFZJOIrIolUepUWWOMMcYYY4wxJl67Laki4gd2APPxMv2u\nB+5W1W1xx1wFbFXVWhFZCBSpakFnysbKW0uqMcYYY4wxxlyiursldQ6wW1XLVDUCPA/cGn+Aqq5R\n1drY5lpgWGfLGmOMMX2JiJy2GGOMMeZMHQWpecCBuO3y2L62fBp47SzLGmOMMcYYY4zp4zrK7tvp\nfrgiMg94ALimq2XjJ1UuLCy0zGvGGGOMMcYYc5EqLi6muLj4rMt3NCa1AG+M6cLY9qOAq6rfbXHc\ndOAlYKGq7u5iWRuTaowxpk9o2cXXvv+MMcb0Bd09JrUEGCci+SKSCNwFvNrigiPwAtR7TgSonS1r\njDHGGGOMMcbEa7e7r6pGReRh4HXADzyrqttE5KHY+0uAx4BM4OnYE+KIqs5pq2wP3osxxhhjjDHG\nmItcu919z0sFrLuvMcaYPsK6+xpjjOmLuru7LyKyUES2i8guEfl6K+9PFJE1ItIkIv/Y4r0yEdkk\nIh+IyLrOVsoYY4wxxhhjTN/UbndfEfEDPwbmAxXAehF5tUW33ePAI8BtrZxCgUJVreqm+hpjjDHG\nGGOMuYR11JI6B9itqmWqGgGeB26NP0BVK1W1BIi0cQ6brdwYY4wxxhhjTKd0FKTmAQfitstj+zpL\ngeUiUiIiD3a1csYYY4wxxhhj+pZ2u/viBZnn4hpVPSQi2cAyEdmuqitbHlRUVHRyvbCwkMLCwnO8\nrDHGGGOMMcaY3lBcXExxcfFZl283u6+IFABFqrowtv0o4Krqd1s5djEQVNXvt3GuVt+37L7GGGP6\nCsvua0zvOXjwIHv37iUcDhOJRM5YotEo0WiUYcOGMWnSJIYNG4bP12GOUWNMJ3Q1u29HLaklwDgR\nyQcOAncBd7d17RYVSQX8qlovImnAR4HHO1sxY4wxxhhjzlVJSQmfuf8hNn64FSENb7SbH4m9etun\n1pU6lGrAwScpJCYkk5aawsCB/RgybBBDhuTiOA6NjY2EQiEagyEaG5oIhcI0N0UIh6O4rsukqfnc\netst3H///QwcOLD3/gCd0NjYSElJCVdffTWBQEfhgTE9r8N5UkVkEfAk3if3WVV9QkQeAlDVJSKS\nC6wH+gMuUA9MBgYDL8VOEwB+q6pPtHJ+a0k1xhjTJ1hLqjGnlJeX88Mf/pCqqirmz599dZvJAAAg\nAElEQVTPTTfdREZGRredf9myZXzus19iT1kZfj6Fw6PAsC6cIQgciS2HY8sh/OwH/CjpuPQD0oCU\nFovgYzXCGzjsIjUpk2nTx3DzrTfxqU99iqFDh3bbfZ4L13VZvHgx33niKaIOQIiUxAyGj8jhijnT\nmTdvHrfccguDBw/u7aqai1xXW1I7DFJ7mgWpxhhj+goLUk1bXNft8a6lruvywgsv8Prrr1NYWMgt\nt9zSrUFhZ7z99ts8+eST/PWNd6lrOI6fK4BslA24VJDg78fg7CxmzBrPR+Z+hJtvvpkpU6Z06RpL\nly7lK198lINHKxEeQfkKMKhH7qdzgsC7CCvw8RcctpKU0J+JE0Yw56rLue6661i0aNF5b2197rnn\n+PxD/0iwMQXlaWAhUANsAjbiZzXKe7jsw+9LJXvgQCZPG8XlV1zO3Llzuf7660lNTT2vdTYXLwtS\njTHGmAuUBammpRdffJGv/eO3KN2/B78vjcFZA5k6YyzXXHs1N998MzNnzjyn4NV1XV566SV+8L0f\nsq5kC66bjI85KFtwKSch0J9hQwdzxZXTmD9/Ph/72McYNOjMgM51XaqqqigtLWXfvn2Ul5fT1NRE\ndnY2OTk5DB06lGHDhjFo0KDT6hsOh/nlL3/JL575FR98sJ2I4+JnEQ53AX8DpMddpQnYCmzExzqE\ntThsRxBSk/szoH8aOUMyGZGfx8iRI5kwYQJTp05l+vTpZGRk8NOf/pRvffPbVNU2IHwd5QtAv7P+\n2/WcJrxOiG8TYC0um3Epx+9LY2BGBpOm5HPF7MuZP38+1113HceOHaOsrIwDBw5QUVHBoUOHOHLk\nCMePH+fY0WqcqMOCG+fz+c9/npEjR3Z49bVr13Lnx+9hf0Ul8B3gM7Q/AjAC7AA2InyAn/dw2I5y\njIAvnYEZGYydkMdll8/i2muvZd68edbyas7Q7UGqiCzkVHffZ1omTRKRicAvgVnAP8cnRuqobOwY\nC1KNMcb0CRakGoBoNMq//uu/8uQPfk59QzPCl1E+CxwDNuBjPT7WEmUrEKVf2kDGjh3K5bNnMnHi\nRCZPnsysWbPIzc1t9fyu6/LKK6/w/e/9kLVrN+G4iQifxOUe4HJOpREJAZuB9/GzEmUdLvsI+NPp\nl5ZOc3Mz4WgEx2lGaQYEIR1hAEIGkADU4RJEaQAaAQdIxCcJ+P0JRKMhhByUO1A+Bsyh4xkQ4yne\nbIh78NKjVOBjLz5KUcpxOYJSAwQQMlEeAz4FJHfhGheCMLAL2IywAT/rcPgQpRJIROiH0B8hE2EQ\nSjYuuSiDgQh+/geH90hO6MfMWeO56+47eOCBB+jfv//JK5SXl/Px2+9kXckGfDyCy79wbkF8E7AT\n2AZsIUAJLttwOQgIAV8qKckpZGb0Y+iwLIaNyGP06NGMHz+em266qc3/f82lqVuDVBHx4z06mQ9U\n4D32uVtVt8Udkw2MBG4Dqk8EqZ0pGzvOglRjjDF9ggWpfdvhw4d55JEv8vJLr6NuLi7fAu4EEtso\nocAhYCPwAQHWoezD5TDKcVoGAkPyBgLCe+9vw3H8CHfjci8wmxb5LdsRBrYA5UAGMDC2ZOKNtexM\n+TqgNrZk4f1M7EkuXoA/kI5zgl5sXDof1DfjdSt+DR9/wmEPA9IHcc3cWSQmJPDKK2/g42Yc/p2u\njc3tKsXrNuw9VDix+NmDUIZLBS77GT50OA9/6UG+/OUvk5jY1megdcFgkF/96lfk5eVx6623Whbm\ni0B3B6lXAYvjpqD5BoCqfqeVY0+bYqazZS1INcYY01dYkNo3rVq1ii89/A+8t2Ezfj6Cw7eAa+l8\n4NgaxQsCKzgVDJQjNKDcClx5juc3F78aoBgff0aowOHbeB0fLwSVwG/x8VNUDjLn8uks/tdvsWjR\nojZLrFixgp///Ocsf301x2uO4CPfa0WXBvKHDePm2xfw8MMPM27cuPN3G6bTujtI/QSwQFUfjG3f\nA1ypqo+0cmzLILVTZS1INcYY01dYkHrpa2xs5MUXX+RPf/oTa1Z+wKGjlbgaxcd9uHwVGN3bVTTm\nArMFHz/H5TckJ/q55dYb+PYT3yYlJYWf/OQnvPTCn9i1Zx+OK/j5KA63441nHoT3sGY38L/4eRGH\ntSQn9uPyyydy7333cN9995GcnMzBgwdZv349GzduZOfOnZTuLaN8XyVV1XWEmkNkZWZwy+1/w1e+\n8hUmTZrUq3+NS1V3B6kfBxaeZZDaqbIioosXLz65XVhYSGFhYWfrb4wxxlw0LEi99JSUlLB06VJW\nLH+L7dv209hchY+hCB/BYR5QAEyka+MwjemLosAy/DyNwzLAxc8MHD4OLAKm0XHvgGZgFT7+DLyC\nS0Vsv+IjGx95KGNwGAfkAyOAHKAEP7/DYSUpSf34yNzL+PwXPsfNN99sXYnPUnFxMcXFxSe3H3/8\n8W4NUguAorguu48CbhsJkFoGqZ0qay2pxhhj+goLUi9upaWlLF26lOXLlrPx/V0crzmG4iPALBz+\nBuVqvPGf/Ts6lTGmXfV4D3bSzvE8lXj5WzPpXPf3Jrwu0i+gvIJImCmTxnDPfXdzzz33XDDz216M\nurslNYCX/OgGvAEP62gl+VHs2CKgPi5I7VRZC1KNMcb0FRakXriCwSBHjhzh6NGjVFZWcvz4cSor\nK1mzZg3r12zmcGUljtuMn0koc3G5Bi8gzcfGfhpzKVJgE8If8fEiDjvxSTJZmQOZMi2fq66+iptu\nuomrrrrKWls7oSemoFnEqWlknlXVJ0TkIQBVXSIiuXiZe/vjpSCrByararC1sq2c34JUY4wxfYIF\nqe1bs2YNjY2NzJs3r9t+9JWXl7N69Wo2bNjAtm3b2LOzjIMVVdQ1BIk6YVSjeBlpAZIRkhFSgVSE\nNGAGDnPxAtJJeD9pjDF9j4M3TdBGhBL8rCHKh0CItORMRo8Zwkeuu4o77riDuXPnWuDaQrcHqT3N\nglRjjDF9hQWpp7iuy7Jly/jd735H8fI1lB86jKuCkIASpH9aFpOn5FN4/VzuuOMOLrvssjbPVVpa\nyooVK1i7di0fbt7Knl0VVNfWE44G8cai5eIjH5fxuEzCa/3Mx0u8kh5bujYFhjHGeI7iTRP1PgFW\nEGUdQjMDM7K57IqJLFy0gE9+8pNnzAvrui5btmzh3XffZfPmzWzfvp3SXRUcr6rHcVxUNba4uKqo\ngqoLgE98DM7OZNqscRQUFLBgwQIuv/zyCzow7omW1IWcag19po3xqE/hjWhuBO5X1Q9i+8vwJsty\ngIiqzmmlrAWpxhhj+oRLMUhtbGzk0UcfpXjFSvpnpJORMYCBAweSlZVFdnY22dnZ5ObmMnToUPbu\n3cvSpUt5p/g9jhw7AqTgYy4Oi4C5wHi8rrNHgBJgDQGKibIRQcnKyGLGZeNITEpk+5ZSjhytJtRc\nh+LiYzg+JhFlJl6iorHAKLxA1LrjGmPOF8WbZ/hdfLyN8CYOO0kI9CN3cBb1dY0EGxuIukEgGT95\nCKNxmIQyDm8O20S80KutJQRsw0cJQgkOO4FmUpMyGD58MDMvn0xBQQGzZ89m9uzZXZ6Htid095hU\nP9640vl4E3Ctp8W4UhG5EXhYVW8UkSuBH6lqQey9UuByVa1q5xoWpBpjjOkTLqUg9cMPP+SRL3yR\n4rfXIjoBl7uABnwcQziGUA3UoNThUo8SREjFxzwcFuIFpSPpXACpQBmwHh+rAAeXacAEvMB2SCfP\nY4wxvaEZr7V1M1424fzYkt6N16gEtgCb8bMe2IhLOUodPkklLTmd7OwB5I8dyoQJE5g2bRpz585l\nypQp3ViHtnV3kHoVsDguQ+83AFT1O3HH/Ax4U1WXxra3A9ep6pFYkHqFqh5v5xoWpBpjjOkTejtI\n3b9/PytXrqSkpIQtW7awZ0c5DQ1NzLhsPDfetIi//du/PaNLWkvPPfcc//Lov7GvfD9+PobD14AZ\n5+cGjDHGdFEzsB8oBcoQduFnG8oeHPYh4iOz/0CmzhjD3LnXcuutt3LZZZd1e9fh7g5SPwEsaG+u\nUxH5E/CEqq6ObS8Hvqaq74vIXqAWr7vvElX9z1auYUGqMcaYPqE7gtRoNMpTTz3Fr3/5WyKRKH6/\nD7/fjz/BT8Dvwx/wEUgI4Pf7CTeHKdt7hONVtTSFg4CDjzx8jMFhGspEIB0fqxGKcdhJYqAf+flD\nmVtYwMc+9jEWLFhAU1MTjz76KM8s+T2hZhf4CspDQFZ3/FmMMcb0CsULXt9HWIuPd3DYjKAM6JfJ\n5Cn5zLxsBmPGjGH8+PFMmjSJUaNGnVUA29UgNdCJmnfqum3sv1ZVD4pINrBMRLar6sqWBxUVFZ1c\nLywspLCwsJOXNcYYY/qGNWvW8M1v/DNvrywBHYTLA3iJ9Z02lgje1/xovPGZY4HBuAhui3O7fDK2\n1kw4uoGdu99l7+7lPPvM38e66frwMRGHnwG30PHPB2OMMRc+wfuOGI3yCRwAFKWcmvr3WP3ueta9\n+z7KKpRKXKqBMCIpJAaSSUtJITMznUf+4SG+9KUvnXbm4uJiiouLz75mHbSkFgBFcd19HwXc+ORJ\nse6+xar6fGz7ZHffFudaDARPzKMat99aUo0xxvQJXW1Jramp4bHHHuPXv3yR2mAdPu7C5fPAZZy/\nMZiH8GaXG3+ermeMMebC1YSX3O5w7PV5pkzYypbtG9ot1d0tqSXAOBHJBw4CdwF3tzjmVeBh4PlY\nUFsTG4+aCvhVtV5E0oCPAo93tmLGGGNMX+C6LjU1NVRXV1NdXU1tbS07d+7kqR8+zfZdu/AzE4cf\nALfhktwLNRwSW4wxxphkvKR3I2PbZcDWbr9Ku0GqqkZF5GHgdbx8x8+q6jYReSj2/hJVfU1EbhSR\n3UAD8KlY8VzgpdhT4wDwW1V9o9vvwBhjjDmPamtrKS0tpayszHvdsYPSbdsoLStj/9GjRBwHv8+H\nX8R7PbH4/WecSyQBiOJ9TSYAiQhJCP3xngl/GufkDwFjjDGmb+hwntQer4B19zXGGBNTU1PD22+/\nzZuvv86bf/kLjaEQ06dPZ+a11zJj5kxmzpzJsGHDzug221mhUIiysrJTAebu3ZR++CFlpaWEmpra\nLeu6LoeqqohEo4xKSWEUkN/UxKhwmHy8GTlH4s1ud2JUaJTTR4meGW424D2VvnAnYDfGGGPa9hRT\nJvzivHf3NcYYY9oVjUY5cuQIFRUVVFRUcPDgQRobG8nJySEnJ4fc3Fxyc3MZNGjQGa2J9fX1rFy5\nkjffeIM3X3uNHfv2UZCczLz6ep5WpT+w8fBhNq5YwU9SU9kQiRAWYcaECcwoKGDmnDlkZmbS0NBA\nMBg8tdTUEKyuJlhbS7CujkMHD1JaUUFNQwMjUlPJF2FUJMKoUIjL6Pxsdbl4+WwlEun036cI2AP8\nptV3U4GpwE/x5g01xhhjTIctqSKyEHgSr7vvM/FJk+KOeQpYBDQC96vqB10oay2pxvSC4uJiy6Rt\nUFVc18VxHBzHIRKJUFtbS21tLXV1dSfXT+6rqeH4wYMcLCvzAtLKSirr6xmUlEReQgJDgbxwmBTH\n4WhSEof9fo6ocjgcpiYcZlB6OjlZWeTm5FBTW8uW3bu5IiWF64NB5rkuc4CkDup8GG9K9I3AxvR0\n6n0+0lyXdMfxlkiEdFXSgbTYMgQvEB1Kz7RZ/gr4PrAXL9/u7cATwAC8ZAy78YLUMx8h2/efMedX\nMVDYy3Uw5lLSCy2pIuIHfgzMByqA9SLyqqpuizvmRmCsqo4TkSuBp4GCzpQ1xvQeC1IvbKpKMBik\nsrKSY8eOUVNTc3rQWFND3fHj1B47Rm1VFfW1tTQ3NxMOhwlHIieXSDTqrUejhKNRHFUc1/WW2Dpw\ncvxkos9H/4QEBvj9DPD5GAD0d10GRKMMiEToH40yBfgbIC+25AAJoRCEQqffRIvWxghQWVfH4bo6\njpSWkgwUACldaJUErzUzF1gAEAx29U/b7b4PfA/4NXADUA58Hu9vtAoLQ425sBRjQaoxF76OuvvO\nAXarahmAiDwP3ArEB5q3AP8FoKprRSRDRHLxhud0VNYYcwFTVRoaGlpvUYvta2pqIjk5mZSUFFJS\nUk5bP7EkJSXh9/vbXVzXPb27ZmyJ78YZjUZJTExsd4lGo2eWr672un/W1BCsr6e5qclrOYxGT2tF\ndOLWExISSElOJiU1lZTUVJJTUkhJTz+19OtHOBQ6de66Om8JBgk2NBBsbKShqYmkhATSU1JIT0sj\nPT2d9H79SO/fn7QBA0jPyCClXz9qKys5dvAglYcPU3nsGJU1NRyrr8cPZCclMSgQIAMYoMoAx2FA\nJMKAcJgReK12A2KvSXjjIeOXhBbr/lYWn/cfGxzHW7oYNHZWAl5L5tAeOXvvqMPrzvtLvBT24I07\n/W+8L8Hn8FpPw8B9bZ4lH3gWL8RtBr4OvBB7707gu3j/BY0xxpi+oaMgNQ84ELddDlzZiWPy8H6H\ndFS2Q6+++iq33nprV4sZYzrh8cf73qxQfiBJxGs5xGtB9MX2x++LqBJSJeS6NKniduLc6T4f6SKk\n+3yk+XwM8vmINDURrK3lgOsSjC3RNsr39/nI9vsZ7vczKymJVF+sY6p76uoNPh8NSUkcTOqoU6w5\nHw5FozSEQvx3ejovtEjmlBYKUQT08/nYHg5zdUrKGa3NAW4kylF8fBMfWTjsRDmOn8sBcPgVwp/w\n2xylxnQLh134Wdfb1TDmkuFQhvi6/zdJR0FqZ3spndOM4mebpdEYY7rKARpVvZbDbhZ0XYLgtUae\nhTrXpc512dNDLZmm57zQiW7HK1t2hwai/AUAl3WnPQhx+OvJdaWGKHvOuY7GGE+U3b1dBWMuKVu2\ndX8811GQWgEMj9sejtci2t4xw2LHJHSibJcG0BpjjDEXkliCwD8BSarqtnjvv/C+C3fg5W64N7Y/\nHy/HUkBVXREpBT6tqitEpBG4/ET+BhGZCGxUVWs6N8YY02d0lOSwBBgnIvkikgjcBbza4phXgb8H\nEJECoEZVj3SyrDHGGHMxW4M3kPTj8TtFJB1YCCzv4vkO4g1SPWFEbJ8xxhjTZ7QbpKpqFHgYeB3Y\nCixV1W0i8pCIPBQ75jVgr4jsBpbgJTVss2yP3YkxxhhznqlqLd4sM/8hIgtEJCHWUvrfeHkZTuRO\n6qzfA/8iIoNEZBDwGG1NsWqMMcZcojrq7ouq/gVig2ZO7VvSYvvhzpY1xhhjLiWq+j0ROQ78OzAG\nL+nvy8DdqhoWEeXMHA9tDYr+P3jJmjfFtv87ts8YY4zpM0Q7SB4SG2/zJF7yy2dU9bst3v874Gt4\nT4rrgc+p6qbYe2V4X9YOEFHVOd19A8YYY4wxxhhjLh3tBqki4sdL+DAfL0HSerwnw9vijrkK2Kqq\ntbGAtkhVC2LvleIlgKjqwXswxhhjjDHGGHOJ6Chx0hxgt6qWqWoEeB44bdJSVV0TG5MDsBYvu288\ny95rjDHGGGOMMaZTOgpS8/ASP5xQHtvXlk8Dr8VtK7BcREpE5MGzq6IxxhhjjDHGmL6io8RJnZ7t\nXkTmAQ8A18TtvkZVD4lINrBMRLar6soW5Tp9DWOMMcYYY4wxFx9V7XQP246C1ApgeNz2cLzW1NOI\nyHTgP4GFqlodV5FDsddKEXkZr/vwypblO0reZIzpfkVFRRQVFfV2NYzpk+zzZ0zvsM+eMb1DpGsj\nQDvq7lsCjBORfBFJBO4CXm1xwRHAS8A9qro7bn+qiPSLracBHwU2d6l2xhhjjDHGGGP6lHZbUlU1\nKiIPA6/jTUHzrKpuE5GHYu8vwZtoPBN4OhYhn5hqJhd4KbYvAPxWVd/osTsxxhhjjDHGGHPR66i7\nL6r6F+AvLfYtiVv/DPCZVsrtBWZ2Qx2NMT2gsLCwt6tgTJ9lnz9jeod99oy5OLQ7T+p5qYCI9nYd\njDHGGGOMMcb0DBHpUuKkjsakIiILRWS7iOwSka+38v7fichGEdkkIqtiSZQ6VdYYY4wxxhhjjInX\nbkuqiPiBHcB8vEy/64G7VXVb3DFXAVtVtVZEFgJFqlrQmbKx8taSaowxxhhjjDGXqO5uSZ0D7FbV\nMlWNAM8Dt8YfoKprVLU2trkWGNbZssYYY0xfIiKnLcYYY4w5U0dBah5wIG67PLavLZ8GXjvLssYY\nY4wxxhhj+riOsvt2uh+uiMwDHgCu6WrZ+EmVCwsLLfOaMcYYY4wxxlykiouLKS4uPuvyHY1JLcAb\nY7owtv0o4Krqd1scNx14CVioqru7WNbGpBpjjOkTWnbxte8/Y4wxfUF3j0ktAcaJSL6IJAJ3Aa+2\nuOAIvAD1nhMBamfLGmOMMcYYY4wx8drt7quqURF5GHgd8APPquo2EXko9v4S4DEgE3g69oQ4oqpz\n2irbg/dijDHGGGOMMeYi12533/NSAevua4wxpo+w7r7GGGP6ou7u7ouILBSR7SKyS0S+3sr7E0Vk\njYg0icg/tnivTEQ2icgHIrKus5UyxhhjjDHGGNM3tdvdV0T8wI+B+UAFsF5EXm3Rbfc48AhwWyun\nUKBQVau6qb7GGGOMMeYSE41GaWxsJBgMEgqFaGhoOPna1NREQUEBAwcO7O1qGmPOk46moJkD7FbV\nMgAReR64FTgZpKpqJVApIje1cQ6brdwYY4wxpo9zXZcNGzbwxz/+kbfffoctG/dQXVuFqw147Rr+\n2BIAAkjsFfwgtdz5iZv5xa9+QWpqai/ehTHmfOgoSM0DDsRtlwNXduH8CiwXEQdYoqr/2cX6GWOM\nMcaYi0xdXR2rVq3izTff5J23V7Ft635q66tQfPiZisu1KJ8DLgNG4P0kbTFm+7SNEl584Yu8+Idc\nPv+F+/nBD35AINDRz1hjzMWqo0/3uWZ0uEZVD4lINrBMRLar6sqWBxUVFZ1cLywspLCw8Bwva4wx\nxhhz8aurq+PNN99k1apV7Nixg2g02u7xruvS1NREY7CJUGMTTU0Rwk0RmsMRIhGHSNQBIDMznWEj\nshmZP4IxY8YwZcoUZs6cybhx4/D5OkxZAkBVVRUrV65k7dq1bN68mZ3byjh0qIqGUD2uhhAG4Wc8\nUa4HZuMFpHk4Z9XJ7gocVoP7V37yH4+w5GfZfOuxf+Sb3/xmu/UtLy/nJz/5CX/471fZW1ZO5oAB\n/NM3HuarX/1qp+/TGNN1xcXFFBcXn3X5drP7ikgBUKSqC2PbjwKuqn63lWMXA0FV/X4b52r1fcvu\na4wxpq+w7L6mLZs2bWLZsmWUlJTw4eYdHNh3lLqGOlwN4WMwPsbjMhElqYMzCUo/IAVIjnuNX3eB\nQ8B+/OxGKMOlApdKoBmfpJIQSELVxVUXjV9wASd2DmJ1G4vLFFymAGNjywggodv/Th4FXkb4Mmmp\nzXz/h//GZz/7WcAL0l988UWefeYXrFq5gYamavzMwuEOvBQra/DxXXz+am67/aP86EdPMnTo0B6q\npzHmhK5m9+0oSA0AO4AbgIPAOuDu1uY7FZEioP5EECoiqYBfVetFJA14A3hcVd9oUc6CVGOMMX2C\nBakmXjAY5Fvf+hbP/vz31DfW4Wc8MAWHWcCE2DKKngv2WtOIlyuzKnbdxNiS0OI1EUjCG0PaW6LA\nrxC+QVZmCunpqew7UA70x8ctONwKXAektSinwNv4+S4ObzJ5wgT+/YdPsGjRojOu4Lou7733Hn/8\n4x95Z+UqtmzaS01dDSI+EgNJpKQk069fClmD+pGdk0VOTg65ubmMHDmSgoICZs6caS22xtDNQWrs\nhIuAJ/H+FXpWVZ8QkYcAVHWJiOQC64H+eI/V6oHJwGDgpdhpAsBvVfWJVs5vQaoxxpg+wYJUA7Bi\nxQq+/k//zHvvb8THJBy+CnwMOmwlNa1rQvg5ig9YiNeS21nlCD9GeZoB6al89nP3oqqsfGsV27ft\npy5YhRKIG0c7B5iO15p8HDgWez2OcBgfBxGOoBzFYT8QJimhP4MGZjBu4jCmz5hOQUEBN9xwA4MH\nD+7uP4QxF6xuD1J7mgWpxhhj+goLUvuuYDDI4sWLeWbJ76hrCOLjPlwewWstNb2vGXgBP08ipBPl\nI8SPoz37ySqqgJ3ADoQP8bMBlx24HERIRCRw8t8FQbx1EQQfIpAQCHDVNTP48j98kRtvvLEb7tOY\n3mFBqjHGGHOBsiD10hAMBnn22WdZ+vsX2LhhF66r+P1+An4/gYCfxMQAiYkJJCUlkJyaSCQSYfuO\n3fiYGNdqmtzbt2F6lYM3ki7MqTG+TovFBY7j40VcXibgc7ls1iQ++7nPcN9991l2Y3NR6Ynuvgs5\n1d33mZZJk0RkIvBLYBbwz/GJkToqGzvGglRjjDF9ggWpFyfXdXnttdf4xS9+wVt/XU9VXSU+RqLc\ngrIA6AeEgKbY0nLdAf4fYFIv3YG5+LlACcILwPMgVUwYO5r7Hvg7Hn74YdLT03u7gsa0q7sTJ/nx\nEifNxxtFv54WiZNi08uMBG4DquMSJ3VYNnacBanGGGP6BAtSe144HOb555+nsrKSoUOHkpeXx8iR\nI8nLy2u35SkajVJaWsrevXvZt28fBw4c4MCBA7y94l3KystBU/CxAIdb8PJJZp23ezLmTNsR/oCP\n3+Gwm9SkTEaPHkLBNVewcOFCbrrpJpKTrbXeXDi6O0i9ClgcNwXNNwBU9TutHHvaFDOdLWtBqjHG\nmL7CgtSe8c477/Czn/2M5f+7iiPHD+NjMEIWSg1KHUoQr1tlIj5JIuBPIDEhEcdxCEebcdxmvDGJ\nyQgD8JGBMAjIIUoh8FFgHGc/LtGYnlQNfAC8R4CVuLyHSyXJCQMYMTKXK6+axYIFC/j4xz9ugavp\nNV0NUjvqzJ4HHIjbLgeu7OS5z6WsMcYYY0yr9u/fz9NPP80f//Bndu3Zj+OCn+txeByYj0tr815G\ngVpcrSYcrSYcrcGbNzQLGARkAgEUr3OuMRePTOB64Hqi/FNsXz1NkY3s3P0+e2x2yvIAACAASURB\nVHev5Le/+Qb33PMA6SkDmTZ9NAtvXMC9997LqFGjOjy767qUlpaybt066urqmDBhAtOnT2fgwIFd\nqmVdXR2bN29m+/btzJgxgyuuuKLLd2r6jo6C1HN5xNvpskVFRSfXCwsLKSwsPIfLGmOMMeZSUVVV\nxSuvvMKyZctY/+5GDpQfoTlSj5/LcLgXb8qRaTgdtnIG8AJS66Zr+oJ+wLXAtUT5YmxfNcHQu7y7\n9i3WrX2VxYu/TcCfwsjhQyi84WqmTZvGjh072LNnD2W7Kzh6tIZgqJGoEwQCsR4KKbgcQ6kB/AR8\nKSQnpTCgfxqDczPIG55LIBCg/MBBjhyspqaugVBTiKgTAiII/RGycSlnyODBLP7Xb/Dggw/aXLKX\noOLiYoqLi8+6fEfdfQuAorguu48CbhsJkFp29+1UWevua4wxpq/oy919Xdfl2LFjhMNhr5ttOHza\neiQSIRKJsGXLFv7617/y3totHDxcScQJ4mM4whwcrsWbEmQmXiuoMebsRYFNwCoCvIFSijASh4ko\no/FSzowERgADWpRVvG7Gh05bfOwDwriMAoa0WAYBJ4LROoRngO+RlBDm/k/fyfe+9z1LAHUJ6+4x\nqQG85Ec34OXJXkcryY9ixxYB9XFBaqfKWpBqjDGmr+grQequXbt47bXXeOedd9j0/jYOVFQSaq7B\ny1Dqx/uhKrFXb11i+30MAq7E4Rq8gHQqkNQ7N2KM6WEO8Bp+/j9cNjP3I3P48U//g6lTp/Z2xUw3\n64kpaBZxahqZZ1X1CRF5CEBVl4hILl7m3v543z71wGRVDbZWtpXzW5BqjDGmT7iUgtTy8nLWrl17\ncozZ9g93UVp6mPqGGhQXP2OBWTjMwQs0p+K1pBhjTGu24Oe7OLzIyGEj+MxD93L33XczZsyY3q6Y\n6QbdHqT2NAtSjTHG9BUXW5Dqui5Lly7lueeeo3R3OUcOV1Pf2EAk2gAoPgbjYxjKOBwmA9Niy3As\nE64x5uwcR1iCjz/g8CF+XzJ5Q3K46tpZ3Hzzzdx+++2kpqb2diVNF/VES+pCTrWGPtPGeNSngEVA\nI3C/qn4Q218G1OG15UdUdU4rZS1INcYY0ydcLEHqO++8Q9Hix3nrrRIcJwHhdlwmc2p82khgIBaI\nGmN6lgN8CKzFzwqU1bgcIjVpIBMmDmf8xDFkZWWRk5Nzcl7k4cOHk5+fb+NbLzDdPSbVjzeudD5Q\ngdet97RxpSJyI/Cwqt4oIlcCP1LVgth7pcDlqlrVzjUsSDXGGNMnnK8g9ejRo7z88sts3LiR8ePH\nM3v2bGbPnk1iYmKbZfbs2cNjjz3GKy8tp6GpAT+fwOFB4GosGDXGXDjqgPUIa/CxG6ESqEKpxj05\nL3ID4EckkaRAKpkZ/Rg+cjATJo1j6tSpXHHFFRQUFFiL7HnU3UHqVcDiuAy93wBQ1e/EHfMz4E1V\nXRrb3g5cp6pHYkHqFap6vJ1rWJBqjDGmT+hskNrY2EhycnKnpmXYt28ff/jDH3jrrbd4f91WDh89\nRtRtwMcofIxBKcflAEo9fkkjNTWdnOwMxkwYzoQJE1BVnn/uFSqrj+DnBhwewpvWpe2A1hhjLmyK\n18GzCigH9gJ78fMhsDP2b2IVPkklNTmdcePymL/geu655x6mT5/emxW/ZHV3kPoJYIGqPhjbvge4\nUlUfiTvmT8ATqro6tr0c+Jqqvi8ie4FavLb6Jar6n61cw4JUY4wxfULLILW0tJQ33niD1atXs/GD\nLZSVHqYuWIer3phPCCAkIBLA7wvg9/tJCARITEhABKpqa3C1CT/jUQpwuRovI+5kIKHF1ZuAfUAp\nsBdhJ362oTThcD/wMc6cZsIYYy5VEWA/sAdhLX5eJ8r7+CSBobmDufa6K7j99tu57bbb2u2FYjqn\nq0FqoIP3Oxs9tnXBa1X1oIhkA8tEZLuqrmx5UFFR0cn1wsJCCgsLO3lZY4wx5sLmui7l5eXs2LHj\njPdGjZqIn3yEqUT5BDAFmASMih3RgNKAagOu00DEaaAp3AAE8Z7/TgHG4+DvRE2SgQmxxfuCj57r\nzRljzEUrARgDjEH5KFG+Bbi4up3yQ6t44fnlLH3+H1DupV9qFgmBAFHHxXVdXNfBdRVXXVx1UXVB\nlcSEZPr3SyNnSAYj8vMYPXo0EydOZNq0acycObNPjZMtLi6muLj4rMt31JJaABTFdfd9FHDjkyfF\nuvsWq+rzse2T3X1bnGsxEDwxj2rcfmtJNcYYc9FzXZdNmzaxZcsWdmzdyo4PPmDH9u3srqggIyGB\nCQkJvFlb27IUNt7TGGMuZJVACV7LayJecJvYYj0Bb87no3jdiyvwsRcfe1D243IEpQYhiYRACqnJ\nKWRmpjMkbyB5w/PIz89n7NixTJ48malTp5KRkXFONd61axebNm1i0aJFF8y42+7u7hvAS5x0A3AQ\nWEf7iZMKgCdVtUBEUgG/qtaLSBrwBvC4qr7R4hoWpBpjjOk19fX1rF69mtTUVKZPn86AAZ3v8lpZ\nWckbb7zB//7hD7y+fDmZqswCJjQ0MEGVCcB4oF/s+DO/ne37zxhj+gYHL4g9FLccxE8pwn6Ug7hU\notQS8KWTO3gQl82ZzLx587j99tsZOXJkq2cNBoO89NJL/PnPf2bNOx9w6EgljtuEMBDlGKlJmUyc\nNJLr51/HnXfeyezZs8/fLcfpiSloFnFqCppnVfUJEXkIQFWXxI75MV6WhQbgU7HxqKOBl2KnCQC/\nVdUnWjm/BanGGGPOm+bmZtasWcOKN97gr6++ysadO7k8JYVmYEsoRPaAAcyYNo0Z11zDjFmzmDlz\nJvn5+fh8PqLRKOvWreN///xn/vell9hRWsq8pCQW1tezgFOddNtiQaoxxpj2hYGtwPv4WI3wLg67\n8Esy2VlZzLhsPKNGj+LdVevZsfMAoeZqfAxFuAaHeUAB3rARP15qIC8Tsp/lRPkAEZdBmYOYfeUU\nFi5ayB133EFubm6P31W3B6k9zYJUY4wxXdHc3MzRo0dPLpFIhOTkZJKTk0lJSWl1fdu2bfx12TL+\n+sorvLthA5OTk7m+sZEbolGuBk50hnKA3cBGYKPfz8a0NDZGo9Q6DpNGjWLX/v0M9/tZFAqxMFa2\nK+k0LEg1xhjTdQ6wEy9wfRcfu4lyLXAVcAXQv5PnUbzkeWvx8RbCShx2EfCnkZc7mDlXz2DRokXc\nfvvt59zluCULUo0xxlzQ6urqKC0t5ejRo4RCIZqammhqajq5fnJfMEhNZSVHKyo4evgwR48f50hN\nDaFwmOzkZHICAbJFSFKlSYQQXv7aJlVCrktTbAlFo4xKSeGG5mZuCIeZC3T1q/c43nTyY4GhsX1T\ngZ8Cc7twnjO/nTszJnU/XoKkuk4ca4wxxnRFBNgCrMfPSpTVuOwnMTCA/BG5TJ4+jszMTLKyssjK\nyiI7O5ucnBxyc3NPLoFAR7l4e6a770JOdfd9Jj5pUtwxTwGL8CYkul9VP+hCWQtSjekFxcXFlknb\ndDvHcaivr6eyspLS0lL27t1L6a5dlH74IaV797K3ooKmcJjRqankipCiSrIqya5LsuuS4jgkx5YU\nvLGcObFlcGzJ4PyEaguBK4HHW+x/Bfh/gQq8NBldYS2pxvS2YqCwl+tgzIUuBGwASvCzGaEaqAbq\nUOpQgl7meUJAmHnXXc+K4uXtnrFbp6ARET/wY2A+3vfxehF5tZXESWNVdZyIXAk8DRR0pqwxpvdY\nkNpzTgRq9fX1+P3+k11Pk5KS8PnaD2scx6GxsZFgMEhDQwMNDQ0Eg0EqKytPdXE9cIAj+/d7rYvH\njnG0poaqhgYA/D4fPhH8IqfWfT5v3efrMLgTEZITE/8ve3ceHkd1Jfz/e7q6pdbmTZb3fcFgjGO8\nyjGLHJxgCBOWTAaYkIQJISQZ8oa8SSYhmXcQmYRlfk8WMiSMEwiZJAQcBgcIgcFAEIuRt5jN+yrb\nsmRrX1pSq5c6vz+qbLeFrMWWLNs6n+epp6uq61bdaj9y96l777lkpKcT9peMjAzCGRlkZGYSzsgg\n4Dg01tXRUF9PQ2MjDZEIDU1NNLS00BKPkx0KkZuWxiTHYWIsxsSWFq7DG685EcgDpKGhBz7p3nUz\n8D0+GKT+DriJjgPUJHRpUhhjzKlWhAWpxnQmA68r8UKSnR77ABUHH+3xGnTWNjsf2KmqJQAi8gRw\nNZAaaH4C+G8AVV0jIoNEZATeb5HOyhpjzlCu6xKNRkkmk2RmZuI4vfeTPJFIEI1GSSQ6ntVRVY8c\n21430sNLMtnxf7mqSjweP/Yczc1Em5qIRiJEm5tpaWoiUl/fbqAWTSTICYXIDgZxgZZkkmgySWsi\nQchxyAiFCKelEQ6FSE9LIxqL0RSN0tTaSmsiQWYoRFYwSJbjkBUIkC1CnirDEgmGRaNMdl0WcrRl\ncTgwxK+7m0ySxAuSXP81db0zLn6XWX9pafMa9c8z4DhLJhCIxSAW68LVTm9X47WYvgFc7O+rBf4C\nrAEmAL8GPgIU4nWWygCeBX4CLAY+h/csegFelt8PcvHC3QK8jsN/Bd7D+3HwByAXKAEm4c1qGgAe\nBf4/vGkO8oBvA188+Rs2xhhjuq13+jZ1FqSOBvanbJfifdd2dsxovGE7nZXt1J/+9Ceuu+667hYz\nxnTB3Xe3bSMy3SHAgECAAY7DgECAiYEAOenpZGVkHPtftj9WQ4FW1yWqSjSRIBqLEY1EGCrC+ECA\nrHCYjPZaO/0hEbXBILXZ2Wzr/VszvsEtLXwWmJ2RAcDuWIz0WIy7s7OpbGzkrowMfh4MsjkaZVss\nxoKMDJaEQjyryream8l1HC5LT6c2meSXzc0fOL/DxxCEBO8Ca3G4EJiLyzpgLg5TUFpIokeOdalC\nGIIwGqWWJF/G4RGky4kzjOm/kuzCYVVfV8OYs0aSfYjT83OxdhakdnWwzEmF0CKWCMIYc+ZRoN51\nqXfdvq6K6WUl8fgx2yv87spvtgk8V7e0QEvLke2aZJIdHbQqJ3mlzfZbKVu1JCg57rHHllt73PeM\nMcdKsKevq2DMWWXj5p6P5zoLUg8AY1O2x+K1iHZ0zBj/mFAXynZrAK0xxhhzqonIDuBfgfV4Q1ZG\nq2qliOwBblHVv4pIIV5+hpv8MvnAM6o6POU89wBjVfUzIjIB2A0EVdUVkVeB36nqr/1jb/bPfXE7\nx14B3AVMxev/mwncp6p39fJHYYwxxpwSnSUmXA9MFZEJIpIGXI833CbVs8Bn4ciXcp2qHupiWWOM\nMeZ091u877mbgP9V1crjHJfa+6gcGCIiGSn7xp1sRUQkHXgK+A9gmKoOBp7H5qYxxhhzFukwSFXV\nBHA78CKwGViuqltE5DYRuc0/5nlgt4jsBJYBX+mobK/diTHGGNM7fgt8FPgCfqLAzqjqXryHtYUi\nEhKRhcBVdDyMpiuBZpq/VAGHW1U/1pU6GWOMMWeKTmdeVdUXgBfa7FvWZvv2rpY1xhhjziSquldE\nVgEzOX6PIOWDAeingd8A1cBaYDnHzkzT9nhts952G1VtFJH/A/wRSAf+jDd1qzHGGHPWENWOcyOJ\nyFLgp3hfrA+r6v1t3v808C94T4AbgS+r6nv+eyVAA96MBXFVnd/TN2CMMcacCURkObBZVS2ttjHG\nGNOBDoNUEXGAbcASvARJ64AbU7vt+l2YNqtqvR/QFqpqvv/eHmCOqtb04j0YY4wxpx0RmYs3teoe\n4HJgBZCvqu/2acWMMcaY01xn3X3nAztVtQRARJ7Am9/8SJCqqsUpx6/By+6bypI5GGOM6Y9G4AWm\nuXjzhn/JAlRjjDGmc50FqaPxvlgPKwUWdHD8LXhZBg9T4GURSQLLVPVXJ1RLY4wx5gyjqs8Bz/V1\nPYwxxpgzTWdBascDVlOIyGLg88CilN2LVLVcRPKAl0Rkq6q+0aZcl69hjDHGGGOMMebMo6pd7mHb\nWZB6ABibsj0WrzX1GCIyE/gVsFRVa1MqUu6/VorIn/C6D7/RtnxnyZuMMT2vsLCQwsLCvq6GMf2S\n/f0Z0zfsb8+YviHSvRGgHc6TijfH21QRmSAiacD1tEm/LyLj8Mbc3KSqO1P2Z4pIjr+ehTeP2/vd\nqp0xxhhjjDHGmH6lw5ZUVU2IyO3Ai3hT0DyiqltE5Db//WXAvwGDgYf8CPnwVDMjgBX+viDwmKqu\n7LU7McYYY4wxxhhzxuusuy+q+gLwQpt9y1LWvwB8oZ1yu4FZPVBHY0wvKCgo6OsqGNNv2d+fMX3D\n/vaMOTN0OE/qKamAiPZ1HYwxxhhjjDHG9A4R6VbipM7GpCIiS0Vkq4jsEJFvt/P+p0XkXRF5T0RW\n+UmUulTWGGOMMcYYY4xJ1WFLqog4wDZgCV6m33XAjaq6JeWYhcBmVa0XkaVAoarmd6WsX95aUo0x\nxhhjjDHmLNXTLanzgZ2qWqKqceAJ4OrUA1S1WFXr/c01wJiuljXGGGP6ExE5ZjHGGGPMB3UWpI4G\n9qdsl/r7jucW4PkTLGuMMcYYY4wxpp/rLLtvl/vhishi4PPAou6WTZ1UuaCgwDKvGWOMMcYYY8wZ\nqqioiKKiohMu39mY1Hy8MaZL/e07AVdV729z3ExgBbBUVXd2s6yNSTXGGNMvtO3ia99/xhhj+oOe\nHpO6HpgqIhNEJA24Hni2zQXH4QWoNx0OULta1hhjjDHGGGOMSdVhd19VTYjI7cCLgAM8oqpbROQ2\n//1lwL8Bg4GH/CfEcVWdf7yyvXgvxhhjjDHGGGPOcB129z0lFbDuvsYYY/oJ6+5rjDGmP+rp7r6I\nyFIR2SoiO0Tk2+28f66IFItIVES+0ea9EhF5T0TeFpG1Xa2UMcYYY4wxxpj+qcPuviLiAA8CS4AD\nwDoRebZNt91q4KvANe2cQoECVa3pofoaY4wxxhhjjDmLddaSOh/YqaolqhoHngCuTj1AVStVdT0Q\nP845bLZyY4wxxhhjjDFd0lmQOhrYn7Jd6u/rKgVeFpH1InJrdytnjDHGGGOMMaZ/6bC7L16QeTIW\nqWq5iOQBL4nIVlV9o+1BhYWFR9YLCgooKCg4ycsaY4wxxhhjjOkLRUVFFBUVnXD5DrP7ikg+UKiq\nS/3tOwFXVe9v59i7gIiq/ug452r3fcvua4wxpr+w7L7GGGP6o57O7rsemCoiE0QkDbgeePZ4125T\nkUwRyfHXs4CPAe93tWLGGGOMMcYYY/qfDoNUVU0AtwMvApuB5aq6RURuE5HbAERkhIjsB74O/KuI\n7BORbGAE8IaIvAOsAZ5T1ZW9eTPGGGOMObs9++yzfPe73+WRRx6huLiYSCTSK9dxXZeysjJisVi3\ny5aWlvLwww/zuc99jpkzZpM7cByXXFzAo48+SiKR6IXaep555hnmzJ7PU0891WvXMMaYU6HD7r6n\npALW3dcYY0w/Yd19T1xVVRVLP3YVf3t7E0Fm43IQpRKlAQgRcjIJp4cZNDCL4aMGM2BgNoFAAMdx\ncByHYDB4ZPvwekNDAxWHqqipbKChoZmm5iixRIxkshUlipe6I4EQJhTMIDOcweDB2YwYNYTRY0cx\nYcIExo0bx+bNm/nburfZuaOM+sY6XI0SYCIBPkSCBcAEArwBrECpYuzoMVzzySu44447mDhx4kl/\nNq7r8pmbPssfHv8TwmdQniB3UA733P//+OIXv3jS5zfGmJPV3e6+FqQaY4wxp8jpFKTu2LGDH/zg\nB6xd/TazZp/PZZddxjXXXMPQoUP7rE7Hc9999/Gv3/shuB8hyS+B4SnvunhTtpf7y0H/tRFIIiTw\nAs0EQhzFPbJPGYTLKCA3ZRmasp4GJICKlPN7i8MehH0oFQjj/WB0JjADmAA4x7mbEuAFHJ4gyRqy\nwgNZdPGFfOnLt3Httdd2+7PZu3cv+fMupaIyiMtzwLlAC/AIwr+TlQnf+e7/4c477yQQ6GyUV/ua\nm5vZvXs3u3btYt++fZSWllJeXk5OTg63334755133gmd1xjTf/R4kCoiS4Gf4v1v+3DbpEkici7w\nKHAh8L3UxEidlfWPsSDVGGNMv9DXQerhwPSZFS9RH6nFoYAkS3D4G8o6XPYSdLIYMWwos+dNZ/Hi\nxVx77bWMHz+euro6Nm7cyObNm9m5cyclJSUcKC2jvLSa2roIsVgcEUEE/zWACAQCAQIiBALCyFFD\n+NI/38qtt95KMNjZBAOwbds2PnbZVew/UI/yG+DKXv+MTq0W4DUCPIWygswMh699/VbuvvvuLn0+\nv/zlL/nyl74BegMu/wmE2xwRB5YT4F8JhRr4yu03c99995GWlnbMUXv37uXll19m3bp1vP/eRnbt\nKKOuvpF4IoarUbxAPYsAAxEGIwwFhgFVJFhFOC2bBfkz+MKtt3DDDTd0qe7GmP6lR4NUEXGAbcAS\n4ACwDrhRVbekHJMHjAeuAWoPB6ldKesfZ0GqMcaYfqEvgtT2A9PP4wV8WW2OjgGbgLcJ8BbCGpJs\n999LIgwiQB7CSJSxJJkIjAJGAgPxZq5LpizuMevCOwi/Q6WSGedN5cu3f7HdgNV1Xb7y5a/wy1/+\njgA3k+R+ILtXPp/TRwJ4igCFSKCM66+/iv988D8ZMmTIB46MxWJccfnH+WvRGuB3wNWdnNsFniPA\nd5HAfhZ+eCYH9lVx6FAtLa0NKC4BxhFgGgkuBM4DJgJ5eC3KAzh+GpMY8AYBnkZ5GqhhwtixXPup\nj/O1r32NcePGHa2F61JTU0NlZSXV1dVUV1dTU1PDuHHjWLx48Qm39BpjTn89HaQuBO5KmYLmOwCq\nel87xx4zxUxXy1qQaowxpr84FUFqNBpl+fLlLF/+R1a9/jYNTbU4LCbJP9F+YNqZJNAADKJNIv+T\nsAXh8XYD1jfffJNrP3EjDY3ZuPwBmNdD1zxTKPAGDoW4rOaiRfN5aNnPOf/88wHYsGEDiy+9kkhk\nNC5PA2O7ee4ihOdRpgDTgHPwHjL01L/tbuB5vzvzegKShmoCJYHXshsE0hHCCBkIGbjUoETIyRzC\nueeN4+JLF3HdddexcOFCC1yNOUv0dJD698Dlqnqrv30TsEBVv9rOsW2D1C6VtSDVGGNMf9FekOq6\nLsXFxTz33HO88fqbbNm4l/pIA5nhTMaMzWPGzGnk5+ezZMkSZsyY8YEf7c3NzfzhD3/gyT/+D2tX\nb6SusZIAI4EluFwJXE73A9NT6diAVVUR/hXlm0CoryvXx7bicA9JnmTqpElc+pEP88jDf0C4A5e7\n8QK+01kz3hjeLH/J5PhjdSuAvyGsxuE1ErwDtDIoO5fpF0xk9pxZTJ8+ndmzZ3PhhRd+oMuyMeb0\n1tNB6ieBpScYpHaprIjoXXfddWS7oKCAgoKCrtbfGGOMOWO0DVJzskbR2FQDpBPkApIsQpmP18JV\nCmzBYQPwLkl2AQky0gYwckQuI0YPYdP7JdRHqggwBmEJSS4HLsFL/nMm2ooXzHSndbA/qEB4AOHP\nuDyI92/cH5QB6xGKcXgXpYQkZUAjAckinJ7JkMEDGDsuj4mTJ3DBBReQn59Pfn4+4XDb8bnHV1VV\nxUsvvcQbb7zBxvc3gcCAATkMGjSIIUOGkJubS15eHkOHDmXEiBGMHTu2R7IyG3M2Kyoqoqio6Mj2\n3Xff3aNBaj5QmNJl907APU4CpLZBapfKWkuqMcaYs5nruqxdu5ann3yS+3/84zbv/gUv7+DILp6t\nEtiCN3V5KV5X2IuBD45bNObs1YqX7mQfsBcowWEbsB2XEpQ6nEA2OZk5jB6TyznnTWLmzJnMnTuX\nAwcOUFxczHvvbGLP7nIaIg242kKA0QSYToJZQIgA1Qi1CLVAPUo9SgSlCaWRzPAAPv2Za7jnnntO\ny4zYxpxuerolNYiX/OgyvMdZa2kn+ZF/bCHQmBKkdqmsBanGGGPONq2trbz66qs8/cQTPPP00wxJ\nJrkmGuWeRKLNkfb9Z0zPiwJ7gJ3ATgJsJMBmkpT4GYpnkGAOMN1fJtK9rtNx4C84/Jgk65g+bRrf\n/+H/45Of/GSP34kxZ4vemILmCo5OI/OIqt4rIrcBqOoyERmBl7l3AF76uEZguqpG2ivbzvktSDXG\nGNPjVJWVK1fy03//d0r27CEjHCacnk44HCackUFGRgbhzExvycoid+RIRo4ezciRI48sw4cPJz09\n/bjnr6+vp6qq6kim0oMHD7JyxQr+95VXmJGWxjWNjVytylS/zAe/ne37z5gz216EZSjLyEgPcP2N\nV3H//fczbNiwkz6z67pUVVX1yLmM6Ws9HqT2NgtSjTHG9KTW1lYef/xxfnT33UhVFd+IRJiH17YS\nxZuZsu16C1AFHMzIoDwUolyE8nicimiUnHCYkbm5jBg2jNbWVqpqaqhuaKC2uZnMYJDctDSGOg65\nQF4iwaVNTVwFDG+nbhakGnO2SgAv+K2rq5k6aRLz8mcxY8YM5s6dy8KFC8nOPv40SolEgldffZXn\nn3+eVW8Us33rfuqbaoA46aFB5C+8gNu+9EWuv/56y3hszki90ZK6lKOtoQ8fZzzqz4Ar8NK43ayq\nb/v7S/Dy1ieBuKrOb6esBanGGGNOWm1tLf/185/z4I9+xIxEgm9GIizh5CbWcIFqvPykB4Ew3qyR\nQ/FGgXY396wFqcb0B6XA4zi8jTdOdh9KDSIZZKZnMzR3ABOmjGLUqJFsfG8ru3eV0xStQRiAw0yS\nXIwyD2+8eh7wJgH+B2UFSCPnTJ7AjTd9iq997WsMGjSoT+/UmK7q6TGpDt640iV4I9TX0WZcqYhc\nCdyuqleKyALgAVXN99/bA8xR1ZoOrmFBqjHG9GPxeJzdu3ezfft2tm3bxvZ33mHbxo1s37OHhOsy\nYeRIJk6ezMTp05k4dSoTJ05k4sSJjB8/nvT0dHbv3s1P77+f3//ud3wC+L8tLczs65s6DgtSjemv\nEsB+vLGy3nhZh70kmQXMxgtIczs5hwLbEJ4mwOMk2cbQwcP4+Cc+wh132p18bwAAIABJREFU3MGs\nWbN6rfaRSIQDBw6Qk5PDqFGjeu065uzV00HqQuCulAy93wFQ1ftSjvkv4FVVXe5vbwUuVdVDfpA6\nV1WrO7iGBanGGHOGc12XLVu2sGbNGtYUFVGyfTuBQIBAIIDjON4SDBI4vO441NfVsW37dvZVVTE6\nHGZaIMA50SjTYjHOwZuEJcTRn3R7gD0ZGexJS2NPMklpSwtDc3JojUa5NZnkq/E4p/tPJwtSjTE9\npwqvi/HjJCkiLZjB3LnT+dw/fYabb765S3PJbtq0iSeffJJXX32NnVv30xKNEYvFiSfiJJJxXI0D\nMf/oMBAn5GQxcfwoLr3sw1x//fUsXrzYuiCbTvV0kPr3wOUdzXUqIn8G7lXVt/ztl4F/UdUNIrIb\nqMfr7rtMVX/VzjUsSDXGmDPMwYMHvYB01SrW/PWvrN+0ibxgkAWqLGhqOpIoKNnO4vqv2XiB6GSg\n/dREHUvgdfHJ9c91JrAg1RjTO+JAMQGeAVbgcogxI0fxiWsv54477mDq1Kns2rWLJ598kr++8irv\n/G0b1XXVuJrE4QJcLkGZCQzCy4WauuTg/S8t/nXeBd4iyEqSrEZpIndQHnMXnM9VV32cSy65hFAo\nRFpaGsFg8Jj1tLQ00tLSjjzENP1HTwepnwSWdiFIvU9VV/nbqUHqKFUtE5E84CXgq6r6Rptr6F13\n3XVku6CggIKCgq7W3xhjTC+JxWLs2bOHXbt2sXPnTnZu2sTOjRvZtG0bDZEI89PTyY9EWOC6zMcb\np2k6ZkGqMebU2A88T5DlJChGxEE1gcN5KBfj8mFgLt5jwpMZuX/4WsUEeBV4FZcyvMeR6r+mrh9+\nFSCEECIQCOIEggQdh2AwRHpakPRwGhd8aCrf/d6dXHTRRSdZP9MXioqKKCoqOrJ9991392iQmg8U\npnT3vRNwU5Mn+d19i1T1CX/7SHffNue6C4gcnkc1Zb+1pBpjzCmiqkQikSNTplRXVx+dQqWqioMl\nJezavJmdJSWU1dYyNiODyYEAU1pbvQWv9XMKYM/Au8+CVGPMqdeKF0hOxMuDejqIAU0dLBEcXibJ\n04RD6Sy+bEG3A9ZEIsHq1auZPn06Q4YM6YV7MN3R0y2pQbzESZcBZcBaOk6clA/8VFXzRSQTcFS1\nUUSygJXA3aq6ss01LEg1xpzVWltbaWhooKGhgcbGxiPrDQ0NNDU1MXDgQIYNG3ZkGTJkyHG7QUWj\nUUpKStizZ4+3bN/Ons2bKd2/n1gsRtJ1cV2XZDJJMuXVVSUWj1PT1ERQhKHp6eQ6DrkiDHVdcmMx\ncltbGYYXgE4GxtP97LWmYxakGmNMdySAV3H47XEDVtd1eeedd3jxxRdZvXo17729nfKD1bTG64As\noJmB2UMpuGweX/jCF7jyyiutq3Ef6I0paK7g6BQ0j6jqvSJyG4CqLvOPeRBYivfo45/8rr6TgBX+\naYLAY6p6bzvntyDVGHNWaG1tZcOGDby1ahVvrVzJ2vXrqWhoQFUZkJbGgGCQAYEAA0TIAQa4LlnJ\nJPWOQ0UgQIXrcigWozEeZ2h2NsMGD2ZYXh6DhgyhrLSUPQcOUBOJMC4zk4mBABNjMSa2tDARGIs3\nYsjBa+F02lkP4U2bEu6TT8eABanGGHPijg1Y00NhnIBDc2sdEMJhGsocXOYAFwDTgYF46XFeweFp\nXF5AJMqkCeO49u8/zu233864ceOOXOHgwYOsWrWKDRs2sGXLFnZuL6HsQDWNkQggOAEv8V/QcQiF\nHEJph7snh8jKyWDK1InMmTOHSy65hDlz5hAMBvvgczo99XiQ2tssSDXG9KZ4PE59fT21tbXU1dVR\nW1tLY2MjTU1Nxy51dTTV1xOpr6c5EiErJ4dh48YxbNQohg8ffkxLZ15eHmlpaRw8eJC33nqLt157\njeJXXuGd7ds5NyODha2tfLi1lXxgNN1PChTDy9lY4S+1wCi8jlqjsG62ZzILUo0xpickgNf99RnA\nsC6WU2Ar8L84/A9J1pORPhDXVVrjTUCCAMMJMAGXabici/ftOx7v27cFiKYsqdsRHDYBG0myB2gm\nFMwhd9BAJp8zmgtmzmDhwoVcddVV/bL7sQWpxpizQjKZpLKykvLycsrLyzl48CC1tbVEo1GiLS20\nNDYSbWryluZmWpqbiba00NjQQF19PbWNjdQ1N9MSizEoPZ1BwSCDHYdBQI4qWcmktyQS3oKXITYL\nyMTrFlIBVIRCVKSnU+E4VKhSkUhQGY2SHgoRAhamp/PhxkY+rN7U62dKllnTfW8At+L9vDlRvROk\nXgncCHymB85ljDH9SQtQDGTgBaPDOflEUofVAzuA7cBmgryDy2ZcSgk52Ywcnsfs+dP5yEc+wrXX\nXsuYMWPaPUtzczNr165l3bp1bNy4kR3bdnKwrIaAEyAtFCSU5pCWHiItPUR6OO1INuWcnBzOOecc\nPvShDzFv3rzjnv9U6Y3uvks52t334dSkSSnH/Ay4AmgGblbVt7tR1oJUY/pAUVHRMZm0VZVoNHpM\ni2NzczOZmZlkZWV9YOluFxbXdamtrT02Uc/h9YoKKktLKd+3zwtKq6qoikQYkp7OiFCIkSKMTCQY\nHI+TkUgQdl3CeF8p4TZLDl4C/cH+aw4993VzmOJ99QzAWjXPdPfiBZ/Pp+yb6i9t9/0Q+IeTvJ61\npBrT14qAgj6ug+nfWoFNwAYCFCOsJslOnECYvCG5TJwykvID1VRV1dMcbcLVZoRBBBgHTCHJdLx+\nWi7elEAx/zUORBFaEaIEqAN247Ifl0OAEHKyyMnKYtjwQUyYPJphw4bR0tJydGluoaWplZaWGK3R\nOK2xBOH0IBdcOI38/HyuvPJKZsyYcUJjens6cZKDlzhpCd50dOvoOHHSAuABP3FSp2X98hakGtNL\nVJWamhpKS0s5cOCA97p/P6U7dvD6qlXkZmdT19BAXSRCbVMTAIPT0hgcDDIoECAT7xljkypNrktT\nMukt8TjBQICs9HTCoRAiHf+fE43FqI9GGRAKkRsKkes4DAVyEwkvYU88Th4wMmUZhiXtMb3vLbx2\nyFq8ALIc+DDeT4hSvIcQ5Xg/B8qAESd5PQtSjelrhf5izOkkiRc2vY3X+joar2X3cNaJtJM8v+J9\n0+07sgg7cTiIkomShUs2Xl+ytk0AjQT4G8IGkmwHkmSFBzFu3DBmzTmfiy66iBtuuKHTLszdDVI7\nawqZD+xU1RL/5E8AVwOpgeYngP8GUNU1IjJIREbgfaqdlTVnsEQiQUtLi9f90l9aW1u9bKL+ciTL\naMp6Vx5KiAhpaUe7LLT3Chxz/bZ1aWlpwXVdAoEAjuMcs6TuU9Vj6tle3SORiNe6WFlJXUWF91pT\nQ21dnRfkNTWhqqQFg4SCwaOvoZBXZ/+1sydPx9Sl7Wfnbx+5J38i7GPuzd+nqpRXVHCgpoaw4zA6\nPZ0xIoyOxxnT0sICVVqA2zm21THD+1A7/fdRoDWZpKm5mWinR3v/tQ4Ggq2t0NrahRLGnBpz8Z49\nvwNciNequhjY7e+b7e+bjPfzYR7eRA4A9wP/CTTgjRX+BfARvJ8a9wO/xusyfg7wNNB+R6tiYKG/\nXgBcAvwVeM/f/wcgF2+80xeA//WvMBX4C5Dnl/sMcAvwG+Bhv+wjeH/Zv8DLbWiMMeb05OAlepre\nS+cXvNSJQ4BZgPdbLtHF0u6RNQUO0RTdyJbtm9i+fS1PPH4PD//yUTa8s65Ha9xZkDqao9/H4D1Y\nXtCFY0bjfWd3VrZTK1as4JOf/GR3ixnT68Ii3hhHx2Gq4yBAPBYj1tpKXJWYKs3+6+HtrrSZOCJe\nRlYRLyur/xo4/Ir3o9oFkqokU15d/1WAEcEgF4XDZB0OjFWpDgapzsnhXWBrNMp/hC3PqzGZTU18\nNhhkcno677W0MNBxaHZdPidyZF8S+F4oRFVLC9fk5NCYTFLc3MwlWVmEAwGaXZd7gZ8FAuxobaU0\nHmdeRgYzHIeGZJIvHv47bGxsc/VLcMhHCJHgXWAtDjOBC3FZC8zGYTIuB1CqCXA+3v8CEeCTCEGS\nvItwiACP4VKOyzYCVCF8CKUMl6sJsuiUfZ7GnM6S7ME5knDHGHPyMojHuxrudl1nQWpX+yGd1JCv\nzroKGnM6iqpSnkhQnuj5P8yeUJtMsqWTVsttsdgpqo0xp7eqZJKNh/9e4vEj+zem/A3t8/c/09Bw\nZN+Lkchxz/mK34W+YwmSvHnMniRrUrbqSLAv5b32f1wrdbgpKZ1ctuG1/R6+yqtdqIsx/UOCkr6u\ngjFnlY2bez6e6yxIPYDXEfqwsXgtoh0dM8Y/JtSFst3qm2yMMcb0NBFZDCwHpgEbVXW0iAzAS8l4\nHlCJ1+N3IvA7VR3rl7sR+ApwPvAi8H9VtVxEmoB5qrq5zXW+DcxR1X9I2fc48J4/B/mr/vl/7b93\nM3CLql4sIkHgu8D1eH14fw98T1UTqeVSy6RcwwWmqOrunvzcjDHGmN7SWWqm9cBUEZkgIml4X47P\ntjnmWeCzACKSD9Sp6qEuljXGGGP62mq8Gd9vBVYBqGoDXq6kLwIHVHVv20Kq+rgfDI7H63l0OIP9\nfmBKO9c54B+bary/v0OqmlDV76vq+Xi5na7C/+41xhhjzjYdBqmqmsDLrfIisBlYrqpbROQ2EbnN\nP+Z5YLeI7ASW4T1VPm7ZXrsTY4wx5gSoagveg9X/C8f0p32znX0AiMg5IvIREUnHSwYcxctoBF7m\non8XkSnimSkiQ/BmtTlHRG4UkaCIXA+cCzyXeur26igii0XkAj9zfiPe0PRke8caY4wxZ7pOJzpU\n1ReAF9rsW9Zm+/auljXGGGNOQ68B+XDMANE3gH/m2CD1cK6GdLxpVs/DCxhX4bW6AvzYf38lMBQv\nq/21qlomIlcBDwAP4c0zcJWq1rRz/sPrh7eH+2XG4GVNegL4XTv3kVqmvXMaY4wxp70O50kFEJGl\nwE/xciM/rKr3t3n/08C/4D39bQS+rKrv+e+V4GXnTwJxVZ3f0zdgjDHGGGOMMebs0WGQ6ncr2gYs\nwRszsw64MbXbrogsBDarar0f0Baqar7/3h68JBE1Hzy7McYYY4wxxhhzrM4SJ80HdqpqiarG8boX\nXZ16gKoWq2q9v7mGD85Xbtl7jTHGGGOMMcZ0SWdB6mi8LIWHlfr7jucWvMQQhynwsoisF5FbT6yK\nxhhjjDHGGGP6i84SJ3U52YI/z9zngUUpuxf5c8blAS+JyFZVfaNNOUvoYIwxxhhjjDFnMVXtcg/b\nzoLUA8DYlO2xeK2pxxCRmcCvgKWqWptSkXL/tVJE/oTXffiNtuU7S95kjOl5hYWFFBYW9nU1jOmX\n7O/PmL5hf3vG9A2R7o0A7ay773pgqohMEJE04Hrg2TYXHAesAG5S1Z0p+zNFJMdfzwI+BrzfrdoZ\nY4wxxhhjjOlXOmxJVdWEiNwOvIg3Bc0jqrpFRG7z318G/BswGHjIj5APTzUzAljh7wsCj6nqyl67\nE2OMMcYYY4wxZ7zOuvuiqi8AL7TZtyxl/QvAF9optxuY1QN1NMb0goKCgr6ugjH9lv39GdM37G/P\nmDNDh/OknpIKiGhf18EYY4wxxhhjTO8QkW4lTupsTCoislREtorIDhH5djvvf1pE3hWR90RklZ9E\nqUtljTHGGGOMMcaYVB22pIqIA2wDluBl+l0H3KiqW1KOWQhsVtV6EVkKFKpqflfK+uWtJdUYY4wx\nxhhjzlI93ZI6H9ipqiWqGgeeAK5OPUBVi1W13t9cA4zpalljjDGmPxGRYxZjjDHGfFBnQepoYH/K\ndqm/73huAZ4/wbLGGGOMMcYYY/q5zrL7drkfrogsBj4PLOpu2dRJlQsKCizzmjHGGGOMMcacoYqK\niigqKjrh8p2NSc3HG2O61N++E3BV9f42x80EVgBLVXVnN8vamFRjjDH9Qtsuvvb9Z4wxpj/o6TGp\n64GpIjJBRNKA64Fn21xwHF6AetPhALWrZY0xxhhjjDHGmFQddvdV1YSI3A68CDjAI6q6RURu899f\nBvwbMBh4yH9CHFfV+ccr24v3YowxxhhjjDHmDNdhd99TUgHr7muMMaafsO6+xhhj+qOe7u6LiCwV\nka0iskNEvt3O++eKSLGIREXkG23eKxGR90TkbRFZ29VKGWOMMcYYY4zpnzrs7isiDvAgsAQ4AKwT\nkWfbdNutBr4KXNPOKRQoUNWaHqqvMcYYY4wxxpizWGctqfOBnapaoqpx4Ang6tQDVLVSVdcD8eOc\nw2YrN8YYY4wxxhjTJZ3Nkzoa2J+yXQos6Mb5FXhZRJLAMlX9VTfrZ4wxxhhjzlKxWIw9e/awb98+\nSktLKSsro6KigsrKSqqrq6muqqOuuhHXVdLDITIy0sjISicjM4OMDG/JysoiMzOTT33qUxQUFPT1\nLRljekBnQerJZnRYpKrlIpIHvCQiW1X1jbYHFRYWHlkvKCiw/2CMMcYYY3pBcXExzz77LDNnzmTx\n4sWMGDGi1661Y8cOiouL2bFjB7t376a09ABl+yupqW6kqaWFWLwFpQUII2QTYCDCICAXZSguF6Dk\nAYPwfrK2AFF/iRCgCSGC0Awc4he/+DsGZufw+Vtv4Pvf/z7Z2dm9dm/GmI4VFRVRVFR0wuU7zO4r\nIvlAoaou9bfvBFxVvb+dY+8CIqr6o+Ocq933LbuvMcaY/sKy+5q+8Oabb3LvPffx11fWEI21EGQW\nLqW4lCGEyAjnMCxvEOecO54ZF8xgwYIFzJkzh9zcXAYMGEAgcPzRYdFolNdee40333yTDRs2sHXj\nbg4eqqG5tQGAAKMIMAplHEkmAmOAkf4yChgOhHroTqPAn3D4MS6bmD1rBvf+xw/56Ec/2kPnP7W2\nbNnC2rVrufHGG0lLS+vr6hhzUrqb3bezIDUIbAMuA8qAtcCN7c13KiKFQOPhIFREMgFHVRtFJAtY\nCdytqivblLMg1RhjTL9gQao5VYqKirj/vv/g1b+upTXeisM1JPksUMDRoNDF+3m3A9hBgC0EeA+X\nnbhU4qUbSeC1YjoIQUQcAhIgEAjiugkSbiNCLg5TcfkQLjOBacC5eAFoX6Um2UGAX+Dya3IyM/jc\n5z/FD3/4QwYMGNCts+zdu5c1a9bw7rvvsm3bNnbvLOFgWR1pIYeBg7MYkjeQIUOGkJeXx7Bhwxg5\nciSjR49mwoQJTJkyhczMzC5dJxqN8tRTT/HnP/+ZVa//jfJDFSTdOEIuSC3582dxz30/sN6G5ozV\no0Gqf8IrgJ8CDvCIqt4rIrcBqOoyERkBrAMG4P1v1whMB4YBK/zTBIHHVPXeds5vQaoxxph+wYJU\n01tc1+X555/nZw88yOuvrac1Hk8JTC+l8xFexz0z0IrXStnS5jUNmAJknPwN9JoY8AwOPybJu4TT\nsglIAMcJEAgECDoOjhMgGHQIBR2coENdXYTGpibiyQggBBhOgHEoU0hyLl5rcAyoBaoJcIgAlUA1\nSi1KPS4NQBOQRjAQJpyeQXZWmNyhAxk5ZigjR45k4MCBrClex+ZNJTRFawkwAmERST4CLATOw8tx\n+j4BHsLl9+RkZnLT567jBz/4AUOGDOmTT9SYE9HjQWpvsyDVGGNMf2FBqulJDQ0N/OIXv+Cx3y5n\n89ZdqKYhXI3LTcDFnHhgerbahzejYhwvyDz8GmuzLw8Y7y+DOfHWYBdvpsZDQIX/eggox2EvQh1J\nFqAswptQY1An52sFnsXhpyTZwLlTp/C9f/s2//iP/0ggEKCiooL333+fzZs3s3PnTvbt28f+fWUc\nKq8lHosz7fwJLFq0kI9//OMsXLiww27cxvQ0C1KNMcaY05QFqf2X67pUVFRQVlbGwYMHOXToEJWV\nlVRVVVFVVUUkEiE3N5fRo0czbtw4Jk2axKRJkxgxYsQxwcSmTZv4yU9+wp+ffoWK6nIcpuByPcrV\nwAXYzH/9xT4CPILyXyBNeDNFughDCDAMYTTKeH8c8CgghPAODm+RZCNKC9kZg5k0eRTz82fz0Y9+\nlCuvvNKSTZle0xvdfZdytLvvw22TJonIucCjwIXA91ITI3VW1j/GglRjjDH9ggWpZ5/169ezbt06\nSkpK2L9/P2VlZRwqq6GmupFIUwut8ShJN4rXShdCyEDIQsgGBiAMBAah5CBUAxW4VKPUoTQASYQw\nQScdEYglWnC4lCQ3AFfgja4y/ZcL7AaGAgPp+kOKCuBd4G2CrMLlbVwqGD50JNf+/VK++c1vMnny\n5F6qs+mPejpxkoOXOGkJXv+IdbRJnORPLzMeuAaoTUmc1GlZ/zgLUo0xxvQLFqSe+VzX5fe//z3L\nHvoV69ZtIp6M4zAOYTjKaJKMw8tcOxwvgByO1310ICfW/bYFr8toFd440Nl4Y0GN6WmVwHM4PEaS\nVWRnDmLJx/L5xje+wUUXXdRuiYaGBl566SVef/11NvztbXZsLWXQ4Gx+94dHmDdv3qmtvjmt9XSQ\nuhC4K2UKmu8AqOp97Rx7zBQzXS1rQaoxxpj+woLUM1MkEuGBBx7g9795gm279oAORPgULp8C8vE6\njBlzNmkGXsbhCZI8RygYZP6885k56wLefed9dmzdT019PUk34ieWOp8Ec4ELCLAOl4e4cOYMlv/P\nY0ydOrWvb8acBrobpHb2SG80sD9luxRY0MVzn0xZY4wxxpgeFYvF+OMf/8jvf/8Yq1e9RzyeJBQK\nkpYWIhxOIzMjnaycdHIGZpGdnU04HOat1zdQXlnmj/38NMq1wLnY4wVzdssEPkGSTwBJ4onVFBc/\nyZri7SQowBv/fD4wFZc03JSSLjcA3+S9977LOed8iEsvWcgTyx9jxIgRp/42zBmrsyD1ZP4P7nLZ\nwsLCI+sFBQU2B5QxxhhjTprrurzyyis8+uijvLKymIrqgwQYinAFSW4DcqG1CYjgTRdyeD1CgDqE\nCEm+C/wdSYb34Z0Y05ccYBEui44JRjs2kiSPAt/jzde/xaiRk7nmmsv5zX//psO5ahOJBOvWreOt\nt97CcRxmz57N3LlzuzzfrDl9FBUVUVRUdMLlO+vumw8UpnTZvRNwj5MAqW133y6Vte6+xhhj+gvr\n7nt8ruuyZ88egBNO2BKJRHj++ed56aWXeGXlm+zdX4qrIRwu81uELsPLdGqMObXexeEOVNZz883/\nwOdv+TyrV69mw4YNbN28nb0lFdQ3NpBIRoBsHMYDissBlDpEMshIy2TwoAGMHZ/HpCkTOe+88/jM\nZz7D+PHj+/rmTBf09JjUIF7yo8uAMmAt7SQ/8o8tBBpTgtQulbUg1RhjTH/R34PUSCRCcXExa9eu\nZePGjWzftov9eyupb2gklojgZSZVhAAZ6QMYOWII586YxOzZs7n00ktZtGgR4XAYgH379vHMM8/w\n6quv8va6TZQdrCKWaCDACALMIsHleLkbp2HTshhzuniTAF/DZRcOE4DzSDITmOovU4CsNmUSeDlY\n9/pLCQ5bgW0k2cSo4SP5yldv4Vvf+hZpaZZU7HTVG1PQXMHRaWQeUdV7ReQ2AFVdJiIj8DL3DsDL\ng90ITFfVSHtl2zm/BanGGGP6hf4WpCYSCR577DEe/uUjrFu7mdZEHUIuDuNRppHkfGBSyjLYL3kQ\n/B+hAd4lwHsk2YFSSzCQjatJXI3hMAWYR5KFwCxgBt5YOmNM/1ADPE6AB1HZx5wLZ3LX3d/jqquu\n6rDUO++8w5NPPknRX19j6+Z9zJp9Dj954MfMnDnz1FS7H+rxILW3WZBqjDGmv+gPQerBgwd54IEH\n+OPjT7Nn7z5gCMJ1uFwHfBgIncTZW4AdQBiYjGXVNcYctYkAv8LlN4TTQlxz3RLuueceHMfhySef\nZOWLL/H2+q1U1VWhqjh8CJcClFk4/JkkTzEibzjf/PbtfP3rXycQCPT1DZ1VeqMldSlHW0MfPs54\n1J/hzSjdDNysqm/7+0vAn4ka4qo6v52yFqQaY4zpF87GIDUajfKXv/yF5cuXs/KFVdRHqnCYRZIb\ngL/D675njDGnSgJYicPPSfIyAA7TUC7BZREwH6/nRtt4qR74LQF+hARq+fiVBfz4pz/u0hj5WCzG\n3r17GT9+vHU5Po6eHpPq4I0rXYLXGXwdbcaVisiVwO2qeqWILAAeUNV8/709wBxVrengGhakGmOM\n6RfO9CC1ubmZ5557jhdeeIHVq/5Gyd6DRGN1CHkEWESSfwA+Cgzs66oaYwxetu4wnU9okkqBVTj8\nmCQvMH7MOL757a+SkZHB1q1b2bVrFyW793GwrJb6hgjRWDOuNgPpQIyAZJKRnkXukAGMmzicyVMm\nMX36dC688EIWLFjQYXbjs1lPB6kLgbtSMvR+B0BV70s55r+AV1V1ub+9FbhUVQ/5QepcVa3u4BoW\npBpjjOkXTlWQWlFRweOPP87q1atJS0sjMzOTzMxMcnJyyMrKYsCAAWRnZ5OTk0N6ejp1dXVUV1dT\nW1tLfX09DQ0N1NfX09jYSFNTE2X7K9m77xCt8XoCDEeYQ5JLgTl4Y0H7548uY8zZrgrhEYRlCEGE\nsbhMwmUKMBYY4y+j8ILhVmA/R5M87SHIVpRduJSi1JIWHMjUKWO47GOXctNNNzFv3ry+urlTqqeD\n1L8HLlfVW/3tm4AFqvrVlGP+DNyrqm/52y8D/6KqG0RkN17beRJYpqq/aucaFqQaY4zpF3ojSD08\nF+hTTz3Fa6+sYndJObFEAw5TgQuBJEIz+IsSBVpQWoEoShIhAyETIQvIBnJQBqAMxGUQMNw/1yz/\nfWOMMd3XCvwNeJ0gK0mwloAII4cN56KCuVx33XVcc801BAIBqqqqjizV1dVUV1dTV1dHXV0dTU1N\npKenk56eTjgcJhwOk5GRQUZGBuFwmMzMTEaMGMG8efNOm7G13Q1SO2v77uq35/EueJGqlolIHvCS\niGxV1TfaHlRYWHhkvaCggIKCgi5e1hhjjDm7xWIxdu3axZ49eyjBuKASAAAKJklEQVQpKWH//v2U\nl5ezt2Qv7769k9qGKoQsHBaQ4PPAQuBCkqR3+Rr2qNgYY06FdLwEch8mwXcAxdXtHDj0Jv+z/CX+\nuPzrKDfiTZgSAsII4SMPEiELIRvIAOJ4QW8cJQbE/PU4EMelEWghI30QY8fk8aHZ07n44ou56qqr\nmDhxYq/faVFREUVFRSdcvrOW1HygMKW7752Am5o8ye/uW6SqT/jbR7r7tjnXXUDk8DyqKfutJdUY\nY8xZT1U/8ET7lltu8Z+S11BTWU99fTNNTVFaW2PEEq0k3Va8HyGZCAMIMAQhDxhOkrEo+UA+MPrU\n35AxxpheEAXSgJ5oAa0G3gfex2E18DZJdiMSZGD2IObMPY+fP/Qg06ZNO+ErJBIJAILBjts+e7q7\nbxAvcdJlQBmwlo4TJ+UDP1XVfBHJBBxVbRSRLGAlcLeqrmxzDQtSjTHGnJFUlcbGRqqrq6mqqqKy\nspKDBw9SXl5O2e7dlO/dS3lZGeUVFZTX1RFLJo8pH+Q6XPJwGQ4MwZsn9PAy1F8GY1OtGGOM6Rku\nUIIXuP6RJCuYNmUyDzz4Iy6//PIun6W4uJhvf+tOVr21nrmzL2DN+uIOj++NKWiu4OgUNI+o6r0i\nchuAqi7zj3kQWAo0Af/kj0edBKzwTxMEHlPVe9s5vwWpxhhj+pyq0tTU9IFxQFVVVVRVVFB14ADV\nBw9669XVVNXXUxWJkBYIMDQtjVzHYagIIxIJRkWjjEwmGQWMTFkyP3jVU3yXxhhjTKpDBPgJLr9g\n6OBB3P2D7/ClL32p3bGs0WiU73//+/zXz39LbUMtAW7EJY3zp73Fxq3vdHiVHg9Se5sFqcYYY05W\nPB6nvLyc0tJSSktL2b9/P6W7d1O6cyel+/fTGIngui5J1yWZTHqvrnt0n+tS39JCUISh6ekMDQYZ\nCuQmkwyNxRgai5EL5AG5HG3jzMXL59hVH/x2tu8/Y4wxp4Mm4FGEe0hPi/Hlf/4s99xzD+FwmNdf\nf53vfOtOVq99B2EyLt8APoX36PVnnD/t1xakGmOM6V9isRhlZWVe4OkHoaW7dnnL/v2UHjpEZUMD\nw8JhxoZCjHFdxkSjjInHj0wOMACvO1DAf01dDu8bgJeKojdZkGqMMeb0lgSeJUAhyG6yM7NpaGrE\n4SaSfBU4v83xvROkdjqzrYgs5Wh334dTkyalHPMz4Aq8/PY3q+rbXS1rjOkbRUVFlknbnDRV9Vom\nk0mvVdJf78rDx+bmZg4dOkRFRQWHDh3y1svKOLR3LxXl5Rw6d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cbLKzgy03N5cePXpwww03HJAlgRSkikiLV1VVxdSpU3lz0iTefOklFn/+Oedk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gktDVKb6kAcAiwxsxWxkz8PnIb39ckWpwJ/AzCzWc65LOdcV6B3M+rusnTgiNY8oUgHUUjHe0yL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Eud3093rxNtES6wLCjKEGSImOJqUZ92gxEQrdbgojIth3Ke+jlDqj3jhkD/r5ptTlZJGJm7Vt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\nwbBIlSRJkiQNxr9N48Mh+Ai6KgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -9034,7 +9122,7 @@ } ], "source": [ - "fig, axes = plt.subplots(len(tossup), 1, sharex=True, figsize=(16, 8))\n", + "fig, axes = plt.subplots(len(tossup), 1, sharex=True, figsize=(16, 12))\n", "\n", "for state, ax in zip(tossup, axes):\n", " ax.fill_between(bins[1:], 0, histograms[state], facecolor='red')\n", @@ -9045,7 +9133,7 @@ }, { "cell_type": "code", - "execution_count": 308, + "execution_count": 189, "metadata": { "collapsed": false }, @@ -9071,7 +9159,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 190, "metadata": { "collapsed": true }, @@ -9091,7 +9179,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 191, "metadata": { "collapsed": false }, @@ -9149,12 +9237,12 @@ " 0\n", " 6\n", " 0\n", - " 6\n", + " 0\n", " 18\n", " 13\n", " 10\n", " 237\n", - " 299\n", + " 293\n", " \n", " \n", " 3\n", @@ -9165,9 +9253,9 @@ " 6\n", " 18\n", " 0\n", - " 10\n", + " 0\n", " 237\n", - " 280\n", + " 270\n", " \n", " \n", " 4\n", @@ -9255,8 +9343,8 @@ " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin Solid States Total\n", "0 9 29 6 0 6 18 13 10 237 328\n", "1 9 29 6 0 6 18 13 10 237 328\n", - "2 9 0 6 0 6 18 13 10 237 299\n", - "3 9 0 0 0 6 18 0 10 237 280\n", + "2 9 0 6 0 0 18 13 10 237 293\n", + "3 9 0 0 0 6 18 0 0 237 270\n", "4 9 29 6 4 6 18 13 10 237 332\n", "5 9 0 0 4 6 18 13 10 237 297\n", "6 9 29 6 0 6 18 13 10 237 328\n", @@ -9265,7 +9353,7 @@ "9 0 29 0 0 6 18 0 10 237 300" ] }, - "execution_count": 313, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -9276,7 +9364,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 192, "metadata": { "collapsed": false }, @@ -9334,12 +9422,12 @@ " 29\n", " 0\n", " 4\n", - " 0\n", + " 6\n", " 0\n", " 0\n", " 0\n", " 206\n", - " 239\n", + " 245\n", " \n", " \n", " 3\n", @@ -9350,9 +9438,9 @@ " 0\n", " 0\n", " 13\n", - " 0\n", + " 10\n", " 206\n", - " 258\n", + " 268\n", " \n", " \n", " 4\n", @@ -9440,8 +9528,8 @@ " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin Solid States Total\n", "0 0 0 0 4 0 0 0 0 206 210\n", "1 0 0 0 4 0 0 0 0 206 210\n", - "2 0 29 0 4 0 0 0 0 206 239\n", - "3 0 29 6 4 0 0 13 0 206 258\n", + "2 0 29 0 4 6 0 0 0 206 245\n", + "3 0 29 6 4 0 0 13 10 206 268\n", "4 0 0 0 0 0 0 0 0 206 206\n", "5 0 29 6 0 0 0 0 0 206 241\n", "6 0 0 0 4 0 0 0 0 206 210\n", @@ -9450,7 +9538,7 @@ "9 9 0 6 4 0 0 13 0 206 238" ] }, - "execution_count": 314, + "execution_count": 192, "metadata": {}, "output_type": "execute_result" } @@ -9461,7 +9549,7 @@ }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 193, "metadata": { "collapsed": false }, @@ -9469,18 +9557,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 319, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -9493,7 +9581,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 194, "metadata": { "collapsed": false }, @@ -9501,18 +9589,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 320, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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10MfoVNXhJO/v7ufOxr+e5MHu/rWquj7Joe6+fnYRp/cmeVE2TxH+SJLv6+6uqk8meW2S\n40n+MMnbuvv2M+7Hx+jswMfoPDF33nnno39AMIJMMZpMMZI8MZpMMdqyH6OzYwFbVbckeWmSp2Wz\n3/XfJ/nvSW5N8r1JNpK8srtPzbZ/fZKfSfKtJK/r7g/N1j8/ybuTXJDkA9392i3uSwG7AwUsAACw\nqpYtYHc8hbi7X3WWm370LNu/Kcmbtlj/qSTPfUKzAwAAgJkn7fcEYDedPg8fRpEpRpMpRpInRpMp\npkYBCwAAwEpY6CJOe0UP7M70wAIAAKtq2R5YR2ABAABYCQpY1pq+DUaTKUaTKUaSJ0aTKaZGAQsA\nAMBK0AO7BD2wAAAAi9MDCwAAwIGggGWt6dtgNJliNJliJHliNJliahSwAAAArAQ9sEvQAwsAALA4\nPbAAAAAcCApY1pq+DUaTKUaTKUaSJ0aTKaZGAQsAAMBK0AO7BD2wAAAAi9MDCwAAwIGggGWt6dtg\nNJliNJliJHliNJliahSwAAAArAQ9sEvQAwsAALA4PbAAAAAcCApY1pq+DUaTKUaTKUaSJ0aTKaZG\nAQsAAMBK0AO7BD2wAAAAi9MDCwAAwIGggGWt6dtgNJliNJliJHliNJliahSwAAAArAQ9sEvQAwsA\nALA4PbAAAAAcCApY1pq+DUaTKUaTKUaSJ0aTKaZGAQsAAMBK0AO7BD2wAAAAi9MDCwAAwIGggGWt\n6dtgNJliNJliJHliNJliahSwAAAArAQ9sEvQAwsAALA4PbAAAAAcCEsVsFW1UVWfrapPV9Xx2boL\nq+qOqvpiVX24qg7NbX9DVd1bVfdU1RXLTh52om+D0WSK0WSKkeSJ0WSKqVn2CGwnOdrdz+vuF83W\nXZ/kju5+TpKPzsapqsuSXJPksiRXJvmtqnIEGAAAgIWMKCDPPH/55Uluni3fnOQVs+Wrk9zS3Q93\n90aSLyV5UWAXHT16dL+nwJqRKUaTKUaSJ0aTKaZmxBHYj1TVn1bVz87WXdTd98+W709y0Wz5GUlO\nzu17Mskzl7x/AAAADojzl9z/Jd39lar6niR3VNU98zd2d1fVdpfKfdxtx44dy+HDh5Mkhw4dypEj\nRx595+f0OfhTGT/44ANJPp/kBXt6/6ft9+NfhfGJEydy3XXXTWY+xqs/Pr1uKvMxXv3xmdna7/kY\nr/ZYnoxHj2+66aZJvx43nv74xIkTOXXqVJJkY2Mjyxr2MTpV9YYk30jys0mOdvdXq+riJB/r7u+v\nquuTpLvfPNv+9iRv6O5Pzn0PH6OzAx+j88Tceeedj/4BwQgyxWgyxUjyxGgyxWj79jE6VfWdVfXU\n2fLfTnJFkruS3Jbk1bPNXp3kfbPl25L8RFU9paqeneTSJMfP9f5ZHVX16Nde84TLaDLFaDLFSPLE\naDLF1CxzCvFFSf5gVpScn+S/dPeHq+pPk9xaVa9JspHklUnS3XdX1a1J7k7yrSTXrtThVpa0edQY\nAADgXJ3zEdju/nJ3H5l9/VB3/+ps/V91949293O6+4ruPjW3z5u6+/u6+/u7+0MjHgBs5/R5+DCK\nTDGaTDGSPDGaTDE151zAAgAAwF4adhGnEVzEaWereBGnVZwzAAAw3r5dxAkAAAD2kgKWtaZvg9Fk\nitFkipHkidFkiqlRwAIAALAS9MAuQQ/sYlZxzgAAwHjL9sAu8zmwsO9mn0P8KAUyAACsL6cQswZ6\n9vV4+jYYTaYYTaYYSZ4YTaaYGgUsAAAAK0EP7BL0wC5mN+f82PfOrnx/Dp7509LlCQBgLJ8DCzCc\nwhUAYIoUsKw1fRuMJlOMJlOMJE+MJlNMjQIWAACAlaAHdgl6YBejB5ZVsop/YwAAq0IPLAAAAAeC\nApa1pm+D0WSK0WSKkeSJ0WSKqVHAAgAAsBL0wC5BD+xi1q0H1ueE7r75n3Gytz/nVfwbAwBYFXpg\nYV8obHZfx88ZAIB5CljWmr4NRpMpRpMpRpInRpMppkYBCwAAwEo4f78nwN7Yz57C/XT06NH9ngJr\nRqYYTaYYSZ4YTaaYGgXsgfLYxY44OA7qmxcAAKwfpxCz1vRtnOaCSKNMMVNV9egXq2eKmWJ1yROj\nyRRTo4AFWAveoAAA1p9TiFlrU+zbcErvaptiplhtMsVI8sRoW2XKaxn2kwIW9oV+ZFgXXsgBB5PX\nMuwPpxCz1vRtMJpMTdf+9gKfe5+5TDGSPDGaTDE1jsAC8DjzReBqHVHsOBoAAOtLActaOyi9QE5h\nfGKWKc7OlqnVLfi2oxjcC+vyPOV5aBrWJU9Mh0wxNQpYWBt6UZ6Y3SjOFHzLUgStOs9DsIz1fDMU\nxtIDyxBT/RxKfRu7b6q/+90iU3vhYH1usUwxkjytg2k9/8kUU+MILANN7+jTy172skeXvZO5m574\n796RNlaJvK4uR7QA1osClgNgeoU1p63e6Yb71QvkRfgU7E5e9yNTBy9PB+f/wNT6FVfxzZ9VnPNu\nmlqmQAELsDIOzotwlrdzkSpPu13Ib/f9p/gmwu4Vbqv3ZuVqzhkOhj3tga2qK6vqnqq6t6p+ZS/v\nG2CEdeoFOtf+5f3se17HnuutMjX/OJd7rGMLoxGZmd7vbreLx+2+//j7Xv456mD1oLOzdfq/x3rY\nswK2qs5L8h+SXJnksiSvqqof2Kv7h1Uw7Rd507cXP78TJ04M/X77//s+1xeqW++3yONZ/vc0jaJs\n1P2+7GUvO8t9b11I7P/zxDKZOfff3f7/rayG0c9Rp+1W7vxep2+3MgXnai+PwL4oyZe6e6O7H07y\nX5NcvYf3D99mu3+a+3tkan3e/d6fFya7+/M7derULnzX9fh9P2aRx7O3Od85i/v1O+gkbzjH/dYt\nN4s49zdOVsky/5925znqtN3K3RP/nvv9xtM65W0nu5speOL2soB9ZpI/mxufnK2DfbQbp3at1ovK\n3Sjkv91q/Ty2U1V54xvfeGBetKyf9cniOlm306XH2dtTj1fT9j+H3fsdHtQ3kGAa9vIiTmv3l37e\neckFF/xaqs7LeeddvN/TWTtTvMDF+truYi4u9PLtXp3k5ie0x35m2d/R/lrsojgbezKXaZvqBXO2\nfv6b6t/V6Xm98Y1vnNS8ztWYn7P/Ycva2NjY7ynAt6m9eoKrqh9OcmN3Xzkb35Dkke7+tbltVv/Z\nFgAAgLPq7nN+Z2kvC9jzk/yvJP8oyZ8nOZ7kVd39hT2ZAAAAACttz04h7u5vVdUvJPlQkvOSvFPx\nCgAAwKL27AgsAAAALGMvPwf2kqr6WFV9vqo+V1Wvna2/sKruqKovVtWHq+rQ3D43VNW9VXVPVV2x\nV3NlNWyTqd+oqi9U1Weq6ver6u/O7SNTnNXZMjV3+7+pqkeq6sK5dTLFWW2Xqar6xdlz1eeqav56\nEDLFWW3zv+9IVX2iqj5dVX9SVS+c20em2FJV/a2q+mRVnZjl6cbZeq/POSfbZGrY6/O97IF9epKn\nd/eJqvquJJ9K8ook/zLJX3b3r1fVryT57u6+vqouS/LeJC/M5sftfCTJc7r7kT2ZMJO3TaaeleSj\n3f1IVb05SWSKRZwtU939haq6JMl/SvL3kzy/u/9KptjJNs9TT0/y+iQ/1t0PV9X3dPdfyBQ72SZT\nb03ym939oaq6Kskvd/fLZIqdVNV3dvffzK5X88dJXpfkn8Xrc87RWTL1dzLo9fmeHYHt7q9294nZ\n8jeSfCGbk3x5HvtMipuz+SScJFcnuaW7H+7ujSRfSvKivZov03eWTD2ju++YC/0ns1nQJjLFDs6W\nqdnNb0nyy2fsIlNsa5v/fT+f5Fe7++HZbX8x20Wm2NY2mXokyekjGoeS3Ddblim21d1/M1t8SpIn\nZ/Ozh7w+55xtkalHRr4+37MCdl5VHU7yvGxO/qLuvn920/1JLpotPyPJybndTmbzCRoe54xMzfuZ\nJB+YLcsUC5vPVFVdneRkd3/2jM1kioWd8Tz1nCSXz075vLOqXjDbTKZY2FymPpHkuiS/UVX/J8lv\nJLlhtplMsa2qelJVncjm6/APd/fxeH3OErbI1J+csclSr8/3vICdne7ye0le191fn7+tN89n3u6c\nZlec4nFmmfrdbGbqG3Pr/12Sh7r7vdvsLlM8znymsnlU4/VJ3jC/yTa7yxSPc8bz1Nez+SkA393d\nP5zk3ya5dZvdZYrH2eJ/37VJruvu703yS0netc3uMsWjuvuR7j6SzSNiL66qHzrjdq/PeUK2yNQP\nnr5txOvzPS1gq+rJ2Sxe/3N3v2+2+v5ZP0eq6uIkD8zW35fkkrndn5XHToeBJN+Wqd+Zy1Sq6liS\nH0vyL+Y2lyl2tEWm/l6Sw0k+U1VfzmZuPlVVF0WmWMBZnqdOJvn9JJm9M/1IVT0tMsUCzpKpn+7u\nP5gt/24eOwVPplhId//fJB9L8k/i9TkDzGXqymTc6/O9vApxJXlnkru7+6a5m25L8urZ8quTvG9u\n/U9U1VOq6tlJLk1yfK/my/SdLVNVdWU2j2hc3d3fnNtFptjWVpnq7ru6+6LufnZ3Pzubhcc/mJ1a\nJVNsa5v/fe9L8iOzbZ6T5Cnd/ZeRKXawTab+vKpeOlv+kSRfnC3LFGdVVU87fYXhqrogyT/OZl+1\n1+eck7NlauTr8/N3Z+pbekmSn0zy2ar69GzdDUnenOTWqnpNko0kr0yS7r67qm5NcneSbyW5tn1o\nLd9uq0y9Psnbstk0fsfm//n8z+6+VqZYwJaZ6u4Pzm3zaGZkigWc7X/fu5K8q6ruSvJQkp9OZIqF\nnO1/388meevsqp//L8m/SmSKHV2c5OaqOi+bB7b+W3d/oKo+Ea/POTdny9S9GfT6fM8+RgcAAACW\nsS9XIQYAAIAnSgE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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -9525,7 +9613,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 195, "metadata": { "collapsed": false }, @@ -9533,10 +9621,10 @@ { "data": { "text/plain": [ - "0.93440000000000001" + "0.88929999999999998" ] }, - "execution_count": 321, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" } From c152e668debc882401c51b674c9d85aebf3bbba7 Mon Sep 17 00:00:00 2001 From: Ritesh Bansal Date: Wed, 25 May 2016 00:10:11 -0400 Subject: [PATCH 09/11] final comments --- data/2012-predicted.csv | 74 +- silver_model.ipynb | 5152 ++++++++++++++++++++++++--------------- 2 files changed, 3175 insertions(+), 2051 deletions(-) diff --git a/data/2012-predicted.csv b/data/2012-predicted.csv index 566dede..08de6dc 100644 --- a/data/2012-predicted.csv +++ b/data/2012-predicted.csv @@ -1,42 +1,42 @@ State,poll -Arizona,-5.47338235568 +Arizona,-5.36244790596 California,19.9664750649 -Colorado,2.70582862386 -Connecticut,8.98092251576 +Colorado,2.66784319603 +Connecticut,8.9362274122 Florida,2.17096302191 -Georgia,-8.79052658316 -Hawaii,18.6970465167 -Illinois,15.5789784841 -Indiana,-7.36662226941 -Iowa,2.02234485831 -Kansas,-9.77854656124 -Maine,12.1942940297 -Maryland,16.4950271772 -Massachusetts,14.2424602294 -Michigan,8.32254279018 -Minnesota,6.8269245287 -Mississippi,-8.16652344925 -Missouri,-2.22766788175 -Montana,-7.25170680045 -Nebraska,-8.84173818768 -Nevada,4.78033849449 -New Hampshire,-1.52243896396 -New Jersey,10.6670169688 -New Mexico,9.65142194494 +Georgia,-8.85859171804 +Hawaii,18.584803377 +Illinois,15.4778671922 +Indiana,-7.22011474159 +Iowa,2.11176030586 +Kansas,-9.70849453844 +Maine,12.3537974257 +Maryland,16.3842951633 +Massachusetts,14.1746314895 +Michigan,8.39317008802 +Minnesota,6.74991517435 +Mississippi,-8.34658151627 +Missouri,-2.15293038253 +Montana,-7.18813752641 +Nebraska,-8.78919577642 +Nevada,5.09313003222 +New Hampshire,-1.54705616998 +New Jersey,10.6412190369 +New Mexico,10.7094653488 New York,23.4735500439 -North Carolina,-0.39008891323 -North Dakota,-9.34916878139 -Ohio,4.16473342997 -Oregon,8.65947940788 -Pennsylvania,5.42298139475 -Rhode Island,13.3495112334 -South Carolina,-6.27767039939 -South Dakota,-1.54303362486 -Tennessee,-2.47362790033 +North Carolina,-0.466602437954 +North Dakota,-9.28719433084 +Ohio,4.22939440827 +Oregon,8.79474159474 +Pennsylvania,5.50255611546 +Rhode Island,13.2368530126 +South Carolina,-6.4647998996 +South Dakota,-1.42359773058 +Tennessee,-2.63022570014 Texas,-2.29550733867 -Utah,-29.181185755 -Vermont,15.0056796275 -Virginia,2.44301244125 -Washington,12.3462816771 -West Virginia,-8.71977421619 -Wisconsin,4.50963521112 +Utah,-29.1702386849 +Vermont,14.8917641789 +Virginia,2.42024421684 +Washington,12.3125048739 +West Virginia,-9.53945115143 +Wisconsin,4.6277436276 diff --git a/silver_model.ipynb b/silver_model.ipynb index b69596e..e35d205 100644 --- a/silver_model.ipynb +++ b/silver_model.ipynb @@ -26,12 +26,7 @@ "from pandas import DataFrame, Series\n", "from scipy import stats\n", "np.set_printoptions(precision=4, suppress=True)\n", - "%matplotlib inline\n", - "\n", - "#pandas.set_options(notebook_repr_html=False,\n", - "# precision=4,\n", - "# max_columns=12, column_space=10,\n", - "# max_colwidth=25)" + "%matplotlib inline\n" ] }, { @@ -76,6 +71,13 @@ "6. Simulation: Simulate our results 10,000 times based on the results of the projection to account for the uncertainty in our estimates. The end result is a robust probabilistic assessment of what will happen in each state as well as in the nation as a whole. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ritesh" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -255,13 +257,247 @@ " 1.5\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 12\n", + " 12\n", + " 12\n", + " Douglas Duncan\n", + " Fannie Mae\n", + " 1.8\n", + " 1.7\n", + " \n", + " \n", + " 13\n", + " 13\n", + " 13\n", + " Robert Dye\n", + " Comerica Bank\n", + " 2.5\n", + " 2.2\n", + " \n", + " \n", + " 14\n", + " 14\n", + " 14\n", + " Maria Fiorini Ramirez/Joshua Shapiro\n", + " MFR, Inc.\n", + " 1.4\n", + " 1.2\n", + " \n", + " \n", + " 15\n", + " 15\n", + " 15\n", + " Ethan Harris\n", + " Bank of America Securities- Merrill Lynch\n", + " 1.3\n", + " 1.0\n", + " \n", + " \n", + " 16\n", + " 16\n", + " 16\n", + " Maury Harris\n", + " UBS\n", + " 1.5\n", + " 1.8\n", + " \n", + " \n", + " 17\n", + " 17\n", + " 17\n", + " Jan Hatzius\n", + " Goldman, Sachs & Co.\n", + " 2.3\n", + " 1.5\n", + " \n", + " \n", + " 18\n", + " 18\n", + " 18\n", + " Tracy Herrick\n", + " Avidbank\n", + " 1.8\n", + " 1.8\n", + " \n", + " \n", + " 19\n", + " 19\n", + " 19\n", + " Stuart Hoffman *\n", + " PNC Financial Services Group\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 20\n", + " 20\n", + " 20\n", + " Gene Huang\n", + " FedEx Corp.\n", + " 1.9\n", + " 1.7\n", + " \n", + " \n", + " 21\n", + " 21\n", + " 21\n", + " William B. Hummer\n", + " Wintrust Wealth Management\n", + " 1.7\n", + " 1.9\n", + " \n", + " \n", + " 22\n", + " 22\n", + " 22\n", + " Bruce Kasman\n", + " JP Morgan Chase & Co.\n", + " 1.5\n", + " 2.0\n", + " \n", + " \n", + " 23\n", + " 23\n", + " 23\n", + " Joseph LaVorgna\n", + " Deutsche Bank Securities Inc.\n", + " 2.7\n", + " 2.8\n", + " \n", + " \n", + " 24\n", + " 24\n", + " 24\n", + " Edward Leamer/David Shulman\n", + " UCLA Anderson Forecast\n", + " 1.3\n", + " 1.5\n", + " \n", + " \n", + " 25\n", + " 25\n", + " 25\n", + " Don Leavens/Tim Gill\n", + " NEMA Business Information Services\n", + " 1.7\n", + " 1.7\n", + " \n", + " \n", + " 26\n", + " 26\n", + " 26\n", + " John Lonski\n", + " Moody's Investors Service\n", + " 1.5\n", + " 1.3\n", + " \n", + " \n", + " 27\n", + " 27\n", + " 27\n", + " Dean Maki\n", + " Barclays Capital\n", + " 2.0\n", + " 2.5\n", + " \n", + " \n", + " 28\n", + " 28\n", + " 28\n", + " Aneta Markowska *\n", + " Societe Generale\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 29\n", + " 29\n", + " 29\n", + " Jim Meil/Arun Raha\n", + " Eaton Corp.\n", + " 1.2\n", + " 2.1\n", + " \n", + " \n", + " 30\n", + " 30\n", + " 30\n", + " Mark Nielson\n", + " MacroEcon Global Advisors\n", + " 2.2\n", + " 2.8\n", + " \n", + " \n", + " 31\n", + " 31\n", + " 31\n", + " Michael P. Niemira\n", + " International Council of Shopping Centers\n", + " 2.3\n", + " 2.2\n", + " \n", + " \n", + " 32\n", + " 32\n", + " 32\n", + " Jim O'Sullivan\n", + " High Frequency Economics\n", + " 2.5\n", + " 2.0\n", + " \n", + " \n", + " 33\n", + " 33\n", + " 33\n", + " Nicholas S. Perna\n", + " Perna Associates\n", + " 2.2\n", + " 1.5\n", + " \n", + " \n", + " 34\n", + " 34\n", + " 34\n", + " Dr. Joel Prakken/ Chris Varvares\n", + " Macroeconomic Advisers\n", + " 1.5\n", + " 1.4\n", + " \n", + " \n", + " 35\n", + " 35\n", + " 35\n", + " David Resler\n", + " Nomura Securities International\n", + " 1.9\n", + " 1.7\n", + " \n", + " \n", + " 36\n", + " 36\n", + " 36\n", + " John Ryding/Conrad DeQuadros\n", + " RDQ Economics\n", + " 2.1\n", + " 2.4\n", + " \n", + " \n", + " 37\n", + " 37\n", + " 37\n", + " John Silvia\n", + " Wells Fargo & Co.\n", + " 1.6\n", + " 1.7\n", + " \n", + " \n", + " 38\n", + " 38\n", + " 38\n", + " Allen Sinai\n", + " Decision Economics, Inc.\n", + " 2.1\n", + " 2.7\n", " \n", " \n", " 39\n", @@ -373,65 +609,114 @@ " \n", " \n", "\n", - "

51 rows × 6 columns

\n", "" ], "text/plain": [ - " Unnamed: 0 Unnamed: 0.1 Forecaster Institution \\\n", - "0 0 0 Paul Ashworth Capital Economics \n", - "1 1 1 Nariman Behravesh IHS Global Insight \n", - "2 2 2 Richard Berner/ David Greenlaw * Morgan Stanley \n", - "3 3 3 Ram Bhagavatula Combinatorics Capital \n", - "4 4 4 Beth Ann Bovino * Standard and Poor's \n", - "5 5 5 Jay Brinkmann Mortgage Bankers Association \n", - "6 6 6 Michael Carey Credit Agricole CIB \n", - "7 7 7 Joseph Carson AllianceBernstein \n", - "8 8 8 Julia Coronado BNP Paribas \n", - "9 9 9 Mike Cosgrove Econoclast \n", - "10 10 10 Lou Crandall Wrightson ICAP \n", - "11 11 11 J. Dewey Daane Vanderbilt University \n", - ".. ... ... ... ... \n", - "39 39 39 James F. Smith Parsec Financial Management \n", - "40 40 40 Sean M. Snaith University of Central Florida \n", - "41 41 41 Sung Won Sohn California State University \n", - "42 42 42 Neal Soss CSFB \n", - "43 43 43 Stephen Stanley Pierpont Securities \n", - "44 44 44 Susan M. Sterne Economic Analysis Associates Inc. \n", - "45 45 45 Diane Swonk Mesirow Financial \n", - "46 46 46 Carl Tannenbaum The Northern Trust \n", - "47 47 47 Bart van Ark The Conference Board \n", - "48 48 48 Brian S. Wesbury/ Robert Stein First Trust Advisors, L.P. \n", - "49 49 49 William T. Wilson Skolkovo Institute for Emerging Market Studies \n", - "50 50 50 Lawrence Yun National Association of Realtors \n", + " Unnamed: 0 Unnamed: 0.1 Forecaster \\\n", + "0 0 0 Paul Ashworth \n", + "1 1 1 Nariman Behravesh \n", + "2 2 2 Richard Berner/ David Greenlaw * \n", + "3 3 3 Ram Bhagavatula \n", + "4 4 4 Beth Ann Bovino * \n", + "5 5 5 Jay Brinkmann \n", + "6 6 6 Michael Carey \n", + "7 7 7 Joseph Carson \n", + "8 8 8 Julia Coronado \n", + "9 9 9 Mike Cosgrove \n", + "10 10 10 Lou Crandall \n", + "11 11 11 J. Dewey Daane \n", + "12 12 12 Douglas Duncan \n", + "13 13 13 Robert Dye \n", + "14 14 14 Maria Fiorini Ramirez/Joshua Shapiro \n", + "15 15 15 Ethan Harris \n", + "16 16 16 Maury Harris \n", + "17 17 17 Jan Hatzius \n", + "18 18 18 Tracy Herrick \n", + "19 19 19 Stuart Hoffman * \n", + "20 20 20 Gene Huang \n", + "21 21 21 William B. Hummer \n", + "22 22 22 Bruce Kasman \n", + "23 23 23 Joseph LaVorgna \n", + "24 24 24 Edward Leamer/David Shulman \n", + "25 25 25 Don Leavens/Tim Gill \n", + "26 26 26 John Lonski \n", + "27 27 27 Dean Maki \n", + "28 28 28 Aneta Markowska * \n", + "29 29 29 Jim Meil/Arun Raha \n", + "30 30 30 Mark Nielson \n", + "31 31 31 Michael P. Niemira \n", + "32 32 32 Jim O'Sullivan \n", + "33 33 33 Nicholas S. Perna \n", + "34 34 34 Dr. Joel Prakken/ Chris Varvares \n", + "35 35 35 David Resler \n", + "36 36 36 John Ryding/Conrad DeQuadros \n", + "37 37 37 John Silvia \n", + "38 38 38 Allen Sinai \n", + "39 39 39 James F. Smith \n", + "40 40 40 Sean M. Snaith \n", + "41 41 41 Sung Won Sohn \n", + "42 42 42 Neal Soss \n", + "43 43 43 Stephen Stanley \n", + "44 44 44 Susan M. Sterne \n", + "45 45 45 Diane Swonk \n", + "46 46 46 Carl Tannenbaum \n", + "47 47 47 Bart van Ark \n", + "48 48 48 Brian S. Wesbury/ Robert Stein \n", + "49 49 49 William T. Wilson \n", + "50 50 50 Lawrence Yun \n", "\n", - " gdp_q3_2012 gdp_q4_2012 \n", - "0 2.0 1.5 \n", - "1 1.5 1.6 \n", - "2 NaN NaN \n", - "3 2.0 4.0 \n", - "4 NaN NaN \n", - "5 1.8 1.9 \n", - "6 1.7 1.6 \n", - "7 2.5 3.5 \n", - "8 1.4 1.6 \n", - "9 1.6 1.6 \n", - "10 1.8 1.8 \n", - "11 1.5 1.5 \n", - ".. ... ... \n", - "39 3.8 4.8 \n", - "40 1.7 1.9 \n", - "41 1.8 1.7 \n", - "42 1.5 2.2 \n", - "43 1.0 2.1 \n", - "44 2.2 1.9 \n", - "45 1.3 1.5 \n", - "46 1.7 2.0 \n", - "47 1.6 1.6 \n", - "48 2.5 3.0 \n", - "49 1.9 2.2 \n", - "50 1.7 2.1 \n", - "\n", - "[51 rows x 6 columns]" + " Institution gdp_q3_2012 gdp_q4_2012 \n", + "0 Capital Economics 2.0 1.5 \n", + "1 IHS Global Insight 1.5 1.6 \n", + "2 Morgan Stanley NaN NaN \n", + "3 Combinatorics Capital 2.0 4.0 \n", + "4 Standard and Poor's NaN NaN \n", + "5 Mortgage Bankers Association 1.8 1.9 \n", + "6 Credit Agricole CIB 1.7 1.6 \n", + "7 AllianceBernstein 2.5 3.5 \n", + "8 BNP Paribas 1.4 1.6 \n", + "9 Econoclast 1.6 1.6 \n", + "10 Wrightson ICAP 1.8 1.8 \n", + "11 Vanderbilt University 1.5 1.5 \n", + "12 Fannie Mae 1.8 1.7 \n", + "13 Comerica Bank 2.5 2.2 \n", + "14 MFR, Inc. 1.4 1.2 \n", + "15 Bank of America Securities- Merrill Lynch 1.3 1.0 \n", + "16 UBS 1.5 1.8 \n", + "17 Goldman, Sachs & Co. 2.3 1.5 \n", + "18 Avidbank 1.8 1.8 \n", + "19 PNC Financial Services Group NaN NaN \n", + "20 FedEx Corp. 1.9 1.7 \n", + "21 Wintrust Wealth Management 1.7 1.9 \n", + "22 JP Morgan Chase & Co. 1.5 2.0 \n", + "23 Deutsche Bank Securities Inc. 2.7 2.8 \n", + "24 UCLA Anderson Forecast 1.3 1.5 \n", + "25 NEMA Business Information Services 1.7 1.7 \n", + "26 Moody's Investors Service 1.5 1.3 \n", + "27 Barclays Capital 2.0 2.5 \n", + "28 Societe Generale NaN NaN \n", + "29 Eaton Corp. 1.2 2.1 \n", + "30 MacroEcon Global Advisors 2.2 2.8 \n", + "31 International Council of Shopping Centers 2.3 2.2 \n", + "32 High Frequency Economics 2.5 2.0 \n", + "33 Perna Associates 2.2 1.5 \n", + "34 Macroeconomic Advisers 1.5 1.4 \n", + "35 Nomura Securities International 1.9 1.7 \n", + "36 RDQ Economics 2.1 2.4 \n", + "37 Wells Fargo & Co. 1.6 1.7 \n", + "38 Decision Economics, Inc. 2.1 2.7 \n", + "39 Parsec Financial Management 3.8 4.8 \n", + "40 University of Central Florida 1.7 1.9 \n", + "41 California State University 1.8 1.7 \n", + "42 CSFB 1.5 2.2 \n", + "43 Pierpont Securities 1.0 2.1 \n", + "44 Economic Analysis Associates Inc. 2.2 1.9 \n", + "45 Mesirow Financial 1.3 1.5 \n", + "46 The Northern Trust 1.7 2.0 \n", + "47 The Conference Board 1.6 1.6 \n", + "48 First Trust Advisors, L.P. 2.5 3.0 \n", + "49 Skolkovo Institute for Emerging Market Studies 1.9 2.2 \n", + "50 National Association of Realtors 1.7 2.1 " ] }, "execution_count": 4, @@ -529,6 +814,28 @@ "national_data2012 = pandas.read_pickle(\"data_nuevo/2012_poll_data_national.pkl\")" ] }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(290, 9)" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "national_data2012.shape" + ] + }, { "cell_type": "code", "execution_count": 9, @@ -621,12 +928,19 @@ "" ], "text/plain": [ - " Pollster Sample MoE Obama (D) Romney (R) Spread obama_spread State poll_date\n", - "0 RCP Average NaN -- 49.1 45.1 Obama +4.0 4 USA 2012-09-28\n", - "1 Rasmussen Tracking 1500 3.0 48.0 47.0 Obama +1 1 USA 2012-09-30\n", - "2 CNN/Opinion Research 783 3.5 50.0 47.0 Obama +3 3 USA 2012-09-29\n", - "3 Gallup Tracking 3050 2.0 50.0 44.0 Obama +6 6 USA 2012-09-28\n", - "4 Quinnipiac 1912 2.2 49.0 45.0 Obama +4 4 USA 2012-09-28" + " Pollster Sample MoE Obama (D) Romney (R) Spread \\\n", + "0 RCP Average NaN -- 49.1 45.1 Obama +4.0 \n", + "1 Rasmussen Tracking 1500 3.0 48.0 47.0 Obama +1 \n", + "2 CNN/Opinion Research 783 3.5 50.0 47.0 Obama +3 \n", + "3 Gallup Tracking 3050 2.0 50.0 44.0 Obama +6 \n", + "4 Quinnipiac 1912 2.2 49.0 45.0 Obama +4 \n", + "\n", + " obama_spread State poll_date \n", + "0 4 USA 2012-09-28 \n", + "1 1 USA 2012-09-30 \n", + "2 3 USA 2012-09-29 \n", + "3 6 USA 2012-09-28 \n", + "4 4 USA 2012-09-28 " ] }, "execution_count": 9, @@ -649,6 +963,28 @@ "state_data2012 = pandas.read_pickle(\"data_nuevo/2012_poll_data_states.pkl\")" ] }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(357, 19)" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2012.shape" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -741,12 +1077,19 @@ "" ], "text/plain": [ - " Pollster State MoE Obama (D) Romney (R) Sample Spread obama_spread poll_date\n", - "0 Rasmussen Reports WA 4.5 52 41 500 Obama +11 11 2012-09-26\n", - "1 Gravis Marketing WA 4.6 56 39 625 Obama +17 17 2012-09-22\n", - "2 Elway Poll WA 5.0 53 36 405 Obama +17 17 2012-09-11\n", - "3 SurveyUSA WA 4.4 54 38 524 Obama +16 16 2012-09-08\n", - "4 SurveyUSA WA 4.4 54 37 524 Obama +17 17 2012-08-02" + " Pollster State MoE Obama (D) Romney (R) Sample Spread \\\n", + "0 Rasmussen Reports WA 4.5 52 41 500 Obama +11 \n", + "1 Gravis Marketing WA 4.6 56 39 625 Obama +17 \n", + "2 Elway Poll WA 5.0 53 36 405 Obama +17 \n", + "3 SurveyUSA WA 4.4 54 38 524 Obama +16 \n", + "4 SurveyUSA WA 4.4 54 37 524 Obama +17 \n", + "\n", + " obama_spread poll_date \n", + "0 11 2012-09-26 \n", + "1 17 2012-09-22 \n", + "2 17 2012-09-11 \n", + "3 16 2012-09-08 \n", + "4 17 2012-08-02 " ] }, "execution_count": 11, @@ -815,7 +1158,43 @@ "9 Boston Globe\n", "10 CBS/NYT/Quinnipiac\n", "11 CNN/Opinion Research\n", + "12 CNN/Time\n", + "13 CNU/Times-Dispatch\n", + "14 Caddell/McLaughlin/SAN (R)\n", + "15 Castleton State College\n", + "16 Chicago Tribune\n", + "17 Civitas (R)\n", + "18 Clarus Research\n", + "19 Columbus Dispatch*\n", + "20 Courier-Journal/SurveyUSA\n", + "21 Critical Insights\n", + "22 Daily Kos/PPP (D)\n", + "23 Dartmouth\n", + "24 Denver Post/SurveyUSA\n", + "25 Des Moines Register\n", + "26 Deseret News\n", + "27 Deseret News/KSL\n", + "28 Detroit News\n", + "29 EPIC-MRA\n", " ... \n", + "90 Siena\n", + "91 Sooner Poll\n", + "92 St. Cloud State U.\n", + "93 Star Tribune/Mason-Dixon*\n", + "94 Strategies 360 (D)\n", + "95 Suffolk University\n", + "96 Suffolk/7News\n", + "97 Suffolk/WSVN\n", + "98 Suffolk/WWBT\n", + "99 Sunshine State News/VSS\n", + "100 SurveyUSA\n", + "101 SurveyUSA/Civitas (R)\n", + "102 Talk Business Poll\n", + "103 Tennessean/Vanderbilt\n", + "104 The Simon Poll/SIU\n", + "105 The Washington Poll\n", + "106 Tribune-Review/Susquehanna\n", + "107 UMass/Boston Herald\n", "108 Virginian-Pilot/ODU\n", "109 Voter/Consumer Res/TIR (R)\n", "110 WBUR/MassINC\n", @@ -948,10 +1327,52 @@ " 1.29\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 12\n", + " Keystone (PA)\n", + " 0.64\n", + " 1.55\n", + " \n", + " \n", + " 13\n", + " LA Times / Bloomberg\n", + " 0.83\n", + " 1.44\n", + " \n", + " \n", + " 14\n", + " Marist (NY)\n", + " 0.69\n", + " 1.73\n", + " \n", + " \n", + " 15\n", + " Mason-Dixon\n", + " 1.10\n", + " 1.15\n", + " \n", + " \n", + " 16\n", + " Mitchell\n", + " 0.96\n", + " 1.43\n", + " \n", + " \n", + " 17\n", + " Ohio Poll\n", + " 1.24\n", + " 1.05\n", + " \n", + " \n", + " 18\n", + " Public Opinion Strategies\n", + " 0.63\n", + " 1.81\n", + " \n", + " \n", + " 19\n", + " Public Policy Polling (PPP)\n", + " 1.05\n", + " 1.60\n", " \n", " \n", " 20\n", @@ -1027,7 +1448,6 @@ " \n", " \n", "\n", - "

32 rows × 3 columns

\n", "" ], "text/plain": [ @@ -1044,7 +1464,14 @@ "9 Fox / Opinion Dynamics 0.79 1.60\n", "10 Franklin Pierce (NH) 0.74 1.60\n", "11 Insider Advantage 0.95 1.29\n", - ".. ... ... ...\n", + "12 Keystone (PA) 0.64 1.55\n", + "13 LA Times / Bloomberg 0.83 1.44\n", + "14 Marist (NY) 0.69 1.73\n", + "15 Mason-Dixon 1.10 1.15\n", + "16 Mitchell 0.96 1.43\n", + "17 Ohio Poll 1.24 1.05\n", + "18 Public Opinion Strategies 0.63 1.81\n", + "19 Public Policy Polling (PPP) 1.05 1.60\n", "20 Quinnipiac 0.95 1.34\n", "21 Rasmussen 1.30 0.88\n", "22 Research 2000 1.01 1.20\n", @@ -1056,9 +1483,7 @@ "28 Univ. New Hampshire 1.08 1.26\n", "29 USA Today / Gallup 0.63 2.01\n", "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74\n", - "\n", - "[32 rows x 3 columns]" + "31 Zogby Interactive 0.43 4.74" ] }, "execution_count": 16, @@ -1094,13 +1519,6 @@ "weights.mean()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clean up the pollster names a bit so we can merge with the weights." - ] - }, { "cell_type": "code", "execution_count": 18, @@ -1257,12 +1675,19 @@ "" ], "text/plain": [ - " Pollster State MoE Obama (D) Romney (R) Sample Spread obama_spread poll_date Weight PIE\n", - "0 Rasmussen WA 4.5 52 41 500 Obama +11 11 2012-09-26 1.3 0.88\n", - "1 Rasmussen WI 4.5 49 46 500 Obama +3 3 2012-09-17 1.3 0.88\n", - "2 Rasmussen WI 4.5 47 48 500 Romney +1 -1 2012-08-15 1.3 0.88\n", - "3 Rasmussen WI 4.5 49 46 500 Obama +3 3 2012-07-25 1.3 0.88\n", - "4 Rasmussen WI 4.5 44 47 500 Romney +3 -3 2012-06-12 1.3 0.88" + " Pollster State MoE Obama (D) Romney (R) Sample Spread \\\n", + "0 Rasmussen WA 4.5 52 41 500 Obama +11 \n", + "1 Rasmussen WI 4.5 49 46 500 Obama +3 \n", + "2 Rasmussen WI 4.5 47 48 500 Romney +1 \n", + "3 Rasmussen WI 4.5 49 46 500 Obama +3 \n", + "4 Rasmussen WI 4.5 44 47 500 Romney +3 \n", + "\n", + " obama_spread poll_date Weight PIE \n", + "0 11 2012-09-26 1.3 0.88 \n", + "1 3 2012-09-17 1.3 0.88 \n", + "2 -1 2012-08-15 1.3 0.88 \n", + "3 3 2012-07-25 1.3 0.88 \n", + "4 -3 2012-06-12 1.3 0.88 " ] }, "execution_count": 22, @@ -1358,7 +1783,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, @@ -1366,7 +1791,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1551,15 +1976,25 @@ "" ], "text/plain": [ - " Pollster State Obama (D) Romney (R) Sample poll_date\n", - "258 Public Policy Polling (PPP) AZ 44 53 993 2012-09-08\n", - "259 Public Policy Polling (PPP) AZ 41 52 833 2012-07-24\n", - "260 Public Policy Polling (PPP) AZ 43 50 500 2012-05-19\n", - "261 Public Policy Polling (PPP) AZ 47 47 743 2012-02-18\n", - "262 Public Policy Polling (PPP) AZ 42 49 500 2011-11-19\n", - "263 Public Policy Polling (PPP) AZ 44 48 623 2011-04-30\n", - "264 Public Policy Polling (PPP) AZ 43 49 599 2011-01-29\n", - "265 Public Policy Polling (PPP) AZ 43 50 617 2010-09-20" + " Pollster State Obama (D) Romney (R) Sample \\\n", + "258 Public Policy Polling (PPP) AZ 44 53 993 \n", + "259 Public Policy Polling (PPP) AZ 41 52 833 \n", + "260 Public Policy Polling (PPP) AZ 43 50 500 \n", + "261 Public Policy Polling (PPP) AZ 47 47 743 \n", + "262 Public Policy Polling (PPP) AZ 42 49 500 \n", + "263 Public Policy Polling (PPP) AZ 44 48 623 \n", + "264 Public Policy Polling (PPP) AZ 43 49 599 \n", + "265 Public Policy Polling (PPP) AZ 43 50 617 \n", + "\n", + " poll_date \n", + "258 2012-09-08 \n", + "259 2012-07-24 \n", + "260 2012-05-19 \n", + "261 2012-02-18 \n", + "262 2011-11-19 \n", + "263 2011-04-30 \n", + "264 2011-01-29 \n", + "265 2010-09-20 " ] }, "execution_count": 30, @@ -1583,12 +2018,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", - " if __name__ == '__main__':\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/pandas/core/frame.py:3167: SettingWithCopyWarning: \n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/pandas/core/frame.py:2915: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " inplace=inplace, kind=kind, na_position=na_position)\n" ] } @@ -1608,23 +2041,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", + "/anaconda/envs/pydata/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", + "/anaconda/envs/pydata/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " from ipykernel import kernelapp as app\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n", + "/anaconda/envs/pydata/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " app.launch_new_instance()\n" ] }, @@ -1731,15 +2164,25 @@ "" ], "text/plain": [ - " Pollster State Obama (D) Romney (R) Sample poll_date cumulative\n", - "258 Public Policy Polling (PPP) AZ 44 53 993 2012-09-08 993\n", - "259 Public Policy Polling (PPP) AZ 41 52 833 2012-07-24 1826\n", - "260 Public Policy Polling (PPP) AZ 43 50 500 2012-05-19 2326\n", - "261 Public Policy Polling (PPP) AZ 47 47 743 2012-02-18 3069\n", - "262 Public Policy Polling (PPP) AZ 42 49 500 2011-11-19 3569\n", - "263 Public Policy Polling (PPP) AZ 44 48 623 2011-04-30 4192\n", - "264 Public Policy Polling (PPP) AZ 43 49 599 2011-01-29 4791\n", - "265 Public Policy Polling (PPP) AZ 43 50 617 2010-09-20 5408" + " Pollster State Obama (D) Romney (R) Sample \\\n", + "258 Public Policy Polling (PPP) AZ 44 53 993 \n", + "259 Public Policy Polling (PPP) AZ 41 52 833 \n", + "260 Public Policy Polling (PPP) AZ 43 50 500 \n", + "261 Public Policy Polling (PPP) AZ 47 47 743 \n", + "262 Public Policy Polling (PPP) AZ 42 49 500 \n", + "263 Public Policy Polling (PPP) AZ 44 48 623 \n", + "264 Public Policy Polling (PPP) AZ 43 49 599 \n", + "265 Public Policy Polling (PPP) AZ 43 50 617 \n", + "\n", + " poll_date cumulative \n", + "258 2012-09-08 993 \n", + "259 2012-07-24 1826 \n", + "260 2012-05-19 2326 \n", + "261 2012-02-18 3069 \n", + "262 2011-11-19 3569 \n", + "263 2011-04-30 4192 \n", + "264 2011-01-29 4791 \n", + "265 2010-09-20 5408 " ] }, "execution_count": 32, @@ -1765,22 +2208,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", + "/anaconda/envs/pydata/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", + "/anaconda/envs/pydata/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " from ipykernel import kernelapp as app\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/pandas/core/generic.py:2862: SettingWithCopyWarning: \n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/pandas/core/generic.py:2602: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self._update_inplace(new_data)\n" ] } @@ -2008,7 +2451,43 @@ "9 0.629961\n", "10 0.329877\n", "11 0.140308\n", + "12 0.024803\n", + "13 0.009184\n", + "14 0.004284\n", + "15 0.000615\n", + "16 0.723635\n", + "17 0.090454\n", + "18 0.050766\n", + "19 0.890899\n", + "20 0.378929\n", + "21 0.014919\n", + "22 0.004809\n", + "23 0.644685\n", + "24 0.238710\n", + "25 0.101532\n", + "26 0.038473\n", + "27 0.017538\n", + "28 0.146943\n", + "29 0.040293\n", " ... \n", + "393 0.017948\n", + "394 0.002637\n", + "395 0.000370\n", + "396 0.629961\n", + "397 0.106333\n", + "398 0.049606\n", + "399 0.004385\n", + "400 0.000675\n", + "401 0.000119\n", + "402 0.723635\n", + "403 0.198425\n", + "404 0.046284\n", + "405 0.831238\n", + "406 0.370274\n", + "407 0.361817\n", + "408 0.000147\n", + "409 0.003817\n", + "410 0.601513\n", "411 0.445449\n", "412 0.217638\n", "413 0.062500\n", @@ -2076,32 +2555,68 @@ { "data": { "text/plain": [ - "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168494\n", - " Rasmussen -10.209446\n", - "CA Field Poll (CA) 23.343924\n", - " Public Policy Polling (PPP) 20.999075\n", - " Rasmussen 22.000000\n", - " SurveyUSA 22.123414\n", - "CO American Research Group 2.000000\n", - " Public Policy Polling (PPP) 5.469907\n", - " Rasmussen -1.573788\n", - "CT Public Policy Polling (PPP) 12.757757\n", - " Quinnipiac 7.293983\n", - " Rasmussen 8.000000\n", - " ... \n", - "VA Mason-Dixon 1.000000\n", - " Public Policy Polling (PPP) 5.095802\n", - " Quinnipiac 0.578138\n", - " Rasmussen 0.891780\n", - "VT Public Policy Polling (PPP) 20.000000\n", - "WA Public Policy Polling (PPP) 13.050886\n", - " Rasmussen 11.000000\n", - " SurveyUSA 15.310208\n", - "WI CNN / Opinion Research 4.000000\n", - " Public Policy Polling (PPP) 5.392554\n", - " Rasmussen 2.116005\n", - "WV Public Policy Polling (PPP) -19.756631\n", + "State Pollster \n", + "AZ Public Policy Polling (PPP) -9.168494\n", + " Rasmussen -10.209446\n", + "CA Field Poll (CA) 23.343924\n", + " Public Policy Polling (PPP) 20.999075\n", + " Rasmussen 22.000000\n", + " SurveyUSA 22.123414\n", + "CO American Research Group 2.000000\n", + " Public Policy Polling (PPP) 5.469907\n", + " Rasmussen -1.573788\n", + "CT Public Policy Polling (PPP) 12.757757\n", + " Quinnipiac 7.293983\n", + " Rasmussen 8.000000\n", + "FL American Research Group 5.000000\n", + " Mason-Dixon -3.543178\n", + " Public Policy Polling (PPP) 3.125154\n", + " Quinnipiac 3.075653\n", + " Rasmussen 0.882884\n", + " Suffolk (NH/MA) -0.003377\n", + " SurveyUSA 4.168952\n", + "GA Insider Advantage -19.174054\n", + " Mason-Dixon -17.000000\n", + " Public Policy Polling (PPP) -3.000000\n", + " SurveyUSA -7.983856\n", + "HI Public Policy Polling (PPP) 27.000000\n", + "IA American Research Group 7.000000\n", + " Mason-Dixon -3.000000\n", + " Public Policy Polling (PPP) 5.878693\n", + " Rasmussen -2.749416\n", + "IL Chicago Trib. / MarketShares 21.000000\n", + "IN Rasmussen -16.000000\n", + " ... \n", + "OH Ohio Poll 3.000406\n", + " Public Policy Polling (PPP) 4.141640\n", + " Quinnipiac 7.729397\n", + " Rasmussen 0.865613\n", + "OR Public Policy Polling (PPP) 9.130153\n", + " SurveyUSA 8.675504\n", + "PA Public Policy Polling (PPP) 6.160027\n", + " Quinnipiac 6.047221\n", + " Rasmussen 10.874768\n", + " SurveyUSA 0.000000\n", + "RI Public Policy Polling (PPP) 17.000000\n", + "SC Public Policy Polling (PPP) -14.558484\n", + "SD Public Policy Polling (PPP) -6.000000\n", + "TN Public Policy Polling (PPP) -7.000000\n", + "TX Public Policy Polling (PPP) -6.998595\n", + "UT Mason-Dixon -51.000000\n", + " Public Policy Polling (PPP) -32.000000\n", + "VA American Research Group 2.000000\n", + " Mason-Dixon 1.000000\n", + " Public Policy Polling (PPP) 5.095802\n", + " Quinnipiac 0.578138\n", + " Rasmussen 0.891780\n", + "VT Public Policy Polling (PPP) 20.000000\n", + "WA Public Policy Polling (PPP) 13.050886\n", + " Rasmussen 11.000000\n", + " SurveyUSA 15.310208\n", + "WI CNN / Opinion Research 4.000000\n", + " Public Policy Polling (PPP) 5.392554\n", + " Rasmussen 2.116005\n", + "WV Public Policy Polling (PPP) -19.756631\n", "dtype: float64" ] }, @@ -2114,6 +2629,13 @@ "state_polls" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jim" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2134,61 +2656,7 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "State object\n", - "Obama int64\n", - "McCain int64\n", - "Pollster object\n", - "poll_date datetime64[ns]\n", - "dtype: object" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state_data2008.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "State object\n", - "Kerry int64\n", - "Bush int64\n", - "Pollster object\n", - "poll_date datetime64[ns]\n", - "dtype: object" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state_data2004.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 46, + "execution_count": 46, "metadata": { "collapsed": false }, @@ -2453,9 +2921,139 @@ " 64.000000\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", + " IA\n", + " 41.407407\n", + " 50.037037\n", + " \n", + " \n", + " ID\n", + " 60.000000\n", + " 30.500000\n", + " \n", + " \n", + " IL\n", + " 36.900000\n", + " 55.600000\n", + " \n", + " \n", + " IN\n", + " 47.500000\n", + " 44.961538\n", + " \n", + " \n", + " KS\n", + " 53.562500\n", + " 37.750000\n", + " \n", + " \n", + " KY\n", + " 54.842105\n", + " 37.526316\n", + " \n", + " \n", + " LA\n", + " 52.166667\n", + " 39.083333\n", + " \n", + " \n", + " MA\n", + " 38.800000\n", + " 52.200000\n", + " \n", + " \n", + " MD\n", + " 38.666667\n", + " 53.833333\n", + " \n", + " \n", + " ME\n", + " 38.187500\n", + " 50.562500\n", + " \n", + " \n", + " MI\n", + " 42.052632\n", + " 47.368421\n", + " \n", + " \n", + " MN\n", + " 41.739130\n", + " 50.260870\n", + " \n", + " \n", + " MO\n", + " 47.428571\n", + " 45.571429\n", + " \n", + " \n", + " MS\n", + " 51.200000\n", + " 40.500000\n", + " \n", + " \n", + " MT\n", + " 48.214286\n", + " 43.857143\n", + " \n", + " \n", + " NC\n", + " 47.522727\n", + " 46.090909\n", + " \n", + " \n", + " ND\n", + " 45.571429\n", + " 42.714286\n", + " \n", + " \n", + " NE\n", + " 51.714286\n", + " 37.142857\n", + " \n", + " \n", + " NH\n", + " 42.756757\n", + " 48.918919\n", + " \n", + " \n", + " NJ\n", + " 39.766667\n", + " 49.766667\n", + " \n", + " \n", + " NM\n", + " 43.592593\n", + " 48.740741\n", + " \n", + " \n", + " NV\n", + " 44.843750\n", + " 46.937500\n", + " \n", + " \n", + " NY\n", + " 36.864865\n", + " 52.432432\n", + " \n", + " \n", + " OH\n", + " 44.974684\n", + " 46.658228\n", + " \n", + " \n", + " OK\n", + " 61.700000\n", + " 32.000000\n", + " \n", + " \n", + " OR\n", + " 40.851852\n", + " 50.333333\n", + " \n", + " \n", + " PA\n", + " 42.080000\n", + " 48.893333\n", " \n", " \n", " RI\n", @@ -2519,7 +3117,6 @@ " \n", " \n", "\n", - "

51 rows × 2 columns

\n", "" ], "text/plain": [ @@ -2537,7 +3134,33 @@ "FL 46.393939 46.121212\n", "GA 51.346154 43.153846\n", "HI 30.000000 64.000000\n", - "... ... ...\n", + "IA 41.407407 50.037037\n", + "ID 60.000000 30.500000\n", + "IL 36.900000 55.600000\n", + "IN 47.500000 44.961538\n", + "KS 53.562500 37.750000\n", + "KY 54.842105 37.526316\n", + "LA 52.166667 39.083333\n", + "MA 38.800000 52.200000\n", + "MD 38.666667 53.833333\n", + "ME 38.187500 50.562500\n", + "MI 42.052632 47.368421\n", + "MN 41.739130 50.260870\n", + "MO 47.428571 45.571429\n", + "MS 51.200000 40.500000\n", + "MT 48.214286 43.857143\n", + "NC 47.522727 46.090909\n", + "ND 45.571429 42.714286\n", + "NE 51.714286 37.142857\n", + "NH 42.756757 48.918919\n", + "NJ 39.766667 49.766667\n", + "NM 43.592593 48.740741\n", + "NV 44.843750 46.937500\n", + "NY 36.864865 52.432432\n", + "OH 44.974684 46.658228\n", + "OK 61.700000 32.000000\n", + "OR 40.851852 50.333333\n", + "PA 42.080000 48.893333\n", "RI 32.000000 53.000000\n", "SC 53.300000 41.000000\n", "SD 50.375000 39.875000\n", @@ -2549,9 +3172,7 @@ "WA 40.424242 51.515152\n", "WI 41.921053 49.684211\n", "WV 48.692308 42.538462\n", - "WY 59.333333 32.666667\n", - "\n", - "[51 rows x 2 columns]" + "WY 59.333333 32.666667" ] }, "execution_count": 49, @@ -2594,6 +3215,81 @@ "state_groups.aggregate(dict(Obama=np.mean, McCain=np.mean)).mean()" ] }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ARG': 'American Research Group',\n", + " 'American Res.Group': 'American Research Group',\n", + " 'American Research': 'American Research Group',\n", + " 'Bloomberg News': 'LA Times / Bloomberg',\n", + " 'CNN/Opinion Research': 'CNN / Opinion Research',\n", + " 'Chicago Tribune': 'Chicago Trib. / MarketShares',\n", + " 'Columbus Dispatch': 'Columbus Dispatch (OH)',\n", + " 'Columbus Dispatch*': 'Columbus Dispatch (OH)',\n", + " 'EPIC/MRA': 'EPIC-MRA',\n", + " 'Fairleigh Dickinson': 'Fairleigh-Dickinson (NJ)',\n", + " 'Fairleigh Dickinson U.': 'Fairleigh-Dickinson (NJ)',\n", + " 'Fairleigh Dickinson Univ.': 'Fairleigh-Dickinson (NJ)',\n", + " 'Fairleigh-Dickinson Univ.': 'Fairleigh-Dickinson (NJ)',\n", + " 'Field': 'Field Poll (CA)',\n", + " 'Field Poll': 'Field Poll (CA)',\n", + " 'Fox/Opinion Dyn.': 'Fox / Opinion Dynamics',\n", + " 'Franklin Pierce Coll.': 'Franklin Pierce (NH)',\n", + " 'Gallup': 'USA Today / Gallup',\n", + " 'Inside Advantage': 'Insider Advantage',\n", + " 'InsiderAdvantage': 'Insider Advantage',\n", + " 'Keystone Poll': 'Keystone (PA)',\n", + " 'LA Times': 'LA Times / Bloomberg',\n", + " 'Los Angeles Times': 'LA Times / Bloomberg',\n", + " 'Marist': 'Marist (NY)',\n", + " 'Marist Coll.': 'Marist (NY)',\n", + " 'Marist College': 'Marist (NY)',\n", + " 'Market Shares': 'Chicago Trib. / MarketShares',\n", + " 'Mason-Dixon*': 'Mason-Dixon',\n", + " 'Mitchell Research': 'Mitchell',\n", + " 'NY Times': 'CBS / New York Times',\n", + " 'Ohio Poll/Univ of Cin.': 'Ohio Poll',\n", + " 'Ohio U.': 'Ohio Poll',\n", + " 'Ohio Univ.': 'Ohio Poll',\n", + " 'Opinion Dynamics': 'Fox / Opinion Dynamics',\n", + " 'Opinion Res.': 'CNN / Opinion Research',\n", + " 'Opinion Research': 'CNN / Opinion Research',\n", + " 'PPP (D)': 'Public Policy Polling (PPP)',\n", + " 'Pub. Opin. Strat.': 'Public Opinion Strategies',\n", + " 'Pub. Opinion Strat.': 'Public Opinion Strategies',\n", + " 'Quinnipiac U.': 'Quinnipiac',\n", + " 'Quinnipiac Univ': 'Quinnipiac',\n", + " 'Quinnipiac Univ.': 'Quinnipiac',\n", + " 'Rasmussen Reports': 'Rasmussen',\n", + " 'Selzer & Co.': 'Selzer',\n", + " 'Star Tribune': 'Star Tribune (MN)',\n", + " 'Star-Tribune': 'Star Tribune (MN)',\n", + " 'Suffolk U.': 'Suffolk (NH/MA)',\n", + " 'Suffolk Univ.': 'Suffolk (NH/MA)',\n", + " 'Suffolk University': 'Suffolk (NH/MA)',\n", + " 'Survey USA': 'SurveyUSA',\n", + " 'U. of New Hampshire': 'Univ. New Hampshire',\n", + " 'Univ. of New Hampsh.': 'Univ. New Hampshire',\n", + " 'Univ. of New Hampshire': 'Univ. New Hampshire',\n", + " 'Zogby': 'Zogby'}" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pollster_map" + ] + }, { "cell_type": "code", "execution_count": 51, @@ -2618,6 +3314,122 @@ "state_data2008 = state_data2008.merge(weights, how=\"inner\", on=\"Pollster\")" ] }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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4AR4551SurveyUSA2004-10-231.910.720.7937011970-01-01 00:00:00.000000000
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" + ], + "text/plain": [ + " State Kerry Bush Pollster poll_date Weight PIE time_weight \\\n", + "0 AL 39 57 SurveyUSA 2004-10-25 1.91 0.72 0.831238 \n", + "1 AZ 41 56 SurveyUSA 2004-10-28 1.91 0.72 0.890899 \n", + "2 AZ 43 54 SurveyUSA 2004-10-17 1.91 0.72 0.690956 \n", + "3 AR 46 51 SurveyUSA 2004-11-01 1.91 0.72 0.977160 \n", + "4 AR 45 51 SurveyUSA 2004-10-23 1.91 0.72 0.793701 \n", + "\n", + " newest_poll \n", + "0 1970-01-01 00:00:00.000000001 \n", + "1 1970-01-01 00:00:00.000000001 \n", + "2 1970-01-01 00:00:00.000000000 \n", + "3 1970-01-01 00:00:00.000000001 \n", + "4 1970-01-01 00:00:00.000000000 " + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_data2004.head() ## Weight and PIE added in by previous step" + ] + }, { "cell_type": "code", "execution_count": 53, @@ -2707,7 +3519,43 @@ "9 True\n", "10 True\n", "11 False\n", + "12 False\n", + "13 False\n", + "14 True\n", + "15 True\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 False\n", " ... \n", + "704 True\n", + "705 True\n", + "706 True\n", + "707 True\n", + "708 True\n", + "709 True\n", + "710 False\n", + "711 False\n", + "712 False\n", + "713 True\n", + "714 False\n", + "715 True\n", + "716 True\n", + "717 False\n", + "718 True\n", + "719 True\n", + "720 False\n", + "721 True\n", "722 True\n", "723 False\n", "724 False\n", @@ -2729,7 +3577,7 @@ } ], "source": [ - "(date2004 - state_data2004.poll_date) < datetime.timedelta(21)" + "(date2004 - state_data2004.poll_date) < datetime.timedelta(21) ## Restrict to 3 weeks before the election" ] }, { @@ -2803,6 +3651,17 @@ "state_data2008.dtypes" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## Add time weight to each poll that we have in the filtered 3 week set using the exp_decay function defined earlier" + ] + }, { "cell_type": "code", "execution_count": 61, @@ -2894,6 +3753,17 @@ " return x == x.max()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## Assign the latest poll date to all polls for each (state,pollster) group. Why?" + ] + }, { "cell_type": "code", "execution_count": 64, @@ -2906,6 +3776,13 @@ "state_data2008[\"newest_poll\"] = state_data2008.groupby((\"State\", \"Pollster\")).poll_date.transform(max_date)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ritesh" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3010,61 +3887,164 @@ " D+12\n", " \n", " \n", - " ...\n", - " ...\n", + " Idaho\n", + " R+17\n", " \n", " \n", - " Rhode Island\n", - " D+11\n", + " Illinois\n", + " D+8\n", " \n", " \n", - " South Carolina\n", - " R+8\n", + " Indiana\n", + " R+6\n", " \n", " \n", - " South Dakota\n", - " R+9\n", + " Iowa\n", + " D+1\n", " \n", " \n", - " Tennessee\n", - " R+9\n", + " Kansas\n", + " R+12\n", " \n", " \n", - " Texas\n", + " Kentucky\n", " R+10\n", " \n", " \n", - " Utah\n", - " R+20\n", + " Louisiana\n", + " R+10\n", " \n", " \n", - " Vermont\n", - " D+13\n", + " Maine\n", + " D+5\n", " \n", " \n", - " Virginia\n", - " R+2\n", + " Maryland\n", + " D+9\n", " \n", " \n", - " Washington\n", - " D+5\n", + " Massachusetts\n", + " D+12\n", " \n", " \n", - " West Virginia\n", - " R+8\n", + " Michigan\n", + " D+4\n", " \n", " \n", - " Wisconsin\n", + " Minnesota\n", " D+2\n", " \n", " \n", - " Wyoming\n", - " R+20\n", + " Mississippi\n", + " R+10\n", " \n", - " \n", - "\n", - "

51 rows × 1 columns

\n", - "" + " \n", + " Missouri\n", + " R+3\n", + " \n", + " \n", + " Montana\n", + " R+7\n", + " \n", + " \n", + " Nebraska\n", + " R+13\n", + " \n", + " \n", + " Nevada\n", + " D+1\n", + " \n", + " \n", + " New Hampshire\n", + " D+2\n", + " \n", + " \n", + " New Jersey\n", + " D+4\n", + " \n", + " \n", + " New Mexico\n", + " D+2\n", + " \n", + " \n", + " New York\n", + " D+10\n", + " \n", + " \n", + " North Carolina\n", + " R+4\n", + " \n", + " \n", + " North Dakota\n", + " R+10\n", + " \n", + " \n", + " Ohio\n", + " R+1\n", + " \n", + " \n", + " Oklahoma\n", + " R+17\n", + " \n", + " \n", + " Oregon\n", + " D+4\n", + " \n", + " \n", + " Pennsylvania\n", + " D+2\n", + " \n", + " \n", + " Rhode Island\n", + " D+11\n", + " \n", + " \n", + " South Carolina\n", + " R+8\n", + " \n", + " \n", + " South Dakota\n", + " R+9\n", + " \n", + " \n", + " Tennessee\n", + " R+9\n", + " \n", + " \n", + " Texas\n", + " R+10\n", + " \n", + " \n", + " Utah\n", + " R+20\n", + " \n", + " \n", + " Vermont\n", + " D+13\n", + " \n", + " \n", + " Virginia\n", + " R+2\n", + " \n", + " \n", + " Washington\n", + " D+5\n", + " \n", + " \n", + " West Virginia\n", + " R+8\n", + " \n", + " \n", + " Wisconsin\n", + " D+2\n", + " \n", + " \n", + " Wyoming\n", + " R+20\n", + " \n", + " \n", + "\n", + "" ], "text/plain": [ " PVI\n", @@ -3081,7 +4061,33 @@ "Florida R+2 \n", "Georgia R+7 \n", "Hawaii D+12\n", - "... ...\n", + "Idaho R+17\n", + "Illinois D+8 \n", + "Indiana R+6 \n", + "Iowa D+1 \n", + "Kansas R+12\n", + "Kentucky R+10\n", + "Louisiana R+10\n", + "Maine D+5 \n", + "Maryland D+9 \n", + "Massachusetts D+12\n", + "Michigan D+4 \n", + "Minnesota D+2 \n", + "Mississippi R+10\n", + "Missouri R+3 \n", + "Montana R+7 \n", + "Nebraska R+13\n", + "Nevada D+1 \n", + "New Hampshire D+2 \n", + "New Jersey D+4 \n", + "New Mexico D+2 \n", + "New York D+10\n", + "North Carolina R+4 \n", + "North Dakota R+10\n", + "Ohio R+1 \n", + "Oklahoma R+17\n", + "Oregon D+4 \n", + "Pennsylvania D+2 \n", "Rhode Island D+11\n", "South Carolina R+8 \n", "South Dakota R+9 \n", @@ -3093,9 +4099,7 @@ "Washington D+5 \n", "West Virginia R+8 \n", "Wisconsin D+2 \n", - "Wyoming R+20\n", - "\n", - "[51 rows x 1 columns]" + "Wyoming R+20" ] }, "execution_count": 66, @@ -3131,7 +4135,33 @@ "Florida -2\n", "Georgia -7\n", "Hawaii 12\n", - " ..\n", + "Idaho -17\n", + "Illinois 8\n", + "Indiana -6\n", + "Iowa 1\n", + "Kansas -12\n", + "Kentucky -10\n", + "Louisiana -10\n", + "Maine 5\n", + "Maryland 9\n", + "Massachusetts 12\n", + "Michigan 4\n", + "Minnesota 2\n", + "Mississippi -10\n", + "Missouri -3\n", + "Montana -7\n", + "Nebraska -13\n", + "Nevada 1\n", + "New Hampshire 2\n", + "New Jersey 4\n", + "New Mexico 2\n", + "New York 10\n", + "North Carolina -4\n", + "North Dakota -10\n", + "Ohio -1\n", + "Oklahoma -17\n", + "Oregon 4\n", + "Pennsylvania 2\n", "Rhode Island 11\n", "South Carolina -8\n", "South Dakota -9\n", @@ -3321,12 +4351,220 @@ " 16.7\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " Michigan\n", + " 47.7\n", + " 36.6\n", + " 11.1000\n", + " 5056\n", + " 15.7\n", + " \n", + " \n", + " Minnesota\n", + " 48.4\n", + " 38.2\n", + " 10.2000\n", + " 3873\n", + " 13.4\n", + " \n", + " \n", + " Washington\n", + " 47.5\n", + " 37.7\n", + " 9.8000\n", + " 5333\n", + " 14.8\n", + " \n", + " \n", + " Oregon\n", + " 47.2\n", + " 39.1\n", + " 8.1000\n", + " 3002\n", + " 13.7\n", + " \n", + " \n", + " Pennsylvania\n", + " 46.4\n", + " 41.2\n", + " 5.2000\n", + " 8443\n", + " 12.4\n", + " \n", + " \n", + " Maine\n", + " 43.8\n", + " 39.4\n", + " 4.4000\n", + " 1040\n", + " 16.8\n", + " \n", + " \n", + " New Mexico\n", + " 44.7\n", + " 41.1\n", + " 3.6000\n", + " 1555\n", + " 14.2\n", + " \n", + " \n", + " Ohio\n", + " 44.1\n", + " 40.5\n", + " 3.6000\n", + " 6426\n", + " 15.4\n", + " \n", + " \n", + " West Virginia\n", + " 45.3\n", + " 41.9\n", + " 3.4000\n", + " 1202\n", + " 12.8\n", + " \n", + " \n", + " Wisconsin\n", + " 45.0\n", + " 42.2\n", + " 2.8000\n", + " 4140\n", + " 12.8\n", + " \n", + " \n", + " Iowa\n", + " 43.2\n", + " 41.4\n", + " 1.8000\n", + " 2337\n", + " 15.4\n", + " \n", + " \n", + " Florida\n", + " 43.0\n", + " 42.3\n", + " 0.7000\n", + " 9965\n", + " 14.7\n", + " \n", + " \n", + " Arkansas\n", + " 41.5\n", + " 40.8\n", + " 0.7000\n", + " 2071\n", + " 17.7\n", + " \n", + " \n", + " Kentucky\n", + " 43.5\n", + " 43.1\n", + " 0.4000\n", + " 2898\n", + " 13.4\n", + " \n", + " \n", + " North Carolina\n", + " 43.4\n", + " 43.2\n", + " 0.2000\n", + " 6213\n", + " 13.4\n", + " \n", + " \n", + " New Hampshire\n", + " 42.3\n", + " 43.8\n", + " -1.5000\n", + " 873\n", + " 13.9\n", + " \n", + " \n", + " Virginia\n", + " 41.2\n", + " 44.2\n", + " -3.0000\n", + " 5313\n", + " 14.6\n", + " \n", + " \n", + " Missouri\n", + " 40.1\n", + " 44.0\n", + " -3.9000\n", + " 3727\n", + " 15.9\n", + " \n", + " \n", + " Georgia\n", + " 40.3\n", + " 44.3\n", + " -4.0000\n", + " 5110\n", + " 15.4\n", + " \n", + " \n", + " Nevada\n", + " 39.2\n", + " 43.4\n", + " -4.2000\n", + " 1348\n", + " 17.4\n", + " \n", + " \n", + " Louisiana\n", + " 40.3\n", + " 45.1\n", + " -4.8000\n", + " 2655\n", + " 14.6\n", + " \n", + " \n", + " Colorado\n", + " 39.9\n", + " 45.1\n", + " -5.2000\n", + " 3671\n", + " 15.0\n", + " \n", + " \n", + " Texas\n", + " 38.3\n", + " 44.1\n", + " -5.8000\n", + " 11325\n", + " 17.6\n", + " \n", + " \n", + " South Dakota\n", + " 41.5\n", + " 47.5\n", + " -6.0000\n", + " 607\n", + " 11.0\n", + " \n", + " \n", + " Indiana\n", + " 39.0\n", + " 45.7\n", + " -6.7000\n", + " 4197\n", + " 15.3\n", + " \n", + " \n", + " Mississippi\n", + " 40.1\n", + " 47.1\n", + " -7.0000\n", + " 1763\n", + " 12.8\n", + " \n", + " \n", + " Arizona\n", + " 39.8\n", + " 47.3\n", + " -7.5000\n", + " 4325\n", + " 12.9\n", " \n", " \n", " Tennessee\n", @@ -3426,7 +4664,6 @@ " \n", " \n", "\n", - "

51 rows × 5 columns

\n", "" ], "text/plain": [ @@ -3444,7 +4681,33 @@ "California 48.3 34.6 13.7000 16197 17.1\n", "Illinois 48.4 35.8 12.6000 5888 15.8\n", "New Jersey 47.4 35.9 11.5000 4239 16.7\n", - "... ... ... ... ... ...\n", + "Michigan 47.7 36.6 11.1000 5056 15.7\n", + "Minnesota 48.4 38.2 10.2000 3873 13.4\n", + "Washington 47.5 37.7 9.8000 5333 14.8\n", + "Oregon 47.2 39.1 8.1000 3002 13.7\n", + "Pennsylvania 46.4 41.2 5.2000 8443 12.4\n", + "Maine 43.8 39.4 4.4000 1040 16.8\n", + "New Mexico 44.7 41.1 3.6000 1555 14.2\n", + "Ohio 44.1 40.5 3.6000 6426 15.4\n", + "West Virginia 45.3 41.9 3.4000 1202 12.8\n", + "Wisconsin 45.0 42.2 2.8000 4140 12.8\n", + "Iowa 43.2 41.4 1.8000 2337 15.4\n", + "Florida 43.0 42.3 0.7000 9965 14.7\n", + "Arkansas 41.5 40.8 0.7000 2071 17.7\n", + "Kentucky 43.5 43.1 0.4000 2898 13.4\n", + "North Carolina 43.4 43.2 0.2000 6213 13.4\n", + "New Hampshire 42.3 43.8 -1.5000 873 13.9\n", + "Virginia 41.2 44.2 -3.0000 5313 14.6\n", + "Missouri 40.1 44.0 -3.9000 3727 15.9\n", + "Georgia 40.3 44.3 -4.0000 5110 15.4\n", + "Nevada 39.2 43.4 -4.2000 1348 17.4\n", + "Louisiana 40.3 45.1 -4.8000 2655 14.6\n", + "Colorado 39.9 45.1 -5.2000 3671 15.0\n", + "Texas 38.3 44.1 -5.8000 11325 17.6\n", + "South Dakota 41.5 47.5 -6.0000 607 11.0\n", + "Indiana 39.0 45.7 -6.7000 4197 15.3\n", + "Mississippi 40.1 47.1 -7.0000 1763 12.8\n", + "Arizona 39.8 47.3 -7.5000 4325 12.9\n", "Tennessee 38.1 46.5 -8.4000 4231 15.4\n", "Alaska 35.9 44.3 -8.4402 NaN 19.8\n", "Oklahoma 38.6 48.0 -9.4000 2583 13.4\n", @@ -3456,9 +4719,7 @@ "Nebraska 33.1 52.1 -19.0000 1351 14.8\n", "Wyoming 26.7 56.6 -29.9000 600 16.7\n", "Idaho 27.5 57.8 -30.3000 1336 14.7\n", - "Utah 24.5 63.8 -39.3000 2256 11.7\n", - "\n", - "[51 rows x 5 columns]" + "Utah 24.5 63.8 -39.3000 2256 11.7" ] }, "execution_count": 70, @@ -3509,18 +4770,177 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 199, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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per_blackper_hispper_whiteeduc_hseduc_collaverage_incomemedian_incomepop_densityvote_popolder_popper_olderper_vote
State
Alabama26.54.066.881.421.7229844208194.43001712.500672383.6000.1400.625
Alaska3.65.863.790.727.030726665211.2475548.44458540.1580.0810.658
Arizona4.530.157.485.026.3256805044856.33934880.535920515.7100.1420.607
Arkansas15.66.674.281.919.1212743926756.01798043.148428944.9340.1460.612
California6.638.139.780.730.12918860883239.124009747.9444409953.7040.1170.637
\n", + "
" + ], + "text/plain": [ + " per_black per_hisp per_white educ_hs educ_coll \\\n", + "State \n", + "Alabama 26.5 4.0 66.8 81.4 21.7 \n", + "Alaska 3.6 5.8 63.7 90.7 27.0 \n", + "Arizona 4.5 30.1 57.4 85.0 26.3 \n", + "Arkansas 15.6 6.6 74.2 81.9 19.1 \n", + "California 6.6 38.1 39.7 80.7 30.1 \n", + "\n", + " average_income median_income pop_density vote_pop \\\n", + "State \n", + "Alabama 22984 42081 94.4 3001712.500 \n", + "Alaska 30726 66521 1.2 475548.444 \n", + "Arizona 25680 50448 56.3 3934880.535 \n", + "Arkansas 21274 39267 56.0 1798043.148 \n", + "California 29188 60883 239.1 24009747.944 \n", + "\n", + " older_pop per_older per_vote \n", + "State \n", + "Alabama 672383.600 0.140 0.625 \n", + "Alaska 58540.158 0.081 0.658 \n", + "Arizona 920515.710 0.142 0.607 \n", + "Arkansas 428944.934 0.146 0.612 \n", + "California 4409953.704 0.117 0.637 " + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "census_data.set_index(\"State\", inplace=True)" + "census_data.head()" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "census_data.set_index(\"State\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, "metadata": { "collapsed": false }, @@ -3656,7 +5076,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 203, "metadata": { "collapsed": false }, @@ -3672,183 +5092,54 @@ " obama_give\n", " romney_give\n", " \n", - " \n", - " State\n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " Alabama\n", - " 0.244651\n", - " 0.365672\n", - " \n", - " \n", - " Alaska\n", - " 1.111870\n", - " 0.498678\n", - " \n", - " \n", - " Arizona\n", - " 0.568634\n", - " 0.672651\n", - " \n", - " \n", - " Arkansas\n", - " 0.246781\n", - " 0.216652\n", - " \n", - " \n", - " California\n", - " 1.128145\n", - " 0.617581\n", - " \n", - " \n", - " Colorado\n", - " 1.056366\n", - " 0.796661\n", - " \n", - " \n", - " Connecticut\n", - " 1.206698\n", - " 1.544816\n", - " \n", - " \n", - " Delaware\n", - " 0.766860\n", - " 0.358712\n", - " \n", - " \n", - " District of Columbia\n", - " 326.863621\n", - " 2.535392\n", - " \n", - " \n", - " Florida\n", - " 0.503180\n", - " 0.874699\n", - " \n", - " \n", - " Georgia\n", - " 0.467529\n", - " 0.526246\n", - " \n", - " \n", - " Hawaii\n", - " 1.006632\n", - " 0.225184\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " Rhode Island\n", - " 0.713200\n", - " 0.358394\n", - " \n", - " \n", - " South Carolina\n", - " 0.317250\n", - " 0.351393\n", - " \n", - " \n", - " South Dakota\n", - " 0.270970\n", - " 0.518931\n", - " \n", - " \n", - " Tennessee\n", - " 0.376523\n", - " 0.522332\n", - " \n", - " \n", - " Texas\n", - " 0.476729\n", - " 0.690927\n", - " \n", - " \n", - " Utah\n", - " 0.379436\n", - " 2.394654\n", - " \n", - " \n", - " Vermont\n", - " 1.602222\n", - " 0.249971\n", - " \n", - " \n", - " Virginia\n", - " 1.000185\n", - " 0.938508\n", + " 0\n", + " 6.658811e-08\n", + " 9.952707e-08\n", " \n", " \n", - " Washington\n", - " 1.190590\n", - " 0.475625\n", + " 1\n", + " 2.081808e-06\n", + " 9.336994e-07\n", " \n", " \n", - " West Virginia\n", - " 0.260437\n", - " 0.321333\n", + " 2\n", + " 1.171138e-07\n", + " 1.385368e-07\n", " \n", " \n", - " Wisconsin\n", - " 0.455410\n", - " 0.237802\n", + " 3\n", + " 1.108137e-07\n", + " 9.728485e-08\n", " \n", " \n", - " Wyoming\n", - " 0.746122\n", - " 1.080021\n", + " 4\n", + " 3.969586e-08\n", + " 2.173072e-08\n", " \n", " \n", "\n", - "

51 rows × 2 columns

\n", "" ], "text/plain": [ - " obama_give romney_give\n", - "State \n", - "Alabama 0.244651 0.365672\n", - "Alaska 1.111870 0.498678\n", - "Arizona 0.568634 0.672651\n", - "Arkansas 0.246781 0.216652\n", - "California 1.128145 0.617581\n", - "Colorado 1.056366 0.796661\n", - "Connecticut 1.206698 1.544816\n", - "Delaware 0.766860 0.358712\n", - "District of Columbia 326.863621 2.535392\n", - "Florida 0.503180 0.874699\n", - "Georgia 0.467529 0.526246\n", - "Hawaii 1.006632 0.225184\n", - "... ... ...\n", - "Rhode Island 0.713200 0.358394\n", - "South Carolina 0.317250 0.351393\n", - "South Dakota 0.270970 0.518931\n", - "Tennessee 0.376523 0.522332\n", - "Texas 0.476729 0.690927\n", - "Utah 0.379436 2.394654\n", - "Vermont 1.602222 0.249971\n", - "Virginia 1.000185 0.938508\n", - "Washington 1.190590 0.475625\n", - "West Virginia 0.260437 0.321333\n", - "Wisconsin 0.455410 0.237802\n", - "Wyoming 0.746122 1.080021\n", - "\n", - "[51 rows x 2 columns]" + " obama_give romney_give\n", + "0 6.658811e-08 9.952707e-08\n", + "1 2.081808e-06 9.336994e-07\n", + "2 1.171138e-07 1.385368e-07\n", + "3 1.108137e-07 9.728485e-08\n", + "4 3.969586e-08 2.173072e-08" ] }, - "execution_count": 81, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "giving = demo_data[[\"obama_give\", \"romney_give\"]].div(demo_data[[\"vote_pop\", \"older_pop\"]].sum(1), axis=0)\n", - "giving" + "giving.head()" ] }, { @@ -3895,7 +5186,8 @@ { "data": { "text/plain": [ - "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" + "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1.])" ] }, "execution_count": 85, @@ -3918,7 +5210,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, @@ -3975,107 +5267,107 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + "//anaconda/envs/pydata/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] } @@ -4115,7 +5407,8 @@ " 'Delaware': (Index([[u'Delaware', u'Michigan', u'Washington', u'Oregon', u'Missouri', u'Illinois', u'Rhode Island']], dtype='object', name=u'State'),\n", " array([[ 0. , 2.8113, 2.9256, 2.926 , 3.0637, 3.0731, 3.1641]])),\n", " 'District of Columbia': (Index([[u'District of Columbia', u'Maryland', u'Massachusetts', u'Connecticut', u'Virginia', u'New Jersey', u'New York']], dtype='object', name=u'State'),\n", - " array([[ 0. , 13.03 , 13.3261, 13.6773, 14.119 , 14.1531, 14.5409]])),\n", + " array([[ 0. , 13.03 , 13.3261, 13.6773, 14.119 , 14.1531,\n", + " 14.5409]])),\n", " 'Florida': (Index([[u'Florida', u'Pennsylvania', u'New York', u'Ohio', u'Arizona', u'Michigan', u'Illinois']], dtype='object', name=u'State'),\n", " array([[ 0. , 3.6013, 4.1043, 4.154 , 4.3315, 4.3721, 4.3781]])),\n", " 'Georgia': (Index([[u'Georgia', u'North Carolina', u'Louisiana', u'South Carolina', u'Tennessee', u'Alabama', u'Illinois']], dtype='object', name=u'State'),\n", @@ -4279,7 +5572,9 @@ { "data": { "text/plain": [ - "array([3, 4, 3, 3, 0, 4, 4, 4, 1, 0, 3, 4, 3, 4, 3, 2, 2, 3, 3, 2, 4, 4, 2, 2, 3, 2, 2, 2, 3, 4, 4, 3, 0, 3, 2, 2, 3, 2, 2, 4, 3, 2, 3, 0, 3, 2, 4, 4, 3, 2, 2])" + "array([0, 4, 1, 0, 1, 2, 2, 2, 3, 1, 0, 2, 4, 2, 0, 4, 4, 0, 0, 4, 2, 2, 0,\n", + " 4, 0, 0, 4, 4, 1, 4, 2, 1, 2, 0, 4, 0, 0, 4, 0, 2, 0, 4, 0, 1, 4, 4,\n", + " 2, 2, 0, 4, 4])" ] }, "execution_count": 94, @@ -4332,13 +5627,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "['California' 'Florida' 'New York' 'Texas']\n", + "['Alabama' 'Arkansas' 'Georgia' 'Indiana' 'Kentucky' 'Louisiana' 'Michigan'\n", + " 'Mississippi' 'Missouri' 'North Carolina' 'Ohio' 'Oklahoma' 'Pennsylvania'\n", + " 'South Carolina' 'Tennessee' 'West Virginia']\n", + "['Arizona' 'California' 'Florida' 'Nevada' 'New Mexico' 'Texas']\n", + "['Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland'\n", + " 'Massachusetts' 'New Jersey' 'New York' 'Rhode Island' 'Virginia'\n", + " 'Washington']\n", "['District of Columbia']\n", - "['Iowa' 'Kansas' 'Maine' 'Michigan' 'Minnesota' 'Missouri' 'Montana' 'Nebraska' 'North Dakota' 'Ohio' 'Oregon' 'Pennsylvania' 'South Dakota' 'Vermont'\n", - " 'Wisconsin' 'Wyoming']\n", - "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Idaho' 'Indiana' 'Kentucky' 'Louisiana' 'Mississippi' 'Nevada' 'New Mexico' 'North Carolina' 'Oklahoma'\n", - " 'South Carolina' 'Tennessee' 'Utah' 'West Virginia']\n", - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'New Hampshire' 'New Jersey' 'Rhode Island' 'Virginia' 'Washington']\n" + "['Alaska' 'Idaho' 'Iowa' 'Kansas' 'Maine' 'Minnesota' 'Montana' 'Nebraska'\n", + " 'New Hampshire' 'North Dakota' 'Oregon' 'South Dakota' 'Utah' 'Vermont'\n", + " 'Wisconsin' 'Wyoming']\n" ] } ], @@ -4359,7 +5658,9 @@ { "data": { "text/plain": [ - "array([2, 1, 2, 2, 3, 1, 1, 1, 4, 3, 2, 1, 0, 1, 2, 0, 0, 2, 2, 0, 1, 1, 2, 1, 2, 2, 0, 0, 2, 1, 1, 2, 3, 2, 0, 2, 2, 0, 2, 1, 2, 0, 2, 3, 0, 1, 1, 1, 2, 0, 0], dtype=int32)" + "array([1, 0, 3, 1, 4, 0, 0, 0, 2, 4, 1, 0, 3, 0, 3, 3, 3, 1, 1, 3, 0, 0, 3,\n", + " 0, 1, 3, 3, 3, 0, 0, 0, 3, 4, 1, 3, 3, 3, 3, 3, 0, 1, 3, 1, 4, 3, 0,\n", + " 0, 0, 1, 3, 3], dtype=int32)" ] }, "execution_count": 98, @@ -4382,13 +5683,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Idaho' 'Iowa' 'Kansas' 'Maine' 'Montana' 'Nebraska' 'North Dakota' 'Oregon' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", - "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois' 'Maryland' 'Massachusetts' 'Minnesota' 'New Hampshire' 'New Jersey' 'Rhode Island' 'Vermont'\n", - " 'Virginia' 'Washington']\n", - "['Alabama' 'Arizona' 'Arkansas' 'Georgia' 'Indiana' 'Kentucky' 'Louisiana' 'Michigan' 'Mississippi' 'Missouri' 'Nevada' 'New Mexico' 'North Carolina' 'Ohio'\n", - " 'Oklahoma' 'Pennsylvania' 'South Carolina' 'Tennessee' 'West Virginia']\n", - "['California' 'Florida' 'New York' 'Texas']\n", - "['District of Columbia']\n" + "['Alaska' 'Colorado' 'Connecticut' 'Delaware' 'Hawaii' 'Illinois'\n", + " 'Maryland' 'Massachusetts' 'Minnesota' 'Nevada' 'New Hampshire'\n", + " 'New Jersey' 'Rhode Island' 'Vermont' 'Virginia' 'Washington']\n", + "['Alabama' 'Arkansas' 'Georgia' 'Kentucky' 'Louisiana' 'Mississippi'\n", + " 'North Carolina' 'South Carolina' 'Tennessee' 'West Virginia']\n", + "['District of Columbia']\n", + "['Arizona' 'Idaho' 'Indiana' 'Iowa' 'Kansas' 'Maine' 'Michigan' 'Missouri'\n", + " 'Montana' 'Nebraska' 'New Mexico' 'North Dakota' 'Ohio' 'Oklahoma'\n", + " 'Oregon' 'Pennsylvania' 'South Dakota' 'Utah' 'Wisconsin' 'Wyoming']\n", + "['California' 'Florida' 'New York' 'Texas']\n" ] } ], @@ -4465,8 +5769,9 @@ { "data": { "text/plain": [ - "array(['New Mexico', 'North Carolina', 'Nevada', 'Ohio', 'Pennsylvania', 'Indiana', 'Arizona', 'Missouri', 'Michigan', 'Georgia', 'West Virginia',\n", - " 'South Carolina', 'Tennessee', 'Mississippi'], dtype=object)" + "array(['Wisconsin', 'New Mexico', 'North Dakota', 'Nebraska', 'Ohio',\n", + " 'Pennsylvania', 'Indiana', 'Iowa', 'Arizona', 'Maine', 'Missouri',\n", + " 'Michigan', 'Montana', 'Kansas', 'Oregon', 'South Dakota', 'Utah'], dtype=object)" ] }, "execution_count": 105, @@ -4501,18 +5806,11 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" - ] - }, { "data": { - "image/png": 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CdUlo0yZ/p/3mm/2yaZMP2o8+Gj75SaisLHYLRUREpFieeMJXV1+61Ce4SKIg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\n1FMG65IkSVJPGaxLkiRJPfX/AUPYrPK0/kgsAAAAAElFTkSuQmCC\n", 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dK7ihd2++iy78M/bhPBzJCWW9WN4t1nzsU2OmdCRqy47pJz1YVtYv6b65SJlq\nbm6bXz2buetTr3v0PdenYgA/3n9/8oc/jNFGPdodt6ysX1opTFAajIJ1EW7d6map+d3vyO9/nzzh\nBLK21gXxBx9M/sd/uLnk//hH8h//UE58J5XNbB5Bm6UjqdZW8u9/J//wBxdUT5lC7r032aMHedhh\n5Hnn8Yoe/XkCLuEReJJ1eJYHYA7H9h7qPgPvv++mXN2yxf186y03DeuiReTdd7vUtBkzyAsv5BMD\n9uBjqOYKDOE7GMytKOf7qOLrVX3cf5BnzHDTuD79NPnxx+2KmUm75upaJDuu3zP/tJ/hI3fnLrSO\ns8GcxIG4kT/Fd/gvVPERjOLsgw93z/ZIuH/sdqrpuzf3xnWcgKf4fYT4Gir4Bgbyh5jFYVhDYLy3\nT21UOZK3ZcdZWMantG8uZg5yxxyf0XEzmUkqsg6GHbwHh/HxgUM6zODm2qhju6Qzk0+8YF2zwYiU\nku7dY89Ss3kz8OqrwEsvueWmm4AXXwS2bt21fdsyapRmp5Fg2rRp1338wgsuhWDFCjc70+jRwCGH\nAN/4BvCjH7mZl7p2BQDc+cCf8PHmsQC+5B1oJWq6dgP23LP98SsqgN69gb33jnn60FsfYMmSdQAu\nBNAEQysG4GZ8ff8/4KYpU1x6QzgM3HyzS1vr3h3Yd19gv/1wyt/fQCWew2sYj9XYG9tz1UYSCP2x\nEVdgJc5BGHfjXOyH1/EeFqNh4MLMUxXNsBoDsBpH4M84Aj/CwxiHRpyO9ViOcVgEwzVYh9f8rUpJ\nmY2ZGIqPMefAsajv0iV/J44VwWtRz7oISder+Oij5A03kGee6eaKr6wk99nHPfBp1iz3ZNc339Q8\n8UWkU6TBfPop+ac/kXPmkMcf79JTKivJ0aPJb37TPVF48WJ3DyeRzQwTkTp+Pf+r+GkwkQ9VmzeP\na046iY90KecqDOAWlPE168r3vvAF8soryV/+kvzLX8j16zucT2kwxZUGM7hiN87BYfwIxltwCPdA\n3ww/ex3bKTrdYlfay6/YG7fyB10r+B6M92EPjkZPKg0mvTSYM3Au38JuHFoR+zOdyzQYDTBNQANM\nRWLYvh1Ytcr1vEcuH3/set0je+EPOgjo0yfh4Qo9WMyv8xe6HukqqgGmJPDWW8DTT+9a3njD9ZSP\nH++W0aOB2tqdveXpCtIA026tOzD9lGNwRP/+HQeP9+zp6uotT2zdijm/eQgwK+gAUyD2QMZY+8e6\n9z766H3DkMeXAAAgAElEQVRs3LgRn3yyGX379kB1dU27gX/R+zzwwCMpt3Eq92s693SyAbIx9//o\nI+Dmm7HtxhvxaFUNbu3TD++W90LMAaYJzhtvUGVbm6xatRpm3bDPPnvhmmumA2h/Hbpu3Yo3r/gB\nTnjzFTzf2hVzrBvW7L4HRo0alfA6TZt2PpYvX44bbrgL27ZtxsCBA1Fd3XPnNYu+DvH+vkRe77Z6\nR5d9wIDeABD3XojXFsnuieiBpRs3fogPPtiEbt267fzMx7uOvz73XBz9y7swqUsfrO1dji1bduCz\nz7ahe/eeGDlyxM7zzZkzB6HQDdi61VBZ2R0zZlyU8LjRNMBUPesiufXJJ+STT7p54M8/nxw/3g3I\nGzqU/NrXXI78//6vG/jqDWgtdC+ZX+cvdD06nche8+OOc4M8hwxxed833OByvrduLXQp86+11eXR\nL1zonox84onkwIHkoEFuEPkNN5Avv5z3YkXf/5E9un70umZ6/Fj7+jvHfApzoK9e7Z6P0bcvee65\nbsyDD20c3T4pf6Pjbd+nYgAvwBlcjf58vEs3/vX661M6XyrXLL1vZqZ5vdHV3DXoNvL3zAaBJvtG\nJtY9FO86PnnnnXwPxkmophucG1ne9u2d7b8F0ABTBesiebdjh0uRefBBlzJz0kkuhaaykhwzhs2D\nh/K7+A9OwhLugX/S8Mu8Dhbza7BaMQ96K7ht28jnnyfnzSPPO8+lsfToQX7hC+Rll7kZjf75z0KX\nMrhaW11A+JvfkBde6P5TM3KkG8z6+ut5KULH+9/fwYeZHj/2vh23Tefzm3yArFv3rcO/TJ56Ktmv\nH/n//h+5bp3PbRzdPpm1SRm2sQnn8N2KHuRXv0q+8ELC86VyzdIboNxW9rbynxT1uz/XOZV7KNZ1\nPGnisXynsifPxV7ee7VR5U2v7snEC9Y1wFREcqdLF5eaUFvbbi5abNoEvPwyXjnrW9h73Qc4ASHs\nizfQG+vx3tOVwMknu4F3I0a4n/vuC/TvD1jHbweliLS2Am++CSxbBjzzjFtefBEYOtTNWXzoocA5\n57gUj+7dC13a4mAG7LWXW047Dbj1VmD5cuDee4Ejj3SfvfPPB/7jP/SMhZwjjsKjuAI/wegX3gJO\nCQF33AFUVxe6YHFtRzfMx5fw4YSP8H9HTwIaG4FJk7D75n8Xumg7dcEOjMNyTMZDOHn5U8D++7u0\nov32A045xQ0az4FyfI5ZK/6KPw8YjDvf7peTc6QsVgSvRT3rIvkQ/ZXhbhW78c+33OLSZa6+mjz9\ndPLww91XyL17k4ce6ua2njWL/O1vyWeecdPp+XR+pcH4aPt2l/L0m9+Q06aRX/mKu4bDhrl0luuu\nIx9/3M13LrmxbZtLmWloIAcPdtOyRk0Z6YdST4OZM3s2z+hWzecwlC9hMM/tVs3www9n2aqp1yOT\nNJi49d24kbzqKn5WXc3bulZyIG5st01+0mCquA/68jw08D6Ucz16cgWG8MayHlweCrlUr3ffdVMM\nn3km2acP19XXs768L4G7UjhnKmkwd/E3XSv43oQJbF60KKJ9C5MGowGmCWiAqUjupTywa/16N7D1\njTd2/Wz7vWdP1ws/YgQweDAwcCAwYAAwfLjrWUzQK1+qA0x99dlnwMsvuycuPv+8+7lyJbD77sCY\nMUBdnVvGjHHXRfJvxQrghhuAhx92PfDHH+963n36BiPZgNNsn26Z6fFj7evXANOKHdtx7cgh2H/R\nInxcVYUbuvbCM/13x2X/dUFOPv+JypjuwOak9f3wQ/z9ggtQ88f/wx+H7IVB18zGl70e7FSuWToD\nlCceWYe/3bMAQ996C1/a8TkmchsA4MmuFVjWpw9eHDAEGDQ4/nXZsAG46y5s/slPsG7TZjw5cA/s\nN+0STLjggnZ/91O5h8LhMOZefwdOX/0qju+6A71feAHo0aNd+3bvvgMbNvw75gDTlNo2AQ0wVc+6\nFEhzc/POXLZ0/oed6X6FUNCytraSa9e6KfB+8Qs38O7ii92Au7FjyT593BMoR48mp04lr7iCvOMO\nNyXlmjWuB7jEpXT92qYafOIJ186XX+4eqrX//tzevTtX96rm4kFD+eoFF7htsvjGo1REt3te/la8\n8w5XnXYaX+pdww1dy7iwZiC/P/qLGfcEx6pDXd0E1tTUsq5uYsxe7ETvZ3reVLYLhUIdzp1Rm3/w\nAVeddho/6dadTw4YzKd/+tOMyl5XN4G1tQflpS3Saa/INmqZP9+NJenTx+XgP/FE+tP0btvm/tYu\nX04+9pgbw3TXXe6bnhNOcE9sHTzYfWs6b557aqv3QL7Ich911FEsKxvAsrIBbGpq6linHTu47Jpr\nuGDPWr5b0YP/HjiQ/Pa33bdLGzcmbI9QKMSKihoOQx8u6dKd6wYP3jnOINO2y+R6QgNMFaxL/mX6\nlVgxpVUURVnXryeXLSPvuYcMhcizziKPPNI9Tr57d3LECJcqcNZZ5H//N3nbbeTDD5PPPUd+8EGn\nfpJr9PUbWLEb/3LTTS7NaOZM91TbsWPdf3j69ye/+EX31fOPfkT+/vdcettt7FsxINjXP4CySe1I\ndJzU5+qexkHoy0twGpdiH34E4z8nTybD4ZRn2olVh7Ky3nHTMdJN18i2zrHTLSLnwO6depu3tpJ/\n/jN59tnc1qsX/6drJffFNWnf85HtHy+Vwu+2SKe94l6fTz4hf/YzctQo97dg4kTXIXL66e5v6s03\nk9df72Zw+v73yTPOcH9jhw4ly8vdwOfRo91+J5zg3v+v/yLvvZd8++2Yf2Pbl3sCo+d579q1/Rz1\n0Wks47rX8PWzzyYnTXIzkx15JHnddXzsvvvatUdZWU8ORDm/i+78EL14JU5mV1QxFAr503ZpULCu\nYF0KINOR4cU0u0gxlTWmzZtdDuSiRa7HeOZMN8Xa0UeTBx1E1tS4gH6vvcgjjnC9S9OmucfM33ef\n+wf87bdd71GQ7dhBvvOO6xn73e/cP7wzZvCPewzngxjDpfgi38VAforuXFXVm/z6191/XO6+m/zr\nXzs8kKdN0V//AslmhpPEx0m8X7zHrQ/BXN6+70FujEhlpbv3TzmF/OEP3f2yYgW5aVMKdYhfj3Rn\nLcm2zrFnHUmzzT/4gJw7l9x/f3Lffclrr+UpR3414zq0b//8tEV67ZVCmdavdw8cu+8+98CuK68k\nL7qI/N73yOnT3bebv/ylm3519eqM/za2L/cAJrt2sWZz2Vn2TZvc3/imJm7u2pXPY0/+AufwelzG\nt9CV62G8H+O4H15tdzzf2y6JeMG6ZoMRkdJWWQkccIBb4tm8GVi3DnjnHWDtWresXg089dSudR98\nAPTr5/K0a2qAvn3j/6ysdDPlRC5mqa0jgU8/Bf71L2DjxvY/Y61r+7lunXtAVW0tMGQIsNtuwIAB\neLOqD57EF/ABvoHV2BvrsAQN4x/G4nvvzd81kIJ7B/3wwLARuGDxA+5+f/114NVX3YOYHnwQuPpq\nYM0aoHdvYO+9gb33xhlvvoI90BerMRyrsTfWgiA6wYxNra3Ao48Cd94JLF4MnHCCm9XliCMAM2x4\n9G+FLmFh1dQADQ2FLkV6evYEjjkGOOYYnPzOBnz02MH4MnpgN3yI49Afr6IMxMUARha6pLHFiuC1\nqGdd/KE0mBLy+eeu53r5cnLJEtfrdPvt5DXXuPzuc89188zX17sHRh1+uJvdZuxYsq6OPOQQ8uCD\n3dfMBxzg5sred1+XolNb63r2hw1zy6hR5IQJ5DHHuJ7+Cy5w5wiFyJtuIufPJxcscD1by5e7HNCo\nXtE22V4/Xf/MBCENJtUHzuy0Y4cbH/LUU+T8+Vx12mn8TdcKPoURXIs+3ALwNXThInTjLfgKL8M3\neEpZNf98663kv/4V6DSYLvglv9i9hqu++U33GRszhvz5z136R5ZtHnvfIkuDKYDs0mBSvS9cGgzQ\nPer41UqDKZZFwbr4QQNMpU0+2ymdc2VbrmK+/oUseyEGmEYOgqutPYh1dROzGpgY+f7iP/yBT91x\nB8/ZfU9+p0sv3tKtiq+PHOn+c9mjB9mvHzfsuy+b+/TnTyv68MdD9uHz3/8+/3bddTz7iw2cUv81\nNj/ySMIy19VN7DBQNBQKxS1jZPmamprYu+fuHGJV/Gp5P167Ry2XH3YYV/Tpz3+VdeOnQ4eS3/kO\n+eyzSduwtvYAlpUNYFXVUIZCoYTbx2uzuroJHDRoeMbHiVXHbAeYhkIh9uo1iGVlA1hbOzrt+zLW\ntqFQiDU1tezVaxBra0endb+nNcA0zvnjlamiooZAP5aX17BXr74EKgnUsLx8YLtr0Xb/9eo1iJWV\nu7G8vDe7dt2twzWL3K6qamhGg0wVrCtYF5ECymcPtHq7U1Nq7ZR5L3zq7RN3n9ZW8r33yL/8xc29\nf9VV5EUX8d0jjuDSLt34OgZyAyr5GcAt/fu7nu1jjuE7Rx3FO7uU8zZU8GZM4o2o41yU8zoczevR\nyFu6lPOOLhWchyN5Fybwnq4VfPeII9y3Tl/6kvvWasQIbu3bl5sAbofxPRgfx368FV/h97pVcdmP\nfuTKlmIb+tX7HLT7L1Z5Uu2tjrd/U1OT12M9LarNMvsmya86ud70tnIl/oZj1zWv5q451tv3wCc6\nXzr1UrCuYF1ECiifAzE16DM1pdZOmQ9GTb19sj1Hd8zjaUcc7WZvevhh/uSAMTwPe/FCnMGLcRO/\ng4N5Gb7By3EtL8P1vBTD+C2cznMxj2filzwN5zN00GHugTktLS4N7PXXeeqXjmFP3E7DlKyuuV8D\nCTNpq1yLVZ6EgzZT2L+srG1gqD8Dqv2r05CIciUuy65rPj5iv/bHS3S+dOoVL1jXAFMREREJhM9Q\njg8qewCHHgoAWHzTXVjySjmArwBoAtAC4BjvdwD4PYBJEa+7oGH3jzDj2GPbHXd9RSX+jQqgMwyA\nldITK4LXop51EfGX0mCCp9TaqaBpMBlu3zHtpP0j7lNNp8hqYG3UcZQGozSYok6DATAEwEMA3gDw\nJoAbAXQDcCaAm/08lw9l3QvA3wCsAvC/ALrF2CblBhYRSSaoA0xLWam1U7r1zaR9/D5HrAGmmQzM\njRzYmWxgbbLy+vEU1sgyBeX+S3WAZjr7ZzPANJd1ahskXFm5GwcN2jvh9YwcPBpvgGmi86Uq58E6\n3HdLywA0ea+7ALgTwHVw308VJFj3/qMwM8b6+wB83fv9NgAXxtgmrUYWCYKg/fEvBLXBLmqLziHR\ndfTrGvsZhCYqV+T66JldUgkQsw0oUyljKtsl2jdegBrv2Km2u5/XOtF/ePy6F7I9TrL/qKVznFj/\nUUv0H7hC/O3MR7A+CcATUeuqAHwE4FsA/gDgca/X/YcR2ywAsBzASwDOi1i/yQv0XwKwBMBhcMlq\nbwE4zttmOIAnATzrLV+IUa6m6GDd+4/FhwC6eK/HA2iOsW9OLoZIrgTta9VCUBvsorboHBJdR7+u\nsZ/pHYnK1X59x7nPy8t3S5h6kW2qRiplTGW7ROcMhUIxUz/izYGearv7ea0TzfFfXt6HZWW9s74X\nsr2nOu7fPgUq/escLxWn43XK9J7KVj6C9UsB3BBj/XMALgGwDkBfABUAVgIY673f1/tZ6a1ve90K\noNH7/UEAYQBdARwM4PmIfbp7v48A8EyM83foWQfQH8CqiNd7AlgZY9+YjTlz5kwC0KJFixYtWrRo\n0aIlrWXmzJlpBet+zgbDJO8vIfkJAJjZgwCOgOsN/46Znehtsydc0L0MwDaSYW/9SgBbSe4ws5fg\netQBoBzALWZ2CIAdAPb1jt8PwKPeNjUAyiPO8U0A76daqVmzZu38vb6+HvX19anuKiIiIiISU0tL\nC1paWpJu52ew/gqAkyNXmFk1gKEAtqN9MG8AaGb1cOkz40luNbPH4XreAeDziO1bAWwDAJKtZtZW\n7u8BeJfk6WbWFcBWb5v1AOq8MjQBGEbyqohyGYA+ZtaFZCvcwNi1sSoVGayLiIiIiPghuhN49uzZ\nMbfr4tcJST4GoIeZnQ4AXvA8F8BdADYDaDCzvmZWCeAEAEsBVAP4xAvUR8LljqejGsB73u9nwKXJ\nRDNETazqfdXwOIBTvFVNcDn1KZk1a1beB8pq0ZLq0tzcjIaGk9DQcBKam5tzvl8Ql85Ul2zr3Nna\nwq/6FFu7JLq+dXUTUFNTi7q6iVm3iV/HSlbmtvWhUCjmOSO3aWpqQk1NLWpqahEKhWIeN9H19KPt\noo+RbN9QKISamlr06jUItbWjE95n2ZTDr2sT63Vt7QEoKxuAqqqhCIVC7craq9cgVFUNTVreUCiE\nXr0GoaxsAGprRyMUCqVV/uhyNDU1Jb1fYh03styVlbuhqmooamsPwqBBw1FWNgBlZT3QpUt/lJUN\nQFNTk69tHWtJuyPYz5PD9VAvxK6pG38Gl6rSBDeQ9E/eez/wti8HsAiuV77t/SO99zZGHHcmgMsi\nXm/0fu4DYAWAFwD8OHKfiG2bEDGgNWJ95NSN90JTN0oJ00DE4lYq1y9XA+yKtb06Sz0S2TVY81eM\nNa91MqkNdE2v7Uqh3eMNqt0133j6A2IzmVs9lWOkMhi04yDTyPnVp2Z1j/kJcXLWfQ3WO9uiYF1K\nRbaPSJbCKpXr51c9O0t7dZZ6JBLrcfeRj3dPJl4bZdN2pdDuserorsV4b0le/47HSG2/dI8R6x6J\nPu6u40T+bDtWdveYn+IF676lwYiIiIiIiM9iRfBa1LMupaUUvtbtzErl+ikNpr3OUo9ElAZTGEqD\nURpM0SwK1qWUNOtJl0WtVK6fX/XsLO3VWeqRSNvTQGtqajMKouK1UTZtVwrtHquOzd4TRXv1GsSq\nqqFpP3E1k3ZL5RipHLdtm7anldbVTWBt7UGsqanloEHDWVU1NON7zC8K1hWsi4jslG2w0RmClVTr\nUKx1TafcuQpc24K7dB43n8/rkk0b5fu+yHd9M90niJ+rbP/DFymX5VawrmBdRIRk9l/jd4Y0gFTr\nUKx1TafcuUoJaW5O/3Hz+bwu2bRRJikd2ch3fTPdJ4ifq2xTqSLlutwK1hWsi4iQzH42i84wG0aq\ndSjWuqZT7lzNjOLeS28GkHxel+zaKP2ZTbKR7/pmuk8QP1fZzigUKdfljhesazYYEREREZGgihXB\na1HPuoh0XkqDCebX9X5SGkx2ZU+2rdJgstteaTCxQWkwCtZFOrNiHQRYKBpgmv+BcEEekJjNIMJQ\nKJTXAaZt77fN6uH3zCK52NcPfg4wjZwJJdl1iRycedRRRyUdqJmonKneN9nUO9b2fgwwjb7vMrn/\nklGwrmBdpNMq1t5PKR2d6R4tZF1y9S1AKUnnG4/2bZbdfORB6tlPV76+WVGwrmBdpNMq1kGAUjo6\n0z1ayLrkajBsKUln4G/7NstuoGaQBrimK18DjOMF6xpgKiIiIiISVLEieC3qWRcpJvp6W4KuM92j\nSoMpbkqDSV+h02DMvSexmBnVPiLFIRwOY+7ceQCAadPOR2NjY4FLJNJeZ7pHC1mXbM7dma5BNsLh\nMKZPvxpvv/0ehg0bgmuumR63LSLbbPDgKjz88FIAwGWXnYUZM2akfd502z/dfXJ1jaOPC8D385gZ\nSFqH9QpG41OwLiIiQQzw8lmmWOdK9fz5CpyCcE0y1Znqkq5Srnss8YL1gqeaBHmB0mBEREpaEFMn\n8lmmWOcKhUIFnUs7iNckU52pLukq5brHA80Go2BdRETSE8QZRPJZpljnivX49nw+Uj6I1yRTnaku\n6SrluscTL1jXbDAiIiIiIgFVVugCiIiIBNW0aedj6dImbNniXldWXolp0+aXTJlineuyyy7BnDlX\nJj1/rsoZxGuSqc5Ul3SVct3TpQGmCWiAqYgERSkMxApqHYNYLg0wdcf96KP3AZShf/9+gbk26SrE\n/ZXonIW+t4IqH2XVAFPlrItIkSqFgVilUEfxl+6ZzCRqN7VpbPlqF2ie9fSpZ11EgmDy5KlYsuR4\nAE3emvloaFiIxYsfKGSxfFUKdRR/6Z7JTKJ2U5vGlq92idezrgGmIiIiIiIBpQGmIiIBVwoDsUqh\njuIv3TOZSdRuatPYCt0uSoNJQGkwIhIUxTQQK1OlUEfxl+6ZzARlgGkxKeQAUwXrCShYF5HOpJD/\nCCsAkGLix/3q9z2vz1BqirmdNBuMZoMRkRJWyFkeNMOEFBM/7le/73l9hlJT7O0EzQaTPvWsi0hn\nUchZHjTDhBQTP+5Xv+95fYZSU+ztpNlgRERERESKjGaDEREpAYWczaDQMymIpMOP+9Xve16fodR0\n1nZSGkwCSoMRkUwFcZCTBphKZxXEwZzpHiPZ9vn+DKVyvlTLlM+yF/PfGg0w1QBTEcmTYh/kJFJM\nOsPnLWh1SKU8qZY5aHULMmiAafrUsy4imSj2QU4ixaQzfN6CVodUypNqmYNWtyDTAFMRERERkSKj\nAaYiIj7rrIOcRIKoM3zeglaHVMqTapmDVrdipDSYBJQGIyKZKuZBTiLFpjN83oJWh2IdYFrM4qXB\nKFhPQMG6iIjkg4IZEVGwngEF6yIikmvhcBhTpjRhy5ZrAbg0gQUL5itgFykxCtYzoGBdRERyTbNl\niAig2WBERERERIqOZoMREREpIM2WISKJqGddRESkgBobG7FggUt9aWhYqHz1DITDYUyePBWTJ09F\nOBwudHFyLtX6Brldgly2aLHKmtfyx3qsqRa3uOYRERGRoCq1x9mnWt8gt0uQyxYtVllDoVBOyu/F\nnR3iUQ0wTUADTEVERIKt1AboplrfILdLkMsWLVZZa2quxscf/wB+l18DTEVEREREikzCAaZmtgPA\ni952awCcTvJf+SiYH8ysBsADAMYB+BXJSyLeGwvgVwAqACwi+Z2CFFJEREQyVmoDdFOtb5DbJchl\nixarrJdddgnmzLkyb+VPmAZjZp+SrPJ+/xWAN0j+KGelyYKZ/Z3k8Kh1PQDUARgFYFRUsL4MwMUk\nl5nZIgA3kWyO2l9pMCIiIgFXak+ATbW+QW6XIJctWqyy5qL8GT0UKSpYvwDAwSS/bWajAdwOoBLA\nWwDOJrnBzFoAPAfgSwB6AjgDwPfhguV7Sf7AzIYDeATAUwC+CGAtgBNIbjWzWgC3ANgNwGYA5wFY\nB2AFgH1JbjezagAvABhBckdEWdeQ3CtOPc4EMLYtWDezQQD+RHJ/7/WpAOpJXhi1n4J1ERGRIlRM\nwaAUhp/3iB/Hyipn3cy6ApgE4CFv1d0ALid5CICVAGZ66wngM5KHwgXzDwH4FlywfqaZ9fW22wfA\nLSRHAdgAYKq3fh6AS0iOA3A5gJ+T/BRAC4BjvW1OBfBAZKCeguiIew8A70S8XuutExERkSIXDocx\nZUoTliw5HkuWHI8pU5oCPz2g5Jef90iu77dkD0WqNLPn4QLZVwEsMbPeAHqTfMrbZj6A+yP2Wej9\nfAnAyyTfBwAzWw1gTwAbAawh+aK33bMAhptZT7ie9vvNdv6notz7eSeAK+CC/zMBnOsdcwaAk71t\nBntlBYClkSkvIiIiUjrmzp2HLVuuRdtsHVu2uHXqXZc2ft4jub7fkgXrW0jWmVklgDCAi+GC80jR\n3fWfeT9bI35ve10WtQ0A7IAb5NkFwCck66ILQfIvZjbczOoBdCX5ird+DoA5wM40mA77xrEWwJCI\n10O8dR3MmjVr5+/19fWor69P8RQiIiIiIrG1tLSgpaUl6XbJgnUAAMktZnYpgD8A+DmAT8zsCJJL\nAZwOl6aSDSP5qZmtMbOTSf7eXPf6wSRXeNvcDeC3AK7K5PiRL0i+a2YbzexwAMvg6nBTrB0jg3UR\nEREJvmKabUQKw897JNNjRXcCz549O+Z2yXLWd+Z6k3wBbhrHU+H6+X9iZisAHIzYATTRMVe8w3Gj\nXp8G4BwzewEujea4iG1+B6AvgHtSPCYAN0sMgLlwOfP/NLOR3lsXwaXXrALwZvRMMCIixaSYHt0t\nkmuNjY1YsMA9qKahYSEWLJjfaVJg9FnvKJM28fMeyfX9VjRPMDWzkwEcR7Ip6cb+nVOzwYhI4LUN\nbnI5k65XpzMFJyLi6LPeUWdqk4ymbgwKM7sZQCOAr5J8M4/nVbAuIoFXTI/uFpHM6bPeUWdqk3jB\neko564WmmV1EREREpBQVRbAuIiLxaTCdSGnQZ72jUmiTokiDKRSlwYhIsdDTGkVKgz7rHXWWNinq\nnPVCUbAuIpK9zvIPqUiQ6XPmj0K2o4L1DChYFxHJTmeaqUEkqPQ580eh21HBegYUrIuIZKczzdQg\nElT6nPmj0O0YL1hP9lAkEREREREpEM0GIyIiOVMKMzWIFJo+Z/4IajsqDSYBpcGIiGRPA99Km65/\nfgS1ndMpVxDqoAGmRUbBuoiISOYKPWBPCiud6697RcF6RhSsi4iIZK7QA/aksNK5/rpXNMBURERE\nRKToaICpiIiI5ERQB+xJfqRz/XWvxKc0mASUBiMiIpKdIAwalMIptgGmhaSc9QwoWBcRERFpr9SD\n6lxRsJ4BBesiIiIiu2jWltxRsJ4BBesiIiIiu2jWltzRbDAiIiIiIkVGs8GIiIiISEo0a0v+KQ0m\nAaXBiIiIiLRXiAGmpTCoVTnrGVCwLiIiIlJYpTKoVcF6BhSsi4iIiBRWqQxq1QBTEREREZEiowGm\nIiIiIhJYpT6oVWkwCSgNRkRERKTwNMBUYlKwLiIiIlJcijWwV7CeAQXrIiIiIsWjmGeOUbCeAQXr\nIiIiIsWjmGeO0WwwIiIiIiJFRrPBiIiIiEin0BlnjlHPuoiIiEhAhcNhTJ48FZMnT0U4HC50cQKv\nsbERCxa41JeGhoUJ89WLpW2Vs56ActZFRESkUIp5sGTQBbFtNcA0AwrWRUREpFCKebBk0AWxbTXA\nVERERESkyGiAqYiIiEgAdcbBkkFRTG2rNJgElAYjIiIihVSsT+MsBkFrW+WsZ0DBuoiIiIjkg3LW\nRfZ+mysAABvTSURBVERERESKjIJ1EREREZGAUrAuIiIiIhJQCtZFRERERAJKwbqIiIiISEApWBcR\nERERCSgF6yIiIiIiAaVgXUREREQkoBIG62a2w8yeN7OVZrbQzHrnq2B+MLMGM1tuZi96P78c8d5Y\nr16rzOxnhSyniIiIiEgsyXrWN5OsI3kQgI8BfDsPZcqImf09xuoPAXyN5MEAmgD8OuK92wCcQ3IE\ngBFmdnTuSykiIiIikrp00mCeBrAHAJjZaDP7q5mtMLMHzayPt77FzG4ws2fM7BUzG+e9/4aZXe1t\nM9zMXjWzeWb2kpmFzazCe6/WzB7xesGfNLP9zKzKzFabWZm3TbX3umtU+RhdYJIvkHzPe/kKgEoz\n62ZmgwBUkVzmvXc3gBPTaAsRERERkZxLKVj3AuNJAB7yVt0N4HKShwBYCWCmt54APiN5KIDbve2/\nBWAUgDPNrK+33T4AbiE5CsAGAFO99fMAXEJyHIDLAfyc5KcAWgAc621zKoAHSO5Is65TATxL8nO4\n/3S8E/HeWm+diIiIiEhglCV5v9LMnocLZF8FsMTLW+9N8ilvm/kA7o/YZ6H38yUAL5N8HwDMbDWA\nPQFsBLCG5Iveds8CGG5mPQF8EcD9ZtZ2rHLv550AroAL/s8EcK53zBkATva2GeyVFQCWkryk7SBm\ndiCAHwNoSFJfEREREZHASBasbyFZZ2aVAMIALoYLziNZ1OvPvJ+tEb+3vS6L2gYAdgCogOvl/4Rk\nXXQhSP7FS5+pB9CV5Cve+jkA5gCAma2Jta+ZDQHwIIDTSa7xVq8FMCRisyHeug5mzZq18/f6+nrU\n19fH2kxEREREJGUtLS1oaWlJul2yYB0AQHKLmV0K4A8Afg7gEzM7guRSAKfDpalkw0h+amZrzOxk\nkr83171+MMkV3jZ3A/gtgKtSPqjLpf8/AFeSfDqiPu+a2UYzOxzAMq8ON8U6RmSwLiIiIiLih+hO\n4NmzZ8fcLlnO+s5BmyRfAPAiXM54E4CfmNkKAAcjdgBNxBj0GX3cqNenATjHzF6AS6M5LmKb3wHo\nC+CeFI8JuG8CagHM9KagfN7M+nvvXQSXXrMKwJskm+McV0RERESkIIyMF08Hi5mdDOA4kk15PCeL\npX1EREREpHiZGUhGp5enlgZTaGZ2M4BGAF8tdFlERERERPKlaHrWC0E96yIiIiKSD/F61tN5KJKI\niIiIiOSRgnURERERkYBSsC4iIiIiElAK1kVEREREAkrBuoiIiIhIQClYFxEREREJKAXrIiIiIiIB\npWBdRERERCSgFKyLiIiIiASUgnURERERkYBSsC4iIiIiElAK1kVEREREAkrBuoiIiIhIQClYFxER\nEREJKAXrIiIiIiIBpWBdRERERCSgFKyLiIiIiASUgnURERERkYBSsC4iIiJFIxwOY/LkqZg8eSrC\n4XChiyOSc0ay0GUILDOj2kdERCQYwuEwpkxpwpYt1wIAKiuvxIIF89HY2Fjgkolkz8xA0jqsVzAa\nn4J1ERGR4Jg8eSqWLDkeQJO3Zj4aGhZi8eIHClksEV/EC9aVBiMiIiIiElBlhS6AiIiISCqmTTsf\nS5c2YcsW97qy8kpMmza/sIUSyTGlwSSgNBgREZFgCYfDmDt3HgAXvCtfXToL5axnQMG6iIiIiOSD\nctZFRERERIqMgnURERERkYBSsC4iIiIiElAK1kVEREREAkrBuoiIiIhIQClYFxEREREJKAXrIiIi\nIiIBpWBdRERERCSgFKyLiIiIiASUgnURERERkYBSsC4iIiIiElAK1kVEREREAkrBuoiIiIhIQClY\nFxEREREJKAXrIiIiIiIBpWBdRERERCSgFKyLiIiIiASUgnURERERkYBSsC4iIiIiElAJg3Uz22Fm\nz5vZSjNbaGa981UwP5jZYV75nzezF8zsxIj3xnr1WmVmPytkOUVEREREYknWs76ZZB3JgwB8DODb\neShTRszs7zFWrwQwlmQdgKMB3GFmbXW+DcA5JEcAGGFmR+enpCIiIiIiqUknDeZpAHsAgJmNNrO/\nmtkKM3vQzPp461vM7AYze8bMXjGzcd77b5jZ1d42w83sVTObZ2YvmVnYzCq892rN7BEzW25mT5rZ\nfmZWZWarzazM26bae901qnyMLjDJLSRbvZc9ALR6xxgEoIrkMu+9uwGcGL2/iIiIiEghpRSse4Hx\nJAAPeavuBnA5yUPgeq9neusJ4DOShwK43dv+WwBGATjTzPp62+0D4BaSowBsADDVWz8PwCUkxwG4\nHMDPSX4KoAXAsd42pwJ4gOSOFMt+mJm9DGAFgAu94H0PAO9EbLbWWyciIiIiEhhlSd6vNLPn4QLZ\nVwEs8fLWe5N8yttmPoD7I/ZZ6P18CcDLJN8HADN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zl16K//hFF9kn+++vspa096eyFpW1qKwlrycl563Mhx+a/eQnZueea7ZuXdDR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\ne6UoSZUSskCEBkWrcBkVyQIVBaVsJzPVVhAKtfs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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4617,15 +5908,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" - ] - } - ], + "outputs": [], "source": [ "trends = []\n", "for i, group in kmeans_groups:\n", @@ -4652,43 +5935,43 @@ { "data": { "text/plain": [ - "[['Wisconsin', 4.5081131780406549],\n", - " ['North Dakota', 4.5081131780406549],\n", - " ['Nebraska', 4.5081131780406549],\n", - " ['Iowa', 4.5081131780406549],\n", - " ['Maine', 4.5081131780406549],\n", - " ['Montana', 4.5081131780406549],\n", - " ['Kansas', 4.5081131780406549],\n", - " ['Oregon', 4.5081131780406549],\n", - " ['South Dakota', 4.5081131780406549],\n", - " ['Utah', 4.5081131780406549],\n", - " ['Washington', 4.636017894647817],\n", - " ['New Hampshire', 4.636017894647817],\n", - " ['New Jersey', 4.636017894647817],\n", - " ['Colorado', 4.636017894647817],\n", - " ['Connecticut', 4.636017894647817],\n", - " ['Virginia', 4.636017894647817],\n", - " ['Massachusetts', 4.636017894647817],\n", - " ['Rhode Island', 4.636017894647817],\n", - " ['Hawaii', 4.636017894647817],\n", - " ['Vermont', 4.636017894647817],\n", - " ['Maryland', 4.636017894647817],\n", - " ['Minnesota', 4.636017894647817],\n", - " ['Illinois', 4.636017894647817],\n", - " ['New Mexico', 4.3030153035887064],\n", - " ['North Carolina', 4.3030153035887064],\n", - " ['Nevada', 4.3030153035887064],\n", - " ['Ohio', 4.3030153035887064],\n", - " ['Pennsylvania', 4.3030153035887064],\n", - " ['Indiana', 4.3030153035887064],\n", - " ['Arizona', 4.3030153035887064],\n", - " ['Missouri', 4.3030153035887064],\n", - " ['Michigan', 4.3030153035887064],\n", - " ['Georgia', 4.3030153035887064],\n", - " ['West Virginia', 4.3030153035887064],\n", - " ['South Carolina', 4.3030153035887064],\n", - " ['Tennessee', 4.3030153035887064],\n", - " ['Mississippi', 4.3030153035887064],\n", + "[['Washington', 4.5126101123209397],\n", + " ['New Hampshire', 4.5126101123209397],\n", + " ['New Jersey', 4.5126101123209397],\n", + " ['Nevada', 4.5126101123209397],\n", + " ['Colorado', 4.5126101123209397],\n", + " ['Connecticut', 4.5126101123209397],\n", + " ['Virginia', 4.5126101123209397],\n", + " ['Massachusetts', 4.5126101123209397],\n", + " ['Rhode Island', 4.5126101123209397],\n", + " ['Hawaii', 4.5126101123209397],\n", + " ['Vermont', 4.5126101123209397],\n", + " ['Maryland', 4.5126101123209397],\n", + " ['Minnesota', 4.5126101123209397],\n", + " ['Illinois', 4.5126101123209397],\n", + " ['North Carolina', 2.990114760057434],\n", + " ['Georgia', 2.990114760057434],\n", + " ['West Virginia', 2.990114760057434],\n", + " ['South Carolina', 2.990114760057434],\n", + " ['Tennessee', 2.990114760057434],\n", + " ['Mississippi', 2.990114760057434],\n", + " ['Wisconsin', 5.689142600619455],\n", + " ['New Mexico', 5.689142600619455],\n", + " ['North Dakota', 5.689142600619455],\n", + " ['Nebraska', 5.689142600619455],\n", + " ['Ohio', 5.689142600619455],\n", + " ['Pennsylvania', 5.689142600619455],\n", + " ['Indiana', 5.689142600619455],\n", + " ['Iowa', 5.689142600619455],\n", + " ['Arizona', 5.689142600619455],\n", + " ['Maine', 5.689142600619455],\n", + " ['Missouri', 5.689142600619455],\n", + " ['Michigan', 5.689142600619455],\n", + " ['Montana', 5.689142600619455],\n", + " ['Kansas', 5.689142600619455],\n", + " ['Oregon', 5.689142600619455],\n", + " ['South Dakota', 5.689142600619455],\n", + " ['Utah', 5.689142600619455],\n", " ['Florida', 3.3877002862540975],\n", " ['California', 3.3877002862540975],\n", " ['New York', 3.3877002862540975],\n", @@ -4773,16 +6056,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", - " if __name__ == '__main__':\n" - ] - } - ], + "outputs": [], "source": [ "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" ] @@ -4812,14 +6086,6 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: irow(i) is deprecated. Please use .iloc[i]\n", - " if __name__ == '__main__':\n" - ] - }, { "data": { "text/plain": [ @@ -4834,10 +6100,10 @@ "poll_date 2012-09-11 00:00:00\n", "Weight 0.65\n", "PIE 1.76\n", - "ESS 173.017\n", - "MESS 173.017\n", - "time_weight 0.615572\n", - "kmeans_labels 1\n", + "ESS 173.0171\n", + "MESS 173.0171\n", + "time_weight 0.6155722\n", + "kmeans_labels 0\n", "pollster_state American Research Group-Colorado\n", "Name: 168, dtype: object" ] @@ -4917,16 +6183,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", - " if __name__ == '__main__':\n" - ] - } - ], + "outputs": [], "source": [ "state_data2012.sort(columns=[\"pollster_state\", \"poll_date\"], inplace=True);" ] @@ -5016,31 +6273,67 @@ "data": { "text/plain": [ "pollster_state\n", - "American Research Group-Colorado 1\n", - "American Research Group-Florida 1\n", - "American Research Group-Iowa 1\n", - "American Research Group-Nevada 1\n", - "American Research Group-New Hampshire 3\n", - "American Research Group-North Carolina 1\n", - "American Research Group-Ohio 1\n", - "American Research Group-Virginia 1\n", - "CNN / Opinion Research-Wisconsin 1\n", - "Chicago Trib. / MarketShares-Illinois 1\n", - "Columbus Dispatch (OH)-Ohio 2\n", - "EPIC-MRA-Michigan 8\n", - " ..\n", - "Suffolk (NH/MA)-Florida 2\n", - "SurveyUSA-California 4\n", - "SurveyUSA-Florida 2\n", - "SurveyUSA-Georgia 4\n", - "SurveyUSA-Kansas 2\n", - "SurveyUSA-Michigan 1\n", - "SurveyUSA-New Jersey 1\n", - "SurveyUSA-New York 1\n", - "SurveyUSA-North Carolina 2\n", - "SurveyUSA-Oregon 4\n", - "SurveyUSA-Pennsylvania 1\n", - "SurveyUSA-Washington 4\n", + "American Research Group-Colorado 1\n", + "American Research Group-Florida 1\n", + "American Research Group-Iowa 1\n", + "American Research Group-Nevada 1\n", + "American Research Group-New Hampshire 3\n", + "American Research Group-North Carolina 1\n", + "American Research Group-Ohio 1\n", + "American Research Group-Virginia 1\n", + "CNN / Opinion Research-Wisconsin 1\n", + "Chicago Trib. / MarketShares-Illinois 1\n", + "Columbus Dispatch (OH)-Ohio 2\n", + "EPIC-MRA-Michigan 8\n", + "Fairleigh-Dickinson (NJ)-New Jersey 3\n", + "Field Poll (CA)-California 6\n", + "Insider Advantage-Georgia 2\n", + "LA Times / Bloomberg-New Hampshire 1\n", + "Marist (NY)-New York 3\n", + "Mason-Dixon-Florida 3\n", + "Mason-Dixon-Georgia 1\n", + "Mason-Dixon-New Hampshire 1\n", + "Mason-Dixon-North Dakota 1\n", + "Mason-Dixon-Utah 1\n", + "Mason-Dixon-Virginia 1\n", + "Mitchell-Michigan 3\n", + "Ohio Poll-Ohio 2\n", + "Public Policy Polling (PPP)-Arizona 7\n", + "Public Policy Polling (PPP)-California 2\n", + "Public Policy Polling (PPP)-Colorado 6\n", + "Public Policy Polling (PPP)-Connecticut 3\n", + "Public Policy Polling (PPP)-Florida 8\n", + " ..\n", + "Rasmussen-Iowa 3\n", + "Rasmussen-Maine 1\n", + "Rasmussen-Massachusetts 4\n", + "Rasmussen-Michigan 2\n", + "Rasmussen-Missouri 7\n", + "Rasmussen-Montana 5\n", + "Rasmussen-Nebraska 2\n", + "Rasmussen-Nevada 3\n", + "Rasmussen-New Hampshire 1\n", + "Rasmussen-New Jersey 1\n", + "Rasmussen-New Mexico 3\n", + "Rasmussen-North Carolina 4\n", + "Rasmussen-North Dakota 1\n", + "Rasmussen-Ohio 7\n", + "Rasmussen-Pennsylvania 4\n", + "Rasmussen-Virginia 5\n", + "Rasmussen-Washington 1\n", + "Rasmussen-Wisconsin 7\n", + "Suffolk (NH/MA)-Florida 2\n", + "SurveyUSA-California 4\n", + "SurveyUSA-Florida 2\n", + "SurveyUSA-Georgia 4\n", + "SurveyUSA-Kansas 2\n", + "SurveyUSA-Michigan 1\n", + "SurveyUSA-New Jersey 1\n", + "SurveyUSA-New York 1\n", + "SurveyUSA-North Carolina 2\n", + "SurveyUSA-Oregon 4\n", + "SurveyUSA-Pennsylvania 1\n", + "SurveyUSA-Washington 4\n", "dtype: int64" ] }, @@ -5154,7 +6447,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 204, "metadata": { "collapsed": false }, @@ -5255,169 +6548,43 @@ " -0.219418\n", " EPIC-MRA\n", " \n", - " \n", - " 10\n", - " EPIC-MRA-Michigan\n", - " 2012-06-04\n", - " Michigan\n", - " 1.427470\n", - " EPIC-MRA\n", - " \n", - " \n", - " 11\n", - " EPIC-MRA-Michigan\n", - " 2012-07-28\n", - " Michigan\n", - " 2.361416\n", - " EPIC-MRA\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 309\n", - " SurveyUSA-Kansas\n", - " 2011-11-10\n", - " Kansas\n", - " -26.128872\n", - " SurveyUSA\n", - " \n", - " \n", - " 310\n", - " SurveyUSA-Kansas\n", - " 2011-11-20\n", - " Kansas\n", - " -6.973400\n", - " SurveyUSA\n", - " \n", - " \n", - " 311\n", - " SurveyUSA-North Carolina\n", - " 2012-04-28\n", - " North Carolina\n", - " 3.435788\n", - " SurveyUSA\n", - " \n", - " \n", - " 312\n", - " SurveyUSA-North Carolina\n", - " 2012-09-30\n", - " North Carolina\n", - " 1.008145\n", - " SurveyUSA\n", - " \n", - " \n", - " 313\n", - " SurveyUSA-Oregon\n", - " 2011-11-20\n", - " Oregon\n", - " 7.001203\n", - " SurveyUSA\n", - " \n", - " \n", - " 314\n", - " SurveyUSA-Oregon\n", - " 2012-03-17\n", - " Oregon\n", - " 10.204604\n", - " SurveyUSA\n", - " \n", - " \n", - " 315\n", - " SurveyUSA-Oregon\n", - " 2012-05-09\n", - " Oregon\n", - " 1.934380\n", - " SurveyUSA\n", - " \n", - " \n", - " 316\n", - " SurveyUSA-Oregon\n", - " 2012-09-12\n", - " Oregon\n", - " 8.172477\n", - " SurveyUSA\n", - " \n", - " \n", - " 317\n", - " SurveyUSA-Washington\n", - " 2011-11-22\n", - " Washington\n", - " 12.315353\n", - " SurveyUSA\n", - " \n", - " \n", - " 318\n", - " SurveyUSA-Washington\n", - " 2012-05-09\n", - " Washington\n", - " 8.655616\n", - " SurveyUSA\n", - " \n", - " \n", - " 319\n", - " SurveyUSA-Washington\n", - " 2012-08-02\n", - " Washington\n", - " 14.386038\n", - " SurveyUSA\n", - " \n", - " \n", - " 320\n", - " SurveyUSA-Washington\n", - " 2012-09-08\n", - " Washington\n", - " 9.553699\n", - " SurveyUSA\n", - " \n", " \n", "\n", - "

321 rows × 5 columns

\n", "" ], "text/plain": [ - " pollster_state poll_date State m Pollster\n", - "0 American Research Group-New Hampshire 2012-03-17 New Hampshire 6.436534 American Research Group\n", - "1 American Research Group-New Hampshire 2012-06-23 New Hampshire 0.071010 American Research Group\n", - "2 American Research Group-New Hampshire 2012-09-26 New Hampshire 4.054884 American Research Group\n", - "3 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio 1.875520 Columbus Dispatch (OH)\n", - "4 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio 7.679307 Columbus Dispatch (OH)\n", - "5 EPIC-MRA-Michigan 2011-02-15 Michigan -4.201071 EPIC-MRA\n", - "6 EPIC-MRA-Michigan 2011-07-10 Michigan -3.096961 EPIC-MRA\n", - "7 EPIC-MRA-Michigan 2011-11-15 Michigan -4.201071 EPIC-MRA\n", - "8 EPIC-MRA-Michigan 2012-01-23 Michigan 6.398112 EPIC-MRA\n", - "9 EPIC-MRA-Michigan 2012-04-02 Michigan -0.219418 EPIC-MRA\n", - "10 EPIC-MRA-Michigan 2012-06-04 Michigan 1.427470 EPIC-MRA\n", - "11 EPIC-MRA-Michigan 2012-07-28 Michigan 2.361416 EPIC-MRA\n", - ".. ... ... ... ... ...\n", - "309 SurveyUSA-Kansas 2011-11-10 Kansas -26.128872 SurveyUSA\n", - "310 SurveyUSA-Kansas 2011-11-20 Kansas -6.973400 SurveyUSA\n", - "311 SurveyUSA-North Carolina 2012-04-28 North Carolina 3.435788 SurveyUSA\n", - "312 SurveyUSA-North Carolina 2012-09-30 North Carolina 1.008145 SurveyUSA\n", - "313 SurveyUSA-Oregon 2011-11-20 Oregon 7.001203 SurveyUSA\n", - "314 SurveyUSA-Oregon 2012-03-17 Oregon 10.204604 SurveyUSA\n", - "315 SurveyUSA-Oregon 2012-05-09 Oregon 1.934380 SurveyUSA\n", - "316 SurveyUSA-Oregon 2012-09-12 Oregon 8.172477 SurveyUSA\n", - "317 SurveyUSA-Washington 2011-11-22 Washington 12.315353 SurveyUSA\n", - "318 SurveyUSA-Washington 2012-05-09 Washington 8.655616 SurveyUSA\n", - "319 SurveyUSA-Washington 2012-08-02 Washington 14.386038 SurveyUSA\n", - "320 SurveyUSA-Washington 2012-09-08 Washington 9.553699 SurveyUSA\n", + " pollster_state poll_date State m \\\n", + "0 American Research Group-New Hampshire 2012-03-17 New Hampshire 6.436534 \n", + "1 American Research Group-New Hampshire 2012-06-23 New Hampshire 0.071010 \n", + "2 American Research Group-New Hampshire 2012-09-26 New Hampshire 4.054884 \n", + "3 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio 1.875520 \n", + "4 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio 7.679307 \n", + "5 EPIC-MRA-Michigan 2011-02-15 Michigan -4.201071 \n", + "6 EPIC-MRA-Michigan 2011-07-10 Michigan -3.096961 \n", + "7 EPIC-MRA-Michigan 2011-11-15 Michigan -4.201071 \n", + "8 EPIC-MRA-Michigan 2012-01-23 Michigan 6.398112 \n", + "9 EPIC-MRA-Michigan 2012-04-02 Michigan -0.219418 \n", "\n", - "[321 rows x 5 columns]" + " Pollster \n", + "0 American Research Group \n", + "1 American Research Group \n", + "2 American Research Group \n", + "3 Columbus Dispatch (OH) \n", + "4 Columbus Dispatch (OH) \n", + "5 EPIC-MRA \n", + "6 EPIC-MRA \n", + "7 EPIC-MRA \n", + "8 EPIC-MRA \n", + "9 EPIC-MRA " ] }, - "execution_count": 133, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m_dataframe" + "m_dataframe.head(10)" ] }, { @@ -5433,7 +6600,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 205, "metadata": { "collapsed": false }, @@ -5492,7 +6659,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 1\n", + " 0\n", " \n", " \n", " 1\n", @@ -5516,7 +6683,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 1\n", + " 0\n", " \n", " \n", " 2\n", @@ -5540,7 +6707,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 1\n", + " 0\n", " \n", " \n", " 3\n", @@ -5564,7 +6731,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 1\n", + " 0\n", " \n", " \n", " 4\n", @@ -5588,7 +6755,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 1\n", + " 0\n", " \n", " \n", " 5\n", @@ -5612,7 +6779,7 @@ " 0.961563\n", " 0.733997\n", " 4\n", - " 1\n", + " 0\n", " \n", " \n", " 6\n", @@ -5635,8 +6802,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 2\n", - " 2\n", + " 0\n", + " 3\n", " \n", " \n", " 7\n", @@ -5659,8 +6826,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 2\n", - " 2\n", + " 0\n", + " 3\n", " \n", " \n", " 8\n", @@ -5683,8 +6850,8 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 2\n", - " 2\n", + " 0\n", + " 3\n", " \n", " \n", " 9\n", @@ -5707,466 +6874,85 @@ " -1\n", " 0.377548\n", " 0.427662\n", - " 2\n", - " 2\n", - " \n", - " \n", - " 10\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2011-03-12\n", - " Ohio\n", - " 4.430867\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 2\n", - " 2\n", - " \n", - " \n", - " 11\n", - " Public Policy Polling (PPP)-Ohio\n", - " 2011-05-21\n", - " Ohio\n", - " 2.960337\n", - " Public Policy Polling (PPP)\n", - " 12.4\n", - " 3.2\n", - " 81.0\n", - " 87.4\n", - " 24.1\n", - " ...\n", - " 1650927.993\n", - " 0.143\n", - " 0.624\n", - " 3.6\n", - " 15.4\n", - " -1\n", - " 0.377548\n", - " 0.427662\n", - " 2\n", - " 2\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 309\n", - " Public Policy Polling (PPP)-Wisconsin\n", - " 2012-09-19\n", - " Wisconsin\n", - " 6.146157\n", - " Public Policy Polling (PPP)\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 310\n", - " Rasmussen-Wisconsin\n", - " 2011-10-26\n", - " Wisconsin\n", - " 3.197725\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 311\n", - " Rasmussen-Wisconsin\n", - " 2012-02-27\n", - " Wisconsin\n", - " 4.357351\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 312\n", - " Rasmussen-Wisconsin\n", - " 2012-03-27\n", - " Wisconsin\n", - " 10.707061\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 313\n", - " Rasmussen-Wisconsin\n", - " 2012-05-09\n", - " Wisconsin\n", - " 2.821784\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 314\n", - " Rasmussen-Wisconsin\n", - " 2012-06-12\n", - " Wisconsin\n", - " -1.913946\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 315\n", - " Rasmussen-Wisconsin\n", - " 2012-07-25\n", - " Wisconsin\n", - " 1.932597\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 316\n", - " Rasmussen-Wisconsin\n", - " 2012-09-17\n", - " Wisconsin\n", - " 1.932597\n", - " Rasmussen\n", - " 6.5\n", - " 6.1\n", - " 83.1\n", - " 89.4\n", - " 25.8\n", - " ...\n", - " 793935.613\n", - " 0.139\n", - " 0.629\n", - " 2.8\n", - " 12.8\n", - " 2\n", - " 0.455410\n", - " 0.237802\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 317\n", - " Rasmussen-Nebraska\n", - " 2012-03-05\n", - " Nebraska\n", - " -19.056922\n", - " Rasmussen\n", - " 4.7\n", - " 9.5\n", - " 81.8\n", - " 90.0\n", - " 27.7\n", - " ...\n", - " 250599.176\n", - " 0.136\n", - " 0.614\n", - " -19.0\n", - " 14.8\n", - " -13\n", - " 0.335630\n", - " 0.351093\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 318\n", - " Rasmussen-Nebraska\n", - " 2012-05-16\n", - " Nebraska\n", - " -20.181630\n", - " Rasmussen\n", - " 4.7\n", - " 9.5\n", - " 81.8\n", - " 90.0\n", - " 27.7\n", - " ...\n", - " 250599.176\n", - " 0.136\n", - " 0.614\n", - " -19.0\n", - " 14.8\n", - " -13\n", - " 0.335630\n", - " 0.351093\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 319\n", - " SurveyUSA-Kansas\n", - " 2011-11-10\n", - " Kansas\n", - " -26.128872\n", - " SurveyUSA\n", - " 6.1\n", - " 10.8\n", - " 77.8\n", - " 89.2\n", - " 29.3\n", - " ...\n", - " 381874.654\n", - " 0.133\n", - " 0.615\n", - " -16.9\n", - " 14.3\n", - " -12\n", - " 0.392000\n", - " 0.469934\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 320\n", - " SurveyUSA-Kansas\n", - " 2011-11-20\n", - " Kansas\n", - " -6.973400\n", - " SurveyUSA\n", - " 6.1\n", - " 10.8\n", - " 77.8\n", - " 89.2\n", - " 29.3\n", - " ...\n", - " 381874.654\n", - " 0.133\n", - " 0.615\n", - " -16.9\n", - " 14.3\n", - " -12\n", - " 0.392000\n", - " 0.469934\n", - " 2\n", - " 0\n", + " 0\n", + " 3\n", " \n", " \n", "\n", - "

321 rows × 24 columns

\n", + "

10 rows × 24 columns

\n", "" ], "text/plain": [ - " pollster_state poll_date State m Pollster \\\n", - "0 American Research Group-New Hampshire 2012-03-17 New Hampshire 6.436534 American Research Group \n", - "1 American Research Group-New Hampshire 2012-06-23 New Hampshire 0.071010 American Research Group \n", - "2 American Research Group-New Hampshire 2012-09-26 New Hampshire 4.054884 American Research Group \n", - "3 Public Policy Polling (PPP)-New Hampshire 2011-04-02 New Hampshire 3.118546 Public Policy Polling (PPP) \n", - "4 Public Policy Polling (PPP)-New Hampshire 2011-07-03 New Hampshire -0.240062 Public Policy Polling (PPP) \n", - "5 Public Policy Polling (PPP)-New Hampshire 2012-05-12 New Hampshire 10.469450 Public Policy Polling (PPP) \n", - "6 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio 1.875520 Columbus Dispatch (OH) \n", - "7 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio 7.679307 Columbus Dispatch (OH) \n", - "8 Ohio Poll-Ohio 2011-09-16 Ohio 4.166959 Ohio Poll \n", - "9 Ohio Poll-Ohio 2012-08-19 Ohio 1.501578 Ohio Poll \n", - "10 Public Policy Polling (PPP)-Ohio 2011-03-12 Ohio 4.430867 Public Policy Polling (PPP) \n", - "11 Public Policy Polling (PPP)-Ohio 2011-05-21 Ohio 2.960337 Public Policy Polling (PPP) \n", - ".. ... ... ... ... ... \n", - "309 Public Policy Polling (PPP)-Wisconsin 2012-09-19 Wisconsin 6.146157 Public Policy Polling (PPP) \n", - "310 Rasmussen-Wisconsin 2011-10-26 Wisconsin 3.197725 Rasmussen \n", - "311 Rasmussen-Wisconsin 2012-02-27 Wisconsin 4.357351 Rasmussen \n", - "312 Rasmussen-Wisconsin 2012-03-27 Wisconsin 10.707061 Rasmussen \n", - "313 Rasmussen-Wisconsin 2012-05-09 Wisconsin 2.821784 Rasmussen \n", - "314 Rasmussen-Wisconsin 2012-06-12 Wisconsin -1.913946 Rasmussen \n", - "315 Rasmussen-Wisconsin 2012-07-25 Wisconsin 1.932597 Rasmussen \n", - "316 Rasmussen-Wisconsin 2012-09-17 Wisconsin 1.932597 Rasmussen \n", - "317 Rasmussen-Nebraska 2012-03-05 Nebraska -19.056922 Rasmussen \n", - "318 Rasmussen-Nebraska 2012-05-16 Nebraska -20.181630 Rasmussen \n", - "319 SurveyUSA-Kansas 2011-11-10 Kansas -26.128872 SurveyUSA \n", - "320 SurveyUSA-Kansas 2011-11-20 Kansas -6.973400 SurveyUSA \n", + " pollster_state poll_date State \\\n", + "0 American Research Group-New Hampshire 2012-03-17 New Hampshire \n", + "1 American Research Group-New Hampshire 2012-06-23 New Hampshire \n", + "2 American Research Group-New Hampshire 2012-09-26 New Hampshire \n", + "3 Public Policy Polling (PPP)-New Hampshire 2011-04-02 New Hampshire \n", + "4 Public Policy Polling (PPP)-New Hampshire 2011-07-03 New Hampshire \n", + "5 Public Policy Polling (PPP)-New Hampshire 2012-05-12 New Hampshire \n", + "6 Columbus Dispatch (OH)-Ohio 2012-08-20 Ohio \n", + "7 Columbus Dispatch (OH)-Ohio 2012-09-24 Ohio \n", + "8 Ohio Poll-Ohio 2011-09-16 Ohio \n", + "9 Ohio Poll-Ohio 2012-08-19 Ohio \n", + "\n", + " m Pollster per_black per_hisp per_white \\\n", + "0 6.436534 American Research Group 1.3 2.9 92.2 \n", + "1 0.071010 American Research Group 1.3 2.9 92.2 \n", + "2 4.054884 American Research Group 1.3 2.9 92.2 \n", + "3 3.118546 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", + "4 -0.240062 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", + "5 10.469450 Public Policy Polling (PPP) 1.3 2.9 92.2 \n", + "6 1.875520 Columbus Dispatch (OH) 12.4 3.2 81.0 \n", + "7 7.679307 Columbus Dispatch (OH) 12.4 3.2 81.0 \n", + "8 4.166959 Ohio Poll 12.4 3.2 81.0 \n", + "9 1.501578 Ohio Poll 12.4 3.2 81.0 \n", "\n", - " per_black per_hisp per_white educ_hs educ_coll ... older_pop per_older per_vote dem_adv \\\n", - "0 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", - "1 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", - "2 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", - "3 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", - "4 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", - "5 1.3 2.9 92.2 90.9 32.9 ... 184547.160 0.140 0.648 -1.5 \n", - "6 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", - "7 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", - "8 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", - "9 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", - "10 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", - "11 12.4 3.2 81.0 87.4 24.1 ... 1650927.993 0.143 0.624 3.6 \n", - ".. ... ... ... ... ... ... ... ... ... ... \n", - "309 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "310 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "311 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "312 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "313 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "314 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "315 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "316 6.5 6.1 83.1 89.4 25.8 ... 793935.613 0.139 0.629 2.8 \n", - "317 4.7 9.5 81.8 90.0 27.7 ... 250599.176 0.136 0.614 -19.0 \n", - "318 4.7 9.5 81.8 90.0 27.7 ... 250599.176 0.136 0.614 -19.0 \n", - "319 6.1 10.8 77.8 89.2 29.3 ... 381874.654 0.133 0.615 -16.9 \n", - "320 6.1 10.8 77.8 89.2 29.3 ... 381874.654 0.133 0.615 -16.9 \n", + " educ_hs educ_coll ... older_pop per_older per_vote \\\n", + "0 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "1 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "2 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "3 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "4 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "5 90.9 32.9 ... 184547.160 0.140 0.648 \n", + "6 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "7 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "8 87.4 24.1 ... 1650927.993 0.143 0.624 \n", + "9 87.4 24.1 ... 1650927.993 0.143 0.624 \n", "\n", - " no_party PVI obama_give romney_give kmeans_group kmeans_labels \n", - "0 13.9 2 0.961563 0.733997 4 1 \n", - "1 13.9 2 0.961563 0.733997 4 1 \n", - "2 13.9 2 0.961563 0.733997 4 1 \n", - "3 13.9 2 0.961563 0.733997 4 1 \n", - "4 13.9 2 0.961563 0.733997 4 1 \n", - "5 13.9 2 0.961563 0.733997 4 1 \n", - "6 15.4 -1 0.377548 0.427662 2 2 \n", - "7 15.4 -1 0.377548 0.427662 2 2 \n", - "8 15.4 -1 0.377548 0.427662 2 2 \n", - "9 15.4 -1 0.377548 0.427662 2 2 \n", - "10 15.4 -1 0.377548 0.427662 2 2 \n", - "11 15.4 -1 0.377548 0.427662 2 2 \n", - ".. ... ... ... ... ... ... \n", - "309 12.8 2 0.455410 0.237802 2 0 \n", - "310 12.8 2 0.455410 0.237802 2 0 \n", - "311 12.8 2 0.455410 0.237802 2 0 \n", - "312 12.8 2 0.455410 0.237802 2 0 \n", - "313 12.8 2 0.455410 0.237802 2 0 \n", - "314 12.8 2 0.455410 0.237802 2 0 \n", - "315 12.8 2 0.455410 0.237802 2 0 \n", - "316 12.8 2 0.455410 0.237802 2 0 \n", - "317 14.8 -13 0.335630 0.351093 2 0 \n", - "318 14.8 -13 0.335630 0.351093 2 0 \n", - "319 14.3 -12 0.392000 0.469934 2 0 \n", - "320 14.3 -12 0.392000 0.469934 2 0 \n", + " dem_adv no_party PVI obama_give romney_give kmeans_group \\\n", + "0 -1.5 13.9 2 0.961563 0.733997 4 \n", + "1 -1.5 13.9 2 0.961563 0.733997 4 \n", + "2 -1.5 13.9 2 0.961563 0.733997 4 \n", + "3 -1.5 13.9 2 0.961563 0.733997 4 \n", + "4 -1.5 13.9 2 0.961563 0.733997 4 \n", + "5 -1.5 13.9 2 0.961563 0.733997 4 \n", + "6 3.6 15.4 -1 0.377548 0.427662 0 \n", + "7 3.6 15.4 -1 0.377548 0.427662 0 \n", + "8 3.6 15.4 -1 0.377548 0.427662 0 \n", + "9 3.6 15.4 -1 0.377548 0.427662 0 \n", "\n", - "[321 rows x 24 columns]" + " kmeans_labels \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 3 \n", + "7 3 \n", + "8 3 \n", + "9 3 \n", + "\n", + "[10 rows x 24 columns]" ] }, - "execution_count": 135, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m_regression_data" + "m_regression_data.head(10)" ] }, { @@ -6309,16 +7095,27 @@ "" ], "text/plain": [ - " PVI per_black per_hisp older_pop average_income romney_give obama_give educ_coll educ_hs\n", - "PVI 1.000000 -0.294799 0.116418 0.150510 0.595083 0.291997 0.669193 0.494291 0.225624\n", - "per_black -0.294799 1.000000 -0.173355 0.278531 -0.064176 0.111333 -0.280984 -0.110643 -0.497133\n", - "per_hisp 0.116418 -0.173355 1.000000 0.403386 0.099982 0.289653 0.307853 0.113554 -0.564734\n", - "older_pop 0.150510 0.278531 0.403386 1.000000 0.023183 0.237119 -0.036660 -0.074438 -0.478205\n", - "average_income 0.595083 -0.064176 0.099982 0.023183 1.000000 0.717860 0.704609 0.888344 0.249691\n", - "romney_give 0.291997 0.111333 0.289653 0.237119 0.717860 1.000000 0.554900 0.630611 -0.024673\n", - "obama_give 0.669193 -0.280984 0.307853 -0.036660 0.704609 0.554900 1.000000 0.835424 0.084808\n", - "educ_coll 0.494291 -0.110643 0.113554 -0.074438 0.888344 0.630611 0.835424 1.000000 0.272766\n", - "educ_hs 0.225624 -0.497133 -0.564734 -0.478205 0.249691 -0.024673 0.084808 0.272766 1.000000" + " PVI per_black per_hisp older_pop average_income \\\n", + "PVI 1.000000 -0.294799 0.116418 0.150510 0.595083 \n", + "per_black -0.294799 1.000000 -0.173355 0.278531 -0.064176 \n", + "per_hisp 0.116418 -0.173355 1.000000 0.403386 0.099982 \n", + "older_pop 0.150510 0.278531 0.403386 1.000000 0.023183 \n", + "average_income 0.595083 -0.064176 0.099982 0.023183 1.000000 \n", + "romney_give 0.291997 0.111333 0.289653 0.237119 0.717860 \n", + "obama_give 0.669193 -0.280984 0.307853 -0.036660 0.704609 \n", + "educ_coll 0.494291 -0.110643 0.113554 -0.074438 0.888344 \n", + "educ_hs 0.225624 -0.497133 -0.564734 -0.478205 0.249691 \n", + "\n", + " romney_give obama_give educ_coll educ_hs \n", + "PVI 0.291997 0.669193 0.494291 0.225624 \n", + "per_black 0.111333 -0.280984 -0.110643 -0.497133 \n", + "per_hisp 0.289653 0.307853 0.113554 -0.564734 \n", + "older_pop 0.237119 -0.036660 -0.074438 -0.478205 \n", + "average_income 0.717860 0.704609 0.888344 0.249691 \n", + "romney_give 1.000000 0.554900 0.630611 -0.024673 \n", + "obama_give 0.554900 1.000000 0.835424 0.084808 \n", + "educ_coll 0.630611 0.835424 1.000000 0.272766 \n", + "educ_hs -0.024673 0.084808 0.272766 1.000000 " ] }, "execution_count": 136, @@ -6353,7 +7150,43 @@ "9 44 days\n", "10 570 days\n", "11 500 days\n", + "12 353 days\n", + "13 332 days\n", + "14 247 days\n", + "15 150 days\n", + "16 101 days\n", + "17 24 days\n", + "18 3 days\n", + "19 445 days\n", + "20 375 days\n", + "21 348 days\n", + "22 333 days\n", + "23 305 days\n", + "24 263 days\n", + "25 235 days\n", + "26 193 days\n", + "27 157 days\n", + "28 150 days\n", + "29 102 days\n", " ... \n", + "291 56 days\n", + "292 40 days\n", + "293 19 days\n", + "294 507 days\n", + "295 227 days\n", + "296 108 days\n", + "297 315 days\n", + "298 146 days\n", + "299 61 days\n", + "300 24 days\n", + "301 254 days\n", + "302 29 days\n", + "303 1 days\n", + "304 584 days\n", + "305 500 days\n", + "306 409 days\n", + "307 220 days\n", + "308 87 days\n", "309 13 days\n", "310 342 days\n", "311 218 days\n", @@ -6414,7 +7247,7 @@ " Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", "\n", "\n", - " Time: 22:40:17 Log-Likelihood: -1457.5\n", + " Time: 22:44:53 Log-Likelihood: -1457.5\n", "\n", "\n", " No. Observations: 321 AIC: 2927.\n", @@ -6476,7 +7309,7 @@ "Model: WLS Adj. R-squared: 0.700\n", "Method: Least Squares F-statistic: 150.4\n", "Date: Tue, 24 May 2016 Prob (F-statistic): 3.09e-81\n", - "Time: 22:40:17 Log-Likelihood: -1457.5\n", + "Time: 22:44:53 Log-Likelihood: -1457.5\n", "No. Observations: 321 AIC: 2927.\n", "Df Residuals: 315 BIC: 2950.\n", "Df Model: 5 \n", @@ -6548,7 +7381,7 @@ "data": { "image/png": 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PtBBCCBFgkwe83gMMOe/UEQ4/XtYDXk0kVVTMI4MNhRBCCJFWf38/N920lrff/hlwufPq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\n8+CPf/xjYAz4FnAP03tn73H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spdFmzVqYc2kvk2vLu1EqzO3yY8Xug3bt7Nxqt+dTO1sn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5YQzsVUM9uJkLHcK3eMZZGOnR+6+6fS70Y4Sq9w93kZ9p5EzAPTiBzuX3iG7F3B3t3A0919XSG9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\ntupS50wVCeWRtBGwk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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -6597,11 +7430,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", + "/anaconda/envs/pydata/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " from ipykernel import kernelapp as app\n" ] } @@ -6632,10 +7465,15 @@ { "data": { "text/plain": [ - "array([-15.4217, -21.6313, -5.0082, -10.561 , 16.1516, 1.9687, 10.9842, 14.074 , 2.0836, -4.6565, 18.1919, -25.1584, 15.7641, -7.8211, 1.3951,\n", - " -17.1674, -14.4888, -9.6044, 6.7223, 17.5189, 18.47 , 8.243 , 2.4987, -8.0447, -3.1677, -11.2639, -19.1112, 5.3443, 0.8928, 8.2712,\n", - " 10.5977, 19.1728, -1.8028, -16.6034, -0.1732, -24.4563, 6.9845, 4.29 , 18.3768, -7.1117, -14.4658, -11.14 , -8.1462, -29.3198, 18.9368,\n", - " -0.9413, 7.9092, -12.3842, 3.4667, -31.8014])" + "array([-15.4217, -21.6313, -5.0082, -10.561 , 16.1516, 1.9687,\n", + " 10.9842, 14.074 , 2.0836, -4.6565, 18.1919, -25.1584,\n", + " 15.7641, -7.8211, 1.3951, -17.1674, -14.4888, -9.6044,\n", + " 6.7223, 17.5189, 18.47 , 8.243 , 2.4987, -8.0447,\n", + " -3.1677, -11.2639, -19.1112, 5.3443, 0.8928, 8.2712,\n", + " 10.5977, 19.1728, -1.8028, -16.6034, -0.1732, -24.4563,\n", + " 6.9845, 4.29 , 18.3768, -7.1117, -14.4658, -11.14 ,\n", + " -8.1462, -29.3198, 18.9368, -0.9413, 7.9092, -12.3842,\n", + " 3.4667, -31.8014])" ] }, "execution_count": 146, @@ -6691,7 +7529,7 @@ "data": { "image/png": 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P80eSVJ9isciVK1eAzpZC+CO3P1RLmu9q5IlijJ+IMf488DVNiUyS+tTytFqV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('Vermont', 1.9907421659897),\n", + " ('Virginia', 1.2108135939642675),\n", + " ('Washington', 1.5580686388761644),\n", + " ('West Virginia', 0.76184689415056239),\n", + " ('Wisconsin', 1.3837649018595504),\n", " ('Wyoming', 0.0)]" ] }, @@ -6828,6 +7666,13 @@ "trends = trends.product(axis=1)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Jim (finally)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -6845,32 +7690,68 @@ { "data": { "text/plain": [ - "State Pollster \n", - "AZ Public Policy Polling (PPP) -9.168494\n", - " Rasmussen -10.209446\n", - "CA Field Poll (CA) 23.343924\n", - " Public Policy Polling (PPP) 20.999075\n", - " Rasmussen 22.000000\n", - " SurveyUSA 22.123414\n", - "CO American Research Group 2.000000\n", - " Public Policy Polling (PPP) 5.469907\n", - " Rasmussen -1.573788\n", - "CT Public Policy Polling (PPP) 12.757757\n", - " Quinnipiac 7.293983\n", - " Rasmussen 8.000000\n", - " ... \n", - "VA Mason-Dixon 1.000000\n", - " Public Policy Polling (PPP) 5.095802\n", - " Quinnipiac 0.578138\n", - " Rasmussen 0.891780\n", - "VT Public Policy Polling (PPP) 20.000000\n", - "WA Public Policy Polling (PPP) 13.050886\n", - " Rasmussen 11.000000\n", - " SurveyUSA 15.310208\n", - "WI CNN / Opinion Research 4.000000\n", - " Public Policy Polling (PPP) 5.392554\n", - " Rasmussen 2.116005\n", - "WV Public Policy Polling (PPP) -19.756631\n", + "State Pollster \n", + "AZ Public Policy Polling (PPP) -9.168494\n", + " Rasmussen -10.209446\n", + "CA Field Poll (CA) 23.343924\n", + " Public Policy Polling (PPP) 20.999075\n", + " Rasmussen 22.000000\n", + " SurveyUSA 22.123414\n", + "CO American Research Group 2.000000\n", + " Public Policy Polling (PPP) 5.469907\n", + " Rasmussen -1.573788\n", + "CT Public Policy Polling (PPP) 12.757757\n", + " Quinnipiac 7.293983\n", + " Rasmussen 8.000000\n", + "FL American Research Group 5.000000\n", + " Mason-Dixon -3.543178\n", + " Public Policy Polling (PPP) 3.125154\n", + " Quinnipiac 3.075653\n", + " Rasmussen 0.882884\n", + " Suffolk (NH/MA) -0.003377\n", + " SurveyUSA 4.168952\n", + "GA Insider Advantage -19.174054\n", + " Mason-Dixon -17.000000\n", + " Public Policy Polling (PPP) -3.000000\n", + " SurveyUSA -7.983856\n", + "HI Public Policy Polling (PPP) 27.000000\n", + "IA American Research Group 7.000000\n", + " Mason-Dixon -3.000000\n", + " Public Policy Polling (PPP) 5.878693\n", + " Rasmussen -2.749416\n", + "IL Chicago Trib. / MarketShares 21.000000\n", + "IN Rasmussen -16.000000\n", + " ... \n", + "OH Ohio Poll 3.000406\n", + " Public Policy Polling (PPP) 4.141640\n", + " Quinnipiac 7.729397\n", + " Rasmussen 0.865613\n", + "OR Public Policy Polling (PPP) 9.130153\n", + " SurveyUSA 8.675504\n", + "PA Public Policy Polling (PPP) 6.160027\n", + " Quinnipiac 6.047221\n", + " Rasmussen 10.874768\n", + " SurveyUSA 0.000000\n", + "RI Public Policy Polling (PPP) 17.000000\n", + "SC Public Policy Polling (PPP) -14.558484\n", + "SD Public Policy Polling (PPP) -6.000000\n", + "TN Public Policy Polling (PPP) -7.000000\n", + "TX Public Policy Polling (PPP) -6.998595\n", + "UT Mason-Dixon -51.000000\n", + " Public Policy Polling (PPP) -32.000000\n", + "VA American Research Group 2.000000\n", + " Mason-Dixon 1.000000\n", + " Public Policy Polling (PPP) 5.095802\n", + " Quinnipiac 0.578138\n", + " Rasmussen 0.891780\n", + "VT Public Policy Polling (PPP) 20.000000\n", + "WA Public Policy Polling (PPP) 13.050886\n", + " Rasmussen 11.000000\n", + " SurveyUSA 15.310208\n", + "WI CNN / Opinion Research 4.000000\n", + " Public Policy Polling (PPP) 5.392554\n", + " Rasmussen 2.116005\n", + "WV Public Policy Polling (PPP) -19.756631\n", "Name: poll, dtype: float64" ] }, @@ -6907,27 +7788,43 @@ "data": { "text/plain": [ "State\n", - "Wisconsin 6.238169\n", - "North Dakota 2.688194\n", - "Nebraska 2.244621\n", - "Iowa 5.871751\n", - "Maine 6.814014\n", - "Montana 3.632644\n", - "Kansas 2.588450\n", - "Oregon 6.860391\n", - "South Dakota 3.066288\n", - "Utah 0.438951\n", - "Washington 7.223234\n", - "New Hampshire 5.946964\n", - " ... \n", - "Arizona 4.523532\n", - "Missouri 4.834265\n", - "Michigan 6.760751\n", - "Georgia 4.582913\n", - "West Virginia 3.278239\n", - "South Carolina 4.168397\n", - "Tennessee 3.488289\n", - "Mississippi 4.010877\n", + "Washington 7.030956\n", + "New Hampshire 5.788660\n", + "New Jersey 7.095052\n", + "Nevada 6.576822\n", + "Colorado 5.979155\n", + "Connecticut 7.575398\n", + "Virginia 5.463930\n", + "Massachusetts 8.900798\n", + "Rhode Island 8.884297\n", + "Hawaii 8.851564\n", + "Vermont 8.983443\n", + "Maryland 8.732396\n", + "Minnesota 6.073006\n", + "Illinois 8.421703\n", + "North Carolina 3.519400\n", + "Georgia 3.184612\n", + "West Virginia 2.278010\n", + "South Carolina 2.896570\n", + "Tennessee 2.423971\n", + "Mississippi 2.787112\n", + "Wisconsin 7.872436\n", + "New Mexico 9.464191\n", + "North Dakota 3.392443\n", + "Nebraska 2.832664\n", + "Ohio 7.059944\n", + "Pennsylvania 8.056202\n", + "Indiana 5.352802\n", + "Iowa 7.410025\n", + "Arizona 5.980694\n", + "Maine 8.599141\n", + "Missouri 6.391523\n", + "Michigan 8.938587\n", + "Montana 4.584319\n", + "Kansas 3.266569\n", + "Oregon 8.657667\n", + "South Dakota 3.869590\n", + "Utah 0.553946\n", "Florida 4.503946\n", "California 6.373831\n", "New York 6.775401\n", @@ -6959,7 +7856,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 206, "metadata": { "collapsed": false }, @@ -6980,260 +7877,117 @@ " \n", " \n", " 0\n", - " Wisconsin\n", - " 6.238169\n", + " Washington\n", + " 7.030956\n", " National\n", " \n", " \n", " 1\n", - " North Dakota\n", - " 2.688194\n", + " New Hampshire\n", + " 5.788660\n", " National\n", " \n", " \n", " 2\n", - " Nebraska\n", - " 2.244621\n", + " New Jersey\n", + " 7.095052\n", " National\n", " \n", " \n", " 3\n", - " Iowa\n", - " 5.871751\n", + " Nevada\n", + " 6.576822\n", " National\n", " \n", " \n", " 4\n", - " Maine\n", - " 6.814014\n", + " Colorado\n", + " 5.979155\n", " National\n", " \n", - " \n", - " 5\n", - " Montana\n", - " 3.632644\n", - " National\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " State poll Pollster\n", + "0 Washington 7.030956 National\n", + "1 New Hampshire 5.788660 National\n", + "2 New Jersey 7.095052 National\n", + "3 Nevada 6.576822 National\n", + "4 Colorado 5.979155 National" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trends.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "polls = pandas.concat((state_polls, trends))" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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7Oregon6.860391National1American Research Group0.651.76
8South Dakota3.066288National2CBS / New York Times0.661.84
9Utah0.438951National3Chicago Trib. / MarketShares1.161.13
10Washington7.223234National
11New Hampshire5.946964National
............
29Arizona4.523532National
30Missouri4.834265National
31Michigan6.760751National
32Georgia4.582913National
33West Virginia3.278239National
34South Carolina4.168397National
35Tennessee3.488289National
36Mississippi4.010877National
37Florida4.503946National
38California6.373831National
39New York6.775401National
40Texas3.144208National
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1American Research Group0.651.76
2CBS / New York Times0.661.84
3Chicago Trib. / MarketShares1.161.13
4CNN / Opinion Research0.771.594CNN / Opinion Research0.771.59
50.791.60
10Franklin Pierce (NH)0.741.60
11Insider Advantage0.951.29
............
20Quinnipiac0.951.34
21Rasmussen1.300.88
22Research 20001.011.20
23Selzer1.470.92
24Star Tribune (MN)0.812.01
25Strategic Vision0.951.45
26Suffolk (NH/MA)0.771.37
27SurveyUSA1.910.72
28Univ. New Hampshire1.081.26
29USA Today / Gallup0.632.01
30Zogby0.641.72
31Zogby Interactive0.434.74
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New Hampshire 1.08 1.26\n", - "29 USA Today / Gallup 0.63 2.01\n", - "30 Zogby 0.64 1.72\n", - "31 Zogby Interactive 0.43 4.74\n", - "\n", - "[32 rows x 3 columns]" + " Pollster Weight PIE\n", + "0 ABC / Washington Post 0.95 1.41\n", + "1 American Research Group 0.65 1.76\n", + "2 CBS / New York Times 0.66 1.84\n", + "3 Chicago Trib. / MarketShares 1.16 1.13\n", + "4 CNN / Opinion Research 0.77 1.59\n", + "5 Columbus Dispatch (OH) 0.50 6.76\n", + "6 EPIC-MRA 0.75 1.65\n", + "7 Fairleigh-Dickinson (NJ) 0.71 1.72\n", + "8 Field Poll (CA) 1.33 0.88\n", + "9 Fox / Opinion Dynamics 0.79 1.60" ] }, - "execution_count": 162, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "weights" + "weights.head(10)" ] }, { @@ -7430,16 +8076,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", - " if __name__ == '__main__':\n" - ] - } - ], + "outputs": [], "source": [ "polls = polls.sort(\"State\")" ] @@ -7593,31 +8230,47 @@ "data": { "text/plain": [ "State\n", - "Arizona -5.768496\n", + "Arizona -5.362448\n", "California 19.966475\n", - "Colorado 2.705829\n", - "Connecticut 8.980923\n", + "Colorado 2.667843\n", + "Connecticut 8.936227\n", "Florida 2.170963\n", - "Georgia -8.644088\n", - "Hawaii 18.697047\n", - "Illinois 15.578978\n", - "Indiana -7.756370\n", - "Iowa 1.832903\n", - "Kansas -9.926964\n", - "Maine 11.856359\n", - " ... \n", - "Pennsylvania 5.211292\n", - "Rhode Island 13.349511\n", - "South Carolina -5.875070\n", - "South Dakota -1.796079\n", - "Tennessee -2.136715\n", + "Georgia -8.858592\n", + "Hawaii 18.584803\n", + "Illinois 15.477867\n", + "Indiana -7.220115\n", + "Iowa 2.111760\n", + "Kansas -9.708495\n", + "Maine 12.353797\n", + "Maryland 16.384295\n", + "Massachusetts 14.174631\n", + "Michigan 8.393170\n", + "Minnesota 6.749915\n", + "Mississippi -8.346582\n", + "Missouri -2.152930\n", + "Montana -7.188138\n", + "Nebraska -8.789196\n", + "Nevada 5.093130\n", + "New Hampshire -1.547056\n", + "New Jersey 10.641219\n", + "New Mexico 10.709465\n", + "New York 23.473550\n", + "North Carolina -0.466602\n", + "North Dakota -9.287194\n", + "Ohio 4.229394\n", + "Oregon 8.794742\n", + "Pennsylvania 5.502556\n", + "Rhode Island 13.236853\n", + "South Carolina -6.464800\n", + "South Dakota -1.423598\n", + "Tennessee -2.630226\n", "Texas -2.295507\n", - "Utah -29.204379\n", - "Vermont 15.005680\n", - "Virginia 2.443012\n", - "Washington 12.346282\n", - "West Virginia -9.075658\n", - "Wisconsin 4.259403\n", + "Utah -29.170239\n", + "Vermont 14.891764\n", + "Virginia 2.420244\n", + "Washington 12.312505\n", + "West Virginia -9.539451\n", + "Wisconsin 4.627744\n", "Name: poll, dtype: float64" ] }, @@ -7673,16 +8326,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jim/INSTALL/anaconda3/envs/pydata538/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", - " if __name__ == '__main__':\n" - ] - } - ], + "outputs": [], "source": [ "electoral_votes.sort(\"State\", inplace=True)\n", "electoral_votes.reset_index(drop=True, inplace=True)" @@ -7738,130 +8382,312 @@ " romney\n", " \n", " \n", - " State\n", - " \n", - " \n", - " \n", - " \n", + " State\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " Alabama\n", + " 9\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Alaska\n", + " 3\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Arizona\n", + " 11\n", + " -5.362448\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Arkansas\n", + " 6\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " California\n", + " 55\n", + " 19.966475\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Colorado\n", + " 9\n", + " 2.667843\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Connecticut\n", + " 7\n", + " 8.936227\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Delaware\n", + " 3\n", + " NaN\n", + " 1\n", + " 0\n", + " \n", + " \n", + " District of Columbia\n", + " 3\n", + " NaN\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Florida\n", + " 29\n", + " 2.170963\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Georgia\n", + " 16\n", + " -8.858592\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Hawaii\n", + " 4\n", + " 18.584803\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Idaho\n", + " 4\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Illinois\n", + " 20\n", + " 15.477867\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Indiana\n", + " 11\n", + " -7.220115\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Iowa\n", + " 6\n", + " 2.111760\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Kansas\n", + " 6\n", + " -9.708495\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Kentucky\n", + " 8\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Louisiana\n", + " 8\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Maine\n", + " 4\n", + " 12.353797\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Maryland\n", + " 10\n", + " 16.384295\n", + " 1\n", + " 0\n", " \n", - " \n", - " \n", " \n", - " Alabama\n", - " 9\n", - " NaN\n", + " Massachusetts\n", + " 11\n", + " 14.174631\n", + " 1\n", " 0\n", + " \n", + " \n", + " Michigan\n", + " 16\n", + " 8.393170\n", " 1\n", + " 0\n", " \n", " \n", - " Alaska\n", + " Minnesota\n", + " 10\n", + " 6.749915\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Mississippi\n", + " 6\n", + " -8.346582\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Missouri\n", + " 10\n", + " -2.152930\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Montana\n", " 3\n", - " NaN\n", + " -7.188138\n", " 0\n", " 1\n", " \n", " \n", - " Arizona\n", - " 11\n", - " -5.768496\n", + " Nebraska\n", + " 5\n", + " -8.789196\n", " 0\n", " 1\n", " \n", " \n", - " Arkansas\n", + " Nevada\n", " 6\n", - " NaN\n", + " 5.093130\n", + " 1\n", + " 0\n", + " \n", + " \n", + " New Hampshire\n", + " 4\n", + " -1.547056\n", " 0\n", " 1\n", " \n", " \n", - " California\n", - " 55\n", - " 19.966475\n", + " New Jersey\n", + " 14\n", + " 10.641219\n", " 1\n", " 0\n", " \n", " \n", - " Colorado\n", - " 9\n", - " 2.705829\n", + " New Mexico\n", + " 5\n", + " 10.709465\n", " 1\n", " 0\n", " \n", " \n", - " Connecticut\n", - " 7\n", - " 8.980923\n", + " New York\n", + " 29\n", + " 23.473550\n", " 1\n", " 0\n", " \n", " \n", - " Delaware\n", - " 3\n", - " NaN\n", - " 1\n", + " North Carolina\n", + " 15\n", + " -0.466602\n", " 0\n", + " 1\n", " \n", " \n", - " District of Columbia\n", + " North Dakota\n", " 3\n", - " NaN\n", - " 1\n", + " -9.287194\n", " 0\n", + " 1\n", " \n", " \n", - " Florida\n", - " 29\n", - " 2.170963\n", + " Ohio\n", + " 18\n", + " 4.229394\n", " 1\n", " 0\n", " \n", " \n", - " Georgia\n", - " 16\n", - " -8.644088\n", + " Oklahoma\n", + " 7\n", + " NaN\n", " 0\n", " 1\n", " \n", " \n", - " Hawaii\n", - " 4\n", - " 18.697047\n", + " Oregon\n", + " 7\n", + " 8.794742\n", " 1\n", " 0\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " Pennsylvania\n", + " 20\n", + " 5.502556\n", + " 1\n", + " 0\n", " \n", " \n", " Rhode Island\n", " 4\n", - " 13.349511\n", + " 13.236853\n", " 1\n", " 0\n", " \n", " \n", " South Carolina\n", " 9\n", - " -5.875070\n", + " -6.464800\n", " 0\n", " 1\n", " \n", " \n", " South Dakota\n", " 3\n", - " -1.796079\n", + " -1.423598\n", " 0\n", " 1\n", " \n", " \n", " Tennessee\n", " 11\n", - " -2.136715\n", + " -2.630226\n", " 0\n", " 1\n", " \n", @@ -7875,42 +8701,42 @@ " \n", " Utah\n", " 6\n", - " -29.204379\n", + " -29.170239\n", " 0\n", " 1\n", " \n", " \n", " Vermont\n", " 3\n", - " 15.005680\n", + " 14.891764\n", " 1\n", " 0\n", " \n", " \n", " Virginia\n", " 13\n", - " 2.443012\n", + " 2.420244\n", " 1\n", " 0\n", " \n", " \n", " Washington\n", " 12\n", - " 12.346282\n", + " 12.312505\n", " 1\n", " 0\n", " \n", " \n", " West Virginia\n", " 5\n", - " -9.075658\n", + " -9.539451\n", " 0\n", " 1\n", " \n", " \n", " Wisconsin\n", " 10\n", - " 4.259403\n", + " 4.627744\n", " 1\n", " 0\n", " \n", @@ -7923,7 +8749,6 @@ " \n", " \n", "\n", - "

51 rows × 4 columns

\n", "" ], "text/plain": [ @@ -7931,31 +8756,55 @@ "State \n", "Alabama 9 NaN 0 1\n", "Alaska 3 NaN 0 1\n", - "Arizona 11 -5.768496 0 1\n", + "Arizona 11 -5.362448 0 1\n", "Arkansas 6 NaN 0 1\n", "California 55 19.966475 1 0\n", - "Colorado 9 2.705829 1 0\n", - "Connecticut 7 8.980923 1 0\n", + "Colorado 9 2.667843 1 0\n", + "Connecticut 7 8.936227 1 0\n", "Delaware 3 NaN 1 0\n", "District of Columbia 3 NaN 1 0\n", "Florida 29 2.170963 1 0\n", - "Georgia 16 -8.644088 0 1\n", - "Hawaii 4 18.697047 1 0\n", - "... ... ... ... ...\n", - "Rhode Island 4 13.349511 1 0\n", - "South Carolina 9 -5.875070 0 1\n", - "South Dakota 3 -1.796079 0 1\n", - "Tennessee 11 -2.136715 0 1\n", + "Georgia 16 -8.858592 0 1\n", + "Hawaii 4 18.584803 1 0\n", + "Idaho 4 NaN 0 1\n", + "Illinois 20 15.477867 1 0\n", + "Indiana 11 -7.220115 0 1\n", + "Iowa 6 2.111760 1 0\n", + "Kansas 6 -9.708495 0 1\n", + "Kentucky 8 NaN 0 1\n", + "Louisiana 8 NaN 0 1\n", + "Maine 4 12.353797 1 0\n", + "Maryland 10 16.384295 1 0\n", + "Massachusetts 11 14.174631 1 0\n", + "Michigan 16 8.393170 1 0\n", + "Minnesota 10 6.749915 1 0\n", + "Mississippi 6 -8.346582 0 1\n", + "Missouri 10 -2.152930 0 1\n", + "Montana 3 -7.188138 0 1\n", + "Nebraska 5 -8.789196 0 1\n", + "Nevada 6 5.093130 1 0\n", + "New Hampshire 4 -1.547056 0 1\n", + "New Jersey 14 10.641219 1 0\n", + "New Mexico 5 10.709465 1 0\n", + "New York 29 23.473550 1 0\n", + "North Carolina 15 -0.466602 0 1\n", + "North Dakota 3 -9.287194 0 1\n", + "Ohio 18 4.229394 1 0\n", + "Oklahoma 7 NaN 0 1\n", + "Oregon 7 8.794742 1 0\n", + "Pennsylvania 20 5.502556 1 0\n", + "Rhode Island 4 13.236853 1 0\n", + "South Carolina 9 -6.464800 0 1\n", + "South Dakota 3 -1.423598 0 1\n", + "Tennessee 11 -2.630226 0 1\n", "Texas 38 -2.295507 0 1\n", - "Utah 6 -29.204379 0 1\n", - "Vermont 3 15.005680 1 0\n", - "Virginia 13 2.443012 1 0\n", - "Washington 12 12.346282 1 0\n", - "West Virginia 5 -9.075658 0 1\n", - "Wisconsin 10 4.259403 1 0\n", - "Wyoming 3 NaN 0 1\n", - "\n", - "[51 rows x 4 columns]" + "Utah 6 -29.170239 0 1\n", + "Vermont 3 14.891764 1 0\n", + "Virginia 13 2.420244 1 0\n", + "Washington 12 12.312505 1 0\n", + "West Virginia 5 -9.539451 0 1\n", + "Wisconsin 10 4.627744 1 0\n", + "Wyoming 3 NaN 0 1" ] }, "execution_count": 176, @@ -8057,7 +8906,7 @@ " \n", " Arizona\n", " 11\n", - " -5.768496\n", + " -5.362448\n", " 0\n", " 1\n", " \n", @@ -8078,14 +8927,14 @@ " \n", " Colorado\n", " 9\n", - " 2.705829\n", + " 2.667843\n", " 1\n", " 0\n", " \n", " \n", " Connecticut\n", " 7\n", - " 8.980923\n", + " 8.936227\n", " 1\n", " 0\n", " \n", @@ -8113,49 +8962,231 @@ " \n", " Georgia\n", " 16\n", - " -8.644088\n", + " -8.858592\n", " 0\n", " 1\n", " \n", " \n", " Hawaii\n", " 4\n", - " 18.697047\n", + " 18.584803\n", " 1\n", " 0\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " Idaho\n", + " 4\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Illinois\n", + " 20\n", + " 15.477867\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Indiana\n", + " 11\n", + " -7.220115\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Iowa\n", + " 6\n", + " 2.111760\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Kansas\n", + " 6\n", + " -9.708495\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Kentucky\n", + " 8\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Louisiana\n", + " 8\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Maine\n", + " 4\n", + " 12.353797\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Maryland\n", + " 10\n", + " 16.384295\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Massachusetts\n", + " 11\n", + " 14.174631\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Michigan\n", + " 16\n", + " 8.393170\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Minnesota\n", + " 10\n", + " 6.749915\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Mississippi\n", + " 6\n", + " -8.346582\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Missouri\n", + " 10\n", + " -2.152930\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Montana\n", + " 3\n", + " -7.188138\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Nebraska\n", + " 5\n", + " -8.789196\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Nevada\n", + " 6\n", + " 5.093130\n", + " 1\n", + " 0\n", + " \n", + " \n", + " New Hampshire\n", + " 4\n", + " -1.547056\n", + " 0\n", + " 1\n", + " \n", + " \n", + " New Jersey\n", + " 14\n", + " 10.641219\n", + " 1\n", + " 0\n", + " \n", + " \n", + " New Mexico\n", + " 5\n", + " 10.709465\n", + " 1\n", + " 0\n", + " \n", + " \n", + " New York\n", + " 29\n", + " 23.473550\n", + " 1\n", + " 0\n", + " \n", + " \n", + " North Carolina\n", + " 15\n", + " -0.466602\n", + " 0\n", + " 1\n", + " \n", + " \n", + " North Dakota\n", + " 3\n", + " -9.287194\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Ohio\n", + " 18\n", + " 4.229394\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Oklahoma\n", + " 7\n", + " NaN\n", + " 0\n", + " 1\n", + " \n", + " \n", + " Oregon\n", + " 7\n", + " 8.794742\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Pennsylvania\n", + " 20\n", + " 5.502556\n", + " 1\n", + " 0\n", " \n", " \n", " Rhode Island\n", " 4\n", - " 13.349511\n", + " 13.236853\n", " 1\n", " 0\n", " \n", " \n", " South Carolina\n", " 9\n", - " -5.875070\n", + " -6.464800\n", " 0\n", " 1\n", " \n", " \n", " South Dakota\n", " 3\n", - " -1.796079\n", + " -1.423598\n", " 0\n", " 1\n", " \n", " \n", " Tennessee\n", " 11\n", - " -2.136715\n", + " -2.630226\n", " 0\n", " 1\n", " \n", @@ -8169,42 +9200,42 @@ " \n", " Utah\n", " 6\n", - " -29.204379\n", + " -29.170239\n", " 0\n", " 1\n", " \n", " \n", " Vermont\n", " 3\n", - " 15.005680\n", + " 14.891764\n", " 1\n", " 0\n", " \n", " \n", " Virginia\n", " 13\n", - " 2.443012\n", + " 2.420244\n", " 1\n", " 0\n", " \n", " \n", " Washington\n", " 12\n", - " 12.346282\n", + " 12.312505\n", " 1\n", " 0\n", " \n", " \n", " West Virginia\n", " 5\n", - " -9.075658\n", + " -9.539451\n", " 0\n", " 1\n", " \n", " \n", " Wisconsin\n", " 10\n", - " 4.259403\n", + " 4.627744\n", " 1\n", " 0\n", " \n", @@ -8217,7 +9248,6 @@ " \n", " \n", "\n", - "

51 rows × 4 columns

\n", "" ], "text/plain": [ @@ -8225,31 +9255,55 @@ "State \n", "Alabama 9 NaN 0 1\n", "Alaska 3 NaN 0 1\n", - "Arizona 11 -5.768496 0 1\n", + "Arizona 11 -5.362448 0 1\n", "Arkansas 6 NaN 0 1\n", "California 55 19.966475 1 0\n", - "Colorado 9 2.705829 1 0\n", - "Connecticut 7 8.980923 1 0\n", + "Colorado 9 2.667843 1 0\n", + "Connecticut 7 8.936227 1 0\n", "Delaware 3 NaN 1 0\n", "District of Columbia 3 NaN 1 0\n", "Florida 29 2.170963 1 0\n", - "Georgia 16 -8.644088 0 1\n", - "Hawaii 4 18.697047 1 0\n", - "... ... ... ... ...\n", - "Rhode Island 4 13.349511 1 0\n", - "South Carolina 9 -5.875070 0 1\n", - "South Dakota 3 -1.796079 0 1\n", - "Tennessee 11 -2.136715 0 1\n", + "Georgia 16 -8.858592 0 1\n", + "Hawaii 4 18.584803 1 0\n", + "Idaho 4 NaN 0 1\n", + "Illinois 20 15.477867 1 0\n", + "Indiana 11 -7.220115 0 1\n", + "Iowa 6 2.111760 1 0\n", + "Kansas 6 -9.708495 0 1\n", + "Kentucky 8 NaN 0 1\n", + "Louisiana 8 NaN 0 1\n", + "Maine 4 12.353797 1 0\n", + "Maryland 10 16.384295 1 0\n", + "Massachusetts 11 14.174631 1 0\n", + "Michigan 16 8.393170 1 0\n", + "Minnesota 10 6.749915 1 0\n", + "Mississippi 6 -8.346582 0 1\n", + "Missouri 10 -2.152930 0 1\n", + "Montana 3 -7.188138 0 1\n", + "Nebraska 5 -8.789196 0 1\n", + "Nevada 6 5.093130 1 0\n", + "New Hampshire 4 -1.547056 0 1\n", + "New Jersey 14 10.641219 1 0\n", + "New Mexico 5 10.709465 1 0\n", + "New York 29 23.473550 1 0\n", + "North Carolina 15 -0.466602 0 1\n", + "North Dakota 3 -9.287194 0 1\n", + "Ohio 18 4.229394 1 0\n", + "Oklahoma 7 NaN 0 1\n", + "Oregon 7 8.794742 1 0\n", + "Pennsylvania 20 5.502556 1 0\n", + "Rhode Island 4 13.236853 1 0\n", + "South Carolina 9 -6.464800 0 1\n", + "South Dakota 3 -1.423598 0 1\n", + "Tennessee 11 -2.630226 0 1\n", "Texas 38 -2.295507 0 1\n", - "Utah 6 -29.204379 0 1\n", - "Vermont 3 15.005680 1 0\n", - "Virginia 13 2.443012 1 0\n", - "Washington 12 12.346282 1 0\n", - "West Virginia 5 -9.075658 0 1\n", - "Wisconsin 10 4.259403 1 0\n", - "Wyoming 3 NaN 0 1\n", - "\n", - "[51 rows x 4 columns]" + "Utah 6 -29.170239 0 1\n", + "Vermont 3 14.891764 1 0\n", + "Virginia 13 2.420244 1 0\n", + "Washington 12 12.312505 1 0\n", + "West Virginia 5 -9.539451 0 1\n", + "Wisconsin 10 4.627744 1 0\n", + "Wyoming 3 NaN 0 1" ] }, "execution_count": 179, @@ -8512,17 +9566,29 @@ "" ], "text/plain": [ - " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", - "0 4.873036 -1.835269 -1.584515 -3.218906 2.596223 -6.904616 5.234435 -2.283621\n", - "1 0.957117 -0.748111 4.386324 -6.180422 -0.967252 -1.152163 3.401308 -3.299674\n", - "2 -0.517285 -2.633575 0.126641 1.748446 -3.301858 3.434171 2.704772 1.507483\n", - "3 2.702568 -2.051184 -0.368671 -2.807308 -0.803664 1.591066 -2.074982 -1.190261\n", - "4 -2.061518 -2.535617 -2.013738 -0.037994 -3.351931 0.703247 4.979407 2.226132\n", - "5 -0.575507 -2.662887 -2.241475 5.077364 0.152423 -1.910987 0.572746 6.300765\n", - "6 0.360477 1.851609 0.900511 -1.056750 -3.427555 -1.048028 -0.626683 1.759870\n", - "7 2.516950 2.793306 0.856762 2.655423 -2.263194 3.758604 1.538789 -0.894279\n", - "8 1.465554 -0.226715 3.394888 4.559450 6.556726 -4.189489 -4.332341 -1.513398\n", - "9 0.480111 2.628507 0.946905 -6.066604 -0.918612 2.483924 0.690284 2.286034" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia \\\n", + "0 4.873036 -1.835269 -1.584515 -3.218906 2.596223 -6.904616 5.234435 \n", + "1 0.957117 -0.748111 4.386324 -6.180422 -0.967252 -1.152163 3.401308 \n", + "2 -0.517285 -2.633575 0.126641 1.748446 -3.301858 3.434171 2.704772 \n", + "3 2.702568 -2.051184 -0.368671 -2.807308 -0.803664 1.591066 -2.074982 \n", + "4 -2.061518 -2.535617 -2.013738 -0.037994 -3.351931 0.703247 4.979407 \n", + "5 -0.575507 -2.662887 -2.241475 5.077364 0.152423 -1.910987 0.572746 \n", + "6 0.360477 1.851609 0.900511 -1.056750 -3.427555 -1.048028 -0.626683 \n", + "7 2.516950 2.793306 0.856762 2.655423 -2.263194 3.758604 1.538789 \n", + "8 1.465554 -0.226715 3.394888 4.559450 6.556726 -4.189489 -4.332341 \n", + "9 0.480111 2.628507 0.946905 -6.066604 -0.918612 2.483924 0.690284 \n", + "\n", + " Wisconsin \n", + "0 -2.283621 \n", + "1 -3.299674 \n", + "2 1.507483 \n", + "3 -1.190261 \n", + "4 2.226132 \n", + "5 6.300765 \n", + "6 1.759870 \n", + "7 -0.894279 \n", + "8 -1.513398 \n", + "9 2.286034 " ] }, "execution_count": 182, @@ -8676,17 +9742,29 @@ "" ], "text/plain": [ - " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", - "0 8.710056 2.001751 2.252505 0.618114 6.433243 -3.067596 9.071456 1.553400\n", - "1 0.472249 -1.232979 3.901456 -6.665290 -1.452120 -1.637031 2.916440 -3.784542\n", - "2 -2.403524 -4.519815 -1.759598 -0.137794 -5.188097 1.547932 0.818533 -0.378756\n", - "3 -1.191917 -5.945668 -4.263155 -6.701793 -4.698149 -2.303418 -5.969467 -5.084745\n", - "4 1.793951 1.319852 1.841730 3.817475 0.503538 4.558716 8.834875 6.081601\n", - "5 -2.572048 -4.659428 -4.238016 3.080822 -1.844118 -3.907528 -1.423795 4.304224\n", - "6 -0.158740 1.332393 0.381295 -1.575966 -3.946771 -1.567245 -1.145899 1.240653\n", - "7 2.745183 3.021539 1.084994 2.883656 -2.034961 3.986837 1.767022 -0.666046\n", - "8 1.963082 0.270812 3.892416 5.056978 7.054254 -3.691961 -3.834814 -1.015870\n", - "9 -3.459845 -1.311450 -2.993052 -10.006560 -4.858569 -1.456033 -3.249672 -1.653923" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia \\\n", + "0 8.710056 2.001751 2.252505 0.618114 6.433243 -3.067596 9.071456 \n", + "1 0.472249 -1.232979 3.901456 -6.665290 -1.452120 -1.637031 2.916440 \n", + "2 -2.403524 -4.519815 -1.759598 -0.137794 -5.188097 1.547932 0.818533 \n", + "3 -1.191917 -5.945668 -4.263155 -6.701793 -4.698149 -2.303418 -5.969467 \n", + "4 1.793951 1.319852 1.841730 3.817475 0.503538 4.558716 8.834875 \n", + "5 -2.572048 -4.659428 -4.238016 3.080822 -1.844118 -3.907528 -1.423795 \n", + "6 -0.158740 1.332393 0.381295 -1.575966 -3.946771 -1.567245 -1.145899 \n", + "7 2.745183 3.021539 1.084994 2.883656 -2.034961 3.986837 1.767022 \n", + "8 1.963082 0.270812 3.892416 5.056978 7.054254 -3.691961 -3.834814 \n", + "9 -3.459845 -1.311450 -2.993052 -10.006560 -4.858569 -1.456033 -3.249672 \n", + "\n", + " Wisconsin \n", + "0 1.553400 \n", + "1 -3.784542 \n", + "2 -0.378756 \n", + "3 -5.084745 \n", + "4 6.081601 \n", + "5 4.304224 \n", + "6 1.240653 \n", + "7 -0.666046 \n", + "8 -1.015870 \n", + "9 -1.653923 " ] }, "execution_count": 183, @@ -8728,130 +9806,142 @@ " \n", " \n", " 0\n", - " 11.415885\n", + " 11.377900\n", " 4.172714\n", - " 4.085408\n", - " -0.904325\n", - " 11.467091\n", - " 0.925123\n", - " 11.514468\n", - " 5.812802\n", + " 4.364265\n", + " -0.928942\n", + " 11.526373\n", + " 1.161799\n", + " 11.491700\n", + " 6.181143\n", " \n", " \n", " 1\n", - " 3.178078\n", + " 3.140093\n", " 0.937984\n", - " 5.734359\n", - " -8.187729\n", - " 3.581729\n", - " 2.355688\n", - " 5.359453\n", - " 0.474861\n", + " 6.013216\n", + " -8.212346\n", + " 3.641010\n", + " 2.592363\n", + " 5.336685\n", + " 0.843202\n", " \n", " \n", 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8.563627\n", + " -2.126256\n", + " 1.533766\n", + " 3.249012\n", + " 0.321866\n", + " 0.996449\n", + " 8.931968\n", " \n", " \n", " 6\n", - " 2.547089\n", + " 2.509104\n", " 3.503356\n", - " 2.214198\n", - " -3.098405\n", - " 1.087077\n", - " 2.425474\n", - " 1.297113\n", - " 5.500056\n", + " 2.493055\n", + " -3.123022\n", + " 1.146359\n", + " 2.662150\n", + " 1.274345\n", + " 5.868397\n", " \n", " \n", " 7\n", - " 5.451011\n", + " 5.413026\n", " 5.192502\n", - " 2.917898\n", - " 1.361217\n", - " 2.998887\n", - " 7.979555\n", - " 4.210034\n", - " 3.593357\n", + " 3.196755\n", + " 1.336600\n", + " 3.058169\n", + " 8.216231\n", + " 4.187266\n", + " 3.961697\n", " \n", " \n", " 8\n", - " 4.668911\n", + " 4.630925\n", " 2.441775\n", - " 5.725319\n", - " 3.534539\n", - " 12.088102\n", - " 0.300757\n", - " -1.391801\n", - " 3.243533\n", + " 6.004176\n", + " 3.509922\n", + " 12.147384\n", + " 0.537433\n", + " -1.414570\n", + " 3.611874\n", " \n", " \n", " 9\n", - " -0.754017\n", + " -0.792002\n", " 0.859513\n", - " -1.160148\n", - " -11.528999\n", - " 0.175280\n", - " 2.536686\n", - " -0.806660\n", - " 2.605480\n", + " -0.881291\n", + " -11.553616\n", + " 0.234561\n", + " 2.773362\n", + " -0.829428\n", + " 2.973821\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", - "0 11.415885 4.172714 4.085408 -0.904325 11.467091 0.925123 11.514468 5.812802\n", - "1 3.178078 0.937984 5.734359 -8.187729 3.581729 2.355688 5.359453 0.474861\n", - "2 0.302305 -2.348852 0.073305 -1.660233 -0.154249 5.540650 3.261545 3.880646\n", - "3 1.513912 -3.774705 -2.430252 -8.224232 0.335699 1.689300 -3.526455 -0.825343\n", - "4 4.499779 3.490815 3.674634 2.295036 5.537386 8.551434 11.277888 10.341004\n", - "5 0.133780 -2.488465 -2.405113 1.558383 3.189730 0.085190 1.019217 8.563627\n", - "6 2.547089 3.503356 2.214198 -3.098405 1.087077 2.425474 1.297113 5.500056\n", - "7 5.451011 5.192502 2.917898 1.361217 2.998887 7.979555 4.210034 3.593357\n", - "8 4.668911 2.441775 5.725319 3.534539 12.088102 0.300757 -1.391801 3.243533\n", - "9 -0.754017 0.859513 -1.160148 -11.528999 0.175280 2.536686 -0.806660 2.605480" + " Colorado Florida Iowa New Hampshire Nevada Ohio \\\n", + "0 11.377900 4.172714 4.364265 -0.928942 11.526373 1.161799 \n", + "1 3.140093 0.937984 6.013216 -8.212346 3.641010 2.592363 \n", + "2 0.264319 -2.348852 0.352162 -1.684850 -0.094967 5.777326 \n", + "3 1.475926 -3.774705 -2.151395 -8.248849 0.394981 1.925976 \n", + "4 4.461794 3.490815 3.953491 2.270419 5.596668 8.788110 \n", + "5 0.095795 -2.488465 -2.126256 1.533766 3.249012 0.321866 \n", + "6 2.509104 3.503356 2.493055 -3.123022 1.146359 2.662150 \n", + "7 5.413026 5.192502 3.196755 1.336600 3.058169 8.216231 \n", + "8 4.630925 2.441775 6.004176 3.509922 12.147384 0.537433 \n", + "9 -0.792002 0.859513 -0.881291 -11.553616 0.234561 2.773362 \n", + "\n", + " Virginia Wisconsin \n", + "0 11.491700 6.181143 \n", + "1 5.336685 0.843202 \n", + "2 3.238777 4.248987 \n", + "3 -3.549223 -0.457002 \n", + "4 11.255119 10.709345 \n", + "5 0.996449 8.931968 \n", + "6 1.274345 5.868397 \n", + "7 4.187266 3.961697 \n", + "8 -1.414570 3.611874 \n", + "9 -0.829428 2.973821 " ] }, "execution_count": 184, @@ -8984,15 +10074,25 @@ "" ], "text/plain": [ - " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin\n", - "Colorado 1.000000 0.495928 0.493505 0.503438 0.500832 0.505176 0.499251 0.502516\n", - "Florida 0.495928 1.000000 0.504859 0.484577 0.495763 0.494672 0.493993 0.501656\n", - "Iowa 0.493505 0.504859 1.000000 0.493213 0.489625 0.502977 0.496665 0.505508\n", - "New Hampshire 0.503438 0.484577 0.493213 1.000000 0.505407 0.500251 0.505616 0.506102\n", - "Nevada 0.500832 0.495763 0.489625 0.505407 1.000000 0.497530 0.491802 0.504529\n", - "Ohio 0.505176 0.494672 0.502977 0.500251 0.497530 1.000000 0.491498 0.493094\n", - "Virginia 0.499251 0.493993 0.496665 0.505616 0.491802 0.491498 1.000000 0.499111\n", - "Wisconsin 0.502516 0.501656 0.505508 0.506102 0.504529 0.493094 0.499111 1.000000" + " Colorado Florida Iowa New Hampshire Nevada \\\n", + "Colorado 1.000000 0.495928 0.493505 0.503438 0.500832 \n", + "Florida 0.495928 1.000000 0.504859 0.484577 0.495763 \n", + "Iowa 0.493505 0.504859 1.000000 0.493213 0.489625 \n", + "New Hampshire 0.503438 0.484577 0.493213 1.000000 0.505407 \n", + "Nevada 0.500832 0.495763 0.489625 0.505407 1.000000 \n", + "Ohio 0.505176 0.494672 0.502977 0.500251 0.497530 \n", + "Virginia 0.499251 0.493993 0.496665 0.505616 0.491802 \n", + "Wisconsin 0.502516 0.501656 0.505508 0.506102 0.504529 \n", + "\n", + " Ohio Virginia Wisconsin \n", + "Colorado 0.505176 0.499251 0.502516 \n", + "Florida 0.494672 0.493993 0.501656 \n", + "Iowa 0.502977 0.496665 0.505508 \n", + "New Hampshire 0.500251 0.505616 0.506102 \n", + "Nevada 0.497530 0.491802 0.504529 \n", + "Ohio 1.000000 0.491498 0.493094 \n", + "Virginia 0.491498 1.000000 0.499111 \n", + "Wisconsin 0.493094 0.499111 1.000000 " ] }, "execution_count": 185, @@ -9026,8 +10126,8 @@ " \n", " \n", " Colorado\n", - " 0.7468\n", - " 0.2532\n", + " 0.7453\n", + " 0.2547\n", " \n", " \n", " Florida\n", @@ -9036,33 +10136,33 @@ " \n", " \n", " Iowa\n", - " 0.6704\n", - " 0.3296\n", + " 0.6937\n", + " 0.3063\n", " \n", " \n", " New Hampshire\n", - " 0.3561\n", - " 0.6439\n", + " 0.3541\n", + " 0.6459\n", " \n", " \n", " Nevada\n", - " 0.8845\n", - " 0.1155\n", + " 0.8871\n", + " 0.1129\n", " \n", " \n", " Ohio\n", - " 0.8233\n", - " 0.1767\n", + " 0.8373\n", + " 0.1627\n", " \n", " \n", " Virginia\n", - " 0.7250\n", - " 0.2750\n", + " 0.7235\n", + " 0.2765\n", " \n", " \n", " Wisconsin\n", - " 0.8508\n", - " 0.1492\n", + " 0.8717\n", + " 0.1283\n", " \n", " \n", "\n", @@ -9070,14 +10170,14 @@ ], "text/plain": [ " DemWinPct RepWinPct\n", - "Colorado 0.7468 0.2532\n", + "Colorado 0.7453 0.2547\n", "Florida 0.6937 0.3063\n", - "Iowa 0.6704 0.3296\n", - "New Hampshire 0.3561 0.6439\n", - "Nevada 0.8845 0.1155\n", - "Ohio 0.8233 0.1767\n", - "Virginia 0.7250 0.2750\n", - "Wisconsin 0.8508 0.1492" + "Iowa 0.6937 0.3063\n", + "New Hampshire 0.3541 0.6459\n", + "Nevada 0.8871 0.1129\n", + "Ohio 0.8373 0.1627\n", + "Virginia 0.7235 0.2765\n", + "Wisconsin 0.8717 0.1283" ] }, "execution_count": 186, @@ -9112,9 +10212,9 @@ "outputs": [ { "data": { - "image/png": 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cbLKzgy03N5cePXpwww03HJAlgRSkikiLV1VVxdSpU3lz0iTefOklFn/+Oedk\nZXHhtm0MIcgzuBl2ZpndsW0GtqSnU5KezsCKCq50p6GDcBYAv8/I4K+hEEf178/8JUu4Ari7ooJj\nG/EzlgHTgfdDId5v1YoFsRjnA1eWl3MRkNuI95LGoyBVRET2LEIwL3YNQTqg1YRZjlFIgnUkKCKY\nXZlOEODmECIP4zCMzjhdiNMD6ESQnKoKqCD4Or0C2E6IMowyjHKcz4kzl7RwNn16duVLQ8/h2muv\n5eyzz270JX4UpIrIfuXufPzxx0x88UUmjh/PirVrGfqlL3HZ177G8OHDad++fb2vtWzZMl577TUm\n/eMfrF61ih49etDziCPoddRR9OzZc+fWpUsXwuEv1oiLRCIUFxezceNGioqKvnhdt46Pp0zhg48/\n5pjMTC7cvp0Lkwl+6pMcqDFVEGSLO41gBosIKEgVEZF9FQW2EcyLbYz1c2PAJ8D7pPEaMT7AiNGp\nQ0cGn3sy5513Ht27d6dHjx707du3QX/npVKQKiKNrqKigsmTJzPxn/9k4ksv0SoW47KqKi6PRukL\nvAZMbN2ad6qqOHngQC679louHzFit+EjFRUVvPvuu0x68UUmvfwy20pKGGbG8PJyjiBY36oQKExL\nozA7m8JQiMJolOKqKrq0a0dGejpFW7dSVlXF4ZmZdEhPp6MZHRIJOlZV0SESYSBwPsF/3SLNjYJU\nERFp3hxYAUwhzJvAHJytJCgl6MV1jEzCoQzS0zPIzszkm9+6jl88tFt+3F0oSBU5SLk7q1atIi8v\nj9atW++3+6xfv57XXnuN6e++S1paGlm5uWS3bk1Wco5CVlbWzvkKW7du5T/jx1PwwQecmJnJ5aWl\nXOZO/z1cuwJ4G5iYlcVEM9q0b89lV19N9z59eP2f/+T9WbM4PjOT4WVlDE8kOJH6LWESIQhgIwRD\nddvVs55Ic6MgVUREDm4V7DpR6m8MHDCdzxbOqbXW/liCZhhfLEHztLs/VEOZx4HhQDnwDXef3YC6\nClKlRXJ3li1bxjvvvEPBK69Q8N57xKqqKI1GaZWVRd/u3el35JH0PfZY+vXvT9++fenbty9du3Zt\n0DyBWCzGtGnTmDRxIpP+/W9WrlnDkLQ0ztm+HfhilkJlKERFWhoV4XDwPhQiM5Fg2PbtDAcaOrgj\nAXwMTAyHWZuRwYUVFVyIkvhIy6YgVUREDi2PM3DAHw9skGpmYYLVEIYQzOCdCYx09wUpZS4GRrn7\nxWZ2OvBxOcQ9AAAgAElEQVSYu59Rn7rJ+gpS5aASiUSYP38+c+fOZe7MmcyZNo3PliwhOyODnl26\n0LNPH3oOGEDPvn3p1avXznmVrVu3ZsWKFUFQ+p//8E5BAV5ZyfmhEOdv304+0Dd5j/XA8uS2zIzl\nOTksT0tjWSRCSTRK53bt6NqxI127d6drnz506d2brl277tyys7MpKCjg1eef561336V3WhrDKyoY\nHotxBsF0+wIgvwl+fiItmYJUkaZWgJ5+Io1p/wSpaXWcHwQsdfeVyYuPB0YQJLDc4XLgzwDuPt3M\n2plZZ6BPPepKC+PuJBKJA3a/UCiEWcMW+6iqqmLLli07t82bN7N48WLmTJ3K3I8/ZsmaNfTJyuIE\nd04oK2M0cBzBUNTPN22i8LPPKJw4kTnZ2byckUFhIsHnlZUkgHZpaeQng9KfAEdQ81IkXZLbYAB3\nSPZ6QjBcYV1xMWuLi1k3fz5rgbVpaczLymJtOMzaRIKtsRhnh8NcXFbG49S8NmYBekyLiEhLU4Ce\nfiLNX11BajdgVcr+auD0epTpRpDQsq66h5yKigq2bNmye+bRDRvYWFhI0dq1bNywgU0lJbRv146e\nvXvv7HVLzWaal5fX4OCqviKRCKWlpbtsZWVlVFZWUlFRsftrRQUVZWVUlZcTraoiFo0SjUSIRSLB\n+2iUWDRKLBYjUlUV1KuspLKqKniNRKiIRKiMRqmMxXD3/fbZqnN3MtPSyM7IICs9nezMTLIyMsjO\nyiIrM5Ps7OwgKN26lc3btrFl+3ai8Th5GRm0T08nLxQiDziiooLzIhG+CwwEsqPRGu/XO3WnoiLY\nCPpKSoHW0WiD18esLgfol9x2isWgrGwfrywiIiIi0vTqClLrOw5pv0UcmzZt4rnnniMej9daLh6P\nE4vFdm7RZNC0cz8SIZyWtjMpTGpymNT3iURilwBtt6CtvJytxcVsKSpiy6ZNbCkpYcu2bWwpK6My\nGbhkhsMcnp5Oh3CYju50iMXoGInQh6BruiPB/L4ta9dSOH8+ha++ytSMDJ5LT6cwkWB1MrDp2LYt\nmenp+/wzTCQSlFVWUlpRQSJlaHWrtDRap6XROhSilRnZQDaQlUiQ7U5WPE52PB7sEySrSU9uackt\nvYbXbCCrltcwBL2DB0ACqIzFqIzFdlklasdrBZBJME9yx5YLWGUlVFbWeM3K5LY3Svay3v6wY9q7\niDQl/RaKHFh6+ok0rvL9ctW6gtQ1QI+U/R4EPaK1lemeLJNej7oAB6xX7UCpisdZE4+zpiGVIpFg\nS7Fx69ZGbVd1ZbEYZbEY6/brXaQ5+9+mboBIi7d3682JyL7Q00+kMc1b1PjxXF1B6izgSDPrDawF\nvgqMrFbmZWAUMN7MzgBK3H2DmW2qR90GTaAVERFpCma2kmAgzI5hPQ5cBEwF0ty90Sfbm9kK4BZ3\nn9zY1xYREWnOag1S3T1mZqOA1wlGaT7j7gvM7Pbk+bHu/qqZXWxmSwlWeL2ptrr788OIiIjsJw5c\nmhowJr+E3StmFnb32uexiIiItFB19aTi7pOASdWOja22P6q+dUVERA5FZtYVeIogMfdm4CF3fzp5\nbgxwLMGEuMuBu82sB9DP3W9IlrkB+DnB1PiHq117EPAYcFTyGv8G7nb3mrO4iYiIHMRCTd0AERGR\ng0Rd01PGA4UEqz5dDfw/Mzs/5fzlwD/dvS0wjpTkhGZ2DPA74DqC7PiHEeR42CEGfC95/EzgAuDO\nffkwIiIizZWCVBERkboZ8KKZbUluL7BrkNkDOAsY7e4Rd58LPA3cmHKND9z9ZQB3r2TXoPdqYKK7\nv+/uEeDHBAnKSZb/2N1nuHvC3T8Hfg+ct38+qoiISNOqc7iviIiI4MCIWuakdgU2u/v2lGOFwKkp\n+zVmuE+pv/O8u5cnExDuuFd/giHApxAsl5xGkNxQRETkkKOeVBERkX23FmhvZq1SjvVk18C0tgWi\n15KybJuZ5RAM7d3hSWA+cERyuPD96BkuIiKHKD3gRERE9pG7rwI+AH5hZplmdjxwM/C3el7i38Cl\nZjbYzDKAn7LrM7oVUAqUm9lRwB2N13oREZHmRUGqiIjI3kvtHR0J9CboFX0B+EnK8GBn957Uncfc\nfR7wbeDvyfqbgVUpZX8AXAtsI5iPOr6G64mIiBwSzL32Z5yZDQMeJVjr9Gl3f6ja+euAewkSQJQC\nd7j7J8lzKwkeqHEg6u6DGvsDiIiIiIiIyKGj1iDVzMLAImAIsAaYCYx09wUpZc4E5rv71mRAO8bd\nz0ieWwGc4u6b9+NnEBERERERkUNEXcN9BwFL3X1lcsHw8cCI1ALuPs3dtyZ3p7Prum5Q97pyIiIi\nIiIiIkDdQWo3dp0Tszp5bE9uAV5N2XfgLTObZWa37V0TRUREREREpKWoa53UeidlMLPzCTIZDk45\nPNjd15lZB+BNM1vo7lP2op0iIiIiIiLSAtQVpK4hZd225PvdFiNPptr/AzDM3bfsOO7u65KvRWY2\ngWD48JRqdZWdUERERERE5BDm7vWeBlpXkDoLONLMehOkxP8qQYr9ncysJ0Gq/evdfWnK8Rwg7O6l\nZpYLDAUe3EOD69teEWkkY8aMYcyYMU3dDJEWSb9/Ik1Dv3siTcOsYWmKag1S3T1mZqOA1wmWoHnG\n3ReY2e3J82OBnwB5wJPJm+9YaqYz8ELyWBowzt3faNjHERERERERkZakrp5U3H0SMKnasbEp728F\nbq2h3nLgxEZoo4iIiIiIiLQQdWX3FZFDVH5+flM3QaTF0u+fSNPQ757IwcGaej6omXlTt0FERERE\nRET2DzNrUOIk9aSKiIiIiIhIs6EgVURERERERJoNBakiIiIiIiLSbNQZpJrZMDNbaGZLzGx0Deev\nM7O5ZvaJmU01s+PrW1dEREREREQkVa2Jk8wsDCwChgBrgJnASHdfkFLmTGC+u281s2HAGHc/oz51\nk/WVOElEREREROQQ1diJkwYBS919pbtHgfHAiNQC7j7N3bcmd6cD3etbV0RERERERCRVXUFqN2BV\nyv7q5LE9uQV4dS/rioiIHNLMbJdNREREdpdWx/l6j8M1s/OBm4HBDa0rIiIiIiIiAnUHqWuAHin7\nPQh6RHeRTJb0B2CYu29pSF2AMWPG7Hyfn59Pfn5+Hc0SERERERGR5qigoICCgoK9rl9X4qQ0guRH\nFwBrgRnsnjipJzAZuN7dP2xI3WQ5JU4SEZEWofoQXz3/RESkJWho4qRae1LdPWZmo4DXgTDwjLsv\nMLPbk+fHAj8B8oAnkw/fqLsP2lPdvfpUIiIiIiIi0iLU2pN6QBqgnlQREWkh1JMqIiItUWMvQSMi\nIiIiIiJywChIFRERERERkWZDQaqIiIiIiIg0GwpSRUREREREpNlQkCoiIiIiIiLNRp1BqpkNM7OF\nZrbEzEbXcP4oM5tmZpVmdk+1cyvN7BMzm21mMxqz4SIiIiIiInLoqXWdVDMLA08AQ4A1wEwze7na\neqebgO8AV9RwCQfy3X1zI7VXREREREREDmF19aQOApa6+0p3jwLjgRGpBdy9yN1nAdE9XKPe6+GI\niIiIiIhIy1ZXkNoNWJWyvzp5rL4ceMvMZpnZbQ1tnIiIiIiIiLQstQ73JQgy98Vgd19nZh2AN81s\nobtP2cdrioiIiIiIyCGqriB1DdAjZb8HQW9qvbj7uuRrkZlNIBg+vFuQOmbMmJ3v8/Pzyc/Pr+8t\nREREREREpBkpKCigoKBgr+ub+547S80sDVgEXACsBWYAI6slTtpRdgxQ6u6/Tu7nAGF3LzWzXOAN\n4EF3f6NaPa+tDSIiIocKs13TNOj5JyIiLYGZ4e71zlVUa0+qu8fMbBTwOhAGnnH3BWZ2e/L8WDPr\nDMwE2gAJM/secAzQEXgh+UBOA8ZVD1BFREREREREUtXak3pAGqCeVBERaSHUkyoiIi1RQ3tS68ru\nKyIiIiIiInLAKEgVERERERGRZkNBqoiIiIiIiDQbClJFRERERESk2VCQKiIiIiIiIs1GnUGqmQ0z\ns4VmtsTMRtdw/igzm2ZmlWZ2T0PqioiIiIiIiKSqdQkaMwsDi4AhwBqC9VBHuvuClDIdgF7AFcAW\nd/91fesmy2kJGhERaRG0BI2IiLREjb0EzSBgqbuvdPcoMB4YkVrA3YvcfRYQbWhdERERkUNZJBJh\n0qRJJBKJpm6KiMhBo64gtRuwKmV/dfJYfexLXRERETkIJBIJHn74Ye68807Ky8ubujm1ikQiPPHE\nE5x43CmkhdvRIa8H//M//0MsFmvU+xQWFvL973+fnl2PJDOzLRdf/BV69+jP5s2bG/U+IiKHqrQ6\nzu/LOCSNYRIRETlEFRcXc/fddzP+uYnEYm0wOjH2qU5ce+0VPPnUk7Rq1arB11y/fj3vvvsuRUVF\nbNq0ic2bN7N161a2bt3Ktm3b2FpSRtm2CtrlteKkU07g3HPP5aKLLqJ9+/Z7vGZJSQmPPvoof/vT\nP1j++ecYXXCuxRlBcckcfvKjh3jgJw8xbNh5PPb4o/Tr12+vfh5vvvkmT/zmCSa/PYOy8i2EGUSc\nu4FLgcNYu/Y6unU5gvfef53TTjttr+5xICUSCUIh5dcUkaZRV5C6BuiRst+DoEe0Pupdd8yYMTvf\n5+fnk5+fX89biIiIyIE0ffp0Rt15F7M+nkuY04nzPDAEx8Cn8ty4H/L3v3fha1+7nLG/H1tnsFpS\nUsIvfvEL/vLs86wvWkeIzhitMVoDbYC2OB2JcxTQDmiFUcxHM2fw+6f+iwQ3ErYc2rZpyxFHduOk\nU07grLPOYubMmfxr/CusL15HmKOIcytwJU7flLufSoJbIDGd1179FUe8eiy9e/TkwZ/fz4033lhj\nexOJBB9//DHvvfceH330EZ/Mmc/ChSuIJSDMpcQZC1xAnNxd6sV5AY/8P04/PZ/f/vZX3HHHHfX+\nmZeVlfGNr9/ExJcnc1heO04ffDzDhw/nmmuuoV27dvW+zp6UlJTw/PPP8+qrrzLjg0/ZUFyEe5RT\nTj6RXz/yEOeee+4+30NEWpaCggIKCgr2un5diZPSCJIfXQCsBWZQQ/KjZNkxQGlK4qR61VXiJBER\naSkOVOKkRYsW8eCDDzLjwzncdMt1jB49mrS0ur6X3rNEIsFTTz3Fzx74X9YXFxHiRhLcAxyxhxrT\nCPND3Oby1a9exu//8PtdgtXKykoeeeQRfv/kn1i5qpAwA4nzTeDLwGENbF0MWAbMw/iMMDNIMA+j\nJ3FuBC4DOtbzWkUYfwAeIzMjzldHXkpOTg6fzP2U5UvWsmnLViKxbUA2YfoAxxDnJOB84GTqt7Lf\nq8BXueG6K/nL3/5Sa8lEIsHo0aN55OGxkDieOGOA1YQpwHmfBJ+Tmd6Ofn27ce75Z3LVVVdxwQUX\n7NIDGolEqKys3LlVVVWxfPlyXnzxRd595wOWL19LVXQrIXphnEOc84DTASPEb0jwJ9q3bcd3v38b\n999//z79O6qupKSEkV+9lq0l2/i/hx9i8ODBjXZtEWleGpo4qdYgNXnB4cCjQBh4xt1/YWa3A7j7\nWDPrTJC5tw2QAEqBY9y9rKa6NVxfQaqIiBzUotEos2bNYsaMGZxzzjmcfPLJNZbbn0Hq2rVr+elP\nf8rzz73Clm3FhLmQOOcQ4mmwdeSfdzr/9+v/3WPbqkskEkyYMIFnnvkjk9/6kEg0C+de4GagdT1b\n9WEyWJ3NV66+lHPzz+W3j49lwaKlGD1JcCvwNZpfyoo48BphHsdIJ8aJwNFA/+TWdh+vvwjjQo7q\nn8es2dPIycnZrcTvfvc7fnD3T6iqyiPBb4GhNVynHPgY+JA0JhNnBs7W5LlEcjOC4DlEMIAuhJFL\nmFOI8SXgTIIAO3sPbS0HxhPiIbB1XHTROTzy6MMMGDBgrz99IpHgrrvu4rdPPIv5mTj9SPAXDmvX\nnrvuuZ377ruvUYNhEWl6jR6k7m8KUkVE5GATiUSYNWsWBZMnUzBxIh/OnUu/zExOrarijXCYLr17\nc+fo0VxzzTVkZWXtrNfYQWr1obJhzkj2SF4OpA6z/YgwjxPnn3Rofzijvncr9913HxkZGbtcb8WK\nFfzmN7/hxX9PYmXhKiAX41ISXANcSP16CmsynTD34hTi3IhzA3vuhW0pthLiCnJz5zN9ZgFHH300\nAJMmTeLr199O8eYKnIeB62jYz72MLwLStAbWrY0DMwnzK+JMpFe3Htwz+jvcdtttu/wbr8vTTz/N\nd0fdR1XVYSQYC+Qnz2wHniPEQ1howz7PERaR5kVBqoiINHvxeJyqqqoae5AOtMLCQsb99a8Ur19P\nVqtWZOfmkpWVRXZ29i6v6enpfDJnzs6g9MisLPIrKsiPRjkHyEteL04woPN3rVoxC7jpllv41ne/\nS9++ffcpSK2srOSDDz5gypQpzJkzh9kz5/H5mlUNHCpbRtAr9muw1Zxz9ql8+StXMeGFF/lw2mdU\nVJUkE/58BRhOEEjW+28KabA4IUaDjeUXv/wxz/7hryxcuhLjv3DuBuof/B1YmzD+gPEHEqyle5du\nXPWVS7j77rvp1atXjTWmTp3KV6++kTXrtwC/Ar5BzQG0E3yp8Svi/KfOOcIicnBQkCoiIs3asmXL\nGHn55cxbsoRh55/PyNtu45JLLiE7e0/DDRtfRUUFL774Is8+/jgfzZnDV93pV1VFJVBhRmVaGhXh\nMJWhEBWhEJWhEJXA0cmg9Gy+CEprsxR4Kj2dP4XDDDrtNCZNmbLLeXcnkUhQXFzM+vXr2bBhA8XF\nxaxbt445c+Yw79MFfL5yI1u3bSOWKMVoT5i+JDiOBCcT9Jju7VDZ2YT5Dc4U4EISXAGcw56Hfcr+\n83fgTsJcR5yf0vB5uU1pFfAKYZ4jzgxa5eSR/6XT+O53v8OFF17I2rVruXLE1cyYNQfj+zj/DdWS\nSu3ZF3OEnS0Y6YRCaYRD6aSnpZOenkZ2VgY5OZnkts4mHktQVlZBRXkVVZEokUiUWDxGPBEl4VEg\nipFJRnoubVvn0rlrHn369eTII4/k2GOP5aSTTuKYY47Z56HGlZWVvPLKKwwdOpQ2bdrs07VEDhUK\nUkVEpNn6+7hxfO/22/lxRQXXJxJMAJ5r3ZqPYjEuv+QSRt5yCxdccAHp6em1XicWi7Fy5UoWL16M\nmdG3b1969epV67BDd+ejjz7i2SefZPz48ZwaDnNTaSlXsP/7qyqAfwA37XYmA4gSDMvMwsjGyMVo\nAwwgzonAAIJ5kEccgJaK7IvtwNuE+SdxXiEcchKJKCEuIc7DQPe9vK4nr12aspVV2y8l+D1qTTDU\nvXUN73OBEqCQILguJMwSjCUkWEWC9UAF2ZmHccIJRzDiysu4+eab6dix7sRbM2fO5IknnuC1/7zH\nxk3rMNrhbKVnt+5c//Vr+OEPf9gomZhFDlYKUkVEpNkpKyvjO7feygcTJzK+vJyTqp1fBzxvxnOt\nWrEcuPrqq7n25pvp168fixcvDrZ581g8Zw6LlyxhxcaNdMnMpH+yx2N5PE5hRQUd27ShT48e9O3f\nn77HHkvffv3o3bs3s2bO5I+PP05ZURE3VVby9Xicngf6h0BNA2dLCP5wVpIYOdQkgNkEX8Qc18Rt\naYgy4EOMtwnxKnEWkp3ZluOPD4LWm266ic6dO7Nt2zbGjh3L+HHP8+lny4jGI4T5EnGuJkhy1Zlg\ncYt/EeZZ4iygR5fuXP+Na7j33nsVsEqLoyBVRESaldmzZ/O1yy/nrOJiflNZSe2rZsJyYHwoxHO5\nuayPRhmQmUn/aJT+5eU7+xT7sXufYpxgMe7lO7ZQiOU5OawIhzkyEuHmigrOo/HSyOyN3Z/Oev6J\nNG/lBEHrW4SYRJz5ZKS1IhIrI8yRJLgK51LgFILFLPZkHfDvZMA6j+6duzPyhqu46aabdibNEjmU\nKUgVEZFmwd15/JFH+PmPfsRjFRVc29QNagYUpIoc7CqAOQRfl+3t3OH1BAHr34gzl5Cl06VjBwad\ndTwXX3wx11xzjeayyiFnf6yTOowv1jp92t0fqqHM4wRpAMuBb7j77OTxlcA2gi+4o+4+qIa6ClJF\nRA4xxcXF3HTNNayfPp3x5eVoEYmAglQR2VUCWEiQ0bgA530SrCIzvS1HHNGNUwedSFpaGtFodOcW\ni8WIRCLEYjFisRjuTm5uLm3btiUvL4+8vDzat2/P4YcfTocOHejUqRO9e/fWEGNpUo0apJpZGFgE\nDAHWADOBke6+IKXMxcAod7/YzE4HHnP3M5LnVgCnuPvmWu6hIFVE5CBXVVXFokWLmD9/PvM++YQ/\njR3LyLIyfh6JkFF39RZDQaqI1G078BHwIWlMTx5Lx0kHMnAykvsZOJkAhNiKsQWjBNiGU4pThrMd\np5z/z969x0dV3/kff31mJjeScJMghIuAEhQoKiJ4qRqrW1FbL61Wsa69aW2tbreXn233t7ti3bZ2\nd+26tb+22Fp1WxV70XpZrYoaRaQIilCQq+FOIIEEksl1Zs7n98eZ4ICQBEhIgPfz8TiPM9+Z7/ec\n7xkSJp/5fs/nCw1kxXozYvggzj53MldeeSWXXHLJQWcyFumozg5SzwTucPep6fJ3Adz97ow6vwRe\ndffH0+XlwHnuvjUdpE5y9+1tnENBqojIfsrMbrtixQrWrlhBc0MDyUSCREsLyURi15ZI73Py8ij9\n5CeZevHFlJSUfGjNzo5oaWlh2bJlLF26lPf+9jeWvvUW7y1bxvqqKkbm5TEOGBuP83fufLTzL/uw\npyBVRLpHM+E05XlEeZmAeTg1FPY6hnHjR3Lhxz/GWWedRUNDA7W1tcTj8V37+vp64vE4jY2NuDu9\ne/emX79+HHPMMfTv35+ioiIGDhzIoEGDOPbYY3vE+tfS83R2kHoVcJG735QuXw9McffbMuo8A/zI\n3d9Ml2cBt7v7O2ZWDuwknO47w91/tZdzKEgVEdkLd2fLli27ZbddsXAhK1evZm1lJcW5uZREo5Q0\nNTGyuZlehDliM7esjMe1wMt5eTxvRlZ+PlMvvZSLr7yS888/n8LCwr2ef926dcybN495s2fz11df\nZdGqVRyXm8t4wmB0nDtjgdGgEdMOUJAqIj1HFfAWxhwivErAWowcIAcjFyMX6EW4dnIenk57Z+wE\nduLppYDCEdsGnCagCcgiFs2noFc+RUV9OG7UYI4//nhOOukkTj31VCZOnEhBQXsp9ORI09lB6qeB\nqR0IUu929znpcmaQWuzum82sCHgJuM3dZ+9xDgWpInJYCIJg1z1AiUSCmpoaqqqqqKyspKqqKtwq\nKqhcv56qLVvYvn07ffr0YfDQoRQff3y4Ly5m8ODBu/Y5OTnU1tZ+EIguX87KhQvD/YYN5JoxOieH\nMYkEYxoaKGHf2W07yoGlwF/M+EthIfOamjh9/HimfuYzjP/IR3h34ULmzZrFX99+m0gyyZRYjCnx\nOGe4M4lwtUE5MApSReTI5kANrevQwnqMNURZgbOOgM04OzByyIrlkZ/Xi/79CygeNoChw4YycuRI\nSkpKGDduHKeccoqmIx9BOjtIPQOYnjHd93tAkJk8KT3dt8zdZ6bLu6b77nGsO4C4u9+zx/N+xx13\n7CqXlpZSWlra0f6LiOyXIAjYtm0bmzdvpqKiItxv3szm8nIq1q0Ly1VV1Dc1kQwCkqkUySAgEQS4\nO1mRCLH01jcri4GxGEVAUTJJUXMzA5PJsAz0Jxy9rCBcLa8iJ4fNOTlUmFGRTLKlqYmcWIwgCBid\nl0eJOyX19ZQEAWMIRyf7HYL3JA6UAX/JyWFZbi6nNDRwRiLBFGAYewus5EApSBURSQJbCT8dWz8h\nNxGlHGMDTgUpKoFmBvQdyNnnncq0adP49Kc/3eGgNQgC5s+fzyuvvMKQIUP4xCc+Qf/+/bvsiuTD\nysrKKCsr21W+8847OzVIjREmTrqA8CfoLdpOnHQGcK+7n2FmvYCou9eZWT7wInCnu7+4xzk0kioi\nXa6iooJf//KX3P+zn9HY0EBxdjaDzShOJBjc1ERxEDAYKAYGAwV8eLpshM4N2ALCILZPJx9Xei4F\nqSIiHVUBvEaU5wl4BaeKY/oO5OxzT+Uzn/kMV199NbFYjHnz5vHSSy8xf/58lixaxZat1TS17ATy\niHI8TjUBG4lFChg44BjGn3w8Z519FpdeeikTJ04kEunO1bOPHl2xBM3FfLAEzQPu/iMzuxnA3Wek\n6/wMmEqYjuwL6am+o4An0oeJAY+4+4/2cnwFqSLSJdyd119/nZ//x3/w4qxZfMaMW5qaOLm7OyZH\nLQWpIiIHagth0PoXAmbhVBF+3ZtPjBICTiVgIjA2vWWOnLYAy4DFRJhPhHkkWQYkKMjrx4ABfSge\n0p8hw4YwcuRIRo8ezbhx4/jIRz6i+2c7SacHqV1NQaqI7Es8HmfNmjWUl5dTXl5OEASMGjWKUaNG\nMXLkyH0udl5bW8vvfvtbfv4f/0Fq2zZuaWjgBnf6HOL+i+xJQaqISGepJJzvdKA3xjjhtOPFhPfQ\nbiLKGox1OJsJqMLZSZgIKo+IRds8mhn0KSxg1AmDGfeRsUyePJnzzz+f0aNHH2D/jiwKUkXksOHu\nVFZWsnLlSlavXk35qlWUL1lC+erVlG/cSG1jIyPz8hhlxqimJiLAmtxcyoHyhgbycnIYNWQIo044\ngVHjxzPyhBNYOHcuM2fO5IJIhFvq6ylFU2ml51CQKiJyOAmAbYSjuIl26iaBtRjLiPIuznJSrAMg\nN7uQgQP6MfqkYYwdO5ZTTz2Vs846i9GjRx81040VpIpIjxOPx1m1ahUrVqxg5YoVYfbaZctYuX49\nEXfG5OZyQjLJ8Q0NjHJnFDAKGER4H+jeOOF3qOWtmxnleXmMaGnhxmSSIYfm0kT2i4JUEZGjSetf\nKyuBlRjvEWUpAe8TsAlIkRUroG9hIUOHF1Fy4vGMHTuWk08+mdNPP53i4uIOnSUIAt5++21efPFF\n5phX7dEAACAASURBVM+fz98WrqC6Ok5eXg69+/Si/4BC+h/TjwEDBjBw4ECOPfZYiouLmThxImPG\njOnC6/+AglSRI9iiRYt46s9/pmjgQEpKShgzZgzFxcUd/hauubmZdevWUV5ezoYNG2hoaKCxsZGm\npiYa6+poisdprK+nqb6exoYGmpuaSCQSJBOJD5ZeSe+TySTJVCosp1K7P05nxU2kH8ciEUb36hVm\nr21oYEwqtWsplWO69B0T6VkUpIqIyAdqgDW0fuUeZSnGcgI2EVAFGFnRfAry8xk4sO+uNWePOeYY\n3n33XZYuXk3Flu3pRFG5RBmNcyoBpwFDCdMz1gA1RNhKhEpgG041zg4CtpAVy2PcSaO48qrL+PKX\nv8ygQYO65EoVpIocYaqrq3n0kUf4zU9/yrbNm7m6pYW6rCxWZmezMpFgZyLB6KFDKRkzhjGnnkrJ\nSScxdOhQKioqKH///XD67IoVlG/YwNadOxmWl8eoaJThiQT5ySR5qRS5qRS5hMt1Z+5zCO/2yMxw\nu2fG246WNeVWREGqiIh0lAPVhOvNhuvOGu8TZSVQjTOB1G6Jog7ka/8kMB/jRSI8RYql5Of24/Qp\nY5l23bXccMMN5OYe6Krsu1OQKnIESKVSzJo1i9/cdx8vzJrFJdEoX2ho4GOEabYz1QKrSE8kMWNl\nfj4bolGKg4BRDQ2MSqV2TZ8dShg0ikj3UJAqIiI9Vz0wmwj/CzxHwCZ65fQhFouRFYuRlRUlOzuL\nnNwscnOz6JWfS15+Ll/60pe4/vrr2zxyVyxBM5UPlqD5tbv/eC91fgpcDDQAn3f3hfvRVkGqHDWC\nIPhg2mzGFNrWbefOnfzh0Ud56Fe/YlAiwRfq6pjGgeetE5GeRUGqiIgcPiqB5UAT0JjemvbYv8a4\nEytZsmxhm0fa3yC1zUEVM4sCPwMuBDYB883saXdfllHnEuAEdx9tZlOAXwBndKStyMFyd+rq6qio\nqGDz5s0f7NetY/OaNTQ3NpLbqxd5+fnhvqCA3IKCsJybS15eHs3NzdRUV1OzZQs1lZXUbN9OTU0N\nNTt2UBOPs6OhAXcnNyuLvOzscJ+TQ25ODnl5eeTm5JCbl0cqmQzv72xuprGpKdy3tNDU0hLuk0kC\nd7IiEWKRyK59zIyYGVlm5EQiXNrczHMtLXyki9+7MqC0i88hIiLSs5ShTz+RjhqY3tpSAP6bTj9z\nezP/JgOr3X0tgJnNBC4nXA231WXAwwDuPs/M+prZIGBkB9r2eO5OdXX1rrUa16xZQ3Z2NsOHD9+1\nFRUV9dj00alUitraWhoaGmhqagoT5LQmymln3xSP0xiPh0l06utpaW6Gdka9gyD4IMlOIvGh0cJE\nRsKdPZPu7Eq0k0rhEAZy0SixjC0rFtv1uKGpiYqaGnCnODeX4kiEwakUxc3NDE4kmEh4b+XevvNp\nMGN7LEZjLEZOKkW/lhZOIByx3NtmQGMqFb4vezlmE+Ev097u62zd56brEATh1s3K0Me0iIgcbcrQ\np59Iz9dekDqE8E7dVhuBKR2oMwQo7kDb/ebuNDc3fyjgSiaT7bZra6pla3nr1q1hkpmlS8OkMxUV\nWBBwfG4uo9wZ0dhISyTC67m54W3MLS3UJpMM7d+f4UOGMGzkSIafeCL9BwygsLCQgoICCgsLd22t\n5fz8fBoaGqipqWHHjh3hyF3mtnUrO7dtAyCWnU1WdjaxrCxi2dlhOSeHWFYWkUiE2upqarZupaaq\niurWUcC6OmriceLNzRRmZdErFiM3EiEvEiHX7IPAyZ0893CfTqDTuu9LuARIa91s2k9+Y7SfRGfP\n5/ZWH8JbuTO3xB7lXMIfskKARHtrV33oByJssx/tOue2cRERERERaUt7QWpHb5bpssSdr7zyChdc\ncEFXHX6fjjHj+GiUS6JR+kUi0NICQH0sfMsGt7QwmDDqbohG2VhTw/rt25m7cCFNndSHGOEb25Ew\nKgr0M6NfJEI/M05MP+6TkxOuM9nB0btkJEJdJEJdVla7deXwNj+RoFL/ziKHVtPunxBRvtBNHRE5\nOgUsJMK67u6GyBEj4L0uiQTbC1I3AcMyysMIR0TbqjM0XSerA22B8Ebanma7O9uTSd5qZ4S2K+3P\nmVPANne2pVJd1R05Ai3Qz4tIt0rxUHd3QeSok2JRd3dB5IiydHnnx3PtBakLgNFmNgLYDFwDTNuj\nztPArcBMMzsD2OHuW81sewfa7leWJxERkZ7GzNYAX3L3V7q7LyIiIkeCNrP9uHuSMAB9AXgPeNzd\nl5nZzWZ2c7rOc0C5ma0GZgC3tNW2y65ERESkG1non81srZltNbOHzax3+rWHzeyb6cdDzCwws1vS\n5ePTX+xiZv3M7FkzqzSzajN7xsyGdN9ViYiIHHrtrpMqIiIi+9Y6kgqMAL4H/B1QBfwPUO/uN5jZ\nF4Ar3f0yM7sO+D6wwN2vNbMvAp909yvNrD9wHvA84Wyn3wBZ7n7lIb8wERGRbtIz100RERE5vBhw\nHXCPu69193rCgPVaM4sArwMftfCmnXOAfwfOTrc9D3gNwN2r3f1Jd29y9zjww/TrIiIiRw0FqSIi\nIp2jGHZLG7qecDT0WHd/H6gHTiEMUp8FNptZCXAu6SDVzHqZ2Yz0lOGd6ef7WE/MMCgiItJFFKSK\niIh0js2EU35bDSdM1L41XX4NuJpw+u7mdPnzQD/g3XSdbwElwGR370M4imp04VJvIiIiPY2CVBER\nkc7xGPANMxthZgWEU3VnunvrItWvESYUfD1dLkuXZ/sHCSIKgEZgZ/r+1DsOVedFRER6CgWpIiIi\nB88Jkxz9ljAILQcagNsy6rxOGIS2BqlzgLyMMsC96ee2AW8SJlBShkMRETmqtJvd18ymEn5oRoFf\nu/uP93j9s8DthFOR6oCvuvvi9GtrgVogBSTcfXJnX4CIiIiIiIgcOdoMUs0sCqwALgQ2AfOBaZnr\nnZrZmcB77r4zHdBOd/cz0q+tAU5z9+ouvAYRERERERE5QrQ33XcysDqdTj8BzAQuz6zg7nPdfWe6\nOA8YuscxlOxBREREREREOqS9IHUIsCGjvDH93L58CXguo+zALDNbYGY3HVgXRURERERE5GgRa+f1\nDidrMLPzgS/yweLkAGe7e4WZFQEvmdlyd599AP0UERERERGRo0B7QeomYFhGeRjhaOpuzGwC8Ctg\nqrvXtD7v7hXpfZWZPUk4fXj2Hm2VtVBEREREROQI5u4dvg20vSB1ATDazEYQLlJ+DTAts4KZDQee\nAK5399UZz/cCou5eZ2b5wMeBO/fR4Y72V0Q6yfTp05k+fXp3d0PkqKTfP5Huod89ke5htn9pitoM\nUt09aWa3Ai8QLkHzgLsvM7Ob06/PAP4V6Af8In3y1qVmBgFPpJ+LAY+4+4v7dzkiIiIiIiJyNGlv\nJBV3f55wMfHM52ZkPL4RuHEv7cqBUzqhjyIiIiIiInKUaC+7r4gcoUpLS7u7CyJHLf3+iXQP/e6J\nHB6su+8HNTPv7j6IiIiIiIhI1zCz/UqcpJFUERERERER6TEUpIqIiIiIiEiPoSBVREREREREeox2\ng1Qzm2pmy81slZl9Zy+vf9bMFpnZYjObY2YTOtpWREREREREJFObiZPMLAqsAC4ENgHzgWnuviyj\nzpnAe+6+08ymAtPd/YyOtE23V+IkERERERGRI1RnJ06aDKx297XungBmApdnVnD3ue6+M12cBwzt\naFsRERERERGRTO0FqUOADRnljenn9uVLwHMH2FZEROSIZma7bSIiIvJhsXZe7/A8XDM7H/gicPb+\nthURERERERGB9oPUTcCwjPIwwhHR3aSTJf0KmOruNfvTFmD69Om7HpeWllJaWtpOt0RERERERKQn\nKisro6ys7IDbt5c4KUaY/OgCYDPwFh9OnDQceAW43t3/uj9t0/WUOElERI4Ke07x1eefiIgcDfY3\ncVKbI6nunjSzW4EXgCjwgLsvM7Ob06/PAP4V6Af8Iv3hm3D3yftqe0BXJSIiIiIiIkeFNkdSD0kH\nNJIqIiJHCY2kiojI0aizl6AREREREREROWQUpIqIiIiIiEiPoSBVREREREREegwFqSIiIiIiItJj\nKEgVERERERGRHkNBqoiIiIiIiPQY7QapZjbVzJab2Soz+85eXj/RzOaaWZOZfWuP19aa2WIzW2hm\nb3Vmx0VEREREROTIE2vrRTOLAj8DLgQ2AfPN7Gl3X5ZRbTtwG3DFXg7hQKm7V3dSf0VEREREROQI\n1t5I6mRgtbuvdfcEMBO4PLOCu1e5+wIgsY9jdHjRVhERERERETm6tRekDgE2ZJQ3pp/rKAdmmdkC\nM7tpfzsnIiIiIiIiR5c2p/sSBpkH42x3rzCzIuAlM1vu7rMP8pgiIiIiIiJyhGovSN0EDMsoDyMc\nTe0Qd69I76vM7EnC6cMfClKnT5++63FpaSmlpaUdPYWIiIiIiIj0IGVlZZSVlR1we3Pf92CpmcWA\nFcAFwGbgLWDaHomTWutOB+rc/Z50uRcQdfc6M8sHXgTudPcX92jnbfVBRETkSGG2e5oGff6JiMjR\nwMxw9w7nKmpzJNXdk2Z2K/ACEAUecPdlZnZz+vUZZjYImA/0BgIz+zowFhgIPJH+QI4Bj+wZoIqI\niIiIiIhkanMk9ZB0QCOpIiJylNBIqoiIHI32dyS1vey+IiIiIiIiIoeMglQRERERERHpMRSkioiI\niIiISI+hIFVERERERER6DAWpIiIiIiIi0mO0G6Sa2VQzW25mq8zsO3t5/UQzm2tmTWb2rf1pKyIi\nIkeHBQsWcM899/D+++93d1dERKSHa3MJGjOLAiuAC4FNhOuhTnP3ZRl1ioDjgCuAGne/p6Nt0/W0\nBI2IiBwVjsYlaLZt28anr7ya1994iygjSbGaiGUzoN8xTDj1BErPL+Wqq65izJgx3d1VERHpIp29\nBM1kYLW7r3X3BDATuDyzgrtXufsCILG/bUVEROTIFAQBX//61zl24AjmvJEPLCXFEqCBwOdTWf0D\nXnn5JO745yc48cRTiEYKKep/HF/96lcJgqC7uy8iIt2ovSB1CLAho7wx/VxHHExbEREROUw98sgj\n9C44lp/99HkCf44UzwIj0q9GgDHAdQT8lBRvA/UEvpBtNT/m/l++Sp/CQTz99NPd1f398txzz7Ft\n27bu7oaIyBGlvSD1YOYhHflzmERERGSXJUuWcMKIk7j++q9R3/hvBCwDzu1AywhwAnAtAUupb/hn\nLr/8Ok4/7UwqKyu7ttMHoKmpiW9961v0yi3i0kuvo6hoKIOKRvKNb3yDLVu2dHf3PiQIAubNm8c/\n/dM/8dGzz+GkkgncfffdNDU1dXfXRET2KtbO65uAYRnlYYQjoh3R4bbTp0/f9bi0tJTS0tIOnkJE\nRES6W3V1Nddf9/c8/8KrRPgS8G9AnwM8WhTnH4DPsPCdrzF40Chu/85t/OAHPyASaX9RgrKyMh58\n8EFaWloYPXo0EyZM4PTTT2fYsGEdat+WjRs3cuvXbuWZZ17GfBQpZhDeyVTD1m1/5r57H+Tee0cw\n8JhBfGbaJ/ne975HcXHxQZ1zfwVBwJtvvsnTTz/N7NfmsOy9tdTGa3BixJhAio/i9OH/fu9h/ul7\n3+fEktHc9o9f4aabbiIWa+/PQhGRjikrK6OsrOyA27eXOClGmPzoAmAz8BZ7SX6UrjsdqMtInNSh\ntkqcJCIih7O6ujpeffVVXnjqKV6bNYux48bxiWuv5eKLL6aoqGi3uh1NnBQEwUEHVF0tCAIefvhh\nfvzDe1ix+n2inE2KnwMlnXymVzE+R78+Kf7050c+9EX20qVLmTFjBs89M4s16zYSeIQopUBvjPcJ\n2EjAViAgFi0gP68XRQP6MGzksYwZM4YJEyZw5plnMmHChH2+53PmzOHWW/6RdxcvIcqFpPhnYMo+\n+lsNPEWUB0nxFgP6DeTTn7mEG2+8kUmTJnXau5IpCAIefPBB/v1H/8XK998HcokxgSTnEKYIOQ0o\nBvbMWbIa41GM34Bt59STT+Ib3/4606ZN69DP3+HwcyoiPcP+Jk5qM0hNH/Bi4F4gCjzg7j8ys5sB\n3H2GmQ0izNzbGwiAOmCsu8f31nYvx1eQKiIihw13Z/Hixbzwl7/wl9//nvlLljA5N5epdXWc585S\n4NmCAma1tDDuhBP4xLRpXPrJT+41CHJ3tm3bxhNPPMGsWbOY/9fFbKqoIpHcAcSIWDbRaDbZsWzy\ncnMoKMyjb998jhnYl759+7YbIKRSKerq6qjdUUddbSP1dY00NiVoam4hkUiQTCXBYMSwQZReeDbX\nXnstpaWlbR538eLFfO+7/8SLL84hlcoFbsb5IjD8oN/bfWshwo8JuJvS886k9Pxz+POfnuG9ZWto\nSTYSZQopriT8XnwsHw7GAHYC6wnTZazHKCfKMpzVpNgENBKLFtI7v4DBxf0ZfeJIhg8fzh9nPsPm\nyq1E+CIB3yZc0KCjaoBniPJbUswlYsbggcfy0dJJfOpTn+KKK64gOzv7gN+VxYsX893vfI+XXppD\nKtUL+ArO5/azj63+RoT/wfkfIpEmRh03lObmJI2NzTQ3J2hJhj8vQZAk8ATQQsR6cc01n+SXM35J\n7969D/g69iYIAh5//HHu+c//ZuHCpbg7xw4YyJnnnMKnPvUprrrqKnJzczv1nCLSdTo9SO1qClJF\nRKSncne2bNlCeXk5q1evpuy553jhhRfolUoxNZFganMzpUDBXto2A68Dz2Zn80xWFomcHDZWV+9W\nJxbtSzJVT4TjMKaQ4qPARGA80ALsIAx0MrcdwDaiVNFe+gcnitMXp1+6l4XpLfNxE/BXYrxAirlA\nMwP6F3HGWRO4/IrLueaaawC46667eOD+R9m+YxtRLiPF14Bz2HtA2FXWEeWrwBYCLsO5CDid9u9e\n6og4sAYoB9YQZRnGSlJ8EucmwvfqYDiwEniDKC8RMBtnG33yB3DKpDFcdNHfMW7cOEaPHs3xxx+/\nz+A1Ho/z/e9/nwfuf4zqnduJcjkpbgE+Suf8Wzjh2MN8IJ8P/7xklt8hyu24LeTTV13K/ffPoG/f\nvgd19ueff54f/eBu5s5dRCrIwbiBgL9P92UOUWbhvEbAFgp7HcOEU05g6sUf54YbbmD48K78okRE\nDoaCVBERkbSWlhZmz55NZWUlsVhs15aVlbVbORqNUllZyZo1ayhftozy996jfM0a1lZWUhiLMTI7\nm1GpFGfH41xEmOJnfziwnHCMb3dvA+OAnIO+1s7hhKONc4gwCygjYCMQJcoYUnwduIqDD9gkVAW8\nSYRXMV7H2UrATqAByCUWySEnO5eC/Dz69ivACVi1upwIJ5LiHwj/Lfb2FcmhNo8o3yGwBVx5xVQe\n+M2v9ytYnTt3Lt+/8y5eeXkeLcmACNcS8HnCqcr7+pu2BpiL8SoRZpHiPcaOOYn577xJr169Dv6S\nRKRTKUgVEZGjWmVlJc899xzPzpzJrNde48TsbEa6kwQSZiTNSEJYTu+TQJE7o5qbGdXczEhgFDCS\nzg0BPvzpfDh8/u1MbxqlOnSSwHbCILYKqEzvm4ErgOO7r2ttWkCU2wlsHpdf9nEe+M0D9O/fHwin\n777zzju8/vrrvP3227y3ZAXr11ayM15LKkgS5UpSfJEwG3T0AM69kwiXUVCwnHfefZPjj++a9yiZ\nTPLTn/6UVCrFyJEjOeGEEygpKVFgLNIOBakiInJUcXcWLVrEs089xbOPP87y8nL+LiuLT8TjXAwM\n7O4OZjg8g1SR/bUwHFllDkXHDGTHzlpaknVALlFGAmNJcQphkq3WLasTzpsiwj9ikYd44slHuOyy\nyzrhmKHa2lpuu+02Hv3dnwmCARh9cLYTUAPUA1lEI7lkZ+XQKy+XAQMKuf5z13L77bcf1H3HIkcK\nBakiItJttm/fzq9nzGDZwoWcd8klXHDBBV1yn1jrMhszH3qIPz/xBLktLXwykeATLS2cA/TUPwkV\npMrRZTHwN8IgdDRwcPerdtyDwK1Mn347d9xxx0Edaf369dx801d44aXXiPippPg+cD67/zYHhNOP\nqzK2ciL8GreNTD5tAnf+2x1cdNFFB9UXkcOZglQRETnkFi1axH3//u/86YknuNyMyY2NvJafzyup\nFP369ePCqVO54NJLOf/883dN/9tf7s7ChQt57OGHefyRR+jd3My0hgY+HQSM4dCm7zlQClJFDpU3\ngUuZ+vGz+N/nn9nvpXIWLFjAV276Gm+/u5goF5PiDuDkA+jHEiL8koDf0is3h6uvuYQf/vCHHVo/\nt6mpiR07djBo0KADOK9Iz9IVS9BM5YNlZH7t7j/eS52fAhcT3un/eXdfmH5+LVALpICEu0/eS1sF\nqSIiB8jdWbVqFUuXLmXo0KGMHj36oLNrdlQymeSpp57ivh/+kFXLlnFLSws3pVK7Ta8NCMdSXgZm\nFRYyp7mZkuOO48LLLmPSGWcwcOBAioqKKCoqon///nv9Q3LZsmXM/N3vmPnQQyR37uTa5mamJZOM\nPyRX2bkUpIocShuIcCFDhwQsWjK/3f8b4/E4TzzxBNP/5YesWb+eKDeQ4nsc2JI+e0oAzxHlPlK8\nwXFDhvOFmz5Lc3Mza9euZcP6DVRs3E51TZz6hgYSyUacJiDK6FGjefTxh7tsnV2RQ6FTg1QziwIr\ngAuBTYT5yKe5+7KMOpcAt7r7JWY2Bfhvdz8j/doa4DR3r/7w0Xe1V5AqItJBzc3NvP3228x54w3m\nvPACb86fT24qxYRYjM3AysZG8nNzKRkxgpKxYyk59VRKSkooKSlh6NChxONxampqPrxVV1OzdSvN\nDQ0cU1xM0aBBu4LHzC03N3fXlN7/95OfMLylhdvq6vgUHbujrAX4KzArEmFJQQFVZlQGAVUtLdQm\nEvTv1Yuivn0pGjCAooEDWbV6NVUVFVyTSnFtSwunc3iMmO6LglSRQ62eKNeSnTOXN/86i1NOOQWA\nJUuW8OyzzzJnzhwWvbOCLZXbSSRrMQZhfJGAfwCO6aI+VWI8TITHMApxhpNiBDAUGJzeignvqK8l\nwvcJ+BWnThjP4398hNGjR3d6j4IgIAgCYrHOWM5J5MM6O0g9E7jD3aemy98FcPe7M+r8EnjV3R9P\nl5cD57n71nSQOsndt7dxDgWpIiL70NTUxMsvv8zrr7zCnBdfZOGKFYzJy+Ps5uZwA4Zl1HeggnA1\nxpXAyliMlb16scqdDU1NFMZi9IvF6BeJ0M+dfkFAv0SCfs3N9ANySecUzc4Ot2iUSneqkkmqmprI\nycoiAlwZiXBbYyOndeK1JvhwPtPBhKs/Hkiuz55IQapId3Ai/AvYvRT06kNd/Q4cJ8qJOJMJOJ1w\nKu9YoKdm6d1ElP9Lit9Tet5ZPDbzdx2aBrxx40Z++9vfMmfOHLZvq6Z6Wx11tQ3UNzTT3NJMMpUg\nFbQQZo6OMfCYYj55xQV84xvfYNy4cV1+VXL06Owg9SrgIne/KV2+Hpji7rdl1HkG+JG7v5kuzwJu\nd/d3zKycMG99Cpjh7r/ayzkUpIqIZEgmk7zyyis89sADPPXss3wkFuNj8ThnBwFT6L4VKp3w/g0D\nendTHw53ClJFutNrQByYQDhqeTjOy1hNlG8T8BJXXHERDz38EL17f/A/8ty5c3n00Ud5+aXXef/9\njbQk40Q5CedMAoYA/fax9QV2AM8TZSYpXiM3O58zz5rAzV/5MldfffV+39crkqmzg9RPA1M7EKTe\n7e5z0uXMILXY3TebWRHwEnCbu8/e4xwKUkXkqNearfaxBx/kj3/4AyOAafE4V7szpLs7J51GQaqI\ndI5FRPlH3BZwzrmnsfK9DWzZthX3CDGmkOQiwnkoEzmwfOctwGwi/AnnSbA6Ro8cwZSzJ5JKpUgk\nEru2ZDL5wb45SWNDM/G6Rurrm2lubqG5JUEimSAVJAmCJE4LsUgvJp56El/+6o187nOf0zTjo0Bn\nB6lnANMzpvt+Dwgykyelp/uWufvMdHnXdN89jnUHEHf3e/Z43jPTg5eWllJaWtrR/ouIdCp3Z86c\nOVRWVlJYWEhhYSEFBQW7Pe6sNe8SiQQLFy7kD488wuOPPEKflham1ddzTRDQNcvQS3dTkCoinesN\nIjyWnrJ8NnACnT9C7IQ3kDxNjLdxsoAsnJz0PhsnG8gBYul9YXor2MvjAmAlxh+AmUA1Y04YyQ1f\nvI7bbruNgoKC/epdEARs27aNyspKtm7dSmVlJdu3b2f79u3U1tby5S9/mTFjxnTKOyEdV1ZWRllZ\n2a7ynXfe2alBaowwcdIFwGbgLdpOnHQGcK+7n2FmvYCou9eZWT7wInCnu7+4xzk0kioi7XJ33L3L\nphu1tLTw+OOP85M776Rp61ZOjESoMyMO1LlTl0oRTyapSySImNE7L4/jhw5lwqRJnHzGGUyYMIEJ\nEybQp0+fffZ//fr1zJs3j3mzZzOvrIx3V65kRE4OVzQ2cu1hmq1W9o+CVBGRPa3AeJIIvyPFaoYc\nO4Srp13GiSeeyLp169i0aRNbtmxh6+ZtbN9eS11dI41NTSRTzQTeQpjRIAbkYuRh5GMUYBTgZBGw\ngOuuu5KHHn5II7bdqCuWoLmYD5agecDdf2RmNwO4+4x0nZ8BU4F64Avpqb6jgCfSh4kBj7j7j/Zy\nfAWpIrJX7s68efP446OP8sfHHmP9tm3kZWVRmJdHYV4eBb16UVhQQGHv3hQUFlLYty8nTJjAeaWl\nTJo0iays9vPNVldXM+PnP+dn99zD2GSSb8bjXATsKxR2wvQSO4FVwCJgcW4ui3NyWNLQwDF9+nDy\n+PFMOPNMTho/nrXl5cx7+WXmvfMOlkgwJRZjcjzOFHcmAXsPaeVIpSBVRKQtFcDTRPktsB2jCBhM\niqE4g4GiPbZ+QD5hqLEvi4hwAzk5G3ngwZ8xbdq0Dvdm/vz5fPnGW3h38d8o7NWPz95wBXfd7bw9\n9QAAIABJREFUdRcDBgw40As8anV6kNrVFKSKHHni8TgVFRVs3ryZiooKkskko0ePZvTo0fTv37/N\ntkEQMHfuXP746KP86fHHyW9u5urGRq5KpRhPuBhzXXqL7+Xx0uxsynJzKW9u5oyTT+a8T3yC0o99\njNNPP323aborV67k3rvv5rHHHuMKM77R2MiEg7zuACgnHbhGIryXn89xjY1MSSaZQpiF93BM0yGd\nR0GqiEh3CIAHgG/xkbEn8NwLTzN06NB91n7qqaf4+q23s27jBqJ8gRTfABYS5aekWMAJI0fxf777\ndW688UYllOogBaki0ilqa2tZsmQJ9fX1NDU10djYuGuf+bhu+3Yq1q6lYuNGNm/dSkVNDalUisG5\nuRRHowwOAqLurI5GWdHQQHZ2NiXHHUfJSSeFa3iOGUNJSQk7d+7kD7/7HX/6/e/pl0pxVUMDV6dS\njOXAArsaYDbwWlYWZXl5rGxqYvJHPsI5l1zCO2+8wV/nzuUrySS3JJO0n8RfpHMoSBUR6U7biPJ1\nAvszt976Je69995dQWYQBPz85z9n+j/fzfadcYxv4txGOFqbaRPGQ8DPiUbrueBjZ/LDu3/AxIkT\nD/G1HF4UpIocJdydurq63UYsKyoq2LxuHTsqKyk55RQmnHwyJ598MoMHD8as7f8XduzYwezZs3lt\n1izKnn+e5WvXMq5XL3oDue7kupPnTm4qRV4qtWtfwAdLj7cuP96bvQeWTrj25W5reOblsRLIAj7d\n0MBVqRQnddq79IGdwBvA67EYo5JJ/p6euxqeHLkUpIqI9ARvEuEGCgriPPLYr5k9ezb3/fcDNDVn\n4/wr8HnClcPb4sCbRPk5KZ6kb2F/zvroyUy9eCrXXHMNAwcO7PKr2JtkMsnixYupra3l3HPPPaiR\n3o0bN7Jp0yamTJly0P1SkCpyhEqlUjz55JPc/5//yZq1a9lcXY0Bg3Nydo1YFjc1MTiRoDewMiuL\nRb16sai5GY9GOfnEE5kwZQonn346EyZMoLi4mL/+9a+89tJLvPbCC6xav54z8vI4Lx7nvCBgMmF+\nPhHpPApSRUR6iiTGvTj/QoSRBNwFXEGYhmd/1QPPEuUVYDYpVpMVK+S4YYM4+9zTufzyy7n00kv3\nuTpAbW0tFRUVVFVVUV1dTVZWFjk5OeTk5JCXl0dOTg65ubnk5eWRm5tLQ0MD8+bN4+233+a9995j\n1fK1VGzeTl19nGQQB/KACNGIM/bEUVz/uWnccsstHcqc/PLLL3PffffxyktvUddQDWQRMeP4kcO4\n9LKP85WvfOWAsiUrSBU5wjQ2NvLwgw/yn3fdRVE8zjficU4mHLEs7EB7B7YAi0nfK1lQwKJIhI3N\nzUzOyaG0ro7z0kl8OmdhFRHZFwWpIiI9TQvhfK7OzBrRTPhX1zyivEzAPJxq8nL64u4kUwmCIEng\nCcLsxEaYnTjcnABIAUmcFOwqt24RIhxLhBE4JxHOQRuV3kYQLvPjwGKMpzB+T8BqBh4zmEsvO59v\nfvObjB8frinQ0NDA/fffz/88+Ah/W7KKZABRPkGKqwkXeMkHlgCziPJnUswjO5bP+PHH86mrLuem\nm27q0KixglQ5YiWTSSoqKli/fj0bNmwgJyeHyZMnM2TIkO7u2j65O1VVVaxcuZLy8nKGDh3KxIkT\n6du3b7ttq6ur+cV993HfT37C5FSK2+vrORsl3hE5nClIFRE5Wm0nDPay+GDt2NZ1Yw/F3LUq4Hli\nzCRJGbnZ+fQuLKBye0U62L0a5wrgVPa9xgGEQfVbGC8Q4WlSLONTV3ySPz35xzbP3hVL0EzlgyVo\nfu3uP95LnZ8CFxMm3vy8uy/cj7YKUmWXVCrF/PnzWbBgARvWrGH9ihVsWLuW9RUVbNm5k6KcHIZl\nZTE8CKg3Y15LC7l5eUyZNIkpF1zA5ClTmDRp0n4vBN2WeDxOU1NTm3WCIGDjxo2sXLmSlcuXs3Lh\nwnC/YQORIGBMbi4jUinWRyIsamxkUP/+nDZxIhPPPZfTJk1i4sSJu7Lerl+/nv+6+24efughrgC+\n3djI2E67GhHpTgpSRUSk+7UQZurYAHycMKvIgfpvxo15kCXL322zVqcGqWYWBVYAFwKbgPnANHdf\nllHnEuBWd7/EzKYA/+3uZ3Skbbq9gtTDVHNzM+vWraO8vJw1a9ZQvmIF9bW1nDxlCqeddhrjx48n\nN7e9m85h8+bNvPDCC/zlj39kVlkZQ6JRzkokOK6pieGEy3YMJ5zeuud0VCdc8mMe8FZ2NvNyc1nc\n2MjxxcVM+ehHGTl27K75+/vax+PxDxIPrVvH5jVrqNi4kYrKSjZXVxMEAb06sPjzkOxsStwpqa+n\nJAgoAUqAY/aolyL8xXgbeCcri7fz8ni3qYkBffowasQIFv7tb3wpleLriQRdOUZcBpR24fFF5MMU\npIp0tzL06SfSmX7KuDG/6fQgtb2/vCcDq919bfrgM4HLgcxA8zLgYQB3n2dmfc1sEDCyA22lB3J3\nduzYQVVV1W5bxebNrFm6lPKVK1mzYQNbd+5kaF4eo6JRRra0MKqxkaHAvJkz+XlWFqsaGhgzbBgT\np0zhtHPO4bTTTmPChAlEIhHmzJnDX555hr88+SQbt2zh72IxptbX8xPYr8DMgOPT23UtLdDSQguw\naN065q1bx6ZIhMpYjKZolKZIhMZIhCYzGs1oAhoJZ9oPTqUobmqiOJlkErtnqi0ELJlsvzPtjLa2\nigJj09vfJxKQSBAAq6qqWF5VxXlA+5OBD14Z+pgWEZGjTRn69BPp+doLUocQjgO32gjsmYN4b3WG\nEP59317bDkkkEuzYsYOamhpqamp2e1xTU0NNZSWpZJKCfv0o7N2bgoICCgsLd+1bHwPU1dURj8ep\nq6vb7XG8ro666mryCgoYPHQoxcXFDB48eNfWkRHBg5VMJne7vh07dtDS0kIikSCZTO7a2isnW1rC\nLZEg0dxMMpH4oJzet26J9L6puZlt27dTVVPD9vp68qJRirKzKYpGKXKnKJnk2KYmzg4C/p7wG4hh\nQCwe//CFNDZCYyONwN/WrOHtNWt4+6mn+HVWFssbGohGIozLzWVqPM79QcDpQLS5udPex2zg9PRG\nEEBLS6cdu6tEgDHpTURERETkaNZekNrReUhdlsvlmWee4bLLLgMgLxqlbyxGv0iEfkC/VIp+yST9\ngoAoYQbTVdEo8WiUukiEuBl1QDwIqEulACiMRimMRCggfatyKkVhessHaoD3srPZHI1S4c7m5maC\n9HTkPr16UTxwIAX5+cRiMWJZWWRlZRHLytpVjmVlEcvOJkildgsGk+lAsjWgTLS0UFtXR01tLTXx\n+K5zAORGo/SLxegbiZBD+I+Uld7H3MOyOzF3ou7h4yDY9VystW56y81sv+fx0lsOMAAoSu9zUql2\ng7utHfj3G5LeLksHrs2Eo5d9M469pQPHkc5XSzgPX0S6k34LRQ4tffqJdK4dXXLU9oLUTYQDZq2G\nEY6ItlVnaLpOVgfaAuEc5Y5oTKVoTKWoaKtSKhVu+1DXkWmb+wjOdjY0sHPt2vbbH6SmVIqK9q5T\npBP8V3d3QOSoN7S7OyByFNKnn0hnWrqi4/FcR7UXpC4ARpvZCGAzcA0wbY86TwO3AjPN7Axgh7tv\nNbPtHWi7XzfQioiItDKztYQrlo9094b0czcCn3X387v4vF9y95cznvt8+rlzuuq8+8vMHgQ2uPu/\ndndfRERE9kdbi+Dg7knCAPQF4D3gcXdfZmY3m9nN6TrPAeVmthqYAdzSVtsuuxIRETkaRYCvH+Jz\nOkd4Wl4za/PvAxERka7U7oeQuz/v7mPc/QR3/1H6uRnuPiOjzq3p109293faaisiItJJHPhP4Ntm\n1mdvFczsRDN7ycy2m9lyM7s6/fxIM6vJqPcrM9uaUf6tme1P8Ltb0Gpm3zWz1WZWa2ZLzeyKjNc+\nb2ZzzOwnZlZjZu+b2Znp59eb2VYzuyGj/kNm9kszezF9vDIzG57x+n+l2+w0s8Vmlrm0cn8zezbd\n7q9mNiqjXdBaTp/jF2b2nJnFgVIzKzazP5lZpZmVm9lt+/F+iIiIHDB9UyoiIoezBYRrSnx7zxfM\nLB94CfgdYV64a4Gfm9mJ7r4GqDWzU9PVzwXqzOzEjHJZG+fd81aVPcurgY+6e2/gTuB3ZnZsxuuT\ngUVAf+BR4HHgNMIVta4HfmZmvTLqXwd8nzC33bvAI+lrvAg4Bxjt7n2Aq4HqjD5dC0wH+qX79IM2\nrmkacJe7FwBzgWeAhYTZ+i8A/tHMPt5GexERkU6hIFVERA5nDvwrcJuZDdjjtU8Aa9z9YXcP3P1d\n4AngM+nXXyMcMRyUPs4fgfPMbCTQ290X7eOcBvw5PQpakx6R/X9kjKa6+x/dfUv68e+BVey+DFtr\nvxz4PWEGpe+7e8LdXwJagBMy6j/r7m+4ewvwf4EzzWxIul4hcJKZRdx9Ret50/15wt0XuHuKMLA9\npY338s/uPjf9eAIwwN3/zd2T6aD+14RBr4iISJdSkCoiIoc1d18KPAt8l92n3R4HTNkjmLwOaB3R\nfA0oJRyJfD1dPo9wFHV2W6cELnf3fq0bYT6GXaOpZnaDmS3MOO944JiMY2Su4tWYvo6qPZ4ryDjf\nruz47l5POFpa7O6vAj8jDJK3mtkMMyts4zwF7N1u5yB874r3eO++BwzcR3sREZFOoyBVRESOBHcA\nNxEuzdxqPfBaZjDp7oXu/rX0668RBqilhFN73wDOJgxUy/bz/JkB6nHA/cDXgP7pIHYJB76muJGx\npJuZFRBOE94M4O73ufskYCxQAvyfAzxPZoC/nnC0N/O96+3unzjAY4uIiHSYglQRETnsufv7hPd1\nZiY7+l+gxMyuN7Os9HZ6632n7r4aaCK8B/Q1d68DKoFPEwawByqfMODbBkTM7AuEI6kH4xIzO9vM\nsoG7gLnuvsnMJpnZFDPLAhoIr6d1sfD9CYr3rPsW4T26t5tZnplFzWy8mU06yOsQERFpl4JUERE5\nUnwf6EV6RDAddH6c8D7KTUAF8CMgO6NNGbDN3TdllAHeYf/sWpbG3d8D7iFMPrSFMEB9Y29193iu\nrWM/SjhavB04lTCwBuhNOGpbDawlDIz/o4Pn2fNx5j21AeE9vacA5UBV+jy92+iniIhIp7AwZ0Mb\nFcymAvcCUeDX7v7jPV7/LHA74bewdcBX3X1x+rW1QC3ht7oJd5/c2RcgIiJyJDOzB4GN7v4v3d0X\nERGRQyHW1otmFiVMyHAh4bfQ883saXdfllGtHDjX3XemA9r7gTPSrzlQ6u7ViIiIyIE40HtZRURE\nDkvtTfedDKx297XungBmApdnVnD3ue6+M12cR5hGP5M+XEVERA7c3qbtioiIHLHaHEklzJK4IaO8\nkd3XedvTl4DnMsoOzDKzFDDD3X91QL0UERE5Srn7F7q7DyIiIodSe0Fqh7+5NbPzgS8Spu9vdba7\nV5hZEfCSmS1397bWnhMREREREZGjWHtB6iYy1mZLP964ZyUzmwD8Cpjq7jWtz7t7RXpfZWZPEk4f\nnr1HW01hEhEREREROYK5e4dvA20vSF0AjDazEYSLhl8DTMusYGbDgSeA69NrzrU+3wuIunudmeUT\nLgNw5z463NH+ikgnmT59OtOnT+/ubogclfT7J9I99Lsn0j3M9i9NUZtBqrsnzexW4AXCJWgecPdl\nZnZz+vUZwL8C/YBfpE/eutTMIOCJ9HMx4BF3f3H/LkdERERERESOJu2NpOLuzwPP7/HcjIzHNwI3\n7qVdOeEi4CIiIiIiIiId0t4SNCJyhCotLe3uLogctfT7J9I99Lsncniw7r4f1My8u/sgIiIiIiIi\nXcPM9itxkkZSRUREREREpMdQkCoiIiIiIiI9hoJUERERERER6THaDVLNbKqZLTezVWb2nb28/lkz\nW2Rmi81sjplN6GhbERERERERkUxtJk4ysyiwArgQ2ATMB6a5+7KMOmcC77n7TjObCkx39zM60jbd\nXomTREREREREjlCdnThpMrDa3de6ewKYCVyeWcHd57r7znRxHjC0o21FREREREREMrUXpA4BNmSU\nN6af25cvAc8dYFsREZEjmpnttomIiMiHxdp5vcPzcM3sfOCLwNn721ZEREREREQE2g9SN/1/9u48\nvqri/v/4a+7NvieQsAYFUVERUQRUKkZBi9Qq1pWq1Lpb16871SpUW621FZdqcakttVV/bXGrG7gE\n0SqLuKGArCZAIGELWe/6+f1xLhhCyAIJRPJ+Ph7nce+5Z+acOZF48rkz8xkgv85+Pl6P6DZiyZKe\nBEaZ2caW1AWYMGHC1vcFBQUUFBQ00SwRERERERFpjwoLCyksLNzp+k0lTorDS340AlgNzGb7xEm9\ngHeB883s45bUjZVT4iQREekQ6g/x1fNPREQ6gpYmTmq0J9XMws65q4G3AD/wtJktcM5dHjs+GbgT\nyAYejz18Q2Y2ZEd1d+quREREREREpENotCd1tzRAPakiItJBqCdVREQ6otZegkZERERERERkt1GQ\nKiIiIiIiIu2GglQRERERERFpNxSkioiIiIiISLuhIFVERERERETajSaDVOfcKOfcQufcYufcrQ0c\n7+ec+8g5V+ucu7HesRXOuS+cc58652a3ZsNFRERERERk79PoOqnOOT/wKDASWAXMcc69Um+90/XA\nNcCYBk5hQIGZbWil9oqIiIiIiMherKme1CHAEjNbYWYh4HngtLoFzKzMzOYCoR2co9nr4YiIiIiI\niEjH1lSQ2gMorrO/MvZZcxnwtnNurnPu0pY2TkRERERERDqWRof74gWZu2KYmZU453KB6c65hWY2\ncxfPKSIiIiIiInuppoLUVUB+nf18vN7UZjGzkthrmXPuRbzhw9sFqRMmTNj6vqCggIKCguZeQkRE\nRERERNqRwsJCCgsLd7q+M9txZ6lzLg5YBIwAVgOzgbH1EidtKTsBqDCzP8T2UwC/mVU451KBacBE\nM5tWr5411gYREZG9hXPbpmnQ809ERDoC5xxm1uxcRY32pJpZ2Dl3NfAW4AeeNrMFzrnLY8cnO+e6\nAnOADCDqnLsOOBjIA6bGHshxwD/qB6giIiIiIiIidTXak7pbGqCeVBER6SDUkyoiIh1RS3tSm8ru\nKyIiIiIiIrLbKEgVERERERGRdkNBqoiIiIiIiLQbClJFRERERESk3VCQKiIiIiIiIu1Gk0Gqc26U\nc26hc26xc+7WBo73c8595Jyrdc7d2JK6IiLSfpgZFRUVe7oZIiIi0sE1GqQ65/zAo8AovLVPxzrn\nDqpXbD1wDfDATtQVEZF2YPXq1Zw8fDh5OTn85amn9nRzREREpAOLa+L4EGCJma0AcM49D5wGLNhS\nwMzKgDLn3I9aWldERPa8F55/nmsvu4wra2q4Pxzm7OuuY/YHH/DQ5MkkJia2+HwzZ87kP889R6i2\nllAwSDgYJBwKEQ6Hvf3Y+wMPO4xbfvUr8vLy2uCuRERE5PuqqSC1B1BcZ38lMLSZ596VuiIi0sY2\nbNjA1RddxLzp0/lvdTWDY5/Prq7mZ//v/3HcJ5/w7zfeoGfPns0639q1a7nlmmt497XXuLq6mjS8\nh0x87LX+9s6sWRz09NP84tprufG228jKymrV+/vmm2949q9/5fm//pV+Bx3EA48/zgEHHNCq1xAR\nEZHW19ScVNuFc+9KXRERaUPTpk3jsP33J/eNN5hXJ0AFyAD+U1PDaQsXMuTQQ5kxY0aj54pEIjz2\n6KP0328/8l5+ma+rq7kVuAq4HLgIGAf8FDgb+AlwKvBQMMi8mhpWPvww++fn87vf/pbq6upduq+y\nsjIeefhhhh50EMMHDqTiD3/g2ZIShs+YwbCBA7nxmmvYtGnTLl1DRERE2lZTPamrgPw6+/l4PaLN\n0ey6EyZM2Pq+oKCAgoKCZl5CRGTvNn/+fP70hz+wvqSE2poaaqqrqa2tpaa2ltpAgNpAgJpgEIAB\nBx/M0BNOYOgxxzBkyBA6deq03fmqqqq49brreOW553imupqRO7iuDxgfDjNo0ybOOflkbp04ketv\nugnn3DblZs+ezS/GjSNl5Ureq6qifwvvbx/gmZoaFgC/+s1v2P/3v+eOe+7h4ksvJSEhoVnnqKmp\n4ZVXXuHvjz/OBx9/zCl+PxNj97blITckEuGCmhrueOop+k2ZwsT77uOSyy7D7/e3sMUiIiLSlMLC\nQgoLC3e6vjPbcYency4OWASMAFYDs4GxZrbdvFLn3ASgwsz+0JK6zjlrrA0iIrtLbW0tkx9/nPsm\nTiS/Wzf+7847OfPMM4mPj9/tbVmwYAETb72Vwrff5ppAgL7RKMlAUmyr/z4CfArM9vuZlZrK3Npa\ncrOzGTp0KENHjGDIkCGEw2EuOucchm7cyCM1NTR3cO0K4CepqfQ78USefPZZUlNT2bBhA7ffeCMv\nvfAC99fUcD7gmjhPc8wF7khNZXFaGhMfeIBTTjmFdevWUVZWtu22ahVlq1ZRWlLCrM8/Z3BcHBdU\nVHA6kNbENT4Frk9NpbxrVyY99dRu/WK0fpCv55+IiHQEzjnMrNl/KjQapMZOeDIwCfADT5vZvc65\nywHMbLJzriswB2+EWBSoAA42s8qG6jZwfgWpIh3c/PnzKS4u5qSTTtojPVuBQICnn3qK3/7qVwwK\nBrmzqopVwINpaSxNTOTqG2/k0iuuIDs7u83bsnjxYn5922289frr3BAKcXUk0mTQ1ZAI3reEs4DZ\nSUnMSkigNBRiUk0NZ+7E+WqAK5KS+LR7dy6+5hruvesuzggEuCcQoC1+KjOA29PS+DwQIDcxkVy/\nn1wzcsNhcoNB7xXIBQ4Hurfw/Ab8B7gpJYVBw4fz+z/9iT59+jS7fiAQYNOmTWzcuHGbbfPmzeTk\n5NCrVy/y8/Pp1q3bNv+mFaSKiEhH1OpBaltTkCrScRUVFXHnzTfz5quv0svvpyw5matuuomLL710\ntwSEoVCIvz7zDPfcfjv9a2qYWFXFkfXKzAMeTE7mNeC888/nultuoW/fvo2eNxqNUlJSQlFRETk5\nOeyzzz4kJSU1WmfZsmXcc/vtvPrSS1wXDnNtOEzGLt1d6zPgcZ+P/6ak8OvKyu1+Vt9HNcAf4+L4\nY1wceTk5jZY1oKK6mo1VVYQiEbITEsiOjyfb5yMbyI5ESI9EWB8XR7HPR3EoxLpAgG5ZWfTq1o38\nfffluf/+d9tztvHzr7S0tM2zJweDQYqLi0lOTqZ795Z+XSAiIh2BglQRafc2bNjAvRMm8JennuIX\noRA3xwKyOcAjycm8asZZZ53FNTffzKGHHtrq1w+Hw/x9yhTuHj+evlVVTKyq4ugm6qwC/hQfz5N+\nP8OGDeO6228nOzubZcuWsXz5cpZ9/TXLFixg+bff8m1pKVnx8eQnJLAxEqGopobcjAz69OxJ7/33\np0///vTebz/69OlDZmYmj/z+90z917+4Khzm/8LhZg/DldazAVjbjHLpQBaQSvOGNwfx/u0U4aW7\nv6De8ThfFl1yO3H44IMYMWIEP/nJT+jVq1cLWr69oqIibrnlFl6aOp1AqJzMtDwuuPAMJk6cSE4T\ngXh9paWlPPDAA7z7zgw2rttMRWUtNTUBgqEg4UiQqAVjd5kERIj3p9Knd3eOH3ksY8eO5Qc/+AE+\nX1M5GkVEZG+nIFVE2q2amhoeefBBfv/b33JGJMJdtbV0a6DcWuAJv58/JyRwwMEHc80vf8mpp55K\nXFxTud48oVBo61DM+kMy161bx5THH6dnRQW/rqzk2BbeQxUwxTkeT0vDnKMP0Ke2lt7BIH2A3sC+\neEHMFhG8rHHLgWXAcp+PZSkpLPP7WRMOMzYY5KZQiJaFD/J9tP3T+XNgHj7+h+MjInyD35dEbqdO\nHDXsMMaMGcNZZ51FSkpKo+eNRqM8+eST3PebP7KiuAg/BUT4P2AY8Ap+HiHCpxywXx9uGX8DP//5\nz3cYPC5YsID777+fV198h/Xla/FzOBFOxRtcnY0XpmfX2TLwZvWE8Gb8foCfaUT4GEeIvE55HH3s\nQMaMGcMZZ5xBWtrODGAXEZHvMwWpItLuRCIR/j5lCnfefDNH1tTw2+pq+jWjXhCYCjySns7KxESO\nHTaMwJYMtzU122S5rQkEqA0GqaitpSYYJHPLUEy/3/uzOholOxwmOxjkh5EIx7ftLYs0aPunc/3n\n35bZxHPw8y7GDKKUkJacw6ED+vDDk09i3Lhx9O7dG/DmMN9808289toMIpEU4BqMi4CGhvgW4/gL\nMBm/v5qRI4/h3vt+y8CBA3n33Xd58MFJvPf2LKpqN8eC3POB0bDTs44N76uZ/+Hn7di9FOP3pZKR\nmkH+Prkc3P9ADj/8cI499lgGDx7c7C+iRETk+0VBqojsNoWFhfzr738nGg43Wu6D998ns6yM+6uq\nOGYnrzUP+BIvk+2WzLYNZbtNx8vuqgGG0h41HaQ2ZBPwEY4Z+JhOhPnE+VPISEtjQ/k6/JxMhOuA\n4Q1eYXsGfIifx4jwEn5fHJGow8+pRBgLnID329QWAniB6zfAN/j5HPiKKMsxKon3Z5CclEx8nJ+E\nhDgS4uNITEogKSWB5OQEUtKSSUpKIi8vj379+jFgwACOPPLINp93KyIiu0ZBqoi0uS+//JJbr76a\nhXPn8ovq6m2GtjakN/BDWmeJEpHvs50LUusL4Q0TXoz3m7UrA8Wr8ALGAXhDdvekCmAJUArU4qW1\nqvtaC1ThowIfJRjLiLIaowyIIyEulbTUVPLyMtl3vx7k5eWRlZVFTk4OOTk5dO7cmc6dO9OlSxe6\ndu1K586dNV9WRGQ3UZAq0kFt3ryZ4uJiioqKWLt2LUlJSaSnp5OWlrbNa3p6OikpKTv1x1ndbLy/\nDAS4IholoQ3uRWRv1TpBqmzLgHV4qam8FFWOpfhZA5QDmzEqMKpiWzVewBsm3p9F376nBZ1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gktDVKb6kAcAiwxsxWxkz8PnIb39ckWpwJ/AzCzWc65LOdcV6B3M+rusnTgiNY8oUgHUUjHe0yL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\nwbBIlSRJkiQNxr9N48Mh+Ai6KgAAAABJRU5ErkJggg==\n", 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ju/Kd73yHPn36cNppp9VpL9oDmYJUEZE4ZWVlzJ49m6lTpzL144+ZPmMGxWVl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -9340,17 +10440,29 @@ "" ], "text/plain": [ - " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin Solid States Total\n", - "0 9 29 6 0 6 18 13 10 237 328\n", - "1 9 29 6 0 6 18 13 10 237 328\n", - "2 9 0 6 0 0 18 13 10 237 293\n", - "3 9 0 0 0 6 18 0 0 237 270\n", - "4 9 29 6 4 6 18 13 10 237 332\n", - "5 9 0 0 4 6 18 13 10 237 297\n", - "6 9 29 6 0 6 18 13 10 237 328\n", - "7 9 29 6 4 6 18 13 10 237 332\n", - "8 9 29 6 4 6 18 0 10 237 319\n", - "9 0 29 0 0 6 18 0 10 237 300" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin \\\n", + "0 9 29 6 0 6 18 13 10 \n", + "1 9 29 6 0 6 18 13 10 \n", + "2 9 0 6 0 0 18 13 10 \n", + "3 9 0 0 0 6 18 0 0 \n", + "4 9 29 6 4 6 18 13 10 \n", + "5 9 0 0 4 6 18 13 10 \n", + "6 9 29 6 0 6 18 13 10 \n", + "7 9 29 6 4 6 18 13 10 \n", + "8 9 29 6 4 6 18 0 10 \n", + "9 0 29 0 0 6 18 0 10 \n", + "\n", + " Solid States Total \n", + "0 237 328 \n", + "1 237 328 \n", + "2 237 293 \n", + "3 237 270 \n", + "4 237 332 \n", + "5 237 297 \n", + "6 237 328 \n", + "7 237 332 \n", + "8 237 319 \n", + "9 237 300 " ] }, "execution_count": 191, @@ -9525,17 +10637,29 @@ "" ], "text/plain": [ - " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin Solid States Total\n", - "0 0 0 0 4 0 0 0 0 206 210\n", - "1 0 0 0 4 0 0 0 0 206 210\n", - "2 0 29 0 4 6 0 0 0 206 245\n", - "3 0 29 6 4 0 0 13 10 206 268\n", - "4 0 0 0 0 0 0 0 0 206 206\n", - "5 0 29 6 0 0 0 0 0 206 241\n", - "6 0 0 0 4 0 0 0 0 206 210\n", - "7 0 0 0 0 0 0 0 0 206 206\n", - "8 0 0 0 0 0 0 13 0 206 219\n", - "9 9 0 6 4 0 0 13 0 206 238" + " Colorado Florida Iowa New Hampshire Nevada Ohio Virginia Wisconsin \\\n", + "0 0 0 0 4 0 0 0 0 \n", + "1 0 0 0 4 0 0 0 0 \n", + "2 0 29 0 4 6 0 0 0 \n", + "3 0 29 6 4 0 0 13 10 \n", + "4 0 0 0 0 0 0 0 0 \n", + "5 0 29 6 0 0 0 0 0 \n", + "6 0 0 0 4 0 0 0 0 \n", + "7 0 0 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 13 0 \n", + "9 9 0 6 4 0 0 13 0 \n", + "\n", + " Solid States Total \n", + "0 206 210 \n", + "1 206 210 \n", + "2 206 245 \n", + "3 206 268 \n", + "4 206 206 \n", + "5 206 241 \n", + "6 206 210 \n", + "7 206 206 \n", + "8 206 219 \n", + "9 206 238 " ] }, "execution_count": 192, @@ -9557,7 +10681,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 193, @@ -9566,9 +10690,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -9621,7 +10745,7 @@ { "data": { "text/plain": [ - "0.88929999999999998" + "0.89610000000000001" ] }, "execution_count": 195, From dadbef746bda7bfc556a8ebf1d4e54f4a75581b5 Mon Sep 17 00:00:00 2001 From: Jim Date: Wed, 25 May 2016 12:11:27 -0400 Subject: [PATCH 10/11] remove extra files --- Simulation.ipynb | 548 ----------------------------------------------- Slides.ipynb | 112 ---------- 2 files changed, 660 deletions(-) delete mode 100644 Simulation.ipynb delete mode 100644 Slides.ipynb diff --git a/Simulation.ipynb b/Simulation.ipynb deleted file mode 100644 index d7b0492..0000000 --- a/Simulation.ipynb +++ /dev/null @@ -1,548 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 270, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from pandas import Series, DataFrame\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 381, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(1)\n", - "N = 10000\n", - "states = [\"s1\", \"s2\", \"s3\", \"s4\", \"s5\"]\n", - "state_electorial_college_votes = Series(np.array([5, 10, 15, 20, 25]), index=states)\n", - "local_vote_predictions = Series(np.array([0.53, 0.54, 0.56, 0.52, 0.46]), index=states)\n", - "local_margin_of_error = Series(np.array([0.07, 0.05, 0.04, 0.02, 0.04]), index=states)\n", - "national_margin_of_error = 0.03" - ] - }, - { - "cell_type": "code", - "execution_count": 316, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "local_error_sim = DataFrame(np.random.randn(N, len(states)), columns=states).multiply(local_margin_of_error)\n", - "national_error_sim = Series(np.random.randn(N) * national_margin_of_error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "LocalError = \\sqrt{TotalError^{2} + NationalError^{2}}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "TotalError = \\sqrt{LocalError^{2} + NationalError^{2}}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# total_error_signs = DataFrame(np.random.choice([-1, 1], size=local_error_sim.shape), columns=states)\n", - "# total_error_sim = (local_error_sim**2).add(national_error_sim**2, axis='rows') * total_error_signs\n", - "total_error_sim = local_error_sim.add(national_error_sim, axis='rows')" - ] - }, - { - "cell_type": "code", - "execution_count": 318, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "simulated_vote_predictions = total_error_sim.add(local_vote_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - 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" - ], - "text/plain": [ - " s1 s2 s3 s4 s5\n", - "s1 1.000000 0.199057 0.219443 0.320138 0.239901\n", - "s2 0.199057 1.000000 0.307852 0.431101 0.298213\n", - "s3 0.219443 0.307852 1.000000 0.507409 0.356670\n", - "s4 0.320138 0.431101 0.507409 1.000000 0.486735\n", - "s5 0.239901 0.298213 0.356670 0.486735 1.000000" - ] - }, - "execution_count": 319, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulated_vote_predictions.corr()" - ] - }, - { - "cell_type": "code", - "execution_count": 355, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "bins = np.arange(0, 1, 0.01)\n", - "histograms = {s: np.histogram(simulated_vote_predictions[s], bins=bins, density=True)[0] for s in states}" - ] - }, - { - "cell_type": "code", - "execution_count": 359, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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/4oQTTsi6KElSOysUChQKhTafpy09o6OAGSmlzZrffxw4PaV0SIt29oxKkipe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\n8UPATufcQ2Z2B9DKOXfnAd7rtPG3iIgkO7Nv7/Wt7z4REUk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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rep_electorial_college_votes.hist()" - ] - }, - { - "cell_type": "code", - "execution_count": 409, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.65849999999999997" - ] - }, - "execution_count": 409, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# DEM chance of winning\n", - "sum(dem_simulated_electorial_college['total'] > rep_simulated_electorial_college['total']) / float(N)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Slides.ipynb b/Slides.ipynb deleted file mode 100644 index 5802643..0000000 --- a/Slides.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction - Nate Silver's Election Prediction Meta-Analysis\n", - "\n", - "* Presenters: Ritesh Bansal and James Schmitz\n", - " * We don't know Nate Silver, but we are motivated by curiousity and respect of his work\n", - "* Skipper Seabold put together original presentation and Jupyter Notebooks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nate Silver's Analysis Goal\n", - "\n", - "\"...but somewhat contrary to the media portrayal of election forecasters as wizards who conjure up spells from their spreadsheets, our goal is not to divine some magic formula that miraculously predicts every election. Instead, it’s to make sense of publicly available information in a rigorous and disciplined way.\" - Nate Silver" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Meta-Analysis\n", - "\n", - "(insert definition)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Polling - Combining Poll Results\n", - "\n", - "* Remove historical biases and de-emphasize old polls or polls with methodology flaws\n", - "* Why are polls not accurate?\n", - "* Likely votes vs Actual Voters\n", - "* Is the sample of atual voters representative of the actual population?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Poll Time Decay\n", - "\n", - "* Polls decay over time\n", - "* Adjust older polls for trends" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Macroeconomic Prediction\n", - "\n", - "(is this the right term for this? how about fundamental analysis based on incumbant status, etc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combine Poll results with Macroeconomic Prediction\n", - "\n", - "* ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Project out by allocating the undecided voters\n", - "\n", - "* ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Prediction Uncertainty and Simulate\n", - "\n", - "* Guestimate Electorial college votes" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 5f74c8cea9e02492281bd2616bf59e2cc1fa4bca Mon Sep 17 00:00:00 2001 From: Jim Date: Wed, 1 Jun 2016 16:23:47 -0400 Subject: [PATCH 11/11] remove our names --- silver_model.ipynb | 28 ---------------------------- 1 file changed, 28 deletions(-) diff --git a/silver_model.ipynb b/silver_model.ipynb index e35d205..23ac87c 100644 --- a/silver_model.ipynb +++ b/silver_model.ipynb @@ -71,13 +71,6 @@ "6. Simulation: Simulate our results 10,000 times based on the results of the projection to account for the uncertainty in our estimates. The end result is a robust probabilistic assessment of what will happen in each state as well as in the nation as a whole. " ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ritesh" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -2629,13 +2622,6 @@ "state_polls" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Jim" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3776,13 +3762,6 @@ "state_data2008[\"newest_poll\"] = state_data2008.groupby((\"State\", \"Pollster\")).poll_date.transform(max_date)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ritesh" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -7666,13 +7645,6 @@ "trends = trends.product(axis=1)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Jim (finally)" - ] - }, { "cell_type": "markdown", "metadata": {},