Johns_Ipython_Session_with_Rosslyn_Data.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johnkabler/anaconda/lib/python2.7/site-packages/pytz/__init__.py:35: UserWarning: Module argparse was already imported from /Users/johnkabler/anaconda/python.app/Contents/lib/python2.7/argparse.pyc, but /Users/johnkabler/anaconda/lib/python2.7/site-packages is being added to sys.path\n",
        "  from pkg_resources import resource_stream\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009 = pd.read_csv('2009_Custis_Rosslyn10.15_12.31_15mins.csv', parse_dates=['Date'], index_col='Date')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2010 = pd.read_csv('2010_Custis_Rosslyn01.01_12.31_15mins.csv', parse_dates=['Date'], index_col='Date')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 7488 entries, 2009-10-15 00:00:00 to 2009-12-31 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "Unnamed: 1             7488  non-null values\n",
        "Custis Rosslyn         7444  non-null values\n",
        "Custis Rosslyn Ped     7444  non-null values\n",
        "Custis Rosslyn Bike    7444  non-null values\n",
        "dtypes: float64(3), object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 7488 entries, 2009-10-15 00:00:00 to 2009-12-31 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "Unnamed: 1             7488  non-null values\n",
        "Custis Rosslyn         7444  non-null values\n",
        "Custis Rosslyn Ped     7444  non-null values\n",
        "Custis Rosslyn Bike    7444  non-null values\n",
        "dtypes: float64(3), object(1)"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009 = kb_2009.drop(['Unnamed: 1'], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009 = kb_2009.sort_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009['Custis Rosslyn Bike']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "Date\n",
        "2009-01-11    1\n",
        "2009-01-11    0\n",
        "2009-01-11    0\n",
        "2009-01-11    0\n",
        "2009-01-11    0\n",
        "2009-01-11    0\n",
        "2009-01-11    1\n",
        "2009-01-11    0\n",
        "2009-01-11    1\n",
        "2009-01-11    1\n",
        "2009-01-11    0\n",
        "2009-01-11    1\n",
        "2009-01-11    0\n",
        "2009-01-11    0\n",
        "2009-01-11    0\n",
        "...\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    0\n",
        "2009-12-31    1\n",
        "Name: Custis Rosslyn Bike, Length: 7488, dtype: float64"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009['Custis Rosslyn Bike'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1072c4e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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wez5xmfJOb9++Xeb38/PFdaxc+ZLLOGDAgAo/Hx4eivr1FS+/rOnS7rI+D4QiPV355ZO3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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1072cb510>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2010"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 35040 entries, 2010-01-01 00:00:00 to 2010-12-31 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "Unnamed: 1             35040  non-null values\n",
        "Custis Rosslyn         35036  non-null values\n",
        "Custis Rosslyn Ped     35036  non-null values\n",
        "Custis Rosslyn Bike    35036  non-null values\n",
        "dtypes: float64(3), object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 35040 entries, 2010-01-01 00:00:00 to 2010-12-31 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "Unnamed: 1             35040  non-null values\n",
        "Custis Rosslyn         35036  non-null values\n",
        "Custis Rosslyn Ped     35036  non-null values\n",
        "Custis Rosslyn Bike    35036  non-null values\n",
        "dtypes: float64(3), object(1)"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2010 = kb_2010.drop('Unnamed: 1', axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2010 = kb_2010.sort_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009_10 = pd.concat([kb_2009, kb_2010])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Question:  Why the hell are there so many more bikes in the plot below in 2010 than in 2009?  I suspect that the sensor became much more accurate, but I'm not sure.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kb_2009_10['Custis Rosslyn Bike'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1072d9f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UnnAgN4FUd2kxwmONP7lvH5CWZnjf1q226WHkj1RWmyHK9TM7LUEu3rNcdAKO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\nMtJg/tu3b6OgoAALFy7EyJEjkZWVJYo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       "text": [
        "<matplotlib.figure.Figure at 0x1072d9390>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}