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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DS_Unit_1_Sprint_Challenge_2.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/axrd/DS-Sprint-02-Storytelling-With-Data/blob/master/DS_Unit_1_Sprint_Challenge_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-n_5en3ER1o",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Data Science Unit 1 Sprint Challenge 2\n",
+        "\n",
+        "# Storytelling with Data\n",
+        "\n",
+        "In this sprint challenge you'll work with a dataset from **FiveThirtyEight's article, [Every Guest Jon Stewart Ever Had On ‘The Daily Show’](https://fivethirtyeight.com/features/every-guest-jon-stewart-ever-had-on-the-daily-show/)**!"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Thm2n5FF2Fnp",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 0 — Run this starter code\n",
+        "\n",
+        "You don't need to add or change anything here. Just run this cell and it loads the data for you, into a dataframe named `df`.\n",
+        "\n",
+        "(You can explore the data if you want, but it's not required to pass the Sprint Challenge.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0rTHgzJIuRS7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/daily-show-guests/daily_show_guests.csv')\n",
+        "df.rename(columns={'YEAR': 'Year', 'Raw_Guest_List': 'Guest'}, inplace=True)\n",
+        "\n",
+        "def get_occupation(group):\n",
+        "    if group in ['Acting', 'Comedy', 'Musician']:\n",
+        "        return 'Acting, Comedy & Music'\n",
+        "    elif group in ['Media', 'media']:\n",
+        "        return 'Media'\n",
+        "    elif group in ['Government', 'Politician', 'Political Aide']:\n",
+        "        return 'Government and Politics'\n",
+        "    else:\n",
+        "        return 'Other'\n",
+        "      \n",
+        "df['Occupation'] = df['Group'].apply(get_occupation)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "OS0nW1vz1itX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 1 — What's the breakdown of guests’ occupations per year?\n",
+        "\n",
+        "For example, in 1999, what percentage of guests were actors, comedians, or musicians? What percentage were in the media? What percentage were in politics? What percentage were from another occupation?\n",
+        "\n",
+        "Then, what about in 2000? In 2001? And so on, up through 2015.\n",
+        "\n",
+        "So, **for each year of _The Daily Show_, calculate the percentage of guests from each occupation:**\n",
+        "- Acting, Comedy & Music\n",
+        "- Government and Politics\n",
+        "- Media\n",
+        "- Other\n",
+        "\n",
+        "#### Hints:\n",
+        "1. Use pandas to make a **crosstab** of **`Year`** & **`Occupation`**. ([This documentation](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#cross-tabulations) has examples and explanation.)\n",
+        "2. To get percentages instead of counts, use crosstab's **`normalize`** parameter to normalize over each _row._ ([This documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.crosstab.html) describes the parameter and its options.)\n",
+        "3. You'll know you've calculated the crosstab correctly when the percentage of \"Acting, Comedy & Music\" guests is 90.36% in 1999, and 45% in 2015."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "40MltYfaTtmV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "41757f8d-aad8-4925-82af-b6c3947d069d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Let's see what we're working with here...\n",
+        "df.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>GoogleKnowlege_Occupation</th>\n",
+              "      <th>Show</th>\n",
+              "      <th>Group</th>\n",
+              "      <th>Guest</th>\n",
+              "      <th>Occupation</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>actor</td>\n",
+              "      <td>1/11/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>Michael J. Fox</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>Comedian</td>\n",
+              "      <td>1/12/99</td>\n",
+              "      <td>Comedy</td>\n",
+              "      <td>Sandra Bernhard</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>television actress</td>\n",
+              "      <td>1/13/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>Tracey Ullman</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>film actress</td>\n",
+              "      <td>1/14/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>Gillian Anderson</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>actor</td>\n",
+              "      <td>1/18/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>David Alan Grier</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Year GoogleKnowlege_Occupation     Show   Group             Guest  \\\n",
+              "0  1999                     actor  1/11/99  Acting    Michael J. Fox   \n",
+              "1  1999                  Comedian  1/12/99  Comedy   Sandra Bernhard   \n",
+              "2  1999        television actress  1/13/99  Acting     Tracey Ullman   \n",
+              "3  1999              film actress  1/14/99  Acting  Gillian Anderson   \n",
+              "4  1999                     actor  1/18/99  Acting  David Alan Grier   \n",
+              "\n",
+              "               Occupation  \n",
+              "0  Acting, Comedy & Music  \n",
+              "1  Acting, Comedy & Music  \n",
+              "2  Acting, Comedy & Music  \n",
+              "3  Acting, Comedy & Music  \n",
+              "4  Acting, Comedy & Music  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l_SV1p4WSrrx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 104
+        },
+        "outputId": "2504f7a4-e47b-4220-b1aa-95fc78011334"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.Occupation.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count                       2693\n",
+              "unique                         4\n",
+              "top       Acting, Comedy & Music\n",
+              "freq                        1203\n",
+              "Name: Occupation, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sRMc0H_5z6ff",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 233
+        },
+        "outputId": "0de41c94-147f-4f5f-ba75-56e16b39992d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Simple crosstab will give us what we need. Need to normalize around the index to avoid calculation errors. \n",
+        "year_occ = pd.crosstab(df.Year, df.Occupation, normalize = 'index')\n",
+        "year_occ.head()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Occupation</th>\n",
+              "      <th>Acting, Comedy &amp; Music</th>\n",
+              "      <th>Government and Politics</th>\n",
+              "      <th>Media</th>\n",
+              "      <th>Other</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Year</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1999</th>\n",
+              "      <td>0.903614</td>\n",
+              "      <td>0.012048</td>\n",
+              "      <td>0.066265</td>\n",
+              "      <td>0.018072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2000</th>\n",
+              "      <td>0.739645</td>\n",
+              "      <td>0.082840</td>\n",
+              "      <td>0.124260</td>\n",
+              "      <td>0.053254</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2001</th>\n",
+              "      <td>0.726115</td>\n",
+              "      <td>0.038217</td>\n",
+              "      <td>0.197452</td>\n",
+              "      <td>0.038217</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2002</th>\n",
+              "      <td>0.622642</td>\n",
+              "      <td>0.069182</td>\n",
+              "      <td>0.264151</td>\n",
+              "      <td>0.044025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2003</th>\n",
+              "      <td>0.560241</td>\n",
+              "      <td>0.102410</td>\n",
+              "      <td>0.246988</td>\n",
+              "      <td>0.090361</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Occupation  Acting, Comedy & Music  Government and Politics     Media  \\\n",
+              "Year                                                                    \n",
+              "1999                      0.903614                 0.012048  0.066265   \n",
+              "2000                      0.739645                 0.082840  0.124260   \n",
+              "2001                      0.726115                 0.038217  0.197452   \n",
+              "2002                      0.622642                 0.069182  0.264151   \n",
+              "2003                      0.560241                 0.102410  0.246988   \n",
+              "\n",
+              "Occupation     Other  \n",
+              "Year                  \n",
+              "1999        0.018072  \n",
+              "2000        0.053254  \n",
+              "2001        0.038217  \n",
+              "2002        0.044025  \n",
+              "2003        0.090361  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ITiBNDMFVWsL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 601
+        },
+        "outputId": "e7bebf8e-f564-4bd1-960d-91138981e8b7"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Showing all years and their respective percentage values for Occupation:\n",
+        "year_occ.head(17)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Occupation</th>\n",
+              "      <th>Acting, Comedy &amp; Music</th>\n",
+              "      <th>Government and Politics</th>\n",
+              "      <th>Media</th>\n",
+              "      <th>Other</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Year</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1999</th>\n",
+              "      <td>0.903614</td>\n",
+              "      <td>0.012048</td>\n",
+              "      <td>0.066265</td>\n",
+              "      <td>0.018072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2000</th>\n",
+              "      <td>0.739645</td>\n",
+              "      <td>0.082840</td>\n",
+              "      <td>0.124260</td>\n",
+              "      <td>0.053254</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2001</th>\n",
+              "      <td>0.726115</td>\n",
+              "      <td>0.038217</td>\n",
+              "      <td>0.197452</td>\n",
+              "      <td>0.038217</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2002</th>\n",
+              "      <td>0.622642</td>\n",
+              "      <td>0.069182</td>\n",
+              "      <td>0.264151</td>\n",
+              "      <td>0.044025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2003</th>\n",
+              "      <td>0.560241</td>\n",
+              "      <td>0.102410</td>\n",
+              "      <td>0.246988</td>\n",
+              "      <td>0.090361</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2004</th>\n",
+              "      <td>0.384146</td>\n",
+              "      <td>0.225610</td>\n",
+              "      <td>0.274390</td>\n",
+              "      <td>0.115854</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2005</th>\n",
+              "      <td>0.370370</td>\n",
+              "      <td>0.160494</td>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>0.135802</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2006</th>\n",
+              "      <td>0.360248</td>\n",
+              "      <td>0.192547</td>\n",
+              "      <td>0.291925</td>\n",
+              "      <td>0.155280</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2007</th>\n",
+              "      <td>0.255319</td>\n",
+              "      <td>0.170213</td>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>0.241135</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2008</th>\n",
+              "      <td>0.207317</td>\n",
+              "      <td>0.201220</td>\n",
+              "      <td>0.469512</td>\n",
+              "      <td>0.121951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2009</th>\n",
+              "      <td>0.208589</td>\n",
+              "      <td>0.208589</td>\n",
+              "      <td>0.361963</td>\n",
+              "      <td>0.220859</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2010</th>\n",
+              "      <td>0.351515</td>\n",
+              "      <td>0.181818</td>\n",
+              "      <td>0.303030</td>\n",
+              "      <td>0.163636</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2011</th>\n",
+              "      <td>0.337423</td>\n",
+              "      <td>0.165644</td>\n",
+              "      <td>0.312883</td>\n",
+              "      <td>0.184049</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2012</th>\n",
+              "      <td>0.268293</td>\n",
+              "      <td>0.201220</td>\n",
+              "      <td>0.317073</td>\n",
+              "      <td>0.213415</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2013</th>\n",
+              "      <td>0.427711</td>\n",
+              "      <td>0.126506</td>\n",
+              "      <td>0.307229</td>\n",
+              "      <td>0.138554</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014</th>\n",
+              "      <td>0.392638</td>\n",
+              "      <td>0.128834</td>\n",
+              "      <td>0.325153</td>\n",
+              "      <td>0.153374</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2015</th>\n",
+              "      <td>0.450000</td>\n",
+              "      <td>0.170000</td>\n",
+              "      <td>0.240000</td>\n",
+              "      <td>0.140000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Occupation  Acting, Comedy & Music  Government and Politics     Media  \\\n",
+              "Year                                                                    \n",
+              "1999                      0.903614                 0.012048  0.066265   \n",
+              "2000                      0.739645                 0.082840  0.124260   \n",
+              "2001                      0.726115                 0.038217  0.197452   \n",
+              "2002                      0.622642                 0.069182  0.264151   \n",
+              "2003                      0.560241                 0.102410  0.246988   \n",
+              "2004                      0.384146                 0.225610  0.274390   \n",
+              "2005                      0.370370                 0.160494  0.333333   \n",
+              "2006                      0.360248                 0.192547  0.291925   \n",
+              "2007                      0.255319                 0.170213  0.333333   \n",
+              "2008                      0.207317                 0.201220  0.469512   \n",
+              "2009                      0.208589                 0.208589  0.361963   \n",
+              "2010                      0.351515                 0.181818  0.303030   \n",
+              "2011                      0.337423                 0.165644  0.312883   \n",
+              "2012                      0.268293                 0.201220  0.317073   \n",
+              "2013                      0.427711                 0.126506  0.307229   \n",
+              "2014                      0.392638                 0.128834  0.325153   \n",
+              "2015                      0.450000                 0.170000  0.240000   \n",
+              "\n",
+              "Occupation     Other  \n",
+              "Year                  \n",
+              "1999        0.018072  \n",
+              "2000        0.053254  \n",
+              "2001        0.038217  \n",
+              "2002        0.044025  \n",
+              "2003        0.090361  \n",
+              "2004        0.115854  \n",
+              "2005        0.135802  \n",
+              "2006        0.155280  \n",
+              "2007        0.241135  \n",
+              "2008        0.121951  \n",
+              "2009        0.220859  \n",
+              "2010        0.163636  \n",
+              "2011        0.184049  \n",
+              "2012        0.213415  \n",
+              "2013        0.138554  \n",
+              "2014        0.153374  \n",
+              "2015        0.140000  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Nqf9oJJDDu-d",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 2 — Recreate this explanatory visualization:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scozkHQc0_eD",
+        "colab_type": "code",
+        "outputId": "6d9acfab-93c5-4e70-dbb2-ea5ebb7d1cad",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import display, Image\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2015/08/hickey-datalab-dailyshow.png'\n",
+        "example = Image(url, width=500)\n",
+        "display(example)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZBtnkfptMLmk+J6HR1RolfR2cZ4kCMURmERt2i1wRFHvq1F3TaMmmGBsU6Ssc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\n4JhflzBydcIRb30AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 500
+            }
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "W7lw3JzAE6BJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Hint:** use the crosstab you calculated in part 1!\n",
+        "\n",
+        "**Expectations:** Your plot should include:\n",
+        "- 3 lines visualizing \"occupation of guests, by year.\" The shapes of the lines should look roughly identical to 538's example. Each line should be a different color. (But you don't need to use the _same_ colors as 538.)\n",
+        "- Legend or labels for the lines. (But you don't need each label positioned next to its line or colored like 538.)\n",
+        "- Title in the upper left: _\"Who Got To Be On 'The Daily Show'?\"_ with more visual emphasis than the subtitle. (Bolder and/or larger font.)\n",
+        "- Subtitle underneath the title: _\"Occupation of guests, by year\"_\n",
+        "\n",
+        "Any visual element not specifically mentioned in the expectations is an optional bonus, but it's _not_ required to pass the Sprint Challenge.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "v1_6SJr0YJ-g",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "c28dea15-678a-4dac-a0b6-d33213db7917"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Taking a first look:\n",
+        "year_occ.plot();\n",
+        "\n",
+        "# That's pretty close to the graph posted above. Just need to remove the 'Other' line, add some title/sub text, and \n",
+        "#maybe make it a grey backround  with grid lines for extra brownie points?"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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oSdIVSZKuSpI0uYA2vSVJCpckKUySpPXqDbNi0dfRZtr/ubNyoD9xyZl0WXSM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\n0WjckP8H/0uJ61BPYysAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f153873c400>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "U_9eywKNaz5j",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 313
+        },
+        "outputId": "6b98fc79-95d8-487e-ca90-ca8c3e7ce390"
+      },
+      "cell_type": "code",
+      "source": [
+        "year_occ['Acting, Comedy & Music'].plot(legend=True);\n",
+        "year_occ.Media.plot(legend=True);\n",
+        "year_occ['Government and Politics'].plot(legend=True);\n",
+        "plt.title(\"Who Got To Be On 'The Daily Show'?\", x = 0.25, y = 1.08, fontweight='bold')\n",
+        "plt.text(1996.5,.98,'Occupation of guests, by year');\n",
+        "plt.grid()\n",
+        "plt.yticks([0, 0.25, 0.5, 0.75]);\n",
+        "plt.xticks([2000, 2004, 2008, 2012]);"
+      ],
+      "execution_count": 162,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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DRX9LKR82jyiUR7JvgDApZaQQ4l1u/47j0Wg0JYo76TmsBlyEEI8CCCFsgc+AWVLKBJSN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\nSlDBQVEURSlBBQdFURSlhP8H1iih/761z/gAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f1538889668>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LuacMjSf2ses",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 3 — Who were the top 10 guests on _The Daily Show_?\n",
+        "\n",
+        "**Make a plot** that shows their names and number of appearances.\n",
+        "\n",
+        "**Hint:** you can use the pandas `value_counts` method.\n",
+        "\n",
+        "**Expectations:** This can be a simple, quick plot: exploratory, not explanatory. \n",
+        "\n",
+        "If you want, you can add titles and change aesthetics, but it's _not_ required to pass the Sprint Challenge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tbwfBN3HsFlh",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "top = df.Guest.value_counts()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NZjSp13Qkvw0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "top_ten = top[:10]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wTMhNhITklI1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "top_ten.columns = ['Guest', 'Appearances']"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "AZcfxO43mIw1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "d65d19c3-f19c-455a-bd06-65890b0a95c5"
+      },
+      "cell_type": "code",
+      "source": [
+        "top_ten.plot.bar(x = 'Guest', y = 'Appearances');\n",
+        "plt.title('Top 10 Guest Appearances');\n",
+        "plt.ylabel('Number of Apperances (1999-2015)')\n",
+        "plt.ylim(5,20);"
+      ],
+      "execution_count": 111,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AzoijE2KAzohjZsSwlO1+V+18RySCmUHSWNu1LmvRCTF0ShydEEOnxNEJMXRK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\nIe7/Afh0MIq5ftNBAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f1538553e48>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb b/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb
new file mode 100644
index 0000000..56502ba
--- /dev/null
+++ b/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb
@@ -0,0 +1,6649 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/axrd/DS-Sprint-02-Storytelling-With-Data/blob/master/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-8-trVo__vRE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "\n",
+        "# Choose appropriate visualizations\n",
+        "\n",
+        "\n",
+        "Recreate this [example by FiveThirtyEight:](https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ya_w5WORGs-n",
+        "colab_type": "code",
+        "outputId": "b34c4294-8d00-49fc-f79d-473079a86286",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import display, Image\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png'\n",
+        "example = Image(url=url, width=400)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HioPkYtUG03B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Using this data:\n",
+        "\n",
+        "https://github.com/fivethirtyeight/data/tree/master/inconvenient-sequel\n",
+        "\n",
+        "### Stretch goals\n",
+        "\n",
+        "Recreate more examples from [FiveThityEight's shared data repository](https://data.fivethirtyeight.com/).\n",
+        "\n",
+        "For example:\n",
+        "- [thanksgiving-2015](https://fivethirtyeight.com/features/heres-what-your-part-of-america-eats-on-thanksgiving/) ([`altair`](https://altair-viz.github.io/gallery/index.html#maps))\n",
+        "- [candy-power-ranking](https://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/) ([`statsmodels`](https://www.statsmodels.org/stable/index.html))"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N89pO3kX_-7p",
+        "colab_type": "code",
+        "outputId": "e26f0ab6-7abd-4404-9dda-b1586758c6a1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "display(example)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5uz0eEaEN-GO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "outputId": "16fcefbc-825e-405a-d15e-6a2722adb758"
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "plt.style.use('fivethirtyeight')\n",
+        "\n",
+        "fake = pd.Series([38, 3, 2, 1, 2, 4, 6, 5, 5, 33], \n",
+        "                 index=range(1,11))\n",
+        "\n",
+        "fake.plot.bar(color='#EC713B', width=0.9);"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEJCAYAAACpATGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBsoGACAERQMAMAICgYAYMT/AODrZXzeeAE9AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f3dc1784f28>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HNX09eJOOtes",
+        "colab_type": "code",
+        "outputId": "f3fe6b53-b02a-4baf-f230-04e55dd696a7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "display(example)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KZ0VLOV8OyRr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "fake2 = pd.Series(\n",
+        "    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+        "     2, 2, 2, \n",
+        "     3, 3, 3,\n",
+        "     4, 4,\n",
+        "     5, 5, 5,\n",
+        "     6, 6, 6, 6,\n",
+        "     7, 7, 7, 7, 7,\n",
+        "     8, 8, 8, 8,\n",
+        "     9, 9, 9, 9, \n",
+        "     10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8X9JNW2cScRq",
+        "colab_type": "code",
+        "outputId": "27675a69-0d1a-4cc0-8e09-5bb06417d3d3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 285
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "fake2.value_counts().sort_index().plot.bar(color='C1', width=0.9);"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hYaH69OmjCRMmaOjQoXI4QnOXsXXrVq1bt07Xr1/XjBkz9OGHHyo1NVW1tbWaP3++Nm/e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\ngg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f3dc179e198>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "klu6rw8_SkfE",
+        "colab_type": "code",
+        "outputId": "26f08f5b-961e-4f1d-e97e-a25e6cc1ebee",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "display(example)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gFS1hEoSTPUE",
+        "colab_type": "code",
+        "outputId": "74fbf060-c799-40fe-8bff-42fd36191616",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 370
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight')\n",
+        "\n",
+        "fake = pd.Series([38, 3, 2, 1, 2, 4, 6, 5, 5, 33], \n",
+        "                 index=range(1,11))\n",
+        "\n",
+        "ax = fake.plot.bar(color='#EC713B', width=0.9)\n",
+        "\n",
+        "ax.set(xlabel='Rating', \n",
+        "       ylabel='Percent of total votes', \n",
+        "       yticks=range(0,50,10))\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive\", \n",
+        "        fontsize=16, fontweight='bold')\n",
+        "\n",
+        "ax.text(x=-2, y=46, s=\"IMDb ratings for the film as of Aug. 29\")\n",
+        "\n",
+        "ax.tick_params(labelrotation=0)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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9vT0sLCyQk5OD0NDQSpe9tLNnz8LPzw+//vorAgIC8PTpU9y+fVvm98+YMUPq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\natQIOTk5cHd3x/Pnz6Gvr48lS5aIGY6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/fPgQp9NJc3MzLS0tfPnyhatXryp/v3//Pq2t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\nE0IIETEk1IQQQkQMCTUhhBAR42/RDeTlxIK1QwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f3dc179eba8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Q3ECPtB2TcVK",
+        "colab_type": "code",
+        "outputId": "43172934-9031-48f5-e1bc-f68fe7de1cb6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "display(example)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3SOHJckDUPI8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/inconvenient-sequel/ratings.csv')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UP4v1yTqaR8G",
+        "colab_type": "code",
+        "outputId": "30362eff-c2d0-4fb7-c5c0-0dde218db9c8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(80053, 27)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "79tLHNusafCS",
+        "colab_type": "code",
+        "outputId": "62ad9b71-061d-41e7-ba24-7919eac52079",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "width, height = df.shape\n",
+        "width * height"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2161431"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MKgNi_DjaneG",
+        "colab_type": "code",
+        "outputId": "bdc96a89-092a-49f4-954b-93f2cbe26bb3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 337
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>respondents</th>\n",
+              "      <th>category</th>\n",
+              "      <th>link</th>\n",
+              "      <th>average</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>median</th>\n",
+              "      <th>1_votes</th>\n",
+              "      <th>2_votes</th>\n",
+              "      <th>3_votes</th>\n",
+              "      <th>...</th>\n",
+              "      <th>1_pct</th>\n",
+              "      <th>2_pct</th>\n",
+              "      <th>3_pct</th>\n",
+              "      <th>4_pct</th>\n",
+              "      <th>5_pct</th>\n",
+              "      <th>6_pct</th>\n",
+              "      <th>7_pct</th>\n",
+              "      <th>8_pct</th>\n",
+              "      <th>9_pct</th>\n",
+              "      <th>10_pct</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2017-07-17 12:28:32.785639</td>\n",
+              "      <td>402</td>\n",
+              "      <td>Males</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-male</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>197</td>\n",
+              "      <td>7</td>\n",
+              "      <td>7</td>\n",
+              "      <td>...</td>\n",
+              "      <td>49.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>33.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2017-07-17 12:28:33.025600</td>\n",
+              "      <td>78</td>\n",
+              "      <td>Females</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>10</td>\n",
+              "      <td>16</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20.5</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>62.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2017-07-17 12:28:33.273919</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Aged under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_1</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2017-07-17 12:28:33.495325</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Males under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2017-07-17 12:28:33.722849</td>\n",
+              "      <td>130</td>\n",
+              "      <td>Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_2</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>...</td>\n",
+              "      <td>31.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>49.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 27 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    timestamp  respondents        category  \\\n",
+              "0  2017-07-17 12:28:32.785639          402           Males   \n",
+              "1  2017-07-17 12:28:33.025600           78         Females   \n",
+              "2  2017-07-17 12:28:33.273919            4   Aged under 18   \n",
+              "3  2017-07-17 12:28:33.495325            4  Males under 18   \n",
+              "4  2017-07-17 12:28:33.722849          130      Aged 18-29   \n",
+              "\n",
+              "                                                link  average  mean  median  \\\n",
+              "0   http://www.imdb.com/title/tt6322922/ratings-male      4.6   5.0       2   \n",
+              "1  http://www.imdb.com/title/tt6322922/ratings-fe...      6.9   7.7      10   \n",
+              "2  http://www.imdb.com/title/tt6322922/ratings-age_1      4.2   4.2       3   \n",
+              "3  http://www.imdb.com/title/tt6322922/ratings-ma...      4.2   4.2       3   \n",
+              "4  http://www.imdb.com/title/tt6322922/ratings-age_2      6.3   6.5       9   \n",
+              "\n",
+              "   1_votes  2_votes  3_votes   ...    1_pct  2_pct  3_pct  4_pct  5_pct  \\\n",
+              "0      197        7        7   ...     49.0    1.7    1.7    0.7    1.7   \n",
+              "1       16        1        0   ...     20.5    1.3    0.0    1.3    1.3   \n",
+              "2        2        0        0   ...     50.0    0.0    0.0    0.0   25.0   \n",
+              "3        2        0        0   ...     50.0    0.0    0.0    0.0   25.0   \n",
+              "4       41        0        3   ...     31.5    0.0    2.3    0.8    1.5   \n",
+              "\n",
+              "   6_pct  7_pct  8_pct  9_pct  10_pct  \n",
+              "0    1.7    2.7    2.0    5.0    33.6  \n",
+              "1    0.0    3.8    5.1    3.8    62.8  \n",
+              "2    0.0    0.0    0.0    0.0    25.0  \n",
+              "3    0.0    0.0    0.0    0.0    25.0  \n",
+              "4    2.3    4.6    3.1    4.6    49.2  \n",
+              "\n",
+              "[5 rows x 27 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qG_pKiVjavJJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.options.display.max_columns = None"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "GMKcPPjIbAEQ",
+        "colab_type": "code",
+        "outputId": "821b2807-5f9c-4b24-bd52-4dc11b4a0650",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 884
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df.sample(1).T"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>60390</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>timestamp</th>\n",
+              "      <td>2017-08-22 18:50:02.398321</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>respondents</th>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>category</th>\n",
+              "      <td>Aged under 18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>link</th>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>average</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1_votes</th>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5_votes</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9_votes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10_votes</th>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1_pct</th>\n",
+              "      <td>50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5_pct</th>\n",
+              "      <td>8.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9_pct</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10_pct</th>\n",
+              "      <td>41.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                         60390\n",
+              "timestamp                           2017-08-22 18:50:02.398321\n",
+              "respondents                                                 12\n",
+              "category                                         Aged under 18\n",
+              "link         http://www.imdb.com/title/tt6322922/ratings-age_1\n",
+              "average                                                    3.5\n",
+              "mean                                                       5.1\n",
+              "median                                                       3\n",
+              "1_votes                                                      6\n",
+              "2_votes                                                      0\n",
+              "3_votes                                                      0\n",
+              "4_votes                                                      0\n",
+              "5_votes                                                      1\n",
+              "6_votes                                                      0\n",
+              "7_votes                                                      0\n",
+              "8_votes                                                      0\n",
+              "9_votes                                                      0\n",
+              "10_votes                                                     5\n",
+              "1_pct                                                       50\n",
+              "2_pct                                                        0\n",
+              "3_pct                                                        0\n",
+              "4_pct                                                        0\n",
+              "5_pct                                                      8.3\n",
+              "6_pct                                                        0\n",
+              "7_pct                                                        0\n",
+              "8_pct                                                        0\n",
+              "9_pct                                                        0\n",
+              "10_pct                                                    41.7"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c-sumPXccePB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df.timestamp = pd.to_datetime(df.timestamp)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "YmsqfFn0bJj8",
+        "colab_type": "code",
+        "outputId": "9033d817-12c4-453e-9744-89b6975a59d8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 140
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df.timestamp.describe()"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count                          80053\n",
+              "unique                         80053\n",
+              "top       2017-07-24 23:00:02.760781\n",
+              "freq                               1\n",
+              "first     2017-07-17 12:28:32.785639\n",
+              "last      2017-08-29 23:10:06.218251\n",
+              "Name: timestamp, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2t79rYZLcvjj",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df.set_index('timestamp', inplace=True)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3z2SbbDkdGtu",
+        "colab_type": "code",
+        "outputId": "81476ee3-e390-4dc2-a39c-25c0e5d13ba7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 3278
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df['2017-08-29']"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>respondents</th>\n",
+              "      <th>category</th>\n",
+              "      <th>link</th>\n",
+              "      <th>average</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>median</th>\n",
+              "      <th>1_votes</th>\n",
+              "      <th>2_votes</th>\n",
+              "      <th>3_votes</th>\n",
+              "      <th>4_votes</th>\n",
+              "      <th>5_votes</th>\n",
+              "      <th>6_votes</th>\n",
+              "      <th>7_votes</th>\n",
+              "      <th>8_votes</th>\n",
+              "      <th>9_votes</th>\n",
+              "      <th>10_votes</th>\n",
+              "      <th>1_pct</th>\n",
+              "      <th>2_pct</th>\n",
+              "      <th>3_pct</th>\n",
+              "      <th>4_pct</th>\n",
+              "      <th>5_pct</th>\n",
+              "      <th>6_pct</th>\n",
+              "      <th>7_pct</th>\n",
+              "      <th>8_pct</th>\n",
+              "      <th>9_pct</th>\n",
+              "      <th>10_pct</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:02.248882</th>\n",
+              "      <td>1682</td>\n",
+              "      <td>Males</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-male</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>671</td>\n",
+              "      <td>50</td>\n",
+              "      <td>29</td>\n",
+              "      <td>12</td>\n",
+              "      <td>42</td>\n",
+              "      <td>79</td>\n",
+              "      <td>107</td>\n",
+              "      <td>90</td>\n",
+              "      <td>83</td>\n",
+              "      <td>519</td>\n",
+              "      <td>39.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>30.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:02.499363</th>\n",
+              "      <td>257</td>\n",
+              "      <td>Females</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>7.3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>53</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>14</td>\n",
+              "      <td>13</td>\n",
+              "      <td>26</td>\n",
+              "      <td>131</td>\n",
+              "      <td>20.6</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>51.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:02.727204</th>\n",
+              "      <td>15</td>\n",
+              "      <td>Aged under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_1</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>46.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:03.024270</th>\n",
+              "      <td>13</td>\n",
+              "      <td>Males under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>46.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:03.248207</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Females under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:03.441529</th>\n",
+              "      <td>450</td>\n",
+              "      <td>Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_2</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>8</td>\n",
+              "      <td>117</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>17</td>\n",
+              "      <td>24</td>\n",
+              "      <td>35</td>\n",
+              "      <td>28</td>\n",
+              "      <td>23</td>\n",
+              "      <td>190</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>42.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:03.636825</th>\n",
+              "      <td>402</td>\n",
+              "      <td>Males Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>112</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>15</td>\n",
+              "      <td>20</td>\n",
+              "      <td>33</td>\n",
+              "      <td>25</td>\n",
+              "      <td>21</td>\n",
+              "      <td>163</td>\n",
+              "      <td>27.9</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>40.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:03.839446</th>\n",
+              "      <td>41</td>\n",
+              "      <td>Females Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.1</td>\n",
+              "      <td>7.9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>23</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>7.3</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>56.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:04.030663</th>\n",
+              "      <td>771</td>\n",
+              "      <td>Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_3</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>287</td>\n",
+              "      <td>21</td>\n",
+              "      <td>12</td>\n",
+              "      <td>4</td>\n",
+              "      <td>21</td>\n",
+              "      <td>36</td>\n",
+              "      <td>53</td>\n",
+              "      <td>38</td>\n",
+              "      <td>49</td>\n",
+              "      <td>250</td>\n",
+              "      <td>37.2</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>32.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:04.225962</th>\n",
+              "      <td>679</td>\n",
+              "      <td>Males Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>270</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>20</td>\n",
+              "      <td>33</td>\n",
+              "      <td>51</td>\n",
+              "      <td>34</td>\n",
+              "      <td>39</td>\n",
+              "      <td>200</td>\n",
+              "      <td>39.8</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>7.5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>29.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:04.539312</th>\n",
+              "      <td>76</td>\n",
+              "      <td>Females Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.5</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>9</td>\n",
+              "      <td>47</td>\n",
+              "      <td>14.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>61.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:04.776104</th>\n",
+              "      <td>539</td>\n",
+              "      <td>Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_4</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>245</td>\n",
+              "      <td>28</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>19</td>\n",
+              "      <td>24</td>\n",
+              "      <td>32</td>\n",
+              "      <td>35</td>\n",
+              "      <td>132</td>\n",
+              "      <td>45.5</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>24.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:04.973766</th>\n",
+              "      <td>421</td>\n",
+              "      <td>Males Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>209</td>\n",
+              "      <td>25</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>18</td>\n",
+              "      <td>16</td>\n",
+              "      <td>25</td>\n",
+              "      <td>20</td>\n",
+              "      <td>86</td>\n",
+              "      <td>49.6</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>20.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:05.176998</th>\n",
+              "      <td>109</td>\n",
+              "      <td>Females Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>9</td>\n",
+              "      <td>31</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>15</td>\n",
+              "      <td>44</td>\n",
+              "      <td>28.4</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.3</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>13.8</td>\n",
+              "      <td>40.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:05.366554</th>\n",
+              "      <td>8</td>\n",
+              "      <td>IMDb staff</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-im...</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>12.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:05.556598</th>\n",
+              "      <td>41</td>\n",
+              "      <td>Top 1000 voters</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-to...</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>26.8</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>14.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:05.767397</th>\n",
+              "      <td>922</td>\n",
+              "      <td>US users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-usa</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>405</td>\n",
+              "      <td>33</td>\n",
+              "      <td>20</td>\n",
+              "      <td>8</td>\n",
+              "      <td>9</td>\n",
+              "      <td>30</td>\n",
+              "      <td>54</td>\n",
+              "      <td>46</td>\n",
+              "      <td>58</td>\n",
+              "      <td>259</td>\n",
+              "      <td>43.9</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>28.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:05.964362</th>\n",
+              "      <td>556</td>\n",
+              "      <td>Non-US users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-in...</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>129</td>\n",
+              "      <td>11</td>\n",
+              "      <td>10</td>\n",
+              "      <td>8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>44</td>\n",
+              "      <td>50</td>\n",
+              "      <td>35</td>\n",
+              "      <td>35</td>\n",
+              "      <td>202</td>\n",
+              "      <td>23.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>7.9</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>36.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:00:06.288486</th>\n",
+              "      <td>2646</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1020</td>\n",
+              "      <td>67</td>\n",
+              "      <td>39</td>\n",
+              "      <td>24</td>\n",
+              "      <td>55</td>\n",
+              "      <td>107</td>\n",
+              "      <td>152</td>\n",
+              "      <td>145</td>\n",
+              "      <td>144</td>\n",
+              "      <td>893</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:02.437655</th>\n",
+              "      <td>1682</td>\n",
+              "      <td>Males</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-male</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>671</td>\n",
+              "      <td>50</td>\n",
+              "      <td>29</td>\n",
+              "      <td>12</td>\n",
+              "      <td>42</td>\n",
+              "      <td>79</td>\n",
+              "      <td>107</td>\n",
+              "      <td>90</td>\n",
+              "      <td>83</td>\n",
+              "      <td>519</td>\n",
+              "      <td>39.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>30.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:02.715850</th>\n",
+              "      <td>257</td>\n",
+              "      <td>Females</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>7.3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>53</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>14</td>\n",
+              "      <td>13</td>\n",
+              "      <td>26</td>\n",
+              "      <td>131</td>\n",
+              "      <td>20.6</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>51.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:02.974317</th>\n",
+              "      <td>15</td>\n",
+              "      <td>Aged under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_1</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>46.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:03.242796</th>\n",
+              "      <td>13</td>\n",
+              "      <td>Males under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>46.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:03.471974</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Females under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:03.701915</th>\n",
+              "      <td>450</td>\n",
+              "      <td>Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_2</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>8</td>\n",
+              "      <td>117</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>17</td>\n",
+              "      <td>24</td>\n",
+              "      <td>35</td>\n",
+              "      <td>28</td>\n",
+              "      <td>23</td>\n",
+              "      <td>190</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>42.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:03.922894</th>\n",
+              "      <td>402</td>\n",
+              "      <td>Males Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>112</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>15</td>\n",
+              "      <td>20</td>\n",
+              "      <td>33</td>\n",
+              "      <td>25</td>\n",
+              "      <td>21</td>\n",
+              "      <td>163</td>\n",
+              "      <td>27.9</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>40.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:04.168584</th>\n",
+              "      <td>41</td>\n",
+              "      <td>Females Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.1</td>\n",
+              "      <td>7.9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>23</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>7.3</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>56.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:04.391835</th>\n",
+              "      <td>771</td>\n",
+              "      <td>Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_3</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>287</td>\n",
+              "      <td>21</td>\n",
+              "      <td>12</td>\n",
+              "      <td>4</td>\n",
+              "      <td>21</td>\n",
+              "      <td>36</td>\n",
+              "      <td>53</td>\n",
+              "      <td>38</td>\n",
+              "      <td>49</td>\n",
+              "      <td>250</td>\n",
+              "      <td>37.2</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>32.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:04.630956</th>\n",
+              "      <td>679</td>\n",
+              "      <td>Males Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>270</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>20</td>\n",
+              "      <td>33</td>\n",
+              "      <td>51</td>\n",
+              "      <td>34</td>\n",
+              "      <td>39</td>\n",
+              "      <td>200</td>\n",
+              "      <td>39.8</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>7.5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>29.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 00:10:04.819991</th>\n",
+              "      <td>76</td>\n",
+              "      <td>Females Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.5</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>9</td>\n",
+              "      <td>47</td>\n",
+              "      <td>14.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>61.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:03.683160</th>\n",
+              "      <td>775</td>\n",
+              "      <td>Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_3</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>287</td>\n",
+              "      <td>21</td>\n",
+              "      <td>12</td>\n",
+              "      <td>4</td>\n",
+              "      <td>21</td>\n",
+              "      <td>38</td>\n",
+              "      <td>53</td>\n",
+              "      <td>39</td>\n",
+              "      <td>50</td>\n",
+              "      <td>250</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>32.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:03.924760</th>\n",
+              "      <td>682</td>\n",
+              "      <td>Males Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>270</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>20</td>\n",
+              "      <td>35</td>\n",
+              "      <td>51</td>\n",
+              "      <td>35</td>\n",
+              "      <td>39</td>\n",
+              "      <td>200</td>\n",
+              "      <td>39.6</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>7.5</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>29.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:04.189034</th>\n",
+              "      <td>77</td>\n",
+              "      <td>Females Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.6</td>\n",
+              "      <td>8.1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>10</td>\n",
+              "      <td>47</td>\n",
+              "      <td>14.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>61.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:04.381826</th>\n",
+              "      <td>544</td>\n",
+              "      <td>Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_4</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>246</td>\n",
+              "      <td>29</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>19</td>\n",
+              "      <td>25</td>\n",
+              "      <td>32</td>\n",
+              "      <td>36</td>\n",
+              "      <td>133</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>24.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:04.619175</th>\n",
+              "      <td>424</td>\n",
+              "      <td>Males Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>209</td>\n",
+              "      <td>26</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>18</td>\n",
+              "      <td>17</td>\n",
+              "      <td>25</td>\n",
+              "      <td>20</td>\n",
+              "      <td>87</td>\n",
+              "      <td>49.3</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>20.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:04.820031</th>\n",
+              "      <td>111</td>\n",
+              "      <td>Females Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>32</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>16</td>\n",
+              "      <td>44</td>\n",
+              "      <td>28.8</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>14.4</td>\n",
+              "      <td>39.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:05.028872</th>\n",
+              "      <td>8</td>\n",
+              "      <td>IMDb staff</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-im...</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>12.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:05.231674</th>\n",
+              "      <td>41</td>\n",
+              "      <td>Top 1000 voters</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-to...</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>26.8</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>14.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:05.442596</th>\n",
+              "      <td>925</td>\n",
+              "      <td>US users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-usa</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>405</td>\n",
+              "      <td>34</td>\n",
+              "      <td>20</td>\n",
+              "      <td>8</td>\n",
+              "      <td>9</td>\n",
+              "      <td>30</td>\n",
+              "      <td>54</td>\n",
+              "      <td>46</td>\n",
+              "      <td>60</td>\n",
+              "      <td>259</td>\n",
+              "      <td>43.8</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>28.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:05.634805</th>\n",
+              "      <td>565</td>\n",
+              "      <td>Non-US users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-in...</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>129</td>\n",
+              "      <td>11</td>\n",
+              "      <td>10</td>\n",
+              "      <td>8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>47</td>\n",
+              "      <td>52</td>\n",
+              "      <td>37</td>\n",
+              "      <td>35</td>\n",
+              "      <td>204</td>\n",
+              "      <td>22.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>36.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:05.829558</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:02.207706</th>\n",
+              "      <td>1693</td>\n",
+              "      <td>Males</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-male</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>671</td>\n",
+              "      <td>51</td>\n",
+              "      <td>29</td>\n",
+              "      <td>13</td>\n",
+              "      <td>42</td>\n",
+              "      <td>82</td>\n",
+              "      <td>109</td>\n",
+              "      <td>91</td>\n",
+              "      <td>83</td>\n",
+              "      <td>522</td>\n",
+              "      <td>39.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>30.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:02.398710</th>\n",
+              "      <td>261</td>\n",
+              "      <td>Females</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>7.3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>54</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>14</td>\n",
+              "      <td>14</td>\n",
+              "      <td>28</td>\n",
+              "      <td>131</td>\n",
+              "      <td>20.7</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>10.7</td>\n",
+              "      <td>50.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:02.622019</th>\n",
+              "      <td>15</td>\n",
+              "      <td>Aged under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_1</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>46.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:02.908857</th>\n",
+              "      <td>13</td>\n",
+              "      <td>Males under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>46.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:03.088635</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Females under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:03.288272</th>\n",
+              "      <td>455</td>\n",
+              "      <td>Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_2</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>8</td>\n",
+              "      <td>117</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>8</td>\n",
+              "      <td>17</td>\n",
+              "      <td>25</td>\n",
+              "      <td>36</td>\n",
+              "      <td>29</td>\n",
+              "      <td>23</td>\n",
+              "      <td>191</td>\n",
+              "      <td>25.7</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>7.9</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>42.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:03.501039</th>\n",
+              "      <td>406</td>\n",
+              "      <td>Males Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>112</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>7</td>\n",
+              "      <td>15</td>\n",
+              "      <td>21</td>\n",
+              "      <td>34</td>\n",
+              "      <td>25</td>\n",
+              "      <td>21</td>\n",
+              "      <td>164</td>\n",
+              "      <td>27.6</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>40.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:03.725960</th>\n",
+              "      <td>42</td>\n",
+              "      <td>Females Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.1</td>\n",
+              "      <td>7.9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>23</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>54.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:03.942442</th>\n",
+              "      <td>775</td>\n",
+              "      <td>Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_3</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>287</td>\n",
+              "      <td>21</td>\n",
+              "      <td>12</td>\n",
+              "      <td>4</td>\n",
+              "      <td>21</td>\n",
+              "      <td>38</td>\n",
+              "      <td>53</td>\n",
+              "      <td>39</td>\n",
+              "      <td>50</td>\n",
+              "      <td>250</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>32.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:04.147698</th>\n",
+              "      <td>682</td>\n",
+              "      <td>Males Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>270</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>20</td>\n",
+              "      <td>35</td>\n",
+              "      <td>51</td>\n",
+              "      <td>35</td>\n",
+              "      <td>39</td>\n",
+              "      <td>200</td>\n",
+              "      <td>39.6</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>7.5</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>29.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:04.406759</th>\n",
+              "      <td>77</td>\n",
+              "      <td>Females Aged 30-44</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>7.6</td>\n",
+              "      <td>8.1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>10</td>\n",
+              "      <td>47</td>\n",
+              "      <td>14.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>61.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:04.603751</th>\n",
+              "      <td>544</td>\n",
+              "      <td>Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_4</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>246</td>\n",
+              "      <td>29</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>19</td>\n",
+              "      <td>25</td>\n",
+              "      <td>32</td>\n",
+              "      <td>36</td>\n",
+              "      <td>133</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>24.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:04.905270</th>\n",
+              "      <td>424</td>\n",
+              "      <td>Males Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>209</td>\n",
+              "      <td>26</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>18</td>\n",
+              "      <td>17</td>\n",
+              "      <td>25</td>\n",
+              "      <td>20</td>\n",
+              "      <td>87</td>\n",
+              "      <td>49.3</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>20.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:05.172451</th>\n",
+              "      <td>111</td>\n",
+              "      <td>Females Aged 45+</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>32</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>16</td>\n",
+              "      <td>44</td>\n",
+              "      <td>28.8</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>14.4</td>\n",
+              "      <td>39.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:05.369510</th>\n",
+              "      <td>8</td>\n",
+              "      <td>IMDb staff</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-im...</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>12.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:05.598331</th>\n",
+              "      <td>41</td>\n",
+              "      <td>Top 1000 voters</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-to...</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>26.8</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>14.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:05.794141</th>\n",
+              "      <td>925</td>\n",
+              "      <td>US users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-usa</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>405</td>\n",
+              "      <td>34</td>\n",
+              "      <td>20</td>\n",
+              "      <td>8</td>\n",
+              "      <td>9</td>\n",
+              "      <td>30</td>\n",
+              "      <td>54</td>\n",
+              "      <td>46</td>\n",
+              "      <td>60</td>\n",
+              "      <td>259</td>\n",
+              "      <td>43.8</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>28.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:06.022268</th>\n",
+              "      <td>565</td>\n",
+              "      <td>Non-US users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-in...</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>129</td>\n",
+              "      <td>11</td>\n",
+              "      <td>10</td>\n",
+              "      <td>8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>47</td>\n",
+              "      <td>52</td>\n",
+              "      <td>37</td>\n",
+              "      <td>35</td>\n",
+              "      <td>204</td>\n",
+              "      <td>22.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>36.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:06.218251</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2660 rows × 26 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            respondents            category  \\\n",
+              "timestamp                                                     \n",
+              "2017-08-29 00:00:02.248882         1682               Males   \n",
+              "2017-08-29 00:00:02.499363          257             Females   \n",
+              "2017-08-29 00:00:02.727204           15       Aged under 18   \n",
+              "2017-08-29 00:00:03.024270           13      Males under 18   \n",
+              "2017-08-29 00:00:03.248207            2    Females under 18   \n",
+              "2017-08-29 00:00:03.441529          450          Aged 18-29   \n",
+              "2017-08-29 00:00:03.636825          402    Males Aged 18-29   \n",
+              "2017-08-29 00:00:03.839446           41  Females Aged 18-29   \n",
+              "2017-08-29 00:00:04.030663          771          Aged 30-44   \n",
+              "2017-08-29 00:00:04.225962          679    Males Aged 30-44   \n",
+              "2017-08-29 00:00:04.539312           76  Females Aged 30-44   \n",
+              "2017-08-29 00:00:04.776104          539            Aged 45+   \n",
+              "2017-08-29 00:00:04.973766          421      Males Aged 45+   \n",
+              "2017-08-29 00:00:05.176998          109    Females Aged 45+   \n",
+              "2017-08-29 00:00:05.366554            8          IMDb staff   \n",
+              "2017-08-29 00:00:05.556598           41     Top 1000 voters   \n",
+              "2017-08-29 00:00:05.767397          922            US users   \n",
+              "2017-08-29 00:00:05.964362          556        Non-US users   \n",
+              "2017-08-29 00:00:06.288486         2646          IMDb users   \n",
+              "2017-08-29 00:10:02.437655         1682               Males   \n",
+              "2017-08-29 00:10:02.715850          257             Females   \n",
+              "2017-08-29 00:10:02.974317           15       Aged under 18   \n",
+              "2017-08-29 00:10:03.242796           13      Males under 18   \n",
+              "2017-08-29 00:10:03.471974            2    Females under 18   \n",
+              "2017-08-29 00:10:03.701915          450          Aged 18-29   \n",
+              "2017-08-29 00:10:03.922894          402    Males Aged 18-29   \n",
+              "2017-08-29 00:10:04.168584           41  Females Aged 18-29   \n",
+              "2017-08-29 00:10:04.391835          771          Aged 30-44   \n",
+              "2017-08-29 00:10:04.630956          679    Males Aged 30-44   \n",
+              "2017-08-29 00:10:04.819991           76  Females Aged 30-44   \n",
+              "...                                 ...                 ...   \n",
+              "2017-08-29 23:00:03.683160          775          Aged 30-44   \n",
+              "2017-08-29 23:00:03.924760          682    Males Aged 30-44   \n",
+              "2017-08-29 23:00:04.189034           77  Females Aged 30-44   \n",
+              "2017-08-29 23:00:04.381826          544            Aged 45+   \n",
+              "2017-08-29 23:00:04.619175          424      Males Aged 45+   \n",
+              "2017-08-29 23:00:04.820031          111    Females Aged 45+   \n",
+              "2017-08-29 23:00:05.028872            8          IMDb staff   \n",
+              "2017-08-29 23:00:05.231674           41     Top 1000 voters   \n",
+              "2017-08-29 23:00:05.442596          925            US users   \n",
+              "2017-08-29 23:00:05.634805          565        Non-US users   \n",
+              "2017-08-29 23:00:05.829558         2662          IMDb users   \n",
+              "2017-08-29 23:10:02.207706         1693               Males   \n",
+              "2017-08-29 23:10:02.398710          261             Females   \n",
+              "2017-08-29 23:10:02.622019           15       Aged under 18   \n",
+              "2017-08-29 23:10:02.908857           13      Males under 18   \n",
+              "2017-08-29 23:10:03.088635            2    Females under 18   \n",
+              "2017-08-29 23:10:03.288272          455          Aged 18-29   \n",
+              "2017-08-29 23:10:03.501039          406    Males Aged 18-29   \n",
+              "2017-08-29 23:10:03.725960           42  Females Aged 18-29   \n",
+              "2017-08-29 23:10:03.942442          775          Aged 30-44   \n",
+              "2017-08-29 23:10:04.147698          682    Males Aged 30-44   \n",
+              "2017-08-29 23:10:04.406759           77  Females Aged 30-44   \n",
+              "2017-08-29 23:10:04.603751          544            Aged 45+   \n",
+              "2017-08-29 23:10:04.905270          424      Males Aged 45+   \n",
+              "2017-08-29 23:10:05.172451          111    Females Aged 45+   \n",
+              "2017-08-29 23:10:05.369510            8          IMDb staff   \n",
+              "2017-08-29 23:10:05.598331           41     Top 1000 voters   \n",
+              "2017-08-29 23:10:05.794141          925            US users   \n",
+              "2017-08-29 23:10:06.022268          565        Non-US users   \n",
+              "2017-08-29 23:10:06.218251         2662          IMDb users   \n",
+              "\n",
+              "                                                                         link  \\\n",
+              "timestamp                                                                       \n",
+              "2017-08-29 00:00:02.248882   http://www.imdb.com/title/tt6322922/ratings-male   \n",
+              "2017-08-29 00:00:02.499363  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:00:02.727204  http://www.imdb.com/title/tt6322922/ratings-age_1   \n",
+              "2017-08-29 00:00:03.024270  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:00:03.248207  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:00:03.441529  http://www.imdb.com/title/tt6322922/ratings-age_2   \n",
+              "2017-08-29 00:00:03.636825  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:00:03.839446  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:00:04.030663  http://www.imdb.com/title/tt6322922/ratings-age_3   \n",
+              "2017-08-29 00:00:04.225962  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:00:04.539312  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:00:04.776104  http://www.imdb.com/title/tt6322922/ratings-age_4   \n",
+              "2017-08-29 00:00:04.973766  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:00:05.176998  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:00:05.366554  http://www.imdb.com/title/tt6322922/ratings-im...   \n",
+              "2017-08-29 00:00:05.556598  http://www.imdb.com/title/tt6322922/ratings-to...   \n",
+              "2017-08-29 00:00:05.767397    http://www.imdb.com/title/tt6322922/ratings-usa   \n",
+              "2017-08-29 00:00:05.964362  http://www.imdb.com/title/tt6322922/ratings-in...   \n",
+              "2017-08-29 00:00:06.288486        http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 00:10:02.437655   http://www.imdb.com/title/tt6322922/ratings-male   \n",
+              "2017-08-29 00:10:02.715850  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:10:02.974317  http://www.imdb.com/title/tt6322922/ratings-age_1   \n",
+              "2017-08-29 00:10:03.242796  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:10:03.471974  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:10:03.701915  http://www.imdb.com/title/tt6322922/ratings-age_2   \n",
+              "2017-08-29 00:10:03.922894  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:10:04.168584  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 00:10:04.391835  http://www.imdb.com/title/tt6322922/ratings-age_3   \n",
+              "2017-08-29 00:10:04.630956  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 00:10:04.819991  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "...                                                                       ...   \n",
+              "2017-08-29 23:00:03.683160  http://www.imdb.com/title/tt6322922/ratings-age_3   \n",
+              "2017-08-29 23:00:03.924760  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 23:00:04.189034  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:00:04.381826  http://www.imdb.com/title/tt6322922/ratings-age_4   \n",
+              "2017-08-29 23:00:04.619175  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 23:00:04.820031  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:00:05.028872  http://www.imdb.com/title/tt6322922/ratings-im...   \n",
+              "2017-08-29 23:00:05.231674  http://www.imdb.com/title/tt6322922/ratings-to...   \n",
+              "2017-08-29 23:00:05.442596    http://www.imdb.com/title/tt6322922/ratings-usa   \n",
+              "2017-08-29 23:00:05.634805  http://www.imdb.com/title/tt6322922/ratings-in...   \n",
+              "2017-08-29 23:00:05.829558        http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 23:10:02.207706   http://www.imdb.com/title/tt6322922/ratings-male   \n",
+              "2017-08-29 23:10:02.398710  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:10:02.622019  http://www.imdb.com/title/tt6322922/ratings-age_1   \n",
+              "2017-08-29 23:10:02.908857  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 23:10:03.088635  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:10:03.288272  http://www.imdb.com/title/tt6322922/ratings-age_2   \n",
+              "2017-08-29 23:10:03.501039  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 23:10:03.725960  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:10:03.942442  http://www.imdb.com/title/tt6322922/ratings-age_3   \n",
+              "2017-08-29 23:10:04.147698  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 23:10:04.406759  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:10:04.603751  http://www.imdb.com/title/tt6322922/ratings-age_4   \n",
+              "2017-08-29 23:10:04.905270  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-08-29 23:10:05.172451  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-08-29 23:10:05.369510  http://www.imdb.com/title/tt6322922/ratings-im...   \n",
+              "2017-08-29 23:10:05.598331  http://www.imdb.com/title/tt6322922/ratings-to...   \n",
+              "2017-08-29 23:10:05.794141    http://www.imdb.com/title/tt6322922/ratings-usa   \n",
+              "2017-08-29 23:10:06.022268  http://www.imdb.com/title/tt6322922/ratings-in...   \n",
+              "2017-08-29 23:10:06.218251        http://www.imdb.com/title/tt6322922/ratings   \n",
+              "\n",
+              "                            average  mean  median  1_votes  2_votes  3_votes  \\\n",
+              "timestamp                                                                      \n",
+              "2017-08-29 00:00:02.248882      5.3   5.3       6      671       50       29   \n",
+              "2017-08-29 00:00:02.499363      6.9   7.3      10       53        3        4   \n",
+              "2017-08-29 00:00:02.727204      4.5   5.3       5        7        0        0   \n",
+              "2017-08-29 00:00:03.024270      5.3   5.9       7        5        0        0   \n",
+              "2017-08-29 00:00:03.248207      1.0   1.0       1        2        0        0   \n",
+              "2017-08-29 00:00:03.441529      6.4   6.6       8      117        3        6   \n",
+              "2017-08-29 00:00:03.636825      6.3   6.5       8      112        3        4   \n",
+              "2017-08-29 00:00:03.839446      7.1   7.9      10        4        0        2   \n",
+              "2017-08-29 00:00:04.030663      5.6   5.6       7      287       21       12   \n",
+              "2017-08-29 00:00:04.225962      5.5   5.3       6      270       20       10   \n",
+              "2017-08-29 00:00:04.539312      7.5   8.0      10       11        0        2   \n",
+              "2017-08-29 00:00:04.776104      4.4   4.8       2      245       28       13   \n",
+              "2017-08-29 00:00:04.973766      4.0   4.3       2      209       25       13   \n",
+              "2017-08-29 00:00:05.176998      6.7   6.7       9       31        3        0   \n",
+              "2017-08-29 00:00:05.366554      8.4   7.2       8        1        0        0   \n",
+              "2017-08-29 00:00:05.556598      4.6   4.7       5       11        4        2   \n",
+              "2017-08-29 00:00:05.767397      4.8   5.0       4      405       33       20   \n",
+              "2017-08-29 00:00:05.964362      6.4   6.5       7      129       11       10   \n",
+              "2017-08-29 00:00:06.288486      5.4   5.6       7     1020       67       39   \n",
+              "2017-08-29 00:10:02.437655      5.3   5.3       6      671       50       29   \n",
+              "2017-08-29 00:10:02.715850      6.9   7.3      10       53        3        4   \n",
+              "2017-08-29 00:10:02.974317      4.5   5.3       5        7        0        0   \n",
+              "2017-08-29 00:10:03.242796      5.3   5.9       7        5        0        0   \n",
+              "2017-08-29 00:10:03.471974      1.0   1.0       1        2        0        0   \n",
+              "2017-08-29 00:10:03.701915      6.4   6.6       8      117        3        6   \n",
+              "2017-08-29 00:10:03.922894      6.3   6.5       8      112        3        4   \n",
+              "2017-08-29 00:10:04.168584      7.1   7.9      10        4        0        2   \n",
+              "2017-08-29 00:10:04.391835      5.6   5.6       7      287       21       12   \n",
+              "2017-08-29 00:10:04.630956      5.5   5.3       6      270       20       10   \n",
+              "2017-08-29 00:10:04.819991      7.5   8.0      10       11        0        2   \n",
+              "...                             ...   ...     ...      ...      ...      ...   \n",
+              "2017-08-29 23:00:03.683160      5.7   5.6       7      287       21       12   \n",
+              "2017-08-29 23:00:03.924760      5.5   5.3       6      270       20       10   \n",
+              "2017-08-29 23:00:04.189034      7.6   8.1      10       11        0        2   \n",
+              "2017-08-29 23:00:04.381826      4.4   4.8       2      246       29       13   \n",
+              "2017-08-29 23:00:04.619175      4.0   4.3       2      209       26       13   \n",
+              "2017-08-29 23:00:04.820031      6.6   6.6       9       32        3        0   \n",
+              "2017-08-29 23:00:05.028872      8.4   7.2       8        1        0        0   \n",
+              "2017-08-29 23:00:05.231674      4.6   4.7       5       11        4        2   \n",
+              "2017-08-29 23:00:05.442596      4.8   5.0       4      405       34       20   \n",
+              "2017-08-29 23:00:05.634805      6.4   6.5       7      129       11       10   \n",
+              "2017-08-29 23:00:05.829558      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 23:10:02.207706      5.3   5.4       6      671       51       29   \n",
+              "2017-08-29 23:10:02.398710      7.0   7.3      10       54        3        4   \n",
+              "2017-08-29 23:10:02.622019      4.5   5.3       5        7        0        0   \n",
+              "2017-08-29 23:10:02.908857      5.3   5.9       7        5        0        0   \n",
+              "2017-08-29 23:10:03.088635      1.0   1.0       1        2        0        0   \n",
+              "2017-08-29 23:10:03.288272      6.4   6.6       8      117        3        6   \n",
+              "2017-08-29 23:10:03.501039      6.3   6.5       8      112        3        4   \n",
+              "2017-08-29 23:10:03.725960      7.1   7.9      10        4        0        2   \n",
+              "2017-08-29 23:10:03.942442      5.7   5.6       7      287       21       12   \n",
+              "2017-08-29 23:10:04.147698      5.5   5.3       6      270       20       10   \n",
+              "2017-08-29 23:10:04.406759      7.6   8.1      10       11        0        2   \n",
+              "2017-08-29 23:10:04.603751      4.4   4.8       2      246       29       13   \n",
+              "2017-08-29 23:10:04.905270      4.0   4.3       2      209       26       13   \n",
+              "2017-08-29 23:10:05.172451      6.6   6.6       9       32        3        0   \n",
+              "2017-08-29 23:10:05.369510      8.4   7.2       8        1        0        0   \n",
+              "2017-08-29 23:10:05.598331      4.6   4.7       5       11        4        2   \n",
+              "2017-08-29 23:10:05.794141      4.8   5.0       4      405       34       20   \n",
+              "2017-08-29 23:10:06.022268      6.4   6.5       7      129       11       10   \n",
+              "2017-08-29 23:10:06.218251      5.4   5.6       7     1021       69       38   \n",
+              "\n",
+              "                            4_votes  5_votes  6_votes  7_votes  8_votes  \\\n",
+              "timestamp                                                                 \n",
+              "2017-08-29 00:00:02.248882       12       42       79      107       90   \n",
+              "2017-08-29 00:00:02.499363        4        4        5       14       13   \n",
+              "2017-08-29 00:00:02.727204        0        1        0        1        0   \n",
+              "2017-08-29 00:00:03.024270        0        1        0        1        0   \n",
+              "2017-08-29 00:00:03.248207        0        0        0        0        0   \n",
+              "2017-08-29 00:00:03.441529        7       17       24       35       28   \n",
+              "2017-08-29 00:00:03.636825        6       15       20       33       25   \n",
+              "2017-08-29 00:00:03.839446        1        2        2        2        3   \n",
+              "2017-08-29 00:00:04.030663        4       21       36       53       38   \n",
+              "2017-08-29 00:00:04.225962        2       20       33       51       34   \n",
+              "2017-08-29 00:00:04.539312        2        0        2        1        2   \n",
+              "2017-08-29 00:00:04.776104        3        8       19       24       32   \n",
+              "2017-08-29 00:00:04.973766        3        6       18       16       25   \n",
+              "2017-08-29 00:00:05.176998        0        2        0        8        6   \n",
+              "2017-08-29 00:00:05.366554        0        0        1        1        2   \n",
+              "2017-08-29 00:00:05.556598        2        5        4        5        1   \n",
+              "2017-08-29 00:00:05.767397        8        9       30       54       46   \n",
+              "2017-08-29 00:00:05.964362        8       32       44       50       35   \n",
+              "2017-08-29 00:00:06.288486       24       55      107      152      145   \n",
+              "2017-08-29 00:10:02.437655       12       42       79      107       90   \n",
+              "2017-08-29 00:10:02.715850        4        4        5       14       13   \n",
+              "2017-08-29 00:10:02.974317        0        1        0        1        0   \n",
+              "2017-08-29 00:10:03.242796        0        1        0        1        0   \n",
+              "2017-08-29 00:10:03.471974        0        0        0        0        0   \n",
+              "2017-08-29 00:10:03.701915        7       17       24       35       28   \n",
+              "2017-08-29 00:10:03.922894        6       15       20       33       25   \n",
+              "2017-08-29 00:10:04.168584        1        2        2        2        3   \n",
+              "2017-08-29 00:10:04.391835        4       21       36       53       38   \n",
+              "2017-08-29 00:10:04.630956        2       20       33       51       34   \n",
+              "2017-08-29 00:10:04.819991        2        0        2        1        2   \n",
+              "...                             ...      ...      ...      ...      ...   \n",
+              "2017-08-29 23:00:03.683160        4       21       38       53       39   \n",
+              "2017-08-29 23:00:03.924760        2       20       35       51       35   \n",
+              "2017-08-29 23:00:04.189034        2        0        2        1        2   \n",
+              "2017-08-29 23:00:04.381826        3        8       19       25       32   \n",
+              "2017-08-29 23:00:04.619175        3        6       18       17       25   \n",
+              "2017-08-29 23:00:04.820031        0        2        0        8        6   \n",
+              "2017-08-29 23:00:05.028872        0        0        1        1        2   \n",
+              "2017-08-29 23:00:05.231674        2        5        4        5        1   \n",
+              "2017-08-29 23:00:05.442596        8        9       30       54       46   \n",
+              "2017-08-29 23:00:05.634805        8       32       47       52       37   \n",
+              "2017-08-29 23:00:05.829558       25       55      110      154      147   \n",
+              "2017-08-29 23:10:02.207706       13       42       82      109       91   \n",
+              "2017-08-29 23:10:02.398710        4        4        5       14       14   \n",
+              "2017-08-29 23:10:02.622019        0        1        0        1        0   \n",
+              "2017-08-29 23:10:02.908857        0        1        0        1        0   \n",
+              "2017-08-29 23:10:03.088635        0        0        0        0        0   \n",
+              "2017-08-29 23:10:03.288272        8       17       25       36       29   \n",
+              "2017-08-29 23:10:03.501039        7       15       21       34       25   \n",
+              "2017-08-29 23:10:03.725960        1        2        2        2        4   \n",
+              "2017-08-29 23:10:03.942442        4       21       38       53       39   \n",
+              "2017-08-29 23:10:04.147698        2       20       35       51       35   \n",
+              "2017-08-29 23:10:04.406759        2        0        2        1        2   \n",
+              "2017-08-29 23:10:04.603751        3        8       19       25       32   \n",
+              "2017-08-29 23:10:04.905270        3        6       18       17       25   \n",
+              "2017-08-29 23:10:05.172451        0        2        0        8        6   \n",
+              "2017-08-29 23:10:05.369510        0        0        1        1        2   \n",
+              "2017-08-29 23:10:05.598331        2        5        4        5        1   \n",
+              "2017-08-29 23:10:05.794141        8        9       30       54       46   \n",
+              "2017-08-29 23:10:06.022268        8       32       47       52       37   \n",
+              "2017-08-29 23:10:06.218251       25       55      110      154      147   \n",
+              "\n",
+              "                            9_votes  10_votes  1_pct  2_pct  3_pct  4_pct  \\\n",
+              "timestamp                                                                   \n",
+              "2017-08-29 00:00:02.248882       83       519   39.9    3.0    1.7    0.7   \n",
+              "2017-08-29 00:00:02.499363       26       131   20.6    1.2    1.6    1.6   \n",
+              "2017-08-29 00:00:02.727204        0         6   46.7    0.0    0.0    0.0   \n",
+              "2017-08-29 00:00:03.024270        0         6   38.5    0.0    0.0    0.0   \n",
+              "2017-08-29 00:00:03.248207        0         0  100.0    0.0    0.0    0.0   \n",
+              "2017-08-29 00:00:03.441529       23       190   26.0    0.7    1.3    1.6   \n",
+              "2017-08-29 00:00:03.636825       21       163   27.9    0.7    1.0    1.5   \n",
+              "2017-08-29 00:00:03.839446        2        23    9.8    0.0    4.9    2.4   \n",
+              "2017-08-29 00:00:04.030663       49       250   37.2    2.7    1.6    0.5   \n",
+              "2017-08-29 00:00:04.225962       39       200   39.8    2.9    1.5    0.3   \n",
+              "2017-08-29 00:00:04.539312        9        47   14.5    0.0    2.6    2.6   \n",
+              "2017-08-29 00:00:04.776104       35       132   45.5    5.2    2.4    0.6   \n",
+              "2017-08-29 00:00:04.973766       20        86   49.6    5.9    3.1    0.7   \n",
+              "2017-08-29 00:00:05.176998       15        44   28.4    2.8    0.0    0.0   \n",
+              "2017-08-29 00:00:05.366554        2         1   12.5    0.0    0.0    0.0   \n",
+              "2017-08-29 00:00:05.556598        1         6   26.8    9.8    4.9    4.9   \n",
+              "2017-08-29 00:00:05.767397       58       259   43.9    3.6    2.2    0.9   \n",
+              "2017-08-29 00:00:05.964362       35       202   23.2    2.0    1.8    1.4   \n",
+              "2017-08-29 00:00:06.288486      144       893   38.5    2.5    1.5    0.9   \n",
+              "2017-08-29 00:10:02.437655       83       519   39.9    3.0    1.7    0.7   \n",
+              "2017-08-29 00:10:02.715850       26       131   20.6    1.2    1.6    1.6   \n",
+              "2017-08-29 00:10:02.974317        0         6   46.7    0.0    0.0    0.0   \n",
+              "2017-08-29 00:10:03.242796        0         6   38.5    0.0    0.0    0.0   \n",
+              "2017-08-29 00:10:03.471974        0         0  100.0    0.0    0.0    0.0   \n",
+              "2017-08-29 00:10:03.701915       23       190   26.0    0.7    1.3    1.6   \n",
+              "2017-08-29 00:10:03.922894       21       163   27.9    0.7    1.0    1.5   \n",
+              "2017-08-29 00:10:04.168584        2        23    9.8    0.0    4.9    2.4   \n",
+              "2017-08-29 00:10:04.391835       49       250   37.2    2.7    1.6    0.5   \n",
+              "2017-08-29 00:10:04.630956       39       200   39.8    2.9    1.5    0.3   \n",
+              "2017-08-29 00:10:04.819991        9        47   14.5    0.0    2.6    2.6   \n",
+              "...                             ...       ...    ...    ...    ...    ...   \n",
+              "2017-08-29 23:00:03.683160       50       250   37.0    2.7    1.5    0.5   \n",
+              "2017-08-29 23:00:03.924760       39       200   39.6    2.9    1.5    0.3   \n",
+              "2017-08-29 23:00:04.189034       10        47   14.3    0.0    2.6    2.6   \n",
+              "2017-08-29 23:00:04.381826       36       133   45.2    5.3    2.4    0.6   \n",
+              "2017-08-29 23:00:04.619175       20        87   49.3    6.1    3.1    0.7   \n",
+              "2017-08-29 23:00:04.820031       16        44   28.8    2.7    0.0    0.0   \n",
+              "2017-08-29 23:00:05.028872        2         1   12.5    0.0    0.0    0.0   \n",
+              "2017-08-29 23:00:05.231674        1         6   26.8    9.8    4.9    4.9   \n",
+              "2017-08-29 23:00:05.442596       60       259   43.8    3.7    2.2    0.9   \n",
+              "2017-08-29 23:00:05.634805       35       204   22.8    1.9    1.8    1.4   \n",
+              "2017-08-29 23:00:05.829558      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 23:10:02.207706       83       522   39.6    3.0    1.7    0.8   \n",
+              "2017-08-29 23:10:02.398710       28       131   20.7    1.1    1.5    1.5   \n",
+              "2017-08-29 23:10:02.622019        0         6   46.7    0.0    0.0    0.0   \n",
+              "2017-08-29 23:10:02.908857        0         6   38.5    0.0    0.0    0.0   \n",
+              "2017-08-29 23:10:03.088635        0         0  100.0    0.0    0.0    0.0   \n",
+              "2017-08-29 23:10:03.288272       23       191   25.7    0.7    1.3    1.8   \n",
+              "2017-08-29 23:10:03.501039       21       164   27.6    0.7    1.0    1.7   \n",
+              "2017-08-29 23:10:03.725960        2        23    9.5    0.0    4.8    2.4   \n",
+              "2017-08-29 23:10:03.942442       50       250   37.0    2.7    1.5    0.5   \n",
+              "2017-08-29 23:10:04.147698       39       200   39.6    2.9    1.5    0.3   \n",
+              "2017-08-29 23:10:04.406759       10        47   14.3    0.0    2.6    2.6   \n",
+              "2017-08-29 23:10:04.603751       36       133   45.2    5.3    2.4    0.6   \n",
+              "2017-08-29 23:10:04.905270       20        87   49.3    6.1    3.1    0.7   \n",
+              "2017-08-29 23:10:05.172451       16        44   28.8    2.7    0.0    0.0   \n",
+              "2017-08-29 23:10:05.369510        2         1   12.5    0.0    0.0    0.0   \n",
+              "2017-08-29 23:10:05.598331        1         6   26.8    9.8    4.9    4.9   \n",
+              "2017-08-29 23:10:05.794141       60       259   43.8    3.7    2.2    0.9   \n",
+              "2017-08-29 23:10:06.022268       35       204   22.8    1.9    1.8    1.4   \n",
+              "2017-08-29 23:10:06.218251      146       897   38.4    2.6    1.4    0.9   \n",
+              "\n",
+              "                            5_pct  6_pct  7_pct  8_pct  9_pct  10_pct  \n",
+              "timestamp                                                              \n",
+              "2017-08-29 00:00:02.248882    2.5    4.7    6.4    5.4    4.9    30.9  \n",
+              "2017-08-29 00:00:02.499363    1.6    1.9    5.4    5.1   10.1    51.0  \n",
+              "2017-08-29 00:00:02.727204    6.7    0.0    6.7    0.0    0.0    40.0  \n",
+              "2017-08-29 00:00:03.024270    7.7    0.0    7.7    0.0    0.0    46.2  \n",
+              "2017-08-29 00:00:03.248207    0.0    0.0    0.0    0.0    0.0     0.0  \n",
+              "2017-08-29 00:00:03.441529    3.8    5.3    7.8    6.2    5.1    42.2  \n",
+              "2017-08-29 00:00:03.636825    3.7    5.0    8.2    6.2    5.2    40.5  \n",
+              "2017-08-29 00:00:03.839446    4.9    4.9    4.9    7.3    4.9    56.1  \n",
+              "2017-08-29 00:00:04.030663    2.7    4.7    6.9    4.9    6.4    32.4  \n",
+              "2017-08-29 00:00:04.225962    2.9    4.9    7.5    5.0    5.7    29.5  \n",
+              "2017-08-29 00:00:04.539312    0.0    2.6    1.3    2.6   11.8    61.8  \n",
+              "2017-08-29 00:00:04.776104    1.5    3.5    4.5    5.9    6.5    24.5  \n",
+              "2017-08-29 00:00:04.973766    1.4    4.3    3.8    5.9    4.8    20.4  \n",
+              "2017-08-29 00:00:05.176998    1.8    0.0    7.3    5.5   13.8    40.4  \n",
+              "2017-08-29 00:00:05.366554    0.0   12.5   12.5   25.0   25.0    12.5  \n",
+              "2017-08-29 00:00:05.556598   12.2    9.8   12.2    2.4    2.4    14.6  \n",
+              "2017-08-29 00:00:05.767397    1.0    3.3    5.9    5.0    6.3    28.1  \n",
+              "2017-08-29 00:00:05.964362    5.8    7.9    9.0    6.3    6.3    36.3  \n",
+              "2017-08-29 00:00:06.288486    2.1    4.0    5.7    5.5    5.4    33.7  \n",
+              "2017-08-29 00:10:02.437655    2.5    4.7    6.4    5.4    4.9    30.9  \n",
+              "2017-08-29 00:10:02.715850    1.6    1.9    5.4    5.1   10.1    51.0  \n",
+              "2017-08-29 00:10:02.974317    6.7    0.0    6.7    0.0    0.0    40.0  \n",
+              "2017-08-29 00:10:03.242796    7.7    0.0    7.7    0.0    0.0    46.2  \n",
+              "2017-08-29 00:10:03.471974    0.0    0.0    0.0    0.0    0.0     0.0  \n",
+              "2017-08-29 00:10:03.701915    3.8    5.3    7.8    6.2    5.1    42.2  \n",
+              "2017-08-29 00:10:03.922894    3.7    5.0    8.2    6.2    5.2    40.5  \n",
+              "2017-08-29 00:10:04.168584    4.9    4.9    4.9    7.3    4.9    56.1  \n",
+              "2017-08-29 00:10:04.391835    2.7    4.7    6.9    4.9    6.4    32.4  \n",
+              "2017-08-29 00:10:04.630956    2.9    4.9    7.5    5.0    5.7    29.5  \n",
+              "2017-08-29 00:10:04.819991    0.0    2.6    1.3    2.6   11.8    61.8  \n",
+              "...                           ...    ...    ...    ...    ...     ...  \n",
+              "2017-08-29 23:00:03.683160    2.7    4.9    6.8    5.0    6.5    32.3  \n",
+              "2017-08-29 23:00:03.924760    2.9    5.1    7.5    5.1    5.7    29.3  \n",
+              "2017-08-29 23:00:04.189034    0.0    2.6    1.3    2.6   13.0    61.0  \n",
+              "2017-08-29 23:00:04.381826    1.5    3.5    4.6    5.9    6.6    24.4  \n",
+              "2017-08-29 23:00:04.619175    1.4    4.2    4.0    5.9    4.7    20.5  \n",
+              "2017-08-29 23:00:04.820031    1.8    0.0    7.2    5.4   14.4    39.6  \n",
+              "2017-08-29 23:00:05.028872    0.0   12.5   12.5   25.0   25.0    12.5  \n",
+              "2017-08-29 23:00:05.231674   12.2    9.8   12.2    2.4    2.4    14.6  \n",
+              "2017-08-29 23:00:05.442596    1.0    3.2    5.8    5.0    6.5    28.0  \n",
+              "2017-08-29 23:00:05.634805    5.7    8.3    9.2    6.5    6.2    36.1  \n",
+              "2017-08-29 23:00:05.829558    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 23:10:02.207706    2.5    4.8    6.4    5.4    4.9    30.8  \n",
+              "2017-08-29 23:10:02.398710    1.5    1.9    5.4    5.4   10.7    50.2  \n",
+              "2017-08-29 23:10:02.622019    6.7    0.0    6.7    0.0    0.0    40.0  \n",
+              "2017-08-29 23:10:02.908857    7.7    0.0    7.7    0.0    0.0    46.2  \n",
+              "2017-08-29 23:10:03.088635    0.0    0.0    0.0    0.0    0.0     0.0  \n",
+              "2017-08-29 23:10:03.288272    3.7    5.5    7.9    6.4    5.1    42.0  \n",
+              "2017-08-29 23:10:03.501039    3.7    5.2    8.4    6.2    5.2    40.4  \n",
+              "2017-08-29 23:10:03.725960    4.8    4.8    4.8    9.5    4.8    54.8  \n",
+              "2017-08-29 23:10:03.942442    2.7    4.9    6.8    5.0    6.5    32.3  \n",
+              "2017-08-29 23:10:04.147698    2.9    5.1    7.5    5.1    5.7    29.3  \n",
+              "2017-08-29 23:10:04.406759    0.0    2.6    1.3    2.6   13.0    61.0  \n",
+              "2017-08-29 23:10:04.603751    1.5    3.5    4.6    5.9    6.6    24.4  \n",
+              "2017-08-29 23:10:04.905270    1.4    4.2    4.0    5.9    4.7    20.5  \n",
+              "2017-08-29 23:10:05.172451    1.8    0.0    7.2    5.4   14.4    39.6  \n",
+              "2017-08-29 23:10:05.369510    0.0   12.5   12.5   25.0   25.0    12.5  \n",
+              "2017-08-29 23:10:05.598331   12.2    9.8   12.2    2.4    2.4    14.6  \n",
+              "2017-08-29 23:10:05.794141    1.0    3.2    5.8    5.0    6.5    28.0  \n",
+              "2017-08-29 23:10:06.022268    5.7    8.3    9.2    6.5    6.2    36.1  \n",
+              "2017-08-29 23:10:06.218251    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "\n",
+              "[2660 rows x 26 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "f0JtcmwYdNJY",
+        "colab_type": "code",
+        "outputId": "7a92fdf9-41d9-41df-fece-2f147a92a338",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df.category.value_counts()"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "IMDb users            4216\n",
+              "Males Aged 30-44      4216\n",
+              "Females               4216\n",
+              "Top 1000 voters       4216\n",
+              "Aged under 18         4216\n",
+              "Males Aged 18-29      4216\n",
+              "Females Aged 45+      4216\n",
+              "Females Aged 30-44    4216\n",
+              "IMDb staff            4216\n",
+              "Males under 18        4216\n",
+              "US users              4216\n",
+              "Females Aged 18-29    4216\n",
+              "Aged 30-44            4216\n",
+              "Aged 18-29            4216\n",
+              "Males Aged 45+        4216\n",
+              "Aged 45+              4216\n",
+              "Non-US users          4216\n",
+              "Males                 4216\n",
+              "Females under 18      4165\n",
+              "Name: category, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TA43A_SLdsua",
+        "colab_type": "code",
+        "outputId": "716857f1-b090-43ce-ad54-37bc1d997a91",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df.link.value_counts()"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "http://www.imdb.com/title/tt6322922/ratings-top_1000         4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-male_age_3       4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-female           4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-female_age_4     4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-age_4            4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-male_age_1       4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-female_age_2     4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-usa              4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-imdb_staff       4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings                  4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-male_age_2       4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-age_2            4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-female_age_3     4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-male_age_4       4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-age_3            4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-male             4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-age_1            4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-international    4216\n",
+              "http://www.imdb.com/title/tt6322922/ratings-female_age_1     4165\n",
+              "Name: link, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Tw_H00g3d577",
+        "colab_type": "code",
+        "outputId": "65ee3b1b-6aeb-4695-99e9-145ccb29e911",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 3039
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df[df.category=='IMDb users']"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>respondents</th>\n",
+              "      <th>category</th>\n",
+              "      <th>link</th>\n",
+              "      <th>average</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>median</th>\n",
+              "      <th>1_votes</th>\n",
+              "      <th>2_votes</th>\n",
+              "      <th>3_votes</th>\n",
+              "      <th>4_votes</th>\n",
+              "      <th>5_votes</th>\n",
+              "      <th>6_votes</th>\n",
+              "      <th>7_votes</th>\n",
+              "      <th>8_votes</th>\n",
+              "      <th>9_votes</th>\n",
+              "      <th>10_votes</th>\n",
+              "      <th>1_pct</th>\n",
+              "      <th>2_pct</th>\n",
+              "      <th>3_pct</th>\n",
+              "      <th>4_pct</th>\n",
+              "      <th>5_pct</th>\n",
+              "      <th>6_pct</th>\n",
+              "      <th>7_pct</th>\n",
+              "      <th>8_pct</th>\n",
+              "      <th>9_pct</th>\n",
+              "      <th>10_pct</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:36.697246</th>\n",
+              "      <td>678</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>304</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.8</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:21.250099</th>\n",
+              "      <td>678</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>304</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.8</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 13:00:06.506688</th>\n",
+              "      <td>678</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>304</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.8</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 14:00:06.727742</th>\n",
+              "      <td>679</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>305</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 15:00:06.055125</th>\n",
+              "      <td>679</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>305</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 16:00:06.023053</th>\n",
+              "      <td>679</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>305</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 17:00:06.142993</th>\n",
+              "      <td>679</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>305</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 18:00:06.510891</th>\n",
+              "      <td>680</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>306</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 19:00:06.666467</th>\n",
+              "      <td>680</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>306</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 20:00:05.841494</th>\n",
+              "      <td>681</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>307</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.1</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 21:00:06.449770</th>\n",
+              "      <td>681</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>307</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>18</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.1</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 22:00:06.123852</th>\n",
+              "      <td>682</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>307</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>19</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 23:00:06.458620</th>\n",
+              "      <td>682</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>307</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>19</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 00:00:06.001629</th>\n",
+              "      <td>682</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>307</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>19</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 01:00:06.085490</th>\n",
+              "      <td>682</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>307</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>19</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 02:00:05.876108</th>\n",
+              "      <td>685</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>309</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>20</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 03:00:06.266496</th>\n",
+              "      <td>685</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>309</td>\n",
+              "      <td>14</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>20</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 04:00:05.839574</th>\n",
+              "      <td>686</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>20</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 05:00:05.497594</th>\n",
+              "      <td>686</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>20</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 06:00:06.040353</th>\n",
+              "      <td>686</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>20</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 07:00:05.818742</th>\n",
+              "      <td>686</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>20</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>256</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 08:00:05.724752</th>\n",
+              "      <td>690</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>24</td>\n",
+              "      <td>31</td>\n",
+              "      <td>257</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>37.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 09:00:05.167180</th>\n",
+              "      <td>690</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>24</td>\n",
+              "      <td>31</td>\n",
+              "      <td>257</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>37.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 10:00:06.849305</th>\n",
+              "      <td>693</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>24</td>\n",
+              "      <td>31</td>\n",
+              "      <td>260</td>\n",
+              "      <td>44.7</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 11:00:05.242209</th>\n",
+              "      <td>693</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>310</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>24</td>\n",
+              "      <td>31</td>\n",
+              "      <td>260</td>\n",
+              "      <td>44.7</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 12:00:06.474276</th>\n",
+              "      <td>698</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>312</td>\n",
+              "      <td>13</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>25</td>\n",
+              "      <td>31</td>\n",
+              "      <td>262</td>\n",
+              "      <td>44.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 13:00:06.029593</th>\n",
+              "      <td>698</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>312</td>\n",
+              "      <td>13</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>25</td>\n",
+              "      <td>31</td>\n",
+              "      <td>262</td>\n",
+              "      <td>44.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 14:00:07.095179</th>\n",
+              "      <td>701</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>312</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>25</td>\n",
+              "      <td>31</td>\n",
+              "      <td>264</td>\n",
+              "      <td>44.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 15:00:06.790957</th>\n",
+              "      <td>701</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>312</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>25</td>\n",
+              "      <td>31</td>\n",
+              "      <td>264</td>\n",
+              "      <td>44.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-18 16:00:06.159608</th>\n",
+              "      <td>701</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>312</td>\n",
+              "      <td>14</td>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>21</td>\n",
+              "      <td>25</td>\n",
+              "      <td>31</td>\n",
+              "      <td>264</td>\n",
+              "      <td>44.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>37.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 18:20:06.215486</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 18:30:06.738669</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 18:40:05.771150</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 18:50:06.051326</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 19:00:06.002542</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 19:10:06.066863</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 19:20:06.286871</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 19:30:06.395003</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 19:40:06.372808</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 19:50:06.030788</th>\n",
+              "      <td>2654</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>108</td>\n",
+              "      <td>152</td>\n",
+              "      <td>146</td>\n",
+              "      <td>144</td>\n",
+              "      <td>896</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>33.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 20:00:06.213617</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 20:10:06.353175</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 20:20:06.692848</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 20:30:06.047404</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 20:40:06.341599</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 20:50:06.265539</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 21:00:07.056294</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 21:10:06.940833</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 21:20:06.241187</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 21:30:06.277390</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 21:40:06.384083</th>\n",
+              "      <td>2660</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>152</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 21:50:06.071510</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 22:00:06.565253</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 22:10:07.085246</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 22:20:06.947696</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 22:30:06.423181</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 22:40:06.233659</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 22:50:06.592571</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:00:05.829558</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:06.218251</th>\n",
+              "      <td>2662</td>\n",
+              "      <td>IMDb users</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1021</td>\n",
+              "      <td>69</td>\n",
+              "      <td>38</td>\n",
+              "      <td>25</td>\n",
+              "      <td>55</td>\n",
+              "      <td>110</td>\n",
+              "      <td>154</td>\n",
+              "      <td>147</td>\n",
+              "      <td>146</td>\n",
+              "      <td>897</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>4216 rows × 26 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            respondents    category  \\\n",
+              "timestamp                                             \n",
+              "2017-07-17 12:28:36.697246          678  IMDb users   \n",
+              "2017-07-17 12:34:21.250099          678  IMDb users   \n",
+              "2017-07-17 13:00:06.506688          678  IMDb users   \n",
+              "2017-07-17 14:00:06.727742          679  IMDb users   \n",
+              "2017-07-17 15:00:06.055125          679  IMDb users   \n",
+              "2017-07-17 16:00:06.023053          679  IMDb users   \n",
+              "2017-07-17 17:00:06.142993          679  IMDb users   \n",
+              "2017-07-17 18:00:06.510891          680  IMDb users   \n",
+              "2017-07-17 19:00:06.666467          680  IMDb users   \n",
+              "2017-07-17 20:00:05.841494          681  IMDb users   \n",
+              "2017-07-17 21:00:06.449770          681  IMDb users   \n",
+              "2017-07-17 22:00:06.123852          682  IMDb users   \n",
+              "2017-07-17 23:00:06.458620          682  IMDb users   \n",
+              "2017-07-18 00:00:06.001629          682  IMDb users   \n",
+              "2017-07-18 01:00:06.085490          682  IMDb users   \n",
+              "2017-07-18 02:00:05.876108          685  IMDb users   \n",
+              "2017-07-18 03:00:06.266496          685  IMDb users   \n",
+              "2017-07-18 04:00:05.839574          686  IMDb users   \n",
+              "2017-07-18 05:00:05.497594          686  IMDb users   \n",
+              "2017-07-18 06:00:06.040353          686  IMDb users   \n",
+              "2017-07-18 07:00:05.818742          686  IMDb users   \n",
+              "2017-07-18 08:00:05.724752          690  IMDb users   \n",
+              "2017-07-18 09:00:05.167180          690  IMDb users   \n",
+              "2017-07-18 10:00:06.849305          693  IMDb users   \n",
+              "2017-07-18 11:00:05.242209          693  IMDb users   \n",
+              "2017-07-18 12:00:06.474276          698  IMDb users   \n",
+              "2017-07-18 13:00:06.029593          698  IMDb users   \n",
+              "2017-07-18 14:00:07.095179          701  IMDb users   \n",
+              "2017-07-18 15:00:06.790957          701  IMDb users   \n",
+              "2017-07-18 16:00:06.159608          701  IMDb users   \n",
+              "...                                 ...         ...   \n",
+              "2017-08-29 18:20:06.215486         2654  IMDb users   \n",
+              "2017-08-29 18:30:06.738669         2654  IMDb users   \n",
+              "2017-08-29 18:40:05.771150         2654  IMDb users   \n",
+              "2017-08-29 18:50:06.051326         2654  IMDb users   \n",
+              "2017-08-29 19:00:06.002542         2654  IMDb users   \n",
+              "2017-08-29 19:10:06.066863         2654  IMDb users   \n",
+              "2017-08-29 19:20:06.286871         2654  IMDb users   \n",
+              "2017-08-29 19:30:06.395003         2654  IMDb users   \n",
+              "2017-08-29 19:40:06.372808         2654  IMDb users   \n",
+              "2017-08-29 19:50:06.030788         2654  IMDb users   \n",
+              "2017-08-29 20:00:06.213617         2660  IMDb users   \n",
+              "2017-08-29 20:10:06.353175         2660  IMDb users   \n",
+              "2017-08-29 20:20:06.692848         2660  IMDb users   \n",
+              "2017-08-29 20:30:06.047404         2660  IMDb users   \n",
+              "2017-08-29 20:40:06.341599         2660  IMDb users   \n",
+              "2017-08-29 20:50:06.265539         2660  IMDb users   \n",
+              "2017-08-29 21:00:07.056294         2660  IMDb users   \n",
+              "2017-08-29 21:10:06.940833         2660  IMDb users   \n",
+              "2017-08-29 21:20:06.241187         2660  IMDb users   \n",
+              "2017-08-29 21:30:06.277390         2660  IMDb users   \n",
+              "2017-08-29 21:40:06.384083         2660  IMDb users   \n",
+              "2017-08-29 21:50:06.071510         2662  IMDb users   \n",
+              "2017-08-29 22:00:06.565253         2662  IMDb users   \n",
+              "2017-08-29 22:10:07.085246         2662  IMDb users   \n",
+              "2017-08-29 22:20:06.947696         2662  IMDb users   \n",
+              "2017-08-29 22:30:06.423181         2662  IMDb users   \n",
+              "2017-08-29 22:40:06.233659         2662  IMDb users   \n",
+              "2017-08-29 22:50:06.592571         2662  IMDb users   \n",
+              "2017-08-29 23:00:05.829558         2662  IMDb users   \n",
+              "2017-08-29 23:10:06.218251         2662  IMDb users   \n",
+              "\n",
+              "                                                                   link  \\\n",
+              "timestamp                                                                 \n",
+              "2017-07-17 12:28:36.697246  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 12:34:21.250099  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 13:00:06.506688  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 14:00:06.727742  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 15:00:06.055125  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 16:00:06.023053  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 17:00:06.142993  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 18:00:06.510891  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 19:00:06.666467  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 20:00:05.841494  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 21:00:06.449770  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 22:00:06.123852  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-17 23:00:06.458620  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 00:00:06.001629  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 01:00:06.085490  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 02:00:05.876108  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 03:00:06.266496  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 04:00:05.839574  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 05:00:05.497594  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 06:00:06.040353  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 07:00:05.818742  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 08:00:05.724752  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 09:00:05.167180  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 10:00:06.849305  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 11:00:05.242209  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 12:00:06.474276  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 13:00:06.029593  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 14:00:07.095179  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 15:00:06.790957  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-07-18 16:00:06.159608  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "...                                                                 ...   \n",
+              "2017-08-29 18:20:06.215486  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 18:30:06.738669  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 18:40:05.771150  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 18:50:06.051326  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 19:00:06.002542  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 19:10:06.066863  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 19:20:06.286871  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 19:30:06.395003  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 19:40:06.372808  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 19:50:06.030788  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 20:00:06.213617  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 20:10:06.353175  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 20:20:06.692848  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 20:30:06.047404  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 20:40:06.341599  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 20:50:06.265539  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 21:00:07.056294  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 21:10:06.940833  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 21:20:06.241187  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 21:30:06.277390  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 21:40:06.384083  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 21:50:06.071510  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 22:00:06.565253  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 22:10:07.085246  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 22:20:06.947696  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 22:30:06.423181  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 22:40:06.233659  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 22:50:06.592571  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 23:00:05.829558  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "2017-08-29 23:10:06.218251  http://www.imdb.com/title/tt6322922/ratings   \n",
+              "\n",
+              "                            average  mean  median  1_votes  2_votes  3_votes  \\\n",
+              "timestamp                                                                      \n",
+              "2017-07-17 12:28:36.697246      4.9   5.4       5      304       14        8   \n",
+              "2017-07-17 12:34:21.250099      4.9   5.4       5      304       14        8   \n",
+              "2017-07-17 13:00:06.506688      4.9   5.4       5      304       14        8   \n",
+              "2017-07-17 14:00:06.727742      4.9   5.3       5      305       14        8   \n",
+              "2017-07-17 15:00:06.055125      4.9   5.3       5      305       14        8   \n",
+              "2017-07-17 16:00:06.023053      4.9   5.3       5      305       14        8   \n",
+              "2017-07-17 17:00:06.142993      4.9   5.3       5      305       14        8   \n",
+              "2017-07-17 18:00:06.510891      4.9   5.3       5      306       14        8   \n",
+              "2017-07-17 19:00:06.666467      4.9   5.3       5      306       14        8   \n",
+              "2017-07-17 20:00:05.841494      4.9   5.3       5      307       14        8   \n",
+              "2017-07-17 21:00:06.449770      4.9   5.3       5      307       14        8   \n",
+              "2017-07-17 22:00:06.123852      4.9   5.3       5      307       14        8   \n",
+              "2017-07-17 23:00:06.458620      4.9   5.3       5      307       14        8   \n",
+              "2017-07-18 00:00:06.001629      4.9   5.3       5      307       14        8   \n",
+              "2017-07-18 01:00:06.085490      4.9   5.3       5      307       14        8   \n",
+              "2017-07-18 02:00:05.876108      4.9   5.3       5      309       14        8   \n",
+              "2017-07-18 03:00:06.266496      4.9   5.3       5      309       14        8   \n",
+              "2017-07-18 04:00:05.839574      4.9   5.3       5      310       14        7   \n",
+              "2017-07-18 05:00:05.497594      4.9   5.3       5      310       14        7   \n",
+              "2017-07-18 06:00:06.040353      4.9   5.3       5      310       14        7   \n",
+              "2017-07-18 07:00:05.818742      4.9   5.3       5      310       14        7   \n",
+              "2017-07-18 08:00:05.724752      4.9   5.3       5      310       14        7   \n",
+              "2017-07-18 09:00:05.167180      4.9   5.3       5      310       14        7   \n",
+              "2017-07-18 10:00:06.849305      4.9   5.4       6      310       14        7   \n",
+              "2017-07-18 11:00:05.242209      4.9   5.4       6      310       14        7   \n",
+              "2017-07-18 12:00:06.474276      4.9   5.4       6      312       13        7   \n",
+              "2017-07-18 13:00:06.029593      4.9   5.4       6      312       13        7   \n",
+              "2017-07-18 14:00:07.095179      4.9   5.4       6      312       14        7   \n",
+              "2017-07-18 15:00:06.790957      4.9   5.4       6      312       14        7   \n",
+              "2017-07-18 16:00:06.159608      4.9   5.4       6      312       14        7   \n",
+              "...                             ...   ...     ...      ...      ...      ...   \n",
+              "2017-08-29 18:20:06.215486      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 18:30:06.738669      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 18:40:05.771150      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 18:50:06.051326      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 19:00:06.002542      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 19:10:06.066863      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 19:20:06.286871      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 19:30:06.395003      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 19:40:06.372808      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 19:50:06.030788      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 20:00:06.213617      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 20:10:06.353175      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 20:20:06.692848      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 20:30:06.047404      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 20:40:06.341599      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 20:50:06.265539      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 21:00:07.056294      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 21:10:06.940833      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 21:20:06.241187      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 21:30:06.277390      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 21:40:06.384083      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 21:50:06.071510      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 22:00:06.565253      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 22:10:07.085246      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 22:20:06.947696      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 22:30:06.423181      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 22:40:06.233659      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 22:50:06.592571      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 23:00:05.829558      5.4   5.6       7     1021       69       38   \n",
+              "2017-08-29 23:10:06.218251      5.4   5.6       7     1021       69       38   \n",
+              "\n",
+              "                            4_votes  5_votes  6_votes  7_votes  8_votes  \\\n",
+              "timestamp                                                                 \n",
+              "2017-07-17 12:28:36.697246        5        9       10       18       24   \n",
+              "2017-07-17 12:34:21.250099        5        9       10       18       24   \n",
+              "2017-07-17 13:00:06.506688        5        9       10       18       24   \n",
+              "2017-07-17 14:00:06.727742        5        9       10       18       24   \n",
+              "2017-07-17 15:00:06.055125        5        9       10       18       24   \n",
+              "2017-07-17 16:00:06.023053        5        9       10       18       24   \n",
+              "2017-07-17 17:00:06.142993        5        9       10       18       24   \n",
+              "2017-07-17 18:00:06.510891        5        9       10       18       24   \n",
+              "2017-07-17 19:00:06.666467        5        9       10       18       24   \n",
+              "2017-07-17 20:00:05.841494        5        9       10       18       24   \n",
+              "2017-07-17 21:00:06.449770        5        9       10       18       24   \n",
+              "2017-07-17 22:00:06.123852        5        9       10       19       24   \n",
+              "2017-07-17 23:00:06.458620        5        9       10       19       24   \n",
+              "2017-07-18 00:00:06.001629        5        9       10       19       24   \n",
+              "2017-07-18 01:00:06.085490        5        9       10       19       24   \n",
+              "2017-07-18 02:00:05.876108        5        9       10       20       24   \n",
+              "2017-07-18 03:00:06.266496        5        9       10       20       24   \n",
+              "2017-07-18 04:00:05.839574        5        9       11       20       24   \n",
+              "2017-07-18 05:00:05.497594        5        9       11       20       24   \n",
+              "2017-07-18 06:00:06.040353        5        9       11       20       24   \n",
+              "2017-07-18 07:00:05.818742        5        9       11       20       24   \n",
+              "2017-07-18 08:00:05.724752        6        9       11       21       24   \n",
+              "2017-07-18 09:00:05.167180        6        9       11       21       24   \n",
+              "2017-07-18 10:00:06.849305        6        9       11       21       24   \n",
+              "2017-07-18 11:00:05.242209        6        9       11       21       24   \n",
+              "2017-07-18 12:00:06.474276        6       10       11       21       25   \n",
+              "2017-07-18 13:00:06.029593        6       10       11       21       25   \n",
+              "2017-07-18 14:00:07.095179        6       10       11       21       25   \n",
+              "2017-07-18 15:00:06.790957        6       10       11       21       25   \n",
+              "2017-07-18 16:00:06.159608        6       10       11       21       25   \n",
+              "...                             ...      ...      ...      ...      ...   \n",
+              "2017-08-29 18:20:06.215486       25       55      108      152      146   \n",
+              "2017-08-29 18:30:06.738669       25       55      108      152      146   \n",
+              "2017-08-29 18:40:05.771150       25       55      108      152      146   \n",
+              "2017-08-29 18:50:06.051326       25       55      108      152      146   \n",
+              "2017-08-29 19:00:06.002542       25       55      108      152      146   \n",
+              "2017-08-29 19:10:06.066863       25       55      108      152      146   \n",
+              "2017-08-29 19:20:06.286871       25       55      108      152      146   \n",
+              "2017-08-29 19:30:06.395003       25       55      108      152      146   \n",
+              "2017-08-29 19:40:06.372808       25       55      108      152      146   \n",
+              "2017-08-29 19:50:06.030788       25       55      108      152      146   \n",
+              "2017-08-29 20:00:06.213617       25       55      110      152      147   \n",
+              "2017-08-29 20:10:06.353175       25       55      110      152      147   \n",
+              "2017-08-29 20:20:06.692848       25       55      110      152      147   \n",
+              "2017-08-29 20:30:06.047404       25       55      110      152      147   \n",
+              "2017-08-29 20:40:06.341599       25       55      110      152      147   \n",
+              "2017-08-29 20:50:06.265539       25       55      110      152      147   \n",
+              "2017-08-29 21:00:07.056294       25       55      110      152      147   \n",
+              "2017-08-29 21:10:06.940833       25       55      110      152      147   \n",
+              "2017-08-29 21:20:06.241187       25       55      110      152      147   \n",
+              "2017-08-29 21:30:06.277390       25       55      110      152      147   \n",
+              "2017-08-29 21:40:06.384083       25       55      110      152      147   \n",
+              "2017-08-29 21:50:06.071510       25       55      110      154      147   \n",
+              "2017-08-29 22:00:06.565253       25       55      110      154      147   \n",
+              "2017-08-29 22:10:07.085246       25       55      110      154      147   \n",
+              "2017-08-29 22:20:06.947696       25       55      110      154      147   \n",
+              "2017-08-29 22:30:06.423181       25       55      110      154      147   \n",
+              "2017-08-29 22:40:06.233659       25       55      110      154      147   \n",
+              "2017-08-29 22:50:06.592571       25       55      110      154      147   \n",
+              "2017-08-29 23:00:05.829558       25       55      110      154      147   \n",
+              "2017-08-29 23:10:06.218251       25       55      110      154      147   \n",
+              "\n",
+              "                            9_votes  10_votes  1_pct  2_pct  3_pct  4_pct  \\\n",
+              "timestamp                                                                   \n",
+              "2017-07-17 12:28:36.697246       30       256   44.8    2.1    1.2    0.7   \n",
+              "2017-07-17 12:34:21.250099       30       256   44.8    2.1    1.2    0.7   \n",
+              "2017-07-17 13:00:06.506688       30       256   44.8    2.1    1.2    0.7   \n",
+              "2017-07-17 14:00:06.727742       30       256   44.9    2.1    1.2    0.7   \n",
+              "2017-07-17 15:00:06.055125       30       256   44.9    2.1    1.2    0.7   \n",
+              "2017-07-17 16:00:06.023053       30       256   44.9    2.1    1.2    0.7   \n",
+              "2017-07-17 17:00:06.142993       30       256   44.9    2.1    1.2    0.7   \n",
+              "2017-07-17 18:00:06.510891       30       256   45.0    2.1    1.2    0.7   \n",
+              "2017-07-17 19:00:06.666467       30       256   45.0    2.1    1.2    0.7   \n",
+              "2017-07-17 20:00:05.841494       30       256   45.1    2.1    1.2    0.7   \n",
+              "2017-07-17 21:00:06.449770       30       256   45.1    2.1    1.2    0.7   \n",
+              "2017-07-17 22:00:06.123852       30       256   45.0    2.1    1.2    0.7   \n",
+              "2017-07-17 23:00:06.458620       30       256   45.0    2.1    1.2    0.7   \n",
+              "2017-07-18 00:00:06.001629       30       256   45.0    2.1    1.2    0.7   \n",
+              "2017-07-18 01:00:06.085490       30       256   45.0    2.1    1.2    0.7   \n",
+              "2017-07-18 02:00:05.876108       30       256   45.1    2.0    1.2    0.7   \n",
+              "2017-07-18 03:00:06.266496       30       256   45.1    2.0    1.2    0.7   \n",
+              "2017-07-18 04:00:05.839574       30       256   45.2    2.0    1.0    0.7   \n",
+              "2017-07-18 05:00:05.497594       30       256   45.2    2.0    1.0    0.7   \n",
+              "2017-07-18 06:00:06.040353       30       256   45.2    2.0    1.0    0.7   \n",
+              "2017-07-18 07:00:05.818742       30       256   45.2    2.0    1.0    0.7   \n",
+              "2017-07-18 08:00:05.724752       31       257   44.9    2.0    1.0    0.9   \n",
+              "2017-07-18 09:00:05.167180       31       257   44.9    2.0    1.0    0.9   \n",
+              "2017-07-18 10:00:06.849305       31       260   44.7    2.0    1.0    0.9   \n",
+              "2017-07-18 11:00:05.242209       31       260   44.7    2.0    1.0    0.9   \n",
+              "2017-07-18 12:00:06.474276       31       262   44.7    1.9    1.0    0.9   \n",
+              "2017-07-18 13:00:06.029593       31       262   44.7    1.9    1.0    0.9   \n",
+              "2017-07-18 14:00:07.095179       31       264   44.5    2.0    1.0    0.9   \n",
+              "2017-07-18 15:00:06.790957       31       264   44.5    2.0    1.0    0.9   \n",
+              "2017-07-18 16:00:06.159608       31       264   44.5    2.0    1.0    0.9   \n",
+              "...                             ...       ...    ...    ...    ...    ...   \n",
+              "2017-08-29 18:20:06.215486      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 18:30:06.738669      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 18:40:05.771150      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 18:50:06.051326      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 19:00:06.002542      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 19:10:06.066863      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 19:20:06.286871      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 19:30:06.395003      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 19:40:06.372808      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 19:50:06.030788      144       896   38.5    2.6    1.4    0.9   \n",
+              "2017-08-29 20:00:06.213617      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 20:10:06.353175      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 20:20:06.692848      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 20:30:06.047404      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 20:40:06.341599      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 20:50:06.265539      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 21:00:07.056294      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 21:10:06.940833      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 21:20:06.241187      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 21:30:06.277390      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 21:40:06.384083      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 21:50:06.071510      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 22:00:06.565253      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 22:10:07.085246      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 22:20:06.947696      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 22:30:06.423181      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 22:40:06.233659      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 22:50:06.592571      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 23:00:05.829558      146       897   38.4    2.6    1.4    0.9   \n",
+              "2017-08-29 23:10:06.218251      146       897   38.4    2.6    1.4    0.9   \n",
+              "\n",
+              "                            5_pct  6_pct  7_pct  8_pct  9_pct  10_pct  \n",
+              "timestamp                                                              \n",
+              "2017-07-17 12:28:36.697246    1.3    1.5    2.7    3.5    4.4    37.8  \n",
+              "2017-07-17 12:34:21.250099    1.3    1.5    2.7    3.5    4.4    37.8  \n",
+              "2017-07-17 13:00:06.506688    1.3    1.5    2.7    3.5    4.4    37.8  \n",
+              "2017-07-17 14:00:06.727742    1.3    1.5    2.7    3.5    4.4    37.7  \n",
+              "2017-07-17 15:00:06.055125    1.3    1.5    2.7    3.5    4.4    37.7  \n",
+              "2017-07-17 16:00:06.023053    1.3    1.5    2.7    3.5    4.4    37.7  \n",
+              "2017-07-17 17:00:06.142993    1.3    1.5    2.7    3.5    4.4    37.7  \n",
+              "2017-07-17 18:00:06.510891    1.3    1.5    2.6    3.5    4.4    37.6  \n",
+              "2017-07-17 19:00:06.666467    1.3    1.5    2.6    3.5    4.4    37.6  \n",
+              "2017-07-17 20:00:05.841494    1.3    1.5    2.6    3.5    4.4    37.6  \n",
+              "2017-07-17 21:00:06.449770    1.3    1.5    2.6    3.5    4.4    37.6  \n",
+              "2017-07-17 22:00:06.123852    1.3    1.5    2.8    3.5    4.4    37.5  \n",
+              "2017-07-17 23:00:06.458620    1.3    1.5    2.8    3.5    4.4    37.5  \n",
+              "2017-07-18 00:00:06.001629    1.3    1.5    2.8    3.5    4.4    37.5  \n",
+              "2017-07-18 01:00:06.085490    1.3    1.5    2.8    3.5    4.4    37.5  \n",
+              "2017-07-18 02:00:05.876108    1.3    1.5    2.9    3.5    4.4    37.4  \n",
+              "2017-07-18 03:00:06.266496    1.3    1.5    2.9    3.5    4.4    37.4  \n",
+              "2017-07-18 04:00:05.839574    1.3    1.6    2.9    3.5    4.4    37.3  \n",
+              "2017-07-18 05:00:05.497594    1.3    1.6    2.9    3.5    4.4    37.3  \n",
+              "2017-07-18 06:00:06.040353    1.3    1.6    2.9    3.5    4.4    37.3  \n",
+              "2017-07-18 07:00:05.818742    1.3    1.6    2.9    3.5    4.4    37.3  \n",
+              "2017-07-18 08:00:05.724752    1.3    1.6    3.0    3.5    4.5    37.2  \n",
+              "2017-07-18 09:00:05.167180    1.3    1.6    3.0    3.5    4.5    37.2  \n",
+              "2017-07-18 10:00:06.849305    1.3    1.6    3.0    3.5    4.5    37.5  \n",
+              "2017-07-18 11:00:05.242209    1.3    1.6    3.0    3.5    4.5    37.5  \n",
+              "2017-07-18 12:00:06.474276    1.4    1.6    3.0    3.6    4.4    37.5  \n",
+              "2017-07-18 13:00:06.029593    1.4    1.6    3.0    3.6    4.4    37.5  \n",
+              "2017-07-18 14:00:07.095179    1.4    1.6    3.0    3.6    4.4    37.7  \n",
+              "2017-07-18 15:00:06.790957    1.4    1.6    3.0    3.6    4.4    37.7  \n",
+              "2017-07-18 16:00:06.159608    1.4    1.6    3.0    3.6    4.4    37.7  \n",
+              "...                           ...    ...    ...    ...    ...     ...  \n",
+              "2017-08-29 18:20:06.215486    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 18:30:06.738669    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 18:40:05.771150    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 18:50:06.051326    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 19:00:06.002542    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 19:10:06.066863    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 19:20:06.286871    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 19:30:06.395003    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 19:40:06.372808    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 19:50:06.030788    2.1    4.1    5.7    5.5    5.4    33.8  \n",
+              "2017-08-29 20:00:06.213617    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 20:10:06.353175    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 20:20:06.692848    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 20:30:06.047404    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 20:40:06.341599    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 20:50:06.265539    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 21:00:07.056294    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 21:10:06.940833    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 21:20:06.241187    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 21:30:06.277390    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 21:40:06.384083    2.1    4.1    5.7    5.5    5.5    33.7  \n",
+              "2017-08-29 21:50:06.071510    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 22:00:06.565253    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 22:10:07.085246    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 22:20:06.947696    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 22:30:06.423181    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 22:40:06.233659    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 22:50:06.592571    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 23:00:05.829558    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "2017-08-29 23:10:06.218251    2.1    4.1    5.8    5.5    5.5    33.7  \n",
+              "\n",
+              "[4216 rows x 26 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0pSezjrOeajO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "lastday = df['2017-08-29']"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "waWe9DmgfPh6",
+        "colab_type": "code",
+        "outputId": "19a34138-4569-4bad-eba7-288fd2945c70",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "lastday[lastday.category=='IMDb users'].respondents.plot();"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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/l7KOkWhvjBajkJAQBgwYAIBaraayspL09HSio6MBGq11VFNTQ3JyMrNnz2b3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\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f3dbe3179e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G51BSvv2fU8e",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "final = df.tail(1)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "5BGN3HgAfwma",
+        "colab_type": "code",
+        "outputId": "8e14d435-2d82-430c-b61c-57b84fada1d6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 110
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "columns = ['1_pct', '2_pct', '3_pct', '4_pct', '5_pct', \n",
+        "           '6_pct', '7_pct', '8_pct', '9_pct', '10_pct']\n",
+        "\n",
+        "final[columns]"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1_pct</th>\n",
+              "      <th>2_pct</th>\n",
+              "      <th>3_pct</th>\n",
+              "      <th>4_pct</th>\n",
+              "      <th>5_pct</th>\n",
+              "      <th>6_pct</th>\n",
+              "      <th>7_pct</th>\n",
+              "      <th>8_pct</th>\n",
+              "      <th>9_pct</th>\n",
+              "      <th>10_pct</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2017-08-29 23:10:06.218251</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            1_pct  2_pct  3_pct  4_pct  5_pct  6_pct  7_pct  \\\n",
+              "timestamp                                                                     \n",
+              "2017-08-29 23:10:06.218251   38.4    2.6    1.4    0.9    2.1    4.1    5.8   \n",
+              "\n",
+              "                            8_pct  9_pct  10_pct  \n",
+              "timestamp                                         \n",
+              "2017-08-29 23:10:06.218251    5.5    5.5    33.7  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DCIZWRg2gGlG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "data = final[columns].T"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3mqACt2LhT4c",
+        "colab_type": "code",
+        "outputId": "745e7437-255b-4813-f3f3-a3c2a0072c2d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 358
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "data.index = range(1, 11)\n",
+        "data"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>timestamp</th>\n",
+              "      <th>2017-08-29 23:10:06.218251</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>38.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>5.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>5.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>5.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "timestamp  2017-08-29 23:10:06.218251\n",
+              "1                                38.4\n",
+              "2                                 2.6\n",
+              "3                                 1.4\n",
+              "4                                 0.9\n",
+              "5                                 2.1\n",
+              "6                                 4.1\n",
+              "7                                 5.8\n",
+              "8                                 5.5\n",
+              "9                                 5.5\n",
+              "10                               33.7"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RtT1QWeKgXbw",
+        "colab_type": "code",
+        "outputId": "f4edb4d3-9441-4635-abff-b02b213f2e25",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 368
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight')\n",
+        "\n",
+        "ax = data.plot.bar(color='#EC713B', width=0.9, legend=False)\n",
+        "\n",
+        "ax.set(xlabel='Rating', \n",
+        "       ylabel='Percent of total votes', \n",
+        "       yticks=range(0,50,10))\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive\", \n",
+        "        fontsize=16, fontweight='bold')\n",
+        "\n",
+        "ax.text(x=-2, y=46, s=\"IMDb ratings for the film as of Aug. 29\")\n",
+        "\n",
+        "ax.tick_params(labelrotation=0)"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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h7++PH374AYGBgXj06BFu3Lgh8/tnzpwp9eDYtLQ0KCsrSz2MsWK3v76+PtLS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\nRK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f3dbc9e5358>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yrX6SkWEgcth",
+        "colab_type": "code",
+        "outputId": "3faa28d0-9b87-4f31-d1b0-6de8c565f6ac",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "display(example)"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EO99yJebhkPO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1284
+        },
+        "outputId": "a5cba9c3-391b-4068-e89d-bc539227c6ea"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Using the Polling Data from the article \"How Popular is Donald Trump\"\n",
+        "\n",
+        "polls = pd.read_csv('https://projects.fivethirtyeight.com/trump-approval-data/approval_topline.csv')\n",
+        "polls.head(20)"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>president</th>\n",
+              "      <th>subgroup</th>\n",
+              "      <th>modeldate</th>\n",
+              "      <th>approve_estimate</th>\n",
+              "      <th>approve_hi</th>\n",
+              "      <th>approve_lo</th>\n",
+              "      <th>disapprove_estimate</th>\n",
+              "      <th>disapprove_hi</th>\n",
+              "      <th>disapprove_lo</th>\n",
+              "      <th>timestamp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/14/2018</td>\n",
+              "      <td>43.41381</td>\n",
+              "      <td>47.19595</td>\n",
+              "      <td>39.63168</td>\n",
+              "      <td>52.62209</td>\n",
+              "      <td>56.61587</td>\n",
+              "      <td>48.62830</td>\n",
+              "      <td>12:55:49 14 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/14/2018</td>\n",
+              "      <td>41.23523</td>\n",
+              "      <td>44.67462</td>\n",
+              "      <td>37.79585</td>\n",
+              "      <td>52.89913</td>\n",
+              "      <td>57.05919</td>\n",
+              "      <td>48.73907</td>\n",
+              "      <td>12:55:22 14 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>All polls</td>\n",
+              "      <td>11/14/2018</td>\n",
+              "      <td>42.01041</td>\n",
+              "      <td>46.31419</td>\n",
+              "      <td>37.70662</td>\n",
+              "      <td>52.67213</td>\n",
+              "      <td>56.62647</td>\n",
+              "      <td>48.71780</td>\n",
+              "      <td>12:54:57 14 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>All polls</td>\n",
+              "      <td>11/13/2018</td>\n",
+              "      <td>41.75856</td>\n",
+              "      <td>46.29659</td>\n",
+              "      <td>37.22052</td>\n",
+              "      <td>52.76469</td>\n",
+              "      <td>56.50090</td>\n",
+              "      <td>49.02848</td>\n",
+              "      <td>16:55:50 13 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/13/2018</td>\n",
+              "      <td>40.61781</td>\n",
+              "      <td>44.05400</td>\n",
+              "      <td>37.18161</td>\n",
+              "      <td>53.06430</td>\n",
+              "      <td>56.79879</td>\n",
+              "      <td>49.32982</td>\n",
+              "      <td>16:56:12 13 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/13/2018</td>\n",
+              "      <td>43.42043</td>\n",
+              "      <td>47.37686</td>\n",
+              "      <td>39.46401</td>\n",
+              "      <td>52.75005</td>\n",
+              "      <td>56.53394</td>\n",
+              "      <td>48.96615</td>\n",
+              "      <td>16:56:35 13 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/12/2018</td>\n",
+              "      <td>40.61583</td>\n",
+              "      <td>44.15448</td>\n",
+              "      <td>37.07718</td>\n",
+              "      <td>52.94898</td>\n",
+              "      <td>56.77519</td>\n",
+              "      <td>49.12276</td>\n",
+              "      <td>16:43:10 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/12/2018</td>\n",
+              "      <td>43.75782</td>\n",
+              "      <td>47.78281</td>\n",
+              "      <td>39.73282</td>\n",
+              "      <td>52.54499</td>\n",
+              "      <td>56.32561</td>\n",
+              "      <td>48.76437</td>\n",
+              "      <td>16:43:33 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>All polls</td>\n",
+              "      <td>11/12/2018</td>\n",
+              "      <td>41.80310</td>\n",
+              "      <td>46.43069</td>\n",
+              "      <td>37.17550</td>\n",
+              "      <td>52.68010</td>\n",
+              "      <td>56.45867</td>\n",
+              "      <td>48.90153</td>\n",
+              "      <td>16:42:49 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>All polls</td>\n",
+              "      <td>11/11/2018</td>\n",
+              "      <td>42.16344</td>\n",
+              "      <td>46.75493</td>\n",
+              "      <td>37.57195</td>\n",
+              "      <td>52.48321</td>\n",
+              "      <td>56.16326</td>\n",
+              "      <td>48.80316</td>\n",
+              "      <td>09:47:44 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/11/2018</td>\n",
+              "      <td>43.99274</td>\n",
+              "      <td>48.11128</td>\n",
+              "      <td>39.87421</td>\n",
+              "      <td>52.54802</td>\n",
+              "      <td>56.32327</td>\n",
+              "      <td>48.77278</td>\n",
+              "      <td>09:50:34 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/11/2018</td>\n",
+              "      <td>40.99530</td>\n",
+              "      <td>44.45774</td>\n",
+              "      <td>37.53287</td>\n",
+              "      <td>52.69562</td>\n",
+              "      <td>56.40570</td>\n",
+              "      <td>48.98553</td>\n",
+              "      <td>09:49:18 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>All polls</td>\n",
+              "      <td>11/10/2018</td>\n",
+              "      <td>42.16344</td>\n",
+              "      <td>46.75493</td>\n",
+              "      <td>37.57195</td>\n",
+              "      <td>52.48321</td>\n",
+              "      <td>56.16326</td>\n",
+              "      <td>48.80316</td>\n",
+              "      <td>09:48:31 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/10/2018</td>\n",
+              "      <td>40.99530</td>\n",
+              "      <td>44.45774</td>\n",
+              "      <td>37.53287</td>\n",
+              "      <td>52.69562</td>\n",
+              "      <td>56.40570</td>\n",
+              "      <td>48.98553</td>\n",
+              "      <td>09:49:41 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/10/2018</td>\n",
+              "      <td>43.99274</td>\n",
+              "      <td>48.11128</td>\n",
+              "      <td>39.87421</td>\n",
+              "      <td>52.54802</td>\n",
+              "      <td>56.32327</td>\n",
+              "      <td>48.77278</td>\n",
+              "      <td>09:51:01 12 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/9/2018</td>\n",
+              "      <td>43.99274</td>\n",
+              "      <td>48.11128</td>\n",
+              "      <td>39.87421</td>\n",
+              "      <td>52.54802</td>\n",
+              "      <td>56.32327</td>\n",
+              "      <td>48.77278</td>\n",
+              "      <td>17:57:45  9 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/9/2018</td>\n",
+              "      <td>40.99530</td>\n",
+              "      <td>44.45774</td>\n",
+              "      <td>37.53287</td>\n",
+              "      <td>52.69562</td>\n",
+              "      <td>56.40570</td>\n",
+              "      <td>48.98553</td>\n",
+              "      <td>17:57:18  9 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>All polls</td>\n",
+              "      <td>11/9/2018</td>\n",
+              "      <td>42.16344</td>\n",
+              "      <td>46.75493</td>\n",
+              "      <td>37.57195</td>\n",
+              "      <td>52.48321</td>\n",
+              "      <td>56.16326</td>\n",
+              "      <td>48.80316</td>\n",
+              "      <td>17:56:55  9 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Adults</td>\n",
+              "      <td>11/8/2018</td>\n",
+              "      <td>40.78244</td>\n",
+              "      <td>44.10662</td>\n",
+              "      <td>37.45826</td>\n",
+              "      <td>52.53972</td>\n",
+              "      <td>56.17908</td>\n",
+              "      <td>48.90036</td>\n",
+              "      <td>16:01:22  8 Nov 2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Donald Trump</td>\n",
+              "      <td>Voters</td>\n",
+              "      <td>11/8/2018</td>\n",
+              "      <td>43.97400</td>\n",
+              "      <td>48.09153</td>\n",
+              "      <td>39.85647</td>\n",
+              "      <td>52.43206</td>\n",
+              "      <td>56.15821</td>\n",
+              "      <td>48.70590</td>\n",
+              "      <td>16:01:50  8 Nov 2018</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       president   subgroup   modeldate  approve_estimate  approve_hi  \\\n",
+              "0   Donald Trump     Voters  11/14/2018          43.41381    47.19595   \n",
+              "1   Donald Trump     Adults  11/14/2018          41.23523    44.67462   \n",
+              "2   Donald Trump  All polls  11/14/2018          42.01041    46.31419   \n",
+              "3   Donald Trump  All polls  11/13/2018          41.75856    46.29659   \n",
+              "4   Donald Trump     Adults  11/13/2018          40.61781    44.05400   \n",
+              "5   Donald Trump     Voters  11/13/2018          43.42043    47.37686   \n",
+              "6   Donald Trump     Adults  11/12/2018          40.61583    44.15448   \n",
+              "7   Donald Trump     Voters  11/12/2018          43.75782    47.78281   \n",
+              "8   Donald Trump  All polls  11/12/2018          41.80310    46.43069   \n",
+              "9   Donald Trump  All polls  11/11/2018          42.16344    46.75493   \n",
+              "10  Donald Trump     Voters  11/11/2018          43.99274    48.11128   \n",
+              "11  Donald Trump     Adults  11/11/2018          40.99530    44.45774   \n",
+              "12  Donald Trump  All polls  11/10/2018          42.16344    46.75493   \n",
+              "13  Donald Trump     Adults  11/10/2018          40.99530    44.45774   \n",
+              "14  Donald Trump     Voters  11/10/2018          43.99274    48.11128   \n",
+              "15  Donald Trump     Voters   11/9/2018          43.99274    48.11128   \n",
+              "16  Donald Trump     Adults   11/9/2018          40.99530    44.45774   \n",
+              "17  Donald Trump  All polls   11/9/2018          42.16344    46.75493   \n",
+              "18  Donald Trump     Adults   11/8/2018          40.78244    44.10662   \n",
+              "19  Donald Trump     Voters   11/8/2018          43.97400    48.09153   \n",
+              "\n",
+              "    approve_lo  disapprove_estimate  disapprove_hi  disapprove_lo  \\\n",
+              "0     39.63168             52.62209       56.61587       48.62830   \n",
+              "1     37.79585             52.89913       57.05919       48.73907   \n",
+              "2     37.70662             52.67213       56.62647       48.71780   \n",
+              "3     37.22052             52.76469       56.50090       49.02848   \n",
+              "4     37.18161             53.06430       56.79879       49.32982   \n",
+              "5     39.46401             52.75005       56.53394       48.96615   \n",
+              "6     37.07718             52.94898       56.77519       49.12276   \n",
+              "7     39.73282             52.54499       56.32561       48.76437   \n",
+              "8     37.17550             52.68010       56.45867       48.90153   \n",
+              "9     37.57195             52.48321       56.16326       48.80316   \n",
+              "10    39.87421             52.54802       56.32327       48.77278   \n",
+              "11    37.53287             52.69562       56.40570       48.98553   \n",
+              "12    37.57195             52.48321       56.16326       48.80316   \n",
+              "13    37.53287             52.69562       56.40570       48.98553   \n",
+              "14    39.87421             52.54802       56.32327       48.77278   \n",
+              "15    39.87421             52.54802       56.32327       48.77278   \n",
+              "16    37.53287             52.69562       56.40570       48.98553   \n",
+              "17    37.57195             52.48321       56.16326       48.80316   \n",
+              "18    37.45826             52.53972       56.17908       48.90036   \n",
+              "19    39.85647             52.43206       56.15821       48.70590   \n",
+              "\n",
+              "               timestamp  \n",
+              "0   12:55:49 14 Nov 2018  \n",
+              "1   12:55:22 14 Nov 2018  \n",
+              "2   12:54:57 14 Nov 2018  \n",
+              "3   16:55:50 13 Nov 2018  \n",
+              "4   16:56:12 13 Nov 2018  \n",
+              "5   16:56:35 13 Nov 2018  \n",
+              "6   16:43:10 12 Nov 2018  \n",
+              "7   16:43:33 12 Nov 2018  \n",
+              "8   16:42:49 12 Nov 2018  \n",
+              "9   09:47:44 12 Nov 2018  \n",
+              "10  09:50:34 12 Nov 2018  \n",
+              "11  09:49:18 12 Nov 2018  \n",
+              "12  09:48:31 12 Nov 2018  \n",
+              "13  09:49:41 12 Nov 2018  \n",
+              "14  09:51:01 12 Nov 2018  \n",
+              "15  17:57:45  9 Nov 2018  \n",
+              "16  17:57:18  9 Nov 2018  \n",
+              "17  17:56:55  9 Nov 2018  \n",
+              "18  16:01:22  8 Nov 2018  \n",
+              "19  16:01:50  8 Nov 2018  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cMYfZ-t7lTj9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 105
+        },
+        "outputId": "030fd53e-9cf3-41f9-9f61-a800093d9a74"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Figuring out which part of the data to use. Appears to be sequential. \n",
+        "\n",
+        "polls.timestamp.describe()\n",
+        "# Pandas isn't recognizing the time stamp as temporal element so need to adjust that. "
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count                     1980\n",
+              "unique                    1870\n",
+              "top       11:26:07  2 Mar 2017\n",
+              "freq                         6\n",
+              "Name: timestamp, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "skgavd4GeB2e",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "polls.timestamp = pd.to_datetime(polls.timestamp)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FNDfr4iKl68s",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 140
+        },
+        "outputId": "9c201758-7050-4f05-c86b-f26b2a855963"
+      },
+      "cell_type": "code",
+      "source": [
+        "polls.timestamp.describe()\n",
+        "#Shweeet"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count                    1980\n",
+              "unique                   1870\n",
+              "top       2017-03-02 11:26:09\n",
+              "freq                        6\n",
+              "first     2017-03-02 11:25:44\n",
+              "last      2018-11-14 12:55:49\n",
+              "Name: timestamp, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gmAR4FiKl9cM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 105
+        },
+        "outputId": "d6bdaf18-36ea-4619-b849-c78337532867"
+      },
+      "cell_type": "code",
+      "source": [
+        "polls.modeldate.describe()"
+      ],
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count          1980\n",
+              "unique          660\n",
+              "top       7/30/2018\n",
+              "freq              3\n",
+              "Name: modeldate, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r6ttAt5YmOfk",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Same problem with 'modeldate'\n",
+        "polls.modeldate = pd.to_datetime(polls.modeldate)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "uePNQ0simZL9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 140
+        },
+        "outputId": "b230dd35-bdc1-4340-9ae1-8d2e5432c56a"
+      },
+      "cell_type": "code",
+      "source": [
+        "polls.modeldate.describe()\n",
+        "# Converted"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count                    1980\n",
+              "unique                    660\n",
+              "top       2017-10-30 00:00:00\n",
+              "freq                        3\n",
+              "first     2017-01-23 00:00:00\n",
+              "last      2018-11-14 00:00:00\n",
+              "Name: modeldate, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TGJR9qlombBl",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        },
+        "outputId": "93876ab4-8453-418a-bd16-4f738f2f767f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Now let's explore some of the features of the data frame: \n",
+        "polls.info()\n",
+        "\n",
+        "# We've got 1980 entries for FiveThirtyEight's predictive model that combines a bunch of other polls,\n",
+        "#weights them, and builds a predictive linear regression with an estimate, and a High/Low measure to show\n",
+        "#errors in the regression. The data is split into 2 major categories for approve or disapprove. Then they're stacked\n",
+        "#together to show their change over time. "
+      ],
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1980 entries, 0 to 1979\n",
+            "Data columns (total 10 columns):\n",
+            "president              1980 non-null object\n",
+            "subgroup               1980 non-null object\n",
+            "modeldate              1980 non-null datetime64[ns]\n",
+            "approve_estimate       1980 non-null float64\n",
+            "approve_hi             1980 non-null float64\n",
+            "approve_lo             1980 non-null float64\n",
+            "disapprove_estimate    1980 non-null float64\n",
+            "disapprove_hi          1980 non-null float64\n",
+            "disapprove_lo          1980 non-null float64\n",
+            "timestamp              1980 non-null datetime64[ns]\n",
+            "dtypes: datetime64[ns](2), float64(6), object(2)\n",
+            "memory usage: 154.8+ KB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q8E9fFmImxBs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Importing an image for reference. It's a dynamic plot, not an image. So I'm going to screen print it into an image\n",
+        "#and upload it here. \n",
+        "from google.colab import files"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "b016LXkQpKWP",
+        "colab_type": "code",
+        "colab": {
+          "resources": {
+            "http://localhost:8080/nbextensions/google.colab/files.js": {
+              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7Ci8vIE1heCBhbW91bnQgb2YgdGltZSB0byBibG9jayB3YWl0aW5nIGZvciB0aGUgdXNlci4KY29uc3QgRklMRV9DSEFOR0VfVElNRU9VVF9NUyA9IDMwICogMTAwMDsKCmZ1bmN0aW9uIF91cGxvYWRGaWxlcyhpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IHN0ZXBzID0gdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKTsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIC8vIENhY2hlIHN0ZXBzIG9uIHRoZSBvdXRwdXRFbGVtZW50IHRvIG1ha2UgaXQgYXZhaWxhYmxlIGZvciB0aGUgbmV4dCBjYWxsCiAgLy8gdG8gdXBsb2FkRmlsZXNDb250aW51ZSBmcm9tIFB5dGhvbi4KICBvdXRwdXRFbGVtZW50LnN0ZXBzID0gc3RlcHM7CgogIHJldHVybiBfdXBsb2FkRmlsZXNDb250aW51ZShvdXRwdXRJZCk7Cn0KCi8vIFRoaXMgaXMgcm91Z2hseSBhbiBhc3luYyBnZW5lcmF0b3IgKG5vdCBzdXBwb3J0ZWQgaW4gdGhlIGJyb3dzZXIgeWV0KSwKLy8gd2hlcmUgdGhlcmUgYXJlIG11bHRpcGxlIGFzeW5jaHJvbm91cyBzdGVwcyBhbmQgdGhlIFB5dGhvbiBzaWRlIGlzIGdvaW5nCi8vIHRvIHBvbGwgZm9yIGNvbXBsZXRpb24gb2YgZWFjaCBzdGVwLgovLyBUaGlzIHVzZXMgYSBQcm9taXNlIHRvIGJsb2NrIHRoZSBweXRob24gc2lkZSBvbiBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcCwKLy8gdGhlbiBwYXNzZXMgdGhlIHJlc3VsdCBvZiB0aGUgcHJldmlvdXMgc3RlcCBhcyB0aGUgaW5wdXQgdG8gdGhlIG5leHQgc3RlcC4KZnVuY3Rpb24gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpIHsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIGNvbnN0IHN0ZXBzID0gb3V0cHV0RWxlbWVudC5zdGVwczsKCiAgY29uc3QgbmV4dCA9IHN0ZXBzLm5leHQob3V0cHV0RWxlbWVudC5sYXN0UHJvbWlzZVZhbHVlKTsKICByZXR1cm4gUHJvbWlzZS5yZXNvbHZlKG5leHQudmFsdWUucHJvbWlzZSkudGhlbigodmFsdWUpID0+IHsKICAgIC8vIENhY2hlIHRoZSBsYXN0IHByb21pc2UgdmFsdWUgdG8gbWFrZSBpdCBhdmFpbGFibGUgdG8gdGhlIG5leHQKICAgIC8vIHN0ZXAgb2YgdGhlIGdlbmVyYXRvci4KICAgIG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSA9IHZhbHVlOwogICAgcmV0dXJuIG5leHQudmFsdWUucmVzcG9uc2U7CiAgfSk7Cn0KCi8qKgogKiBHZW5lcmF0b3IgZnVuY3Rpb24gd2hpY2ggaXMgY2FsbGVkIGJldHdlZW4gZWFjaCBhc3luYyBzdGVwIG9mIHRoZSB1cGxvYWQKICogcHJvY2Vzcy4KICogQHBhcmFtIHtzdHJpbmd9IGlucHV0SWQgRWxlbWVudCBJRCBvZiB0aGUgaW5wdXQgZmlsZSBwaWNrZXIgZWxlbWVudC4KICogQHBhcmFtIHtzdHJpbmd9IG91dHB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIG91dHB1dCBkaXNwbGF5LgogKiBAcmV0dXJuIHshSXRlcmFibGU8IU9iamVjdD59IEl0ZXJhYmxlIG9mIG5leHQgc3RlcHMuCiAqLwpmdW5jdGlvbiogdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKSB7CiAgY29uc3QgaW5wdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQoaW5wdXRJZCk7CiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gZmFsc2U7CgogIGNvbnN0IG91dHB1dEVsZW1lbnQgPSBkb2N1bWVudC5nZXRFbGVtZW50QnlJZChvdXRwdXRJZCk7CiAgb3V0cHV0RWxlbWVudC5pbm5lckhUTUwgPSAnJzsKCiAgY29uc3QgcGlja2VkUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBpbnB1dEVsZW1lbnQuYWRkRXZlbnRMaXN0ZW5lcignY2hhbmdlJywgKGUpID0+IHsKICAgICAgcmVzb2x2ZShlLnRhcmdldC5maWxlcyk7CiAgICB9KTsKICB9KTsKCiAgY29uc3QgY2FuY2VsID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnYnV0dG9uJyk7CiAgaW5wdXRFbGVtZW50LnBhcmVudEVsZW1lbnQuYXBwZW5kQ2hpbGQoY2FuY2VsKTsKICBjYW5jZWwudGV4dENvbnRlbnQgPSAnQ2FuY2VsIHVwbG9hZCc7CiAgY29uc3QgY2FuY2VsUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBjYW5jZWwub25jbGljayA9ICgpID0+IHsKICAgICAgcmVzb2x2ZShudWxsKTsKICAgIH07CiAgfSk7CgogIC8vIENhbmNlbCB1cGxvYWQgaWYgdXNlciBoYXNuJ3QgcGlja2VkIGFueXRoaW5nIGluIHRpbWVvdXQuCiAgY29uc3QgdGltZW91dFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgc2V0VGltZW91dCgoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9LCBGSUxFX0NIQU5HRV9USU1FT1VUX01TKTsKICB9KTsKCiAgLy8gV2FpdCBmb3IgdGhlIHVzZXIgdG8gcGljayB0aGUgZmlsZXMuCiAgY29uc3QgZmlsZXMgPSB5aWVsZCB7CiAgICBwcm9taXNlOiBQcm9taXNlLnJhY2UoW3BpY2tlZFByb21pc2UsIHRpbWVvdXRQcm9taXNlLCBjYW5jZWxQcm9taXNlXSksCiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdzdGFydGluZycsCiAgICB9CiAgfTsKCiAgaWYgKCFmaWxlcykgewogICAgcmV0dXJuIHsKICAgICAgcmVzcG9uc2U6IHsKICAgICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICAgIH0KICAgIH07CiAgfQoKICBjYW5jZWwucmVtb3ZlKCk7CgogIC8vIERpc2FibGUgdGhlIGlucHV0IGVsZW1lbnQgc2luY2UgZnVydGhlciBwaWNrcyBhcmUgbm90IGFsbG93ZWQuCiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            }
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "outputId": "27908f2e-fe3d-41b9-9c90-b8b3911d145c"
+      },
+      "cell_type": "code",
+      "source": [
+        "uploaded = files.upload()"
+      ],
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-99094aa6-a2bf-402f-89b3-dbafdb73f270\" name=\"files[]\" multiple disabled />\n",
+              "     <output id=\"result-99094aa6-a2bf-402f-89b3-dbafdb73f270\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Saving poll.png to poll.png\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YiqCQRiSpNC8",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 308
+        },
+        "outputId": "d8842f8d-7dd3-40b6-bbbb-5f65d33298fe"
+      },
+      "cell_type": "code",
+      "source": [
+        "example2 = Image('poll.png', width=500)\n",
+        "display(example2)"
+      ],
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ECOkB3ve+92k/0BvegLDe6gkvtS49QXUkbgpSTM9Q1RBQ1XBQ2BBQ1RBQVV9IfUoxfUNJ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\npevzbxQ3WSZOYl/6OX2h8de0S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o3Hq/oJzwXjn1Vp4hmHEmWnk7tc4+ffmMoDorn3Por0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Mj64/sEeXRzwwHN79mDPPrZxdazSW3qq+EThAEa6heUG2hC4W9la5IebvXaXDZBKdi55coK\nh1ypLitEHauhQq5gVyPkSnU1Qq5UVyrQeHrZFeb5uenq4QZ9SEpqtOXC/AXa5cFzeoN/87ehj1W9\nHZr3NZ1FWKR17+JCUKR6/4shc6yL/jp3phhhrMfjz2iN4gzaca0GTYvwLMP8rNudJHG4/BrqpRMy\nlO3sec8fKtLysui5BaFtnB/2jEegR2LPPluYj2tjGekk3ScXciEXsgxr+5wgSZ/pLd3bfuLt1iO7\n244f6Sqoaz3JthIysuBv0YvN6nyfk0XP1b72CWkJ6jt4e0vjEbLmKB8f8OBvfukrx07cW3HmkZqz\nTx47/bWKGrqAzOAJ00c7C545X/p6oGd0lga0hemyNzsLn+8ofa2lbm9b3b76ur0nqrbsK9+4t/zV\nXWWv7PL9gAy6qnKzb/3x8qXpydTeYLhk9FTRdu7qnbTzmW43KBjtKqltOlrasD+vfHtZ5R55clBS\n9FYHMjkvVTtInWorWJyZ6J7qrx5uLB2sqWw60tZ6+nxPZWPLiYrGg4ixtunIUGcxAAL/LW84UFy/\nr6RhP+iqxkPtbWcW2EnbCfzUdIKOWkSk+Nt00vPoVhju8mQiIkSHSNvaTkMmnlQ3He5uz1/sKqXj\nAJBIJBUEn9gdDyhzWYaeqYH8ttKTLQWVQ/Ut451z+vCCSyBMzE6hg5QN1pYPpkC4r8i5VAOtWtay\nOjipCh8lj/IHgf96P1y0sBBxrEZGXRDMGCLQX7orSOeDnFxIENZ0c71kw6VYqlgm9dUMNR1rSu2u\nwUTWT9c2h0b+mUnaD95Vlu4qHe44l+qrLB2srRiqaxrrIL+VdyjQeLrShbmwuNgx2VM13IDZH387\nJ3sXl/rpfnB6pHakCXKSFFT/+aGa4UZtACrpK69qOFBTs722amtd3Z55vYEl6iLU5tM0uNUdoDOD\nSU3aR7bypX4H8igEbQzS4M3mcxfkwTrIWtNYO7KJzCLLyLj3A3imIjanY/qANG3Wrz9CifcPVdS4\n0HKmveDZhrNP1OU91lrw9GTpJtJ89GksBlA4gYK0jxWkxf5UZ1sedCrvAOD6fZUdBVDwllyt2QZu\nUWfbSrhFgcYT7zeE8yN0rE9nyWJ3WV97fk1/DbO1TXQvZPTmRvGiinsqKadQkiM2qSG69onuqCY9\nfGFMtVLqzo1j7faBOFNz05juq7qKauv2oDXWVL9VVb+3tIfut5mMVcDQpKVNE1EEmaL9aKgvGk9I\nB469zwSxxGt9KzAIIFXjPZ51BjQUjOE2z+XNDqi+0U51Prd/Ww7WEVDmrcOAetrzobpDga9sPNTa\nepruvNO/Zm9YHLowKmvq/NyF1omuyuF6VCtWK6pJ50IuXOnhcjgnCH+PdhVsbz28veUw/z1U+tKZ\nkw90FDzp+854fw/nPXLyyJ093wjuaF9c983NJ79O93OpU3halI/P4bOP871gpScfrDrzMNFH7yqr\n3sq/Ov+y/ImSjTTA2X5AFj1WvMFLVcFz9Q1795a9KjyAIv/uK36u/eyTMkf6b3vl1tk+4eeMJZwe\ncFW8A0UvV5S+yndJnCzberJ0S2njgSn/frRE9vhecX1m/WGPAPCwvbC9dndZx1lvAms5RtqDdX9Z\nDF3TdGRxwI8I0PFiwqsVV5NyGSq/KvB0W3d8GDRqra3gmdC7QHthhvxm6c8MhRW5Pt6cD3AJYNa5\nRIxBmAi9SvFRMlA9OrNmbhBTy5ZLbxGYLIxcGENpG+UPJdL7+Z0OcQU72BisNxjQ4XLGoARh7TbX\nSzlccqWKVU1HUae8tqZqK1Z6GPk94wUWq/6FNb11BxTPlrKOPB4EygZTM8tzl1hyoPF0RQsTK+Sa\nkcAcw6gdaV7CEgsLfkMOlrJRBYUVneQs6SvHejuojsrNffUH6EBGntP5JGPoAMGRwIfIFsD/FboZ\n2YayD2aRTvR5JwoNNTs3eV2Yn0HWZPqBzkl1rHJUoHPfwlcgNR33Et8V3Ig/2Xg0pa8284mh4g2U\n03b/TACt80CgLhAWhb/tBan2PE+L0zpVTylS2DAaf3/cygS0KKzeEZ1WqAA88Wwx4z26BNpq1SXi\n6HrdRcxWNdwQZwxamKdrFnUBMlAa4UBNWlgYGSm/SXdP9Rs/lQ7WTs8HW6JGZsbxsLztlGyNKp1b\nSrvO4afxWfeeMqNJa1CmaDwJJxuYGvTeDAfoGIYE6CEjMyHFY2UHAQrKOc6j7QCNndNs6PP61HbU\nwmBTIxoetze/7VU0HfI2W4xkuE3fDp2TfUY52Ihq0rlzgnLhygmXwzlBJYM121sOkRkIf1sPHyx7\n5VjBkyeO3V1y4n7ymvF9Z6pPff3UkTtPHrlj8o4f1pag8bt+DE/A0162iX18Kmt2nDx6h7IE3VF0\n4v7yUw8xffzEvU2+H5BxTlBHyUZlkXl6pPjlYIyLxSDfFKbSdvbMI+T1I3x/oug9pa8Un/waxyXP\nCQLdmtrT2XpyUdvXMbyK6C6UvakUgsdKa7ZjbGVLEIiapiMefxJomTV7aEDX15/VHx5pOop5DnpY\nSX8lDa+dZ/U4nhxd7f5NDRLNp7xYDLScOd9TaUgwUFK3p+bsk8j4MNQg+W7VDvfpABrZrHj1pCst\nQUDLeKZN/qsf5hbmbXUTwHJlPmbOvpSCWrasyaX1/OJ8+WDKKHkAD/GTx/SOhsiCnRr2lDYDWNXk\nQqawRpvrJR4urVKdmUJ3GG8+EVrmVW6uTu1CB28e76AFp78rc6rltGagKbKvgscBaC+etIsbaDxd\n0cLsmnIvt3qjLuaPCJOzU4YEBnQ8j0OEsZkJg62qYb8uZ0ZLzdu07EQt8LTOF6G2nqVbn+hKrFS6\nendIMWA0Ho076CQiZFukyJSRfkakwwjma2OfL6C1o+pd7Ci92FfTpHQeA6m8R2fKN5Pjj/SwAC0u\nsU3X7CXVqHLHYN2Bkr5ySk/XOU+bajqkUzigfZdWLWhTi1SoADynE4h8c9iwUjsZ1bU7NVvHZI8n\nyA7DrUHpSYS3HXVFWBB6zw+en5s2HjJqR5r4XWhWmOJLektrq7bo5GnU1OwoHqgCPzPLYDfpAP2V\nvY1HzDQDaNKWsjq36Nb6lOIRaH0rOwhkCBN9QZr18e0afLTFeO/Q9HDQ5ARaWk9Rc40/p9wKUxE1\nZSNwxxMhZwnKhSsnXA7nBB3rLvC9gQ7trd16rODJY4VPnjx65+ljd/peM2QxOXnkzlNH7zxz8Jb0\nte/SlqDOJ34Dz9vyn+wseJa9e44UPHbyCFuC7sw/fk/Jya8xjb8VNTu0HxDZgHzat8g801/4Ao1r\n8vtSBK3vC2vLexKSd5f6dh/l+xNDHyiQPkFevJ3lmzg9Y3yDaW+1Ee9g0cusEFSUvYqB1bMEKbu7\nt0dMfyOKovtqApmteTSg8/coaBLkELSL57myTjVz95bSCB7hpxNFV0uzlI63Zo+7DGv3dXcU6Hed\nMsuqtvE3sbaCZ0PvAh3iwGwjv4PejJ4wQO/n6cSwBJUM1Hgc71wYthyCNKB2eEyXdlDLlouosqxc\n4A+DTuAnj+kdDZEFiy6g9TaJzhKPIReiwxptrpd4uLRKdawL3aFLOgT5KOkto5FffMDvq2eHIA+B\n5+xAdeadLKsQaDxd0cKstrwnGKmR7AzHHZO9hgQN28HW8p6oqq00F94N1cqs03gsNL9jPYnqA91d\nEegDUjdoOrmE7eFZFencwlw48QEi3YKmx3SLCqCvbEf6lVvQ+eYT2g+IIGjvexje4nLAX4zn/C4L\naTtLelFbYWvNzvL2M16SugqL0WL71Bc+hYaxVXcLqlEOQYBhCcLzsEOQp3YyeO8VUDFU5wmyA0rJ\nfz2EgQaPQQW5P0sCTdp2CNKYWSDntVFl0HE4BPlg9yV7Q1mUQxBQ1lXQiMZspJlhneg8YjkEaUhn\n8JUdBDIE7RAEoD9ye+O2V7WDTJzK20s511cVNx4w9PnShv10AmmWIcpCbaPR1aRzlqBcuHLC5XBO\n0IHO09on6Ejx82QJyn+CrTmteU90+b4zeHLyyJ1Vr/+NNgMBtZv+Gs9rTn0dPG2Nh5ubjh4+/XXf\nEnTH6aN3Fp24n2m8e7b0Vec5Qb5FBn+VxSETZso2Mz/S1njmUcjfW/zcLuH7E083VW3rVIYkjre9\n/I2WxqOcHu++M+uIou7C51kbqCp6EWOr9gkCxoRfsQVhIsFgzdL4IxvQfi5duYOtQpileJIrbz3p\nDa8N+71xPDFK6vcF0WlIFU2iakdL6ylDgoHy8tc5141nnzRfbz5tRqRB+8azCOzGDBiWIIA1g3cw\nxGgt/dMRV0JcYkEtW9bk0rr3/KBR5hr4yWN6R0NkwfbWeH3cxju0pWUNhTXaXC/xcGmV6lAz+gIW\nzHp1p1GGlfNA9azaO8boSO2WDBWtJ/Q48I4c2Ubj6YoWpr0BllE2mPI4koXGsXZDgobtKQPFTzKQ\nC4YoZEZHag/pJ3r7CTCs3IsmB4iGAmNoBYy2s3SddpYhqyKdiPB+AprGIg7cnejXLSpAW6FIdgEy\nNVr9Nus8Nnr/f/b+A0yy5CoThp//Q3iWXdhl+Vh2l2UN+y8ghySkBeG9EcJKaBFewMqAEEJIyDIj\njTTSyI80Mz0z3T3tp3356vLeu/Teu8rMSlNZ3vf3RsTNyMi4Jk1V9XR153lOxXPq5rnnhrsRJ849\ncWLmDGFDhfDaCE+Wbrfe5mI9tpuW4ICUMY62XJnR5DhgIeNgz5IUKlwXnXq42slQ+QxJUYzpU4L9\nPX6vjIs2hYcCl6PG4EpMusKR7flKbWRBW/19Yt5EZFtE1QqY1KVFZHalg+iMnG1gIa7cX4TkxpJ0\nO8eUoHgc7SBQARLmUoaDo6SzubsJ4t1Evy3ux1QsgFHVlgJv147KcFYR+DfaingPunQDGnA/w4MQ\nJ2ggMc19ggamnxqYempw4qvDfaJPEEmZX0/iKz8uWIL+n4nuD+N6ZPKp+NRzzMenb+prjJOlE4Of\n5rTN0cL9gMRUfMqe+To3Xuybb5BPUqW5ViHW5i9x/vCE6BNUFhVIM20znafP7Q/7ukLuzlCgT8oP\nOcweCznRgFLmE3QRA2u1PkEryRLNZfqHS2M6NAlKRJyKsmuKTSnDa2hQ00/HgLYHjssnKGLgE7Tk\nvbuSIKrhapLs50eRqz8qlYKeTxDRWl5qUG8X51jQ2al+vwFdtpzIpfWyvk/QfRKnSbdisw2foPrh\nhHbX+xzur1qt6BNEQroob43KJ6g0R5QFwb1XQMbTI63Me+ATpN7LHJYcKNKyT5DLem0jNEaawE8/\njzFkkZi3Vgi9qOMThFuO2yfoYK8s8wLG13QerekTBORxjuIkIPRGYLiST9AcKbvaJ4h/4aMmzpJP\nkAo1HSiOFvR8gsgOLH2foPkkiWQEtGb1T39T77BjWO4TxJ+oxqPwCZrjzCIY+gRN1+ATtG3gE1Ty\nBD/aQaACoLPxDEdmCPJ/gfiVgp/p0mnrnL8UqnzO37OQNDOGmqAWnyDD884a0IAHHR6EOEELGSd1\nCGI+Qc+SIEFjxK/HNPQ54jVT9J1x0DhB2x/519wSVPjUf6ZWHhonyPxi2N0ZWbjkG/sq9wmicYWY\nVeiRwaHHuR+QFCcI91LLDnnWZvH4sG3z1cWpZ2OhkbXwxG5w7CA4tusf3DZd3TffyM6c5fxIJye+\nSrx+BN8fA3rEfkMzD5yOh6i3S/le3G3zNff4k87xJxccTRhbuU+QK9BPmDUxGyR+1OIVyITSIA3i\nFAv+QcxwDsdtJU4QMFaM5Vw1LkYmyx7HMDhWKohN2NMeGt9ctM4beh6ROEGTT0MHKsxdKN0ItDUT\nwxaTX6MHkBrYxA+ULEGY1xWOlw529/fMWTfPEkeoSi/JxoQ6gC5bTuTSeu9gz6JV+fdPkCbdihXW\nsWVY16k6Dxuc0O56n8P9Vas7G3djc+tBebFnd7fjBScj//4e/wy+ERpzFRkc9ts8TtBLtfwg4+mR\nVuaijg9CeqM2t9O13Q1N9yLN7Uhqtxqrr5s3hMd2czVQ/GRViNPzsDMkRbsAdrfp7O8i35lExQBo\nbyX2ETDUCLVWqXc5LOUfOL/k2NjVCYaCKSOhCnUMZIF+HG08TlCA6jwSQv3bMV+j4YSkOEHFSEne\nUhiaXGBYiROkhffgxAPuTitZgogvLeoHDUTzydROhixEF8OYnjUNkI/yYpYhO9ytCFyUGjf3tuaL\nLksiYiXC7sXkDv1qPmly2m7w7HGk8Yy04wQZeIrNpa1p/6CcZ2DcpI4TpKd4IFcvWZygtSU55yIW\ngzRxXZpgfGYuOk4wZQ2v1qOlb6ClljRaSo33zyEeDWjASwInzBL07y/+1DecfjlHxR5UCE2kzE2h\nvtvBnju2K8QSNPK5mcFPx6fI9ivF5jJ1Kjb7gr39H7gZCBg69fMj/Y+Gx5+KmS6GHC2JaSV2j2no\nc9T6o9iAkA4OfsblaSfWlqL3jUQnZk+ze1fnLt61Kvu/Ar5eiT9sb6JPIflh/NG5cz5/d6/limLr\nob4/HZYLJnfbnLulTfEGUq4P2q/6/aUzzvTys7loIXM8D9FPv/msLFy22BQ/IGYJsgf6dnjgZLVP\n0AqNupfxg16OL4RDo97gQMJ+u6RglWM6MLiwSL51KJhc0PP90aRDoVGNPDBkxiBbE/EjRQraN8h4\n8vH5BWYM0pG/4GxJzxfNQOzbl62ZTJ9cvt5xrbVAaiMDFVa0BEHPO3gpvveqAeq1OVsWtxhaAuZI\n5ef7Huiy5aQurdd3NyWdDP/iovLzSw1GFbuSlM+pwStzf3Tp+xxObne9n+G+q1UsHWPzWV+/q+iK\nYne1zKcs3uWQMvLvrHNj0LJ/AGwO+y1+wpErH3ipzMFkPD3qylS7MxhF7dWH7NaytHgzqKj0Zq6M\nOW21udsjrrasr28PqgKp+TmyqUoTqP5A9gQ5qCmE6QZAdw/xvqkdaq3S3YM9Zz5Qyjx1IkbxlZ81\nYWejbJcNQ6h59lZyVizTZ9Lu7ehMKWg09Qlyjz9JDpIHG25nbFz/SViUGgiMKgKhHW2vqg9xY0hi\nNt8TiKwm8DjRElQyB2wuc2NQ2kvMf3Zn83zSxNj8K8YnTB1oHIup+hyoaUkBOnI+/JrfLiyUh2R2\n5P27zMhIYYPO+6b4lMN2kw0ODB2OJua4pBc3QO7SRSRB6NWtT1pK27N7fXdDrXhIRsYjHwQqgLBh\ntgzLT9bjW9tm0tbe+ERXdNRXCGvv9asCcltoqQrGIL0u3YgT1ICHB06YJUg0AwHNWQ/3DzJl3X3x\nybZQ35jpvH3iqaK1hRiDkEYczSFna+Hc74mWIPPt/0utRc8E/QOxqWdFfv84iRs93PfocP+jfZNP\n+ny94CHWFup3o0mHbTdxb372HKbV7MIVPf6Q+07EdDkx/Xx09oWI7Ra77vf3zTibe6xX7lgujthv\neLw97Lrdc2fYdqPDfGHAdnXW1VIxD4xOhdkhXC5iQ3F2EtuHt//uonUraQuHRi2+7hlLi9fRWzpo\nTBM36Dyxmo6Ex+ZdrWbrNYvpstnfaw30JYLD1JjiJbhoJerUcvTuwd7Kzpq/EDFlXJ7l0FzaRgwx\njiaz5UWL+bLJdmPO04ErNl8PMuD093sCA97goMnbhTTPAl1rYjZA1p8pJ5kF4wt3g+OKe3MReaGc\n/r7F6BQKlYvNeQKDZm+XLzi0kjCRe/3DRN2BJuQfKnkDMaxxL5gerO1umGL20eAMuuLS5hFYl44Q\noHRCsbNmPfacN7aWvE8cUqoEumy5zxaBtcAeqfykNeexKZV/X5waxqBCxULLzPjJm46BovxolQYY\nwInurvct3I+1urt1NxfajExFMVOHR93LAXnkx+IQMyOZIq1bWX9wOWzOulz5QKpGZ5mjBTKeHkNl\n5rdXvIUwZn+s3A5zHMHm3lZoNW7OumlFVYintr67GViJkVpdDiqBV1bTZLzCqJX1E2OcHmRDigKQ\ncpETuBztxLnGN1BHYFoGdVQp1rfJjYwzH0D+UWR1CGENID0qVgp7zJQi0c2H4n7SvjT7QmDyae/E\n1xdnzuyarxEVSMWmIFOQ0EsTFnLsfdHHBI0oRm6Caqd39vkxwfL2ynzUih6FfiXvp0Zd5UKk+EnH\natbnXyYdD2pnztiUxmE9QwqOTpLx3d3S6Kt6wYAiq4p9E42FJrNk3c68P7mxpDZVQMvCdG9fcrhC\nQ15nq93ZYgkOQivGLcZ2Ddal+RPLCsVaH/p2eUtpQkXF4zgGgQqwVSgzw5HK19gmv7az7l+JDiSm\nu6JjUynzIb+cCS0VwOuGys9uLaNWzRkXuvSq/hDRsAQ14OGBExYnSLIETaX5aWJCmg9EbTfiM2cS\n08/GZs5GbdeDfuIvE7Fc3/7s/+RmoIMPfVu86JsT9nYyDx11GraXzgurJk2Ex3ZSdmOee5DuiVYe\n0b+G0gX/bME3o75eRtPxN7eWMlmvOya+Rr8vPem0XHPabpgj48YzLtsGbzVdVj5M0W9T9qlnFpzN\ni9GpCs+VaOlzzWZBg6duGunRuTlQBbuxCDxiaNTq8UGjYo8DGrV6HNCo1aMCMp42KhN6BWb/XJh8\nzWKawGqKGAhqPz+egVSl+e0VrMOBWHyu7eobpA4PLPq1Wrepib5ft/2+VB11Z39XfRC7+b7Z1n0k\ncD8PAqhnbyHM1nRkS+BLAQ1LUAMeHjjZPkEDiWnuE8RiBlFLkF8zhk5s5szBB76RW4I2vvL6oiXo\nmaj5Gqd5XCFGR8xXJTnGdDgwuBSdMua5B/RyrMxxRkLFEqS6LqCHHevoWXI6584658445887zVeL\nbq7XPWmNfc4cVnbWLMFB58xzJUsQRcfkM8HgsOpZhijFw9vfU65Dd+E8deORKkBEv25o2EcNjVo9\nPmhU7HFAo1aPAxq1elRAxtNGZUKvyPiIOrG7TfyDDLyHqgOxSg/uHviUdayilx7GSaoCbORlraYO\nrNcT6rjhJeyom3tbjnwpFLorH6jKaevkwP08CCxt5vi74z3E7rAGNKAB1cDJtgS1R4cU64/iE0Ro\nT8Yd0oqhk236O24GAnovvWVu6PHI1FPxyVMRd3vR+sO8gUp02HZLklMFPVgj/9HTkeDw1qKydXw9\nUdwcDqTfgir7BOWVYJYOb9m5Jxwd3juMQRO297adtuvEhMTNQMUzLJLuO0bPVdPbKhUtF6RnwR6Q\nz3oSP3S7XKgG+UeqABH9uqFhHzU0avX4oFGxxwGNWj0OaNTqUQEZTxuVub1K8OhArNLl7VW1Xprc\nyBzL1uDtNS3dhro7ZQIV9B9O79wvoeskeMk76s7+ztruxo7hPqwTCvftIHBwcOAthMV3x2APVwMa\n0IDDw8m2BF0PdBVn3CAGDqQzgZ6R6WdG+j85NvzZ+dnnAzRujtXT2WY+n/7Cy7kZ6OAfXzba/bHh\nvkfG+j8ZHv96fPo09wOKCz5BnI5NnoqargR8fcTvxtcTnb8Um34+MfVsdPaFsKuDWGGoPw6eVR/t\n9fUO2a53mi92mC/0WK7YPHfY9XlXK/5tM53vslwasd/ARfW9ejSIQmwuHhwFHQuNugMDJu8ds7fL\nExhY8k6WfIJSLnJsBIvEzNDTdzdXtATZb0k2IIY+ZwtjKIOVJNnDTIP5BR0tzoWLJUtQETfd3SUV\npBpk7riFONnHDsksLBFz4T44gLqzmjA7/X0L3jsWX3coNLKfMJHisJBAkig1rueIwrRooZJtd9cO\nFQ2R6NeVJtfl7RVH3r+QcViynuBK7JAHeK3vbrrygYWM05x1Y+4U9RVIDqxELVk3nuXM+4/xm+Qx\nQzW12oD64F5WLJQ59EP0VfRJfyFyX8VLOlpodNfjgHtTq+vrGb+3x2m7gYnP5uk0pa3SKL29v0Ni\nTGRdpozTtRzUPenppQNp5JcDrBxiRN3d3/UVwlSyE/MOVsjKD8cJB3cPMMdbcx4Ux57zHSYGX2Yz\nDwmQY816Qisx0dFga28b2iPa1JxxgajD+0Os0uBqnOulpoz7TnSkOdzfEurvjU9s1rv7TA0rO2tk\nRE2aofn4Y9N7aOi0625k+m5oQgmonDDfDU/edd6hsd6owrOsnGd6sLcVSjus/l4oTvbYZIVI1QKw\nikItoa7KKqoQP1i0JDxdXle7LTSEekZtKz8dAurrqBzweqKjkmalh2aIG77QB4yOGKsR0JOia4u2\nnJf1UuMNTcfUpcOrcT3fmfxWwZEnbEztxOQbWo2Ph+ZYBjJb+Y29LVQUrxwgnaNL497u/h7efZFB\naX3ozEznhxadC0Oyv1BB7URFof8wIZMp86wgE4isQlVg7w4G2MHEdHtkqDMyPLQ4M7I4ywef++fY\njQY04AGAkx0n6Ly3uWg5Jumku31g+qmhkSf4yV/TE1+Ltrw3+Nwvuy7+ATcDAZee+GHOMzP4Ge4B\nZJzGZs+F/T2xmTNl16efDTtauT9OHWkg0NdtucxOB2Npu+W8yd0+527hV1jab3sxQO+acbaMOG4M\n2q5Num77fKXTxDRTp6+r3XThjuVSh/kCk9MydSbiGtxPu7YWrbuO1gPLzdLZGUDQwdG7m8vJjSWH\n/bZkA2KY8HYrrcKh/GiA7fC423KtZAOiPkHp2bMkKGOV36mQZgNEMlJBMjnGvrira3lreT7QT44J\n83Za6ZmdfvvtvfC0UhxPbwX5i9YyyUB2YlpdQBVsI8UF07Y45wEdef9+vYGKoAhiuhWlYZpkZ1Jg\nCnfkSo7NDKvX9u4rqFirDagb7lnFQr2TzoeG0rz7EhqDNgtkvFryknTziN+LRnc9DrgHtbpSiLvK\nD352OG7Pp8xY0rBRGisftqTkiGXJ2v30vXq/ipGfjKe1V+bO/i7mF1Hs/JLjHnxg8CyHxIcCFzfq\niV6PxbkkB0tNtnLe2N0UbQRA/FurjY9VKfoJiUdb1EjnM056pm3v7VAvSydSJr1zo2oCPEXJbcpK\nDktNmb2+3v3ItKzPAL39d62378bm6OEbZG2PUivL/sWFOV/3XMoCumJwbsDG7papuIZniIoia/Js\n4CA6E3a28BfH6u/Br4eP8FJHR+WwurOO11PMrRqDK2UHV9UNkpUEGFvT9TdHx5OY6+3SyjFbHHmX\nFkGtdjKUTuhXI5+j8e5bsx7pV6A9NLwZGuc9Dd0PnVDikQ5oF2OQA7tjYx2R4Zm0VbxozXnZG9QT\nGxffnduhPs4J1fe445c34gQ14OGBk2QJmomaJUvQM86rGPvoqBE0x6YHpp4ilqChz4z0Kae/I419\n7SdEGxBD58U/KOOZeJr7AUlxgqqhY1PPhn29xBNH8M2pnh613yDnxJvOF0+LN6LHHbcGbdfE63fM\nF/lZY2r5fj8xM0lyrk880zx5OhgYiJiv0rKc2jZfLVmCHG0Y1jdDYwuL8zbPHT67c/TYbpKI1CJA\nI+SaRxG3wxPR6ee9E193jz8ZnHy6MHeRCA9PlswxKjxIu/Kx2dKVQoIEcVRJJri5vMui+qVtc7EJ\nU3DQaVHCGMXdHXftbYW582idqKOF75KTMTR2NzojiwXqHMxZEaiCrau4YBUhGW4YRuo6bXfvYN+s\nddCpd5nYyEKqM32B0IpOopOzca024DBwbypWeU9VHfKotPCaIR+WX/mjjhemUA04OjjuWt3d2/I4\nmvgcx9Hm6URfZWeiS8d+M7TmPHVb848cwvTsbQkx72zvlawPZDytvTLVFhmg5Zhj6C6uy2tdhrW6\nI23sbkoSGCbW01g521W2MyAu6nlYqAHyvclgbC1Fd7Uo8U1cy4GWUD9ZxAZ76Gq2B3RvfILN0YcB\nzONlNo7YlCU0hL6acHfKIxtDXz9xkd5WKi2+nirdW1xazy85NveM/JX0KsoVnz2Izix5e8S3BmiK\nTc4v2Q/pu1H3W79/sK9pvFDj4b+QpTaykkyGmnuaNLt0HRWl16UhX+GgALVT81h6YEVLEJDN0ejV\n0nXgQmLWab3utzeh9Vk3Q/dDu1tCw2VsgtqZ3syJPwGbw3hBeu5ER/mV2SUbXha8O2PJeendQdoX\nn+CcGHxEb80jh4YlqAEPD5wkS1B/YFyyBH3Req5oCQqNWa8yS9DwwGPMvsO8fpY+/6OSGWj/n75p\ntPtjIk9w4mvMshObPRewXQ+Of41aeZjXD7P4VKCj9pvMB4fYX4r+ONXQXl8vtc4wrx9mqamZ7rVd\n8evIN7nb1fzXJ5+5NvGM3dcZn1TOzl+cenbHdE2xBFEzf9zdYQ6PzCdNDttNcYIHbkem5CM2xLMh\nRXR2KDKZk47rDnFg1vHTOVjypCLjyHM6MqlcX8+SQ98lmQzTLvErn83dUZZDZ3ti6lmUKz53NhQY\n3ExapWcROjgqy2TIHJFqB2MNO7aW5LkVEfN09RonhyXVnMoRWrJ0hSMUX+X+kwPGtdqAw8C9qVi9\n5Rxw996bJleS8vvO8BDOgBI0uutxwHHXambJLc4gIs4nFzBKY10n9V6O0nfvlwr2Dw6kjHGMCxth\nyHhaY2ViPSkJ5IjVncJ0DMB3kUhYkxGZOs86J1ImSQjQlHEub69KFzkWdqryeIqvp6F/zkYtXBdl\n3yan0hbFl0FZzRK6I0zWycY2l4qAeVzMJ5Dv4td2C2K4ptgINO3ywPCqsndME1AbEj9Du6t1NTDs\nLnemA9rd7fj1kOb+ut/6ks9UJXTm/co99YI1p21y8hWUGAsiHEmXBoBfksAQjatwUIjqqJ3AaixB\nQD190lYMIZrz9aPXbYTGpubOOKzX0BUlTq522nJe8TpeSf5eTBctkqPEANQzkJhqCQ1I7w7oJuoW\nNLI41xkZAVqyLhTwkG+THjQsQQ14eOAkWYKaPb2SJehTC6eKs29waO70wNRTg5NfHe57ZFjw91n+\n9A9KlqClJ35E4olSnyD32JfHBh7rHfsiroz1f9I8/ERc8P0xpmOmK4EqYveoaRbDqNV0rlXw2amD\nnne1acqfcjap+a9PPHN1/JkF+y2xLOnp08RY4+5iekPA0Wz1ERdf6MGY7EsTvLNlR+0Trt5mxdDd\no1iCgL6BuylnyRYj4G7SkY/NJsKjPP+J8FgmMrWzWSBezZJMhvEFcS6UdrHRw+CUckFaODC0m3KU\nPTQfkQVyTBodi2YAxhq25ncVhrU6ogOia4uSEI4GP/m1VJP7HIxrtQGHgXtTsXo6K/C4Hbw1YMkr\nv+8MlzwKw6Gh0V2PA467VuOxGXEGEdEUI0smg3E1vn5kAUcOA+v63wDERSkZT2uszPz2iiSQY2Q1\noTAdNezu70rP4uioZfUeXk1g0dgaHpQikjA0aFZpw1RmKx9ZWwQ/0ty2YvvDCMb0T8ESpMSsHF6c\nJetY6sugrGkJ3Tu7ZDuk6dBfiIr5nE9ZeF/d0Pu+BaTHgGzv7Yj3iujKG30DQ21I/Ayd1uspj8a5\nIg77TfLr4ewsdb/1sTXB78kQ55ccyj11wYG++dWak+eUnSPq0gBUrCSBo+j67dVy5WNYpSVI7wWx\nO5vRymbzZWDY2eK23cT6a3b+PC6iQ4qc/pUocnJwV66ogfgUfy/64kpmumNj5e+LTDcLfnZDiRm8\na4HymF8NaEADaoWTZAm6ZLkhWYI+MvuV4heY0Kj1Ckai4cHHWPQfnq498n2SJch56S0iz9zQ44mp\nU+6xr4wOPDow9HjP5JP8V8sIMQZRi0+FlPsE1Zp6ynyC6k/H7aUzzsTU5BJ9gpSU+QTZvB1SKfYs\nN++GlW9KMXeHJTTER+2FxKw5PIJ0IaM1cWIdJeocHMOTJJhcaIIYX5ZjihWm3DdnY9EcDg5JOfct\n2T05/9JW/m7SJstkmHIm1tMzadtocr4vPjFqvbxgushUEIv5csB6jZUoPnuGyUyEx8iukNXk3Y3c\n3a1VEm2aRVVUY6ZO3cVYw46s6mqcdWwuSG9qOyQDoZtKVzgeYYjEewbGtdqAw8C9qVj1F2yOO0cR\nMqM2iJvk951hfEFhODQ0uutxwHHXajrtZNOHGhcW59FXC/rOI4ffYHIksHuwJ2WMoxi1hIynNVbm\nxu6WJJBjaiOrMB0D6MV5YWvLKsG3EsE6E+tGrDwlOVBmDIxcYrDtgmLxUbRNbyHM/BlDq3F2hVqC\nlF9ZOpk2kyUrWceWpROphTq+/Yig9i922BXHbSOfoFXFNUNvu5CxWwpqQ+JnaHc2r2j6BLna8Gug\nlpZSQ91v/ZJOcBw12nM+5Z56QYqfxRGdROEQQK9L11pR4JckMJT084jWdlGGVVqC9HzBbB6yF2xi\n7rTTdi3sbA06m7H+Gpp5xm67LnFyh0Rpv15nZIS/F82h/llkKTHDruCFLfoEabxBPJ1Imdm7dqye\niQ1owAMPJ8kSdGr6OckS9L7Jz/LvMLPB/qHRz1MLziNiDKDNj36XaAZa+vyPjvWQU8MYz/TgpyMT\nTyUmT41S60/3xFc65k4P9yv3gqe4U6xCzKAwO+1L8Mepnu6zvki8dQSfnTroScdtTfkeX2+n+SLj\naZ8/3Tvx5f6Rz3X0fqpt4AsBf190/qJYloKjhesNhcAw04Al1FYX1pb4jWWYjxzs7WSW3Lu7W8T+\nwg1ARdxctJIY2OV59odHWZuS2bEQl2UyXFlMbmSaWTjGYE+H88bA9FMW82Wr+crE3JmwoyVOSxS1\nXFPqJDiMSRG3QPNTQjbmgrJMhut1qrbGGrbe5gJNdaEioAia+pwt5z24e4Cqk64zXL8np70cLRjX\nagMOA/emYrHskcJFM3QafoI+LljUsSwvWhWGQ0Ojux4HHHetbm2tuKzyahaIhS76Kkbp/YMDzSWf\nGAjjJQfNaD5AMaw1GU9rrMwD1c4OhpiDyPlBxwaaAe+A1Zvedvf3oEvMpK1s6ThV7q0AZWbvYE9z\nww4u8vMN13c3UbFMJ2ExgEAn1tNruxuMRir6BDEeW97bRJQTop/Qtavi1zCRMh3ShUG9YcfqI2F6\nws6S/iZjbI7v6NczIiwbxv/WqyhHaHg/Mh0v354PZN8RD+n9VPdbT0MpaRu8JNR06Nvc26p+z1FE\nx2tG81Cw0Ip2l87VaE1GxUoSGEr6ucF3wWosQZij9d59c2TMbrk6NH0q6GxBxws7W8fnTkMJH3M2\nSZxc7RS3qpF4QPSV5O9Fe2SI09acZyAxpX53RLop1GfLefj7qDcI7x7sHWs4oQY04AGAk2QJ+sLo\n5yVL0N+MPlKcfUOevH987IvMl0ex4/Q/Oj78ub0Pfgs3A8W+9hPsOnimhz5tH/ki8xwJTnwN1weH\nH2e2le6Jr3I5jlGFB2ls6rmIuy0+pcTW4dfD1uvcn4XYHWqk3f7uDhM71YtZdojnzoTj9ojjRtGL\nR7neabkg8oi01dOpJ9/kaWvDvfOnh/sVX6fezg9b+j5LYui47yRmnlfKMvU8ibXMVYdCQorzD4Qq\nrHviT9pdupfhonV/fxfTJNoImvTaTtESJPgEpSITPJ/B8AijvSmL0qaFEDl1NWlfC0DbUOLSHURn\n1uILEItfyaZiNjeEenvmzw7PnBqeedZnv4VpKT5zOjZzJhQg282IzLSNy0R+yDeE/b27CUsptwyX\nvEpxageqYBspLurvM1Ct6l5FqOMUsmAW+AnauToWgMFhFvczVKzVBtQN96xiy2KUUsT6+Zi291eA\nXEh+5RnmggrDoaHRXY8D7kGtptMOaTXrtN0gzrDFUbqws6q2vx/JadlHBdt7O+qRP1p+KAEZT2uv\nzLXdDfXS+vCHQxnD3sG+RRX3VzP8ih4QIw6d9/sTk9ATOiMjXA5XZtSL6nlaNLY4xzCFJ5b0TKo/\nMDq4GuO0Ok4Q0mIkFLaCJevepmCvf4XkHxk7TKBxyS1oPm11OZu3wxPysFbE9XyYb8VFZ1bbNI0d\nghioj4AE5jaXoaHtRaa9xVhFQOYQ5C8cyiEIcJi33iCWIkdHXiMuONQn30oEFWJgsNvd3+U20L2D\nPXV9enTigqPLHbJLc6hSP9eMIg+saAnic7Tmuw+csN+wW66GXa3Qt5FC8R6cP91C9z9yHlHtRIdn\nRqXJlLkvPiG+FyLdExvDu+PMBxTbkBYP6Mm0WXzXpPPX0Ntj6yn/ShQ8Buf0o2iZrTxDKWh3I05Q\nAx4eOEmWoH/p+5hkCfqTwX8uzr5BX3zW7+9dmD0zNvzZkf5Pjg4/PjfzrM/bw81AwMBzvzLc94hj\n9IvxUnycZ2OTp1wTT/WMf6mNBNNR4ul0zTwzMPSZ4b5HzcNPMLtPdOFy0NsTCgwEPV2x+Yux6edx\nb3T2BQyCxPJC/VmCgm9LTbTb2z1ku95pvthhvtBjuWJytbPrM85m/NtmOt9luTRsv+7z9c27WpkN\niOWT0cO2G2qZIm3zdo3NvzAy9OmxgU9OD37a3v+EZ+jJ+OyZiOlyyNsbWbgUnzkdpgaU7fD43ZTj\n7oYyASc3MNX7MA1Ysx72AY1d14ID4r+zaCNfnxatZMV1sAcdCwMxayOiamSD3AYE3Ek6xHz60lZf\nbCYQGHLnA6xNcW96M7e3vxuMTm7EZsl+rkVrPmV35/GT0u4zSzaod83h/tZw/7CrxeNuD7vbQ547\nYWdLwNfL5PvD45yf54d8V9zfIyvARQuVbCNHlR0CqtGwMSc58n7UJ7QBzFJ1m4EYFLZXXfkAJmxz\n1u1dDm8Kn2ch2V+IQDPAs5x5P+Y55YeTBtXUagPqg3tZsXjd0A/RV9EnocKKhxndU8Abp94ghiuH\nexNFaHTX44B7U6uFQtTv7XHabjjst2yezvnkgjRKM/cQc9Zlyjhdy0G+ur5/QB75VYYqMp7WVZlY\n+noLYSrZiXWauHnq+AAqR2g1bs15UByoItLRSBUhv73C5/2x5HxHdHg0OceUGXHNvLazDh60qTnj\nArG8vUrXkMHI2iI1Ayk6A5OjSavjBDF6bslxJ0r0k5ZQf3dsHLUXWIli1QoeLNEP462AOZ2OqNAl\n3GjxbaxjuTLDh7XY/M6iNZkPsPxw4/vW/vZEamE+42C1Ws0R8gzWqRsUKgoIAgtpchWlyEf2Eua4\n+47X1W4L9tuzXqiO9I5DwSHferyeUJBYs3LbBENTxoVuAA1KYS0Cmh4tztouvZld3VlH9wNCEcWr\nREwGm3lUFwYB9BBmy0ttQFDImvMWe6k3vp4ysCIpXTpbZ5cWoUr9XFI70Q3APB6a463PKkqsHzJH\nC3u32bvPf51JW6fTFis6Xmwq5OsOudpCnq5AeNSe8/TGxidTZjpE+NW+e+jwjryP2HSC3LJTRo8l\nF1hfRerIBwbiU+2Roaai739reKA51HcnOjq/5GA8wrtWcgvCq43ccjm+lYhmY6D3oh25HGmDXsMS\n1ICHB06SJegfuj8gWYJ+v+/v8QLjTfbkfMHgYJB6f4hpxHpLtARlW96r5kHq9ncXvWzkdAFjnIr/\npU1NnrYOM/MhIjkct98KBokxRc0ZiE8HAoOg/eExYrl3tMQWLoStNxxzLwKZLd+XcXqTFg8dClGT\nR7vhFhMVG6NZup4PERtQ0ScoE5kSc+vNesDjW7JzfqRcbeJ2feYNJPJopv6UGTKDwSFfYsGzDE1I\ng+doT8CtW8NugAE0avX44CGt2N0tErqem4GS9ruHC9shQaO7Hgc0avWogIynD01lslW6OO+rP8Bg\nGZzcyETXksDYWjK1mQ2tJqS7KqbqOEHVpIHVWHRtMb6elrJ0eMBqP0NDXItP9K9EiC1jK0/tXKHg\navzgEH5J9wCOr6Pmt1dYnYRW47TpF5EGVmJi61RM0W1Wd9cZ7TlqffK4oe66Pbh7wPqPZp0gRX9m\nnIWd1c297f2Dg+39HW70RIUb3HuYlB9Strhetu5AWlCZ7Nd2N6i1qEyCePB/wxLUgIcHTpIl6K86\n3iNZgn6j653M7uuPTkmxZhgdnX1BtAQt3fmYmofRfdYXqW2lLP5Ol/mSz9enyf/S0n5/n9vbbafB\niTR5gqEhz5KDGLxzPn/SFAiPEgdOZwu1/rQ45l60zzJLUIsvZeO2c/ATz52jgzA0KuH7WDRT2he2\nnbQhw2KePYUgy4OYH05DFARiOiGjtg6PSHuzXm/aTi1cujziuH94IPp1Y7ly1NCo1eODh7pi93bu\nbq+R9Kih0V2PAxq1elRAxtOHpjKxvFfP+75ChO8EWdlZ49f19IRqaHWcoJpoEJJ/2e7B3tpuZf1k\na28by/LkRoaZeJjfSnA1XuVzpT01Rw65rWVDR/IKcEwddXNvu8r6qUiLvieSh9FhHL7uAdRdt/nt\nQoX6KYRQdmprK11ne1SJG47xvYejsX6hGxHk65HVsh2y6Jaa64jw2nGdhNiABtzPcJIsQW9t/UvJ\nEvRz7X+Gt9ebdTO/EmJTKPqYMDox9jXREpQa+qyah9Fub3e39XLRG4hYgjrMF+0+Ggdai/8+p/2L\n80U7Nxnp/NFJZvdhfkCiT5CXukNTToV/d7/+mVsCCOR5gHxv3peLzqwnzPnYjJTnYpToMn6J3j84\noHqbEU9NdHrzKM89oQp2Y7lyxNCo1eODRsUeBzRq9TigUatHBWQ8fWgq078S1dMBQvQECfX1+mjN\nOEESPZW2tEeGmkN9beGhocUZ1zLbBa/weAthcT9OZjOP65J5SA0sDqMop1Za2eF1DLCzv4un+FYi\nqY0M9QQhz/UVwlhvZ7by6og2ajimjprdWlbXw+Fp7pPCILgSuzc7KOuD+uqWOgSx3XNGdYKqUF9P\nbWbLPfq17z0mmr1KBwcHca28cXrlSD8PN6ABJwJOkiXo127/H8kS9OPNf4i3NxAZC+jEx0n2f1q0\nBHk6Pjgx/cyo7eqUs8nvo940Aj8Q1wesV5klqNf64pSjyVeMNaMpX6SDro6o+Wp0/kLMfCVsv12R\nv5zui9huRRYuxRYuRkyXowuXovMXY6ZLYev1oK+v0r39Lm/3uOPWkO3asP36rLPV7+/3Zol9BwOc\nO+8PxKdDrnZNn6CQu4PwKKMh5Seb5I1mr9RGFjMctJbo2iLfdq4JO/s7XCaXHwgOaZbFF5sW88Bp\nb9oWiIxbPR0TrmZ7bHpmydodHeuPT/XExocXZ605sqFM5DemrVkP7sK9A4mpiZQpuHooBygoOvG1\nlK8QIVvY1peyuexxa9jpzRwqH7oUJtRaT6Ld3tuJrSVxL3ILDVjcxw4aVwJUMnh4KMSaYHVnPbya\nQMeAzlf92S4VAVV63LX60ELdFbt3sBdfJz3fX4hC5zvMV9/7DtD5l6MkcnzWf3dlEZqjcr1qaHRX\njCdY+6F7sKASJHDGWuqQ+1/qrlU8NyaM0uKHejRt9dNZHYD3Am+HfyWKp1d8TbAwVt6pFfZOGTkU\nGBQKIBQqiUJt75eN/Ll87n7uosgwso3MowjpjexhNi/tH5BjNI11A1c+OJac709MUo1iDn2VXV/I\nOAcT0z3x8YH41HTaqnmvSOvFCeI0niLFRumOjbnLeVBktl0Lf/5ifCJ0BvHdQcdYWk9HYrNb8fn1\n4Aj0ukBw0J+YddMt8EyOKFNNzyzZSNFiY4OJGbyerJ+gb6A74T1lp+NLgA6GylzcWKJsRElAp8Jr\njomeR88hGdvMowjsrTdlSCwtaGjsueaseygx3Rsfh/I2mSJ15SfRW7Sbd21nPUJ1CUvcCeF0PCnr\n0gpf7VDYXoWWMro4N5CYnl1ip4gY1ZUejXKNJGdbQv1im6I+81sFqk0toSOhR4EntVkWg4nXD5D5\nuR85kJZaZy0VQUtpDrxLmzlLzIn6jKwuapoCWUWBAcORLeflPnRocb06kWjnsn9kca43NtEXnxhL\nzTvzfpFnMmXuj0/2xifQK9A3pHtBo37QRmLd4pXUe1ZRpqUvPqmOPdQVHcPb51oOoETcIindy2g0\n63BiFl2XDD6bucMMPg1owAmCE2MJwvD6mqu/KVmCfvjGm7wpK/crUadLnR8WLUGzre8d7nt0ePBT\nHbPPd1sve7w9Er/D03XHclGME3THfMnt78avXn/Pgqtt1t1scrX7Ar3iXUijjlvsHDGexhYuBv0l\nnxejNNAXm31BvFdM47OnQ54uAwnIT7tFyS1Le23XMephaPPmPLiX+f6IKfcJ8iXNdBxkFnEl1TxW\nEwBtQIoqN7/kMFjzr5IojGWSSZoPenNe3+KCFNeJxCqSOJdD/vgMcjtnOj8w9dTA9FNT82d7LRfJ\nEE/Gd5I2hfownUt36aVQgJqCffxepHeio3WvT1A6KQzheGgulS37KHSEwJRa8XGofKhfys+VYHl7\ndaH89Ad7zsfKvru/58j7xJ8w49b6LQuahygBCAVC+e1wgEXL/bxuOdFQX8Vu7G5ayo9Kwb8bxSOK\nTzZsFkrxVhku2mrdQfaQd1csCKH0i92DIUbLw+zGra9W8USo9WI2sKph0cqrn87qi6Wysbelfk2k\n42k4rO9umLNl+QSz3oq3pkKpcTI8n84e756guiG3tSydzubMByQ7V/WwubdtoA8gteV89HyikkbR\nHOrH9DeanG8S9ASkvfFJAzlIjeMEYa0rSuPpeMokcUbXFst9n+XUm7aH3R1Ml4u62rleF/T1epbJ\nYlvzLp4qh3OX56Q9PDSRMrEKx3sqmQbwL/RJ8UAoIGpJ9A1xLQeHEjMzaStnAA31jGloWPYrQX+L\nT+yOjrkL2tvzUxsZLkTzfCv0kFrPXGcQpQeuzS7ZbyMzNCeDi9OatWSczmXsTaF+XhYxHUvOs0x2\nRkbYldbw4PKWok0NxKeaw/1iLQHZT0cFO/s70qHvpoxT9CzDSsqDIizZBwITvfEJdPX5JbvkHR9b\nS06lLP3xSWBLmJQU3Sa/XUDnNK4Znpoy7tbwgFgzqAflw20h2BUd49eRom9MCucFIx1anBHv5an6\nfWEpZKJHqfnFFG+6VbHzakhAOk17LOPHG/G+T3zwI49+Ai+jUikNaMCDCyfGEpRYT/+Py78gWYL+\n05Wft4cUHxPNeDqZ2+8RLUGTnR9ip8gPDj7WZjrXb70q8gcD/V2WS2KcIEIT56Arc66WDhM/wf1c\np/mi2d3O7w26O6nV5pmEch6ZQpPT5fXzxumY6bL6XpGOzr6gd6/X14NMSnludtzASOpeDob8/dwP\niKgLok/Q3HV/EqOqvJ8WqbcQ1vxWE9Y6jXIh49A7BijDvh6o5DPam3H7QyO8LJ6ivZ/zEI3H2Wq3\nvDgw/RSxBE09NTj9NOgO+zUyWBft/VDd+Hc8vWeBxuyIOVj6VgDakfcr2a0FoJtaVUeBQmuZjVgU\njqMGzMHS44BQiarx39nd31WfnQH00bNL/aqzSIHgr8Z5mwH0Oel2hqmNI9h8R+xADUvQ8UAdFYuR\nwZ4rsxsyxEXNQeMkwb7WsWLAJa/CUB085N1V/OgtIVYCdSvWddQqRmnJFsMQMwJ+rXI629jbCtDT\ndpT/q4OaXhPUibR4Y+jKBxQOAeoolISYp+ajNibtvoLt/R3pcwVDFErhqBEKlWIAkdVjuT5gQE+k\ntfUlRhvHCWJn2KtlYrWs5kd5lT1f6mctB0KeTlGXE+lgeEzmL6enUmaDMnZEhnti44OJ6fmMg72n\n6Pb0vI5gd2wMihaWyrxRRpJzLeEBJ/niSOSPJucgBxJm0orBqJccE677LKS4JSYcMc5gfXeDPwKo\nd9L5QsZp8A1Pc5ApbK+yeydSC2J+ptMWzbrSo+15H3UF0i5XT2yCPsIkXkddQZuaSlsZz1hygeWE\nIYYXJYtHAcinKJwhRgzuYxhfT+HKTNp61dHB8tNFj+LiQxwqajJlJgYgoVxIp5eoZ1wVdeXOB9vC\ng+r6wYPw60hyVroOuinYa8t7mRzkTX0vo5tCvSasFFTPJd1Pi1+ikQG9PGMEJmYggf99n/inDz3y\nMSnAUAMa8EDCibEE4f3/DxfeKFmCvufiG0cdN7lfCbEplNPLl98uWoJG+h9hlqDhvkfvzJ5qNZ1j\n/j6M3+rtEOMEMbrZeYPT4vU203mPv4fdGzFdYf47iu2mSMemnpPyo0H7ejXvleiwt1NTzrSrSZ23\n2952jJjujFPRFUjKvIEU2uzqm42K3kBsNCzRmI+Vei8C9FdxahFRz4cosZ7Wk1+kA/7FeVaW4snx\nJZ5AaAS5HZ97XrEEFdO+2eeU8ZqkxH4/lpyX7lXTTFNh/OK90Ffq+OSY2ypIlQCE1gKsddNWlSB9\nKeWo1qXUkNrISndxRG6lKxyrt+P4V6LSvQwxuSochwCsAOtYBDagGqijYrG4klqZY8WQFvc7rKZl\nGxDHWvZLPszdFesNqVdIiJFTYa0R6qjV7Nay9HSO6/rjnjSdLW3m2DyS3sxWb+vEuyCJ5VjYUR9c\nvSLxcFRv3KivUCKyeUoMSXOfABQGKasc67Myo8n09AHQWHmq9QEDuj0yZKBjGMcJ6ogO68l35P1q\nfj3anzRJupxEuxVp2nKIo4pWHtQ0VuP8Xszj7HpreKAzMtwRGW4JDzD+qbTiCdVc9LJBLeFexQxU\nLlOim8P9uFf64MTsyNMpElAJK/Me/6jYDUREb1HuUUFsPaX2NsKyn904EOdeUSQ/nVEWobKq+kc6\nnjSpy2JMt4T7fYUIvz6YmOalYKhk8dCwtbctSebI/cexksK/qISL9maez5HFWW5vtWQ9zYrHU1lZ\n7kRHqqkf0HMZu2Y9IF3IOElXUV0HPbw4y+R0RUfF6xLdH59SP1dPppq25PhOtLI8K7YkgZ9ZglBX\n9Q0+DWjACYKTYQna2d/FC/mvX3idZAn6V+de12u9wvxKgoK/DKdXTv8mNwPtffBbyNYwxRL0SPfE\nk62mc2Z3O+efcTXjSqvgX9NqvnDbf0ehxeuUHrHfYPdGlb1dz8QFXx5Gh309Yn7UdMjVrnevSEcc\nTZpyhu3X5bxZLrERzRabIspB8XuRSJudPTMR5o2pvedWfZa8gZbJXEvUEKQ7cjXli7RnyREIj6uv\nB73dyHPJBsSQ0k2B7uKoTez3ffEJA/mM7o1NcH7xXug0yL9/JVpTcJz4GvmuIiHTsA22y9UNm3tb\n0rM4epdDCpM+hFbj0l0cY9RfWhNxl3J/JdD8oM3w8IfUEjtQwxJ0PFBHxSYF130Jj8QF7KWEXFg2\nAHHcrOGlfpi7q557IEe9zwYVoY5ajWmN0gwNxj1pOmOzGJtHAivRCD1tGikWogZThoHxHW+QwlQE\nAwvIkmoiNsi5wU8isnlqufyco/sBNL1TGao/TVWE3YM9r3Cuk1o3mGXr1XJ9wJiWYp2ItHGcIGoZ\n0ZDZQqwhGvx6dCA6KelyEu1dshvIoQ+V86BJjy7O8Xtnl2x6/Mz33J7zi9eLViENfom25jx4HQrb\nq/wrgoNuLO0IU8NZsOeWq0vsBsDZtK0rOtoZGTFn3ewWCbb2dpBnzzI5OBxa68buJlKsIPjuy87o\niJiHpmCvZl3p0f3xyYrlUtODiWlOd8fGeHEYKlk/NOS1vlAyjNKju7b3d9i/7eEhaglS8kk96334\nFeor3dWlUZamUJ9enUj0KAmJpV0Pw4uzmteRkrBZVI7kmyPRHZFh6blFS6U2v0RP6cT86sECQcXP\n6uqYPu42oAH3D5wMSxBzSHlZuRkI+LLTrxiyXec+Mup07emf55agrY9+F/MGYmn39FOtpvN23x3O\nv+BpwxVqT1HSVtsVDAqtNn6mWFnaY73M7o3NX6RWG+q/M/VsfPo5hZ48FQz2TTubRxw3Bm3XJl23\nPb5eMYcWb8e45cW5ocfNQ5/zj3+V36VOI65WfpeYjjlvluXKfL7J30kGMmgtiyaqH7CvRmWp2T0A\nxYWOgMwuLqdqK8BOcQpRo6bzNprMQL6c0kFZuh709yO3IzPPSj5B/dPP0PGaWe5JOpSYle5Vp4OJ\nGekuntrzPvD4VyLVfybVVPSZhq3+5Ht4gFIrPYtjsIpT/zXtVgwzW3npCkfcpdxfCTCPSvcyXMg4\nFI5DALEDNSxBxwN1VKyBS0J9sRvuIyjEZQMQx+0a3J0e5u66qf9RmmHd5sI6alUMOCKhwbgXFuY+\nLCAN5hSkGZ0+j3dBEstR/algaTMn8XBUx2szMMUaFEpENk/VYVs5bjDY2laHB1Oi7IQgjdSUdWnq\nA3ppM1kJlyTYcr6R5CwWkP2JqfGkaSZqluSL6fSSVVNmrXFq/IlZTY2Op54si9CsLaFV2bOjkRMp\nnUiVfMZNGZce51iKnFHrLgQ1f62YuvJsHU6eUqCmSe9ymMYJVngu2VumUhZ0gOmUZSZtm12yUTsO\n+XU0Oc8amsHa7gbbL6bX7v3xKUgeWZzjsWBY2hYe1OTXS4u2jJKEatKRxXlOt4eHxL4NZEU4PBgY\n4hc3SGgwtpJClSInF+0tYg57Y+x7aojXsJS2RQb16kRKp9JEvloC0mll55fGr3iPmATUjx4P0u7Y\nuPREZz5gwC+l8xmHeC9PialOxc+qTjOGegMa8CDBidkdhhdSMgMxnJo7E3ERvx51DB3QG199PbcE\nrT7yH0a4T1D/Ix1zp9tM5wN+ejIX5Xf7eohnjehfQ0LS9LS4bkvXGd1jvczuDVtvUnsNiekTmz0T\nnzvL6ODsmW7LZfHedvMFu6eT5W3EfoNdHx58jOSq71HL8BNcDklFeuZ0wNujLqPJ3S7Kb3I3kSGM\neTLnfGFyZJjGt6P54DT3CdLbN6veM6Xn+qHpBYMlgbH8irSf+jTNzJ+TfIJ6Fl5gZaTjNbHfFz2Z\njWTq7T0u6gGMPxSo7uBPzU1VUK+Hg1PHdI6SQyvqBHCpiqDRhR1lh7yE5owLmoEUqZQj08yqAbbz\nXI16zmI1AbEDNSxBxwN1VOz23s68qqGB80uO+3C/SW2wtSIbgBjGF+7WsoH0Ie+umlFsOG7sbmHB\nlttaTm4sYeKovs/UUat6fqwLGQdG6Wqms9xWwWBOYbTmpkiUS28/L96gvYN98Sl6Lp+QwBa3IkiB\nVDguZJwQa83J0evUiHlqJDh9eG/NIwc985k161E4qoaNvS299uK0azmgbCoR9AEDujc+we+lMYPL\n7r3h7HTkfQb9pCcmxySC7kFP9tDm16S9Gaeky5XRnjsG94ImJysZlpHRTcFevMX8Xqy0lagxKn4T\nPaMWPMUdPboy1TTdbSTmM5TcyMyU+x9dtDeTk17jyipdCUJM5UynrWwLGBbq5GxyuqTHa0XW9lpl\nV5b65XkA3R+f1OTXo4mHlJYcY3o+4xCuKyYGjqzTHh6gtGMckIQz5PtMXfkArU9St2I+x1MLrIzE\n1CVc5zS11FSuH6R4YcktqnpoCvbZcz7JJ4vTU2kzk0OCmqvu5fRocl793Dvofjr8Io13lrr1aeSZ\nmKhU/Kg3TBOs3hrQgAcYTowlCO+qZANiaBv7cmLqVGzuXNhP9mFxfxlGbz3xo9wSlP/0f2X7wpD2\njH+lzXx+ztUi8U84biu2Hupl0+K8iRGhyd9Z9LtRrjN6xNUUCAzSe/uo9Yf470RN5CR4Qk+fGjRd\nFPkZfcd60evrFfyPzt2ZeaboqfSIr+QZVBYniEheuMzzKea533qVyWm1XCBDGB3L6Awd8ifmiloC\n+2pE6EB4bDZqKfcJYqNhGa1WcNUfHDAog1/5uRzSm1lj+ZXpfCDk7fLbm4Zmnin5BM083ezrVMZr\nktKjWKuTCU5eP+xepJihJX5oFUoZDEH9ARMatjepEePzSAATufQ4IFQ05edK4NNyvGcxOzRjHnkL\nYXZjNbB/cKBegZDYq0dhGiB2oIYl6HigvorVNPwZRG04SZD1y2Yg4HptbiwPeXc1iHoTWV3EryYh\nej3WLVUegFhfrWq6mbC9z5rfzzEFsBsZRNeSfF7Qm1+A61rbBzT3fLHNcau767hX9B5lpxpJqN5H\nxiCstduX7SMzCOPFEfNUIFXD8H4vAdUi5RZYawCyA7ohWq+9RJo4R5TrA6D74pNtildC6TrWkDSi\nLbmXxAwmlpEyXQLramJT0H+Wazk0llpojww1h/ogfzAxXYwQpM2vR0N/47qcqNeB9qVtxvdiHa6c\nm1EslyZNI7aU3Sv4NJX4xfJasm7B16ZMJvQurfhELGKLnE/XcqCjdJobs1ZoyOyKjYIfrbyzv+tf\niarlqGnUtrLvSZCDdrTXEqeJ0cpeP0GOMd2fmMS9llzJPjKZMvPuXTEuAUYqjA/qjaKasLSp4RgY\nWin5OWKw6ogMIw9i3XZEh11K6YLO5QCPA8XyjxSd1kF95/XqRKLJ4V/Fe7mc8dQCeMwlX7ySfGSJ\n34sHKSa/ch7QeH14Pjk/aPSl28q5wGX8El30dNPO850o66UKP4sTxKyNDWjAgw0nJ2J01inZgBhO\njyh+NLG588QyUh6LZ+dTP8AtQctfeAXxCep/pGfyq+2m8/OuNjW/398/7STRgphtpcXdPJ5cMGVd\nLZZLrYJPEDXiXLBnXe58wBefw71hX290/kKcnBd2I2q+Gps+7XW0iPwiPedq7bO+KF6/M/300MCn\nhvsemRt6HGWBnKIlqIwO+nrVefb6ekfsN4g0x3WMXxjEMfSzMQ4DnC8xF3ZTzyDy7ag1EMOcFJiN\nWozjBCHVVEML26uiC8lQYkbTg2b/YB9CjOVXQ3uy3mCg32e/NTb73MAUiRXd4msjYzoGazqyD8Sn\nnHn6Va0KmVAy+lmwQHpvS6h/lqr+Er9nOVTNATcHdw+g1nP/CCxpfMngsS4CoRCL39uhAFV/vBda\nJCIsikwZl/TpG1f4r+CspgZEgEKG2uMS7DnvUW2uRpUea60+zFBfxaJnLG4scZcHEEnqef4gwMH+\n3XykZAOKL9xd116NG0Cju+a3C+rDCjFabu5taXrKqOMiq6Hevnog7o3FKC0uqKTpLLgSYx6dGM3S\nm1lqTagwpzAaqWYkbMyh4mvCNmgAkAe1HIzA48n5aboXZiHjMNhGh0KJIZCkQmFGFguFaUI8zc2U\ncQZS4fu2i+4d7Iu5RUGqd03lgKlN3UZ69FTaIsa1IYFvCkHMX+Sga6onIG0PD6F1+L3QCTk/52Ee\nFk5yjntVz62fzgf8LFoQswExnyBPpzdFgjdXuBdr5qyHGAKK+cfqd3hxlvs3gRhZnMNiW30vraiS\nFWkoMetSTvlQeKAnkxOjhDoBDQ0NehfW9mIcltbwIHq4Om+MduT93H8KtQqZyBI5rqsosz8+yXyp\nwO8thPXkqGlrzkscUopyUA/0XPOq7hVp6Jxi3B+SCjJHknPc16yJxC6YduVxO7mXeKbT6yOLs6yH\nVxOuC28E7q3ewzqzmRc9g2JrSYwYym/0myJ+Ra2yHov89MTGHeX9FtWieNnQcqFQ3EesyrpCA42R\naEG4nUhAhUwQM5DCM59xkI2KRfm9sUnuT8d4bHnaUuV12x0dQ9/gPNJz0aOIAVfgF+mmYO8k9TnS\nvJfREN6fwAJBufcfPvHBRx77lFJrDWjAAw0nxhI0kJiSbEAMh4Yep1YS4jsTdrZxfxmW7n3su7kl\naPW5Xwt6Oh3uO25/N7GhlHOKqd/f7/J1Ob3dXmIpIBaN6aSpXYgW1Gm5OB9jEexD3qy7dK+/z5u2\n+9JW0JbSSWRyOm6/1W5RXz/XPne6Z+F0dP4SL5GURjztYj7F1Ofrsy7OYapz01yxvCkpBruM25tx\n8vO5VD5BGmlAJwANJpWtvW1MJ0BwYiZWu35gJWAgudbUm/d5lxyOjBPaGCYMqCmYJ0wZN9UGKtyr\nTnEX5ECCS5+n+u8A+wcH67ubWOGgWqBeH7eGjfmcVX71NiAR9g/213c3Nve2Rc2AAWtW/KreFVg9\nYAWFvB3tLqF7UKsPLRymYtHzN0g8zq1ajYYnAPAKbK/f3d28W1fRGt0VwMYTDKQYEDBCsk4S0Dlk\nEIo4u8sADtdXMe4po7RyqQi57eXwaiK8upjazGa28hmybS1DIw3rzg56aWRtEY9Q5BaBviZbeFPE\n1yS6ltSUgKnNnHXH11PqfKrBoFCs8lHz/OhoceQn4+n93UWRbWQeRahcCwLs7u/lt1fQ5YrWAY0a\n1kxR7Vj6ouYljcKe85uzLluOrVFL18n3JLLCZL4DSso8LBayTpHzGNN8ABod9CLfkt2T8XgKZClr\nxF+e2vM+qk2RANi4ItSAkRx3gRiSwOYsO+m1LIXkhYzLlHWpNTQ8DvUJHVV9lzp1LPuRw4nwArtS\n1Ppc9Nx63buqSZENyGHRIY05jVOqSbpRUlZepFwmrU+vSdWjWIqn4zp6uNJxDQFs/N6tvWo1K7zm\nGBnowFtS56CYiSMP6pb0AWIDknPIUr3+X33qyhNvHfQZ9BzpV6xTUEXoS2gO6S6eQttHDmlfMsqn\nmNpItbMWIesFRqNvo0UM7hJT9G3WSz/yyU889unPKHXXgAY80HBiLEG3gj2SDYhhx8Cj1EpC4umE\nrTeYvwyPp3P3/f8PtwQtX/xDdZwdY9qLIaxoM3YsOef8vTPu5gV3uztOTkxg1z3LoUBwiPEHIvQM\nrOUgaIfnDvcDkmIMTTmbuoiTkXwdaa/1SsR8VSNOEKXDni6eN3WePWwHrGDn1qNFnyADfuMlfWYz\nz/j9K1GJExq/nswTQdcX1vT+17BPIjRq9figUbHHAY1a1QO90DwLGafCoQ/HUauFnVXN8f8QdKia\nOHHU0qQrJ7aWVPiOB8h4es+7KNalkjnsyCHNPK0qt9Fh6WFl50uZ3wHzsGA+JnXIbNCaNDRVzesn\nnWa7RKuB8GqC35uvIoSlHuS2ScgzMQ8Pat0eIa3UXQMa8KDDibEEjSXnJRsQwxt9H+NeM8QSVPSR\nCfj7w55ObgYC5m78Nbsu8hjT9EwrNjooY6g373OSQNIOAAD/9ElEQVQr5x2UrjsiY5PO24O2a0P+\nbrIVthAMBIcDgf4O8wXB64dZfAht93aPOfiZX6XroCect8PONsUGVCyXQk+f1ssnUn94VMynMa3y\nCdLmD63GDeJKYori/L5CBHpYfD2Ni9xfl8s5cXRgJaoUshZ4STTsBx4atXp80KjY44BGreqBviWo\n8iGDR16r2/s7euP/Yej8tsY2MREqPhcT6DHaS+75iHpw9yCxscTKWM1pDPUBFBXfSsSgXY6QRo8V\nvIGYJYj4BLWGBzT5G3TdNLVW1HnvSaGhMyudWAXr1O+e89cdjG91Z1393Iehbg9JK9XXgAY86HBi\nLEEzS1bJBsTwfM+HmK0kPnkq5GxjPjI+X5/Z3W6ZOyNagpZa/yEo+NFIdNDbHXG2hG23Qq7OgL+P\nXA8OYVAojg5BT87vTVk8samF+NREcn4h48RF4Eza1uJpY3acJm870wycoWHItHo62s3nO2af7Zr8\nevfkV+/Mnmo1XxiPTnrTNjyUHit2HldabVdagNYrkBDwdIbsTcGJpxyjX3SPfTk4/jWUi1iCpp/z\nudot7o4ZZ7Pd24kCQr7T27XgbptztTq93b7EHMunmGc9GnPAVNi0kHWOJxeg1liy7tmMfTy1sEDd\nVkX++HoKmmtqI7u0mRPPMdk72JNkVkcHvUsOf2LOlzR5sl6Jx5x1TabM40kTsuQuBF35wOySfWxx\nbjY2YQ2PmmJTE4nZ+YyDBv/XkrkImSUfLnvOP7tkm0iZ0FLU51kzPyTS4WBiGjrcaHJe3KssFlYC\nVEhuq5DezGKeFj3zjTVs8K3vbuKu7NbylmGMwI3dTdR2ZjOv3mvAYHtvB0JoBjbFDFQP+wf7Kztr\nqY3M8vZqfRvN9AAdo7C9CsmQz/cmHAaMa/WBBLQpuhbaF90Mba1cPQRs7G2lN3N0F0wit7XMZVZT\nsWjEAu0qaNYjadAHHmrtrux9T5KXcaW+11kNO3SMwri9Vj5GVQT28qK3HDJSJoYmdaECQggYETFE\n6w1oGIfxU3QtaYqRgBfIlYFfCRt8QqtxPAgzV36rYNBjUT/SXFA9jVlyOm3tjAx3x8Ywy4g8FQ8c\nwJBbUT66hMJdBNRDfnsFDYoaqPLYL9RnZiu/VpxNULe4FxJC6Ug6WwrshfyQ6az2rlIR0FIQHlyJ\n8XLxRW9hZ1VvdlPD7v4eehG6pUHrowjO5QD6EvSH+SUH3ZVTuR0NaGc+CGUD+gPal+6iKuPpiY0T\nTa/cJwhPB48954VOOJEi8SXZ5iByVyFoyjgnUuaZtJXuIdJ4riXngQ7WGSHxWXrjE3g657HnfNCF\n8CuXCWVpKm2eSlusgh6FfDKdh6pJEFsmn9NgAwN0P1QUtClNnsPQtpyXZcMk6JPQwfCas/qEboYr\nlpy7nwTnHhxLzqNcKM5kyiLFo4GmCq2MlD25AH0VOmGVeTCmmczhxZmhxRk8Xa33Ire0ikhuNWM/\ngd+cdaM402kbymvwrIq0Le+Nri1iyKWjXwnwMlLn+hK/rxDBW4N3GW+0eogQAUMERkIMvxgSgT66\n3VV6LrUE6ebNmQ+ge6CA6NWOqmNxVk+L+jlqknVmEIeRyWi8X3jL8K7hjaO70ko86O0oFOn5GQd6\nmvo9lWQqtdmABjzocGIsQRhtf+vmW3/k0s9KlqCnu/6R+c7ETJeC1Dtm3tXGovAMDz4mWoLS3f/C\nPWikNGa5WvS+oen06bCvyx8ep+MCsRD7Fk1hV5vJfImdYNU/80ybm3wCmltykLMYAj2t5vMt9uv8\nS1Gz4+asuzno6w+Yzs8MfmZ04NHhvkcnRp+wOVu8OXICRSA64ff1jwYUfqZS9E4+SU8QY+eIKens\n8OORuQsm5+02xW9IO+0K9lW/g3ooOHXZ3sJzK6YtoQHhTIey1FsIcz2M+tVr8Biknqwn7O0qnnZB\n0kDRjwnDbn+cReAry0+rt7V/+hnl1DCSPt3hvNkU7JvLKPGbPFlvSJIZGvEUgiOL82VywgOYY6T8\nIG2L0ACHAidmJvYrlhOaJhJcZ6sXhuiWfGccNVloLwKxMIOCKN6IOV6tduOJmIBFNg8JX11azOAG\nMaAmEGIhXPm5OljZWcPsKwqBbqH8djjAmk08Ynwh44A6ovxWLxjU6gMJ6E6S6wRaXHv1UwVgJQzt\nWZTGkMmsWLFYgEFz4neBxopL+a0BOlBTd5UOjcKSr/oVsh6IseGBUHk3Kx1PA9jZ38X6R7zRX4iK\ng0/1EFlbFOXwQuER4nVNFAc0dDY+Ug0GJhkB/V4zxIY0+DC0Zj168ThQ8+oZoZoUKzdp1kCKxRLn\n2dX/igDA8kwtU0ozW6QL7e7voVAgpGPIzFmX8ToQgMnFTU7DGe2IDGPRK96OmgTSaERknxo7YoIh\nqsv4K0WVkN9eCa7GMHmpS8c+sQRWYnQIqjywpTYyPHtAvdZHPxHOriJHHUE3Uz+9yhTShNPBeptC\nvZPKqUMh57KfGGvKWx8pixMkXW8ND1pzHmvWK2kag4kZ8Yl6MpG68kEl8q7WryztiZGgBMgh8smv\nI/8ohfgUls6QU8BYhGzCiUpjJic1Z30pOTRKyFtbeAjvC7Qv4TSoCmlXbJTHIer2j4q/Nof6sW7X\nfG716WTaLD0RKWspxmPQ+iy1533F0+UUnsHENP+1+tRdCPbGJiAB0lj3xlDJXhAABgGR35kn9kfx\n0DHUkqaOChUisErMr7jLV4hgNBbl8HQ2atW8jpSsa4S3CZ1kOm3V5KwvHS3Xz8UUQ5ZB5KCK6YAS\nyVuR1h4Z4nGp0M/JEKF6IlKx9Xn60UacoAY8NHBiLEF3Vxb9g1+KTz4zPfJ50RL0hc6/S0ydipgu\nh/z9AX//gruV7LeiMXcmuj8sWoIWhz6vGQ8oYn5RIy7P9HPETkGNFP7kQtjZYjJdKp1lTok2jxJ7\nH2mzp4XTGFma3M3Iw8Ls80xmdOpZ//wFdtZDyNfrXg54c15ffHYsucD4kfYsvACxw8OfJefc95ET\n5dm58u3zZ0mJqM+ROq4Qo1vNF24HyEmQxJIt2LY1aQyIl+zNF2zN7Lk8z5zGdEu+zGjdyxf2dHap\n/CxOo7whd4dyzoVw5kUwPIpfRxZnpTyAbvW0DEw/XVbnlO5wXG8K9s4u2bx5n6bMWXdZWzAac4C0\nh19RTQQeRk+kFhiPfyUCXZyVl4FkhWEIvZxN3lgBAhinCHsHe6hP6S6gdBgE1GLJBMAQyzOFQyfk\nKoRX79ezsrOmXi8Bl6o7o9QAsGiRZDKs5nQMA9Cr1QcS0JHQnaQKBKLjKRw1AjRLSRRH9CXjil3W\nOQ68YQwyhuq7a3JjSapbIAaBalbIehBZLbPCMMT6uaJzGUY86S6gwc4FPVjULxSmD+m6JrIBbWN3\nU7zILUHA+SWHZA7Aqon/KiGkqT2DDg4OMMir56mKtDPvl+YLTnMe4xcktBqv+CzmWBSjtiqswfrj\nkwwH4lPQGTD3YbFqbDHEOA99gOWtNz6BehhNzrMTi5gliFUOOUYq2DNFDyxjaM4o01ndgLYjO8R1\nyri0mceMwOh0pUknWW4GAk6kTAOJqWh5KKXgSlRqC0r3op9o5sGYNrFTrlXtO5k2u5YD7TrnE7E4\nQerrevRQohRrkrVC9fdWTfdSu0mpjNNpi5q/KdSLiq2+fgxoNI1afh00FvCQOZ5cUGpV4GkK9mE0\nqyNvjEb/0XtuS7gfKqIpI5xxLvBMpsjJU5CDKbU1xE+jL/Hg9aw1P4pnGZUzmJjBqz2TtmK82j/Y\n393f9bBoYkV+csY5PYtNfB3EyQKvLYZBKHJgNn4uo5lPkPo6Fgi0UEq5eBmnl6ya/LXSo8k5LlOq\nQ0aT1q9LPipHLbMl1O/I+VFX1E6q+1ywofVFmQ1LUAMeHjg5lqCknVqCTrnHnxQtQZ9sf2di6lTY\n3hyicXMG7VeLnjLnZlvfK1qC4pPPMh7uDcTo+NRzig2I+wRRGjxsXAgGB8POVnaQuWKVoGmX9RIZ\nStiYQlKB9nW2L5ydHP5szHQpYr2h2CmKfitkbxSRHOpkulqot8nfxWQOTn296A3ELEGP3pl7tmjx\nUcqlplvtV5kcc7Y4llH5mjSmHMyvZIqV8izQmJM0741SDXVrb1u6XpH2J02s7GI9ENrd7sj7lTG6\nPA93rFfE2uZpz/xZ/Ip515cya8ocmXu+KK1M5njR34flTa/soDkP9GnW+xgsZDSOQAbm6JdbarLQ\nWARC8ZX4OYq+7ljbSL9yZKo5pnzRQUPE6kMJSv4CHDH5KRz1gq8QkWQyDK3EFY66QK9WH0jAGlKq\nPYbVBFJRg7SWlpA4+OSNVmKadk+gv+rjbB9OqLK74nXWG0/Sm/VErGegPr6dobGp18BGU9P+RDJG\n6RYqJzlUGiAGNF8hLF4RLUHA+FpZyFVvObOEPD7r9v4O2/WGtKb5i9NYnarnC0bzr8p4cbAkk+YO\nBrtkV3XlZwFxO6OL3gelZ7WGB6ZSluRGRhGqBf6ViJg3toxsomc5c0vQyOIc4+FnWjNkjkh1wyKJ\nCqRbxsBKFMhotJrxhlPRQxZF5idbI+frRa8oLJv7EpNSWzB6cHFaMw/G9DD5LqXRvl2xMWImUF1n\ntGAJ0rhXTaMR2XPNWbcez+HpkeSsWEZylLsW/0B8mvPUVFcSTY6Z15JfB23JuTujI7RWZR40kEEe\njGnShVTP4vREykxbXyM/XTHyzRISptM28Tqn8XJJzzKm0bfVcvCeoqvj3S/3HCQ7nkQe0XS7XrSJ\n45ZqnstpvThBxK2mvOyM7o1PVJRZDd0eGdKUL9LCvoQa5FM/Jg2ZM0vW8vP+tJ87kVLWZUxmwxLU\ngIcHTogl6GDvbnTWP0B8gqKTz4iWoH9u/asE8Qm6EqQ+Ph3mC8RHhvrLmG++Q7QERUyXGY8YJyjs\n7YLMuOATxOmgpwuDAkaEsLs94GgiZghuj6DYO3+ajCMYRJTRRKADPW3O2+AJEUcVxVdFsVk4W/3R\nCYw15Otikb/NRZgJTj81NPTpYcEnqHviyaLFRykXoy/OPsPpZlcTkzNJjR3FkVFjDy3ojsgw5lcD\nnyDQvcTfWFvO7v5eYj1tIF+TDkTG1fXA6IXEnGYeeubP8DpRCEZPPwWGtvBgIDqhlklaavqpFk+r\nWmYfmcmU/DiXA/y69FykPP/ikgyqJ599JYysEgOZns0C6xCJn6O4WpDc4EVkH5mxepGuc6z+uBlH\nnnweUWN9tgYR9JagtpxX4agL9Gr1gQRpW42IXOGrHrD2loRIGFsy8vjQ9FADmrMuhaMBWlBldzUY\nT0KrdRpPt/a2JVEcjd3KpP1HIuZrcQFDL5Vu54hCGXioSYgBzVS+g1WyBGF8Vh5JQWKW0FsIg2d3\nf9e/EgW9vb9DV001zF+cJp6kqvmC0f2JKZFfs8Lz2ytqmZo08onUuQwlQeNZLeEBjOSKUBXsHexP\npcya+cRavd8/wSqzMzLCrjOnIY5sOpNgjUQRqgzFr0RVlRG0wcbk/YMDnqXZtE304e2KjqIp2XeU\n1EaWGCCEMnIaqo7ecw1oxd6kqnMs9ZWNWuXXGS1YgjTu1aSZDwLxUqmOvw6a2S94GZUtPyp+LM6r\nrx89GsURZR6SHk+SalFqtZwHrV9fPl3LQSJK/7l98UlubVTx9LFYQkPMUKLFQ8PcVJsf9GpNOehj\n0dVFsIn8M0s2kQc1wF8N9H+8BXjl2YhR8bmc1osTpHioqfLWEu7X5K+JduR9Unk16ck08cCqST7U\nEj2Zg4mZ9vBgxeei9SWZdBxqQAMefDghlqD9XWIJGvwy89n51jOv4pagv2v+M1yJmq4wTx8WIajN\nfP4t7e945is/IlqCws427g3E0nbzhaC3U/EDmj4dtV0P25vjcy/gSmzhYiCkRLEJu9v99mbFHiGk\nffPPk6GEjCMaKdncNPVUkJkqSh4rJPVHyYgDTY7zE0tQUfLg2JeYNxBLeyafLHoAKenXJ7/4imu/\n/v0Xf+rWwll2pcnfyeRMlO9n1kzbqSWITLE6OUfaEyO2Kk0J5GOF1nXjNBgZU9cDS00J5cuklPbO\nn1HXOUmnn74dopYgYl2SpfntxBLU6mlTyxS+adDKVz2RpzznzEufgYHPToTuoaAmCw29FqsaiZ+j\naAlCxUq/cmSWIDBL1zlK3vIGoLcYm186rCVoQWcx1rAEVQ9hHY8toGZ0DGOAjigJkdDYEmTNeiR+\nhlh1KxwN0IIqu6uBx1bdlqDNvS1JFEdjS9DiusaWLoYVD8MSwcC8FV6N69kWZ9JW6QoGNMmybGwJ\n0ht8gOPJBbK4WlsM0PCrGNWDJKS0QteaFr0eNGYNLCRETk0vS5RLLdMgFXwB5LQ3NpHcWNIM65Pd\nWoYmoHkX0mZ3DypzKkU3CtErWPuJNaa5JTCwGmO+QnsHezTGkLZdaHFjybhEUupfiYiCNuhh8wx3\nhahSXbExsRRYrotySKzG8jLycqmfWDFl/lNqaU2hvtEyj7CylNosNK4bpNYsiWBCTB6VOOtOqSWo\nVLomnd7bHmb7cTRqo/rUknOrJdedjqcWkF60t6h/lVq/+tRd4JYg7ZRagvRav9dFrQODiRn1ryw1\n1xLDaLbsBLpS2kqDWkr8NLqTwBnqnS6OmSnqG5jZykvyK6Z6cYIEn52ylFqCNPhrSqF/qiWr04k0\n99+vNjUp/noa0oYWZ6S4Tppp0RJUkskGpQY04IGHk7M7bNHK4gQlJk/9uxdexy1Bf3Lzrf6xr5Iz\nv6iPT7/1xVbTuS+Nfe6Hnv6fohno4P3/P+INJPgEmVxt//b8G36//S9iU6cgM2J+kVoTiOEmYrkG\n2hefZftFQ4F+XB+ZebZkj6DYZbloYGNu8neNzj7HZYp+K15ld1iQ7zlv9nUoYqn84dLusEfuzJzi\nvj/npr/+S01/+LJi2f9Px1+T6+YL/LlkB7VgL9ek++OT0FqMfYJGF+cqyqmJ9i/Oq+uB0m2OvLcp\nyL06S3m4Y6GBmYp1wuneudPggR7sXyTxm9Qyh2eebQp0q8s1lpoX8yY+S6I5D/uYzGD/YF9vcxb7\nsKlns9Db8gMU4/sYGHqYug8VXDPED7B6f369LT9iNKL6AJUmyWSIRZfCURfo1WodgNXL8vZKciND\nj944bFze4wB0JKn2GKLj6S29DMDAiYxhNme0Y8ivs91PfCkaoIYqu6vh7rD6g3bp2UTYp2M9MNod\nVktAeoNC4aXDUCBdZNgZIXsiRMxtLRvvDmP7lDlguBZ/5Uh3OvSMLtKR/xDzF6fJ5hHVfMFo1LzE\nL22s2zvYr+lZjBZ9YcTnjiaVOdq/Ei0IXxSYVw40Ab18XrK39PnHoQaUrodI3D1eaWo/HcxNeBYQ\nnSG8mgChuYGRFFBZR9VQxuXt1Q1ywFneV4iI11GuOXK60EKXsqOnVJahxIwopzs6ViqLUN6BOPHS\nqiYPIj24SPqMut7uREeKIVQ0nqV4r6iu69HNIeJhgefifamGvz4aa2CxjKQmtfjJGrjq+tGj3YVg\nk47PUR00FvYdkSGlVst5SOvXm8/OyLDBc8eTC8TQo7qO9E5khMmZTNGA0yqepmAvGMRnGdP2HPRe\nDTlI8Q5K/NacV+LhAYPogXr70rtTDa0XJ6if7bVU5Y0FJjeWWQ3dFh7UlC/SUnyramjXckBzHYF0\nKmXpjRf3Reo/dyw5L8lUxrUGNOBBh5NjCdrIsThBialT//n8T3BL0G9efdPQ0KeHbNeCQRIx2u67\n02o6//4X37z6gW8QLUGZf3qZyd0eEuIE/V77XzAJf972jvjUs2FHM7UmMO+SlrC3y51XxhpfxoHr\nduvVgamnTg09cnrok8Q2Mf1Us7+zJTRgz/tI4DcymjDrMhtZCG0J9JXJpHTI38dGGTex9FOfT8rf\nZS1FpB4afYJZggaGP9tmPt9luYT07PTXv/vc63nBgd905pXPT3+1eGZZT2+cbOlSxjIqX5PGpAJd\nsNwnqJRn0FA9i2c3GMmpjV4OBr3dYj0w2h+HphIaYTHkyvPT7L/TP/MMrxOetrpboHBYyFlgkNmj\nlrkQoOpIeblaQv3sqEiet+K3Hfm5puI5piz/4kJIcw+FPedjq3Q9mwV+09yTpd7S5dUypogOApob\nzSC8ejMBFieaSzUWPuMwoOkzNb/k0PxqXT3o1WqtsHewJx6NNH/ocBjHAWhHTb+JxfXSqc81gWaP\nYojObFyxm3tbmqZPLNsUjgZoQfXdVdMNEB1ADB9WK2g6glmznooyMeJJdwGPNmI0hgJ1jyLHJtDT\nefgVNqCt726Khm/REmTKOKXzufSMnp2REczR7BxoNp6LY3t9tDRfcFrNL7p8Apa3V9Q8FemptFX9\nLPUcDeF4BOotuBpn18niRyufZOp3dbeEB8iSrHh9IrXAaoxPZyJgDmIy6Q4UJW+i+YlBjhzMX225\nqqGLfhNl+W8J99uLZ58zfmVjiMCDFMtCW56fsF7Dc6HRaca7Wcg6wdNNvJPKrjNasASVXUduyZ4s\n1XW2/4U9V4gcXMZTPU139MjXoSY5yIn1pTKaMi51TEawFc9Xqqp+DOiif1OZfNRA0RdJN/8SjQqB\ntNklG63VMh7S+sXToPhzq6cxpEjP4nRbZBDvFG19jfairU/kQIvoIF4zcp71Ymsa0PScNVlOZ3Hv\nm8RfjHSj8LMj/5x5P9QYXyFS8VlqWi9OEN4aYlIRnsVSwVOp5meJ9HTJv0kuO6NZ69chf4z4kcky\n2yPD7kIQ6wXFTFn+LE7T1hfPLGvECWrAQwQnxxJ0965r6kKcxvH5oQs/xa0hv3D1NzrmT7eazo3Z\nbzF/nxlX8z998b+IZiCg+SPf+szkl1hsIPBcmH2aSwD+3853lew1ztaQt8tLbdIYFNgo41ucHzVf\n/PXbbwHzt5x51Xs639XmasLYAZWU8cxl7GRMweBSHFkwKbrzgWB4lMlk1oqgr9eT5doJkT+WnKcj\nVE9ToLvbdH6Q2Tsmvjrc98jg8Gfb588M2a77fH0Tzlv/RXWCPvBV136jyU0my+7oGNN3eZ4N6MHg\n5GV7qzIOlvJMaGiH1Me1Kjk10Z6MixiDFKsNqRN/ZIzxuPI07L8qP63ulv6ZU4oNiJqBOpw3oKXN\npG2CzB5RJj2ZPji6OEeOSi3KaQsPEW8pVd6Iy6hQdqRoDolH1HehIkuRXKBpcUsHNVloLwLBg2lG\nvDG0ElevzXb2d/FEkQ1zvBhT8+DgAEq5yACxtZpalrdXxZ0XCxmHZjTZjdpPs85s5kWXBOid1Yey\n1gODWq0JpIplqF7MvOSwubeNTiVmUjNsR5Wwu7/n0TIGhVfj6MwVKxZLO7w4/C407n1oPrvfoPru\nivc/egynyEsybTlvNcY7DD6iqRSIwaeOU+RRKGmQxMqNFwoDAoYF8VcSrYYcwzzD/hUHNHQ2Pp5w\nSxB0ek2ztTT4ALGSFOdo9fhfH013wcjzZnF1WsafoYfBc4isJqqRr6bJoct4XPFZVc7RjmU/dZYp\nyydSrK4v2poh07UcUOLjkjOJplFjfDpDu6MXRdcW0ZRATfmgJec1uudONz/10SxeDM8/FsBsTS7x\nT6XNSrQgWkawofWrka9JoypahPOhsDAuRpMN2nN+tY8SUmYJUlabxeu0pdxoLMV2QK830ZjH4nP1\nZILG6zCqBCfi14El3Qb/9sUn8eYih6JPBPI/r5yjX1bGqbSFZLJ4LyoNuqvEUzeNbNAeVWqvdnKM\ntwfvYLmuVZZ/ie6OjTEDFmT2+Eep44zCQ1qfed4dIp+TabMokz23PTxIoxQTHrQ+dcRT8oPqQt2K\ncqxZLzG9CXmmDVpDHhjtzAfQdqKcO9FRDNea/OgJJOC3kOe++IQjX63Or6b14gSBxrujvE30WU2h\nfnSbamRWSUv6OSs7o7uiY/a8rw6ZSNH9hvj3XSqzMzpizZJA/vh1NmOnhSo9i9PsPZVkNixBDXh4\n4CRZgmzWtpCnK+S4/cOXfo6bQl5/480sVg5Sp7crFBy4NvfcFz79PaIZ6PQnv/u7n/vhP7vz7mCw\n/+NDn/jo0Mf47RzfN/aYf3HeH5u55rzxylu/w2PKvGf8sV/qfMd/u/qrEv8bmt/Gpgc6dpDUkQ9g\nAB1LLkwvWdkOcHbdu+SAZF981pempzAWr/PUmvNinB1PLRADR9rqS8xZwiPz1uvz7lab504wPOJb\nNP1q6x9LGeD4L3NfZ1+r1JL1UswBU5GFmWJu55Yc08hA0oT8F780VpBQZ7oc9Kas/viMLzHvpQq6\n+CsUpsmUGbWK/LgLmCaJfW08MTsXGbWEhhciIxPxqZklK9SmcslUZmJWkgn9YyqFWjWhVgVvIDnF\ns6B5NIcGMCUXv4yV8ah3aqwTV/ZcciOzvL0ifj4lFgv9RSA4l7dX2dYk45gvhZ3V1EYmvZnV89PB\n7cUMrKq/31YDO3u70CHi6ymstTTPBsKjY+Wn81QJ2/s7kJncWEJ6yNOIGRjXapWQ0znhHlqCwnE/\nAe0qyi42dDbl6iFghZxFsuQthH2FMJWpdL9qKhaNKDRoDRuFHlqotbvSrTEa48lhgG23YTJr8jAq\nDj65QzoJ6g2SAPQovI9sk5FvJTKdts1nHFj/aA5orPsR83fUGl1bzG0V9oQdtRKwwSe4EoPk2FoS\n45vByH+YlK14W8ODHdHhUcUXQIMzsZ5WckYHbU2eKlNL1lPfHA3dAJMgVr+z1DSGGb/PPz4WLp2+\nhCUQ1I+ZtFVsKVSjprSXJMW8DC0FOUf+oWXpcWIBOZ22Qo9Cj3KQr2IaPNWnzmU/NAdIm0pbqW9R\n2a/oschPT3wcC9ee2DiUh77AGNP60FJEnaMt5VL8yoFB1D+kTaYsluIZc1K6kHHwOER3oiPQhfiv\n6taH3gJNaSJlpvtoFAnIJ3LLNEn6XVDjKUhtOR/0KOQfGqBKmzqClPUo5Bb6G94Udh1volKfKQt0\nM1xBWe5ER1vDRPVCflAWlGhe+WKnSJuNWqts/ZpSJrM/PoW2G16cI/ksthRLjVsfKcpFtNOkCSVF\neaVfa0rRgoLea8SJdCHjxLuM56K3GHNWTPXiBLEU+iHeI9QA3im8WQac9aWifo6a1Gz9+lJzzo23\nDJLxxmEdIf6K3s5WPehvqG20HWg8HWsNtZyGJagBDw+cLEtQa8Dfb3F3vOrab3A7yCuv/Tq1BJ1r\nM52fdjaFAgN/3PnOm4/8a9ESxDj/14u//A99/8RvVOMn5r52ynntW8/+GOh/ff71p903oZ6KDBL+\nyI3fgkLA95SS9Ojoy9ZLV+efvTJ/+rK37QnLGenRIv7bCz+BNa2eHE16NmqZiSg2/mr4DWhU0Uhy\nDhNqf3wSoyodeeuRc9/SYtBoY6Ami8PaLO4NYMmEMvoKkdRmdm1XY8lHvwOHxDBGLxUcSa2GVnSP\nrz68C8bJhcNXbAPU0KjV6mFpM8fGW4xFyiUdqKNWIby+Mf+o6OCqEqX74O6B6C9zGJmHpzH7A9XX\nue0PufWv0P0mhnIatEgzD4v67m3QerRSq3Xd26CN6UbdVqTZeNiABjzwcMIsQUH/gMXb/vobvy2a\nQrhP0JSzyRfo/cYzr5j7yLdxM1Dfx/+VyFw9vuz0K36j653SRQlf2/QW+n2DjSDMlnwE9GfMz0kP\nEvFjQx//o86/Ea/8Qd/7apKv6IKGPNXQ8xlH0dNY8QfuiA4XvzLVKfN+o72FsPSZWg+IxeKELALX\ndzfEMoZW46LvyZZyEnAocx9sBTqSWtWLkw2sYxPcAwP3vrvuHxxgwZnayOS3V6RQLw8M3PtaPbkQ\nWInx8VaMyKaGOmqVuB3RcUwc6+4xzeaO3FbBgOce08LsX3Y9vKbEhMK7KV5/AOiiB8eC4MFRjxwD\nWllXG/IcH+1aDsxTLyHMaM7yeIgnmqa1Wue9DdqYbtRtRZqNhw1owAMPJ8kSZF644bQ3AX/u5u+J\ndpCvTX6h1XTu1NRXLO6Ofx78CK5k/ull3BJ0+pPfLTIfOf5m519703Zv1u3OBxx5vynjNGVdCxmn\nPecrjiy6+1pFGnO5LW2zJk3TKdO/u/iT0lM4/r8XftLn77V6Ov/d+TeI1y/72iSZnqzHu+T0aMUv\nwBzAfYI08+PMB6AwWVAo6uOjyePI+8S99HzPbUuo31Zedlc+YM15zFmXK0+kSXKqpFGfqFu6gbx0\nHZIr5hMpngseS84Nfj0eTRqPM2VcmuekYE27ube1vrvJN18Qi4XOcgU8G/RkXM68s7+7truxtrOO\n9Eh2UakBi5CtvW3IFyMNMVjeXpHKG1yNgXl9d2P/YL94EjA5vYXxs9zyfEJysey1hRFBTvAIPEg0\nroHSzCf+xcVQOupZDKzurJdu0ILdgz0wQ47yfznonaeO91ThKAeUC/ncLM/nfQK08pWWUi7VC+ns\nktisxw3Is3Q0eE0Bibf3d5Db3X0NPzXj1gfQBt0EDzoS7X5GgApZ2sxhPaxXw+K7r+7SFW0Wu/Rt\nMjZ8HDkcvkvjRuk9Lcrcqk8mbhTH2/xWYWNvCw0EFGWywQd9lf1rDDu0k+AWZNKaxaTjdpJQGmVj\nHVJcn0gtYGwXr2N2wByBmcJF/pXnAk0aszaEWLNeEJo8qDFkhv5LIn3guZgKXYYyDWg8Bc/CE0FU\nM0dr0mz2l66zOTq6lgyuxonqUqNMiSb5zCn51ONBinqgioGbx4VR85gypKXAI12vnsYjlIOxi7pK\nX3yKaGjlukRFGnUCUcgP2lHNI1iC5HtJu+fc0F4W0FVyHroDqIwH/Y3pJ2hTIBRI1B6tujI5ahqS\nUcnjyQUWa5KXcSgxg+v41bnsn0qbJ1OW+YwDj9aTSXo+eVnQXrio/azD0CSf6PlZlB0XleuoB1af\naH29e1mtgkbObXn0KCfqR+LhtC3nRRHQe1EKg3efaem16ud6NJOGvOGh0HvRr9B8qHxcF/lJH6Dr\nAvQB5LN6+Yeh7Tk/esJIco5t75J4eN0iq7NLtsmUWcozqgh5Rm7V99ZBM10aLW7MT/p/sT4NZNL6\nJD2K9VhcRz4hH3nW5JdoVAjtA2XlVdPKpNKABjzocDIsQZtby35vV2/nh4f7Hh3pf/Rvb79dNIL8\nwKWf+fDAR0F829lXI/2O53+Em4GAn3j8ezmnhP/x0k9PL1l/+MabpOt6+KT9Msad3+n5O+n65wY/\nHnA0T8yf7XDeKHrHkLQzMkImfsXGbJTOR8eH5p5nQZF/9/bbJPkcv+nMq56be6bNfL7fevVL458V\nf/qDvn/g0rxLjpC3qxgDuzUYHPQo46PyRMwBQPEKT1FAepKXUormcD/UCE1OzBy8pOq0PTwEzQac\nJD5f0W8IxMjiPNUGZGkGKeqQRBUtSu6MjrJ5YoxEUlQkN4f6xpWYjrKE8aSJ6kksb31jqXkooJqc\nYopJkZ+90hoewKwjHgST2sjyE7jmlxyJ9TTWLZqWIKxmFjeW+Ik5ICKri5hm2L8cMf8ZrGPrgOXt\nFcziXH5wJSbaWTKbebG8M0u2FhqHmzGjA7BpGL9CJb0THUEN9MQnRhbn7DlvbC0JhYZLxuKhmnUg\nnh4WYl2bSTDpAq6jVqEE8+v+QnTvYA/IvXgGA5M8WKymSW53f88rHDgNadKRPQCs2NGLOA/H1EZG\n4SgCFrdivFuoC9n7KUYyMoMs8eyhL3HbYk2wsbuJpuQVi+Y+2u6nBjxR89A6YMUT/bG2F5sPzc2t\nV7sHe+gz/Cd166NzxtbkE/fQ/fCCKBwCoC9JnJ5lWR1EJ9QsCO/S6kGAAzEKCAG8obyuG4YMOxJA\nD0E/4Q+tr0sXtlfL39OIGLoeMtVHj1cE1CQfhTBKY7nOBQJRyRhqMA6zf9FXof0b9FLWpfntGKyK\nY37v0OIMjwSBJTG/zlISz2U5NE7OnSnNU2P60X9YiqUpOaK+KKclpIQoljgLO6vhtYQjD+Yp/kQM\np3NKNF+Z3yCdWbKSiKdCzllqMEdrprNRqzT7Y46WZKIGJoQ4NTWlcxkabrkoDbXEDBAS53zG2RYZ\n5E/si0+irSU5/FeW4opajnHqygcVM5Agh6fVa2iYImkUYeXewcQ0pmyRh66r5bvw9JEkj6CspFLr\no+2UQLblecNFtLhaJkvxdNL9VHcZp02hfmi8krTDa2gGKfJJT8hS8tASHphO23C9YuuzlMWywTsr\n9qj++JTUoyxZT2eUBJ7nPDwV333ojaIm2VF162umjmV/T9mpwXLaG5t0Uout+O6zFH3SlDlUdCHj\nFBO69ESkuCjysLoVTp1TUmin0Pq6YqP8CuoWNax+SpUp1aXHuTS0u2Y8INRVf7xsRNV839GaQlRy\ncpYc/hVbHzlH/qW7eApNHmsT/hQWoFqTsxEnqAEPD5wAS9Du7qbbfstpvUYtQY+M9D062Pcv//3C\nG0U7iIiveOp/iJagf3zyhyUG4OdGP/3Fsc/esF7COz+WnP/+y6UQ1Hr4vZd+mtuM/7DrXeJP33b2\n1ZfGPsvOt+pwKGe6k4GGnXia8xnvRzXHp5Wzsaae+mj3+0TJEr6l7x+ITPftVvP5Dm+7+NP3XHwj\nk+nNuEPCOWjsXK2gt8sj+ONAa9GLE0QmLSH/jJ5M8RNPS/xQhkQeNd0c6hvS4sHcLMoxpjFMow75\nvUxOa2igv/zMBUaPJxckOcq5kuV5wMxR8blkahHkD9OjbbAiRYdMap39HF1bpIYgeTmkee67JmKh\nJRprDgNYDEvCgSiX8jM1TvHyzqTpoZ703DSwTaRMoGnE9CCWXi3K2qNUh3eio8wLnWNwRQmHYQB4\nnHgLQ/USHQjNyZUP8H9FSxBweXtVkVgEvlYUkbWUCFgoYuEt8kRVp/gDAislswLH+8QYhGxIGQNy\nv63qYWd/l9nyxIo9wu6nCYvrGoeLM0RmuGVHDZtah9mjKZn9sWLri/ZHCdUnx0kMDL2CMUjz3Rdx\neXtFzxJEzJFZ2RyJoh23Dc5fiEgPBdbUpVd1zmiXsFZjkK8QYeMttemTsQUDuCSTI+ur5oxLM3jZ\n9v6OaJ4G3iFrg9KYz04mxsKPj2PsOqPJik51nZ0jqTdH0GO5ZDmzSzaJH68naIyZavkmdi61jnyJ\nnuZnyavkMFpzjtakle9Axeuocz2ZGOeleyvSqHm1HFSvxI/1GGUo8YDuJOc9KzwYjtRyQOMNqik/\nZDrTksNpTHDcj0BPDl5S9b3dsTGRh1qC5HvJyd86zzXRM7CoDU43b6AxO6vzA5p0Py3+aujp4jlQ\nkENaX8UzlKAamtZza6WLtpIy+YKVtnSdtL5KDmpVMd0W72U0Kp/zOPI+5VNfOQ+n2bsPNuiNEk81\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EKXKcywFMo5r3gmbnZggeE9rPYp4vUt7Qo0iAIS3+amj0JS6z+KWtjIdqaKKvSlm5qqeV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MLpo9u2q2TvjT7jafap/69/bliNhffzNW//y4avqwR+4mc3XYKpFlMw+G/tTnMe+/If7yp6dn1H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\nvhfhWNUbN/Xg4X0lf2RfvGe7sYpO5r6Hkvpysbb8ry3f0YhDxg+sOoveC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NhF133IsOvg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\n5RFSEmJ6B6pyAFX5gLAcQFUOoKovRHkKMX0DSTmAqnxAWwCAAp6gPEJKQkz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i/9y3/5L/WOy0Bn0QuxNkEWlkWJn1Hx5RZXky6Bw+fSkydI3uu07NlksplU75UUJDZOkKZz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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 500
+            }
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xddw-67zqKi8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# To recap: 2 color-coded lines (disapprove vs approve). Each has it's estimate range in it's respective color."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb b/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb
index 964e477..5650552 100644
--- a/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb
+++ b/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb
@@ -1,652 +1,3641 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Lambda School Data Science_\n",
-    "# Choose appropriate visualizations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Upgrade Seaborn\n",
-    "\n",
-    "Make sure you have at least version 0.9.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade seaborn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fix misleading visualizations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
-    "    \n",
-    "import misleading"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot1()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot2()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot3()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import altair as alt\n",
-    "# alt.renderers.enable('notebook')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot4()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Links\n",
-    "- [How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)\n",
-    "- [Where to Start and End Your Y-Axis Scale](http://stephanieevergreen.com/y-axis/)\n",
-    "- [xkcd heatmap](https://xkcd.com/1138/)\n",
-    "- [Surprise Maps: Showing the Unexpected](https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Use Seaborn to visualize distributions and relationships with continuous and discrete variables\n",
-    "\n",
-    "#### Links\n",
-    "- [Seaborn tutorial](https://seaborn.pydata.org/tutorial.html)\n",
-    "- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html)\n",
-    "- [Chart Chooser](https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Anscombe dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = sns.load_dataset('anscombe')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [count](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.count.html), for each column in each group"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [standard deviation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.std.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use pandas to [plot](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) the groups, as scatter plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Links\n",
-    "- [Seaborn examples: Anscombe's quartet](http://seaborn.pydata.org/examples/anscombes_quartet.html)\n",
-    "- [Wikipedia: Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet)\n",
-    "- [The Datasaurus Dozen](https://www.autodeskresearch.com/publications/samestats)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Tips dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tips = sns.load_dataset('tips')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Describe the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Flights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flights = sns.load_dataset('flights')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Describe the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot year & passengers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot month & passengers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a [pivot table](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html) of passengers by month and year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the pivot table as a [heat map](https://seaborn.pydata.org/generated/seaborn.heatmap.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_122_Choose_appropriate_visualizations.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/axrd/DS-Sprint-02-Storytelling-With-Data/blob/master/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XGr8T-aMFqf8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "# Choose appropriate visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hiRP3LoKFqgA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Upgrade Seaborn\n",
+        "\n",
+        "Make sure you have at least version 0.9.0"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YmRKBhzHFqgC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332
+        },
+        "outputId": "27711a84-6a92-4533-a83a-6acff9650b36"
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade seaborn"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting seaborn\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc/seaborn-0.9.0-py3-none-any.whl (208kB)\n",
+            "\u001b[K    100% |████████████████████████████████| 215kB 25.4MB/s \n",
+            "\u001b[?25hRequirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (2.1.2)\n",
+            "Requirement already satisfied, skipping upgrade: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.1.0)\n",
+            "Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.6)\n",
+            "Requirement already satisfied, skipping upgrade: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\n",
+            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.5.3)\n",
+            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
+            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
+            "Requirement already satisfied, skipping upgrade: pytz in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2018.7)\n",
+            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
+            "Installing collected packages: seaborn\n",
+            "  Found existing installation: seaborn 0.7.1\n",
+            "    Uninstalling seaborn-0.7.1:\n",
+            "      Successfully uninstalled seaborn-0.7.1\n",
+            "Successfully installed seaborn-0.9.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_A2QCuWAFqgI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "66289fbb-6591-40e5-c478-277404be3772"
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.__version__"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'0.9.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YLSo9f_JFqgO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Fix misleading visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sKpSiNGoFqgP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qGf5dxAqFqgU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 228
+        },
+        "outputId": "be75438b-2e5d-4e55-90d3-481d6ca16829"
+      },
+      "cell_type": "code",
+      "source": [
+        "!wget https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
+        "    \n",
+        "import misleading"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2018-11-13 23:09:21--  https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 1641 (1.6K) [text/plain]\n",
+            "Saving to: ‘misleading.py.1’\n",
+            "\n",
+            "\rmisleading.py.1       0%[                    ]       0  --.-KB/s               \rmisleading.py.1     100%[===================>]   1.60K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2018-11-13 23:09:21 (176 MB/s) - ‘misleading.py.1’ saved [1641/1641]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_ighOHAaFqgY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #1"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lc0l9owCFqga",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 315
+        },
+        "outputId": "faf6f1d9-70ce-4fea-dfab-e621a7dc9ed3"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot1();"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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J4GvACOCRiFgTEf/QzzklSVVUPUPPzAs7Gb67H7JIkvrAO0UlqRAWuiQVwkKXpEJY6JJU\nCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw\n0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtd\nkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCVC30iJgfERsjYl27sYMi4pGIWF/5emD/xpQk\nVdOdM/QFwJkdxq4GfpCZRwM/qKxLkuqoaqFn5mPA6x2GzwfuqSzfA3y4xrkkST0UmVl9p4gxwJLM\nPL6y/mZmjqwsB/BG63onr50NzK6sjgNe7HtsVRwCvFbvEFIn/NmsrfdkZlO1nfbu61EyMyOiy98K\nmXkncGdfj6PfFhErM3NyvXNIHfmzWR+9vcrllxExCqDydWPtIkmSeqO3hb4YmFFZngE8WJs4kqTe\n6s5liwuBJ4BxEdEcEZ8E5gGnR8R64E8q69rznMrSQOXPZh10601RSdLA552iklQIC12SCmGhS1Ih\n+nwduvasynNzjqDdv11mrqpfIkkDhYXeQCJiLnAp8BLQ+m52AqfWK5MEEBFnAdcA760MPQfclJkP\n1y/V4GOhN5Y/BY7KzG31DiK1iohZwKeBq4CVleHJwLyIGF25W1x7gJctNpCIWARcnpnemasBIyKe\nB/5zZr7eYfxg4EeZOb4+yQYfz9Aby/8AVleeTf/r1sHMPK9+kSSiY5kDZObmlmf3aU+x0BvLPcBN\nwLPAzjpnkVr9e0RMyMxn2g9GxATg7TplGpQs9MayJTO/Wu8QUgefAxZHxD8CT1fGJtPynKeL65Zq\nEHIOvYFExC20TLUsZtcpFy9bVF1FxKHAFcBxlaHngb/LzF/UL9XgY6E3kIh4tJPhzEwvW1TdREQT\n0JSZz3cYfy+wKTM31SfZ4OOUS4OIiCHA7Zn5rXpnkTq4Dfj7TsYPBq4FPr5n4wxenqE3ED8FRgPR\n7n4uI2Jd60dXqv/5LJfG8v2I+HxEHBERB7X+qXcoDXojdrNt6B5LIadcGswFla9XtBtL4A/qkEVq\n9dOIOLvjbf6VxwH8rE6ZBiWnXCT1SUQcDTwEPM6uly1OBc7NzH+tV7bBxkJvMBFxPC0PQBrWOpaZ\nX69fIgkiYh9a3vxsnS9/Dvjfmbm1fqkGHwu9gUTE9cDJtBT6w8BZtDwr4yP1zCVpYLDQG0hEPAtM\nAFZn5oTKzRzfyMzT6xxNg1hEvM1/PM55l0203CdxwB6ONGj5pmhjeTczd0bEjog4ANhIy4ddSHWT\nmbu7ykV7kJctNpaVETESuIuWN59WAU/UN5IGu4g4td3y2A7bpu35RIOXUy4NKiLGAAdk5to6R9Eg\nFxGrMvMPOy53tq7+5Rl6A4iIP2+3fBxAZm6wzDVARBfLna2rH1nojWFmu+X/VbcUUueyi+XO1tWP\nfFO08XjGo4HmDyJiMS0/m63LVNbHdv0y1ZqF3hhGRsR/o+V/VAd0fKMpM++vTywJgPPbLd9c+Zod\n1rUH+KZoA6h8EkxXMjNn7ma71K8i4nxgdGb+XWV9BdBES6n/VWb+Uz3zDSYWuqQ+iYgfAx/LzH+r\nrK8BTgOGA/+YmafVM99g4pSLpL76ndYyr/hRZm4GNkfE8HqFGoy8ykVSXx3YfiUz/7zdatMezjKo\nWeiS+urJiJjVcTAiPg2sqEOeQcs59AYSEfsBnwOOzMxZledQj8vMJXWOpkEsIn4PeAD4NS2PowA4\nEdgH+HBm/rJe2QYbC72BRMR9tDzD5ZLMPL5S8I9n5sQ6R5Nan+lyXGX1ucz8v/XMMxhZ6A2k9cN4\nI2J1Zk6qjD2TmRPqnU1S/TmH3li2RcS+VG7aiIijaPlvriR52WKD+Wvge8AREXEv8EfAn9U1kaQB\nwymXBhMRBwMfoOU5GT/JzNfqHEnSAGGhN5CI+EHHu+46G5M0ODnl0gAiYhiwH3BIRBzIfzxx8QDg\n8LoFkzSgWOiN4dPAlcBhtFy22Fro/w58rV6hJA0sTrk0kIj4i8y8rd45JA1MFnqDiYjjgfcCw1rH\nMvPr9UskaaCw0BtIRFwPnExLoT8MnEXLk+0+Us9ckgYGbyxqLB+h5TnTv8jMPwMmAL9b30iSBgoL\nvbG8m5k7gR0RcQCwETiizpkkDRBe5dJYVkbESOAuWq52+RXwRH0jSRoonENvUBExBjggM9fWOYqk\nAcIplwYSET9oXc7MDZm5tv2YpMHNKZcG4J2ikrrDQm8M3ikqqSrn0BuId4pK2h0LvQFExEnAv2Xm\nLyrrlwDTgZeBv87M1+uZT9LA4JuijeEOYBtARPxXYB7wdeAt4M465pI0gDiH3hj2ancWfgFwZ2Yu\nAhZFxJo65pI0gHiG3hj2iojWX76nAe0/Td1fypIAy6BRLASWR8RrwLvADwEi4j/RMu0iSb4p2igi\n4gPAKGBpZr5TGTsG2D8zV9U1nKQBwUKXpEI4hy5JhbDQJakQFrokFcJCl6RC/H/SiPUgIAVT3wAA\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49821e0160>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XDtVlWFUVt4n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The visual ratio between the 2 bars is misleading because the y-axis starts at 10. Changing it to start at 0 solves this:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VRX_4BGdFqgf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332
+        },
+        "outputId": "74bb7354-ea0d-45f4-86d2-2e96a8f04753"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance = pd.DataFrame(\n",
+        "    index=['State Farm', 'GEICO'], \n",
+        "    data={'Market Share %': [18.07, 12.79]})\n",
+        "\n",
+        "insurance.plot.bar()"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4982213be0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49821cd2e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FKCE_XINFqgk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #2"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TZXOyiFuFqgk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 311
+        },
+        "outputId": "315bbaa9-f85d-4054-981c-fcb4f28a036c"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot2();"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hWiNVsdWxa5qihhsJYIwxu+U4W1Y4MfdSfHQSQWexVQHzb+KJCtshWivVDLp+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\n2JRSofP/LrNWcuvLhmgAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f498206cd68>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0Up7ugeRV_c2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "So obviously these 2 don't have total market share between them. You need to understand the entire space in order to deduce anything about their relative size. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RPMyvEUqFqgq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "other = pd.DataFrame(\n",
+        "    index=['Other'], \n",
+        "    data={'Market Share %': [100 - 18.07 - 12.79]})\n",
+        "\n",
+        "insurance = insurance.append(other)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "B8Jzqg53XmDC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 141
+        },
+        "outputId": "0937cdbd-4e02-4806-cf80-90ce8d2efdb3"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Market Share %</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>State Farm</th>\n",
+              "      <td>18.07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>GEICO</th>\n",
+              "      <td>12.79</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Other</th>\n",
+              "      <td>69.14</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Market Share %\n",
+              "State Farm           18.07\n",
+              "GEICO                12.79\n",
+              "Other                69.14"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_jDhQJxMX_QM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Create a category \"Other\" that's 100% - Geico and Statefarm. Now you can better visualize their relationship. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tuD-GgsEX4Wb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 311
+        },
+        "outputId": "b067fba6-9a80-42f9-884f-a9b8fc76bbb6"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance.plot.pie('Market Share %', figsize=(5, 5));"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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fnGz5eCh7Y8w8Y8wZSc7VWWcCPWyH8ByRvJk9S1amanWzmr5c3UBeQarWp5hk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\nxaiUUjG0GJVSKoYWo1JKxfj/axgFR7ARzBoAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981f6d7f0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-NrSoxEFqgw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #3"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ggFkJa0cFqgx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 337
+        },
+        "outputId": "26c3aa36-a435-4499-921c-f2ce135bd9f3"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot3()"
+      ],
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SK4GP0p4i/FV0GG0YixWQFdUWtOe3Hb6W1u7TtHOufMEQWVFtC7wD2JdhLuQ5NMuB9wAH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\nJGlGLFaSJEkz8v8B7VusKzwbg08AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981f763c8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N9KmfKxsYOx9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Since the circles are sized by the area, there's a perception issue. Need to set the radii to the square root of the data. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-eAHd_dUYaSu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "0b59e450-f344-45a3-a7c4-ec8861099a6c"
+      },
+      "cell_type": "code",
+      "source": [
+        "from math import sqrt\n",
+        "sqrt(18.07)**2 / sqrt(12.79)**2\n"
+      ],
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.4128225175918685"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cfft0RHGYpaw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 337
+        },
+        "outputId": "97d73c46-32c4-4c61-8824-f14a12728a0f"
+      },
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "ax.set_xlim((0, 80))\n",
+        "ax.set_ylim((0, 40))\n",
+        "plt.axis('off')\n",
+        "\n",
+        "circle = plt.Circle(xy=(20, 20), radius=sqrt(18.07))\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "circle = plt.Circle(xy=(60, 20), radius=sqrt(12.79))\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "plt.title('State Farm vs GEICO - Corrected')\n",
+        "plt.show()"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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9i7GV7yU5YT4df23oQYDV6V9x/H+T3HroWZJ8PMn95tPxd4cehM3HhzCzZv2m+Ldka0ZV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\nFQBAI8IKAKCR/w9/udGtxYRAaQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981eca3c8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "H71CS8UZYwUl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Use a common factor to scale fairly. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BfXt40vtYzn1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 337
+        },
+        "outputId": "03dc705c-4863-4f46-e288-29f09e8e41af"
+      },
+      "cell_type": "code",
+      "source": [
+        "scale = 18.07 / sqrt(18.07)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "ax.set_xlim((0, 80))\n",
+        "ax.set_ylim((0, 40))\n",
+        "plt.axis('off')\n",
+        "\n",
+        "circle = plt.Circle(xy=(20, 20), radius=sqrt(18.07) * scale)\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "circle = plt.Circle(xy=(60, 20), radius=sqrt(12.79) * scale)\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "plt.title('State Farm vs GEICO - Corrected')\n",
+        "plt.show()"
+      ],
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Qxaou82XAqdE5JC2Ywb+YzjD4d9xSoMGPn0EUq87gv9mSVmlM43tMJVFabIN/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\n5P8D6hGpplR2vBwAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981e7e080>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "nlQmkGcXY3pB",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is accurate as opposed to the original visualization. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mAwA2RVdFqg6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #4"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vImQ51P4Fqg8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zopEbyjKFqg8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# import altair as alt\n",
+        "# alt.renderers.enable('notebook')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "aBtbggV7FqhB",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 349
+        },
+        "outputId": "f466508c-dd56-455b-995e-8fe668a35e1a"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot4()"
+      ],
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Chart({\n",
+              "  data: UrlData({\n",
+              "    format: TopoDataFormat({\n",
+              "      feature: 'states',\n",
+              "      type: 'topojson'\n",
+              "    }),\n",
+              "    url: 'https://vega.github.io/vega-datasets/data/us-10m.json'\n",
+              "  }),\n",
+              "  encoding: EncodingWithFacet({\n",
+              "    color: Color({\n",
+              "      shorthand: 'State Farm policy premiums',\n",
+              "      type: 'quantitative'\n",
+              "    })\n",
+              "  }),\n",
+              "  height: 300,\n",
+              "  mark: 'geoshape',\n",
+              "  projection: Projection({\n",
+              "    type: 'albersUsa'\n",
+              "  }),\n",
+              "  transform: [LookupTransform({\n",
+              "    from: LookupData({\n",
+              "      data:                    state  id  State Farm policy premiums  \\\n",
+              "      0                Alabama   1                      768213   \n",
+              "      1                 Alaska   2                      128613   \n",
+              "      2                Arizona   4                      760003   \n",
+              "      3               Arkansas   5                      454931   \n",
+              "      4             California   6                     3655463   \n",
+              "      5               Colorado   8                      827143   \n",
+              "      6            Connecticut   9                      169773   \n",
+              "      7               Delaware  10                      191319   \n",
+              "      8   District of Columbia  11                       67231   \n",
+              "      9                Florida  12                     2806322   \n",
+              "      10               Georgia  13                     1761224   \n",
+              "      11                Hawaii  15                      133498   \n",
+              "      12                 Idaho  16                      130613   \n",
+              "      13              Illinois  17                     2256543   \n",
+              "      14               Indiana  18                      861382   \n",
+              "      15                  Iowa  19                      368898   \n",
+              "      16                Kansas  20                      351343   \n",
+              "      17              Kentucky  21                      680433   \n",
+              "      18             Louisiana  22                     1415980   \n",
+              "      19                 Maine  23                      101111   \n",
+              "      20              Maryland  24                      883401   \n",
+              "      21         Massachusetts  25                           0   \n",
+              "      22              Michigan  26                     1580398   \n",
+              "      23             Minnesota  27                      851940   \n",
+              "      24           Mississippi  28                      467887   \n",
+              "      25              Missouri  29                      883774   \n",
+              "      26               Montana  30                      156816   \n",
+              "      27              Nebraska  31                      273727   \n",
+              "      28                Nevada  32                      404688   \n",
+              "      29         New Hampshire  33                      103340   \n",
+              "      30            New Jersey  34                      619623   \n",
+              "      31            New Mexico  35                      261922   \n",
+              "      32              New York  36                     1666653   \n",
+              "      33        North Carolina  37                      842368   \n",
+              "      34          North Dakota  38                       67282   \n",
+              "      35                  Ohio  39                     1240075   \n",
+              "      36              Oklahoma  40                      636063   \n",
+              "      37                Oregon  41                      564416   \n",
+              "      38          Pennsylvania  42                     1654528   \n",
+              "      39          Rhode Island  44                           0   \n",
+              "      40        South Carolina  45                      874436   \n",
+              "      41          South Dakota  46                      101912   \n",
+              "      42             Tennessee  47                      889329   \n",
+              "      43                 Texas  48                     3395165   \n",
+              "      44                  Utah  49                      300403   \n",
+              "      45               Vermont  50                       30003   \n",
+              "      46              Virginia  51                      878312   \n",
+              "      47            Washington  53                      794882   \n",
+              "      48         West Virginia  54                      323608   \n",
+              "      49             Wisconsin  55                      443680   \n",
+              "      50               Wyoming  56                       90412   \n",
+              "      51           Puerto Rico  72                           0   \n",
+              "      \n",
+              "          BERKSHIRE HATHAWAY GRP  STATE TOTAL  \n",
+              "      0                   282947      3025561  \n",
+              "      1                    81366       454623  \n",
+              "      2                   604049      4373055  \n",
+              "      3                    96908      1777917  \n",
+              "      4                  2091720     25255501  \n",
+              "      5                   365797      4057463  \n",
+              "      6                   486498      2795415  \n",
+              "      7                   129033       802005  \n",
+              "      8                   115149       325239  \n",
+              "      9                  3952147     17333354  \n",
+              "      10                  821326      7571988  \n",
+              "      11                  184743       722550  \n",
+              "      12                   87222       863636  \n",
+              "      13                  412978      7028885  \n",
+              "      14                  201200      3506499  \n",
+              "      15                   58105      1650388  \n",
+              "      16                   76533      1725608  \n",
+              "      17                  186915      2805864  \n",
+              "      18                  369492      4136224  \n",
+              "      19                   68109       699558  \n",
+              "      20                 1059508      4554586  \n",
+              "      21                  473913      4924736  \n",
+              "      22                       0      8462142  \n",
+              "      23                  108942      3412460  \n",
+              "      24                  100827      1760180  \n",
+              "      25                  214191      3606590  \n",
+              "      26                   33538       668513  \n",
+              "      27                   61046      1167135  \n",
+              "      28                  262815      2129828  \n",
+              "      29                  103403       806545  \n",
+              "      30                 1441019      7375823  \n",
+              "      31                  174865      1284430  \n",
+              "      32                 3926684     12633982  \n",
+              "      33                  560700      5543700  \n",
+              "      34                   15516       454376  \n",
+              "      35                  405652      6303148  \n",
+              "      36                  151388      2515842  \n",
+              "      37                  223187      2647434  \n",
+              "      38                  602848      8270326  \n",
+              "      39                   97597       867558  \n",
+              "      40                  426033      3583918  \n",
+              "      41                   15682       508569  \n",
+              "      42                  292357      3769652  \n",
+              "      43                 2127633     19180816  \n",
+              "      44                  139315      1723931  \n",
+              "      45                   52939       359996  \n",
+              "      46                  884489      5105360  \n",
+              "      47                  478922      4780616  \n",
+              "      48                  110806      1209872  \n",
+              "      49                  131845      2950711  \n",
+              "      50                   30187       374169  \n",
+              "      51                       0       444480  ,\n",
+              "      fields: ['State Farm policy premiums'],\n",
+              "      key: 'id'\n",
+              "    }),\n",
+              "    lookup: 'id'\n",
+              "  })],\n",
+              "  width: 500\n",
+              "})"
+            ],
+            "text/html": [
+              "\n",
+              "<!DOCTYPE html>\n",
+              "<html>\n",
+              "<head>\n",
+              "  <style>\n",
+              "    .vega-actions a {\n",
+              "        margin-right: 12px;\n",
+              "        color: #757575;\n",
+              "        font-weight: normal;\n",
+              "        font-size: 13px;\n",
+              "    }\n",
+              "    .error {\n",
+              "        color: red;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega@3.3.1\"></script>\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega-lite@2.6.0\"></script>\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega-embed@3.14\"></script>\n",
+              "\n",
+              "</head>\n",
+              "<body>\n",
+              "  <div id=\"vis\"></div>\n",
+              "  <script type=\"text/javascript\">\n",
+              "    var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"url\": \"https://vega.github.io/vega-datasets/data/us-10m.json\", \"format\": {\"feature\": \"states\", \"type\": \"topojson\"}}, \"mark\": \"geoshape\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"State Farm policy premiums\"}}, \"height\": 300, \"projection\": {\"type\": \"albersUsa\"}, \"transform\": [{\"lookup\": \"id\", \"from\": {\"data\": {\"name\": \"data-b615a17f98b0ca7d11f99dcf36616c5d\"}, \"key\": \"id\", \"fields\": [\"State Farm policy premiums\"]}}], \"width\": 500, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-b615a17f98b0ca7d11f99dcf36616c5d\": [{\"state\": \"Alabama\", \"id\": 1, \"State Farm policy premiums\": 768213, \"BERKSHIRE HATHAWAY GRP\": 282947, \"STATE TOTAL\": 3025561}, {\"state\": \"Alaska\", \"id\": 2, \"State Farm policy premiums\": 128613, \"BERKSHIRE HATHAWAY GRP\": 81366, \"STATE TOTAL\": 454623}, {\"state\": \"Arizona\", \"id\": 4, \"State Farm policy premiums\": 760003, \"BERKSHIRE HATHAWAY GRP\": 604049, \"STATE TOTAL\": 4373055}, {\"state\": \"Arkansas\", \"id\": 5, \"State Farm policy premiums\": 454931, \"BERKSHIRE HATHAWAY GRP\": 96908, \"STATE TOTAL\": 1777917}, {\"state\": \"California\", \"id\": 6, \"State Farm policy premiums\": 3655463, \"BERKSHIRE HATHAWAY GRP\": 2091720, \"STATE TOTAL\": 25255501}, {\"state\": \"Colorado\", \"id\": 8, \"State Farm policy premiums\": 827143, \"BERKSHIRE HATHAWAY GRP\": 365797, \"STATE TOTAL\": 4057463}, {\"state\": \"Connecticut\", \"id\": 9, \"State Farm policy premiums\": 169773, \"BERKSHIRE HATHAWAY GRP\": 486498, \"STATE TOTAL\": 2795415}, {\"state\": \"Delaware\", \"id\": 10, \"State Farm policy premiums\": 191319, \"BERKSHIRE HATHAWAY GRP\": 129033, \"STATE TOTAL\": 802005}, {\"state\": \"District of Columbia\", \"id\": 11, \"State Farm policy premiums\": 67231, \"BERKSHIRE HATHAWAY GRP\": 115149, \"STATE TOTAL\": 325239}, {\"state\": \"Florida\", \"id\": 12, \"State Farm policy premiums\": 2806322, \"BERKSHIRE HATHAWAY GRP\": 3952147, \"STATE TOTAL\": 17333354}, {\"state\": \"Georgia\", \"id\": 13, \"State Farm policy premiums\": 1761224, \"BERKSHIRE HATHAWAY GRP\": 821326, \"STATE TOTAL\": 7571988}, {\"state\": \"Hawaii\", \"id\": 15, \"State Farm policy premiums\": 133498, \"BERKSHIRE HATHAWAY GRP\": 184743, \"STATE TOTAL\": 722550}, {\"state\": \"Idaho\", \"id\": 16, \"State Farm policy premiums\": 130613, \"BERKSHIRE HATHAWAY GRP\": 87222, \"STATE TOTAL\": 863636}, {\"state\": \"Illinois\", \"id\": 17, \"State Farm policy premiums\": 2256543, \"BERKSHIRE HATHAWAY GRP\": 412978, \"STATE TOTAL\": 7028885}, {\"state\": \"Indiana\", \"id\": 18, \"State Farm policy premiums\": 861382, \"BERKSHIRE HATHAWAY GRP\": 201200, \"STATE TOTAL\": 3506499}, {\"state\": \"Iowa\", \"id\": 19, \"State Farm policy premiums\": 368898, \"BERKSHIRE HATHAWAY GRP\": 58105, \"STATE TOTAL\": 1650388}, {\"state\": \"Kansas\", \"id\": 20, \"State Farm policy premiums\": 351343, \"BERKSHIRE HATHAWAY GRP\": 76533, \"STATE TOTAL\": 1725608}, {\"state\": \"Kentucky\", \"id\": 21, \"State Farm policy premiums\": 680433, \"BERKSHIRE HATHAWAY GRP\": 186915, \"STATE TOTAL\": 2805864}, {\"state\": \"Louisiana\", \"id\": 22, \"State Farm policy premiums\": 1415980, \"BERKSHIRE HATHAWAY GRP\": 369492, \"STATE TOTAL\": 4136224}, {\"state\": \"Maine\", \"id\": 23, \"State Farm policy premiums\": 101111, \"BERKSHIRE HATHAWAY GRP\": 68109, \"STATE TOTAL\": 699558}, {\"state\": \"Maryland\", \"id\": 24, \"State Farm policy premiums\": 883401, \"BERKSHIRE HATHAWAY GRP\": 1059508, \"STATE TOTAL\": 4554586}, {\"state\": \"Massachusetts\", \"id\": 25, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 473913, \"STATE TOTAL\": 4924736}, {\"state\": \"Michigan\", \"id\": 26, \"State Farm policy premiums\": 1580398, \"BERKSHIRE HATHAWAY GRP\": 0, \"STATE TOTAL\": 8462142}, {\"state\": \"Minnesota\", \"id\": 27, \"State Farm policy premiums\": 851940, \"BERKSHIRE HATHAWAY GRP\": 108942, \"STATE TOTAL\": 3412460}, {\"state\": \"Mississippi\", \"id\": 28, \"State Farm policy premiums\": 467887, \"BERKSHIRE HATHAWAY GRP\": 100827, \"STATE TOTAL\": 1760180}, {\"state\": \"Missouri\", \"id\": 29, \"State Farm policy premiums\": 883774, \"BERKSHIRE HATHAWAY GRP\": 214191, \"STATE TOTAL\": 3606590}, {\"state\": \"Montana\", \"id\": 30, \"State Farm policy premiums\": 156816, \"BERKSHIRE HATHAWAY GRP\": 33538, \"STATE TOTAL\": 668513}, {\"state\": \"Nebraska\", \"id\": 31, \"State Farm policy premiums\": 273727, \"BERKSHIRE HATHAWAY GRP\": 61046, \"STATE TOTAL\": 1167135}, {\"state\": \"Nevada\", \"id\": 32, \"State Farm policy premiums\": 404688, \"BERKSHIRE HATHAWAY GRP\": 262815, \"STATE TOTAL\": 2129828}, {\"state\": \"New Hampshire\", \"id\": 33, \"State Farm policy premiums\": 103340, \"BERKSHIRE HATHAWAY GRP\": 103403, \"STATE TOTAL\": 806545}, {\"state\": \"New Jersey\", \"id\": 34, \"State Farm policy premiums\": 619623, \"BERKSHIRE HATHAWAY GRP\": 1441019, \"STATE TOTAL\": 7375823}, {\"state\": \"New Mexico\", \"id\": 35, \"State Farm policy premiums\": 261922, \"BERKSHIRE HATHAWAY GRP\": 174865, \"STATE TOTAL\": 1284430}, {\"state\": \"New York\", \"id\": 36, \"State Farm policy premiums\": 1666653, \"BERKSHIRE HATHAWAY GRP\": 3926684, \"STATE TOTAL\": 12633982}, {\"state\": \"North Carolina\", \"id\": 37, \"State Farm policy premiums\": 842368, \"BERKSHIRE HATHAWAY GRP\": 560700, \"STATE TOTAL\": 5543700}, {\"state\": \"North Dakota\", \"id\": 38, \"State Farm policy premiums\": 67282, \"BERKSHIRE HATHAWAY GRP\": 15516, \"STATE TOTAL\": 454376}, {\"state\": \"Ohio\", \"id\": 39, \"State Farm policy premiums\": 1240075, \"BERKSHIRE HATHAWAY GRP\": 405652, \"STATE TOTAL\": 6303148}, {\"state\": \"Oklahoma\", \"id\": 40, \"State Farm policy premiums\": 636063, \"BERKSHIRE HATHAWAY GRP\": 151388, \"STATE TOTAL\": 2515842}, {\"state\": \"Oregon\", \"id\": 41, \"State Farm policy premiums\": 564416, \"BERKSHIRE HATHAWAY GRP\": 223187, \"STATE TOTAL\": 2647434}, {\"state\": \"Pennsylvania\", \"id\": 42, \"State Farm policy premiums\": 1654528, \"BERKSHIRE HATHAWAY GRP\": 602848, \"STATE TOTAL\": 8270326}, {\"state\": \"Rhode Island\", \"id\": 44, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 97597, \"STATE TOTAL\": 867558}, {\"state\": \"South Carolina\", \"id\": 45, \"State Farm policy premiums\": 874436, \"BERKSHIRE HATHAWAY GRP\": 426033, \"STATE TOTAL\": 3583918}, {\"state\": \"South Dakota\", \"id\": 46, \"State Farm policy premiums\": 101912, \"BERKSHIRE HATHAWAY GRP\": 15682, \"STATE TOTAL\": 508569}, {\"state\": \"Tennessee\", \"id\": 47, \"State Farm policy premiums\": 889329, \"BERKSHIRE HATHAWAY GRP\": 292357, \"STATE TOTAL\": 3769652}, {\"state\": \"Texas\", \"id\": 48, \"State Farm policy premiums\": 3395165, \"BERKSHIRE HATHAWAY GRP\": 2127633, \"STATE TOTAL\": 19180816}, {\"state\": \"Utah\", \"id\": 49, \"State Farm policy premiums\": 300403, \"BERKSHIRE HATHAWAY GRP\": 139315, \"STATE TOTAL\": 1723931}, {\"state\": \"Vermont\", \"id\": 50, \"State Farm policy premiums\": 30003, \"BERKSHIRE HATHAWAY GRP\": 52939, \"STATE TOTAL\": 359996}, {\"state\": \"Virginia\", \"id\": 51, \"State Farm policy premiums\": 878312, \"BERKSHIRE HATHAWAY GRP\": 884489, \"STATE TOTAL\": 5105360}, {\"state\": \"Washington\", \"id\": 53, \"State Farm policy premiums\": 794882, \"BERKSHIRE HATHAWAY GRP\": 478922, \"STATE TOTAL\": 4780616}, {\"state\": \"West Virginia\", \"id\": 54, \"State Farm policy premiums\": 323608, \"BERKSHIRE HATHAWAY GRP\": 110806, \"STATE TOTAL\": 1209872}, {\"state\": \"Wisconsin\", \"id\": 55, \"State Farm policy premiums\": 443680, \"BERKSHIRE HATHAWAY GRP\": 131845, \"STATE TOTAL\": 2950711}, {\"state\": \"Wyoming\", \"id\": 56, \"State Farm policy premiums\": 90412, \"BERKSHIRE HATHAWAY GRP\": 30187, \"STATE TOTAL\": 374169}, {\"state\": \"Puerto Rico\", \"id\": 72, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 0, \"STATE TOTAL\": 444480}]}};\n",
+              "    var embed_opt = {\"mode\": \"vega-lite\"};\n",
+              "\n",
+              "    function showError(el, error){\n",
+              "        el.innerHTML = ('<div class=\"error\">'\n",
+              "                        + '<p>JavaScript Error: ' + error.message + '</p>'\n",
+              "                        + \"<p>This usually means there's a typo in your chart specification. \"\n",
+              "                        + \"See the javascript console for the full traceback.</p>\"\n",
+              "                        + '</div>');\n",
+              "        throw error;\n",
+              "    }\n",
+              "    const el = document.getElementById('vis');\n",
+              "    vegaEmbed(\"#vis\", spec, embed_opt)\n",
+              "      .catch(error => showError(el, error));\n",
+              "  </script>\n",
+              "</body>\n",
+              "</html>\n"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "llOgfwW_ZH7-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Population density isn't accounted for. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HKefy7hQFqhF",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 349
+        },
+        "outputId": "95cb3eca-77ba-4177-a342-4470b216c314"
+      },
+      "cell_type": "code",
+      "source": [
+        "import altair as alt\n",
+        "from vega_datasets import data\n",
+        "\n",
+        "states = alt.topo_feature(data.us_10m.url, 'states')\n",
+        "\n",
+        "pop_eng_hur = data.population_engineers_hurricanes.url\n",
+        "variable = 'population'\n",
+        "\n",
+        "alt.Chart(states).mark_geoshape().encode(\n",
+        "    alt.Color(variable, type='quantitative')\n",
+        ").transform_lookup(\n",
+        "    lookup='id',\n",
+        "    from_=alt.LookupData(pop_eng_hur, 'id', [variable])\n",
+        ").properties(\n",
+        "    width=500,\n",
+        "    height=300\n",
+        ").project(\n",
+        "    type='albersUsa'\n",
+        ")"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Chart({\n",
+              "  data: UrlData({\n",
+              "    format: TopoDataFormat({\n",
+              "      feature: 'states',\n",
+              "      type: 'topojson'\n",
+              "    }),\n",
+              "    url: 'https://vega.github.io/vega-datasets/data/us-10m.json'\n",
+              "  }),\n",
+              "  encoding: EncodingWithFacet({\n",
+              "    color: Color({\n",
+              "      shorthand: 'population',\n",
+              "      type: 'quantitative'\n",
+              "    })\n",
+              "  }),\n",
+              "  height: 300,\n",
+              "  mark: 'geoshape',\n",
+              "  projection: Projection({\n",
+              "    type: 'albersUsa'\n",
+              "  }),\n",
+              "  transform: [LookupTransform({\n",
+              "    from: LookupData({\n",
+              "      data: 'https://vega.github.io/vega-datasets/data/population_engineers_hurricanes.csv',\n",
+              "      fields: ['population'],\n",
+              "      key: 'id'\n",
+              "    }),\n",
+              "    lookup: 'id'\n",
+              "  })],\n",
+              "  width: 500\n",
+              "})"
+            ],
+            "text/html": [
+              "\n",
+              "<!DOCTYPE html>\n",
+              "<html>\n",
+              "<head>\n",
+              "  <style>\n",
+              "    .vega-actions a {\n",
+              "        margin-right: 12px;\n",
+              "        color: #757575;\n",
+              "        font-weight: normal;\n",
+              "        font-size: 13px;\n",
+              "    }\n",
+              "    .error {\n",
+              "        color: red;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega@3.3.1\"></script>\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega-lite@2.6.0\"></script>\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega-embed@3.14\"></script>\n",
+              "\n",
+              "</head>\n",
+              "<body>\n",
+              "  <div id=\"vis\"></div>\n",
+              "  <script type=\"text/javascript\">\n",
+              "    var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"url\": \"https://vega.github.io/vega-datasets/data/us-10m.json\", \"format\": {\"feature\": \"states\", \"type\": \"topojson\"}}, \"mark\": \"geoshape\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"population\"}}, \"height\": 300, \"projection\": {\"type\": \"albersUsa\"}, \"transform\": [{\"lookup\": \"id\", \"from\": {\"data\": {\"url\": \"https://vega.github.io/vega-datasets/data/population_engineers_hurricanes.csv\"}, \"key\": \"id\", \"fields\": [\"population\"]}}], \"width\": 500, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\"};\n",
+              "    var embed_opt = {\"mode\": \"vega-lite\"};\n",
+              "\n",
+              "    function showError(el, error){\n",
+              "        el.innerHTML = ('<div class=\"error\">'\n",
+              "                        + '<p>JavaScript Error: ' + error.message + '</p>'\n",
+              "                        + \"<p>This usually means there's a typo in your chart specification. \"\n",
+              "                        + \"See the javascript console for the full traceback.</p>\"\n",
+              "                        + '</div>');\n",
+              "        throw error;\n",
+              "    }\n",
+              "    const el = document.getElementById('vis');\n",
+              "    vegaEmbed(\"#vis\", spec, embed_opt)\n",
+              "      .catch(error => showError(el, error));\n",
+              "  </script>\n",
+              "</body>\n",
+              "</html>\n"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sDxiyBz7FqhJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Links\n",
+        "- [How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)\n",
+        "- [Where to Start and End Your Y-Axis Scale](http://stephanieevergreen.com/y-axis/)\n",
+        "- [xkcd heatmap](https://xkcd.com/1138/)\n",
+        "- [Surprise Maps: Showing the Unexpected](https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JauLxUzMFqhL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Use Seaborn to visualize distributions and relationships with continuous and discrete variables\n",
+        "\n",
+        "#### Links\n",
+        "- [Seaborn tutorial](https://seaborn.pydata.org/tutorial.html)\n",
+        "- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html)\n",
+        "- [Chart Chooser](https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-3fPw_cDFqhP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Anscombe dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YgE2omQFqhR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "p5RELkU5FqhU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df = sns.load_dataset('anscombe')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "rVdadmk8Fqhg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lmQ1LiFmFqhj",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "6a076bf5-53f1-49b9-f007-551fb7fbccf9"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(44, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3ZoKUDI6Fqhq",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Mo9zUnPAFqhr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        },
+        "outputId": "4f473938-9959-4763-a67b-61ff64af6d85"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.head(10)"
+      ],
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dataset</th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>I</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>8.04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>I</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>6.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>I</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>7.58</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>I</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>8.81</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>I</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>8.33</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>I</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>9.96</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>I</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>7.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>I</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>I</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>10.84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>I</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>4.82</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  dataset     x      y\n",
+              "0       I  10.0   8.04\n",
+              "1       I   8.0   6.95\n",
+              "2       I  13.0   7.58\n",
+              "3       I   9.0   8.81\n",
+              "4       I  11.0   8.33\n",
+              "5       I  14.0   9.96\n",
+              "6       I   6.0   7.24\n",
+              "7       I   4.0   4.26\n",
+              "8       I  12.0  10.84\n",
+              "9       I   7.0   4.82"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "b4rMmi7tFqhv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xHUlVJuBFqhv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped = df.groupby('dataset')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ebvvP8DoFqhz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mRIZht_4Fqh1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 233
+        },
+        "outputId": "8a3d4c7c-4bd9-4f30-f353-cfabceeb1ae7"
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped.describe()"
+      ],
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"8\" halign=\"left\">x</th>\n",
+              "      <th colspan=\"8\" halign=\"left\">y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "      <td>2.031568</td>\n",
+              "      <td>4.26</td>\n",
+              "      <td>6.315</td>\n",
+              "      <td>7.58</td>\n",
+              "      <td>8.57</td>\n",
+              "      <td>10.84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "      <td>2.031657</td>\n",
+              "      <td>3.10</td>\n",
+              "      <td>6.695</td>\n",
+              "      <td>8.14</td>\n",
+              "      <td>8.95</td>\n",
+              "      <td>9.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500000</td>\n",
+              "      <td>2.030424</td>\n",
+              "      <td>5.39</td>\n",
+              "      <td>6.250</td>\n",
+              "      <td>7.11</td>\n",
+              "      <td>7.98</td>\n",
+              "      <td>12.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "      <td>2.030579</td>\n",
+              "      <td>5.25</td>\n",
+              "      <td>6.170</td>\n",
+              "      <td>7.04</td>\n",
+              "      <td>8.19</td>\n",
+              "      <td>12.50</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            x                                               y            \\\n",
+              "        count mean       std  min  25%  50%   75%   max count      mean   \n",
+              "dataset                                                                   \n",
+              "I        11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500909   \n",
+              "II       11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500909   \n",
+              "III      11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500000   \n",
+              "IV       11.0  9.0  3.316625  8.0  8.0  8.0   8.0  19.0  11.0  7.500909   \n",
+              "\n",
+              "                                                   \n",
+              "              std   min    25%   50%   75%    max  \n",
+              "dataset                                            \n",
+              "I        2.031568  4.26  6.315  7.58  8.57  10.84  \n",
+              "II       2.031657  3.10  6.695  8.14  8.95   9.26  \n",
+              "III      2.030424  5.39  6.250  7.11  7.98  12.74  \n",
+              "IV       2.030579  5.25  6.170  7.04  8.19  12.50  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ld4MUR3TFqh5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [count](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.count.html), for each column in each group"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gJAk0iKFFqh6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "519032ce-7a57-4800-8e7e-f99b73f0d66d"
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped.count()"
+      ],
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          x   y\n",
+              "dataset        \n",
+              "I        11  11\n",
+              "II       11  11\n",
+              "III      11  11\n",
+              "IV       11  11"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pYiLgui1Fqh-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kpqJ6WpWFqh-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "225f1784-83e3-48bb-895d-693c5fc8f177"
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped.mean()"
+      ],
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "           x         y\n",
+              "dataset               \n",
+              "I        9.0  7.500909\n",
+              "II       9.0  7.500909\n",
+              "III      9.0  7.500000\n",
+              "IV       9.0  7.500909"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "exw1JatiFqiB",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [standard deviation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.std.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1aqfauC7FqiC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "6a1b9020-aff6-4ae2-e16c-e14eccb08395"
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped.std()"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.031568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.031657</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.030424</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.030579</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                x         y\n",
+              "dataset                    \n",
+              "I        3.316625  2.031568\n",
+              "II       3.316625  2.031657\n",
+              "III      3.316625  2.030424\n",
+              "IV       3.316625  2.030579"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 59
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LUnSuuc7FqiH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6Y9H5zL9FqiH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 325
+        },
+        "outputId": "9d019b35-3967-4b65-d5e9-a66e7e6a5825"
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped.corr()"
+      ],
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816421</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816421</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">II</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816237</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">III</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816287</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816287</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">IV</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816521</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                  x         y\n",
+              "dataset                      \n",
+              "I       x  1.000000  0.816421\n",
+              "        y  0.816421  1.000000\n",
+              "II      x  1.000000  0.816237\n",
+              "        y  0.816237  1.000000\n",
+              "III     x  1.000000  0.816287\n",
+              "        y  0.816287  1.000000\n",
+              "IV      x  1.000000  0.816521\n",
+              "        y  0.816521  1.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zvADEx0XFqiM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use pandas to [plot](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) the groups, as scatter plots"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9C4FuWTIFqiN",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1081
+        },
+        "outputId": "0d358b2e-c65e-49ed-cbf8-146b1be5ed6f"
+      },
+      "cell_type": "code",
+      "source": [
+        "grouped.plot('x','y', kind = 'scatter');"
+      ],
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f498164f710>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49816044a8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981621f98>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49815494e0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "b_H44apRFqiQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "v7YazExOFqiR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "outputId": "a1cb0bcf-9512-49bd-ea2a-126dfd02b377"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot('x', 'y', col='dataset', data=df, col_wrap=2);"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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rY5AP7JsbuDIA2D1M0oMxMjNTOXJoPvfeftQqFgAwEAEZxszMTOXg/A1DlwEAu5YhFgAA\n0HEFGQBGbNQDJALyYDRhgPFiox5glSEWA1htwrfecV+Ovu59ufWO+/LgI2eystKGLg1g17JRD7BK\nQB6AJgwwfmzUA6wSkAegCQOMHxv1AKsE5AFowgDjx0Y9wCqT9Aaw2oQvnwiiCQMMx0Y9wCoBeQCa\nMMB4slEPkAjIg9GEAQDGk4DMJazPDADsdgIyayySDwBgFQs61mcGABCQ6VifGQBAQKZjfWYAAAGZ\njkXyAQBM0qNjfWYAAAGZy1ifGQDY7QyxAACAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEA\noCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBndugCAGAzVlZa\nTp1dzvL5C5mb3ZMD++YyM1NDlwVMMAEZgIm1stLy4CNncvzEYk6ePpfD+/fmrmMLOXJoXkgGrpsh\nFgBMrFNnl9fCcZKcPH0ux08s5tTZ5YErAybZIAG5qp5WVfdU1Ser6hNV9YIh6gBgsi2fv7AWjled\nPH0uy+cvDFQRMA2GuoL8q0ne1Vr7hiTflOQTA9UBwASbm92Tw/v3XnLs8P69mZvdM1BFwDTY8YBc\nVU9N8h1J3pgkrbXl1toXd7oOACbfgX1zuevYwlpIXh2DfGDf3MCVAZNsiEl6X5dkKclvVNU3JflQ\nkle21s4OUAsAE2xmpnLk0Hzuvf2oVSyALTPEEIvZJN+c5D+21v5ekrNJXnP5k6rqtqparKrFpaWl\nna4RYCrshl46M1M5OH9DnrH/xhycv0E4BjZtiIB8MsnJ1tr9o8/vycXAfInW2p2ttYXW2sLBgwd3\ntECAaaGXAmzcjgfk1trnk3y2qo6MDr0oycd3ug4AALiSoTYKeUWS36yquSR/nuRfDFQHAABcYpCA\n3Fr7SJKFIc4NAABXYyc9AADoDDXEYiKtrLScOrtsKSEAgCkmIF+jlZWWBx85k+MnFnPy9Lm1xeiP\nHJoXkgEApoghFtfo1NnltXCcJCdPn8vxE4s5dXZ54MoAANhKAvI1Wj5/YS0crzp5+lyWz18YqCIA\nALaDgHyN5mb35PD+vZccO7x/b+Zm9wxUEQAA20FAvkYH9s3lrmMLayF5dQzygX1zA1cGAMBWMknv\nGs3MVI4cms+9tx+1igUAwBQTkDdgZqZycP6GocsAAGAbGWIBAAAdARkAADoCMgAAdARkAADoCMgA\nANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYA\ngI6ADAAAndmhC2DzVlZaTp1dzvL5C5mb3ZMD++YyM1NDlwUAMJEE5Am3stLy4CNncvzEYk6ePpfD\n+/fmrmMLOXJoXkgGALgOhlhMuFNnl9fCcZKcPH0ux08s5tTZ5YErA7iylZWWpTNP5OHTj2fpzBNZ\nWWlDlwRwCVeQJ9zy+Qtr4XjVydPnsnz+wkAVAazPXS9gEriCPOHmZvfk8P69lxw7vH9v5mb3DFQR\nwPrc9QImgYA84Q7sm8tdxxbWQvLq1ZgD++YGrgzgy7nrBUwCQywm3MxM5cih+dx7+1GrWABjb/Wu\nVx+S3fUCxo0ryFNgZqZycP6GPGP/jTk4f4NwDIwtd72ASeAKMgA7xl0vYBIIyADsqNW7XgDjyhAL\nAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAJ2pX+ZtZaXl1Nll620CAHBNpjogr6y0PPjImRw/\nsZiTp8+t7dh05NC8kAwAwBVN9RCLU2eX18Jxkpw8fS7HTyzm1NnlgSsDAGBcTXVAXj5/YS0crzp5\n+lyWz18YqCIAAMbdVAfkudk9Obx/7yXHDu/fm7nZPQNVBADAuJvqgHxg31zuOrawFpJXxyAf2Dc3\ncGUAAIyrqZ6kNzNTOXJoPvfeftQqFgAAXJPBAnJV7UmymOTh1trN23WemZnKwfkbtuvlAQCYMkMO\nsXhlkk8MeH4AAPgygwTkqjqc5HuTvGGI8wMAwHqGuoL8K0lenWRloPMDAMAV7XhArqqbkzzaWvvQ\nkzzvtqparKrFpaWlHaoOYLropQAbN8QV5KNJXlxVf5nkbUleWFVvufxJrbU7W2sLrbWFgwcP7nSN\nAFNBLwXYuB0PyK2117bWDrfWnpXkpUn+oLX2wztdBwAAXMlUbxQCAAAbNehGIa219yd5/5A1AABA\nzxVkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCg\nIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQmR26AADG38pKy6mzy1k+fyFzs3ty\nYN9cZmZq6LIAtoWADMBVray0PPjImRw/sZiTp8/l8P69uevYQo4cmheSgalkiAUAV3Xq7PJaOE6S\nk6fP5fiJxZw6uzxwZQDbQ0AG4KqWz19YC8erTp4+l+XzFwaqCGB7CcgAXNXc7J4c3r/3kmOH9+/N\n3OyegSoC2F4CMgBXdWDfXO46trAWklfHIB/YNzdwZQDbwyQ9AK5qZqZy5NB87r39qFUsgF1BQAbg\nSc3MVA7O3zB0GQA7whALAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQA\nAOgIyAAA0BGQAQCgU621oWt4UlW1lOTTm3yZr0ry2BaUs9XUtXHjWtu41pWMb23jWlcyfrU91lq7\naTMvMOW9NBnf2tS1ceNa27jWlYxvbeNW1zX10okIyFuhqhZbawtD13E5dW3cuNY2rnUl41vbuNaV\njHdtQxrn92Vca1PXxo1rbeNaVzK+tY1rXU/GEAsAAOgIyAAA0NlNAfnOoQtYh7o2blxrG9e6kvGt\nbVzrSsa7tiGN8/syrrWpa+PGtbZxrSsZ39rGta6r2jVjkAEA4FrspivIAADwpARkAADoCMgAANAR\nkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQmVlX9m6r6ySd5zi1V9Xe2+LzPqqof3KLX\nuruqvm/08furamH08V9W1VdtxTkArmaaemlV/VxV/fvLHnteVX1iK87D7iEgM+1uSbKlTT3Js5Js\nSVMHmBCT0kvfmuSfX3bspaPjcM0EZCZKVf10VX2qqv5LkiPd8eNV9cGq+mhV/XZV3VhV35bkxUle\nX1UfqapnX+l5o3///VX1wOj4B0bH9lTV60fP/1hV/djodL+Q5NtHr/mqHX4LADZtWntpa+1TSU5X\n1T/oDv8PEZDZoNmhC4BrVVXfkotXAp6Xi1+7H07yodHD72it3TV63s8neXlr7deq6p1Jfre1ds/o\nsS9e/rwkv5bkZ5P849baw1X1tNFrvjzJX7fWvrWqbkhyX1W9O8lrkvxka+3mK9Q4n+Q/r/Of8IOt\ntY9v8m0A2JRd0EvfOvrvu7+qnp/kC621h67hrYE1AjKT5NuT3NtaezxJRg171XNHTfppSZ6S5PfX\neY31nndfkrur6u1J3jE69j1JvnF1jHCSpyZ5TpLl9QpsrZ3JxR86AONq2nvpf0ryh1X1r2J4BddJ\nQGZa3J3kltbaR6vqZUm+ayPPa639y9Etue9N8qHRFZZK8orW2iU/IKpqvdd2BRmYdHdnwntpa+2z\nVfUXSb4zyT9L8oL1ngvrMQaZSfKBJLdU1d5R8/wn3WPzST5XVV+R5Ie642dGj131eVX17Nba/a21\nn02ylORrc/GKyI+Pnpuq+vqq2neF11zTWjvTWnveOn+EY2Ac7IZe+tYkv5zkz1trJ6/h+XAJAZmJ\n0Vr7cC7eOvtokt9L8sHu4Z9Jcn8u3t77ZHf8bUl+qqr+pKqefZXnvb6q/rSqHkjyh6NzvCHJx5N8\neHT8f83Fuy4fS3JhNAnFJD1gouySXvpbSf5uDK/gOlVrbegaAABgbLiCDAAAHQEZAAA6AjIAAHQE\nZAAA6EzEOsg33XRTe9e73jV0GQBDqs2+gF4KcG29dCKuID/22GNDlwAw8fRSgGszEQEZAAB2ioAM\nAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0Zocu\nALjUykrLqbPLWT5/IXOze3Jg31xmZmrosgBg1xCQYYysrLQ8+MiZHD+xmJOnz+Xw/r2569hCjhya\nF5IBYIcYYgFj5NTZ5bVwnCQnT5/L8ROLOXV2eeDKAGD3EJBhjCyfv7AWjledPH0uy+cvDFQRAOw+\nAjKMkbnZPTm8f+8lxw7v35u52T0DVQQAu4+ADGPkwL653HVsYS0kr45BPrBvbuDKAGD3MEkPxsjM\nTOXIofnce/tRq1gAwEAEZBgzMzOVg/M3DF0GAIyFIZY/FZABABhLQy1/agwyAABjaajlTwVkAADG\n0lDLnwrIAACMpaGWPxWQAQAYS0Mtf2qSHgAAY2mo5U8FZAAAxtYQy58aYgEAAB0BGQAAOgIyAAB0\nBGQAAOgIyAAA0BGQAQCgs20BuareVFWPVtUD3bHXV9Unq+pjVXVvVT1tu84PAADXYzuvIN+d5KbL\njr0nyXNba9+Y5FNJXruN5wcAgA3btoDcWvtAki9cduzdrbXzo0//OMnh7To/AABcjyHHIP9okt9b\n78Gquq2qFqtqcWlpaQfLApgeeinAxg0SkKvqp5OcT/Kb6z2ntXZna22htbZw8ODBnSsOYIropQAb\nN7vTJ6yqlyW5OcmLWmttp88PAABXs6MBuapuSvLqJN/ZWnt8J88NAADXYjuXeXtrkj9KcqSqTlbV\ny5P8hyTzSd5TVR+pql/frvMDAMD12LYryK21H7jC4Tdu1/kAAGAr2EkPAAA6AjIAAHQEZAAA6AjI\nAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AG\nAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIA\nAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEA\noCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAA\nHQEZAAA62xaQq+pNVfVoVT3QHXt6Vb2nqh4a/b1/u84PAADXYzuvIN+d5KbLjr0myXtba89J8t7R\n5wAAMDa2LSC31j6Q5AuXHX5JkjePPn5zklu26/wAAHA9dnoM8qHW2udGH38+yaH1nlhVt1XVYlUt\nLi0t7Ux1AFNGLwXYuMEm6bXWWpJ2lcfvbK0ttNYWDh48uIOVAUwPvRRg43Y6ID9SVV+TJKO/H93h\n8wMAwFXtdEB+Z5IfGX38I0l+Z4fPDwAAV7Wdy7y9NckfJTlSVSer6uVJfiHJP6qqh5J89+hzAAAY\nG7Pb9cKttR9Y56EXbdc5AQBgs+ykBwAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdA\nBgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIy\nAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAndmhCwC2z8pKy6mzy1k+fyFzs3tyYN9c\nZmZq6LJgV/D9B5NLQIYptbLS8uAjZ3L8xGJOnj6Xw/v35q5jCzlyaN4Padhmvv9gshliAVPq1Nnl\ntR/OSXLy9LkcP7GYU2eXB64Mpp/vP5hsAjJMqeXzF9Z+OK86efpcls9fGKgi2D18/8FkE5BhSs3N\n7snh/XsvOXZ4/97Mze4ZqCLYPXz/wWQTkGFKHdg3l7uOLaz9kF4dA3lg39zAlcH08/0Hk80kPZhS\nMzOVI4fmc+/tR82ihx3m+w8mm4AMU2xmpnJw/oahy4BdyfcfTC5DLAAAoCMgAwBAR0AGAICOMcgA\nsA1sNQ2TS0AGgC1mq2mYbIZYAMAWs9U0TDYBGQC2mK2mYbIJyACwxWw1DZNNQAaALWaraZhsJukB\nwBaz1TRMNgEZALaBraZhchliAQAAHVeQYZNsBgAA00VAhk2wGQAATB9DLGATbAYAANNHQIZNsBkA\nAEyfQQJyVb2qqv6sqh6oqrdgjqO1AAAPo0lEQVRW1VcOUQdsls0AAGD67HhArqpnJPmJJAuttecm\n2ZPkpTtdB2wFmwEAwPQZapLebJK9VfWlJDcm+auB6oBNsRkAAEyfHQ/IrbWHq+qXknwmybkk726t\nvfvy51XVbUluS5JnPvOZO1skbIDNABhneulwLAEJk+tJh1hU1Suqav9WnXD0Wi9J8nVJ/laSfVX1\nw5c/r7V2Z2ttobW2cPDgwa06PcCuopcOY3UJyFvvuC9HX/e+3HrHfXnwkTNZWWlDlwZcg2sZg3wo\nyQer6u1VdVNVbfbX3+9O8hettaXW2peSvCPJt23yNQFgbFgCEibbkwbk1tq/TvKcJG9M8rIkD1XV\nv6uqZ1/nOT+T5PlVdeMobL8oySeu87XguqystCydeSIPn348S2eecFUH2FKWgITJdk2rWLTWWpLP\nj/6cT7I/yT1V9YsbPWFr7f4k9yT5cJI/HdVw50ZfB66XW5/AdrMEJEy2axmD/Mqq+lCSX0xyX5L/\nrrX240m+Jck/u56TttZ+rrX2Da2157bW/sfW2hPX8zpwPdz6BLabJSBhsl3LKhZPT/JPW2uf7g+2\n1laq6ubtKQu2j1ufwHabmak85+BT8vYfe0HOX1jJ7J6ZfPVTbrCKBUyIJw3IrbWfu8pjxg4zcVZv\nffYh2a1PYCutrLQ8tPRf1+5WrV5BPnJoXkiGCTDIVtMwJLc+ge1mKBdMtqF20oPB2P0O2G6GcsFk\nE5DZlex+B2wnQ7lgshliAQBbzFAumGyuIAPAFjOUCyabgMzEWFlpOXV22Q8bYCIYygWTS0BmIqzu\nfmfJJABguxmDzESwZBIAsFMEZCaCJZMAgJ0iIDMRVpdM6lkyCQDYDgIyE8GSSQDATjFJj4lgySQA\nYKcIyEwMSyYBADvBEAsAAOgIyAAA0DHEgm1l9zsAYNIIyGwbu98BAJPIEAu2jd3vgN1sZaVl6cwT\nefj041k680RWVtrQJQHXyBVkto3d74Ddyh00mGyuILNt7H4H7FbuoMFkE5DZNna/A3Yrd9Bgshli\nwbax+x2wW63eQetDsjtoMDkEZLaV3e+A3ejAvrmc+NG/n0+fejw3zu3J48sX8rcP3OgOGkwIARkA\ntsET51fyM7/zwCWT9IDJYAwyAGwxk/RgsrmCzCXsfAeweSbpwWQTkFlj3U6ArWGSHkw2QyxY45Yg\nwNawzCVMNleQWeOWIMDWsMwlTDYBmTVuCQJsHctcwuQyxII1bgkCALiCTMctQYCtY1UgmFwCMpdw\nSxBg86wKBJPNEAsA2GJWBYLJJiADwBazKhBMNgF5CqystCydeSIPn348S2eeyMpKG7okgF1tdVWg\nnlWBYHIIyBNudZzbrXfcl6Ove19uveO+PPjIGSEZYEBWBYLJZpLehFtvnNu9tx812Q5gIFYFgskm\nIE8449wAxpNVgWByGWIx4YxzAwDYWgLyhDPODQBgaxliMeGMcwMA2FoC8hQwzg0AYOsYYgEAAB0B\nGQAAOoZYDGRlpeXU2WXjhgEAxoyAPIDV3e9WN/hYXXniyKF5IRkAYGCDDLGoqqdV1T1V9cmq+kRV\nvWCIOoay3u53p84uD1wZAABDXUH+1STvaq19X1XNJblxoDoGYfc7AIDxteNXkKvqqUm+I8kbk6S1\nttxa++JO1zEku98BAIyvIYZYfF2SpSS/UVV/UlVvqKp9lz+pqm6rqsWqWlxaWtr5KreR3e+AnTLN\nvRRgu1RrbWdPWLWQ5I+THG2t3V9Vv5rk/22t/cx6/2ZhYaEtLi7uWI07wSoWwAZtukFMYy8F2KBr\n6qVDjEE+meRka+3+0ef3JHnNAHUMyu53AADjaceHWLTWPp/ks1V1ZHToRUk+vtN1AADAlQy1isUr\nkvzmaAWLP0/yLwaqAwC2haF0MLkGCcittY8kWRji3Juh2QFwLWwIBZNtkI1CJtFqs7v1jvty9HXv\ny6133JcHHzmTlZWdneQIwPizIRRMNgH5Gml2AFwrG0LBZBOQr5FmB8C1siEUTDYB+RppdgBcKxtC\nwWQbahWLibPa7C6fcKHZAXC5mZnKcw4+JW//sRfkSxdW8hV7ZvLVT7nBBD2YEALyNZqZqRw5NJ97\nbz9qFQsArmplpeWhpf9qFQuYUIZYbMDq7nfP2H9jDs67EgDAlZnYDZNNQAaALWZiN0w2ARkAtpiJ\n3TDZpj4gr6y0LJ15Ig+ffjxLZ56wsQcA284qFjDZpnqSnq0+ARiCid0w2ab6CrJJEgAMxcRumFxT\nHZBNkgAAYKOmOiCbJAHAUMyBgck11QHZJAkAhrA6B+bWO+7L0de9L7fecV8efOSMkAwTYqon6Zkk\nAcAQ1psDc+/tR3Nw/oaBqwOezFQH5ORvJkkAwE4xBwYm21QPsQCAIZgDA5NNQAaALWYODEy2qR9i\nAQA7zRwYmGwCMgBsA3NgYHIZYgEAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYA\ngI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAA\ndARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgMFpCrak9V/UlV\n/e5QNQAAwOWGvIL8yiSfGPD8AADwZQYJyFV1OMn3JnnDEOcHAID1DHUF+VeSvDrJykDnBwCAK9rx\ngFxVNyd5tLX2oSd53m1VtVhVi0tLSztUHcB00UsBNm6IK8hHk7y4qv4yyduSvLCq3nL5k1prd7bW\nFlprCwcPHtzpGgGmgl4KsHE7HpBba69trR1urT0ryUuT/EFr7Yd3ug4AALgS6yADAEBndsiTt9be\nn+T9Q9YAAAA9V5ABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMg\nAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZ\nAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgA\nANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOtVaG7qGJ1VVS0k+vcmX+aokj21BOVtNXRs3rrWN\na13J+NY2rnUl41fbY621mzbzAlPeS5PxrU1dGzeutY1rXcn41jZudV1TL52IgLwVqmqxtbYwdB2X\nU9fGjWtt41pXMr61jWtdyXjXNqRxfl/GtTZ1bdy41jaudSXjW9u41vVkDLEAAICOgAwAAJ3dFJDv\nHLqAdahr48a1tnGtKxnf2sa1rmS8axvSOL8v41qbujZuXGsb17qS8a1tXOu6ql0zBhkAAK7FbrqC\nDAAAT0pABgCAzq4IyFW1p6r+pKp+d+haelX1tKq6p6o+WVWfqKoXDF1TklTVq6rqz6rqgap6a1V9\n5YC1vKmqHq2qB7pjT6+q91TVQ6O/949JXa8f/b/8WFXdW1VP2+m61qute+xfVVWrqq8al7qq6hWj\n9+3PquoXd7qu9WqrqudV1R9X1UeqarGq/v4QtY0TvXRj9NJN1TZ4Px3XXnq12obup9PUS3dFQE7y\nyiSfGLqIK/jVJO9qrX1Dkm/KGNRYVc9I8hNJFlprz02yJ8lLByzp7iSXL+j9miTvba09J8l7R5/v\ntLvz5XW9J8lzW2vfmORTSV6700WN3J0vry1V9bVJvifJZ3a6oJG7c1ldVfUPk7wkyTe11v5ukl8a\noK7kyu/ZLyb5t6215yX52dHnu51eeo300g25O+PZT+/OePbSZHz76ZfVlQntpVMfkKvqcJLvTfKG\noWvpVdVTk3xHkjcmSWttubX2xWGrWjObZG9VzSa5MclfDVVIa+0DSb5w2eGXJHnz6OM3J7llR4vK\nletqrb27tXZ+9OkfJzm803WN6rjSe5Ykv5zk1UkGmZm7Tl0/nuQXWmtPjJ7z6I4XlnVra0n+m9HH\nT82A3wfjQC+9LnrpNRjXfjquvTQZ3346Tb106gNykl/JxS/klaELuczXJVlK8hujW5ZvqKp9QxfV\nWns4F3/r/EySzyX569bau4et6sscaq19bvTx55McGrKYdfxokt8buohVVfWSJA+31j46dC2X+fok\n315V91fV/1VV3zp0QZ3/Ocnrq+qzufg9MdQdgXGhl26AXrqlxqafjnEvTca3n05kL53qgFxVNyd5\ntLX2oaFruYLZJN+c5D+21v5ekrMZ7vbWmtEYtJfk4g+dv5VkX1X98LBVra9dXKdwrNYqrKqfTnI+\nyW8OXUuSVNWNSf6XXLy1NW5mkzw9yfOT/FSSt1dVDVvSmh9P8qrW2tcmeVVGVyh3I7104/TSrTFO\n/XTMe2kyvv10InvpVAfkJEeTvLiq/jLJ25K8sKreMmxJa04mOdlau3/0+T252OSH9t1J/qK1ttRa\n+1KSdyT5toFrutwjVfU1STL6e5Db8ldSVS9LcnOSH2rjs8j4s3Pxh/RHR98Lh5N8uKr+20Gruuhk\nkne0i/7vXLw6Ociklyv4kVz8+k+S30oyERNLtoleunF66SaNYT8d516ajG8/ncheOtUBubX22tba\n4dbas3JxcsQftNbG4jf41trnk3y2qo6MDr0oyccHLGnVZ5I8v6puHP3m+aKMwYSXy7wzF7/hMvr7\ndwasZU1V3ZSLt6Bf3Fp7fOh6VrXW/rS19tWttWeNvhdOJvnm0dfg0P6PJP8wSarq65PMJXls0Ir+\nxl8l+c7Rxy9M8tCAtQxKL70ueukmjGM/HfNemoxvP53MXtpa2xV/knxXkt8duo7LanpeksUkH8vF\nL+z9Q9c0quvfJvlkkgeS/G9Jbhiwlrfm4vi9L+ViM3p5kgO5OOP6oST/Z5Knj0ld/0+Szyb5yOjP\nr4/Le3bZ43+Z5KvGoa5cbOBvGX2tfTjJC8flPUvy3yf5UJKPJrk/ybcMUdu4/dFLN1SXXnr9tQ3e\nT8e1l17lPRu8n05TL7XVNAAAdKZ6iAUAAGyUgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCg\nIyDDOqrqW6vqY1X1lVW1r6r+rKqeO3RdAJNGP2XS2CgErqKqfj7JVybZm+Rka+3fD1wSwETST5kk\nAjJcRVXNJflgkv8vybe11i4MXBLARNJPmSSGWMDVHUjylCTzuXjlA4Dro58yMVxBhquoqncmeVuS\nr0vyNa21/2ngkgAmkn7KJJkdugAYV1V1LMmXWmv/e1XtSfKHVfXC1tofDF0bwCTRT5k0riADAEDH\nGGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6/z+5lORQ0Ql0OgAAAABJRU5ErkJg\ngg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49816aaf98>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "392cetfbFqiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EGzWwtWHFqiV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "outputId": "ed645de5-650f-4bbe-a41d-518519989e0a"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.lmplot('x', 'y', col='dataset', hue='dataset', data=df, col_wrap=2, ci=None);"
+      ],
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ySU5s6Ty3dHkN5Du3fD6eff64kLfoCXOuhG8+cJ7TfSz85lHwqa5I5dJXnMgJ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\nIIuIiIiIBFGDLCIiIiIS5P8DlDBckGCvtAIAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981451c18>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ul40rb5FFqiZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Links\n",
+        "- [Seaborn examples: Anscombe's quartet](http://seaborn.pydata.org/examples/anscombes_quartet.html)\n",
+        "- [Wikipedia: Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet)\n",
+        "- [The Datasaurus Dozen](https://www.autodeskresearch.com/publications/samestats)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S_4tdrfMFqia",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Tips dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tPMk17S5Fqic",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1HeE2V1pFqid",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "tips = sns.load_dataset('tips')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qxgykyiEFqif",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VosY-3ojFqig",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "04589564-11a3-4fc5-ddc2-b7ee64e7e46f"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.shape"
+      ],
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(244, 7)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UwwHUxlpFqij",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VijsmXfYFqio",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "3853bed8-b287-4abd-fd2f-9b292bce7fa4"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.head()"
+      ],
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>total_bill</th>\n",
+              "      <th>tip</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>smoker</th>\n",
+              "      <th>day</th>\n",
+              "      <th>time</th>\n",
+              "      <th>size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>16.99</td>\n",
+              "      <td>1.01</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10.34</td>\n",
+              "      <td>1.66</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>21.01</td>\n",
+              "      <td>3.50</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>23.68</td>\n",
+              "      <td>3.31</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>24.59</td>\n",
+              "      <td>3.61</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   total_bill   tip     sex smoker  day    time  size\n",
+              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
+              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
+              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
+              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
+              "4       24.59  3.61  Female     No  Sun  Dinner     4"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OPh3kqURFqir",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Eh2UkNo4Fqis",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "b21c667c-171f-49f7-ebf5-f6e4a6f291ee"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.describe()"
+      ],
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>total_bill</th>\n",
+              "      <th>tip</th>\n",
+              "      <th>size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>244.000000</td>\n",
+              "      <td>244.000000</td>\n",
+              "      <td>244.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>19.785943</td>\n",
+              "      <td>2.998279</td>\n",
+              "      <td>2.569672</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8.902412</td>\n",
+              "      <td>1.383638</td>\n",
+              "      <td>0.951100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>3.070000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>13.347500</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>17.795000</td>\n",
+              "      <td>2.900000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>24.127500</td>\n",
+              "      <td>3.562500</td>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>50.810000</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       total_bill         tip        size\n",
+              "count  244.000000  244.000000  244.000000\n",
+              "mean    19.785943    2.998279    2.569672\n",
+              "std      8.902412    1.383638    0.951100\n",
+              "min      3.070000    1.000000    1.000000\n",
+              "25%     13.347500    2.000000    2.000000\n",
+              "50%     17.795000    2.900000    2.000000\n",
+              "75%     24.127500    3.562500    3.000000\n",
+              "max     50.810000   10.000000    6.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 71
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CodWR5LBFqiv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MdQMkHDGFqiw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "20cf8d66-36f6-49df-e83f-2d3c1bcf8b5c"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.distplot(tips.tip);"
+      ],
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lbmZ3Al8FgsDXnXNfuODxVOA7wCagHXivc+50fKNeuvGI4z8PtfIPTx/h2Nk+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\nXUTEh1TuIiI+9P8AyBBshPmmH7UAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49821cda20>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_2HeEAcUbLvw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 285
+        },
+        "outputId": "24ec8657-08b9-4f3a-a894-3b3cce13f0f6"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.distplot(tips.total_bill);"
+      ],
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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UDPv8vHykmY8um2NTNaPEk9vrp/ycjSuL6ewb5qtP7aV/aMTW6jHTEkxPfx1Q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\nJmpDAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49812e8438>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0A9BZe79a3Cq",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "'Size' gives an error since the length's are not the same. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9Ah41eU9bQn-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "18ac4ae8-091d-4aa4-ca4a-aa21f8275c4c"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips['percent'] = tips.tip / tips.total_bill\n",
+        "sns.distplot(tips.percent);"
+      ],
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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oDR0DXOga4FhLH88dbWX8KlNuzRL99MwDt8/7KIHNdWlTM/socLdz7jPJrz8J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\nTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f498134e588>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "atJcQBhXFqiz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NeOLM6rMFqiz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "a6f0447f-70a6-47f8-8ad4-8b8b597bbed7"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot('total_bill', 'tip', data=tips, alpha=0.5);"
+      ],
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7+OPPnKxOwG107BcvTXNlVU91dXv2ul6BG+sdrD/HkN/m2Ynrmy4zutVzO3WJ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\nUkq5RANYKaVcogGslFIu+f8BAgIaCjigk3gAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981201da0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Sy1AC3Vrahfq",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "167b4ebe-1533-4bfc-c59f-d803c89bcb28"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot('total_bill', 'percent', data=tips, alpha=0.5);"
+      ],
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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/foyR6XhhpMKDg+2FjsH8qmady6xadri/hfM3w4VZcyZrSGUN/qJt4adjSV66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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49811ad9e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9LCnl-bQahxR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "uly3dE6yFqi6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZZTXks74Fqi_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 69
+        },
+        "outputId": "7ba4b2f6-fb4e-459a-ddef-2db8edd56e37"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.sex.value_counts()"
+      ],
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Male      157\n",
+              "Female     87\n",
+              "Name: sex, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 81
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l0AE3_JqFqjG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "a19234b4-f191-4ca7-ff8e-641dea2a8a97"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.sex.value_counts().plot.bar();"
+      ],
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49812094e0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zXHV4G11bpqQ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "034e02e3-ea99-45f9-9f24-bf6caafa41a7"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', data=tips, kind='count');"
+      ],
+      "execution_count": 83,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49811ad710>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IS5QMlhsFqjE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4Ag0tHPtb7fw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "da78f603-b538-4023-a51e-38821e576340"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='strip', alpha=0.5);"
+      ],
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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pg0x8UUlJDelZU1Jy7JwJvsW8uSmXT5nOxvlUZQnK9Cwc/eTNrz19fSH4glkl\ncf1HlddoG2b7JQAvQ5ahVbH+qSQvXp3g0ujNhdKbwj4ifjc+t4tkrliuCZzN29SHfEQDbubXd3os\nF2Gfm+aayrvWqVyRpZY7J9a5cAt4g1Ss9/WGzPlc0hy73GaGaxdNXzjLa2bI5ddYoaxkLnHzOfcy\nqaxwy1se/kYmAbhKvTEwy5dODnG8b4ZvnR3lh5fGKx7f11ZDtmAzPJsm6LWoDXrI25pMvsgzuxvZ\n21ZDS42PiN9DY9jHz+xp5vCWuorX2N0aYXG2IeC16G6Q2e+G07AdtpbysJbX5Gm7HzOpgpb95hql\nzJK0mg4TiLufgNaD5rGadtNgczmNOyuXo7ks2PNeaD1QCuQe2PZ2k5oQN5EURJU6PTBbcXxuKMbT\nO5vKKxxcLkXE76G9NkAmX0QpmErkmErm6JtK0Rr182cffxiv5cZtKfwei3gmb9rSl4r3tNcG+PmH\nOjk3NIfX7eKhLXVSkH0jUgp2vBN63mbSAWB6toEJtJ6ASUeEmqDzUXONN2jyu8XsrRtqekNw+NfM\ndma7YPLDNe3mY+dzpni7JWFmJfI3U6WsJRXOLBcsvTdWH/bSEPYxPJsmky9SsDVBr4XPbTGXLnB1\nLMnRbQ3MpfJ8/sQgk/EsQa/Fux5oZWup40ZXfVByvpuFe5ldk0qZzsXLtYx3ucC1iuWK4SbY87PL\nvJ9s1LgdSUFUmVSuwDfPjnBjKsWVsTi5opmxPNRdh8eq/Od6bm8Le9sidNQGqPF72N4Urtiokc6b\nfO5Prk6Ue8ylckW+d2GUoi3NN0VJcsrcTCsW7v61UtOl18rf/lohM+Bq8/2L41wbTxDwWnTWBfBa\nLj76SBeNYW95pcO81qif33hqG4lcAa+l+KtX+klmTdC1XIp9bTVorTk3OMvAjMkVt0UDJLNF0vmi\nVEYT8OYPYOA187kvDA/+yuq2Ji+n90Xof9m0rvcGofUQpKfNUrfORxfSHqJMfgKrzI2pZPnzoNdN\nwbY53jtN71QSn9vi7bua2NdeU75GKSgUNQGPxS8+soU3BmbJFWz2d0RprvFz7PoUY/Es08kc00mY\nS+V5fHuDBF9hlpYNvr5wnE3AjVfMTbQ7lU2Y9vXzq3UmrsLQCWjaa3rEzQ2ZusOigvwUVpnmiL+i\nBGUqV+T6pAnKmXyR718co7shSMjnZjKR5Wunh5lL5/F5XLz7gdZy806tNaNzGY73T7OlPojWmmsT\nCaaSObY2hvjW2RHeubeFy2NxppM5tjWF6KyTXPCmkk8tBMx5ueTy195OIbNwgw8gOWaWts2b6TPr\njH3SPmsxCcBV5h17m/nGmRFYuLUvAAAgAElEQVSuTySoDXrprgswlcrjKt2BK9qa6WSOkM/NC1cm\nmCvVB87mbX5wcYytDSFyRZsvnBhkIp7l/PAcAY9FXdBL2OchGlD4vRaXRuMMzqRJZE3e70T/DO85\n0Mqe1poVxyY2mEibSQ8kJ82W4+nrkJ0zQXLHc3e2eiHUaJaazZertLwQbFx43HJXtrYXgNyEqzoR\nvwfLpQj73Ewnsnznwhg/vTbFlbE4BdvG53HRUmNyaTOpyhsdyWyRbMHm9MAsE6Wbbl31QaaSOaaS\nWRSwpT6ISylyBZtLo5UFVU7fqFz6JjY4peDQx6DjCKSmINoFvqjJ5Z74C9M9404c/EXY8hg07YZH\nfhPquhfeZ+vbl1+FscnJDLjKXBqNmeCp4I3BOaaTWdyWi3gmT3PExy8f7S6vBd7eFOLUoqDZURsg\n4LVIZBbuZtf4PexvryktT0uXC7i7XNCwpJi725L/jzcdXxha98PQcXMcGzIz4enrpnVRoB7CjWZj\nRcfDt34tbxC2P7tw3P24KfITbJBtyCuQAFxlMnmTR4tnCkwlzSw25LUI+z24LRcdtQvrMp/a0Yjb\n5aJ/Oklj2MdTO8yvfLtbI5wbnkNryBdtro4n2NdeQ8TvZnAmzWPb6jm6rYGB6VQ5gFsuxSM9dYhN\nJjZithNbHrN0bHbAnPcGTZ0Il2VWMMRGzBbjO6kj7PaZXXhiRRKAq0x71M9P35xkcCbJbKqAUppY\nuoDHnSHgcfFH37vM1bEEW+qD7G2L4HK52NMa4cGuOs4NzfGtc6O4FLTV+rk+kSSWydNVH8DtMt2U\na4Ne9ndE2d4UJuCxuDA8x7WJJIe6ogS8sgtuQ5sdhBf/yBTPCTebmamyIJ80y8X8taYWRLDBfB4f\nNduJcykzMz79GVOQp3bL7d8rl4Qbr5rcctMeaNl3/7++dUgCcBWxbc1/+uYFBmZSxNIF0gUbBShV\nwF1QvN43w7nhGD63i9f7pon43Dy7p5lr4x4ujsT41rlRCkVTD2ImleMde5uxlKJ3MkW004urtIbY\npRQT8Sx//Uo/pwdmsbVmZC7DVCLPJ57eSo3f4+xfhLj3sgn4ym/D1DXIxUvbjSMmEHv8ZsWCN2y2\nEnuDkE8DCkLNMHoG7LzJ4Z7+DDz88VvXdtAa3vgsJEr1SyYum23KbQfX5EtdTyTpV0WG59JcHUug\nMbUeLGU6IbtdCpdS5Is26VK1smzeJpUrMjJnlqz94OI4haJZUpTKFSnamhtTaZrC5s5zLGNu2NUG\nPexsCXNlLM543PSaA7O6YjyW4c3xZapbifVv5LSZxdqlG7dam0CcnjbrgbVtHvMEYNe7TAGdhz9u\nVkTYefBHTaEebcPY+Vu/V3JiIfjOGzt3f76udU5mwFUk6HXj9Vi4SkvDFOZ/SK/bhQLyRV1ejqaU\nCdJ+t0kb1AY95VKS89f4PS7clouDnVGObm2gPuRlZ0sYn9si6LVu2trscbtkg8ZGFagHlwdQJvgq\nl/kGyyahmDPreOu2mlluy4GFFQst++HM31eWmLzdWl5PwLz+4nXB3tC9/oo2BJkBV5H6kJcPP9SB\nx23hc7vweSwifjcupQj53LTWBGgKe8vXdtQGaK8N4HYp/skTW8tFdSJ+Ny01gXLBncNddbxtVxP7\nO6L4SgF7X3sND7RHCftNwK0NejhYyg2LDai22+RuixmTDnBZEOkwnS2CDaYSWnICtj1TuVysea9Z\nATEv0rJQpnIlvohZATFfPcobgu6n7vVXtCGopYW+q9GRI0f08ePHnR7GmumfSnDqxizbm8K4XYrB\n2TQhn5vDXbWMzGXom0rSFvXTVRdkMpGjpcZPwGth25ozQ7O4lKkDMTKXIeC1aAwvvwBea83QbJrp\nZI7miJ/WqOzV37Cufg8Gj5u1vbHBhYLrdT1mppqNmTTDz/y75Z8fHzWrJOZ7vq1GatrchIt2bcaS\nlKv6S5IALMRmcPKvTD2GxDhMXjbnfFGTLphvmNn+IOx+j3Nj3FhWFYAlBSHEZlC7xSwxmyut87W8\nZmmYx2/SEFvfZgq2SxnJNbXpfi/YTPJFUx9iIp6lsy7I49sbpOPFZlXImsaYszfM7re2g2bpWV0P\n7PsQXP8hvPInJjfc/aTplCHuOwnA68zIXJqX35wikcmzp62Go1vrl20jn8oV+INvXeLyaByloKsu\nSCyT54MPdjgwauGoqWswdBLqt5lW8xOXFoqvtz8EU1dgtLRMrFiA6z8x10oft/tOUhDrSK5g85VT\nwwxMp5hJ5Xnl2hRvDM4te+3rfTNcmzBrerWGgekUl0ZjFIr2steLDSw5sfB5qNFstmjcBYd+EXa+\n8+Y1u7D8OXHPSQCuUjemUnztjWH+4cwIo3MZAMZiGTL5yrbx/VPL12+dTeXKa4TBbOiwlLqp15zY\nBOp6KlcueEOmh1v9ttLjWyuvd1mr224s7poE4Co0Hsvw5VNDXBtPcGUszhdODBDL5KkLecubLOat\ntMRse1OYnsYgHstcH/Ba/NzhjmXTFWKDi7TC3vebgjuz/eZm3OJSk027TAfjYIO5ds/7TL2IvpfM\nUjJx30gOuApdHU9ga43WGqUU+aKmdyLJoa5afmZPMy9cnSBXsOluCPJw9/IVzPZ3RCnYmh3NMWyt\neceeZlqjlR1ui7ZmcCaFz23JGuCNrn67mQXXlmr0Xv2uWQExX92s84j5sItw/NOmSDuYgjoPfdx0\nPhb3nATgKuR2KS6NxJhN5wl6LbY1hYmUdqwd6Iyyty1CvqjL1ctG5tK8MTALKA5vqS0XbH+wq5YH\nu5avw5rOFfnc8QGmkzkAtjeHef/BNpkhb1RzA1DIVZ6bevPm8pIzfQvBF8zqiYFjsPd9932Im5EE\n4Co0Fs+Qs23imQLJbIHagJeehoV+bW7LxXx6dzye4f/50ZtkCzYBr8WPL4/zkSOdHO6qYzqZo3cy\nSV3QtKx3uRSZfJHLo3HODM4yFsuU60FcG08wOJMub2cWG0ywwVQ4S02ZNcChRnNuKVWqFZGagtgw\nZGZNUI4NmU4X4RZo3G0q+s/T2qy0SI6bfHNNuzk/Nwgz/SatIXWBlyUBuAoNTKfJFWz8HvNNPpPK\nEcsUqA1WdrDQWvPnL/ZyZSxBNl9kKpmjKeLjc68PcKp/llSuWK52trethmf3NPGZ124wm8rTO5lk\nOpnlYGdtOQinl9zgExtIIQPpGZMD1rb56Hzk5utqeyA1aSqexYZNQA63wIWvmcBavw2a98ADH1p4\nzrUfmDZGiVJe+aGPm/TG5W8vXNP9uKkzISpIAK5ChaJNoajLgVFjOhwvDcCDM+lymclEtoCtNYls\nAa/l4rW+abY2hMppikujMRrCXmZLfeQaw17GYxkm4lnaawOEfBbdDTL73bAGj5sAGm42xXjcfnOD\nraat8rp80jTTrOs2XZM9QdOeyBc2RdbtIpz/Koyeh2i7Car9r8DwKfO6AD/9E+g6uuT9X4eep80K\nC1EmqyCq0INdtdSHvLgUhH1udrZEVszNNoZ8FQXUQz53ue/brUT8Hva217C1McSDW2r5xSNbypXS\nxAbmcleWllz2GhdEt5RKWC76ngjUmRl0ctxsWY6PwfmvmJmvvdCHkGJ+YTYsbklmwFXocHcdF0fj\n5eLr9SEv25purqfaWRegoy6Ay6WoC3ronUryYFctSike7amvSEHsaa3hYGeUc0Nz5VlwW9TPrxzt\nJiQ1gDe+jocXOlNAKVfbdvN1voi5MTd6zvw52w9Ne81suKbDFHYPNZkUA5ibdPVbzSx5XrQD2g5V\nnut8RGa/y5BqaFUqmS1weSyOx+ViV2t4xdlpvmhzZSxOJl+kIexjIp6lxu9hR3O4fBPOpUxHDMvl\nYndLmLFYFltrdrdGCHol+G4aqWmYvGLKUDbvXTkg2jZMXTU34hp2mLRFYsKcG7toZrfzv5F5AnDk\nN+Cnf2xuugXqTEB+6ONmprx5b8JJOcr1aiqR5UT/DLmizYGOKN0Nb72bwFw6z9+82k+uYJPNF5lI\nZDm6rYGHttSyo/k2nQ2EWCqfgcv/AJNXTbDd9W4zmy7mzbK2TMwE9pr2hdUQm9OqArBMf6pMJl/k\nc8cHy1uO3xxP8NEjXbTXBm7zzOVdGYuTK9jYWnNhJEa2YGP1TTM0k+b9hxQ7mqUDhrgDHj/s//lS\nW6NFMcbymDZE13+80Ipo69ug50lHhrleyE24KtM3layo96A1XB6NL3ttvmhzemCWH10eZ2A6RbZQ\n5ET/ND+5MlFu1jlfDyKeKZAtmB8Md2kN56XR2P38UkQ1sYtmPe+tthZn5mC69+YNG8tZ7qZw30uV\nfeBu/HSh6ppYlsyAq0xomZzsfN+2pb5xZpi+yRQAp/pnKlZKnL4xy4cOd7C7NcLJGzOMzKbJ5IvU\nh7w0lPrKyc23TSI9A6f/zqQHALY8Btufrbxm4HWznldrk9c99EurL0eZnIL4iGlrtJhtVwZkcROZ\nAVeZrvoge1oXcrPNNT4OdERvum4uladvMoXWmuHZNKduzPKTK+PkS+UmbW36w9laY2uzbbkp4jMT\nF22acB5ZoY6E2GD6X1kIvmC2FqdnF44LWej9sQm+YHbM9b6wutceOgmv/39w8etm9ry49GXrgcoG\nn+ImMgWqQu850MYjW+vJFWzaov5l1wBblkIpGJrJMDCTIl80W5cvj8bZXwrYHsvF+eEYs6k8tUEv\ntUEvmXyRR7bW89SORlxSmnJzyCUqj7U2myoCpTohhczNqYLs8mmvm16n94WFwB1pA100S87Czaal\nvbglmQFXqcawj/bawIobMMI+N4c6a5lKZgHwuV1sqQ+SyBbIFor4PC4e7q4jV6j8FdDvsYgGPBJ8\nN5OlBXeCDZUrFPxR0xduscWt6FeiNRSX5It9NabIe9vBynoRYlkyA17Hnt3TTP90ksujcWqDXjwu\nRTpf5Pn9bexuiRDwWrhdipM3ZsgVbJK5AgPTZrZ8eSzO8/tbK3bRiQ2q5QGzQmH8gumEvOXozTfR\n9v+8KT05v/a3/cHbv67LZTZcDJ1cOLea54kyWQdcRRLZAi+/OclUIkdPY5CHttSZQuxBb7kuxFKT\niSxfOTVEPFPA7VI8u6eZ/R1RktkC6XyRxrCPqUSWc8MxfnhxjIDHwldqzLmtKSQ94sTdsW0YfQNi\nI6aLRssDy6+Q2HyqcyOGUqoL+CugBdMp51Na6z++1XM2SwD+zGs3yu2H5tJ5EpkCIZ+F5VIc6a7j\n8JZasgXN0EyaSMDNgY4oSimKtqZvMsHfvXaDuVSeWDrPXCZPc8THjuYw73qglWjAw2dfG2A6mWUk\nlkFp8Hvd/P779tEU8TE8l8HjUjTXSGH2DUtrU1ZSucxuuOHTpuJZTQc0bDV/Lg6eyUmTP/aEzJ+R\ndpjpNZ837zWtjcCssoiPAgqinaZwT2bObH0O1JsKapsvHVG1GzEKwL/SWp9USkWAE0qp72mtLzgw\nlqqRyBbKwVdrzemBWSbiGTyWYiaV5+tvDBP2uckXbTRQ4/dwqKuW//CBBxiPZfj4p19nMpEhU1j4\nD1VhcsPfOTfK49sbOdY7zfBchmQmT8HWeN2KvskE+zsWirhvawrxgUPtUph9oynm4Y3Pmu3CdsFs\nSc4mTKH2YhZaDsLud8Ohj5lNFZe/bSqczfablQ0Nu2DsnMn5uiwTrJ/+V5CehgtfhbEL5nzbQWh+\nAC5/y2xFdrnNhozHfge8Um1vqTUPwFrrEWCk9HlcKXUR6AA2dQD2u134PRapbIELIzH6p5IUbU2x\naKNcikShQL5okyvY1AQ8xDJ53hxP8NXTg3z++BDj8Qz5YuVvMxrIFmymU3kujMSYTubI5ovkStcV\nbOibSpErat6+q4mg1831iSR9Uym2Nr717c+iCo2dN8EXTJ536prZnFEw/+kz02tmrGPnzQ264VMm\nMM8OANoE37lBE5xDjWYmfe7zYPlM0R1dhGIRpq6b0pPzE0C7YHLEw6dkV9wyHP29QCnVAxwGji3z\n2CeVUseVUscnJiaWPrzhuC0X79jbTDxbIJEt4HO78FiKogbb1riUMn3iALsUZ22tefnaFDPJbPmx\npea7IWfyRZQCV+ljfj2w1pDNFyuCdzIru5c2nNyi7tnKMgHTzi865zKz21xiYdlasQDz31WFtHnO\n4o0VqSlzTXHR6xQykEsvLE2D0usu3717s3MsACulwsAXgX+htb5pT6zW+lNa6yNa6yNNTZujIeCu\nlgjvP9TOoc4oT+5opD7oxet24bZcRAMe/B4Lj+XC7VJ4LBchr5v6oI+gz71sykABXkvRGPHR0xii\nxu+hNugpd1a2LEXAY1Eb9JZ7zvk8rmVLX4p1rmmPSQcABOvNmt1AHaDM+UC9WZ7WtNfUAvbXmJTB\nfJ63bpvJBc8fu/2w7R2mKHto0c9nTbtZCWEt2oARaYWWfWvyZa43jqyCUEp5gG8A39Fa/9Htrt8s\nN+EAUrkCf/1KP6lckfF4hqlElqawH6/HRX3IQzav6Z1K0B4N8HOHOzg3FONLJweZiGeYSuQo2hqP\npfB73DSGvTy2rYGmGnMzrjXi51vnR3nhygSpXBG/W3Gwq45/9HgPvZMJLJeLh7bU0rBCq3uxzsWG\nYeiEme22H4bxizB4DAoF05p+69sW1gdn5mDgtdIsN28Cr+WDiQsmd9zztOmGUcyZ5WtDJ8ByQ+ej\nULcVhl43XTiCjbDvA6ZE5eZStasgFPCXwLTW+l+s5jmbKQCDWQFxdnAOW2v2d0Rv2eEils7xH75+\ngfFYhhq/h57GEFsbQ/zCka4Vn1O0zUoKv8clqx42s/SMSRUE6+/d6537oqkd7A3BnvduxjrA86o2\nAD8FvAicBeYTSv+H1vqbKz1nswXgOzUwneLb50ZJZAvUBj2872A7TRGZxYoV2DZc+Iq56QZm9rvv\nQ3e/VOzsF0yd4HneEDz+zzZrJ4zqXIamtX6JVQ5OLJhL5zl5Y4ZMrsi+9pqKIu1d9UE+8dRWErkC\nkRXywcsp2pqCbXNuKMbxvmk08HB3HY/03KMZkahOk5cXgi/AxBWYuHT3edrkZOVxLmlaGfmk8P9K\nZCvyOpAv2nz++ADxjFmdcHkszocPd7JlURdjl0vd0bbil9+c5JtnR0jni6SyBXY0R7BcipeuTtIc\n8d1VFw5R5RZXQpuXWebcnWrYbvK+88LNEnxvY9NtT1mPBmfS5eALJm138S6KqV8YnuN//vBN3hxP\ncHEkxpsTCUZjmfLjw7OZWzxbrHuNuyrTAsoFDTvv7DWSU+am3uIU5rZnTBBOTZnX3/PeezHaDU1m\nwOtA0Gt+WNI5U9+hxu8pn1uNyUSWH14aZzKRpachRO9kotwt2We5zNblVI6OUtuj9lq5MbehhRrg\n4EdNEXZ0qXzkoqVkdvHWeduLXzddk8EsMTv0MdOqKD5qum4EG8xrXP4WPPzrUhviFiQArwMtNX48\nluLVwTnAdLL44OHVFdHRWvMPZ0aYTpqygZdH40wns+WNHb5SecrGsA+/x+Lh7jpJP2wGdT3mY7HY\nMFz6B5PLre2CvR8w64EXGzoJV75rUgu+iAm6I6dNl43hUybwzouPmt1zS0tdijIJwOtAtlBEazjQ\nEaVgayJ+N2cH59jVEmE6mUMBY/EMM8k825pC5boOAKlcsRx859UEvOxts+ifSpIv2jy6t4X/7bld\nUv9hM9MaLnzNLCUDswX56nfhwEcWrhk+DSf/GqavmeP67Wbd8Hy3Ddcy4WS5c6JM/nbWgXxRU7B1\nRQ+3ZLbAV04N0TuZ5OpYHKVgR3OYY71TvO9ge7nbcdBrmdoR6TzTyRyDMykawj7es7+Vdz3QQjTg\nYVdLRILvZlfILATfefHRyuP+lyFYZ4KqXYDZGxDtMJXRwKQyJi6ZFkdg6grXtN3/sa9jchOuis0X\n3wn73GxtDFG0NbmCTbEUjHsnk2QLRaaSOSYTOWZSebSGUzcWfpCUUrxnfyt+j4urY3HcloumiI8z\ng3M0hL1sbQxhuUytiKK99rsiRRUo5MDlqcwDA4Rbbq7pgILWg2aFQ7Ae9n9kIcUQboJHPwm7n4f9\nH5abcKsgM+Aq9dNrk5zsn6Fow/6OGiI+N+eH5xiey9BZG8BrKbTW5AuaRLaAAtK5InVBKmazWmt8\nbhePbWsgnSuWH5tN5fjzF3tpCHuZiGepD5mecc/sbmJPa80KoxIbim3DlW+ZG2oul+nh5g7AbB/E\nx8wW5Ff/FPa+zyxdm+kzGy0C9dC0G7Y+DY07Kl/TFzarKi5/08yEG3aYXLI051yWBOAqNDSb5tj1\n6fLxC1cmmEvnSeWK1AY8zKZzHO+fwaVMC6JcwSadKzAay9BRGyh3O55KZPnSySGGZ9O4LUU6V6Qh\n7MPWmmsTSTrr/FwdSzCVyJKo8eNzW3zv/Bg9DSH8nk25e2lzGTsLI2fM50Xb5Hgf/nXoe4nyL8eZ\nOTj3JRNMw61mppyJQV23WXa2VDZuagnPV02bvAoDr5o6E+ImEoCr0EQ8W3GcyhWZTZkbaUVbMx7L\nEvYVCXhdJLJFuhsCRANeXErx9K4mekq1fL/+xjA/vjxOwdYoBfUhLy1RP+mcTcRnMTKXYWA6TbZQ\nJFe02dkSoWBrZlI52qKBNf+6xRpLjC9zbsx8LBYfMZXQLI9ZYhZsMNXQlpOcrCxZudL7CEBywFWp\nqy5QsXSyJuChLRpAYaqlmeVjLhrCfuqCXhrCfjrrgrTXBogGFv5PPXVjlkIpr6s1zKby/PIjXfze\nO3bSFPGTydv4PeZbIFewSeUKBL0WTVINbXNYugxNuczMdun5hp0mtVDx3BWqm9W035xuWOlaITPg\natQQ9vHeA20c652maGvesbcZj+Xim2dHOHVjhoDHYkdLhNqAh7NDc8x3mG8Ie+lZtIY34nezeMNc\nwGMR8nvweSwOdEYZi2fwWi5qAh7cLhf1IS8ffLAD9woNQMUGkZqG6z8224/DLabzhdsH3U+ZGsE7\n3mk2T0z3msd3vtPUdbj2I5NiaHkAOh5e/rXdPjjwUfP6uUTp2ofW8qtbV6Qr8jqTyRf57Gs3mEmZ\nLgQhn8WBjlpCPovdrRF8bouJeJbzw3NcHo1xbihGMlck6LV4ckcDH3u0G4DrEwm+enq4/Lphn5t/\n/GSPBN+NTms49v9WLjnb9gx0P+7UiDaq6qyGJu6O32Pxy0e7uT6ZQKHY1hSqaFk/Ec/y2dduULA1\nWmsawz7e1l5DWzTAkZ668nXbmsK8/1AbF0biBD0WR3rqJPhuBsmJm9f7Tl2VAOwQCcBVKJktcGUs\njsdysbMljM9duSLB63atuFTs4kisnPdVSlET8HCws5bdrTdXpdrRHGFHc2TR+6WXfT+xgfhqTOeK\n4qK+f8GG1T03NW1WNfjCpRZH8n1ytyQAV5l4Js/fHbtBKmf21J+8McPHHt1SMcu9Fa/75uuWOzcv\nlsnzmbt4P7HOePyw891mm3ExbzZU9Dx9++fFhuHU35odcAD1Z+HQL93fsW4CEoCrzLmhWDkYAkwl\nclyfSC47g13OgY4o54djxNJ5tNa01wborg+ueP35Zd6vdzLJrhap47phtR00M9hcYvXtiAZfXwi+\nYG7Qxccg0nJ/xrhJyDRnHVi+4fzyQj43/+jxbva0RsgWbMZiGb57YZRC0V72+uVeex3clxV3y+29\ns15wy35TyDfK3ZIAXGUe6KghsKjWb13Qw/am8C2ecbNMvsiVsQR+j4Wt4eJInNMDy3c8eKA9WvF+\n9SGvtKUXN+s8UlnZrK7H1AIWd0VSEFWmxu/hVx/r5vJoHLdLsbs1csf52Il4tlxwfd5YLLvstdHA\nwvt5rLf2fmKD0Bp6f2JqQ3hDZnnafDv5aCc88glT7cwXgaa9To50w5AAXIXCPjcPd9fd/sIVtEUD\neCxFvrgQhDvrVt5afLfvJzaI4ZPQ/4r5PBs3LeYf/2fgKX3vBOuh+wnnxrcByVRnAwp4rXJr+rDP\nzaNb6znYGXV6WKLazfRVHhfzZvXDnSjmITFR2RlDrEhmwFVmLJbhxauTxDN5drdEeGxbAy7XnRdL\n72kMlYvyCLEqdtG0F7KLEGkzaYdwc+U1o+dg4Jj5fMvjla3sp6/Dha9CPmNSGFseh8nLZhtzy34z\ne5bC/xUkAFeRQtHmK6eGysvCjvVO4/O4eLj7Du5WC/FWpGdh6pqpcpacNN0udj1f2VZ+bsg05Jx3\n8WsmLTF/M+7yt03wBVPG8uX/bgIvQO8L4A1C++G1+XrWCUlBVJHxeLZiTS5A32TKodGITWW23/zZ\ntMfMXLuOmvKTi830Vh5rbdYDg5k1ZxdVfsrGzcdi09fv7Zg3AAnAVaQu6MW9JN3QGJHSkGINhBal\nGlyWSRUsbVEUWpKOgIUUhcsyTTrneYIQWsXzNzkJwFUk4LV4x96WcjeKLfVBjm6V9INYAzVtpsWQ\n5TbBt+UB0/ttscadpgylyzIfXY9A/baFx/e+zzw+/1pHf2uhNnDDDuh6dO2+nnVCylFWoULRJl/U\nFRskhFgTxbxJJ3hW6HgBC12P3av47axYADu/sJRt85BylOuV23IhBcmEIyzPzbnfpVYTeMuv5zYf\nYlmSghBCCIdIABZCCIdIABZCCIdIABZCCIdIABZCCIdIABZCCIdIABZCCIdIABZCCIdIABZCCIdI\nABZCCIdIABZCCIdIABZCCIesi2poSqkJoN/pcawTjcCk04MQG5J8b63epNb6+dtdtC4CsFg9pdRx\nrfURp8chNh753rr3JAUhhBAOkQAshBAOkQC88XzK6QGIDUu+t+4xyQELIYRDZAYshBAOkQAshBAO\nkQC8DiiltFLqbxYdu5VSE0qpb9zmec/c7hqx8Smlikqp04s+eu7je/26Uup/3q/X32ikXen6kAT2\nK6UCWus08Bww5PCYxPqR1lo/6PQgxM1kBrx+fBN4b+nzjwGfmX9AKfWoUuoVpdQppdRPlVK7lz5Z\nKRVSSn1aKfVa6boPrlBin7kAAAONSURBVNG4RRVSSllKqf+mlHpdKXVGKfVbpfPPKKV+opT6qlLq\nulLqD5RSv1L6vjmrlNpeuu79Sqljpe+l7yulWpZ5jyal1BdL7/G6UurJtf46q50E4PXjs8AvKaX8\nwEHg2KLHLgFPa60PA78P/Odlnv/vgB9qrR8FngX+m1IqdJ/HLKpDYFH64culc58A5rTWjwCPAL+p\nlNpaeuwQ8E+BvcCvAbtK3zd/Bvxu6ZqXgMdK33OfBf7NMu/7x8D/XXqPny89XywiKYh1Qmt9ppS7\n+xhmNrxYFPhLpdROQAOeZV7iXcAHlFL/unTsB7YAF+/LgEU1WS4F8S7goFLqI6XjKLATyAGva61H\nAJRS14Dvlq45i/nPG6AT+HulVBvgBXqXed93AvuUUvPHNUqpsNY6cQ++pg1BAvD68jXgD4FngIZF\n5/8j8COt9YdKQfrHyzxXAT+vtb58f4co1gkF/K7W+jsVJ5V6BsguOmUvOrZZiBl/AvyR1vprpef8\nh2Xew4WZJWfu3bA3FklBrC+fBv5PrfXZJeejLNyU+/UVnvsd4HdVaTqilDp8X0Yo1ovvAL+tlPIA\nKKV23WFKavH33MdXuOa7LKQsUErJjcAlJACvI1rrQa31/1jmof8K/Bel1ClW/q3mP2JSE2eUUudL\nx2Lz+jPgAnBSKXWO/7+dO3bZKYzDOP69wkyK4l/A4lV6FouBGJTlXWUzeP8GgzdJSkxmNspCkjJa\n9A7iNVgVg4HJgPwMzz1YnkHJr875fqZT59z1m67OuU/3BXf5uy/iK8CDJFusrqjcAI6Nn3zvWO4r\n6w8eRZakJr4BS1ITA1iSmhjAktTEAJakJgawJDUxgCWpiQEsSU0MYE3WaIB7kuR1krdJ1pOsjbav\nrSTPkhwY/cqvxpFaklxLstk8vmbALghN2WngY1WdBUiyG3gKnKuqz0nWgc2qupjkAvAwyeWx7njX\n0JoPA1hT9ga4meQ68Bj4AhwGno9KjB3AJ4Cq2k5ybzy3qKrvPSNrTgxgTVZVvU9yFDgDXAVeANtV\ntVix5AjwFdj/n0bUzLkHrMlKchD4VlX3gRsstxX2JVmM+7uSHBrX54G9wAngTpI9TWNrRizj0WQl\nOcUyeH8BP4BLwE/gNss6xZ3ALeAR8BI4WVUfkmwAa1W1qmZR+icMYElq4haEJDUxgCWpiQEsSU0M\nYElqYgBLUhMDWJKaGMCS1OQ3rJ3MJk9i4tUAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981091160>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OYOjzM-UcAFf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "10f34625-bef0-4d2c-9090-19e17f6fca02"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='box');"
+      ],
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f498112f0b8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "38OGGGiRcDLX",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "91df6aaa-2034-48db-8c0a-45071ebe160a"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='bar');"
+      ],
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f49811adbe0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ebaZ0TrscG57",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "dc790f96-3609-4fd9-cec1-9bc0c3418e74"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'total_bill', data=tips, kind='bar');"
+      ],
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981426da0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zc7YaGLOcKCc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "3b860f19-d5f9-4ddb-ab86-6a28fc80d3a1"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'percent', data=tips, kind='bar');"
+      ],
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1YahsX2BVF76zgK8Cf9fXMUhSn3oLYICqWgmsnFB2wdD2o8AZ2+l7A3DihLKfMHg2sSRN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\nGjGAJakRA1iSGjGAJakRA1iSGvn/fYSPGJjcKv0AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4982213eb8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F9ZMfv-ZFqjK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Flights"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "nRjKYfrJFqjL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jwRh2xmPFqjO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "flights = sns.load_dataset('flights')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8vphh01GFqjQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9looZ6qNFqjR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "60466154-a9cf-4c23-fb49-829b681ae557"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.shape"
+      ],
+      "execution_count": 90,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(144, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 90
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ci4iSF_hFqjV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Xmz5_kcZFqjW",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "278fb730-76fd-4eb7-c4ac-ec033f2e22db"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.head()"
+      ],
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>month</th>\n",
+              "      <th>passengers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>January</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>February</td>\n",
+              "      <td>118</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>March</td>\n",
+              "      <td>132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>April</td>\n",
+              "      <td>129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>May</td>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   year     month  passengers\n",
+              "0  1949   January         112\n",
+              "1  1949  February         118\n",
+              "2  1949     March         132\n",
+              "3  1949     April         129\n",
+              "4  1949       May         121"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 91
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7Y28WD_HFqjZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fBBcEfjaFqja",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 386
+        },
+        "outputId": "c5a0341e-fef3-4e5e-b4bd-f54fc73c1c65"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.describe(include='all')"
+      ],
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>month</th>\n",
+              "      <th>passengers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>144.000000</td>\n",
+              "      <td>144</td>\n",
+              "      <td>144.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>unique</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>12</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>top</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>December</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>freq</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>12</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1954.500000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>280.298611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.464102</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>119.966317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1949.000000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>104.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1951.750000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>180.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1954.500000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>265.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1957.250000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>360.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1960.000000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>622.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               year     month  passengers\n",
+              "count    144.000000       144  144.000000\n",
+              "unique          NaN        12         NaN\n",
+              "top             NaN  December         NaN\n",
+              "freq            NaN        12         NaN\n",
+              "mean    1954.500000       NaN  280.298611\n",
+              "std        3.464102       NaN  119.966317\n",
+              "min     1949.000000       NaN  104.000000\n",
+              "25%     1951.750000       NaN  180.000000\n",
+              "50%     1954.500000       NaN  265.500000\n",
+              "75%     1957.250000       NaN  360.500000\n",
+              "max     1960.000000       NaN  622.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 93
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XTidBN_hFqjd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot year & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UQ9HYqHDFqje",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 387
+        },
+        "outputId": "117444ab-515b-4653-a0f3-8f57db575468"
+      },
+      "cell_type": "code",
+      "source": [
+        "# flights.plot('year', 'passengers', kind = 'bar') this works but looks awful. Using Seaborn:\n",
+        "\n",
+        "sns.relplot('year', 'passengers', data = flights);"
+      ],
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.FacetGrid at 0x7f4981f285f8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 95
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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sw/QWily5ch3nLH+IK1euozfgKrJGyDGLhKQALINiB0jlmEWGUwpCBsUOkLFX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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981514d68>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MfhkVTc1Fqjk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot month & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UyTJ92B5Fqjk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 415
+        },
+        "outputId": "d97efa27-d448-403a-9242-d3751d8322ea"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('month', 'passengers', data = flights);\n",
+        "plt.xticks(rotation=90);"
+      ],
+      "execution_count": 101,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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172yv55NbfkNYR7YdefrEbn66/P2YSh4niDNTAQ8q4G76k+Q6ToWDXdS89a+01b2KP6Oc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\neDq3zUu0G+BFrfV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SHKm3NNXP3TOm8NODDYS1Zn52KndWOrcavhuUUiy6xMfc6IqfPr/z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\nrkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4981426630>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lcewAIkrFqjq",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Create a [pivot table](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html) of passengers by month and year"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oGgxhxTuFqjr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "pt = flights.pivot_table('passengers', 'month', 'year')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ghQOdM1EebqY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "outputId": "e0c4b4c0-0332-4e54-8a47-81715004edf8"
+      },
+      "cell_type": "code",
+      "source": [
+        "pt"
+      ],
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>year</th>\n",
+              "      <th>1949</th>\n",
+              "      <th>1950</th>\n",
+              "      <th>1951</th>\n",
+              "      <th>1952</th>\n",
+              "      <th>1953</th>\n",
+              "      <th>1954</th>\n",
+              "      <th>1955</th>\n",
+              "      <th>1956</th>\n",
+              "      <th>1957</th>\n",
+              "      <th>1958</th>\n",
+              "      <th>1959</th>\n",
+              "      <th>1960</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>month</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>January</th>\n",
+              "      <td>112</td>\n",
+              "      <td>115</td>\n",
+              "      <td>145</td>\n",
+              "      <td>171</td>\n",
+              "      <td>196</td>\n",
+              "      <td>204</td>\n",
+              "      <td>242</td>\n",
+              "      <td>284</td>\n",
+              "      <td>315</td>\n",
+              "      <td>340</td>\n",
+              "      <td>360</td>\n",
+              "      <td>417</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>February</th>\n",
+              "      <td>118</td>\n",
+              "      <td>126</td>\n",
+              "      <td>150</td>\n",
+              "      <td>180</td>\n",
+              "      <td>196</td>\n",
+              "      <td>188</td>\n",
+              "      <td>233</td>\n",
+              "      <td>277</td>\n",
+              "      <td>301</td>\n",
+              "      <td>318</td>\n",
+              "      <td>342</td>\n",
+              "      <td>391</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>March</th>\n",
+              "      <td>132</td>\n",
+              "      <td>141</td>\n",
+              "      <td>178</td>\n",
+              "      <td>193</td>\n",
+              "      <td>236</td>\n",
+              "      <td>235</td>\n",
+              "      <td>267</td>\n",
+              "      <td>317</td>\n",
+              "      <td>356</td>\n",
+              "      <td>362</td>\n",
+              "      <td>406</td>\n",
+              "      <td>419</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>April</th>\n",
+              "      <td>129</td>\n",
+              "      <td>135</td>\n",
+              "      <td>163</td>\n",
+              "      <td>181</td>\n",
+              "      <td>235</td>\n",
+              "      <td>227</td>\n",
+              "      <td>269</td>\n",
+              "      <td>313</td>\n",
+              "      <td>348</td>\n",
+              "      <td>348</td>\n",
+              "      <td>396</td>\n",
+              "      <td>461</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>May</th>\n",
+              "      <td>121</td>\n",
+              "      <td>125</td>\n",
+              "      <td>172</td>\n",
+              "      <td>183</td>\n",
+              "      <td>229</td>\n",
+              "      <td>234</td>\n",
+              "      <td>270</td>\n",
+              "      <td>318</td>\n",
+              "      <td>355</td>\n",
+              "      <td>363</td>\n",
+              "      <td>420</td>\n",
+              "      <td>472</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>June</th>\n",
+              "      <td>135</td>\n",
+              "      <td>149</td>\n",
+              "      <td>178</td>\n",
+              "      <td>218</td>\n",
+              "      <td>243</td>\n",
+              "      <td>264</td>\n",
+              "      <td>315</td>\n",
+              "      <td>374</td>\n",
+              "      <td>422</td>\n",
+              "      <td>435</td>\n",
+              "      <td>472</td>\n",
+              "      <td>535</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>July</th>\n",
+              "      <td>148</td>\n",
+              "      <td>170</td>\n",
+              "      <td>199</td>\n",
+              "      <td>230</td>\n",
+              "      <td>264</td>\n",
+              "      <td>302</td>\n",
+              "      <td>364</td>\n",
+              "      <td>413</td>\n",
+              "      <td>465</td>\n",
+              "      <td>491</td>\n",
+              "      <td>548</td>\n",
+              "      <td>622</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>August</th>\n",
+              "      <td>148</td>\n",
+              "      <td>170</td>\n",
+              "      <td>199</td>\n",
+              "      <td>242</td>\n",
+              "      <td>272</td>\n",
+              "      <td>293</td>\n",
+              "      <td>347</td>\n",
+              "      <td>405</td>\n",
+              "      <td>467</td>\n",
+              "      <td>505</td>\n",
+              "      <td>559</td>\n",
+              "      <td>606</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>September</th>\n",
+              "      <td>136</td>\n",
+              "      <td>158</td>\n",
+              "      <td>184</td>\n",
+              "      <td>209</td>\n",
+              "      <td>237</td>\n",
+              "      <td>259</td>\n",
+              "      <td>312</td>\n",
+              "      <td>355</td>\n",
+              "      <td>404</td>\n",
+              "      <td>404</td>\n",
+              "      <td>463</td>\n",
+              "      <td>508</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>October</th>\n",
+              "      <td>119</td>\n",
+              "      <td>133</td>\n",
+              "      <td>162</td>\n",
+              "      <td>191</td>\n",
+              "      <td>211</td>\n",
+              "      <td>229</td>\n",
+              "      <td>274</td>\n",
+              "      <td>306</td>\n",
+              "      <td>347</td>\n",
+              "      <td>359</td>\n",
+              "      <td>407</td>\n",
+              "      <td>461</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>November</th>\n",
+              "      <td>104</td>\n",
+              "      <td>114</td>\n",
+              "      <td>146</td>\n",
+              "      <td>172</td>\n",
+              "      <td>180</td>\n",
+              "      <td>203</td>\n",
+              "      <td>237</td>\n",
+              "      <td>271</td>\n",
+              "      <td>305</td>\n",
+              "      <td>310</td>\n",
+              "      <td>362</td>\n",
+              "      <td>390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>December</th>\n",
+              "      <td>118</td>\n",
+              "      <td>140</td>\n",
+              "      <td>166</td>\n",
+              "      <td>194</td>\n",
+              "      <td>201</td>\n",
+              "      <td>229</td>\n",
+              "      <td>278</td>\n",
+              "      <td>306</td>\n",
+              "      <td>336</td>\n",
+              "      <td>337</td>\n",
+              "      <td>405</td>\n",
+              "      <td>432</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "year       1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  \\\n",
+              "month                                                                         \n",
+              "January     112   115   145   171   196   204   242   284   315   340   360   \n",
+              "February    118   126   150   180   196   188   233   277   301   318   342   \n",
+              "March       132   141   178   193   236   235   267   317   356   362   406   \n",
+              "April       129   135   163   181   235   227   269   313   348   348   396   \n",
+              "May         121   125   172   183   229   234   270   318   355   363   420   \n",
+              "June        135   149   178   218   243   264   315   374   422   435   472   \n",
+              "July        148   170   199   230   264   302   364   413   465   491   548   \n",
+              "August      148   170   199   242   272   293   347   405   467   505   559   \n",
+              "September   136   158   184   209   237   259   312   355   404   404   463   \n",
+              "October     119   133   162   191   211   229   274   306   347   359   407   \n",
+              "November    104   114   146   172   180   203   237   271   305   310   362   \n",
+              "December    118   140   166   194   201   229   278   306   336   337   405   \n",
+              "\n",
+              "year       1960  \n",
+              "month            \n",
+              "January     417  \n",
+              "February    391  \n",
+              "March       419  \n",
+              "April       461  \n",
+              "May         472  \n",
+              "June        535  \n",
+              "July        622  \n",
+              "August      606  \n",
+              "September   508  \n",
+              "October     461  \n",
+              "November    390  \n",
+              "December    432  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 103
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rCf1HsJnFqjv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot the pivot table as a [heat map](https://seaborn.pydata.org/generated/seaborn.heatmap.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DBGXckcaFqjx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 299
+        },
+        "outputId": "d0fd336e-229b-4404-a2ee-173ef962ecc5"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.heatmap(pt);"
+      ],
+      "execution_count": 104,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NlrVSZz0kbSrpckn3Sfo/Sd+T1GvxNUknSBpTIu6ixrbUzGxwLCdKba2gJZON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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4982148048>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8Y9RhpsNegXx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file