Election Analysis - 2012 General Election Polls v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some conversion notes\n",
    "For huffpost, if you want to see the charts:\n",
    "https://elections.huffingtonpost.com/pollster/2016-general-election-trump-vs-clinton\n",
    "if you want to get the data, tack .csv at the end.\n",
    "https://elections.huffingtonpost.com/pollster/2016-general-election-trump-vs-clinton.csv\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer the following questions:\n",
    "- Who was being polled and what was their party affiliation?\n",
    "- Did the poll results favor Romney or Obama?\n",
    "- How do undecided voters effect the poll?\n",
    "- Can we account for the undecided voters?\n",
    "- How did voter sentiment change over time?\n",
    "- Can we see an effect in the polls from the debates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "from __future__ import division\n",
    "# For data\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import numpy as np\n",
    "\n",
    "# For visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "import requests\n",
    "from io import StringIO\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL link\n",
    "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
    "\n",
    "# convert the .csv to text type\n",
    "source = requests.get(url).text\n",
    "\n",
    "poll_data = StringIO(source)\n",
    "poll_df = pd.read_csv(poll_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 586 entries, 0 to 585\n",
      "Data columns (total 17 columns):\n",
      "Pollster                  586 non-null object\n",
      "Start Date                586 non-null object\n",
      "End Date                  586 non-null object\n",
      "Entry Date/Time (ET)      586 non-null object\n",
      "Number of Observations    564 non-null float64\n",
      "Population                586 non-null object\n",
      "Mode                      586 non-null object\n",
      "Obama                     586 non-null float64\n",
      "Romney                    586 non-null float64\n",
      "Undecided                 423 non-null float64\n",
      "Other                     202 non-null float64\n",
      "Pollster URL              586 non-null object\n",
      "Source URL                584 non-null object\n",
      "Partisan                  586 non-null object\n",
      "Affiliation               586 non-null object\n",
      "Question Text             0 non-null float64\n",
      "Question Iteration        586 non-null int64\n",
      "dtypes: float64(6), int64(1), object(10)\n",
      "memory usage: 77.9+ KB\n"
     ]
    }
   ],
   "source": [
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05  2012-11-06T08:40:26Z   \n",
       "1           YouGov/Economist  2012-11-03  2012-11-05  2012-11-26T15:31:23Z   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05  2012-11-06T09:22:02Z   \n",
       "\n",
       "   Number of Observations     Population             Mode  Obama  Romney  \\\n",
       "0                  1000.0  Likely Voters       Live Phone   47.0    47.0   \n",
       "1                   740.0  Likely Voters         Internet   49.0    47.0   \n",
       "2                   872.0  Likely Voters  Automated Phone   48.0    48.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        6.0    NaN  https://elections.huffingtonpost.com/pollster/...   \n",
       "1        3.0    NaN  https://elections.huffingtonpost.com/pollster/...   \n",
       "2        4.0    NaN  https://elections.huffingtonpost.com/pollster/...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None   \n",
       "1  http://cdn.yougov.com/cumulus_uploads/document...  Nonpartisan        None   \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  \n",
       "0            NaN                   1  \n",
       "1            NaN                   1  \n",
       "2            NaN                   1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10bfea9d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is affiliation\n",
    "sns.countplot('Affiliation',data=poll_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f29a390>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affiliation by population\n",
    "sns.countplot('Affiliation',data=poll_df,hue='Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we have a strong showing of likely voters and Registered Voters, so the poll data should hopefully be a good reflection on the populations polled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "averages for Obama, Romney , and the polled people who remained undecided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of Observations    1296.679078\n",
       "Obama                       46.805461\n",
       "Romney                      44.614334\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.mean().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the mean, drop the number of observations, Question Text and Question Iteration to focus the data\n",
    "avg = pd.DataFrame(poll_df.mean()) \n",
    "avg.drop(['Question Text','Question Iteration','Number of Observations'],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the stddev, also dropping number of obserations\n",
    "std = pd.DataFrame(poll_df.std())\n",
    "std.drop(['Question Text','Question Iteration','Number of Observations'],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f5d7550>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now plot avg with yerr=stddev\n",
    "avg.plot(yerr=std,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The polls seem very close, given undecideds. Check out the numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>Average</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>46.805461</td>\n",
       "      <td>2.422058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>44.614334</td>\n",
       "      <td>2.906180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.550827</td>\n",
       "      <td>3.701754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3.376238</td>\n",
       "      <td>2.692726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Average       STD\n",
       "Obama      46.805461  2.422058\n",
       "Romney     44.614334  2.906180\n",
       "Undecided   6.550827  3.701754\n",
       "Other       3.376238  2.692726"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate our Average and Std DataFrames\n",
    "poll_avg = pd.concat([avg,std],axis=1)\n",
    "\n",
    "#Rename columns\n",
    "poll_avg.columns = ['Average','STD']\n",
    "\n",
    "#Show\n",
    "poll_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the polls indicate a fairly close race. But what about the undecided voters? Most of them will likely vote for one of the candidates once the election occurs. Let's assume that if we split the undecided evenly between the two candidates the observed difference should be an unbiased estimate of the final difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 586 entries, 0 to 585\n",
      "Data columns (total 17 columns):\n",
      "Pollster                  586 non-null object\n",
      "Start Date                586 non-null object\n",
      "End Date                  586 non-null object\n",
      "Entry Date/Time (ET)      586 non-null object\n",
      "Number of Observations    564 non-null float64\n",
      "Population                586 non-null object\n",
      "Mode                      586 non-null object\n",
      "Obama                     586 non-null float64\n",
      "Romney                    586 non-null float64\n",
      "Undecided                 423 non-null float64\n",
      "Other                     202 non-null float64\n",
      "Pollster URL              586 non-null object\n",
      "Source URL                584 non-null object\n",
      "Partisan                  586 non-null object\n",
      "Affiliation               586 non-null object\n",
      "Question Text             0 non-null float64\n",
      "Question Iteration        586 non-null int64\n",
      "dtypes: float64(6), int64(1), object(10)\n",
      "memory usage: 77.9+ KB\n"
     ]
    }
   ],
   "source": [
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a messy time series analysis of the voter sentiment by plotting Obama/Romney favor versus the Poll End Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1218237d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is reversed - RHS is earlier - LHS is later:\n",
    "poll_df.plot(x='End Date',y=['Obama','Romney','Undecided'],marker='o',linestyle='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a new feature to feature the difference between Romney and Obama in the polls\n",
    "\n",
    "The Difference column is Obama minus Romney, thus a positive difference indicates a leaning towards Obama in the polls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://elections.huffingtonpost.com/pollster/...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05  2012-11-06T08:40:26Z   \n",
       "1           YouGov/Economist  2012-11-03  2012-11-05  2012-11-26T15:31:23Z   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05  2012-11-06T09:22:02Z   \n",
       "\n",
       "   Number of Observations     Population             Mode  Obama  Romney  \\\n",
       "0                  1000.0  Likely Voters       Live Phone   47.0    47.0   \n",
       "1                   740.0  Likely Voters         Internet   49.0    47.0   \n",
       "2                   872.0  Likely Voters  Automated Phone   48.0    48.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        6.0    NaN  https://elections.huffingtonpost.com/pollster/...   \n",
       "1        3.0    NaN  https://elections.huffingtonpost.com/pollster/...   \n",
       "2        4.0    NaN  https://elections.huffingtonpost.com/pollster/...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None   \n",
       "1  http://cdn.yougov.com/cumulus_uploads/document...  Nonpartisan        None   \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  Difference  \n",
       "0            NaN                   1        0.00  \n",
       "1            NaN                   1        0.02  \n",
       "2            NaN                   1        0.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df['Difference'] = (poll_df.Obama - poll_df.Romney)/100\n",
    "poll_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now to visualize how sentiment in difference changes over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>Start Date</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>686.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start Date  Number of Observations  Obama  Romney  Undecided  Other  \\\n",
       "0  2009-03-13                  1403.0   44.0    44.0       12.0    NaN   \n",
       "1  2009-04-17                   686.0   50.0    39.0       11.0    NaN   \n",
       "2  2009-05-14                  1000.0   53.0    35.0       12.0    NaN   \n",
       "\n",
       "   Question Text  Question Iteration  Difference  \n",
       "0            NaN                   1        0.00  \n",
       "1            NaN                   1        0.11  \n",
       "2            NaN                   1        0.18  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
    "\n",
    "poll_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "positive difference indicates a leaning towards Obama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting marker lines on the dates of the debates and see if there is adds insight. The debate dates were Oct 3rd, Oct 11, and Oct 22nd. Let's plot some lines as markers and then zoom in on the month of October.\n",
    "\n",
    "In order to find where to set the x limits for the figure we need to find out where the index for the month of October in 2012 is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "325\n",
      "352\n"
     ]
    }
   ],
   "source": [
    "# Set row count and xlimit list\n",
    "row_in = 0\n",
    "xlimit = []\n",
    "\n",
    "# Cycle through dates until 2012-10 is found, then print row index\n",
    "for date in poll_df['Start Date']:\n",
    "    if date[0:7] == '2012-10':\n",
    "        xlimit.append(row_in)\n",
    "        row_in +=1\n",
    "    else:\n",
    "        row_in += 1\n",
    "        \n",
    "print(min(xlimit))\n",
    "print(max(xlimit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x121af3450>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WKp4SD7XVtY0eqiqvYt598/S+FZGoqiipoOj1Iko2Bu5EqCytZOW/VrZKWZWllQGX11bW0vPknrgz3Y0CcbDf7gw3CR0Cz/1QvLQ44GtJzUrl2PHHsmnxJr6Y8QUAiamJ9B7Ruz5M9x7RG3eGu1Vea3sU0+EZ1DN1OBrPJK2t16m9DgnPSWlJjPvTOIZcPqTZ7avKqyjfdTBgl+8obxS4P/n7JwG3K9lYwqYlm+gzsg+uBB1KFJHIqK6oZs27ayicXsiXM7+kxlNDQmLCIf/kA2T2zeTODXe2SrmP93s8YLDN7JvJpS9f2ipl5E/Jb5QRwPd5Pv6J8fUZYf+W/WxasolNizfx9eKvWTRlEd5aL64EF91P6N6odzq9Z3qr1Ks9iPnwDOqZCuZw41N7j+xNRu8MOiTrMIw4U7q1lLWz19LzlJ6UbS8L6WhGUloSmWmZZPbJDPj4mnfXBO7VccHzo54ns28mQ68eSt6EPLrndW/JyxFpVTrK137U1tSyYcEGCqcXUvRaEZ59Hjp278iwW4aRNyGP3Wt3M/PmmYeEzvwp+a1Wh2DBtjXLqHt/Hu59m94znSGXD6nvHPHs91C8tLg+TK94dgXL/rIMgM45nRuF6W6DurHy5ZVxuV+0ifAMvp6pjx77iEEXDaJL/y7Rrk7U1VbXBj20VLatjCcGPAEu6NSjE5nZmUF/UrumNnvSgL404sOCXy2gprKGS6dfStbArLCUUfeF0VBSWhLnPHEOie5ECqcX8uEfPmTJ75Zw9NCjGTphKHlX59G5X+ew1EfECR3la/u8Xi9b/reFwumFrPzXSsq2lZGcnszgSwczdMJQcs7MISHRN/yh9/DeuHCF9XvPSbBtrXKO5Dnd6W4GjB3AgLEDAKipqmHrp1vrw/S699bx+UufA5DUMYnqimq8Nb6TEks2lvDWTW9R7anmxBtObLUTEmMxg7i8Xm/za0XJvJfmeRdftxiAhKQEaqt8h1G6H98dc7Fh0MWD6HFijxY1UFFREbm5ua1S36BcLmjFv/OG/25g9h2z2fb5toCPdzy6I/m/y6dkUwklm0rYt2lf/e3qiupG6yamJh42XG9avIl3b333kP+OL3jqgqi/eUMRkfZug7av3M4/TvgHp95xKuc8dk5YyyqcVsjxa48H4LHnHzvkg7BsRxmrX1lN4fRCvl7iOzmnz7f7MHTCUIZcPoSOR3d0XJbaO76Eq70Pd4i9tQ7ji3MPPvhgo/uTJk0Kuu6Ooh2sfHklhdML2bNuDx2SO3Dc+ccxdMJQjj33WJJSk8Jd3XbF6/Wye+1uNi3exKzbZ1FVFvg8l4TEBNwZgcdtOxnLnZKZQlLHJFZOXxmwhz5cGaSgoKBg2LBhJze3Xpvoea77Q/Ua0Qv7puWLN75g0UOL+ODXH5CZnVkfpPuO7lv/n2N7VPJ1CXN/MZdV/15FZnYmp95xKiueWXHIm2rcn8YFfFN5vV7Kd5bXB+mm4XrNO2so3VrabD00jKb9mXv3XNwZbk5/4PSwl5V3TR74v/sCBY+OR3XklB+fwik/PoW9G/ay8l++L75Zt81i9k9mM2DsAPKuycNcZHCn64QWCY/amlq2frqV9fPXBz3KV7KxhJfOfomsgVmNfrr070JiSpv4em2X9hXvq//c2LpiK64EFzln5TD6vtHkXpJLSueUaFexzXK5XHQ9titdj+3KWze+FXS9b9/97YPT5+2toKKkgr0b9h6cVm+f55Bp9A4pq4MLr9cLTYafV5VXMeuOWaQdlUZmdiYZfTJI7pjcGi/PsZD3bmNMAvAkcALgAX5grV3b4PGbgFuAauAha+1MY0w3YDqQCmwBbrDWlh+unMy+jbvoR/50JCN/OpKy7WV8OfNL7JuW5U8tZ9kTy0jNSuW484/DXGwYcPaAiP8xw6W6opoP//ghix5eBF44ffLpnPaL00hKS6L3qb0dH85wuVx0PKojHY/qSM9hPQOX5almX/HBnuo3r38z4HolG0t4bcJr9WOfjh56tE70aqPWzV3H2llrOfvRs0nNSo12dRrp3K8zo+4Zxah7RrGtcFt9D9KMiTNITE3EXGjIm5DHwHMGany/tIjX62XH6h2sn7+eDfM3sGHhBir2VgAEPYEsKS0JT4mHVf9exYHdBw4+4PLNXNM0VGcNzKLLgC5Bv5vaw4U5oqFwWiEDzhlA0WtFFE4vZOMHG8HrOwF63OPjGHLFENKP0clurS0zOzPoEZnmxm57vV4qSyubnb968cOLA25/YPcBpo6bWn8/tWvqYY+id+rR6bAZpW6/yPtN3jCGNf/aQx62YYz5LnChtfZ6Y8wI4JfW2ov8j/UA5gInAynAYv/tPwDLrbX/NMbcA3istY8FK6OoqMjr5BBcZVkl6+asw75hsW9bKvZUkJiayICzBzDo4kEcd/5xpHVLC1ZGzA7b8Hq92Dct7/30Pfau38vgywYz9o9j6dw3cuM/gx2uTEpLIqVzCvu37Ad88/5mn5ZNn1F9yB6VTa9TesVkz4sO4zdWW1PLUyc9RWVpJT9e/WMS3ZFpM9eDvg8x76TQ9ovij4opnF7Iqn+vonxnOSldUhh82WDyJuTR9zt94/Yklnh3JPu31+tlz1d76sPy+gXrKdtWBvhOjMo5K4ecs3Lod2Y/Nszf0Oyh4wO7D7B73W52r/X97Fm7p/522fayRmV3OqbTIaF615pdLJqyiOoD1UHLaKmmY7fDUUa4FU4r5PW1rzda5vrNwR7Krqarb5zv1XlhO3dDfCLxfgqWQdJ7pXPpy5cGPIq+d+NeKvc3ngowISmBjN4ZAYP1ts+3sXDyQqoPVDPqxVHkT8xvtiewJd+Uo4DZANbapcaYhmNETgWWWGs9gMcYsxY43r/Nw/51ZvlvBw3PTiV3TCb3klxyL8mlpqrGN3fhG1/4wvSbFleCi+zR2Qy6eBDmIkOXnC4x/9/3jqIdzP7JbL6a+xVHDTmK6+ZdR85ZORGvR7Azgi946gKGThjK3g172bT44DQ3a95dA0CH5A70PLknfU7zhek+p/UhrWvgf2Akej574TO2fb6Ny/5zWcSCc0u5XC76fLsPfb7dh3GPjWP9vPW+k4BeXsnyp5fj7uymqrSqvqewLZ/cpZ7I1rVv876DYXn++vqLAnU6phP9x/SvD8tdchqflO7k5K7UrFR6ZfWi1ym9DinXs8/jC9ZrDobr3Wt3s3b2Wkq/CT5Urqq8ijeu9100ozXs3bA34DzrM380kz0b9gS8wFFq1pFdUbSl76dqTzUHdh1oNMVlw58Vz66ov8hSHW+Nl+SMZK5feH2Lz4MS5yJx0mOwDDL292PpO7pv0O0qSioOGaJastH3e8PCDezfvL/ZYSOH05Ke52eA16y1s/z3NwH9rbXVxphrgTxr7f/5H3sReBH4h3/5AWNMf+BFa+2oYGV8+umnXrc79DGNXq+XvUV72TxvM5vnbabkS98HZWqPVCp2VuCtPvjaO6R04ORfn0zf84M3RqhyBw+maPVqR+tW7q9k9ZOrWTNtDYlpiQy9bSgDrhoQ1bHcG2dupPCxQsq3lpPWI428u/KC/p08ez3sWrGLHct3sHP5TvYU7qn/sM7on0G3Yd3odpLvp2PvjrhcriN6/ki8hnhRXV7Nu+PfpWOvjpw17ayIfuEM/s9gAFZf4Wy/cKL6QDXfLPyGZfcuo8Zz6FW5UnukcsH8C1qtvHDbOHMjn/zqE2oqDr6W1v6cilQZ0dq/PXs8bF+2ne0fb2f70u3s3+A7UpacmczRw4+u/0nPSY9a4Kour6b061LmXDIn6DrZ52W3Slmb3tkU0nZJGUm4O7txd3GT3CW58e0u7vr7Oz/dyaq/rGq0/3Vwd2DwrYPpdmI3PHs89T+VeysD3q4uqw5ej/QkqvZXweQmD0wGXHDFqitCen0S28LxGVJbXcuB7Qco/6acBRMX1C+PRM/zPqDhIKIEa211kMfSgb0Nlh9osCwot9vd8kPsgwH/fOO71+3GvmmZd++8RsEZoKaihqInijjnF+GZaaC51+Gt9fLpPz9l3i/nUbajjJNuOomzHjqLjkc5n1kgXHJzc4/s7zLy4M2qA1Vs+WRLfc/0pjmb+OqVrwBfb09Gnwy2rthaP5NK+TflLJ+8nF49e7Xqocrlk5fX/+cajjLaooUPLqRiRwUT3pxAn8F9olKH1h5Ck3dSHh/9/KOAjx3YeoCPf/gxA8YNYMDZA+jxrR5Br8wVTV6vl112F2//7u1GoRZ8n1PL7llG4SOFrVLWgd0HDul9qamo4ZMHPmHHnB0kpSWRlJZEYlpi/e0j+VnzzhoKJhdQXe77aojU/v2/e/9H0V+K6i/4k9wpmb6n92Xk7SPJOSuH7sd3j61zNIbBx30/Djp+9IaZN7RKMYebNeQ2e9the3wb/uxet5vyHeWHzN4USI2nhsI/Hfp+TeqYVN+73aVnF9KOP7TXu1EPeFdfD/jj/R6nhACvITtTQ/LaqboMEq5hl8vvXx70pOBgWhKelwAXAP/xj3luuHcsA6YYY1IAN5ALrPRvcy7wT2A8sKgF5R+xrAFZjPzpSOb8PPB/+PuK9/HM8Gfof3Z/Bpw9gN4jekfkWu/FS4uZdccstvxvC71H9mbCuxOCntDX1iSlJtF3dN/6wyveWi/bV22vD9Mr/72yfo7IOlXlVbw+8XVm/nBmq9ShsqwSmhxgqSqvYt698TtjyP5v9vPhIx8y+PLB9BkZneAcLsFOYnFnuKksrWT+ffOZf998Urum0j+/f/3+HuzCLuFWU1nDN8u/OTj8acnXlO8Mfh61t9bL4CsGt0rZnzwZ+IqPNZ4a39Ui/Zdcb/jjJDAdTiT279rqWsp2lHHWlLPIOSuHY4YdE5HP8paIxEUzDldGojuR9J7pR3QVubr3SN1PwxO4GnHBxDkTGwXhUKeIy5+Sf8iY59b+O0l8CbRfNKcl4XkGMNYY8yHgAm4wxvwUWGutfcsY8wS+cJwA3GetrTDGPAS84J+JYycwoQXlhyzol2umm4TEBBY/vJhFDy0iOT2ZnDNz6r9cswZmteqhvdKtpbx/z/t89sJndDqmE5dMvYS8CXnteryWK8FF97zudM/rzik/OoXCl4P0oHnhpJtPapUyl/5pacDlJZtKWDh5IUOvHko3061VymorFjywgJqqGvJ/2/6+cIIFhPOePI+8a/Io21HGV+9/xVdzvmLdnHWs+s8qALoN6la/r/c7vR/JncIzW09FSQXFHxXXh+XNH2+uD6RZA7M47vzj6DOqDwt+tYDSLYeOh83sm8l5fzuvVeqy5p3AV3zM7JvJ95d8P+A2tTW1VFdUHxKqA/3MvDlIQI7A/l3jqWH0vaNbpYxIiMT40dYuIyktqf6kK/C9bwK+n7Iz6T+mf+gVbyDvmjxef7BxeG5LJzxK7Gm4XzgV0xdJcTrbxpFq7gzRir0VrF+wnnVz1rHuvXXsXe8bXdK5X+f6L9ecs3JI7eJwWq8ms23UVNbw8RMf899f/5fqimpG/mwko+8dHZdz1kbi4gPByujg7kBNZQ144Zhhx5A3IY8hVw4ho1dGq5Qbq7Z9vo1/nPgPRtw1gnGPjotKHVoy24YTTk9aqpuebN2cdXw15ys2/HcD1QeqSUhKIPu07Pr9/ZhvHRPyYf59xfvqg/KmxZt8Fzfy+uYwPeZbx9TPUJN9WjadenRq9BrCfSZ7uMuI5v6tC5hEXqRm8ziSi6RI+xGJ2bKcXiQlLsMzHNkZwbvX7a7/cv1q3ldU7q/EleCi16m96H92fwaOG0ivU3sFP6mvQXheO3sts38ym11f7uK484/j7D+dTddju4blNbYF0Q4I/c7sx8p/r2Tl9JVs+WQLuKDfGf3Im5BH7qW5zv9BakOmjpvK5v9t5o61d0RtXudwh+c6R/phW11RzaYlm1j33jrWzVnHts98V/FM65ZG/zH+IR5jB5DROyPgZ8jQq4eyY/WORmG5LtgldUyiz8g+9WG59/DezfZut/XZNqK9f6s3MvIi8Z5VeI5PCs8OhTM8NyjjiBqjpqqGzR9v9vVKz1nHlv9twVvrxZ3hJic/hwFn+05G6tL/4HR4d268iz/1fJSOPTqydflWso7N4pzHz+HYc48N4ytrO2IlIOz6chefxaqnAAAVTklEQVSFLxeycvpKdn25i4SkBI4991jyJuRx3PnHkZTW9i/juva9tUw7ZxrjHhvHiDtHRK0esRqemyrdWspX739Vv7/XzQOc3jOdsu1ljab9cnVw0SG5Q/08vZ16dPJN0+gPyz1O6NGur4AaTKzs39J+KDzHJ4Vnh2IxPDd1YPcB1s8/OMSjbt7QtKPTqNhdQW11LZOYzIP+uXWGXDmES168RFdDixIn7e31evlm+Te+i3D8axX7t+wnuVMygy4ZRN6EPHLyc2L+5KNAamtq+X8n/j+qDlRx6+pbo/oebCvhuSGv18v2wu2sm7OOBQ8sCHjiXFKnJM7967lkj8qmS/8u7fr8hVikiyDFB4Xn+BRL4bltXBUhhqVmpTL4ssEMvmwwXq+X3Wt8Qzzm3j034OVci5cWKzjHOJfLRc9hPek5rCdjHxnLxg82Uji9kKJXi/j8pc9JOyqNIVcMIW9CHr1H9m4zAenT5z9l+8rtXP7q5XoPhsDlctH9+O50P747c++eG3CdqrIqTvzeiRGumYiIRJLCcytyuVx0Pa4rXY/ryqw7ZgVcp65nWtqGhA4J5JyZQ86ZOZz713NZO3stK6evZMWzK/jf3/5H536dGXr1UPIm5LHts20xe+i4srSSBQ8soM9pfcj9rnrmWirYjD11sw6IiEj7pfAcJvpybX8S3YkMumgQgy4ahGe/hy/e+IKV01ey5JElLP7tYt+Ejf5RCLF2Seglf1hC6dZSrnzjyjbTUx7LIjEnr4iIxKb4O3slQvKn5B9ygpm+XNsPd7qbEyaewDWzruFnW35GSpeUwBdiOYJ5I8Nl3+Z9fPiHDxly5RB6D+8d7eq0C3nX5HHBUxeQ2TcTXL5p0TS7g4hIfFDPc5g0mnR7o+/LNZYO40vr6Xh0Ryr2VgR8LBaG6Sx4YAHeGm+7vCBKNOVdk6f9WUQkDik8h1H9l6vrLk3W384FG6aT1i0tCrU5aOtnW/n0n58y8mcj6ZLTJap1ERERaQ80bEOkFQQapoMLyneWs/JfK6NSJ6/Xy9yfzyW1S2qbukyxiIhILFN4FmkFwcbA9h3dl9cmvEbBUwURr9Pa2Wv56v2vOH3S6e3ySokiIiLRoGEbIq0k0BjYvGvyeOWyV5h5y0wqSio47RenRaQutdW1zP35XLIGZnHyD5ud711EREQcUs+zSBglpSZx5YwrGXLlEN6/+33m3TePSFzVc8VzK9ixegdjHhmjC6KIiIi0IvU8i4RZh+QOfHfad3Fnuln88GIq9lZw7l/OxZUQnvmWPfs9LPjVArJHZTPo4kFhKUNERCReKTyLREBChwTO/8f5pGSm8OEfPqRyXyUXPnchHZJav1d4ySNLKNtWxtVvXa0LooiIiLQyhWeRCHG5XIz5/RhSuqQw/975ePZ5uOzfl5GY0nq74b7ifXz06EcMvXoovU7t1WrPKyIiIj4a8ywSQS6Xi9G/HM25fzsX+5Zl+nnT8ez3tNrzz79/Pt5aL/kP64IoIiIi4aDwLBIFp/z4FC556RI2/HcDL419iQO7D7T4Ob9Z8Q2fvfgZw38ynM79OrdCLUVERKQphWeRKDn+2uO54rUr2LpiK/88/Z/s/2Z/yM9Vf0GUrFRG/1IXRBEREQkXhWeRKBp00SAmvDuBPev38Pzo59m7YW9Iz7Pm3TWsn7+eMyafQUrnlFaupYiIiNRReBaJsv75/blu3nUc2H2A50Y9x46iHUe0fW11LXN/MZeux3Vl2C3DwlRLERERgRBn2zDGpAJTgaOB/cD3rLU7mqwzCTgPqAbutNYuM8Z8C5gJrPGv9ndr7b9DrbxIe9F7eG+u/+/1TD17Ks+Pfp5r37uWnsN6Otp2+TPL2Vm0kyvfuDIsU9+JiIjIQaH2PP8IKLTWjgZeBO5v+KAx5iTgdGA4cBXwN/9Dw4A/WWvP8P8oOIv4dc/rzg2LbiC5UzIvnPkCGz/Y2Ow2nn0eFk5aSN/v9MVcaCJQSxERkfgWangeBcz2354FjAnw+BxrrddauwlINMYchS88n2eM+cAY86wxJj3E8kXapayBWXx/8ffJ6JXB1HFTWfPumsOuv/j3iynbXsbZj56tC6KIiIhEQLPDNowxNwJ3NVm8DSjx394PZDZ5PAPY1eB+3TrLgGestQXGmPuAScDPg5Xt8XgoKipqrootUlFREfYyciHsZYgzkWjv1vDtZ77NopsX8fJFLzP898PJHp99yDrl35Tz0aMfkX1+NiUdSygpKgnwTLGtPezfEjvU3vFJbR4fYmn/bjY8W2ufBZ5tuMwY8zpQ12ucDjSdImBfg8cbrjPDWlu37gzgL4cr2+12k5ub21wVW6SoqCjsZQARKUOaF6n2bg25H+by8gUvs/TnS+mW3o1hNzU+GXDGb2cAcMlfL6Fz37Y5r3N72b8lNqi945PaPD5EYv8uKChwtF6owzaWAOf6b48HFgV4fJwxJsEYkw0kWGt3Au8ZY071r5MPOKulSBxKyUzh2tnXMvCcgcy8eSYf/vHD+se+Wf4Nn7/0OSPuGtFmg7OIiEhbFNJsG8DfgReMMYuBSmACgDHmEeBV/8wai4CP8AX0W/3b/Qj4izGmCtgK3NySyou0d0lpSVz1xlXMuG4Gc38xl02LN7F1xVZKNpXgSnDRZUCXaFdRREQkroQUnq215cDlAZbf3eD2ZGByk8eXA6eFUqZIvOqQ3IHvTvsupdtLsW/a+uXeWi/v/eQ9klOTybsmL4o1FBERiR+6SIpIG5DQIYG96w69+mBVeRXz7psXhRqJiIjEJ4VnkTai5OvAs2mUbGp7s2yIiIi0VQrPIm1EZnbTGSEPv1xERERan8KzSBuRPyWfpLSkRsuS0pLIn5IfpRqJiIjEn1Bn2xCRCKs7KXDeffMo2VRCZnYm+VPydbKgiIhIBCk8i7QhedfkKSyLiIhEkYZtiIiIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEOJoWxkjEkFpgJHA/uB71lrdwRYbyAww1qb57/fDZgOpAJbgBusteUh1l1EREREJKJC7Xn+EVBorR0NvAjc33QFY8xE4F/AUQ0W/wqY7t9uBXBLiOWLiIiIiERcqOF5FDDbf3sWMCbAOnuA00PYTkREREQkJjU7bMMYcyNwV5PF24AS/+39QGbT7ay1M/3bN1yc0dx2DXk8HoqKipqrYotUVFSEvYxcCHsZ4kwk2lucaw/7t8QOtXd8UpvHh1jav5sNz9baZ4FnGy4zxrwOpPvvpgN7HZa3z7/+ASfbud1ucnNzHT51aIqKisJeBhCRMqR5kWpvcaa97N8SG9Te8UltHh8isX8XFBQ4Wi/UYRtLgHP9t8cDi8K8nYiIiIhI1IU02wbwd+AFY8xioBKYAGCMeQR41Vq7LMh2D/m3uwnYWbediIiIiEhbEFJ49k8vd3mA5XcHWNajwe1twDmhlCkiIiIiEm26SIqIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEOJoWxkjEkFpgJHA/uB71lrdwRYbyAww1qb57+fBXwJrPSvMsNa++dQ6iAiIiIiEmkhhWfgR0ChtXayMeYq4H7gJw1XMMZM9C87qsHik4CXrbW3h1iuiIiIiEjUhDpsYxQw2397FjAmwDp7gNObLBsGDDPG/NcY84ox5pgQyxcRERERibhme56NMTcCdzVZvA0o8d/eD2Q23c5aO9O/fcPFXwAF1tr3jTHXAH8BLgtWtsfjoaioqLkqtkhFRUXYy8iFsJchzkSivcW59rB/S+xQe8cntXl8iKX9u9nwbK19Fni24TJjzOtAuv9uOrDXYXnzgXL/7RnArw+3stvtJjc31+FTh6aoqCjsZQARKUOaF6n2Fmfay/4tsUHtHZ/U5vEhEvt3QUGBo/VCHbaxBDjXf3s8sMjhds8Al/pv5wPOaikiIiIiEgNCPWHw78ALxpjFQCUwAcAY8wjwqrV2WZDt7gGeM8b8GCgDfhBi+SIiIiIiERdSeLbWlgOXB1h+d4BlPRrcXg+cGUqZIiIiIiLRpoukiIiIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMur9cb7ToEVVBQsAPYGO16iIiIiEi713fYsGFHNbdSTIdnEREREZFYomEbIiIiIiIOKTyLiIiIiDik8CwiIiIi4pDCs4iIiIiIQwrPIiIiIiIOJUa7AodjjEkCngP6AW7gIWA18E/AC6wEbrXW1vrXHwjMsNbm+e9n+7dPBFzAzdZaG6SsO4Ee1tp7/PcvAH4FVAPPWWufDrDNTcAt/nUestbONMY8DpzoX6UHsNdaO6Jlf4n4EOvt7V/vKGAJcLy1tsIY0xGYDnQBKoHvWWs3t+wvER/aaHtnAv8COgEe4Fpr7daW/SXiQ6y3tzHmLuAq/913rbUPNnhsEPAx0N1aWxH6XyF+tMX2NsZkAVOBDGAXcJO1dnvL/hLxIZrt7V+WBswFbrTWfhFgm0PeEy35/o71nudrgV3W2tHAOcBfgT8B9/uXuYCLAIwxE/F9qTWcn+83wF+ttWcADwO/bVqAMSbVGDMNuLXBsiTgMeBs4HTgZmNM9ybb9QDuAE4DxgG/Nca4rbV3+ssbC5QAN7XwbxBPYra9/euNA+bg+6eozk1AgbX2O/g+dO8O6ZXHp7bY3tcDhf76/Rv4RSgvPE7FbHsbY/oD1wDfBkYAZxtjjvc/lgE8iu+fJXGuLbb3vcBia+0o4C/+csWZqLS3f/nJwAfAgEAVO8x7IuTv71gPz68AD/hvu/D9xzAM+K9/2SxgjP/2Hnx/lIZ+Brzjv50IBOoxSAFeAKY0WJYLrLXW7rHWVgKLge802e5UYIm11mOtLQHWAsc3ePx2YI61trC5Fyn1Yrm9AWr95e+uW2CtfbzBc2UDe4O/PGmizbU3UAik+29nAFVBXpscKpbb+2vgHGttjbXWCyQBFcYYF/AUvlBV7vB1ik+ba29gsL9e4DviNKr5lyl+0Wpv8PV0XwIc0uPsF/A90ZLv75getmGtLQUwxqQDrwL3A3/0v9kB9gOZ/nVn+tdtuP1O/zID/BG4OEAZe4A5xpjrGyzOwNdrXKe+HCfrGGOS8Q3nONXpa5WYb2+stXOblulfXmOMmQ/k4TviIA600fbeha+XajWQBYx2+HLjXiy3t7W2CtjpD8t/AFZYa780xkwG3rHWftZ0v5fDa6Pt/SlwIbDC/zsthJcel6LY3lhrlzR9viaCvidC/f6O9Z5njDF9gAXAS9ba6fh6g+qk08x/CsaYM4E3gInWWmuMGWWMWej/OS/IZvs42LtUX44x5hn/dq8EW8d/ewzwgb9HWo5ADLf3YVlrz8IXpF5rbl05qA229yTgEWvtYHyHANXeRyCW29sYkwJM8z/+Y/+61wI3GmMW4hu+M+cIXm7ca4Pt/VugnzHmA3xjd78+gpcb96LU3sGe66G6bfGF5WB5LaTv75juefaPSZkD3GatnedfvMIYc4a1diEwHl9DBdv+TODP+A7PbASw1i4Gzmim6CLgWOM7eaAU3yGfP1prX23w3D2AKf4d0I3vsMBK/8NjOHjoRxyK5fY+TJm/BIqttS/5t61pbhvxaYvtje9wY90/xdvx9WiIA7Hc3v4eyDeB+dba39ctt9YObLDOBnz/MIkDbbG9/es+ba390BhzKb6hG+JAFNs7IGvt/Q2eO4kA74mWfH/HdHjGN86sC/CAMaZuLM1PgCeMb2hEEb7DA8E8DiQDL/i786219pbmCrXWVhljfgq8h693/jnb5AxMa+1WY8wTwCL/OvfZg2dhG+BFh69RDorZ9j6M5/zl3Qh0AG5wuJ20zfZ+AHjGGPNjfOMkdUKwc7Hc3hfjG4PpNsaM9y/7pbX2I2cvTQJoc+0NWOBFf3mbgRubfZVSJyrt7USw94QxJuTvb5fX621+LRERERERif0xzyIiIiIisULhWURERETEIYVnERERERGHFJ5FRERERBxSeBYRERERcSjWp6oTEWnXjDH34JsbPgnfRQV+bq0tMMbkAV2stR84fJ7vAHuttZ83WNYP+BxYju+SuW5gqrX2r4d5nix8c61OD/EliYi0a+p5FhGJEmPMYHyXAR5rrT0duAvf3OEAlwKDj+Dpvg/0DLB8tbX2DP/zjwbGG2MuOMzzHO+vk4iIBKCeZxGR6CkBsoHvG2NmW2s/NcacaozpBVwPVBpjlvvXuRVf77QXuAQYCvweqATeB84BTjLGrLbWbgpUmP9iAX8GrgPeNsb8FjgZ6Ap8Zq29AbgPOMEYczO+K6U+BaQCB4CbrbW6ZLGIxDX1PIuIRIn/ymcXAqcBHxljvgDO9y//J/Ana+0y4DjgPGvtKGA1MM7/FCnW2tHW2geB2cDdwYJzA9uAbsaYDGCPtXYsvgA9wh/ap+C7bPFTwB+BJ6y1Z/hv/661XruISFulnmcRkSgxxgwE9llrv++/fzIwyxizoMmq2/FdRrYUGATUXTbahlBsX6AYX0/y0caYl4FSoBO+nu2G8oB7jTH/h2/MdFUI5YmItCvqeRYRiZ7jgb8aY5L9978E9gI1+E4eTDDGZAIPAlcBP8AXel3+9WsbPFctzXymG2PcwE+AfwHjgT7W2quBe/ENzXA1eZ4vgP/z9zzfArwS6gsVEWkv1PMsIhIl1trXjTG5wP/8vcoJwC+stSXGmALgD0ARsARfb3M1sAffiYHrmzzdx8DvjDHrrbVFDZYPNsYsxBeKk4Bp1tr3jTE9gAeMMR/gG0f9lf951wF5xpg7gZ8DfzfGpOAL1z9p/b+CiEjb4vJ6vdGug4iIiIhIm6BhGyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEMKzyIiIiIiDik8i4iIiIg4pPAsIiIiIuKQwrOIiIiIiEP/H6/DLPRlORlfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple',xlim=(329,356))\n",
    "\n",
    "# Debate on Oct 3 (Oct 1 + 2)\n",
    "plt.axvline(x=329+2, linewidth=1, color='red')\n",
    "# Debate on Oct 11 (Oct 1 + 10)\n",
    "plt.axvline(x=329+10, linewidth=2, color='green')\n",
    "# Debate on Oct 22 (Oct 1 + 21)\n",
    "plt.axvline(x=329+21, linewidth=4, color='grey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Donor Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questions to answer:\n",
    "\n",
    "- How much was donated and what was the average donation?\n",
    "- How did the donations differ between candidates?\n",
    "- How did the donations differ between Democrats and Republicans?\n",
    "- What were the demographics of the donors?\n",
    "- Is there a pattern to donation amounts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "donor_df = pd.read_csv('Election_Donor_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      "cmte_id              1001731 non-null object\n",
      "cand_id              1001731 non-null object\n",
      "cand_nm              1001731 non-null object\n",
      "contbr_nm            1001731 non-null object\n",
      "contbr_city          1001712 non-null object\n",
      "contbr_st            1001727 non-null object\n",
      "contbr_zip           1001620 non-null object\n",
      "contbr_employer      988002 non-null object\n",
      "contbr_occupation    993301 non-null object\n",
      "contb_receipt_amt    1001731 non-null float64\n",
      "contb_receipt_dt     1001731 non-null object\n",
      "receipt_desc         14166 non-null object\n",
      "memo_cd              92482 non-null object\n",
      "memo_text            97770 non-null object\n",
      "form_tp              1001731 non-null object\n",
      "file_num             1001731 non-null int64\n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "donor_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id             cand_nm        contbr_nm contbr_city  \\\n",
       "0  C00410118  P20002978  Bachmann, Michelle  HARVEY, WILLIAM      MOBILE   \n",
       "1  C00410118  P20002978  Bachmann, Michelle  HARVEY, WILLIAM      MOBILE   \n",
       "2  C00410118  P20002978  Bachmann, Michelle    SMITH, LANIER      LANETT   \n",
       "\n",
       "  contbr_st   contbr_zip        contbr_employer      contbr_occupation  \\\n",
       "0        AL   3.6601e+08                RETIRED                RETIRED   \n",
       "1        AL   3.6601e+08                RETIRED                RETIRED   \n",
       "2        AL  3.68633e+08  INFORMATION REQUESTED  INFORMATION REQUESTED   \n",
       "\n",
       "   contb_receipt_amt contb_receipt_dt receipt_desc memo_cd memo_text form_tp  \\\n",
       "0              250.0        20-JUN-11          NaN     NaN       NaN   SA17A   \n",
       "1               50.0        23-JUN-11          NaN     NaN       NaN   SA17A   \n",
       "2              250.0        05-JUL-11          NaN     NaN       NaN   SA17A   \n",
       "\n",
       "   file_num  \n",
       "0    736166  \n",
       "1    736166  \n",
       "2    749073  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What sorts of amounts are we dealing with?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0    178188\n",
       "50.0     137584\n",
       "25.0     110345\n",
       "250.0     91182\n",
       "500.0     57984\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df['contb_receipt_amt'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8079"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df['contb_receipt_amt'].value_counts().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean donation and stddev donation\n",
    "don_mean = round(donor_df['contb_receipt_amt'].mean(),2)\n",
    "don_std = round(donor_df['contb_receipt_amt'].std(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average donation was 298.24 with a std of 3749.67\n"
     ]
    }
   ],
   "source": [
    "print(f'The average donation was {don_mean} with a std of {don_std}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3749.67 is huge for a StdDev. Are large donations or other factors messing with the distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "325136    2014490.51\n",
       "326651    1944042.43\n",
       "344539    1679114.65\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_donor = donor_df['contb_receipt_amt'].copy()\n",
    "top_donor.sort_values(ascending=False, inplace=True)\n",
    "top_donor.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The negative values are due to the FEC recording refunds as well as donations - fixing to only look at positive donation amounts\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0     178188\n",
       "50.0      137584\n",
       "25.0      110345\n",
       "250.0      91182\n",
       "500.0      57984\n",
       "2500.0     49005\n",
       "35.0       37237\n",
       "1000.0     36494\n",
       "10.0       33986\n",
       "200.0      27813\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get rid of the negative values\n",
    "top_donor = top_donor[top_donor >0]\n",
    "\n",
    "top_donor.sort_values(ascending=False, inplace=True)\n",
    "\n",
    "# Look at the top 10 most common donations value counts\n",
    "top_donor.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ranging from `$10`-`$2500`. Are usually made in round number amounts? It appears so, though it would be useful to get better x values for the <`$100` amounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126585810>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "com_don = top_donor[top_donor < 2500]\n",
    "\n",
    "# Set a high number of bins to account for the non-round donations and check histogram for spikes.\n",
    "# figsize (width,height)\n",
    "com_don.hist(bins=250,figsize=(18,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about donations by Party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a list of Candidates\n",
    "candidates = donor_df.cand_nm.unique()\n",
    "candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Break them out by party affiliation\n",
    "# Dictionary of party affiliation\n",
    "party_map = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}\n",
    "\n",
    "donor_df['Party'] = donor_df.cand_nm.map(party_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "      <th>Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
       "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
       "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
       "\n",
       "          contbr_city contbr_st   contbr_zip        contbr_employer  \\\n",
       "0              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "1              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "2              LANETT        AL  3.68633e+08  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  7.24548e+08                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  7.19016e+08                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED              250.0        20-JUN-11          NaN   \n",
       "1                RETIRED               50.0        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED              250.0        05-JUL-11          NaN   \n",
       "3                RETIRED              250.0        01-AUG-11          NaN   \n",
       "4                RETIRED              300.0        20-JUN-11          NaN   \n",
       "\n",
       "  memo_cd memo_text form_tp  file_num       Party  \n",
       "0     NaN       NaN   SA17A    736166  Republican  \n",
       "1     NaN       NaN   SA17A    736166  Republican  \n",
       "2     NaN       NaN   SA17A    749073  Republican  \n",
       "3     NaN       NaN   SA17A    749073  Republican  \n",
       "4     NaN       NaN   SA17A    736166  Republican  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear refunds from Candidates\n",
    "donor_df = donor_df[donor_df.contb_receipt_amt >0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Bachmann, Michelle                 13082\n",
       "Cain, Herman                       20052\n",
       "Gingrich, Newt                     46883\n",
       "Huntsman, Jon                       4066\n",
       "Johnson, Gary Earl                  1234\n",
       "McCotter, Thaddeus G                  73\n",
       "Obama, Barack                     589127\n",
       "Paul, Ron                         143161\n",
       "Pawlenty, Timothy                   3844\n",
       "Perry, Rick                        12709\n",
       "Roemer, Charles E. 'Buddy' III      5844\n",
       "Romney, Mitt                      105155\n",
       "Santorum, Rick                     46245\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Bachmann, Michelle                2.711439e+06\n",
       "Cain, Herman                      7.101082e+06\n",
       "Gingrich, Newt                    1.283277e+07\n",
       "Huntsman, Jon                     3.330373e+06\n",
       "Johnson, Gary Earl                5.669616e+05\n",
       "McCotter, Thaddeus G              3.903000e+04\n",
       "Obama, Barack                     1.358774e+08\n",
       "Paul, Ron                         2.100962e+07\n",
       "Pawlenty, Timothy                 6.004819e+06\n",
       "Perry, Rick                       2.030575e+07\n",
       "Roemer, Charles E. 'Buddy' III    3.730099e+05\n",
       "Romney, Mitt                      8.833591e+07\n",
       "Santorum, Rick                    1.104316e+07\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The candidate Bachmann, Michelle raised 2,711,439 dollars\n",
      "The candidate Cain, Herman raised 7,101,082 dollars\n",
      "The candidate Gingrich, Newt raised 12,832,770 dollars\n",
      "The candidate Huntsman, Jon raised 3,330,373 dollars\n",
      "The candidate Johnson, Gary Earl raised 566,962 dollars\n",
      "The candidate McCotter, Thaddeus G raised 39,030 dollars\n",
      "The candidate Obama, Barack raised 135,877,427 dollars\n",
      "The candidate Paul, Ron raised 21,009,620 dollars\n",
      "The candidate Pawlenty, Timothy raised 6,004,819 dollars\n",
      "The candidate Perry, Rick raised 20,305,754 dollars\n",
      "The candidate Roemer, Charles E. 'Buddy' III raised 373,010 dollars\n",
      "The candidate Romney, Mitt raised 88,335,908 dollars\n",
      "The candidate Santorum, Rick raised 11,043,159 dollars\n"
     ]
    }
   ],
   "source": [
    "# Start by setting the groupby as an object\n",
    "cand_amount = donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()\n",
    "\n",
    "# Our index tracker\n",
    "i = 0\n",
    "\n",
    "for don in cand_amount:\n",
    "    print(f\"The candidate {cand_amount.index[i]} raised {round(don):,} dollars\")\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1219c2990>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing totals\n",
    "cand_amount.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about raw Republican vs Democratic donations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123e5a8d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Groupby party and then count donations\n",
    "donor_df.groupby('Party')['contb_receipt_amt'].sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about Donor Occupation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a pivot table to extract and organize the data by the donor occupation\n",
    "occupation_df = donor_df.pivot_table('contb_receipt_amt',\n",
    "                                index='contbr_occupation',\n",
    "                                columns='Party', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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>Party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIXED-MEDIA ARTIST / STORYTELLER</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA VICE PRESIDENT</th>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESEARCH ASSOCIATE</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEACHER</th>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THERAPIST</th>\n",
       "      <td>3900.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Party                                Democrat  Republican\n",
       "contbr_occupation                                        \n",
       "   MIXED-MEDIA ARTIST / STORYTELLER     100.0         NaN\n",
       " AREA VICE PRESIDENT                    250.0         NaN\n",
       " RESEARCH ASSOCIATE                     100.0         NaN\n",
       " TEACHER                                500.0         NaN\n",
       " THERAPIST                             3900.0         NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many different occupations?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45067, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "45k is going to be too big to view. Need something narrowed down enough to visualize. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a cut off point at 1 milllion dollars of sum contributions\n",
    "occupation_df = occupation_df[occupation_df.sum(1) > 1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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>Party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7477194.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANKER</th>\n",
       "      <td>224084.40</td>\n",
       "      <td>1259468.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS OWNER</th>\n",
       "      <td>449979.30</td>\n",
       "      <td>1323342.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Party                 Democrat  Republican\n",
       "contbr_occupation                         \n",
       "ATTORNEY           11141982.97  7477194.43\n",
       "BANKER               224084.40  1259468.73\n",
       "BUSINESS OWNER       449979.30  1323342.43"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121db7e50>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='bar',figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flipping the chart horizontally for easier reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1223403d0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Horizontal plot, use a convienently colored cmap\n",
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the unavailble occupations\n",
    "occupation_df.drop(['INFORMATION REQUESTED PER BEST EFFORTS','INFORMATION REQUESTED'],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121dc1750>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining C.E.O variations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set new ceo row as sum of the current two\n",
    "occupation_df.loc['CEO'] = occupation_df.loc['CEO'] + occupation_df.loc['C.E.O.']\n",
    "# Drop CEO\n",
    "occupation_df.drop('C.E.O.',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123142710>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}