Election Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use to grab data from the web(HTTP capabilities)\n",
    "import requests\n",
    "\n",
    "# We'll also use StringIO to work with the csv file, the DataFrame will require a .read() method\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the url link for the poll data in csv form\n",
    "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
    "\n",
    "# Use requests to get the information in text form\n",
    "source = requests.get(url).text\n",
    "\n",
    "# Use StringIO to avoid an IO error with pandas\n",
    "poll_data = StringIO(source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# Set poll data as pandas DataFrame\n",
    "poll_df = pd.read_csv(poll_data)\n",
    "\n",
    "# Let's get a glimpse at the data\n",
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>http://elections.huffingtonpost.com/pollster/p...</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>http://elections.huffingtonpost.com/pollster/p...</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>http://elections.huffingtonpost.com/pollster/p...</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</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",
       "3                   IBD/TIPP  2012-11-03  2012-11-05  2012-11-06T08:51:48Z   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05  2012-11-06T08:47:50Z   \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",
       "3                   712.0  Likely Voters       Live Phone   50.0    49.0   \n",
       "4                  1500.0  Likely Voters  Automated Phone   48.0    49.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        6.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1        3.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2        4.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3        NaN    1.0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4        NaN    NaN  http://elections.huffingtonpost.com/pollster/p...   \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",
       "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  \n",
       "0            NaN                   1  \n",
       "1            NaN                   1  \n",
       "2            NaN                   1  \n",
       "3            NaN                   1  \n",
       "4            NaN                   1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview DataFrame\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e3731c518>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Factorplot the affiliation\n",
    "sns.countplot('Affiliation',data=poll_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e373df9b0>"
      ]
     },
     "execution_count": 18,
     "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": [
    "# Factorplot the affiliation by Population\n",
    "sns.countplot('Affiliation',data=poll_df,hue='Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>http://elections.huffingtonpost.com/pollster/p...</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>http://elections.huffingtonpost.com/pollster/p...</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>http://elections.huffingtonpost.com/pollster/p...</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</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",
       "3                   IBD/TIPP  2012-11-03  2012-11-05  2012-11-06T08:51:48Z   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05  2012-11-06T08:47:50Z   \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",
       "3                   712.0  Likely Voters       Live Phone   50.0    49.0   \n",
       "4                  1500.0  Likely Voters  Automated Phone   48.0    49.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        6.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1        3.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2        4.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3        NaN    1.0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4        NaN    NaN  http://elections.huffingtonpost.com/pollster/p...   \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",
       "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  \n",
       "0            NaN                   1  \n",
       "1            NaN                   1  \n",
       "2            NaN                   1  \n",
       "3            NaN                   1  \n",
       "4            NaN                   1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the DataFrame again\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e3747a940>"
      ]
     },
     "execution_count": 20,
     "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": [
    "# First we'll get the average\n",
    "avg = pd.DataFrame(poll_df.mean()) \n",
    "avg.drop('Number of Observations',axis=0,inplace=True)\n",
    "\n",
    "# After that let's get the error\n",
    "std = pd.DataFrame(poll_df.std())\n",
    "std.drop('Number of Observations',axis=0,inplace=True)\n",
    "\n",
    "# now plot using pandas built-in plot, with kind='bar' and yerr='std'\n",
    "avg.plot(yerr=std,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    <tr>\n",
       "      <th>Question Text</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Question Iteration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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\n",
       "Question Text             NaN       NaN\n",
       "Question Iteration   1.000000  0.000000"
      ]
     },
     "execution_count": 21,
     "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": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e3750e9e8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B1aZFwSXo5+tM3esI6cJoUo8yXzsIAUrRroZHLT8Mg0acgJV1O2H0SPMRIOAnaceDPoIpyiqcRT6E39BeigIk4UO5rw2EAP1JO073fYxXkqfgaNKQZqeiAH6ipK4707ceU/zrMcB3IHVsPNmI448bjVd2DEgLdUpjGGnEaMMzoCjAiuRpeDt5gmlaY9vp+4NWt3P9H6LU15XWnpN8G/CNcggk8m1PGqhte7rvY/y7oxx/lCNpoWBpoVv1+H0EF4yuxAM6F8HU/SQ999OX+r+SMXdFm8sC0vvWz8s/wti2t+En6fXoUgK4M34VvsBhuDiwFvcG0tuC9jxuamhF04FOnHnMoaZtO2/FZss4NxcdP4SZT58OY3vxCVWY5N/g2uCZjTefz4M/EFb+vtlC8zFm1WNW4LmU72x5JIgBxfQVmBpJBRmDo56XkuMxO3ZNmh/+3Pi1eDk5Hn6LhlTAzvvWwNK0iW+NjCy7fX0vPqEKiy4bjXLditIBxUE8OP34DL/i0qIAfu17DkGSWbgCZMyRFJMuTPJtTLMzrvgy6hImCYRI+uRXIBHFSVseTvNZLo8E4aM0DW3tAyHqcT2RoPmjTOsPYZJAgGJviMRxa2CpqZ+60S9a88Fm3d9+4QDe+mx32js2635qEKLOTbDaFlDPz45dk3qbvnjvnxCi5LlfKcJLyfFIKsCswHPc/vfPvk9Zrergmrc+y9Ts3SRvpRUAqFSa7PkMFhhVpAnrw9elSQN7lVIQApTjQNonn1FSAHo+g4GeT/ckfLY1/UqlCWujl0ApQvdICiCc/plNkzSMrpSsOt4fn46Xk+Pw1ZVtaF52MzUPq0/dC31rUGVj/iPZ0oBfzZ2DWwNLsZ40YVfRIdg6Zha2DT0fNS99muat0N4Vx4ToW6gK0vNndUGtPvfHp+PWwFJU2ZhkV1q24sIV38XJZDDWHH0jFu86IcODwsxmow7fGTe/53bnjszaWivbKKVUbX0F7wTvp97DiZ1v4dbEUgwJ95zjqZMPCkZ0/hX1DNnUjyR+H3wEtypLcX98OipBz3MAaUv9XMEYV6pIE9aEbkqrd0JRcMLdbzDdJ1ds2Malu3sdr8XSa8UNnHitbPnz9Rjx9d88lUryBdYnI6B+Nj6fmIgf+N+ivr3EFYIk/BlvfW7RpQTwbOIMavldSgC3xK5LDcSLDK6i+ute9p2Fi/FP+JV4xrlnE2cY3DtVu7U3LaObHw/NyVJESFdGnnPj12T4eTvJX6NT8YOAZNX+elt562TX88lNb67mZCnGdqlf+7+//HhcfEIVPnxpCY5dNy9tJaRmFwCqm2QUobQ/+jTseKOZ5alvL6u8aPcj6CdYNG10RniJWc9/TF1NasTMY8cNr5W8lVaGfbM07wdxt/4GmtlZTLrwQ/+bzE/QAFG4BxGO/pZBiMSZ5Wuf4ID5Z3KIxHEJ3sgYxPX5m33q8spEGsnuP4y0PG/xZ34+281fD006sQvts96sTma+4izuj09HuxJKO9ap+BFX7D9k+v6qyStD1y/KWM6u2cWSaBRFrQMLvZ20+hvR8jReZ2wvq7xo94MVXoJnECeApz7kQB4P5H7FucufovT8473WSRl/SZyFhuRgZnqneRvJxv1RUZDSqZ3+XTQrP+X26TN3ufOZ3E9W/lYupWZtWw76mx7NNdALl1UaZv3BWC8zm41vizzQ5i1mxa7Hf8duQHOy1FZfLUePTKFJBt9R6BrwENLMtGUAacOs2PU95QNQQKAA2IFDMDt2DWqVCdT6s+o6gLRl2GlsL31evPdDbyvrdxYKvAuWpZG3GnmC+BDIYgDrhB9h8AWdSsBZWT/0v2k5yDrN2y0IARIgqEIT03Urq/yh4LPwVeoDYZK5mXxklvdX4SuRZPqxsNKZ51kfvrJXpl401zoae5WS1M8X+tYgyXC/26YMZg7iVvMMLJ/zl7pU+erB4KNcfVXvPqsAGD67FmtC5m6SrHMvJceDxIHbQktRoTShEYNwX2w61vU/G7MuknAmgF8v/RgJRVFtiatfKywNX8vTyrdeu2ZN6CaGi7MaokM/VwUAO2pm4lA0YScG4wLfZVx/UKs8dj0E8tj98KuvvsKAlk8cySuEqJIDr9sizdWJN52Zy6PTvGn5ZIMP5vXMpny1rZNUjws7ebCOay5pTtKZnTP+4yWbe2nm/RRAMs3lL0yRa4wunHpo7oVncLq4amlpZdLtyHSfNXM5XZv8HvOcZu9Aku4K+O+Ocjzy7zD+Ubcz5dJo5VJr1j4smpR+ONO3PsN7R3PFNLqRlpEDaa6YVu0b9BPUXPi9lJsmtQ592f3wiJ8sQf3wHyAOn8knHwGCJayTGZhJLl77nycUe5KPE7LJ18t6FTpa2zQkB6MNRZ6UYebyB/S42b2i0N8AaekipAu3Ba1dXFllJgAcUMLUfmHUkWnSjSZpmJ0zc3E0hn1m1VO7N04lpzZYvzHT5kKsXIgHFAczJki9Im+lFUAdzIElUGrKwfy0/s12wOy8gcM7n+mVT2sCgsM7/woAvfZpb4bSLV/kW73yAQUEI3T3zitUXZbej31Q8LLJIMXSoSthHQ6CldYPguM6/4wtjP5q1JHNJA3tnJaPZqWdMAfMeQOQrGIXseZTeNDqSQB8tXCq43yyJa8Hco1Y8aEIte/IPEF8QE2ZrbzWhG5CC0oxMIub5wQFwF+Cv8Vpvrq8HCwVMYgzUTwM/2AsiXUPtiuDUBYJYn80TvVbZvXp7cogrqX59Noo+CL8I2ad2iy8SIzlrkwej/N976XKTIIw8yZQ8GX4R/AjaTm3kwTBXYE/pdZiaGsptnWXqR3XbB/r+zw1v5WAD+0oQimipraw0MJd3xW/Cis2HI+qra9g6PpF+I6yG7uIunbhpAuvd5S3rXrkqx+5nm2vPYSqdfdl7srjEJrvrxv+wFZkq5ULck8+3LN2JYTfJK7FK8p46g5TLB9+lo9+p+KHH0nqqk47aJ5bWqx2Y52MvuNetiUrb+PxmEIQQPr8mRqCARmhGXjLANS2/lviDEwztHWHEsInY+ebDuZ92o9cz75h56RvI0XMoourxBFEAnQ3pTBJZGzlNit2PW6JXdd9TNUk3f4bx9uJNVcs1jmBu+jnLvT/EgqxPfDo0zvx2zcSV3y4P3gj3g6dwdwm8NYg3Yd/v1KESb6N3Evz7UKI6rlFrRNFz+6NOEjG40GKEwQhAIi60El1gQRo3wBmwdxCJI4rKeshIqQLQ9cv4rAgOwpCWgGQHk6yptziYoJATZPu2szWH0DaMLYzczJYr/H1lpatdjS6y11vvx32RfQauB4nerjbenqAKKiZdxeeml1LPU8AVDPC5g4gbRjgsYRIc78lAKot1hXkG34Ag+7e1nPAcoyh5UF33fyO4v06hcIZyPUwtmBKO29xrTHUKG22mxVC1HOIH+g/hG4jI7C+wDkECtaEbsrQU53Mpei3CHRjLiYJBbHfDsOWohYoSs8ntOaTvhelTA2ZmISDdZPPwjPS1mx0kgDf7iB5RBw+vLJhW4+HSWQA0JG5u5STdSEECnbUHOmpXl4Q0koGk+5Qt1yiYdyKiXKtovT4Plf71K2zLgmszciKZ1kwi6z68Nir6TYGI8Dw7GOY5+L5crr824t6dCnm7yukux9c5f8nqn1NqX5Rgg7bNhACDCQH8GDw/6K/bgWkU3wAwrEW+KBquJrfuxYqdyA50L0Wkl6XAElm3G+nsg/LbbeIJNJ88osQBygrec36XbZ9MlvX27/Gz8Sc5ZvVbfg2LUUimrkxc5cSwLvJkcyyzOSdCuzGsevm4cOXMjd0d4PCHMhHTddp5ujRzMuGqsf1O3qkXavq68YGLyZd+G2/5WkhRYuDvgz/1+ZkKfYopaqGbtJ0ccWHvyTOQsLJQBYsAc5/MLPemm176s3T+0OWvvXay1LSxHec98FIKpnaMmv5N2+ebvm0xxVfxtyHnbUEYZJAq1KSYQNP1QJEcUWHdgOaJszro6S/p6uT38vqvpi9pNtdlKUAqTUmO3AI6of/IHM84JhL00/YpkLzrrybGhdov1KEEWQnV11pdnqplxemtALwbcFk3G/v0j8Cy6+jXlrcsQNra87MSJts2YrtymD8KnZDmvxCAHxV9EPQHmsfFNwZ/ylmdG/aa4tYW6ZLZWRgz1eGmaR06WNqm2xaCiy/1rKo7cpgW6Fh6aj+8XpXMw3Nd1g7l31Z9tBCnG6nuKHx1mUgOYA9Sin2ohTlOABf+VDze1AgGDdeYKGA4OnEJPzQ/6Yr98+VOR5CQGpa1MFr01JUvHob8M3f1HORgcC596nPAafOPcP/T0zybVT7SPtGIEq3U3WbzE4q80ovL9yB3IpNS9N3wG7dqv7O0L7SdHVdWh/J3AUe6N66KUzX37X4Eq5p7B17gBd/bn3dyzcB374HfPwMV7ZmoTx5tUDNT9m4a7nWXkBmCFMr3JrQJUSbDGzCVeSfqXyrSRP3myUhwCC9r3UfGMTt0Kn4cJX/n67dEzfi/cSKD0UIUJ/TFTcCSV0c9449wN9/of5sNZcGdh9hXZstu8hgVGSfTQaFKa3wsPLuTL9z7Xea9qzX1Slp9ctxI0G/GpaSomMbQ292ONTYM1AS1pOcsQ5g3ZNc/vZmnVIB8JwyyTTEKNBj621B9jLrbELE2sFqYKa6nNnMo1DJxixN/3aDpAJ8gFHZZRKMYPeo7pealXenD+IaiS713KQ7YOfPhpseYYRkzkN0KCFsHTPLvUJ09N2BvLWBfrxjL1171ss0jLRDSDOqyiO499Lj1NltnY6tgGSE3lzX/2x8MnZ+elkn/gxdxRXq75GB6j83nRxd8GghAEouWZweYlRR426ocwQE2xQ1tsW6/mczd403C2EK2B849Rq3/uc9SimeTpxtGeLUCgUwDW3aWzivDunRjV2Gd67AyA+is4ETfwZHfb77Wd037Bz1d9Yzrp0bNR2sWvLWPZuuQIiq3ycVdWywWhiUDX1XWmF9VpVVW+vrjLS+8mqsvdmwy0d3XgRABYA/dP/r4UwA6TdvyxHXZq7kuu9wuuTTG5QNVfe7fP1sjG3J9JLRdjvR7Iwv6I9AV2vGdb7ybrmKeh+GYmdrFBXg3MuwbCgmdP6BulP71aUfoKb4L6n2c/rwbVd0u9H0htupCc4kCSV7KYi9ht5RVvVFVwKfDlTnq1bezV8/4lffsFfejWO0OS+WTAqgPVKB4k1LkSQ+aiz8JAj8HD2FZOHuS8qGouLmTwCoY4MXkopG330jZ7nv6SUUL9I6YdNSoHO/N3kDgC+oerPoYL516uycNUVCJJgur6RkJY1NS+GLUdzs/CE1H5O23DpmFmIcnj0JEgQm3UGtz7TQu7g98T9pD7ST75suJZAmifHUK1d0LzzMKr31RX4kKcMBy7OJVSezrxlNj07p2EdNRtzPGU1y+Hh1Dqh1q+pu2boVieg+0Ba7Kgqwvm0Qul78L/aGJopi/eUVjGDLYZc5c6PV+n+O6LsDOct9z8rTJdu0TmBpfVYES3qkGZarFfEDFz8CXPR/0uypH/6D7s++bjcuIMNObWd0/W7zKVlJV3cfxVULodKeLx9GW5504fW4g/wS+5NhU5dDf1E/YNR0an3uLnmB6ipmF22XdUCd0OZ/MEjPPYgMtL5WIzJQlRhshGH2jMhA4JJHsQ+ZdfETVVLjCVlhK5ZKogv44g3MJz9HXKG3tgKo/ffEn6lut4a5H/W+05fSn0r+jZDSySxe88nPTOyHvp9etfNyartYovX/HNF3pRWAz0XRi7R2YWp9Wk+jPUFEDeGrwXK1UpI9dujsOUJ3idmgdfEJVebxlM3mIjRM2vJv0VPxLE4FYBISQZdXRn1qKFExHaDfZR0AcycfKrd9pauPSTTOmpbMY+c/aCsMs/uQVP37v3At9TW7hHSldrJ39TultQFPRU/GHWH6W7OiEJCa7i8tRv9m3SfH2yMqybT7tP2ZWpSHHbgc6vt/DujbA3mhYOYS2dVm7S5plYeXsOYiIgOAh47t8eHXPjP1fv2T7sCQ8sEp3ZvprmlmA4eLGQ+ay6gG91JsY91YmqrZ4hSXbHAE8anSHqDqyRSbta3d3A5ZoQC4yL+WmW8LSlB03zEo7tih1tOGVu3BbqPOAAAgAElEQVTUzVGBgmRNOXyKgp3kEEwLXe7Mbq+fOwN9V1opFFj6uD8EHDWZfU6vv5nl4bVON+kOJI06py8IdB3oHpy6J9xW3KjqovpjL9+E33/3i5TuTQ2JYDU3MekOtTw7+NIH1bi/CL/HD9KOPadMsn5HptVt7NX0a1nHAfOQE16jJFL3hvWHK0CSWBh8HCuTxzNdUp3uyXp/YAlWJo/PuO+dih+liKK4oxGAwhzEmcvi7VUlLZ2/O8xtBXbjLvJHav1MybE+DoiBvPdh6eOhUuCLN9jn9FKFWR5ey0OjpqPxpNnpGni4n6qB6knGMo/FOnDSlodTuvfLyfG4P3gj2iOV4J6bGDVdnQPQ69Oku1tHBgK+UPrxE38GXPxoWn0DFz2M8ZfcmNLev1MSQMkli0FO/Fn6cu/DT7eeNzn/QbUMfboTf6YeN7PBOI8Q8ko7pwxxtHtjoJh0YZJvI26L/zwt3KuiqJt7O/XBDpE4Jvk2ZmwF14ZIVnsDUNcJOMhHs7unfhb5RAaq81E51McB8G0sIUnSdwCsA3A2gDiAJ6Ha8wmAX8iybPoNmu3GEmaB1wuRNHuY+qiFPq7XW83yoOmybtEdxkBpbQDR5JPU0mjex4ajjsZQCwyZxvHDY8h/28ifoeqcm53llQ36ejjWzM36jRsQtb2Pmqy+aGRVVxVFAbYZopDWh6+kbrrtxspQu2g+81qk1N8HH7HcbDwF8QFjf2L6hzwnG0tIkhQEsASANmX8IIB5sixPgNqmF1nlITCBpaWVVZuf483DK7QwBjp3MLx8k3rcTrlW1+rKsZJpNK3XqR1aXpUfLnSWVzYY6+EUzb/aM7rb+6MnrOvKEbQK6Ik+uTD4OC70rQGgDpo0EgwPFy8hBGmRUveRfvyJlaTaVq/8t3cVBJ+08gCARwFoLhJjAbzT/fOrAM7yoF4HD2Y+67z+7Ln2ewfYIRC0pdHG+lB82bnqSCuHIdNg5d389TfJ35eIOssrG2h22sWpNku7N9kSjKjzAja0/2LShduC6h9Q2nxJuxLCXxNnWoaO8JJi0oVI0Gd/TmPdk57UR8PUa0WSpKsB7JZl+XVJkuZ0HyayLGt/hvcDsNz9uLOzE3V1dY4rGY1Gs0qfb6TZEzwO/cfehkM2PYpg+07Eig/F7lE/x77gcQDAPqdvD7M8PGq3Y1ob6AtCWhvwGaM+AGzXkVUODaW1AZ/ZtNfUjhz2OWY9AAAEiWA/+GP7mNckQmXYOeZm7Aseh2M69nK1mQIgVlxhuDc7HEsXWl1T93bYOegfGJq654lgP5BkDL5ER/eVmQwhzfhOSQAvt41Hmc+P6xPPYghpxnZlUEp6WZc8Wt042pfuFshqG3a7OpNpQrF92HbqnSm7eIICK0qC2Z/cGN9MNXJJklYBqXmN4wF8DmCMLMuB7vMXAThbluX/MitEaOTp9Al7WCEFyoYC3cuSXeGhY/ld85yUzcrfbTvcqAdvXXnbjGajnfbmyY82vzFqOrct4xa+SQ3LoIWJsKw3yx1Ui0HjwNaG5GBcXvwYZk2R1DUNXG3GngvyXCOXZXmiLMuny7J8BoCNAK4C8KokSWd0X3IugNVWhQj6GLl0d3RTpqFx1OSMQwrjuKfwyGN2pDYrl0xGe314xC/RRZEuHLli0uY3tLkMTltoYRnCfpIeJgJg50eTd0ykywQJmO4opUX83NbS0bOjEE97+3yezrs4mTn4NYC7JEn6F4AQgGXuVkmQ9+TS3ZHmmkcJOeA4hMIXb2QcIozjnsITFoI3dMSo6aoLKAuT9vrVv4/CLYaol3uUUiwn52RE8bSsh9k8CqcttLAMN502OHO1MSu/8x9kl0NJ47/k/+LjsQtSUQv3KKWpiJ8NSTXip+ZZk9pRyKq9ASCZ8HTehcv9MFuEtJJOwdvTW+6OXtCXbNHj0K7DZ9cynWG/Wjg1J3WwojeeH8t24XK5pdvthrQilugL7GMWIrjQyLUtLM3YbRzaNaQ8QtWkh5RTvDSsbHGzbXVlHVF8KBC7h91uqWu39mjk2oKxjr3m7a5bG7ETg3Fv12X4qP/ZKC8OYm975ldoql14wix4+HyIlZ0C+/SGu6NXUGxJ+ou8scVMM3Ybh/eIK3QxwGeLW/3EUFaofQe73dKuRc9EZ8ee7sl5k3Y3rI2owG7cG3wcY/f9AweicQT96b4pae1iNafi8fMhBnKBfQzaYldxhbdhfr2EopM2njTbG1vMNGO3MexexTuPwBW6GOCzxa1w0Hbajdcfn5beZIvHWFJBSSjAbhezORWvw2BDSCsCp+hC024pdM3fEGZ3X10dTAL3OocV8tdsy7Js6LbrM5v3xzJ0McBvixvhoO20m522NF5rssUjALR2xLDxTsabt1ko6hy4sYo3coEgV7A0Ui3kb025+v9chwdwggthIVZs2IZxC9/E4bNrMW7hm6orX7Zl2QlPQAwugYxytBDH1HkCi7S5mjcSA7lAkCtYPvHGkL9e6eYu8uERv0SHYQl9hxLCh0f8kiv9ig3bMGf5Zmxr6YACpPtlG+HV2u1umagk0tuaUo7mN06dJ3BSR48QA7lAkCtomjEt5K9XurmL/OrfR+E2Q+jZ22LX4Ff/Poor/aLXZXTE0ldcpvyyjfDOyZhtmRgZ2BPeWI++rQ3zCjtwCObErsG6/mfT5wlM6pgLXVyP0MgFglxi1IxZW/R5pZu7xPaWDmzDeLzUNT7tOKG4LrLS2znONSdjplPf9hV7Gz59uu5yCNRd7xczcrSqY64Rb+QCQW/Sy9qqU1h6samO7GJ6KmZtuWkpmCGy8ryteRADuUDQmxSoTz63v7lH6amYteXKu8FcZZrnbc2DGMgFgt6kl7VVp3D7m3uUnopZWzJlFyXv25oHoZELBL1NL2qr2cDlb+5heiqstmSGCxjKn7edLQdzFYqhGzGQCwSCvs+kO1RXQ/3KTTsSlrZ8X0uvbTlISI/XkeY6+u17wMfPpF/78k3qzx4N5kJaEQgEfZ9sJSw7Ww6uezJ3oRi6EW/kAoHg4CAbCcuOOyhtRyK7edhEvJELBAKBFXZcFAljc2gRxlYgEAh6ETtbDpptL+cRYiAXCAQCK+xsOWi2vZxHCI1cIBAIeGBp7KxjOXQpFQO5QCAQ6DH6gB81Wd04Ikc+4U7otYE8FouhoaEB0WiU69q6uroc1Co3uG1PUVERqqurEQwGXctTIDgoofmLf/REz/kc+IQ7odcG8oaGBvTr1w/Dhw8HIYxgNt10dHQgEskimE6e4aY9iqKgubkZDQ0NOPzww13JUyA4aOHZKk7zCc+jgbzXJjuj0SgGDRpkOYgLzCGEYNCgQVxfNgKBwAJeX+88CzPcq14rYhB3B9GOAoFL8Pp651no24KZ7FyxYRsWvS5je0sHhpRHMGuK5ErAna1bt+L+++9HS0sLYrEYjjnmGNxyyy2YP38+zjvvPEycONGF2gsEgoKAFpPFSB6GGS6IgfzlzTtw58s9W0Np+/sByGowj0ajuPHGGzF//nyMHj0aAPDiiy/i17/+NQYMsLGJq0Ag6Btouvfya9nX5GGY4YIYyH+/cgtzf79sBvK3334bJ510UmoQB4BLLrkEzz77LMrKyvDMM8/giSeeQCKRwG9/+1sMGzYMv/vd7/DJJ5+gra0NRxxxBO699148/PDD+Oabb7B37160trbiyiuvxBtvvIGvvvoK9913H44//vi0dMOGDcOiRYsc11sgEHjIqOnd7oeMsLd5NogDBbKys7G1k3qcub8fJ1u3bsVhhx2Wcby6uhofffQRxowZg6eeegrXXnstFi1ahAMHDqB///7485//jL/97W/YuHEjdu7cCUB1AXziiScwefJkvPPOO3j00Udx3XXXoba2NiPd5s2bU+kEAkEeUmA7NxXEG3llWRjbKYN5Vvv7ATj00EOxadOmjONff/01TjzxRJx44okAgBNOOAH3338/wuEw9uzZg//+7/9GcXEx2tvbEYupu3Z/97vfBQD069cPRx55JACgrKwMnZ2dpukEAkEeor1153BziGwoiIH8V5OOSNPIARf29wMwadIkPProo9i0aRNGjRoFAHj++ecxcOBA+Hw+bNq0CWPGjMFHH32Eo446CqtWrUJjYyN+//vfY8+ePfjHP/4BRVH3ATTzHDFLJxAI8pQC2rnJciCXJMkP4DEAEoAEgJ9A3Y76Sai7mX4C4BeyLCe9quQFx1UgFAy57rVSUlKCRx99FAsWLEBLSwsSiQQkScKDDz6IBQsW4OOPP8ZVV10FQggWLFiAUCiERx55BNOnT0coFMLQoUOxa9cuy3JGjRqVlq6qqgq7du3C0KE2tpkSCAQCBjxv5BcAgCzL4yRJOgPAg1AH8nmyLL8tSdKjAC4C8KJntYRH+/sBOOyww/Doo49mHF+4cCH1+hdeeCHj2NixY1M/X3HFFamfzzrrLJx11lkZ6fraSlWBQNC7WE52yrK8AsB13b8OA7ATwFgA73QfexXAWZ7UTiAQCASWcGnksizHJUl6CsAlAKYBOF+WZU3k3Q+gzCx9Z2dnRpCoWCyGjg4+rxNFUbivLQS8sKc3A4tFo9E+FdRM2JPfCHsy4Z7slGX5x5Ik3QbgfQB6XaAfgBaztOFwGCNHjkw7VldXxy0v9DUpwgt7gsFgRhvnirq6ul4r2wuEPfnNwWTPunXruPKwlFYkSZohSdKc7l/bASQBfNStlwPAuQBWc5UmEAgEAtfheSNfDuDPkiStAhAE8CsAdQAekyQp1P3zMu+qKBAIBAIzLAdyWZbbANCcKU93vzoCgUCQH9TW12Lx+sXY0bYDFSUVmDlmJqaOmNrb1aJSEAuCAGRuv+TCKqv3338fv/rVr1IrMdva2lBdXY0HHngAoVDIIrVAIOir1NbXoubdGkQTapz/xrZG1LxbAwB5OZgXRKwV/6cvqKElW7cCUHq2W9q0NOu8Tz31VDz99NN4+umnsXz5cgSDQbz55pvZV1ogEBQsi9cvTg3iGtFEFIvXL+6lGplTEG/kgVX3ZsYH9mC7pa6uLuzatQtlZWVYuHBhasb4/PPPx49//GPMnj0bgUAA27dvR1dXF8477zy89dZbaGxsxCOPPILGxkY89thjCAaDaGhowHnnnYcbbrgBjY2NuP3221NxV37zm9/ggw8+wNdff43bbrsNiUQCF198MV544QXxJSAQ5AE72nbYOt7bFMQbOdm3jX7Che2W3nvvPcyYMQPnnXceLr30Upx99tmIRqNoaGjA0qVL8cwzz+CVV16BLMsAgKqqKvzpT3/CiBEj0NDQgMceewyTJ09OvcVv374dDz/8MJ577jk8/vjjAID77rsPM2bMwNNPP42f/exnWLx4MaZOnYqVK1cikUhg9erVOOWUU8QgLhDkAbX1tczYSRUlFTmuDR8F8Uau9K8C2UcZtF3YbunUU0/FQw89hL179+KnP/0pqqursWXLFpx44okghCAYDGL06NHYsmULgJ4oh/3798eIESNSP3d1dQEAjj76aAQCAQQCARQVFQEAPv/8cyxZsgSPP/44FEWBz+dDaWkpTjrpJKxZswbLly/HjTfemLUtAoEgOzRtPKlkho4q8hdh5piZvVArawrijTw+cY7nsYEHDBiARYsWYd68eRg8eHBKVonFYtiwYQOGDRsGwHp/TNr5ESNG4JZbbsHTTz+Nu+66C2effTYAYPr06Xj++efR3NyMY445xjVbBAKBM2jaOAD4iA81p9Xk5UQnUCBv5Inv/ScQCnkeG/jII4/EjBkz8Oabb6K6uhqXX345YrEYzjnnHHzve99znO9tt92GmpoadHZ2IhqN4pZbbgEAjB49Gt988w1++MMfumWCQCDIApYGrihK3g7iQIEM5AA8iQ18yimn4JRTTkk7dsMNNzCv10dE1AZjALj66qvT8tRYu3YtAGDo0KF44oknUse1OCvJZBLFxcU4//zznRkgEAhcpaKkAo1tjdTj+exXXhDSSl9k69atuOSSS3DRRRehtLS0t6sjEAgAzBwzE0X+orRjRf4iTKyeiJp3a9DY1ggFSsqvvLa+tpdqmk7hvJH3MYYOHYq///3vvV0NgUCgQ3vDNr55m/mV58NbuRjIBQKBQMfUEVMzBuc5q+dQr80Xv3IhrQgEAoEFLP/xfPErFwO5QCAQWMDSzvPFr1xIKwKBQGABSzvPB30cKKA38tr6WkxeNhmjnhqFycsmuzJb/P777+Pmm29OO/bAAw9g+fLllmlXrVqF2bNn2ypv+fLlWLlyZcbxcePGcecxffp0NDRkH5pAIBDwoY09mk5+74R78ca0N/JmEAcK5I38tW9ew4KPFhRMSEkWl156KQD0qf1HBYK+TKGEsy2IgfyRzY/k3PXnmmuuyYhiuGXLFsydOxeRSASRSARlZeqe06+++iqefPJJ+Hw+jB07Frfccguam5sxe/Zs7N+/H4qi4L777sPLL7+MwYMH44ILLsDcuXPx5ZdfYujQoak4LcYoiffccw8qKyvx0EMPYfXq1aioqMDevXs9sVcgEGSS726HGgUxkO9s30k97pXrDyEE27dvx0svvYSuri5MmDABN9xwAxYvXoybbroJ48aNwx//+EfU19ejpaUFDz/8MF544QVEIhHMmjULa9euxVtvvYUzzzwTV1xxBf71r39h06ZNqfzXrl2Lzs5OLF26FNu3b8frr78OoCdK4umnn45//etfeOCBB3D99dfjww8/xLJly9De3o7Jkyd7YrNAIMikUMLZFsRAfmjxodjRntlw2br+FBUVpd6GNdrb2xEOh6lRDL/44guMGjUKADBmzBjU19fj22+/xZ49e3DdddcBUHcZ2rp1K7766itMmzYNAPAf//EfAICHH34YALBly5ZUPkOGDEFlZSWAzCiJwWAQX375JY499thUxMSjjz46K5sFAgE/Zkv284mCmOy88bgbPXH9OeKII1BXV4ddu3YBADo7O/Hhhx+ira2NGcVww4YNAIBPPvkEAFBdXY3Kykr86U9/wtNPP40f/ehHGD16NI444ghs3rwZAPDhhx9i0aJFqXyGDx+OjRs3AgB27tyJnTt3pvLXR0mcMmUKDj/8cGzatAnJZBLt7e348ssvs7JZIBDwk+9uhxoF8UZ+zrBzEAqFXHf9KS0txezZs3H99dejqKgIsVgMM2bMwGGHHYZ333034/o777wTN998M5544gkMHDgQ4XAYAwcOxNVXX40ZM2YgkUigqqoK5557Ln7+859j7ty5eOmllwAACxYswIoVKwAA3//+97F582ZcdtllGDJkCAYMGAAgM0rib37zG4wcORLnnHMOpk2bhu985zsYNGhQVjYLBAJ+8t3tUIMoiuJ5IXV1dcrIkSONx2A8xqKjowORSMT6wgLBC3vstKfb9GbZXiDsyW8OJnvWrVu3buzYsSda5VEQ0opAIBAI2IiBXCAQCAocMZALBAJBgdOrA3ku9PmDAdGOAsHBTa8N5EVFRWhubhaDUJYoioLm5uaUr7tAIDj46DX3w+rqajQ0NGD37t2W18ZiMQSDwRzUKje4bU9RURGqq6tdy08gEBQWvTaQB4NBHH744VzXHkzuRgKBQGAX04FckqQggD8BGA4gDGA+gH8DeBKAAuATAL+QZTnpaS0FAoFAwMRKI/8RgGZZlicAOBfA/wB4EMC87mMEwEXeVlEgEAgEZlgN5M8DuF33exzAWADvdP/+KoCzPKiXQCAQCDjhWqIvSVI/AC8BeAzAA7IsD+k+fiaAn8qy/COz9Bs3blTC4bDjSkaj0T7llSHsyW+EPfnNwWRPe3s71xJ9y8lOSZKGAngRwCOyLD8jSdL9utP9ALRY5REOh7Oa3Otrk4PCnvxG2JPfHEz2rFu3jisPU2lFkqRDAbwB4DZZlv/UfXiDJElndP98LoDVXCUJBAKBwBOs3sjnAhgA4HZJkjStfCaAP0iSFAJQB2CZh/UTCAQCgQWmA7ksyzOhDtxGTvemOgKBQCCwiwiaJRAIBAWOGMgFAoGgwBEDuUAgEBQ4YiAXCAQHDbX1tZi8bDJGPTUKk5dNRm19bW9XyRUKYvNlgUAgyJba+lrUvFuDaCIKAGhsa0TNuzUAkHebKdtFvJELBIKDgsXrF6cGcY1oIorF6xf3Uo3cQwzkAoHgoGBH2w5bxwsJIa0IBII+SW19LRavX4wdbTtQUVKBsnAZWjozI4pUlFT0Qu3cRQzkAoGgz0HTwwMkgKAviFgylrquyF+EmWNoax4LCzGQCwSCPgdND48rcZQFy1AcLE69pc8cM7PgJzoBMZALBII+CEv33te1D2uuWJPj2niPGMgFjtDrj4NCg3BL+BbHbzZGLbOvvCUJeo+Kkgo0tjVSj7uJvu/2D/UHIQStna0578fCa0VgG01/bGxrhAIFTV1NqHm3xtHiCmNemm9vX1moIegdZo6ZiSJ/+mYNbuvhxr7b2tWKls6WXunHYiAX2MZNf9y+7Nsr6D2mjpiKmtNqUFlSCQKCypJK1JxW4+obMq3v6sllPxbSisA2bvrj9mXfXkHvMnXE1IyB2y0Zr7a+lirdGMlVPxZv5ALbsHRGJ/qjm3kJBGa4JeNp+fCQq34sBnKBbdzUH3OhZQoEgHsynpWkopHLfiykFYFttE/RNK+VU5x5rRjzEl4rAq9wS8Yzu748XN4rXitiIBc4Qq8/1tXVYeQI57ua07RMPV67J2r5N7Y1wkd8SCpJVH5aWdB/UNx0D803VjetxsxlMzP6A62fAD0vCYQQKIqSkV//UH9MXjbZtH/p+wiLslAZIoEIWjtb3TWYAzGQC/Iar0OPGvNPKklPysklRps091Cg8GwxUltfiyVfL0FXsgtAz33asGsD/v7l39P6ybw180AISS3Jpw3iARJAe7wdrV2tafkBPW1lbE8aPPl4idDIBXmN1+6JZnpnobpB9mWXzsXrF6cGcY1oIornP3+euiRfH1dFw0d8KZfE0lBpxjXGtrLSxH3Ex5WPl4g3ckFe47V7olU+hegG2ZddOlk2aF9SPCiKgk0/3gQAGPXUKMtyrNpNURSmnCLcDwUCOHdP5N3SyyqfQnSDZNVZ04ILdZuz2vpaEEKo53yEfyirKKlAbX0tTv7fk6EgU27RrqH9TEPp/o917rinjsOEv03wtL3FQC7Ia5y4J9rxF6blz1tOvkKzSdNwCzUUgnZPaW/eRf4iXHb0ZQgQa4GhyF+EidUTMWf1HHQkOpjX6O+7WR/hpaWzBbevvd2z9hYDuSCvcbLU2o5GrM8f6Hmz82JJd64wttng0OBe13CzhaVT+4gPNafVYN6p81AaKqWm1WviNafVYFXDKuYbNICM+87qI3aJJWOetbfQyAV5j5V7ohG7GrEx/7q6Oowc6dydMh8wuode/sHl1OsKRTdn1VNRlJSdLJ1ar4kDwJzVc0zLovU1Yx857qnjLOtMw6v27tMDuQiPml/k6n7kKoSpE8zawMv2catNautrce/796bc7MrD5Zh98mzPbTCrv1ammd6trxfLn1xD71M+sXoiXvvqtZS9BMT0bZ7HDi/w19TUeJKxnqampppDDjkkm/Swm17T1LQ9+g7EDmDNtjWoKq3C0QOOdlwXN3BiTz7DY08u78fAooFYs20N4ko8dazIX4TZJ8/mKsur+2PWBl/s/cKz9mlqaoI0VMqqTbT6z1szL01bjiaieKfhHVT3q/bUBtY9nTJ8CpZ8vIS6FyfrGrOB2E/82Ne1L1X/T5s/RWeiM6u66/Oee8rcjLYw62+NjY2NQ4YM+aNV3n1WI+/LvrSFSC7vRy5CmDrBrA28bh832mTx+sVpA6mGpv16aQOr/qsaVjF9vHmu0SAgKA4UI6Eksq4ri9JgqWd9kEtakSTpFAD3ybJ8hiRJRwJ4EoAC4BMAv5Blmd+JM0e46Uvr5HPRzXCZvPmwrrVauuy0fmn5fmqeB+/9cKvd7OrqucBJn2xsa8TkZZNduz9vTHvDVh526mknnVGiifgjAJB62zeTbAaFBmFi9UTLJfNWdSMgado5y6ecl8qSStP6aG/6XmA5kEuSdCuAGQDaug89CGCeLMtvS5L0KICLALzoWQ0d4qYmaHeJuFvLyu3kw7qWZ+myk/rZtZHnfni9HL+3sWoD1iCQi/vDA6v+POmMdZu3Zl7a273RFVBz19Mwhhx4Tn7Oslytr/PWy6l9+vLM8HKOhkda2QLgUt3vYwG80/3zqwDOcrtSbuBWeFQnn4tehstk5cO6lnfpst362bWR5370dTnMrA2sfJW9vj88zBwzk8tXWw/tmWNJNEbMJBtezMox1ssNf3EWQV/Q0zUJlndFluUXJEkarjtEZFnWZgv2AyizyqOzsxN1dXXOagggGo3aTj8CI3DtsGvxbMOzaO5qxqDQIFxRfQVGdI6wlZfZ5zArH6s0vPbYKduNpctmNmVTN4Dvfjhpay9w0t94MGsDAKlzTV1N1PRe3h/e+t9w+A348zd/xoHEAcvrB4cGU585O/Kml+6RxnrR7s+YsjF4d8+7KXt5vVZK/aWpNP0C/XD1YVczxx43+psT90P9yNAPAH26WEc4HM7KL9epX+9IjMR1uM5xuQBQ8Sn7c3jkyJFUTdfsE3rkyJEZ9rB0YauyeeqphWXlspWSL/NaG3XTsLofTvK0gldzzwj76jC+ulWZtDYwXl9OyqleGDztoGnPZq542TyLIzES1024DpOXTTaVEnzEh+auZizbuQy7gruwqmFVyr6ycBnTy8QIIQT9Q/25r+fFR3y4/IPLM+aSlu1chuau5pTr4aqGVWhLtKGypCesMY/t806bx91/zMa3devW8dnDdVU6GyRJOqP753MBrHaQR8Fg9jnMWgo+sXoit6xjtpzcjjzEuvayoy+jLtcO+oJc+bLwYmcft/PkXapvvE4L++pkObXd7cRo1x/oOuDo/mjaszaBaMTNkANWMktSSc5SyaAAABTQSURBVKbseU5+LsM+XpJKktoe2aKvX827NZj/3vyM+2Cst9lzacw71+EPnAzkvwZwlyRJ/wIQArDM3SrlF2ZuWywdclXDKm5XLzMt047LGOvaeafOyzg+f/x83DPunqxc0bxw8XM7T16d2E092W5etOvjShzFgWLb7WCmPWtL2d2aNJ46Yirmj5+PslC6skpAD2qlJ67Eua7TX69vj8Ghwbhcupy5VN7uEnrWXBLtOuNzaXVtriBmK5zcoq6uTukNacVrRj01ivoJa3RrMqK3x2ke+USh3x8374HdvHJRttP8nGBWh2zQ11/rb2ZtB5gv/HGjHhrZ3kMLaWXd2LFjT7TKo08v0beDE/9lN1wczVyeautrmVtOmfmK9w/1ByEkbe9AgO43TtvmrCxUBkIIWjpberY+69YIafmMwAimfVbtSqs3rVyrreD0PsnahJSP+Li29ooEImiPt1Pvjb6OaVvBMerFup+s7cRY1ytQMHnZ5JROy9N+ZkvPzfqksf1o0Py6aUv1eV347MzdAKpWPuqpUagoqcC0Q6ehvr6eaW9FSQU64h22dHXe+tDaMR9CQvTZJfp2cLp83OlScL09A4sG4u2tbyOJzE6kLX3W8mLVc1f7rrQlyJ2JztRn4oHYAby99W28tfWt1EPHSqe9VejTa8fM8hkcGoyxw8bablfjeVa5ZveCtmxcg/VGFk/G05Zh03aRCfqCmHPKnIxl51b1ovWJAAkgrsQz2q2qtAqnVp6acb2GtkT8QOwAV/ux7A2QAHVpOGDefnqMS/FZS/VHDhyJhgMNpnkFfUFMO3oatrRs4XJDBNLbfd3edXi74W3qKkxtSf7Huz6mPlM0ivxFuPSoSy3rw3q2vQwJcdAv0beDU43UDU136oipzPCbxrCXdnzF9bD8xnl0QZ58nm14lnq9Vbvy+gdbacy8g4EGzzLs4kAxcx7ErF60PmEWQpZHb2WVydN+5eFy3HD4Dcw+aaf99H7drKX6H+z8wDKf4kBx2twNkB4++HLp8lT70fTuBBLMLdy0Jfksm4z5m80l0a6zMz+Vy0VsQlpBdsv53VgKbrbrNs+WU3Y+Ud1IZ6S5q5l63Kpd3fAn9srPWHtjd7IVnLFPWG0npl3Pqy9btZ9RmzXzUbbbflbX8/QprW15nh07y+a1kLasMLUExDREQTbPcm+HhOgzA3k2cUZYfq16Xc4q3CiADJ1R0w2N5U07dBpGomdyw0xXVKBg/LPjVT3Qg8kbNyjxl9jSfzW92I49/UP9Mf7Z8aY6rpsU+Yu46qht5aXp8kbtXNuezEyr17R33vbQtFen2iyPJs6CEJL1C4DWZpUllcw5AKvQtDQ0u1nPM69mbRWqNx/pE14rxrgSgPogXnTkRWlxRgBVL9THGWEdM1LkL0LNaTUAkFFWgASQVJJUTc4HH/w+f1reIV8Id4+7O+1hN8aecBMe+7LJG0CGPmjWVnbrYta+TurrVVtomNmfbT20vLXBjtbvjZ/1+ufH676WLaznlied1uY0+4K+IO4Zdw9XsLts0jtBeK10Y6YdG98e4kocxj/ycSWOsmAZioPFqdl/Yzq9Nknz+2WRRBLJZHpeXcmulD4K9AQxmrN6TlZv3ZqXhpnXSjZBgYzlsLwDtLbSPmP1XyNm3gQsbxm7Hgj6t2PaG5+xTq2drVSvFaeY9RVAbb/SUKmlTWZvrEBPv7HjbeVkTgHIfkMFXljPrRl6H/nJyyZT7dPmPKywCtWbr2/leT2Qp7l9fch2+3JDO97XtQ9rrlgDgK3LuTEI6vM65a+noCPekTbAZENSSWLzjzenftfab87qOakysv1joZWj3QeWHrmjbQdVgjLbZqu1qxWVJZWYMnxKavACYGsQp9lP+2zX4+YgrmHWVxRFMZ0XAdSBc+aYmZaDtJU2W1tfiwc2PoDmD5q5XAMJCKZL09OiCwZJEDHFuy8YI3alG/12b6yxgDeErN1wvPlC3kortM9GDePno1XsAx4qSypTb5Bu5GeXIn8RigJFWceUWDhhoelntxtl6PPzER91IIz4I1CgeFo+DSv7nXy2G7HrA22E5yujLFSW5o4J0GUTM8yeIRYRf8TSFTHf4Hl29dewqK2vxezVs7nKcRM3pJW8dT+04/aV7dusMQaF3XCWARKAL8umjCaiUBSFq1yzGBdmrml2yuAhmogy32ajiajn5dOwst+uy6URLX6N3XCuemaOmWm6Z2SRvwiEkKzDBtgN/xr0BbNqm97C6tnljTFj1rZeh6HNlrwdyO24fWWrWxnfcjS/UF7mj5+PBRMWZMSdsMu+rn1U39qyUBnKw+VpsVJYWLmm6cuwE+/CLiz5RivfK6zsd/Imrfdx1nyOjXFG7LTl1BFTTT/1a06rYUovboWANfbV8nA57hl3T956RplBe3ad+HSbtZdXE51ukbcauZWeZ3QlstpmyYy5a+Ziw64NmHequpuIpq3ysnj9YkysnojiYDH2de1ztEQYUG3Sa55GjVfvAsWauNRcJlkubwoULF6/2DIkZ1lInfx1W2Iq8hdh7pq5ttPxyhna0nY7oVIB9mSe9jmtn2+49/17QQjBvq59tsKbapjVr7KkMrUQycx103gu4o8gHAinTXCzyvARH+acMidjYJr/HvsFwQla21m1SzYTqbwLqXjck1nPjHZP7OadS/J2iT5t2asGbfmr2fWAKkf4fX7qYKBAwafNn2JPdA/2d+1PW/bMA20pdTwZZ5bHYuqIqZhYPRGA9fJ2lr3aA2H2YFgtETcuKXeTuBK3/dDyLqHWcNL+PvgQ8AXSrtf6mXGZvjEEgr4tWeEWeOqn79c8S/31xJV4RliGaDxKbWsFSkZ4gfnvzefaPo0XLcSBZsvKb1e6lrcGbRygPTd2Q1RYlaHHaXgPPW4s0c/bgfzoAUejqrQqNUBqCyYqSyqpzvn669tibSgLlSESiKAz0YnKkkrMPWUuzjzszLQB10jdnjp80vSJ6SDO+wmdRBL9gv0wsGgg2mJqYPpjBx2L7W3bmQPZnugezPjuDADAf638r4x6xJU4Pm3+FDO+OyPDXtaCEtZxLa97xt2Tlk9lSSVzAjMXaG8/e6J7UvWZffJsXHPcNVz2aujbn3W/9ShQMu6X1s9o90KPvi2H9huKD3d8iM5Ep2l5tP6h79fG+2v3viSRNG0ffV8CgF+++Uvm9UESRNgfTv1RifgjCPqCzD+qBAS/Hf/bNFue+ewZV/V31jhAu1dJJKluyHV76phL/c3KsCrP2LZWuDGQ563XiiG9q2FSj3vqOOY5q888OyEyswld6mY4VFZ97eblNXZCrfLUUcuP1x7temN/s1NWNmmscPu+6Ms3eyb0Lp089bET6tUJZu3mRjlu9EM7eRwUC4I0P9imD5pshzZl6VZmeivPkmzet3JtKbJ+iW9tfS3zj4UCBRP+NsH07U/L0wirTmb29A/1z2inidUTTcOheol+6bZZiAWz5f/G/Ozo5bTd3nmXiTvZkV2rn3GREi2crxew+pIefdAq470wWwrPe60TBoUGUetjts0iyzZa27oRhjqXIWyBPJZWgB79aV9cneG3E9rUTLdq7mjGp82fZmGRPbQQn7s7dmPJx0tMF1fk0v0rlojh7Ya307TDT5s/tRy4NNc7O28+QV8QJx96smWIU60eNA2TN/yrMS8evVyvhTY1NeGD/R9wz5Ww5mx49XKjhksL5+sEP/Fn/XY6XZqOidUTqc8US+efMnxKxn1zMmdEI+gL4ieH/QQ7Ejuoz/iU4VMy5lJo82OseRc74WeB7EPYAgdBGFu7IUTN0umvn3fqPNOtorwgloxl7cPsNkkkuWN9GN0f+4f7c5dDQHDPuHvw2JTHuNud5fPN2gZPc9GkQds6zSpEqVnfM7qD0tzbzMIT0+rnReyX0mApV/uwuFy6POXJZWdLulUNq0yvzYbiQDEmDJ5ga5tF1taGtNC1+bDloRPyWiO3+uwD1IdKe5OJ+CMAwLUyrThQjAuOuACvffVaziLqFSpuaJ6VJZUZ8ohTPdOJRmpXs7z8g8u582HtIOT16mCeOYDKkkpMO3QarptwHQB7923zjzen2WZWBz1mZej1dqut6Vjn+gX6YX98P7Pe+hg1tF2dAGRENzxmwDF4b8d7qTyCJIiSUEmaOyfP4OzEFbFPa+S8O1DrB2E7S4vb4+2uulv1Zexsb8VCu1bbjZwnDycapluaJUvTLQunL6QxLoPX6puLEA9W4Wy1eiz5egmqqqq45xY05r833zKcgd2+od++0Ow6M03dbBAHVJv1z7b+nsxbMy8jkmZLZ0vaIA4AMSWWKl/fZ63m5fR9gTedG+SttJLLHagF5tCWJs8cM9PxMnVNHjELhaAthbe73DqbJdp6WF+qxuN2l8HTCJAAgr6grTT6JeNWISW0aJs81+qxkgJZ7WrW1ryhNbwK5RBX4o7CIfOER3C605gb5O1Ans+Rxg42WNtbGZepl4fLM7RnFjvadmRsc0ZbCm9Xf3RLs2QtoTce5+mnNJ1ab6tRw+XRs/VLxnm2i9PvRsS7tZzZxKTV1mdW9bC6zutQDk5wslsUTzo3yFtpxe6ne75hFbPbSX5euaGVhcqwP7afmr/ZA6+FE2BpfLX1tZi7Zq6pPGIVhpV13kyL5Nl2yyz96qbVlrv68LrV2YmYZ6yz2dL2xesXY8OuDRmxyq1CN2jX6evEKscqbIEZrDkCY/uVh8uZu3OZ5dMbWMlzvemKmLdv5HYjELLINiqhHjufwElFXVnX2NaIA10HbH8662HJDG4QIAG0x9upg60TSUJD0wvdzlefd2NbY6qNa96t4Z5XMUtfW1+LJV8vodZbayt9OrN7m62dZs+ApgMbbZhYPZGaRt8fjW1FKydAAtS1CbxRAFl58rZfUkma2uMUp5FKee6lW7KeE/LWj5y2RDmp8LvLAeqn/p2n3Ymzhp2FtdvWpvl6FgeKcelRl6Jhf4PlcmoAGcv8eZaJa2jLsWPJmOX1PEvU9SEL9KEIrOpDy5u17NtHfNwR32h+sKxl7XbyZZHtsmiz9O83vk/1YvIRH/qF+mW0lTEUgFUoCTsYw1RYEVfi2BPdg9knzzbto8a2YoUDoDkP9Av2w8KJC23V3ayvmT0bNHt4FuPp+7nxnsw9ZS4mDZuUFkahPFyO4w85Pm2NQ5AE0T/cPxXig+de0mzmSXfQLdG34zpld/mzm0uUWfUB7C2Vt4uVO5fTcAFW0KQVN/JlkW3eTsMZmJ1z4/6ZYTfMgFU6qzp7cf/cCCNh1Q65uBdu06c3lqBhdP0yQ9OlautrMXnZZIx6ahQmL5uc+nw2HmMtUjFbvGJ3KS/rerc0NCu3PDeOZ1sXJ/ka7xerH/DmbVY3p+es6swr+7BwapvT+8A6r4UVoNljZbMbbeu03k5x+z56RcEM5LX1tZb7HGpouhRNC523Zh5uX3t7hrZ48qEnU/O67OjLmOWwdECj5qfVx2sNjeUSaKZrelUnt/Kl3UOarmonb7O6zRwzEyFfiHmOx6ZsNXzeOhuh1cXMHqfl0ezhsdmq3XnalmaPHbvs4MV99Iq81cgN6VGzqQb7Y+yFAOXh8gw9y05Iy4SSUD0w9tRBgQIf8WG6ND21RJkGTRMz6uj6+mjXb9yxER2JDld0VGN9jGFUy8PluOM/7mCW4VTX00PT+NzIF2CHJTULAWuFWd2OHnA0yD6CrV1bmffPyiY3Qpvy1Jk2l0IL78yyh7c8mj5vtIfHZqt252lbvT1uz0kY8eI+0ug1jVySJB+ARwCMBtAJ4BpZlr9kXe+GRs5aMg24E441l9qa22F5exsv7fFSa2eRrT29UWczcmFPLm3O1fOTK5t6UyO/GECRLMv/AWA2gN85zIcbN/XfbK8V5A6v5xW8oBDrbAaPPX3NZqCwbHI6kI8H8BoAyLL8HgDLvxjZ4pb+a6ZhC/KP3vTNdUoh1tkMHnv6ms1AYdnkdGVnfwD6mceEJEkBWZatN1R0iKZ9GaOWmeli+o2K9avfaMfyeYfsgxnWPczn+1WIdTaDx56+ZjNQWDY51cgfBPCeLMtLu39vkGW5mnX9xo0blXA47LiS0WgURUXur2rsLYQ9+Y2wJ785mOxpb2/3NIztWgAXAFgqSdKpAOgrZroJh8NZTU6IycH8RtiT3wh78huLyU6uPJwO5C8COFuSpHcBEAA/cZiPQCAQCLLE0UAuy3ISwM9drotAIBAIHFAwKzsFAoFAQEcM5AKBQFDg5CT64bp163YD+MbzggQCgaBvMWzs2LGW8U1yMpALBAKBwDuEtCIQCAQFjhjIBQKBoMARA7lAIBAUOGIgFwgEggJHDOQCgUBQ4IiBXCAQCAocMZALBAJBgSMGcoFAIChwxEAuEAgEBY7TMLYCQa8gSdIZAJYC+Lfu8G5Zli/jTP8egB/Isvy17tiTAMYA2AP1mWgCcLMsy1+Z5HMJgPdlWd5u0wSBwHXEQC4oRN6UZfkHLud5qyzLrwGAJEkToP6xOMnk+plQQzmLgVzQ64iBXNBnkCTpbQAbARwLdV/Zy2RZ/kaSpN8COAfAVgCDrfKRZXm1JEkxSZKOBFAE4EGoMmQ5gJsADABwPIC/SJI0HsD1AK4EoAD4myzLf3DbNoHADKGRCwqRMyVJelv3b5bu3AeyLJ8F4B8ArpAk6VgAE6G+XV8FoB9nGTuhDvrfA/Dr7jwfBPATWZZrof7BuArAkQAuBzC++9/FkiRJ2ZsoEPAj3sgFhYiZtLKh+/9bAVRAHYg/6t7Vap8kSab7y+oYBqAB6jNyuyRJHVD/COwzXHds97Uru38fAHVwlznLEQiyRryRC/oaxrjMMoCTJUnySZJUAuC7VhlIknQ2gHZZlhsA/AHAnbIs/xjqJuOk+7Ik1OdHBvApgO/LsnwGgCdhsRm5QOA24o1cUIic2a2H6zmXdqEsyxslSXoewIdQJyZ3MfK8X5Kk2QASAPZDlUsA4H8B/F2SpJ1Q39A1jf1dAH8BMBnq2/gaSZLCAD4AsM2JUQKBU8TGEgKBQFDgCGlFIBAIChwxkAsEAkGBIwZygUAgKHDEQC4QCAQFjhjIBQKBoMARA7lAIBAUOGIgFwgEggJHDOQCgUBQ4Px/KMVYX/n8vgwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick plot of sentiment in the polls versus time.\n",
    "poll_df.plot(x='End Date',y=['Obama','Romney','Undecided'],marker='o',linestyle='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For timestamps\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>http://elections.huffingtonpost.com/pollster/p...</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>http://elections.huffingtonpost.com/pollster/p...</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>http://elections.huffingtonpost.com/pollster/p...</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.01</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",
       "3                   IBD/TIPP  2012-11-03  2012-11-05  2012-11-06T08:51:48Z   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05  2012-11-06T08:47:50Z   \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",
       "3                   712.0  Likely Voters       Live Phone   50.0    49.0   \n",
       "4                  1500.0  Likely Voters  Automated Phone   48.0    49.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        6.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1        3.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2        4.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3        NaN    1.0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4        NaN    NaN  http://elections.huffingtonpost.com/pollster/p...   \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",
       "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
       "4  http://www.rasmussenreports.com/public_content...  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  \n",
       "3            NaN                   1        0.01  \n",
       "4            NaN                   1       -0.01  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column for the difference between the two candidates\n",
    "poll_df['Difference'] = (poll_df.Obama - poll_df.Romney)/100\n",
    "# Preview the new column\n",
    "poll_df.head()"
   ]
  },
  {
   "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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-12</td>\n",
       "      <td>638.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-15</td>\n",
       "      <td>577.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.09</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",
       "3  2009-06-12                   638.0   48.0    40.0       12.0    NaN   \n",
       "4  2009-07-15                   577.0   49.0    40.0       11.0    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  \n",
       "3            NaN                   1        0.08  \n",
       "4            NaN                   1        0.09  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set as_index=Flase to keep the 0,1,2,... index. Then we'll take the mean of the polls on that day.\n",
    "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
    "\n",
    "# Let's go ahead and see what this looks like\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "# Plotting the difference in polls between Obama and Romney\n",
    "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x20e376943c8>"
      ]
     },
     "execution_count": 30,
     "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": [
    "# Start with original figure\n",
    "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple',xlim=(329,356))\n",
    "\n",
    "# Now add the debate markers\n",
    "plt.axvline(x=329+2, linewidth=4, color='grey')\n",
    "plt.axvline(x=329+10, linewidth=4, color='grey')\n",
    "plt.axvline(x=329+21, linewidth=4, color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saiful_desv\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: 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": [
    "# Set the DataFrame as the csv file\n",
    "donor_df = pd.read_csv('C:\\\\Users\\\\Saiful_desv\\\\Desktop\\\\Training\\\\Python Learning\\\\Election Analysis\\\\Election_Donor_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "    <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",
       "    </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",
       "    </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  \n",
       "0     NaN       NaN   SA17A    736166  \n",
       "1     NaN       NaN   SA17A    736166  \n",
       "2     NaN       NaN   SA17A    749073  \n",
       "3     NaN       NaN   SA17A    749073  \n",
       "4     NaN       NaN   SA17A    736166  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "\n",
    "# Get a quick overview\n",
    "donor_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 100.00     178188\n",
       " 50.00      137584\n",
       " 25.00      110345\n",
       " 250.00      91182\n",
       " 500.00      57984\n",
       " 2500.00     49005\n",
       " 35.00       37237\n",
       " 1000.00     36494\n",
       " 10.00       33986\n",
       " 200.00      27813\n",
       " 20.00       17565\n",
       " 15.00       16163\n",
       " 150.00      14600\n",
       " 75.00       13647\n",
       " 201.20      11718\n",
       " 30.00       11381\n",
       " 300.00      11204\n",
       " 20.12        9897\n",
       " 5.00         9024\n",
       " 40.00        5007\n",
       " 2000.00      4128\n",
       " 55.00        3760\n",
       " 1500.00      3705\n",
       " 3.00         3383\n",
       " 60.00        3084\n",
       " 400.00       3066\n",
       "-2500.00      2727\n",
       " 110.00       2554\n",
       " 125.00       2520\n",
       " 19.00        2474\n",
       "             ...  \n",
       " 174.80          1\n",
       " 7.27            1\n",
       " 1219.00         1\n",
       " 1884.88         1\n",
       " 162.25          1\n",
       " 218.31          1\n",
       " 78.62           1\n",
       " 203.16          1\n",
       " 53.11           1\n",
       " 499.66          1\n",
       " 19.53           1\n",
       " 188.60          1\n",
       " 47.10           1\n",
       " 19.85           1\n",
       " 28.83           1\n",
       " 202.59          1\n",
       "-5500.00         1\n",
       " 9.25            1\n",
       " 202.66          1\n",
       " 1205.00         1\n",
       " 80.73           1\n",
       " 115.07          1\n",
       " 213.69          1\n",
       " 70.76           1\n",
       " 144.13          1\n",
       " 97.15           1\n",
       " 122.32          1\n",
       " 188.65          1\n",
       " 122.40          1\n",
       " 132.12          1\n",
       "Name: contb_receipt_amt, Length: 8079, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a quick look at the various donation amounts\n",
    "donor_df['contb_receipt_amt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average donation was 298.24 with a std of 3749.67\n"
     ]
    }
   ],
   "source": [
    "# Get the mean donation\n",
    "don_mean = donor_df['contb_receipt_amt'].mean()\n",
    "\n",
    "# Get the std of the donation\n",
    "don_std = donor_df['contb_receipt_amt'].std()\n",
    "\n",
    "print ('The average donation was %.2f with a std of %.2f' %(don_mean,don_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114604     -30800.00\n",
       "226986     -25800.00\n",
       "101356      -7500.00\n",
       "398429      -5500.00\n",
       "250737      -5455.00\n",
       "33821       -5414.31\n",
       "908565      -5115.00\n",
       "456649      -5000.00\n",
       "574657      -5000.00\n",
       "30513       -5000.00\n",
       "562267      -5000.00\n",
       "30584       -5000.00\n",
       "86268       -5000.00\n",
       "708920      -5000.00\n",
       "665887      -5000.00\n",
       "708899      -5000.00\n",
       "708929      -5000.00\n",
       "21172       -5000.00\n",
       "21168       -5000.00\n",
       "21167       -5000.00\n",
       "262328      -5000.00\n",
       "946875      -5000.00\n",
       "7361        -5000.00\n",
       "416403      -5000.00\n",
       "21164       -5000.00\n",
       "707945      -5000.00\n",
       "615101      -5000.00\n",
       "7973        -5000.00\n",
       "54430       -5000.00\n",
       "54434       -5000.00\n",
       "             ...    \n",
       "708022      10000.00\n",
       "708898      10000.00\n",
       "710177      10000.00\n",
       "876244      10000.00\n",
       "709608      10000.00\n",
       "708919      10000.00\n",
       "709739      10000.00\n",
       "91145       10000.00\n",
       "708138      10000.00\n",
       "993178      10000.00\n",
       "709813      10000.00\n",
       "710730      10000.00\n",
       "708928      10000.00\n",
       "709268      10000.00\n",
       "99829       10000.00\n",
       "90076       10000.00\n",
       "709859      10000.00\n",
       "41888       10000.00\n",
       "65131       12700.00\n",
       "834301      25000.00\n",
       "823345      25000.00\n",
       "217891      25800.00\n",
       "114754      33300.00\n",
       "257270     451726.00\n",
       "335187     512710.91\n",
       "319478     526246.17\n",
       "344419    1511192.17\n",
       "344539    1679114.65\n",
       "326651    1944042.43\n",
       "325136    2014490.51\n",
       "Name: contb_receipt_amt, Length: 1001731, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's make a Series from the DataFrame, use .copy() to avoid view errors\n",
    "top_donor = donor_df['contb_receipt_amt'].copy()\n",
    "\n",
    "top_donor.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get rid of the negative values\n",
    "top_donor = top_donor[top_donor >0]\n",
    "\n",
    "# Sort the Series\n",
    "top_donor.sort_values()\n",
    "\n",
    "# Look at the top 10 most common donations value counts\n",
    "top_donor.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2789fd346d8>"
      ]
     },
     "execution_count": 18,
     "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": [
    "\n",
    "# Create a Series of the common donations limited to 2500\n",
    "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",
    "com_don.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab the unique object from the candidate column\n",
    "candidates = donor_df.cand_nm.unique()\n",
    "#Show\n",
    "candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "# Now map the party with candidate\n",
    "donor_df['Party'] = donor_df.cand_nm.map(party_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clear refunds\n",
    "donor_df = donor_df[donor_df.contb_receipt_amt >0]\n",
    "\n",
    "# Preview DataFrame\n",
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby candidate and then displayt the total number of people who donated\n",
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby candidate and then displayt the total amount donated\n",
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The candidate Bachmann, Michelle raised 2711439 dollars \n",
      "\n",
      "\n",
      " The candidate Cain, Herman raised 7101082 dollars \n",
      "\n",
      "\n",
      " The candidate Gingrich, Newt raised 12832770 dollars \n",
      "\n",
      "\n",
      " The candidate Huntsman, Jon raised 3330373 dollars \n",
      "\n",
      "\n",
      " The candidate Johnson, Gary Earl raised 566962 dollars \n",
      "\n",
      "\n",
      " The candidate McCotter, Thaddeus G raised 39030 dollars \n",
      "\n",
      "\n",
      " The candidate Obama, Barack raised 135877427 dollars \n",
      "\n",
      "\n",
      " The candidate Paul, Ron raised 21009620 dollars \n",
      "\n",
      "\n",
      " The candidate Pawlenty, Timothy raised 6004819 dollars \n",
      "\n",
      "\n",
      " The candidate Perry, Rick raised 20305754 dollars \n",
      "\n",
      "\n",
      " The candidate Roemer, Charles E. 'Buddy' III raised 373010 dollars \n",
      "\n",
      "\n",
      " The candidate Romney, Mitt raised 88335908 dollars \n",
      "\n",
      "\n",
      " The candidate Santorum, Rick raised 11043159 dollars \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 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 (\" The candidate %s raised %.0f dollars \" %(cand_amount.index[i],don))\n",
    "    print ('\\n')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a133f6d8>"
      ]
     },
     "execution_count": 25,
     "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": [
    "# PLot out total donation amounts\n",
    "cand_amount.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a1405ba8>"
      ]
     },
     "execution_count": 26,
     "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": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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')\n",
    "\n",
    "# Let's go ahead and check out the DataFrame\n",
    "occupation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45067, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check size\n",
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a14677b8>"
      ]
     },
     "execution_count": 31,
     "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": [
    "# plot out with pandas\n",
    "occupation_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a15c17b8>"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "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": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278b56cf390>"
      ]
     },
     "execution_count": 35,
     "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": [
    "# Repeat previous plot!\n",
    "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}