jeffatennis · August 21, 2015 22:16
diff --git a/gistfile1.txt b/gistfile1.txt
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Election Data Project - Polls and Donors\n",
    "\n",
    "In this Data Project we will be looking at data from the 2012 election.\n",
    "\n",
    "In this project we will analyze two datasets. The first data set will be the results of political polls. We will analyze this aggregated poll data and answer some questions:\n",
    "1.) Who was being polled and what was their party affiliation?\n",
    "2.) Did the poll results favor Romney or Obama?\n",
    "3.) How do undecided voters effect the poll?\n",
    "4.) Can we account for the undecided voters?\n",
    "5.) How did voter sentiment change over time?\n",
    "6.) Can we see an effect in the polls from the debates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division \n",
    "#This takes care of floating points and future is req'd as it's pulling  the functionality from a future version of python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "requests info: here \n",
    "stringIO info: here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 589 entries, 0 to 588\n",
      "Data columns (total 14 columns):\n",
      "Pollster                  589 non-null object\n",
      "Start Date                589 non-null object\n",
      "End Date                  589 non-null object\n",
      "Entry Date/Time (ET)      589 non-null object\n",
      "Number of Observations    567 non-null float64\n",
      "Population                589 non-null object\n",
      "Mode                      589 non-null object\n",
      "Obama                     589 non-null int64\n",
      "Romney                    589 non-null int64\n",
      "Undecided                 422 non-null float64\n",
      "Pollster URL              589 non-null object\n",
      "Source URL                587 non-null object\n",
      "Partisan                  589 non-null object\n",
      "Affiliation               589 non-null object\n",
      "dtypes: float64(2), int64(2), object(10)\n",
      "memory usage: 69.0+ KB\n"
     ]
    }
   ],
   "source": [
    "poll_df = pd.read_csv('http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv')\n",
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x187281d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18728208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Affiliation',data=poll_df.sort('Affiliation'),kind='count',hue='Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</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>2012-11-06 2000-01-01 08:40:26 UTC</td>\n",
       "      <td>1000</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UPI/CVOTER</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-05 2000-01-01 18:30:15 UTC</td>\n",
       "      <td>3000</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</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>2012-11-06 2000-01-01 09:22:02 UTC</td>\n",
       "      <td>872</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JZ Analytics/Newsmax</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06 2000-01-01 07:38:41 UTC</td>\n",
       "      <td>1041</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.jzanalytics.com/</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Rep</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>2012-11-06 2000-01-01 08:47:50 UTC</td>\n",
       "      <td>1500</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48</td>\n",
       "      <td>49</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05   \n",
       "1                 UPI/CVOTER  2012-11-03  2012-11-05   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05   \n",
       "3       JZ Analytics/Newsmax  2012-11-03  2012-11-05   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05   \n",
       "\n",
       "                 Entry Date/Time (ET)  Number of Observations     Population  \\\n",
       "0  2012-11-06 2000-01-01 08:40:26 UTC                    1000  Likely Voters   \n",
       "1  2012-11-05 2000-01-01 18:30:15 UTC                    3000  Likely Voters   \n",
       "2  2012-11-06 2000-01-01 09:22:02 UTC                     872  Likely Voters   \n",
       "3  2012-11-06 2000-01-01 07:38:41 UTC                    1041  Likely Voters   \n",
       "4  2012-11-06 2000-01-01 08:47:50 UTC                    1500  Likely Voters   \n",
       "\n",
       "              Mode  Obama  Romney  Undecided  \\\n",
       "0       Live Phone     47      47          6   \n",
       "1       Live Phone     49      48        NaN   \n",
       "2  Automated Phone     48      48          4   \n",
       "3         Internet     47      47          6   \n",
       "4  Automated Phone     48      49        NaN   \n",
       "\n",
       "                                        Pollster URL  \\\n",
       "0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4  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                                                NaN  Nonpartisan        None  \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None  \n",
       "3                        http://www.jzanalytics.com/      Sponsor         Rep  \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mode\n",
       "Automated Phone    1245.031579\n",
       "IVR/Live Phone     1304.000000\n",
       "IVR/Online         1000.000000\n",
       "Internet           1500.039604\n",
       "Live Phone         1256.728625\n",
       "Name: Number of Observations, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMode = poll_df.groupby('Mode')['Number of Observations'].mean()\n",
    "byMode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Number of Observations</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Dem</th>\n",
       "      <th>None</th>\n",
       "      <th>Rep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Automated Phone</th>\n",
       "      <td>66598</td>\n",
       "      <td>169958</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVR/Live Phone</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7824</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVR/Online</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet</th>\n",
       "      <td>NaN</td>\n",
       "      <td>147497</td>\n",
       "      <td>4007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Live Phone</th>\n",
       "      <td>20099</td>\n",
       "      <td>313360</td>\n",
       "      <td>4601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Number of Observations              \n",
       "Affiliation                        Dem    None   Rep\n",
       "Mode                                                \n",
       "Automated Phone                  66598  169958   NaN\n",
       "IVR/Live Phone                     NaN    7824   NaN\n",
       "IVR/Online                         NaN    1000   NaN\n",
       "Internet                           NaN  147497  4007\n",
       "Live Phone                       20099  313360  4601"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(poll_df,index=['Mode'],columns=['Affiliation'],values=['Number of Observations'],aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>46.772496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>44.573854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.549763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "Obama      46.772496\n",
       "Romney     44.573854\n",
       "Undecided   6.549763"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg = pd.DataFrame(poll_df.mean())\n",
    "avg.drop('Number of Observations',axis=0,inplace='True') #axis = 0 allows us to delete rows labeled # of obs\n",
    "avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>2.448627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>2.927711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>3.702235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "Obama      2.448627\n",
       "Romney     2.927711\n",
       "Undecided  3.702235"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = pd.DataFrame(poll_df.std()) #what if I df has multiple numeric columns, how can I specify which number column to usewanted to do this on the \n",
    "std.drop('Number of Observations',axis=0,inplace='True') #axis = 0 allows us to delete rows labeled # of obs\n",
    "std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x188ad3c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18818128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg.plot(yerr=std,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>46.772496</td>\n",
       "      <td>2.448627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>44.573854</td>\n",
       "      <td>2.927711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.549763</td>\n",
       "      <td>3.702235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Avg       STD\n",
       "Obama      46.772496  2.448627\n",
       "Romney     44.573854  2.927711\n",
       "Undecided   6.549763  3.702235"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_avg = pd.concat([avg,std],axis=1)\n",
    "poll_avg.columns=['Avg','STD']\n",
    "poll_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</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>2012-11-06 2000-01-01 08:40:26 UTC</td>\n",
       "      <td>1000</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UPI/CVOTER</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-05 2000-01-01 18:30:15 UTC</td>\n",
       "      <td>3000</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</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>2012-11-06 2000-01-01 09:22:02 UTC</td>\n",
       "      <td>872</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JZ Analytics/Newsmax</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06 2000-01-01 07:38:41 UTC</td>\n",
       "      <td>1041</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.jzanalytics.com/</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Rep</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>2012-11-06 2000-01-01 08:47:50 UTC</td>\n",
       "      <td>1500</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48</td>\n",
       "      <td>49</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05   \n",
       "1                 UPI/CVOTER  2012-11-03  2012-11-05   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05   \n",
       "3       JZ Analytics/Newsmax  2012-11-03  2012-11-05   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05   \n",
       "\n",
       "                 Entry Date/Time (ET)  Number of Observations     Population  \\\n",
       "0  2012-11-06 2000-01-01 08:40:26 UTC                    1000  Likely Voters   \n",
       "1  2012-11-05 2000-01-01 18:30:15 UTC                    3000  Likely Voters   \n",
       "2  2012-11-06 2000-01-01 09:22:02 UTC                     872  Likely Voters   \n",
       "3  2012-11-06 2000-01-01 07:38:41 UTC                    1041  Likely Voters   \n",
       "4  2012-11-06 2000-01-01 08:47:50 UTC                    1500  Likely Voters   \n",
       "\n",
       "              Mode  Obama  Romney  Undecided  \\\n",
       "0       Live Phone     47      47          6   \n",
       "1       Live Phone     49      48        NaN   \n",
       "2  Automated Phone     48      48          4   \n",
       "3         Internet     47      47          6   \n",
       "4  Automated Phone     48      49        NaN   \n",
       "\n",
       "                                        Pollster URL  \\\n",
       "0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4  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                                                NaN  Nonpartisan        None  \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None  \n",
       "3                        http://www.jzanalytics.com/      Sponsor         Rep  \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Time series\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18d5ba90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "nrBBCLITFXUAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18d706a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot(x='End Date',y=['Obama','Romney','Undecided'],linestyle='',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#improve the time series by editing the data frame\n",
    "#want to plot difference b/w obama and romney\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>567.000000</td>\n",
       "      <td>589.000000</td>\n",
       "      <td>589.000000</td>\n",
       "      <td>422.000000</td>\n",
       "      <td>589.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1296.197531</td>\n",
       "      <td>46.772496</td>\n",
       "      <td>44.573854</td>\n",
       "      <td>6.549763</td>\n",
       "      <td>0.021986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1617.552964</td>\n",
       "      <td>2.448627</td>\n",
       "      <td>2.927711</td>\n",
       "      <td>3.702235</td>\n",
       "      <td>0.037872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>856.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36472.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Observations       Obama      Romney   Undecided  Difference\n",
       "count              567.000000  589.000000  589.000000  422.000000  589.000000\n",
       "mean              1296.197531   46.772496   44.573854    6.549763    0.021986\n",
       "std               1617.552964    2.448627    2.927711    3.702235    0.037872\n",
       "min                328.000000   37.000000   32.000000    1.000000   -0.160000\n",
       "25%                856.500000   45.000000   43.000000    4.000000    0.000000\n",
       "50%               1000.000000   47.000000   45.000000    6.000000    0.020000\n",
       "75%               1500.000000   48.000000   46.000000    8.000000    0.040000\n",
       "max              36472.000000   54.000000   53.000000   28.000000    0.180000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df['Difference']=(poll_df.Obama-poll_df.Romney)/100\n",
    "poll_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>1403</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>686</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>1000</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-12</td>\n",
       "      <td>638</td>\n",
       "      <td>48</td>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-15</td>\n",
       "      <td>577</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start Date  Number of Observations  Obama  Romney  Undecided  Difference\n",
       "0  2009-03-13                    1403     44      44         12        0.00\n",
       "1  2009-04-17                     686     50      39         11        0.11\n",
       "2  2009-05-14                    1000     53      35         12        0.18\n",
       "3  2009-06-12                     638     48      40         12        0.08\n",
       "4  2009-07-15                     577     49      40         11        0.09"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18fbc128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18f73c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot(x='Start Date',y='Difference',figsize=(12,4),marker='o',linestyle = '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329\n",
      "356\n"
     ]
    }
   ],
   "source": [
    "#cycle through index to investigate differences around date of debates; this will create the x limits to create a graph\n",
    "row_in = 0\n",
    "xlimit = []\n",
    "\n",
    "for StartingDate in poll_df['Start Date']:\n",
    "    if StartingDate[0:7] == '2012-10':  #if first 7 digits of start date fall in oct 2012, append it to list\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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a9a53c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1908c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot(x='Start Date',y='Difference',figsize=(12,4),marker='o',linestyle = '-',xlim=(329,356))\n",
    "#Oct 3 debate:\n",
    "plt.axvline(x=329+2, linewidth=4, color='red')\n",
    "\n",
    "#Oct 11 debate:\n",
    "plt.axvline(x = 329+10, linewidth=4, color='red')\n",
    "\n",
    "#Oct 22 debate:\n",
    "plt.axvline(x = 329+21, linewidth=4, color='red')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "For all these polls it is important to remeber how geographical location can effect the value of a poll in predicting the outcomes of a national election. \n",
    "\n",
    "Donor Data Set\n",
    "\n",
    "Let's go ahead and switch gears and take a look at a data set consisting of information on donations to the federal campaign. \n",
    "\n",
    "This is going to be the biggest data set we've looked at so far. You can download it here , then make sure to save it to the same folder your iPython Notebooks are in.\n",
    "\n",
    "The questions we will be trying to answer while looking at this Data Set is:\n",
    "1.) How much was donated and what was the average donation?\n",
    "2.) How did the donations differ between candidates?\n",
    "3.) How did the donations differ between Democrats and Republicans?\n",
    "4.) What were the demographics of the donors?\n",
    "5.) Is there a pattern to donation amounts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py:1170: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data = self._reader.read(nrows)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>3.660103e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</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>3.660103e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50</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>3.686334e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250</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>7.245483e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</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>7.190165e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300</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  3.660103e+08                RETIRED   \n",
       "1              MOBILE        AL  3.660103e+08                RETIRED   \n",
       "2              LANETT        AL  3.686334e+08  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  7.245483e+08                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  7.190165e+08                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED                250        20-JUN-11          NaN   \n",
       "1                RETIRED                 50        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED                250        05-JUL-11          NaN   \n",
       "3                RETIRED                250        01-AUG-11          NaN   \n",
       "4                RETIRED                300        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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors = pd.read_csv(r'C:\\Users\\jtennis\\Python Homework\\Election_Donor_Data.csv')\n",
    "donors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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: 129.9+ MB\n"
     ]
    }
   ],
   "source": [
    "donors.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "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",
       " 180.95          1\n",
       " 223.10          1\n",
       "-1545.12         1\n",
       " 1179.88         1\n",
       "-226.20          1\n",
       " 178.05          1\n",
       " 122.14          1\n",
       " 79.95           1\n",
       " 130.80          1\n",
       " 314.60          1\n",
       " 121.58          1\n",
       "-195.32          1\n",
       " 201.24          1\n",
       " 152.05          1\n",
       " 59.49           1\n",
       " 0.88            1\n",
       " 19.35           1\n",
       " 58.18           1\n",
       " 71.20           1\n",
       " 70.68           1\n",
       " 163.90          1\n",
       " 14.97           1\n",
       " 264.39          1\n",
       " 162.60          1\n",
       " 81.15           1\n",
       " 45.15           1\n",
       " 106.56          1\n",
       " 62.20           1\n",
       " 58.82           1\n",
       " 73.83           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors['contb_receipt_amt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The avg donation was 298.24 with a STDDEV of 3749.67\n"
     ]
    }
   ],
   "source": [
    "don_mean = donors['contb_receipt_amt'].mean()\n",
    "don_std = donors['contb_receipt_amt'].std()\n",
    "\n",
    "print 'The avg donation was %.2f with a STDDEV of %.2f' %(don_mean,don_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "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, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_donor = donors['contb_receipt_amt'].copy()\n",
    "top_donor.sort()\n",
    "top_donor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#want to get rid of the negatives\n",
    "top_donor = top_donor[top_donor>0]\n",
    "top_donor.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "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",
       "110.00       2554\n",
       "125.00       2520\n",
       "19.00        2474\n",
       "112.00       2457\n",
       "            ...  \n",
       "180.00        368\n",
       "4.00          361\n",
       "25.50         355\n",
       "88.00         348\n",
       "38.00         341\n",
       "145.00        337\n",
       "2400.00       331\n",
       "700.00        306\n",
       "13.00         292\n",
       "22.50         275\n",
       "135.00        270\n",
       "800.00        264\n",
       "52.00         254\n",
       "24.00         251\n",
       "144.00        250\n",
       "54.00         244\n",
       "37.50         242\n",
       "113.00        238\n",
       "37.00         238\n",
       "76.00         227\n",
       "143.00        225\n",
       "14.00         222\n",
       "2.00          220\n",
       "51.00         219\n",
       "2250.00       217\n",
       "240.00        213\n",
       "312.00        212\n",
       "162.00        208\n",
       "262.00        205\n",
       "275.00        201\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_donor.value_counts().head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create histogram for common donation amts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f9f4048>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "f6gK0B8Vn43iAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9eeda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_donations = top_donor[top_donor<2500]\n",
    "\n",
    "common_donations.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Break-down common contributions by political party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Roemer, Charles E. 'Buddy' III      62.070530\n",
       "Paul, Ron                          144.149966\n",
       "Bachmann, Michelle                 203.762190\n",
       "Obama, Barack                      224.847985\n",
       "Santorum, Rick                     230.419260\n",
       "Gingrich, Newt                     251.931290\n",
       "Cain, Herman                       350.488133\n",
       "Johnson, Gary Earl                 459.450243\n",
       "McCotter, Thaddeus G               500.405405\n",
       "Huntsman, Jon                      771.017921\n",
       "Romney, Mitt                       794.740686\n",
       "Pawlenty, Timothy                  934.150184\n",
       "Perry, Rick                       1356.280804\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byCandidate = donors.groupby('cand_nm')['contb_receipt_amt'].mean()\n",
    "byCandidate.sort()\n",
    "byCandidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Obama, Barack                     593746\n",
       "Paul, Ron                         143757\n",
       "Romney, Mitt                      107229\n",
       "Gingrich, Newt                     47679\n",
       "Santorum, Rick                     46559\n",
       "Cain, Herman                       20107\n",
       "Perry, Rick                        13575\n",
       "Bachmann, Michelle                 13140\n",
       "Roemer, Charles E. 'Buddy' III      5920\n",
       "Pawlenty, Timothy                   4555\n",
       "Huntsman, Jon                       4156\n",
       "Johnson, Gary Earl                  1234\n",
       "McCotter, Thaddeus G                  74\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors['cand_nm'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Want to associate a party affiliation to each candidate\n",
    "###Lec 36 discusses data mapping, which is most efficient, but a lot of typing.  it is used below\n",
    "###Alternatively, we can loop through the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "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 add new column to DF and map the party to the candidate\n",
    "donors['Party'] = donors.cand_nm.map(party_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm                         Party     \n",
       "McCotter, Thaddeus G            Republican        74\n",
       "Johnson, Gary Earl              Republican      1234\n",
       "Huntsman, Jon                   Republican      4156\n",
       "Pawlenty, Timothy               Republican      4555\n",
       "Roemer, Charles E. 'Buddy' III  Republican      5920\n",
       "Bachmann, Michelle              Republican     13140\n",
       "Perry, Rick                     Republican     13575\n",
       "Cain, Herman                    Republican     20107\n",
       "Santorum, Rick                  Republican     46559\n",
       "Gingrich, Newt                  Republican     47679\n",
       "Romney, Mitt                    Republican    107229\n",
       "Paul, Ron                       Republican    143757\n",
       "Obama, Barack                   Democrat      593746\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verify above mapping worked correctly\n",
    "byCandidate = donors.groupby(['cand_nm','Party'])['contb_receipt_amt'].count()\n",
    "byCandidate.sort()\n",
    "byCandidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>3.660103e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</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>3.660103e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50</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>3.686334e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250</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>7.245483e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</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>7.190165e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300</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  3.660103e+08                RETIRED   \n",
       "1              MOBILE        AL  3.660103e+08                RETIRED   \n",
       "2              LANETT        AL  3.686334e+08  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  7.245483e+08                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  7.190165e+08                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED                250        20-JUN-11          NaN   \n",
       "1                RETIRED                 50        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED                250        05-JUL-11          NaN   \n",
       "3                RETIRED                250        01-AUG-11          NaN   \n",
       "4                RETIRED                300        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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_positive = donors[donors.contb_receipt_amt>0]\n",
    "donors_positive.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to make above group-by more legible?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The candidate ('McCotter, Thaddeus G', 'Republican') raised 37030 dollars\n",
      "The candidate (\"Roemer, Charles E. 'Buddy' III\", 'Republican') raised 367458 dollars\n",
      "The candidate ('Johnson, Gary Earl', 'Republican') raised 566962 dollars\n",
      "The candidate ('Bachmann, Michelle', 'Republican') raised 2677435 dollars\n",
      "The candidate ('Huntsman, Jon', 'Republican') raised 3204350 dollars\n",
      "The candidate ('Pawlenty, Timothy', 'Republican') raised 4255054 dollars\n",
      "The candidate ('Cain, Herman', 'Republican') raised 7047265 dollars\n",
      "The candidate ('Santorum, Rick', 'Republican') raised 10728090 dollars\n",
      "The candidate ('Gingrich, Newt', 'Republican') raised 12011832 dollars\n",
      "The candidate ('Perry, Rick', 'Republican') raised 18411512 dollars\n",
      "The candidate ('Paul, Ron', 'Republican') raised 20722567 dollars\n",
      "The candidate ('Romney, Mitt', 'Republican') raised 85219249 dollars\n",
      "The candidate ('Obama, Barack', 'Democrat') raised 133502591 dollars\n"
     ]
    }
   ],
   "source": [
    "cand_amount = donors.groupby(['cand_nm','Party'])['contb_receipt_amt'].sum()\n",
    "cand_amount.sort()\n",
    "\n",
    "i = 0\n",
    "\n",
    "for donor in cand_amount:\n",
    "    print 'The candidate %s raised %.0f dollars' %(cand_amount.index[i],donor)\n",
    "    #print '\\n' # adds new line\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8d1320>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "rq6uWLt2LU3ohPwPquVOCGkROTk5+PHHH+Hr6/tf/94qlQrfffcdEhISsGvXrv/69yfkr4hW6ITo\n",
       "sKtXr2LDhg1Nfi06OhoWFhb/5+9dVFQEDw+P//M//zIbN25EWloadu/e3SLfn5C/IlqhE0IIIX8D\n",
       "lBRHCCGE/A3QhE4IIYT8DdCETgghhPwN0IROCCGE/A3QhE4IIYT8Dfw/64YCGZRInEQAAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a284a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byCandidate.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "####Let's compare Obama's fundraising to the total of all republicans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2197db38>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "lAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21965cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byParty = donors.groupby('cand_nm')['contb_receipt_amt'].sum()\n",
    "byParty.sort()\n",
    "byParty.plot(kind='barh',cmap='ocean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "McCotter, Thaddeus G              3.703000e+04\n",
       "Roemer, Charles E. 'Buddy' III    3.674575e+05\n",
       "Johnson, Gary Earl                5.669616e+05\n",
       "Bachmann, Michelle                2.677435e+06\n",
       "Huntsman, Jon                     3.204350e+06\n",
       "Pawlenty, Timothy                 4.255054e+06\n",
       "Cain, Herman                      7.047265e+06\n",
       "Santorum, Rick                    1.072809e+07\n",
       "Gingrich, Newt                    1.201183e+07\n",
       "Perry, Rick                       1.841151e+07\n",
       "Paul, Ron                         2.072257e+07\n",
       "Romney, Mitt                      8.521925e+07\n",
       "Obama, Barack                     1.335026e+08\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "lZ+fL5vNJpvNpo8++kjhcFjt7e3at2+fvv/+ew0NDUkau7Sck5MjSXK5XAoGg5KkI0eORCe0zZ8/\n",
       "Xy6X65LndbvdSktLkyRlZWVFP2v69OnyeDySpLS0tOgUuPT0dB05cuQq/uTA9Yd7wEACsVqt501c\n",
       "O3XqlB555BEdP35c8+bN09NPP63R0dHo9+12u6SL7/Oe+38mT558yfOe/ZwLP8tms12UT9JFU+EA\n",
       "xI4CBhLI4sWLtX//fo2MjGhoaEhr165VIBDQc889p2XLlqmzszNaruOVYE5OjlpbWyVJ3d3d8vl8\n",
       "MeegYIH/H5eggQSSm5urY8eOqaioSJFIRGvXrtWvv/6qgoICORwO5eXlKRwOa2hoSBaL5bzV6tmv\n",
       "KyoqVFlZqYKCAs2cOVOZmZn/ec4LP+fcY+PtoGZnNXDlmAUNAIABWAED14GjR4+qurr6X7+3Y8cO\n",
       "zZgxI86JALACBgDAAGzCAgDAABQwAAAGoIABADAABQwAgAEoYAAADEABAwBggH8A4WzQoHgCjDEA\n",
       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abc7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byParty.plot(kind='bar')\n",
    "byParty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Let's look at how different occupations donated to each campaign.\n",
    "#####Did certain occupations donate more or less?  How did it vary by occupation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 17 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",
      "Party                1001731 non-null object\n",
      "dtypes: float64(1), int64(1), object(15)\n",
      "memory usage: 137.6+ MB\n"
     ]
    }
   ],
   "source": [
    "donors.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA VICE PRESIDENT</th>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESEARCH ASSOCIATE</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEACHER</th>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THERAPIST</th>\n",
       "      <td>3900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'MIS MANAGER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>177.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(PART-TIME) SALES CONSULTANT &amp; WRITER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>285.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(RETIRED)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>5000</td>\n",
       "      <td>2114.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>--</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.NET PROGRAMMER</th>\n",
       "      <td>481</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/13/1972</th>\n",
       "      <td>98</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100% DISABLED ARMY VET</th>\n",
       "      <td>NaN</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100% DISABLED VETERAN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2080.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100% DISABLED VIET NAM COMBAT VETER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100% DISABLED VIETNAM VETERAN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>236.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12K ADVOCATE</th>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13B CANNON CREWMEMBER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>71.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13D</th>\n",
       "      <td>721</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15J</th>\n",
       "      <td>NaN</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15U - CHINOOK MECHANIC</th>\n",
       "      <td>NaN</td>\n",
       "      <td>200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1SG RETIRED</th>\n",
       "      <td>210</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1ST ASSISTANT DIRECTOR 2ND UNIT</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1ST ASSISTANT SOUND EDITOR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>292.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1ST GRADE TEACHER</th>\n",
       "      <td>435</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1ST LIEUTENANT (MILITARY OFFICER)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1ST LIEUTENANT (RET)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>449.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1ST LT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH CORRECTIONS SUPERVISOR</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH COUNSELOR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1133.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH COUNTRY CHAIR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH DEVELOPMENT</th>\n",
       "      <td>650</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH DIRECTOR</th>\n",
       "      <td>550</td>\n",
       "      <td>546.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH MINISTER</th>\n",
       "      <td>725</td>\n",
       "      <td>780.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH MINISTRY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH OUTREACH DIRECTOR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH PASTOR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>335.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH PROGRAMS DIRECTOR</th>\n",
       "      <td>205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH SERVICE AIDE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH SERVICE COORDINATOR</th>\n",
       "      <td>425</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH SERVICES LIBRARIAN</th>\n",
       "      <td>290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH SERVICES MANAGER</th>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH SPECIALIST</th>\n",
       "      <td>652</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH SPORTS DIRECTOR</th>\n",
       "      <td>110</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH VOTE DIRECTOR</th>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH WORKER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEN BUDDHIST PRIEST</th>\n",
       "      <td>400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEN TEACHER</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEPPOS AND ASSOCIATES</th>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIMMERMANS DAIRY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>83.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOMBIE SLAYER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZONE MANAGER</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOOKEEPER</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOOLOGIST</th>\n",
       "      <td>400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOOLOGY EDUCATION</th>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\\NONE\\</th>\n",
       "      <td>NaN</td>\n",
       "      <td>250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>~</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45073 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Party                                  Democrat  Republican\n",
       "contbr_occupation                                          \n",
       "   MIXED-MEDIA ARTIST / STORYTELLER         100         NaN\n",
       " AREA VICE PRESIDENT                        250         NaN\n",
       " RESEARCH ASSOCIATE                         100         NaN\n",
       " TEACHER                                    500         NaN\n",
       " THERAPIST                                 3900         NaN\n",
       "'MIS MANAGER                                NaN      177.60\n",
       "(PART-TIME) SALES CONSULTANT & WRITER       NaN      285.00\n",
       "(RETIRED)                                   NaN      250.00\n",
       "-                                          5000     2114.80\n",
       "--                                          NaN       75.00\n",
       ".NET PROGRAMMER                             481         NaN\n",
       "0                                           NaN       20.00\n",
       "07/13/1972                                   98         NaN\n",
       "100% DISABLED ARMY VET                      NaN      350.00\n",
       "100% DISABLED VETERAN                       NaN     2080.78\n",
       "100% DISABLED VIET NAM COMBAT VETER         NaN      100.00\n",
       "100% DISABLED VIETNAM VETERAN               NaN      236.25\n",
       "11B                                         NaN      300.00\n",
       "12K ADVOCATE                                150         NaN\n",
       "13B CANNON CREWMEMBER                       NaN       71.80\n",
       "13D                                         721         NaN\n",
       "15J                                         NaN      150.00\n",
       "15U - CHINOOK MECHANIC                      NaN      200.00\n",
       "1SG RETIRED                                 210         NaN\n",
       "1ST ASSISTANT DIRECTOR 2ND UNIT              35         NaN\n",
       "1ST ASSISTANT SOUND EDITOR                  NaN      292.80\n",
       "1ST GRADE TEACHER                           435         NaN\n",
       "1ST LIEUTENANT (MILITARY OFFICER)           NaN      500.00\n",
       "1ST LIEUTENANT (RET)                        NaN      449.50\n",
       "1ST LT                                      NaN      500.00\n",
       "...                                         ...         ...\n",
       "YOUTH CORRECTIONS SUPERVISOR                 50         NaN\n",
       "YOUTH COUNSELOR                             NaN     1133.05\n",
       "YOUTH COUNTRY CHAIR                         NaN      250.00\n",
       "YOUTH DEVELOPMENT                           650         NaN\n",
       "YOUTH DIRECTOR                              550      546.32\n",
       "YOUTH MINISTER                              725      780.24\n",
       "YOUTH MINISTRY                              NaN      250.00\n",
       "YOUTH OUTREACH DIRECTOR                     NaN     1000.00\n",
       "YOUTH PASTOR                                NaN      335.00\n",
       "YOUTH PROGRAMS DIRECTOR                     205         NaN\n",
       "YOUTH SERVICE AIDE                          NaN       88.60\n",
       "YOUTH SERVICE COORDINATOR                   425         NaN\n",
       "YOUTH SERVICES LIBRARIAN                    290         NaN\n",
       "YOUTH SERVICES MANAGER                      300         NaN\n",
       "YOUTH SPECIALIST                            652         NaN\n",
       "YOUTH SPORTS DIRECTOR                       110         NaN\n",
       "YOUTH VOTE DIRECTOR                         165         NaN\n",
       "YOUTH WORKER                                NaN     1011.74\n",
       "ZEN BUDDHIST PRIEST                         400         NaN\n",
       "ZEN TEACHER                                 100         NaN\n",
       "ZEPPOS AND ASSOCIATES                      1000         NaN\n",
       "ZIMMERMANS DAIRY                            NaN       83.71\n",
       "ZMS                                         NaN       70.12\n",
       "ZOMBIE SLAYER                               NaN      125.00\n",
       "ZONE MANAGER                                135         NaN\n",
       "ZOOKEEPER                                    35         NaN\n",
       "ZOOLOGIST                                   400         NaN\n",
       "ZOOLOGY EDUCATION                            25         NaN\n",
       "\\NONE\\                                      NaN      250.00\n",
       "~                                           NaN       75.00\n",
       "\n",
       "[45073 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation = donors.pivot_table(index='contbr_occupation',columns='Party',values='contb_receipt_amt',aggfunc=np.sum)\n",
    "occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA VICE PRESIDENT</th>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESEARCH ASSOCIATE</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEACHER</th>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THERAPIST</th>\n",
       "      <td>3900</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         NaN\n",
       " AREA VICE PRESIDENT                      250         NaN\n",
       " RESEARCH ASSOCIATE                       100         NaN\n",
       " TEACHER                                  500         NaN\n",
       " THERAPIST                               3900         NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####There are more than 45,000 occupations....need to boil this down.\n",
    "#####Will look only at occupations where contribution exceeded 1MM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LargeDonors = occupation[occupation.sum(1)>1000000]\n",
    "LargeDonors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e3d278>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24e21f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LargeDonors.plot(kind='barh',figsize=(10,12),cmap='hot',sort_columns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "at 22nd minute of video; would like to re-sort high to low on total donation and also drop blank entries, as well as map \"like\"\n",
    "entries (example: CEO and C.E.O.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "occupation.drop(['INFORMATION REQUESTED PER BEST EFFORTS','INFORMATION REQUESTED'],axis=0,inplace=True) #axis=0 tells IPython to delete indexes rather than caolumns\n",
    "occupation.loc['CEO']=occupation.loc['CEO']+occupation.loc['C.E.O.']\n",
    "occupation.drop('C.E.O.',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217d1f28>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2749e278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LargeDonors = occupation[occupation.sum(1)>1000000]\n",
    "LargeDonors.shape\n",
    "LargeDonors.plot(kind='barh',cmap='seismic',sort_columns=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
 }
No results found