lec66-70-選挙のデータの解析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. どのような人たちが調査対象だったのか\n",
    "2. 調査結果は、どちらの候補の有利を示しているか\n",
    "3. 態度未定の人たちが世論調査に与えた影響は？\n",
    "4. 態度未定の人たちの動向は？\n",
    "5. 投票者の気持ちは時間と共にどう変化したか\n",
    "6. 討論会の影響を世論調査の結果から読み取ることはできるか"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
    "\n",
    "source = requests.get(url).text\n",
    "\n",
    "poll_data = StringIO(source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 586 entries, 0 to 585\n",
      "Data columns (total 17 columns):\n",
      "Pollster                  586 non-null object\n",
      "Start Date                586 non-null object\n",
      "End Date                  586 non-null object\n",
      "Entry Date/Time (ET)      586 non-null object\n",
      "Number of Observations    564 non-null float64\n",
      "Population                586 non-null object\n",
      "Mode                      586 non-null object\n",
      "Obama                     586 non-null float64\n",
      "Romney                    586 non-null float64\n",
      "Undecided                 423 non-null float64\n",
      "Other                     202 non-null float64\n",
      "Pollster URL              586 non-null object\n",
      "Source URL                584 non-null object\n",
      "Partisan                  586 non-null object\n",
      "Affiliation               586 non-null object\n",
      "Question Text             0 non-null float64\n",
      "Question Iteration        586 non-null int64\n",
      "dtypes: float64(6), int64(1), object(10)\n",
      "memory usage: 77.9+ KB\n"
     ]
    }
   ],
   "source": [
    "poll_df = pd.read_csv(poll_data)\n",
    "\n",
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster        ...         Question Iteration\n",
       "0  Politico/GWU/Battleground        ...                          1\n",
       "1           YouGov/Economist        ...                          1\n",
       "2           Gravis Marketing        ...                          1\n",
       "3                   IBD/TIPP        ...                          1\n",
       "4                  Rasmussen        ...                          1\n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>ABC/Post</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>AP-GfK</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Allstate/National Journal</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Angus-Reid</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Bloomberg</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CBS</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>CBS/Times</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>CNN</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Clarus</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>DailyKos/SEIU/PPP (D)</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Democracy Corps (D)</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Democracy Corps (D)/Campaign For America's Future</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Democracy Corps (D)/Campaign for America's Future</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Democracy Corps (D)/Center for American Progress</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Democracy Corps (D)/Women's Voices. Women Vote.</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Esquire/Yahoo! News</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>FOX</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Fairleigh Dickinson</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>GWU/Politico (Lake/Tarrance)</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Gallup</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>High Point University</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Ipsos/Reuters</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Ipsos/Reuters (Web)</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>JZ Analytics</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>JZ Analytics/Newsmax</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Rep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Marist/McClatchy</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Mason-Dixon</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>NBC/WSJ</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>NPR</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>NPR</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Newsweek/Daily Beast</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PPP (D-Americans United for Change)</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Dem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Pew</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>Politico / Penn Schoen Berland</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Post/Kaiser</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Purple Strategies</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Quinnipiac</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Reason/Rupe</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Resurgent Republic (R)</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Rep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Suffolk</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>SurveyUSA</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>TIPP/Investor's Business Daily/Christian Scien...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Tarrance (R)</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Rep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>Time</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>UConn/Hartford Courant</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>UPI/CVOTER</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>USA Today/Gallup</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>USAToday/ Gallup</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>USC/LA Times</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>United Technologies/National Journal</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Washington Times/JZ Analytics</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>YouGov</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Zogby (Internet)</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Pollster     ...     Affiliation\n",
       "404                                           ABC/Post     ...            None\n",
       "471                                             AP-GfK     ...            None\n",
       "90                                                 ARG     ...            None\n",
       "132                          Allstate/National Journal     ...            None\n",
       "305                                         Angus-Reid     ...            None\n",
       "122                                          Bloomberg     ...            None\n",
       "63                                                 CBS     ...            None\n",
       "415                                          CBS/Times     ...            None\n",
       "504                                                CNN     ...            None\n",
       "568                                             Clarus     ...            None\n",
       "123                              DailyKos/SEIU/PPP (D)     ...             Dem\n",
       "421                                Democracy Corps (D)     ...             Dem\n",
       "537  Democracy Corps (D)/Campaign For America's Future     ...             Dem\n",
       "547  Democracy Corps (D)/Campaign for America's Future     ...             Dem\n",
       "469   Democracy Corps (D)/Center for American Progress     ...             Dem\n",
       "525    Democracy Corps (D)/Women's Voices. Women Vote.     ...             Dem\n",
       "152                                Esquire/Yahoo! News     ...            None\n",
       "150                                                FOX     ...            None\n",
       "539                                Fairleigh Dickinson     ...            None\n",
       "528                       GWU/Politico (Lake/Tarrance)     ...            None\n",
       "196                                             Gallup     ...            None\n",
       "67                                    Gravis Marketing     ...            None\n",
       "32                               High Point University     ...            None\n",
       "60                                            IBD/TIPP     ...            None\n",
       "309                                      Ipsos/Reuters     ...            None\n",
       "145                                Ipsos/Reuters (Web)     ...            None\n",
       "146                                       JZ Analytics     ...            None\n",
       "33                                JZ Analytics/Newsmax     ...             Rep\n",
       "536                                   Marist/McClatchy     ...            None\n",
       "299                                        Mason-Dixon     ...            None\n",
       "..                                                 ...     ...             ...\n",
       "439                                            NBC/WSJ     ...            None\n",
       "108                                                NPR     ...             Dem\n",
       "230                                                NPR     ...            None\n",
       "531                               Newsweek/Daily Beast     ...             Dem\n",
       "507                                            PPP (D)     ...             Dem\n",
       "9                  PPP (D-Americans United for Change)     ...             Dem\n",
       "93                                                 Pew     ...            None\n",
       "555                     Politico / Penn Schoen Berland     ...           Other\n",
       "459                          Politico/GWU/Battleground     ...            None\n",
       "207                                        Post/Kaiser     ...            None\n",
       "22                                   Purple Strategies     ...            None\n",
       "481                                         Quinnipiac     ...            None\n",
       "435                                          Rasmussen     ...            None\n",
       "136                                        Reason/Rupe     ...            None\n",
       "182                             Resurgent Republic (R)     ...             Rep\n",
       "527                                            Suffolk     ...            None\n",
       "451                                          SurveyUSA     ...            None\n",
       "242  TIPP/Investor's Business Daily/Christian Scien...     ...            None\n",
       "273                                       Tarrance (R)     ...             Rep\n",
       "474                                               Time     ...            None\n",
       "71                              UConn/Hartford Courant     ...            None\n",
       "85                                          UPI/CVOTER     ...            None\n",
       "433                                   USA Today/Gallup     ...            None\n",
       "388                                   USAToday/ Gallup     ...            None\n",
       "190                                       USC/LA Times     ...            None\n",
       "105               United Technologies/National Journal     ...            None\n",
       "62                       Washington Times/JZ Analytics     ...            None\n",
       "18                                              YouGov     ...            None\n",
       "533                                   YouGov/Economist     ...            None\n",
       "450                                   Zogby (Internet)     ...            None\n",
       "\n",
       "[63 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 世論調査の主体とその支持政党を見る\n",
    "poll_df[['Pollster','Partisan','Affiliation']].sort_values('Pollster').drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a190b4da0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# affiliationでカウントプロット(Otherのものを除外)\n",
    "sns.countplot('Affiliation',data=poll_df[poll_df['Affiliation'] != 'Other'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18f4b438>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 調査対象で層別化する\n",
    "sns.countplot('Affiliation',data=poll_df,hue='Population',order=['Rep','Dem','None'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster        ...         Question Iteration\n",
       "0  Politico/GWU/Battleground        ...                          1\n",
       "1           YouGov/Economist        ...                          1\n",
       "2           Gravis Marketing        ...                          1\n",
       "3                   IBD/TIPP        ...                          1\n",
       "4                  Rasmussen        ...                          1\n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1927af60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 候補者の支持率を計算する\n",
    "avg = pd.DataFrame(poll_df.mean())\n",
    "avg.drop('Number of Observations',axis=0,inplace=True)\n",
    "avg.drop('Other',axis=0,inplace=True)\n",
    "avg.drop('Question Text',axis=0,inplace=True)\n",
    "avg.drop('Question Iteration',axis=0,inplace=True)\n",
    "\n",
    "std = pd.DataFrame(poll_df.std())\n",
    "std.drop('Number of Observations',axis=0,inplace=True)\n",
    "std.drop('Other',axis=0,inplace=True)\n",
    "std.drop('Question Text',axis=0,inplace=True)\n",
    "std.drop('Question Iteration',axis=0,inplace=True)\n",
    "\n",
    "avg.plot(yerr=std,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>46.805461</td>\n",
       "      <td>2.422058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>44.614334</td>\n",
       "      <td>2.906180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.550827</td>\n",
       "      <td>3.701754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Average       STD\n",
       "Obama      46.805461  2.422058\n",
       "Romney     44.614334  2.906180\n",
       "Undecided   6.550827  3.701754"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均と標準偏差のDataFrameを連結する\n",
    "poll_avg = pd.concat([avg,std],axis=1)\n",
    "\n",
    "# 列名を付ける\n",
    "poll_avg.columns = ['Average','STD']\n",
    "\n",
    "poll_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster        ...         Question Iteration\n",
       "0  Politico/GWU/Battleground        ...                          1\n",
       "1           YouGov/Economist        ...                          1\n",
       "2           Gravis Marketing        ...                          1\n",
       "3                   IBD/TIPP        ...                          1\n",
       "4                  Rasmussen        ...                          1\n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17bdc4e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 両候補の支持率と未定の割合をプロットする\n",
    "poll_df.plot(x='End Date',y=['Obama','Romney','Undecided'],marker='o',linestyle='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster    ...     Difference\n",
       "0  Politico/GWU/Battleground    ...           0.00\n",
       "1           YouGov/Economist    ...           0.02\n",
       "2           Gravis Marketing    ...           0.00\n",
       "3                   IBD/TIPP    ...           0.01\n",
       "4                  Rasmussen    ...          -0.01\n",
       "\n",
       "[5 rows x 18 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# オバマとロムニーの支持率の差をDifference列に挿入する\n",
    "poll_df['Difference'] = (poll_df.Obama - poll_df.Romney)/100\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>686.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-12</td>\n",
       "      <td>638.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-15</td>\n",
       "      <td>577.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start Date     ...      Difference\n",
       "0  2009-03-13     ...            0.00\n",
       "1  2009-04-17     ...            0.11\n",
       "2  2009-05-14     ...            0.18\n",
       "3  2009-06-12     ...            0.08\n",
       "4  2009-07-15     ...            0.09\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as_index=Falseで元のindexを維持する\n",
    "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 時間と共に支持率がどう変化したかを見る\n",
    "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>1746.000000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2012-10-02</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>1061.500000</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>48.666667</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.028333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>1197.333333</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2012-10-08</td>\n",
       "      <td>2030.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.015000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>1118.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2012-10-11</td>\n",
       "      <td>1335.250000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2012-10-12</td>\n",
       "      <td>1231.500000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2012-10-13</td>\n",
       "      <td>1163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2012-10-14</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2012-10-15</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2012-10-16</td>\n",
       "      <td>1251.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2012-10-17</td>\n",
       "      <td>935.333333</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2012-10-18</td>\n",
       "      <td>1084.500000</td>\n",
       "      <td>47.166667</td>\n",
       "      <td>47.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.001667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>1180.666667</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2012-10-20</td>\n",
       "      <td>1132.333333</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2012-10-22</td>\n",
       "      <td>1364.000000</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2012-10-23</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>1386.500000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2012-10-25</td>\n",
       "      <td>1075.200000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>994.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2012-10-27</td>\n",
       "      <td>2579.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2012-10-28</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>47.333333</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>2012-10-30</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.015000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>10420.250000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.015000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start Date     ...      Difference\n",
       "325  2012-10-01     ...        0.025000\n",
       "326  2012-10-02     ...        0.040000\n",
       "327  2012-10-04     ...       -0.028333\n",
       "328  2012-10-05     ...       -0.005000\n",
       "329  2012-10-06     ...        0.010000\n",
       "330  2012-10-07     ...       -0.003333\n",
       "331  2012-10-08     ...       -0.015000\n",
       "332  2012-10-10     ...        0.010000\n",
       "333  2012-10-11     ...        0.005000\n",
       "334  2012-10-12     ...       -0.005000\n",
       "335  2012-10-13     ...       -0.005000\n",
       "336  2012-10-14     ...       -0.020000\n",
       "337  2012-10-15     ...       -0.060000\n",
       "338  2012-10-16     ...        0.016667\n",
       "339  2012-10-17     ...        0.013333\n",
       "340  2012-10-18     ...       -0.001667\n",
       "341  2012-10-19     ...       -0.013333\n",
       "342  2012-10-20     ...       -0.003333\n",
       "343  2012-10-22     ...       -0.003333\n",
       "344  2012-10-23     ...       -0.005000\n",
       "345  2012-10-24     ...       -0.005000\n",
       "346  2012-10-25     ...        0.006000\n",
       "347  2012-10-26     ...        0.000000\n",
       "348  2012-10-27     ...        0.000000\n",
       "349  2012-10-28     ...       -0.003333\n",
       "350  2012-10-29     ...        0.000000\n",
       "351  2012-10-30     ...        0.015000\n",
       "352  2012-10-31     ...        0.015000\n",
       "\n",
       "[28 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 候補者同士の討論会があった日をグラフに書き込む\n",
    "# 2012年10月のデータのindexを確認する\n",
    "poll_df[poll_df['Start Date'].apply(lambda x:x.startswith('2012-10'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a19579208>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10月のみプロット\n",
    "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple',xlim=(325,352))\n",
    "# 討論会の日程を書き込む\n",
    "plt.axvline(x=326, linewidth=4, color='gray')\n",
    "plt.axvline(x=333, linewidth=4, color='gray')\n",
    "plt.axvline(x=342, linewidth=4, color='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a194b3710>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 寄付のデータ\n",
    "1. 寄付の金額とその平均的な額\n",
    "2. 候補者ごとの寄付の違い\n",
    "3. 民主党と共和党での寄付の違い\n",
    "4. 寄付した人々の属性について\n",
    "5. 寄付の総額に何らかのパターンはあるか"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tsubasa/.pyenv/versions/anaconda3-5.3.1/lib/python3.7/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "donor_df = pd.read_csv('Election_Donor_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      "cmte_id              1001731 non-null object\n",
      "cand_id              1001731 non-null object\n",
      "cand_nm              1001731 non-null object\n",
      "contbr_nm            1001731 non-null object\n",
      "contbr_city          1001712 non-null object\n",
      "contbr_st            1001727 non-null object\n",
      "contbr_zip           1001620 non-null object\n",
      "contbr_employer      988002 non-null object\n",
      "contbr_occupation    993301 non-null object\n",
      "contb_receipt_amt    1001731 non-null float64\n",
      "contb_receipt_dt     1001731 non-null object\n",
      "receipt_desc         14166 non-null object\n",
      "memo_cd              92482 non-null object\n",
      "memo_text            97770 non-null object\n",
      "form_tp              1001731 non-null object\n",
      "file_num             1001731 non-null int64\n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "donor_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id   ...    form_tp file_num\n",
       "0  C00410118  P20002978   ...      SA17A   736166\n",
       "1  C00410118  P20002978   ...      SA17A   736166\n",
       "2  C00410118  P20002978   ...      SA17A   749073\n",
       "3  C00410118  P20002978   ...      SA17A   749073\n",
       "4  C00410118  P20002978   ...      SA17A   736166\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 100.00     178188\n",
       " 50.00      137584\n",
       " 25.00      110345\n",
       " 250.00      91182\n",
       " 500.00      57984\n",
       " 2500.00     49005\n",
       " 35.00       37237\n",
       " 1000.00     36494\n",
       " 10.00       33986\n",
       " 200.00      27813\n",
       " 20.00       17565\n",
       " 15.00       16163\n",
       " 150.00      14600\n",
       " 75.00       13647\n",
       " 201.20      11718\n",
       " 30.00       11381\n",
       " 300.00      11204\n",
       " 20.12        9897\n",
       " 5.00         9024\n",
       " 40.00        5007\n",
       " 2000.00      4128\n",
       " 55.00        3760\n",
       " 1500.00      3705\n",
       " 3.00         3383\n",
       " 60.00        3084\n",
       " 400.00       3066\n",
       "-2500.00      2727\n",
       " 110.00       2554\n",
       " 125.00       2520\n",
       " 19.00        2474\n",
       "             ...  \n",
       " 174.80          1\n",
       " 7.27            1\n",
       " 1219.00         1\n",
       " 1884.88         1\n",
       " 162.25          1\n",
       " 218.31          1\n",
       " 78.62           1\n",
       " 203.16          1\n",
       " 53.11           1\n",
       " 499.66          1\n",
       " 19.53           1\n",
       " 188.60          1\n",
       " 47.10           1\n",
       " 19.85           1\n",
       " 28.83           1\n",
       " 202.59          1\n",
       "-5500.00         1\n",
       " 9.25            1\n",
       " 202.66          1\n",
       " 1205.00         1\n",
       " 80.73           1\n",
       " 115.07          1\n",
       " 213.69          1\n",
       " 70.76           1\n",
       " 144.13          1\n",
       " 97.15           1\n",
       " 122.32          1\n",
       " 188.65          1\n",
       " 122.40          1\n",
       " 132.12          1\n",
       "Name: contb_receipt_amt, Length: 8079, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 寄付の額\n",
    "donor_df['contb_receipt_amt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8079,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df['contb_receipt_amt'].value_counts().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "寄付の平均は298.24で、その標準偏差は3749.67です。\n"
     ]
    }
   ],
   "source": [
    "# 寄付の平均的な額\n",
    "don_mean = donor_df['contb_receipt_amt'].mean()\n",
    "\n",
    "# 標準偏差\n",
    "don_std = donor_df['contb_receipt_amt'].std()\n",
    "\n",
    "print(f'寄付の平均は{don_mean:0.2f}で、その標準偏差は{don_std:0.2f}です。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           250.0\n",
       "1            50.0\n",
       "2           250.0\n",
       "3           250.0\n",
       "4           300.0\n",
       "5           500.0\n",
       "6           250.0\n",
       "7           250.0\n",
       "8           250.0\n",
       "9           250.0\n",
       "10          250.0\n",
       "11          500.0\n",
       "12          250.0\n",
       "13          250.0\n",
       "14          250.0\n",
       "15          300.0\n",
       "16          500.0\n",
       "17         1000.0\n",
       "18          250.0\n",
       "19          300.0\n",
       "20          500.0\n",
       "21          250.0\n",
       "22         2500.0\n",
       "23         2500.0\n",
       "24          150.0\n",
       "25          200.0\n",
       "26          100.0\n",
       "27          250.0\n",
       "28          500.0\n",
       "29          250.0\n",
       "            ...  \n",
       "1001701    2500.0\n",
       "1001702    2500.0\n",
       "1001703   -2500.0\n",
       "1001704   -2500.0\n",
       "1001705    1000.0\n",
       "1001706    2500.0\n",
       "1001707   -2500.0\n",
       "1001708    2500.0\n",
       "1001709   -2500.0\n",
       "1001710   -2500.0\n",
       "1001711    1000.0\n",
       "1001712    2500.0\n",
       "1001713    2500.0\n",
       "1001714     250.0\n",
       "1001715     250.0\n",
       "1001716    1000.0\n",
       "1001717     100.0\n",
       "1001718    2500.0\n",
       "1001719    2500.0\n",
       "1001720     100.0\n",
       "1001721     250.0\n",
       "1001722     100.0\n",
       "1001723     100.0\n",
       "1001724     500.0\n",
       "1001725    2500.0\n",
       "1001726    5000.0\n",
       "1001727    2500.0\n",
       "1001728     500.0\n",
       "1001729     500.0\n",
       "1001730    2500.0\n",
       "Name: contb_receipt_amt, Length: 1001731, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrameの1列からSeriesを作る\n",
    "top_donor = donor_df['contb_receipt_amt'].copy()\n",
    "\n",
    "top_donor.sort_values()\n",
    "\n",
    "top_donor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0     178188\n",
       "50.0      137584\n",
       "25.0      110345\n",
       "250.0      91182\n",
       "500.0      57984\n",
       "2500.0     49005\n",
       "35.0       37237\n",
       "1000.0     36494\n",
       "10.0       33986\n",
       "200.0      27813\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 負の数(払い戻し)を取り除く\n",
    "top_donor = top_donor[top_donor>0]\n",
    "top_donor.sort_values()\n",
    "\n",
    "top_donor.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17aa0908>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2,500ドルまでの寄付データを取り出す\n",
    "com_don = top_donor[top_donor<2500]\n",
    "\n",
    "com_don.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重複のない候補者のデータを作る\n",
    "candidates = donor_df.cand_nm.unique()\n",
    "candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "# 政党列を作成する\n",
    "donor_df['Party'] = donor_df.cand_nm.map(party_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "      <th>Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id     ...     file_num       Party\n",
       "0  C00410118  P20002978     ...       736166  Republican\n",
       "1  C00410118  P20002978     ...       736166  Republican\n",
       "2  C00410118  P20002978     ...       749073  Republican\n",
       "3  C00410118  P20002978     ...       749073  Republican\n",
       "4  C00410118  P20002978     ...       736166  Republican\n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 払い戻しの除去\n",
    "donor_df = donor_df[donor_df.contb_receipt_amt>0]\n",
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Bachmann, Michelle                 13082\n",
       "Cain, Herman                       20052\n",
       "Gingrich, Newt                     46883\n",
       "Huntsman, Jon                       4066\n",
       "Johnson, Gary Earl                  1234\n",
       "McCotter, Thaddeus G                  73\n",
       "Obama, Barack                     589127\n",
       "Paul, Ron                         143161\n",
       "Pawlenty, Timothy                   3844\n",
       "Perry, Rick                        12709\n",
       "Roemer, Charles E. 'Buddy' III      5844\n",
       "Romney, Mitt                      105155\n",
       "Santorum, Rick                     46245\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 候補者の名前でグループ化して、それぞれの寄付件数を表示\n",
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Bachmann, Michelle                2.711439e+06\n",
       "Cain, Herman                      7.101082e+06\n",
       "Gingrich, Newt                    1.283277e+07\n",
       "Huntsman, Jon                     3.330373e+06\n",
       "Johnson, Gary Earl                5.669616e+05\n",
       "McCotter, Thaddeus G              3.903000e+04\n",
       "Obama, Barack                     1.358774e+08\n",
       "Paul, Ron                         2.100962e+07\n",
       "Pawlenty, Timothy                 6.004819e+06\n",
       "Perry, Rick                       2.030575e+07\n",
       "Roemer, Charles E. 'Buddy' III    3.730099e+05\n",
       "Romney, Mitt                      8.833591e+07\n",
       "Santorum, Rick                    1.104316e+07\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# グループ化したあと、寄付の総額で表示\n",
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bachmann, Michelleは、2711439ドル集めた。\n",
      "Cain, Hermanは、7101082ドル集めた。\n",
      "Gingrich, Newtは、12832770ドル集めた。\n",
      "Huntsman, Jonは、3330373ドル集めた。\n",
      "Johnson, Gary Earlは、566962ドル集めた。\n",
      "McCotter, Thaddeus Gは、39030ドル集めた。\n",
      "Obama, Barackは、135877427ドル集めた。\n",
      "Paul, Ronは、21009620ドル集めた。\n",
      "Pawlenty, Timothyは、6004819ドル集めた。\n",
      "Perry, Rickは、20305754ドル集めた。\n",
      "Roemer, Charles E. 'Buddy' IIIは、373010ドル集めた。\n",
      "Romney, Mittは、88335908ドル集めた。\n",
      "Santorum, Rickは、11043159ドル集めた。\n"
     ]
    }
   ],
   "source": [
    "cand_amount = donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()\n",
    "\n",
    "i = 0\n",
    "\n",
    "for don in cand_amount:\n",
    "    print(f\"{cand_amount.index[i]}は、{don:.0f}ドル集めた。\")\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19812c50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cand_amount.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a984cc0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 政党ごとの寄付の額\n",
    "donor_df.groupby('Party')['contb_receipt_amt'].sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 職業ごとに政党別に分けて寄付額をまとめる\n",
    "occupation_df = donor_df.pivot_table('contb_receipt_amt',\n",
    "                                    index='contbr_occupation',\n",
    "                                    columns='Party',aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIXED-MEDIA ARTIST / STORYTELLER</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA VICE PRESIDENT</th>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESEARCH ASSOCIATE</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEACHER</th>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THERAPIST</th>\n",
       "      <td>3900.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Party                                Democrat  Republican\n",
       "contbr_occupation                                        \n",
       "   MIXED-MEDIA ARTIST / STORYTELLER     100.0         NaN\n",
       " AREA VICE PRESIDENT                    250.0         NaN\n",
       " RESEARCH ASSOCIATE                     100.0         NaN\n",
       " TEACHER                                500.0         NaN\n",
       " THERAPIST                             3900.0         NaN"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45067, 2)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 百万ドルより上の額にする\n",
    "occupation_df = occupation_df[occupation_df.sum(1)>1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 2)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1976df60>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P6PNJwIuqenGtYz3++OPlgQMHdtg3b9684EW9vlS26PobVbbo+htVtuj6G1W26PpDyo4aNYoRR/0JgNmn7WT5FE54j5kzZ/a6tja6bFa5uXPnPjpmzJiN6x6gXC7X/CuVSmuUSqXppVJp/8T+DUql0m2xz1NKpdI36x1vxowZ5SRp+7LoS2WLrr9RZYuuv1Fli66/UWWLrj+07JoTby2vOfFW+3D8kB6vv7eUzSo3ffr06eU68rVcLtddKP0EcCdwqKrek/h6JrCuiKwEfABsAUyu24s4juM4DaOeSeMxwIrAcSJS0a1fBCynqr8VkcOBP2O6+UtV9X+Na6rjOI5Tj5pCXVUPAw6r8f0twC3d3SjHcRync/QKj1LHcRyne3Ch7jiO00S4UHccx2kiXKg7juM0ES7UHcdxmggX6o7jOE2EC3XHcZwmwoW64zhOE+FC3XEcp4lwoe44jtNEuFB3HMdpIlyoO47jNBEu1B3HcZoIF+qO4zhNhAt1x3GcJsKFuuM4ThPhQt1xHKeJcKHuOI7TRLhQdxzHaSJcqDuO4zQRLtQdx3GaCBfqjuM4TYQLdcdxnCbChbrjOE4T4ULdcRyniXCh7vQa1l1rjfYPC+cV1xDH6cMsVXQDHKfCUq2D4ITl7cMJ7xXbGMfpo/hI3XEcp4lwoe44jtNEuFB3HMdpIlyoO47jNBEu1B3HcZoIF+qO4zhNRJBJo4hsCpyuqlsm9h8OHAC8Ge36jqpqt7bQcRzHCaauUBeRI4HxwIcpX48G9lXVR7u7YY7jOE5+QtQvzwO7Znw3BjhaRO4XkaO7r1mO4zhOZ2gpl8t1C4nICOBaVR2b2H888CtgDnATcKGq3lrrWI8//nh54MCBHfbNmzeP1tbWoAb3pbJF19+oso2qf9SoUR08SmfOnNmjbSj6ujaqbNH1h5QdNWoUI476EwCzT9vJnoMCnoHeUDar3Ny5cx8dM2bMxnUPUC6X6/6VSqURpVLp4cS+llKptHzs8/dKpdJx9Y41Y8aMcpK0fVn0pbJF19+oso2qv1wul8vHD7G/AtpQ9HVtVNmi6w8tu+bEW8trTrzVPhT0DPSGslnlpk+fPr0cIK+7EvtlCPCUiIzC9O1bA5d24XiO4zhOF8kt1EVkL2CQqv5WRI4B7gPmA/eo6m3d3UDHcRwnnCChrqqzgbHR9tWx/dOAaQ1pmeM4jpMbdz5yHMdpIlyoO47jNBEu1B3HcZoIF+qO4zhNhAt1x3GcJsKFuuM4ThPhQt3JzbprrdH+YeG84hriOE4VXfEodZZQlmod1CFGi+M4vQcfqTuO4zQRLtQdx3GaCBfqjuM4TYQLdcdxnCbChbrjOE4T4ULdcRyniXCh7jiO00S4UHccx2kiXKg7juM0ES7UHcdxmggX6o7jOE2EC3XHcZwmwoW64zhOE+FC3XEcp4lwoe44jtNEuFB3HMdpIlyoO47jNBEu1B3HcZqIQoX6vIWLAfjUiLWLbIbjOE7TUKhQbx3QnxFH/YnllhlYZDMcx3GaBle/OI7jNBEu1B3HcZoIF+qO4zhNhAt1x3GcJsKFuuM4ThMRJNRFZFMR+UvK/p1F5BEReUhEDur21jmO4zi5qCvUReRI4GKgNbF/ADAF2B74EnCwiKzaiEY6juM4YYSM1J8Hdk3ZPwp4TlXfVdUFwP3A5t3ZOMdxHCcfS9UroKq/F5ERKV8NAd6LfX4fWL7e8ebPn8/MmTMBGDVqVNv+yr56zJs3r8+ULbr+RpWN3zeofe/y1N+o4y7p96vo+kPKJu99hZ5+BnpD2TzHTKOuUK/BHGBw7PNg4P/q/WjgwIGpNzDrpiaZOXNmnylbdP2NLBun1m86e8zuPO6Sfr+Krj9v2Tg9/Qz0hrJZ5R599NGgeroi1GcC64rISsAHwBbA5C4cz3Ecx+kiuYW6iOwFDFLV34rI4cCfMd38par6v+5uoOM4jhNOkFBX1dnA2Gj76tj+W4BbGtIyx3EcJzfufOQ4jtNEuFB3HMdpIlyoO47jNBEu1B3HcZoIF+qO4zhNhAt1x3GcJsKFuuM4ThPhQt1xHKeJcKHuOI7TRLhQdxzHaSJcqDuO4zQRLtQdx3GaCBfqjuM4TYQLdcdxnCbChbrjOE4T0TuE+sJ56duO4zhOLrqSzq77GNAKJ0Q5q094r3ZZx3EcJ5PeMVJ3HMdxugUX6o7jOE2EC3XHcZwmwoW64zhOE+FC3XEcp4lwoe44jtNEuFB3HMdpIlyoO47jNBEu1B3HcZoIF+qO4zhNhAt1x3GcJsKFuuM4ThPhQt1xHKeJcKHuOI7TRLhQdxzHaSJcqDuO4zQRdZNkiEg/4AJgQ2A+cKCqPhf7/lzgC8D70a5dVNUzXTiO4xRASOajrwGtqrqZiIwFzgJ2iX0/Gviyqr7ViAY6juM44YSoX74I3AGgqg8DG1e+iEbx6wK/FZEHRGT/hrTScRzHCSJkpD4EiKtTFovIUqq6CFgOOA84G+gP3Cci01X1iayDzZ8/n5kzZwIwatSo1DKV79OYN29eze97U9mi629U2eR966771ajjLun3q+j6Q8o2uyzIUzbPMVMpl8s1/0ql0tmlUmn32OeXY9v9S6XS4NjnM0ql0vhax5sxY0Y5zpoTb7WN44fYXx2Sv+/NZYuuv5Fl692vjxYsKpfL5fIHc+eFHzPguBX8fjXfea058daO8qCH6+8tZbPKTZ8+fXq5jrwul8tB6pcHgHEAkU79ydh3JeB+EekvIgMwVc1jne9inGahdUB/Rhz1J5ZbZmDRTXFysO5aa7R/WDivuIY4nSZE/XITsJ2IPAi0ABNE5HDgOVW9WUSuAh4GFgJTVfXpxjXXcZxGslTrIDhheftwghux9UXqCnVV/Rj4bmL3f2LfnwGc0c3tchzHcTqBOx85juM0ES7UnWDmLVxcdBMcx6mDC/UmpzsXviqLn47j9F5CFkqdPowvfDnOkoWP1J3GEp8duImc4zQcF+pOYxnQajOFE5a3bafHqKyBfGrE2gW3xOlJXKg7TpPS5gC2VLl9p8+Wmh7XqTtOs1OZLYGvqywB+EjdcRyniXCh7jiO00S4UHeWeHxB0WkmXKg7SzweUdJpJlyoO47jNBEu1B3HcZoIF+oRnhzAcZxmwIV6RFuMlII8H71TcRynO3ChXjAVy4uiO5Vmpq3D7AOdpXfuTldxoV4wHs628bR1mH2gs/TO3ekqLtQdxwkmnijF7fp7Jx77JZB5CxfTOqA/4A+zs+QSn1nOPm2nglvjpOFCPRB/mJ1mJj5ocfo2rn7pg/himtPd+NpO8+BCvTNUBGlBAtUX03o/rnvuSNEDkaLr70lcqHeGSnxqF6hOBpWRr8eUMYoeiBRdf0/iQt1poy/ZcxfNkjTy6634PUinzwn1PDey6JteVP3xqX8e+pI9d9EsSSO/3kpR96C3q9b6nPVL242Euqm58pRtBEXV75Y6jtM4evv71edG6o4Dxc/CupPOzqycxlD0DLuro38X6n0Ef/E7Um/q3ZeuV3xR1SmeRql16q1ZdVeyFhfqfQR/8fPRqOvVmzoLX9juW/TUmpULdcfJQW9y0vGF7fr0pk64p3Ch7jhOQ+gNAjW0E+4Nbe0umk6o94ab0xva4HSCuBqjl6o0+tKz1ZdUYJ1uay98ZuoKdRHpJyK/FpGHROQvIrJO4vuDRGS6iDwsIl9pXFPD6A265940Ra9HXxISDafiKdyLVRq94fluoyCB1qverzrPTBHvV8hI/WtAq6puBhwFnFX5QkRWBX4IfAH4MvALEWmIT7QLn8bQq4REHfwZ6GX0gU6waDr1fnWxswwR6l8E7gBQ1YeBjWPffR54QFXnq+p7wHPABrlbEUCv6p2dQuhLHVAHeuEU3enFdLGzbCmXyzULiMjFwO9V9fbo84vA2qq6SET2AT6rqhOj76YCU1X17qzjPfroo28C/83dUsdxnCWbNceMGbNyvUIhYQLmAINjn/up6qKM7wYD/1frYCGNchzHcTpHiPrlAWAcgIiMBZ6MffdPYHMRaRWR5YFRwFPd3krHcRwniBD1Sz/gAkxX3gJMwIT8c6p6s4gcBByMdRCnqurvG9tkx3EcJ4u6Qt1xHMfpOzSd85HjOM6SjAt1x3GcJsKFuuM4ThPR40JdRFp6uk7HcZyeQkQ2KrL+IkbqfxORT3X3QUVkKRHZVUS2iu37hIhc1911dZWib3otuhrmQURWEZH9ReRIEdlLRFaoUXaX6P/yInKmiJwsIst1pf4ade3YoOMuk7JvAxE5SUQuEJFjRGTdRtQd1bVqF38/oYu/Xybx+ejY9jax7Qu7Uk9vJuUZOCu1YA9RhFA/A/iziOwV+gMRGZZ82UXkkESxq4BvAMeJyKEishPwb+DxHPX8LmP/ESLyrIi8LCIviMgRocfMINdNDzz/DoJLRIbGtr+TUva62PZPYl/dnlJ2eRE5XEQmRCauiMhnReTBRLmtgL8BawPzgDHAIyLyhZRjngbsKyL9gfOAQcDbQOrLLyLDYts7ici2GeX2E5FXROR5Edkoavv1wOkZ5UeLyMjY51YRqSorImuKyLkicqKILBvtG0dHvw1EZDfgUuAlLLzG+8DvKx1YomzQdY218xYRuUJEVo72fQ94NKXsvll/KZdgfNp16ez5A9vFto+NH6LGsQ8Vkb+LyAwRuVNE9s8ot2H0f4CIfF9EDqxct0S5pUVksojMip4FjTrZKmdLETkn+6yryoZeg2AaMRjt8cTTqnqLiNwPnBkJ3iti392ZLB/1/AcAS4nIAcCzwHXAe3QUACNVdWMRWRp70OcDW6nqzBzNOyil/h8BJWC0qr4vIkOAKSLyU1U9M1buVaBiH9oSbQ8GllXV/jnakKw/9PwBfkq7UL4B2Dra3gP4TaLsKrHtnWjvaNLUYzcA04GNgDVE5HXgeOAniXI/A7ZQ1Tdi7Z8MTAOSQniMqm4XvWg7AWuo6tzo2ehANAA4SURGAccAOwCvichYVT05UfxwYH1geHROqwF/BPZOOe550TktLyInY9f2d8BdKdfgGuByYM2oLQuAXYH9EuUOA76kqh/G6rkcuDlqR5zQ6wpwEXB0VP8pUSe/GrBFStlRic8V/5K5wNTEd8uKzSSq7ruqPhP7GHr+LRnbqYjICcCqwP7A69HxjxCR1eL3VkQOB/aIBgiTo3L/BaZg1zzOWcCrwChVnScig4Ejo9/9KFH2s/XaGCP0GnxBRF5J7GsByqq6WmL/VcAiYLiIrA/MAi4BgjubJD0u1AFU9V0R+ScmhObTLgSrhDrwLWA9YBhwLfAJ4HRVvTRRbk507AVR7729qr6T1YboRu+K3aAXgRtV9d2Uot/EBNXH0fHniMh3sRFpm1BX1eGJ438XOAITMkny3PTQ868co952GvHv0xwXBqvqMWLrIQrMBj4XF94R/ZL7VPVVkdRBWiXUxOeBp1R1bvR56ZSy+wMbqurCaNYxBhMADwJJof5OdB/fFZH1gO9W4hal8AVVHS3mDX0PsBxwkKrek1L2Y1X9LYCIzAb+il2DZISuRXGBDhANBtJCTIZeV4APK4MeEfkZNhjaR1Wr7peqxlUg62CC6FaqBRrYCPo3VD8jZdoHBRB+/uWM7Sy+HEWArfCkmEroPjre2x2B/xcdcy+gFMmRqlkNNmD4f5UPqvo+NoP/S0rZT4rIwWkNq5xvjNBr8KCqbkUY3TEY7UCPC3URWQu4DHgTGKuqb9f5yTuqugB4RUQ+Ceymqo/V+c3rdQT6usAfsNHTLGxkN1FEvqaqmii+oCLQK0TCZWHGsVfDetr3gU0zzi/PTc9z/lkvVNrLleflmwegqmUR+Qj4asqDDJAVGzdNzbdYRLbHRjm/BxBTqaTFDlqsqh9GQvpNVX01Kp9WX/xe/beGQAeb7aCq70Wd/JdVdXZG2fj9fhvYL02gJuqPk3YNQq8rtHeCAK+o6qSMcm2IyPcxQf5jVb01o9jjqrp1xndxQs+/IiRbEtvJwUqFqvNV1Y9FZFFi98equlhERgMvxAZgaQOW+Rl1pd2bpbGZQlqnliT0GuQh12A0hCJG6n8HJqrqVYHl4xftxRoCbX0RuRq7OZVtAFQ1qb+fDOypqk9UdojINdH+nZP1i8gqCZVXrZLyAAAgAElEQVTCJ0h5QMSiVp4AHKeq19Q9szBCzx9guajD6kf7tLofsGxK2bTr1YLNCmq14e0agmekiJya2NeC6diTHAacio1OLxSRL2PrLbunlO0fqb2+SaReEpHVgQEpZYeKyHbYeQ+JOg4gVb0XP69Xagj0ZNn3arzMHZ69iK5eV4B+IjIAO6+Pou0WMIEQLxh1/pcB7wCfz5iB5iX0/K/GVF/J7az3Ies4VcJaREqYGunm6PP6pA8kWuLXJ0ZaxzpbVU/KaEOttta6BqcEHi9JzcFoKEUI9U1V9X/JnSIyUFXTetgOvX18qpSYHsWFwa/rtGFIXKBHx3pMRFZMKXsycFskrJ4H1sIWgI5JtP/3WLKQo4C36wiTPDc99PzBdKaVfR8ltpNkXa+0azcmmua2AOvFtsvxaS6mU0/j+JR9Q1R1j9jnP0d/aZwFPAG8BnxVRD4PXA8cmlL2MWx6DvAvYM9oO029V+mEWoC14x2Sqh6TKPvFSGXWAqwU206qzNI6JejadQUYgaloiMoo7WrLZKf5FLAAuBf4VVz9lTLA+WZGe5OEnv+LqnpZ4DHjx43TAiTfxeOwtZnZwNEi8iXgSmC3lGOOoOO1Ksf+J6mSRQFtrXcNviAiyfsHQEoHkmcwGkQRQv1sbOEOEfmJqlYW6G6now6vQlbPn7xBc1T1X8kfS4rVAdlWP1XXQ1XvE7MaOARbsHwZODilrjnROXwpsT9NmOS56aHnD/BzVb037bgp9fw16njuiqb/GwLDVfWOlOKhiU/urKhG4ojIpillzyL9fqdxh6qOiB1vATY4eD1ZUFUnRGUGYusPr2cMFqBjJ5TVIVWOm6brTyPPcxicUCZ+/gF8LUfZ34lI6ogzrpbJcf7jsVlCEKHHVdV/Am3PkYg8jOV1qFKD5rlWqrpPjrKh1+C1xOflgIlYh5R8v/MMRsMol8s9+lcqle6Lbd+btj9RviVj/5qJz/Fj3ZW2P7bvl6VS6fuJfYeUSqVfdbb+jDLDSqXSUSn7v5P4O7xUKr1eKpX+0ZX6S6XSH0ql0oxSqXRkqVRauU7bDimVSg+WSqXB0ecNSqXSQ6VS6eCUsjvGtofGz6PGPZhW5x5U7avR1jxllyqVSueXSqXnS6XSA6VSaVb0eUBK2eEZx9g0Zd+E2Pb6se3ju/AcBl3XaN/Rse1tYtsXBlyTwaVS6QelUmlGyneS+Nu9VCq9WiqVzkop+/lSqXRZqVS6t1QqXZ5xnf5RKpXWLaWQ0bbQ6xp8/jnLvloqlV6J/irb75dKpcWdbWviN18slUozS6XScaVSqX/K9xtl/G6X0Gc++Vd0mIB6VhdgVgkAiMiZsf3J0UD8WEtl7K9wLDZafkJEbhKRf2GjxjT789D6iZXbRESuwOxXV09+r6q/qfwBT2OmlOdjq/udrl9VvwZsien77xSR6yTmAJJgP2DryDKASB21HSlmnZiVUoUbYtt7JMrFr/XqGfsrfEHMhjj+92rKVDwvx2Oj85Gq+gVgHWxR6+cpZdvWdURkWmz/L1LKxu25z4ttJ2dmeZ7D0OsK2fbfn04pC4CIrCciF2DGAJ8Bvp0soxHAM5g12InAt1T1J4lj7QCci5nTfhcz/TxPqpPNlzBrmuRf1ig09LrmsX8PvlaqOlxVV4v+hmMj6deBr3ehrRVb+jOjcnup6s9VNU3/H8/5HDelTZppBlOE+iWvyVP8ZRiTsb/WcdNMvj4E9hJz0FkbWyTL0q0F1R+ZJO0JfB9bfR+CTQ/T9NlECzmnYvbbe6VN2fPUXyFa0J0MTI7UHt8WkfNUNblQNze5MKeqH4jI+3XakMsOOSLtPuexAEpbfARS9Y5bqeoXY98vBo6PputJ8nRCodcgjwVSnuua9X3VcUXkG9hzuDTW+YuqVjmgxcqvi9mvPwFsoqofpBQ7EhgXW8h7Rsws+QbMXLJCqDVN2rnUugadNdENGTiGWq0FtVXMY/wyzPns82kqoozf1hsEBFGEUM9jdZGk1g2KWwd02M46WHTj2m6eiHylhulXvfpnYyv8e6vqsyJyew2Bnuemh9afrGMzbGS2GTaqSrJQRIap6lux3wwj/ZkIFVR5O+xQXqHaeSqLBTn3p5HHBLQrz2GjTFCnAr8EzlbVtyMhn4qIHIr5UhwO3BbtWxqqrWqSlhmq+oZ0PZZTI56tXM9hDqu10Lb+A1tj+xLw12ihOmsBPM8zEEQRQj2P1QWEn+gI2i0CwKaT9X6TJC1GR2j952BWFyPEknXXeti746ZXIeYDsC82Y/gPcDHwPU3Y2UechKlorgBeAD6FLQQfmVI21FQyyzogzaoo1QJIRJZJ6Qz/T1X/mlY+hXkiMlJVn48dcyQp9tDke4nippIrRYvMLcBKiXIjyLZSSZLHBDWP/fc6mOnf30XkScxxLYuKmmUKZsRQaXfSqmaAiCwdF/SR8E8uHlZZo0Qz4oNU9bSU+kOva57zDy6b02ottK15Yv3kHozWowihPitn+Syzrw6u0DmtA1JR1SldqP904PTI1OpAYBOxGCLTVDWZtzXrplcFhwqtP+JubAq5tarW1E2r6v0i8k1gH8xN/7/A11X1vynFQ00lWzM6kDSeFZFzgXcxD9m5YnE0zsWEUpw/BR4TzJX+ZhG5hHYT1INIj3GSpxPKMpVMqs3W03bv2Hokr2UtE9Rg++/IAulU4NRoTeUgEZkF/F5Vj0iUXSuwrVcDl4jIYar6jligtnOT9SdmfptgZqdfJn22COHXNY/9e56yeazW/hXY1vU0cnoTkaEVVY6YN3RyxjmC8EFAEEUI9etotxsl2l4HWB5IixCYZfbVoScTc51Oo6yqP0+UPUdVD4u299bIEUpEblLV5ALJhthDeS82hf8UtuiU6qkYjSj/Gj30+2C2tRslynQQnCKyNqYD3Qczw4sTbPYWtWtxdMwOI6jkVDra9wLVJlZp7KKqc5I7RSS58HQ34WaKoXE0AP4pImkxTlDVvyU+/1ssONJ4YHvMDnn7jDWT4E6oYioZwCMi8m1VnR5wzC0Dj4mqnhhaNvG7e4B7otFyVUAvETlaVX8RbW8TlUdELlTVQ2LHuVBEPsae7WGYN+75qnp+4nhpa0trZakis66riHw2US71/EUkbVH5YVXN8nkIrb9KFqnqfiHHJF8Mpv9XbwCWlyICerXFeYgegJOwCH1ZoVH3UNUz4juih+pq7KWtkLRZjtuGJi0f4g/MAbRbQaSFif12VH5aNJpsAX6MTWnbjpshdJ6gxip2NDI9FJv+nQZ8LqVYnl5/BtUdZqqDinQMQFYhKwDZbSKybXxhVUT2xhZk4zFv8uhXQ+NogPkIxKnEJRlIYlQdE1IhkTD/GnXqL9YrGC2kna6q40XkOUxFMgjYNrKhrrAPcJGI3IQlYq+1OHcZGfpzVT0gUXZWRllUNXlvf5FVNoXtaLf2OZZ2a6s0S5GKJUstZhO4thS19cvYc/QO1qG/hM0yvoUNoOrxE2ygGOdEsYilR6YNZhL1X6eRE5zU8ZvJcQ/yLIBfmaynqxQS0Avawmhejo3uNtFsB5FxIvJe9EAhIptjF6LDw1X5PirzRSyq3fnYA5Ikz+r4OCxGTTmqZ3Y0OniQjp3FX7Dp/iOJ45ax4F9tiIW63Q8LDXwWFggrzYwO8vX6I3OMPPMEILsBU2mMw2ZIv8I6oOSUdT0Jt1IJjqOhqpVpLiKyEnABZi6aFqI1LqTqUQkDfbLWD1txDjbrAnhJVbcSkTHYoGSnWFv/FS1ST4yO/bvYd0kP4GsTn1fDOveqSJVUC9kdMT14msrwP3XOJU7QuxCt15yN6cw3w56JD4DxqvpQrGietSWwe/ANTA1xGhY99CVshpy3/RX+H7Y29ICITEhRf8bJE63007Hv/kzHQWWcbl/8zEMRAb36YS72e2GLJw/U+clXgNtFZC7wSWwBaHdV/UfKsUPNBPNc6A+TwkYtoFfS9G9j7JxGY6qaq1Q1a/3gCGw0c5mqPikd45knydPr51F/AGGmXKp6TjSr+iMW/OhebNqYtNrJY6USGkcj3tZxmFXHOar6q4xiQ+OLXXGSC1/aMQz0OGqHgV5JVW9O/P7RqJNJUlnsHEYND+C4ikBE9gQmAT9R1StTys6Pyi2LCfLPYgHInkkpe4WIrFSxVhGLVbQ4ru+OEfou/BK4RFUXicjZmHprBjbL3TJWd561JbBF8GcwE8lLgJNU9aIa7ajV/kobPgZOE5E/AfdGM6ssQ4Q4NQd48YGniCyuMRDNswBeWTNLtqNeWzMpYqT+EKZHPQMzaVy/8kXKSKZiOz0Oi3G9ANhYI4eZOJLPTDAe86ND/I+UsnNFZO1I/1ypa20SN10t0NZjkXpma2CSWFaam+OziIgR2OjknOglXU5EllfV91Lq76zdc10kRwAyVT1TLPb5Nqr604xieaxUQuNoICKDMEE2CthRY5YtKayCTd3T7MerQjtreBjouI41rirsoFqIRumXYIu7m9V48SvlV8I6wsFYiOfMWCSRiu+3WOdzSNasLBKoU0Xkc2rBvDYALo5UTclZQKilyEBVvTnSza+hqndFdaVaaSTWlsaTsrYUEXfIeTFLoGeoC9MsTyrlt8ZmlOdRHUM+TiNG0nliMM2gfcG1WyhCqN8W/R8c/VVIvaBikdnAdNNXAtuKyNNQFcQ/j5lgVsyP41OaMBH4g4jcQ7vp35dJ8c6L2lSOet5VsIWpA6lWFc3H1gSuFot3fSDwbxGZrqrJAEt5ev1g9YfkMOWK6WhbgJKI/AF7GJOBr4KtVDQ8jgaYqmUZ7OU8QDoGqEoG3vqPqqZmzkki+cJAvyci66jqcxW9f3Tvko46U4EDNLGAm1H/zphK4yxVrRn3Q0TOwl7+wzC13TqV65AyWj8ZS9TxbvT9XWKmeJcAmyfKhlqKVDrJbbCZWkWgL59oZ5sRQlT3/2GCNe6BGSf+fPeXWMKO+Hkl1YWx+j6Zsu9C7NmuNVuvEOw3E5NFkEgukmhrqFMdwDxNtzjrNEUslGatYmctlMYF4svAD6PtymJZhWDbUFW9Im2/pKS7UtWnIz3+Ltjo5TFsivh+4rcDsBHcXpir9M3AYWnTYxHZqPKwqepzwFEicizw1ZRm5TF7y6P+yGPKFdfRHpcoG+csEdlFVf8olnhiEjb6/YUmEkcAiEVbPIT2RCUXpqnVsNlEKFkx3dP4O3BUmrojhUnAH0XkIuA5bFZ3INUZlTaKZpcjsAHAi5od0veP2KjueGm33kqdrWBqPQW+l9iffA/AEnV0qFNVnxGzXiGx/0Rom30OA17WdGuMp8TCU4/BTCSHY2sXyQByeTIJQfbznXZebYhZOFWMDJJ5Wj/EZvQhDmd5/Gbi71ZmWyNV5SnYbLwVU21eiwXcS8aJvySgjbloKZd7RHffhoh8G3sYPsLCfr6ALWp+WlXzmO8lj/sYJqTuwFzQ87zclWPcoao7dLL+dzGheg22WNp2YVNGvvdqPlfq0Dbcl3OUkPz9MOBATXcSSZbdGfh+/HqJ5R1dF3tRLsNermcwQbdv4vc7YML6BOwZKGGzppM04dUrIsM1I/pjRicQL7M+lujk2ZTv9sVst6s6nIxjrYrNvtbCFvOmqurLiTLLYS/wUMwSZF3gDSx+/5xE2ZaQtYSo7GitnxymUvavWLiEj2P7+gP3a8csQ0Sdz/WYavMNrIP9ELM6ezVWrgVLI/iSqj4lZnK4LXBu/F0TESXD8ihNvZqH6Nruhw0EVsUGeL9PU3GJyJpR2cqA4fKszlVEVsHW7oZhA8fbohlGvfYslSKkEUuT+Crm0TtPLBfAT7FMVz9KlP0O2RY1nbpeRahffkJgDknIZfb1dWArzETxVyLyLCbg71DVl1KOezBwabTwszmwfmcFesQfo3aOjP7a2kl6mr4gcpw/5HPSiddRcRLZnigLUUa5lbDR6cGYpc/FiSLBeUcJjyUCthi3ddSGaapacST6BdVmZxU1w0jsWfgp8KaIXKyqyfZuDRwrIi/T/qxkJhFW1dewtaBKXV+Jjht/bk4HblDVqbFyB2KpD5PxV16KBPAdwJ81PY1dhcnJc63BlcA10VrRC8Aa2AwrLZnx2cDhcV17dA1/hfkNAG1qxUe0fbH1U8CTKYOnpQnPJEQ0+s8SanvFyp2Hnf9NWGjh81Q1VdUYzQAvwazfHsI61ltF5IDkICAa8V+IOUe9gc1EThSR/TRhxCGWmOU64CuRams3ETkM2DUxu0mm05tDdjq95CyjQp9yPsqTQxICzb4ivdTlwOXRqOIrmOC4gESGHLFkt5/BHv5F2KjrxyKysiYclULRDMcESThRROTJUZrH7O1Nac8YX8ZmQ49pbJE31q7gAGRipnuHYqZi12NT9C+n1J8n72ieWCLxffUCb/0Us+JZKCITMRPHlzCT0w5CvXLPotHqltgzsA7wbEqHSVR2Jayz+A7pHduGqtoheYeqXiyWNDzJSMw8cEvgWjGHl79gncvf0+oPQVUvErPOmoI9L7MxS6s0ob6yJhZPIx38xPg+SU/+/apUJ//Ok0kIwmOIfxHL4fkPrKOqJfR+Duyk7f4Hd4rI7Zj6ZLtE2TzJ0n8NnBlbq7hGLK3lr+moOg1Op1dDHZ1XjdVGEUI9Tw7JYLOvSHWwIybMNwQexkYbackJdiTM9jwYyedEERyhMPT8I5K2zIOwEcI5Wp2oejbhTiIPYuf2WbVcirdllMuTdzQ0lkgtsmYwr0Y64oXRmgVSnfMyTitmRTEE65jSvGdDO7Ysq6uq+iO1wV+Av0TP75bYQujBwMqJ4nkGAqjqtVQPCNLIam/SqiU0+XeeTEJgprF1/QpUdSOxxDIHYbOLFhH5tKqm2eQvrQmHMlV9QVK8RMmXLH2wqv4hUfZ3IpJM6B2cTi8hNyZgqqI8zldVFCHU8+SQBILNvl7FplBnqOqjddrwQVKXqem253noqhNFJoHnj8ayyMd+24oJjqRQz+MksgU2On1aRG7EOos04nlHfy21844GxRKJyGN2tlRM/fNngOjYyyULisWe2TJq7+3ATzXbtyC0Y3tHRDbWWJgAEdkYe2mT9Y+O2jku2vVnbKbxz2RZcgwEROQh0s3/0izB0uz600wFg5J/q+o+Eov+KSI7AfNV9e6M5gY7i6nqg8CDYknCfwBcJSJlVd04UTTpEV1ZE0gT6nmSpWe9I8n9I+gYXLBC2rMblxu/oBvkRhFCPU8OyTxmX/tiI/CLRWQ6Zjp5t6bYtGOJe+vanuckjxNFVoTCZTURDCrH+acSLdSkxX0JdhKJ9JD/EFuo+hawhYj8Iyp7fqzc83RM8JCZd1TTY4mcp+lORXkCb00FZmIv9rYi8hlMJ39OStltMRXKzcDtWZ1lRGjHdgTmffsX2gOKbUt1QnOwBfXrsUXU2TXqzsu3cpR9jHQ76aQpYFDy7ww1zWspapoKQc5iUQd4Caba2w4bQLyHzcaT3CW2aH+Mqn4sZnp5CulrW3mSpf9DRH6oqufG2vUDLBxIvN0j0s4ng646X1XR49YvFSQshyTRiz8XMwuqNDZz2hn1yBtjAn5bzLwruZi2PjYirLI91/p2rVntbLNoEZEH1LLu5D3GP1X184l9uc4/5ZirAn9S1TF1ylWcRPZX1TQnkWT5z2CWMj+K7QsOlCYZlgPdQSR8Pohe6OHAqln3VcxWfSfseRmKzWpu0ww781jHdiA2muvQsUVlWqNjro2pI/6o6Sadm0X1boPZu9+BdS5VKgWx2DtVo11JD1WMiGyoFtxsAKbOmY8ZBoRG0Uweb0dsQfE1TH88gij5t8aslUTkbiwA3IdR59umplHVsSnHfRXrJKpGtRrzN4h04hNV9QkRmYHF2HkOu14d3rVopnZSVOZtbNZxPWa+ujhRNtXfBKpNnyOZ9cvo/F/DYkX9GVtojsdFSgtNUjlmB7+K7pAbSYoIE7AUdmF2xC7MamLuvD/W9CSyqR5r0SghuW8lbEFlC2As9iBXeThqoO15ToKcKOpQNb3LOv80pNqSoBWL0VIVz0VE0vR1f4z+kmWzImAmVQp5AqXdSbtFywStkYG+Rv0kF+VEZEftGADtVWxB7zta7dlLpG45X0SmYoOAH2G689SReCScL8FUR58hJf1f9IJ3sCISkTNU9chEuYcw64yfiZnV7QD8VkRW10SQLnKEKhaRw4E9ROQLmMpoTSy08hQSAeYyVDWV9sUtOG7HBHnld1nJv4PUNDFCncX6RQJ9NWA5jcw7Jd32fhE2SzhGRFbGRsMLxby3k2GR74ttl4GPND2cQmUN5BAR+SE2AHhLzXpuLLaG11Y04HwqLNsNcqMDRahfjifKIQlt9rM/wxYoj6r3Y8lwOhCRp7CLcQ8WUuAETU/LBYCaS34t9+G8dMqJIkHVyyVmepmKVtuxJtUzHwEzMzqrPCGQf4Atdl6D2fGG6BbrBUqLf18vA31o/ZAjAJpYPPnNsWfpY+zZOZEUyyJptypKUm/9psKWKcdswTrdzaO/EjaVT1rUQL5QxTtiC7plImc4tXAIyRgjEKiqiXe8IrK+qj4dbR+vHS04gtQ0MUL9SSqDmx2wGEeVkfPgzF8Aqvpm7ONfMPVNnOR7MEhswX68doy+GT/mQjG/lPFimaMGYtZ0le+TI/xMXwnac7pC5+VGB4oQ6nlySAKZTgfJqH87qupLMbVOaIq4bkFzxMaW9NCoLVjAsiSp7tEZbfhrpJ+8S82ueENMYN2RUjZPCOTh2Mu0Jxa/4/fA71I6zTwLmnn0fqH1Q74AaDtiM4aTNBYiIFKfJEkmJemHPZNz6fzg4BVMd30XNqM8UrMXavOEKv5YVRdHeugXNDLBI30m+N8sVU2iaLzjrdiMQ7VH8llYx/Qa8FUxm/HrsYFYGqn6dBFZUzu6z98tIg9gNvdfFctmdSHptvdZpJ3/Zsl90bEvw2b8ye9GYGbAe0TH20NtATdeJstX4iJVTXqQPol11H/FOsG7NMDxqRZFCPVcOSQl3Ong1ajsOEyHN1zMQuFHaWqd7iZF9dGGVoedzQqNmoxjkis5gogcgr18D9Gugz9eRD6VMqqv/KZuCORoOnsr5sAxCBshXi0ic1U1PtLLEygtLaZN6rQzR/2QIwCaVscsz0xWojHLIjFb9sujNiU9BOPxQSq0YKqwJIKpqHbHMoJ9W0RSvU/JEao41o4J2CJwZbRYNSrOoaoJ6ixzqGkqtEUWFZEztT1Y3GXERqqqerqI3Ay8oZZ3dSQWVuKmjOOmETSQUNXnRSRt1vxHbHF+KjYyvy4p0CNq+Up0EOqq+qVoIFrxVzhYzJzyr5q+sFyXIoR6nhySEO50cDx2wzul1ukGQnKsVgjW40l4cgawkeOXKqO3SAe5XVRfB6Eu+UMgVxiDjf7XxEaYcfIESuusuqpW/ZAvABoAEpaspFL2+5gg/7GmJynPir2TFizsVOB6Va3EaUeyvU/zhCo+DnOemQ0cLWbhdCUp+UMJV9UEdZY51DQV4p3CmIz9AKjqzNj285h1URU5Z8Jpv+9PIlBZ7BgLseBy/VLqqJDLV0JV54vIo9iC7mAszs/okLamUYRQz5NDMo/TQW61TneiKSFnJYqPQiJJBvn0eEHJGSLmJqfjasGl0nTqwSGQxcII7ImNOB7CbMy/myJY8iw6bZm2P40c9UOOsKeSI1mJWDTAy7DF4c/HVBod0Hyxd/J4nwaHKsa8eTeNtf1hzFs4bcYaqqoJTbocqqZJo946TCjBM+GUNauBmHVL1QxAVb8arQ8cgA0yB4nFMLpTO1oV5fGVODwqtwI2a7kVs9LptHahiCiNeXJIVn4TdzrYG5gmFgwp7nSQS63TKKR+fJS8erw8yRkWSszxIzruMNLv8+3Yi5MMgZzGPzDb7zuw67k9sL2IJE20gjsrEfmdRmGGJWax0sX6wSLhJSMHZpEnWclTUd33YrGF2r7QjjFKvompLuYC+6jqI2STx/s01dNWUqzASORJTVOpJY4RoqoJTRCdZ00DGhPP/EpsBvKBqv6lTtnkmtVH2Mw41VlKLYDbiSJyEmYGfSA2cIhbk+XxlfgZ9lz/AlO5dFlVXIRJ49EankMSsbdnMqZz/B3tjjvJ+BJ51TrdioS7kaeSpccjMDlDxElYnIsraLe/PwCLgZOs74TQtmEvfF1ydlZDY9txi5VO1x/xQxE5H9N5X6YdrR+SjCA8WcnXAuv/MZaUYkXabZqzCPY+TSK1Q88G50klXFVznCYiUmYQvKYREc/8s3603UL1wnQeLsDUJ4NEZIy25x1N40WtYU4bR2JRNaP/dwB3iC0Gt6GWeeomOvpK7KfpvhIrY5ZP44BTpd1u/zYNyJ2bRhHqlzw5JMEWFk7Epnl/wnRNb2IXNN7z5VLrNIBQN/JUaujxQpMzoKr3i8g3sHPeCVvw+rqmBOGX7EwyadP5OzUj9G2986rRWSXrrcVqWWqRlPq+JmbzvS/WwT0D/FZV70kpm5Ws5BFV3S1RtoN6LZo17odZZMUTKszX9oB1VdPtBHm8T0OtwNAceVKjGVSIqmYq7QuaR9e4H6FqmgobYx3fO7THZh9Oeq7cUNZT1c3FrHlup/YAsp45bZx7SF/UPY2O8dSDfSWi63wv7YlHdsDURL8iJdxBCEXFfgnKIRmxSNtTZx2mka2niHyQ+G1utU43E+pGnkuPR3hyBsTijs8iMYuR9LjjF2gUkVJEVtP0xAgVgkPfprQpc9Epeun6xbYr1i9JlVmugYBagKbJwOSo4/m2iJynqlXZbGK/qSQrmUQN5xExp5pDMYuV35ORASuiZmelFkju87R7n/4TOFbTvU+DQ89GBOVJjQtoEdmm0vmJyIWqekjGudS6H6FqmgonY+qm4ZjOeRY2kEtTVYSyENrsyes571UW1tMWZpPOP6GLunl8JTam3U/h09j6zhXYbKtTFCHUc+WQpGNUx7gqpepmRS9zkPViEBoAACAASURBVFqnu9HA+CgRwXq8aNS1Le2j75ewOORpU+E8wncr2iNSXpnyfZyg0Lc5O6sRtAvPFtoDIJWpNoHMOxCotGczTOhuhqnu6qLmIZgWfOsb2KL30tjITlQ1aaEC6WadlWOnmaxWeZ9mEBx6VvLlSY0L6GOx0SiYuWWcUHPAPKoygJGqunG09vIoZiO/lcYsXRpMxfknTR6FvhNpM9562xVOxxZTTwb+VUdVFkQRQj04h2REcA7B3oBWu5EfmFKmg2lXtDCVuaAbTd/OyPo+RpDwTdlXT/2RRfIBzNNZjchRT/BAQCyWy77YCPE/2EL19zRfzJO0F2sqpiM/W81O+hsZv80y6+wSmi/0bHCeVMKfgzS1SqVtnU4CQxTmOFJZ9sNm13XXFOqQx1LocQ3PQha6qJvHV2KbwLqDKUKo500zlyeHYGFIeHyUpMfZ/thCZmp2ngzd92BgWVUN1bmlxh2v833ustqe73IEZi75P1V9TkTWwHTN8UQEeeyZ8wwE7sau7dZ1VEpZDmNZzlLrYvrsv4vIk5haI437MvZ3B09hs4X+2PT8ysgCKBl69nNpKpwMQp+DXNFVO8nr3SDQMy2FQhAzPVys6aE1Kou6LViS96xF3dy+Et1JEUL9qvpF2kkuUNVCciQ9bgBxj7kfY2ZtWcQ9zo6idnaeDqNfEfkutsCWtpCUR1BnPaBpMbeDQt+KeXteg1m2/BcoicibWM7LpLlgHnvmPAOBdXJMYbMGBlX7ow7iVMxCYRss+fIsLFzBT2NFZ0d/r0Wf47F1ktc1GLEYIz/B9M+HquqFwIUikuYo9Vy0OF25R++QPVL9ZKQ2a0lsdyjXCbVKsv0/UtVfpnyVNhOv1Fm1CBxYV3AAOGCiiPwLiwmzMxZ64P9E5KeaMCWmPd59PYJ9JRpBEUI9z2pzMBIlPRaRWzEh8SEWW+NCbDreUOKr2iLyreQqd4Lc2XnEPEsvwdz/N9VYrJIYeeKOByf5zjHyOY3q/JznYHrT2YmywXpHVe2QVkxqB0h6JWZtU9HRp85scg4YkrFQPsJGzgfR8UX/JrYg1ootkt2oiRj5nWQvTM89BDNB/DOAqj6eLBgfCEj9ZORX0642i29XJSsRs+P/LuZRuQBbbJ+co/1pQj1rJt4V8gSAOxELub1QRE7GzIafwxY6k0J9mKZYkiXJut5iYbAbThFCPc9qcx7yJD1uNPVGisEeZ9F3+wAnYLbCaZmBgJoOKlVxR0IeztjvQ0c+VR6SmCoj7WEO1jtKjmTSOWc2edgYmz5fiZmvVp7fDovgqnojcGM0W/wmcF208Hq1xlITdoJ5kVXQW9GiYig1n8UUVRdgZnmJzz/GOpXRqvq+WCTGKdGI9syAdqQK1jwdaw7yBIALDulLekINoOMiuJh37glYmOQjVPVNEfkethAdFKqgKxQh1Du72lyPXEmPCybY40xEfo85mBwFvF1rgUpErsQSV8QD9o/CclV2Ja1eZ0LvVtiFlJj25LNnDk4mXSFwZhOMqm4Q3ad9sHvxN+DKyiwrpfx72GL5DKxDuZwcETfr0NmF7SrEkkT8Apt5fBOzrLkIM6+LO4R9A0ul+DGAqs6JOsy/YbFqKscr/H3TfAHg8oT0nUtYrPSLML+ZNYFTIou4T5IS9bERFCHU86w25yFP0uNuR9qTDcR11EDHZAPR5zweZ3Owl+tLseODTe+TC1T3AfeLZR1SETkIc2T4QRdPL3Tk86YkPCQxe960+C957JlzqatCZzZ5UUvzd1RUxxbAL0RkDU1k9BGRDbBz2zE6n4vpmOavMwTrnmMdfwsJc9AUS5WfAOtj9/gsTJf+R6r9IBYkLYiiTjbppKR0zNBVobtCAOSlXgC4PCF9X9NErPQMPqxc52iWewUWMqJHrkERQr0KERmKRQo8rQuHyZP0uBHsicWCuBfTN34KC89Z5f4eCYTKdof9KWZoy6rqHtH3P9HI5VlEquKbqOol0aLPVSLyFvYibaaqryXL5iHHyKfiIXk3NuIbiaVq+0rKMfMsvOUJkBQ8s+kMkdrh69j9Xg5Tx8S/fzravAZby6ksjq0DdEW9mEf3vCfm8LWIjjlI0yxV3tF2D9j1sEBpaSEbyiKySsKC6RN09CNBVddKa5BYdqAeQXIEgNN8IX1DE6LEBxuvqOqkfGfQNYoQ6m3u19HFPxQThkHOIVlojqTHDeLbWDq3aZEuvwWzghlGu5NPhUMSnyuqp4FUL2quHNveifrOVUtjo/hlMe+8ZFzurpI58lELCbAJpnJZE3uhju4G66M8AZIqM5u0qW6nhbqI7IYJik8BN2JCYnZK0Tex+7lN9FeZXXVJvZhT9/woNgJfjFnKVCVJiREXyv/NEOhgzjG3RTrlSkiDY0mJfFghUmPshb3jHbIDNZg8AeCCQ/qq6hGB9feTdm/pj6S2t3S3U4RQnxPp8b6PmRwOAdbSlOS5eRBzOjkb6zQqQbU+wKY9PRF+dxwwVtsD/swWkT2wRbUOQl1V27K3i0VbvADLgJJmix3sJCQix2LT5vFqkRx/iHm5jk+zkggldOQj7TEvrhGLefF2tD81P2goOdVVn844TFenvtdhzkz/xjrvUyuzrLj6Q3OEFG4ge2FrV8tjljK1hHpFPdMCDMma2ajqfdF7+11ssfpl4OC0eyAB2YEaTJfML7uBEbR7SBPbTvOW7naKEOqzsanp3qr6rIjc3lWBHvFL4BI1N++zMNPJGdiIbstuOH49PkwKuUjnmJnMWiw5wy+Bc1T1VxnF8tiel7BY3x9E9Z8rFqDpOqrdvvMQOvLJE/PiaA0M0iX5kkkH5dzsBEFx0kXkHFU9LNreW1WvirZvUtWvN6htSeapBYoKsZR5DBPUi6jjVKTmINZhfUYSYZMlPDtQI+l0ALruQPN5S3c7RQj1c7CRxAgRuZjuW8kfqKo3R/r5NbQ9CFi9gD7dxVwRWVtVX6jsiBb20kz0BmHOSaOw3Kqp072IPGESWisCvaJ/V9V/ikhXww+HjnzyxLzIE6QruLPQHKaaecih/ojb/x9Au7PdCt3bomDqvV9BqhoR2Q9bs6pnJdNCWHagRtLpAHTdQbRmNgWzvDowy0KqURSRJON04HSxuM0HApuIyOmYLvqpLhy68vBuQ3sYy36kRwhsBBOBP4jIPbTHMv8y6VH8nsQe+qnAAdIx4UJS55cnTEKW/r2r5nxXRGsEW2C68hexgP7JFzZPLO08QbrydBa9iaKsP/J4aYaqag4nwEpGw7MDNZI8MZAawanYdRmKdSRpaQQbRmHWL9HI56+RFcN47IHaqAuHfCp6eDfGXLiHYxc0NAtOl1DVp0Vkc2yRcDVsWnuSpseQOCHHcfMskDVE4EVWDrdinnazMHfqs0Vkp8Q0N0/MizzROvMmXiiS3tC+PJYyoaqaUCuZ0OxARdBT92OBRoHWRCTVuauRFG7SqKr/h7n1n9fFQx2B2VKfqqpPichnsUWtrsRlzoWaw8nUgHKhI9+8NEqgnIXlTWxLNBGNvqbQUYedJ+ZFniBdhQZIykla6N2sIGENIedAIE6tgUCQlYwEZgdqML2hY63QU+rfNgoX6t3IqvEHTVWfBJ6MFkeSCSIKJcfINy+NClO8hiYyB6nqHSJyXGJfnpgXeYJ0FRogKSdZoXeP7+mGBBKqqgmykiEwO1CDyRMDqRGkBUkDqjNPNYJmEuqFLo7kJHTkm5dGhSkOEsCSI+aF5gvSlSeZdKFomMdhbyJUVRNqJRMf7dfKDtQwtAuhd7uJrCBpS45HaTdR9OJIHoJGvnnpwrS7Hi+KyM6qektlh4jshJmnxgmOeSE5gnSRL5m0k4Mcz0weh6YKvSFMACJynUZe2T2BZgRJ6ymaSahnUbROLY28iUKK5ggs8uBBmLfdCCzyYjJBcp6YF8FBujRHMmmnYYRayfQmfXaFVYpuQE/STEK9Nz5MWYSOfHsL44BLMROtFswV/pVof3xhOE/Mi1xBurQTyaSLoKdHhT1IqJVMaHagnqRH7cSLppmEetGLI3lIjnzXAj5B9ci3txB/IffE9IRCdeeZJ+ZFrpjyFaQTyaR7mJXrF+nz1FJpBidfaRQi8hVVvbXyWVUP6uH6N8oIn7CLqv6x0fU3jVDvBYsjwajqW5GVwaOYKuMmYH1VTQtRWziqenRlW0TGpjhIVViT9njTLdSOeZEnpvxadD2ZdE8RlEihDxJqJfM/4KvAu6p6H7RZe51L18MPh3I4Zl1W1MzpLNqNNu5S1e2i/YdhDlsNpWmEuohkpawrq+q0Hm1MHUTkBCwuxlRVfUQsANKPRGSYqiYjOvY2aiWeTg27mlE2T5Cu4GTSvYDQRAp9jVArmaswNdzwyKJpFnbvesxfhI4ziSL06fH6l8rY3zCaRqhTrbPrhyXMmIst7PQmdiQwomOzIvmCdOVJJl00oYkU+hQ5rGRGqurGkd79USwS61YaC2/bAxS9vlaoB3TTCPWEimAdzPztVuBHRbWpBh8khZTWiehYJCJyDe1xwbsl4zs5gnSRI5l0LyA0kUKzMgdsDSWKvbS9qr7Tw21I8+olaldPqMDia0sdtnug7uYR6hVE5PuYIP9xfLGkl/GRBEZ07CV0pyNTheAgXdq4ZNLdjqoeISKDsexQlRAQN6pqdycr6Qu8XoBAh2yv3p5iBB3jqVcyXvlIPQ8i8kngMuAdLKb4uwU3qRZ5IjoWTujUW0TuI+PB1eq8tLmnqNLNyaQbQRSX5g/AzZg+eX1gooh8TVWbUdeeJE+EyIaQpf6KVJw9Uf+Inqgni6YR6sBTWAKHe4FfScdwtj3yMIWSM6JjX+K7ic8bYgtkV6eUzRWkSxqUTLoBTAb2VNUnKjsi9dVkeq/JaneSJ0JkT/MT0hNKdyvRTO03wHdU9X0R2RN71w/qiXe8mYT614puQB5CIzr2JSoj0SgC5VGYGeK3Mkb6wUG6pMHJpLuZIXGBDqCqj4lIb/SX6HYaGKqiO+ipkCG/Af6JpdMEWzP6JHAhsE+jK28mod6V6IZONxGNuKcCTwCbaJSJKUlWRMcMGpJMukFkLYY107vWV+mpNas1tGPe2kWYJ/RDPVF5Mz1oWYmNu5TF3QlHRA7FFi8PB26L9i0N1R6lkUPKsVhohNsxHfQAbMr658ShG5VMuhH8S0S+r7GcsyJyCG4V02OIyKtUPxstwEo91IQsp7ikV3VDaBqhnnPk5zSGn0T/pwBn0z7dTfMonYZNS1cE/oaZMr6EjfKTQr1RyaQbwbHARSLyHdqDnz2HqaKcHiBpLVUAzyZDAojIV+khbUJLudwbBzv5iXSWP8MEyyjMTn0+sL+qPlPjp04BiMjfVHWLaPsRVd0k2o67VfdZxBKgr40FNvtf0e1ZkhCRCap6WbS9vqo+HW0f3xNhcaMYRtdg8ZxmAWtgQfD27QmLraYZqWOLEA9G2+cB52MJns/F0tw5PYCIfAM4lHYb7fNVNS3wVjwaY9wioLc5E+UiJVzFqIollqo21cJ4L2Y8Zt4MJgsq6tcv9UTlaik6dxSRT2HWbS/2ZHiLZhLqK6jquZE50QZYXJVylKjB6QFEZDymRjkEs78vAWeIyODKyClG4bk8G0QyXEULMAGz9nGh3jMEO7Y1Aomlr4vYINaxezq7TvAl4O8xN3wX6j3HQcB2qjo/+vyUiOyO6ciTQr2v5fIMoo+Fq2hWCo29Qnv6uiTuUZqTV6PR3vbAydGI/UjMtM7pGRbFBDoAqvqBiFRlemrGoFdx+ki4imYlniQ7vt1T1i8vAdeoaiGJ0XskwEwPcQh2MX+mqn/A3LOHAN8vtFVLFv1FZFB8R9S59mk9eR5E5JMiciewORauwgV6z/MYZjH1rWj7e8DB0XZPsAHwpIhcICKf66E622ga6xeneERkN+zlmUi7Od+ZWLLo3uza322IyLu0h6tIRuLsVeEqmhURGY2lX9wEC81wIfAecISq3txDbRiAhQaYAKwQtecaVZ3b6LqbRv2ScDgoY+c2AzhYVZ8trGFLEKp6g4i8B5yILXi+DJybNlqNUtmlZsjp4zk++1S4iiblFMx8cKGInPz/27v3YDur+ozj35MIpBCqQsQACiEZfKwlOhipGhQUg8DIxVq5hKqDEmNsYaIUxksVwakKKlpGUC5BGBgJCpSBUUSElpuEagcdB4VHSYkUAoZLUSSRCKR/vO9pNtu9czlnn73Ofs/zmWFYe+21z/mdTOaXdda71m9R3V9wL+sPuY25+j7XK4Ar6gtgFlPtBps21t+7MUm904GDumjW16luqo8+qGuxbMrR/fFwQ07PjfPaJxPFJNs/r6t6bmP7ToCWmvx9IWkK8LdUB8+Gn/GNucYk9U5s36oN33wePVTXtuhWenduW9d4uCEnmmn4WeGBVFchImkrYGrXT/SQpLdQldF+C9WdpCfZvqsf3xsantRr25YOYALZnOP84+GGnGimGyT9iOok56GSZlGtq4952d3aqVS1qBa17wbrh8Yk9dZyrLWtqG6fub3D8BgbJ9hePILPlbohJxrI9umSrgFW2X5sOKnbvqpP378vJ1e7aUxSB+a3vV4D/Ijq8Ef0x+zNGFv8hpxortZlPNvLqXZjTQiN3dJYr6FR4tefiUqSgTM6vdd+PFpS19lMHjZGjFxjZuqSXkF1ZdgKqq1EVwHrJH3U9iUlY5tAtgSm8+c1NjrNHH5v+6ftnZIOG4vAIiaKxiR1qs39p1IdBf4e8FqqcpfXUdXujrG3wvZnN3HsGdTV89rK7S6m2jEQESPQpKT+jO0fAkhaPHzgSFLH69RiTGxO3fDW2fwLuvRHxGZqUu2X1iuk/tjSbtLPOK7Z/rNLdSUdIum6DsNLV9KLaKQmzdQ77aYYAl5VNqyJR9J2wAKqOjDLgSUdhk2q62NMam/3LdCIBmpSUj+ipX1Ol3aMIUlzqG49mgt8B3jA9gFdhs8AzPrlluF2ZuoRo9CYpJ5tcOPC7VQ7kGbXJ0Wv7TbQ9oy+RRUxgTQmqce4sA9wLPALSf/GBmptSNoH+CrV/aQLbN/bnxAjmq2xh4+inPpe2KOo1tUnAZfYPqttzG31+9sDH7F9eN8DjWigzNSj52w/RVVG9wJJe1Al73Zrbd8DIOnUfsYX0WRJ6tEzkk7u8tbGinVlx0tEjySpRy8dDzwBLKW69WhDB4l2lrSwHjPcBv68TkxEbLok9eilHakuJpgP7AlcCVxhu9Op3kvr8e3tPOSJGIU8KI0xIWkqVT37dwOrbW/OBRoRMUJZy4yxMgfYG9iVaikmIvogM/XoGUl7US297A8so1pWudl2/pJF9EmSevSMpOeAu6nKHa+lZX3c9ic7jN8BOBiYRjWbv9b2E/2JNqKZsvwSvfQB4IvAz4F7qOq5uG4/j6S3ArcAM6mqas4BfiJp775FG9FA2f0SPWP7IknTgDX1ASQkDQGLOgw/GdjH9qrhDklfprrQZF4/4o1ooszUo2ckfQK4g6r2y9sk7UJV5KvTFXWTWhM6gO2H+hBmRKNlph69dBRV/fppwGXAS4HTbX+zw9hnu3yNTDQiRiFJPXrpcdtrgZWSdgYOt31nl7GzJH2+rW+Iao09IkYoST16qXUr1f0bSOhQral38pkexhMx4SSpRy+11nPZaSP1XK7vtIYu6fVjHGNEo2X9MnppuIbL9Jb28H/tvjXckHRJS/8XxjLAiKbLTD16xvapAJJmALtQLcGs6DK8tYLjy7r0R8RmSlKPnqmLeC2lus1oBbC7pFXAfNu/38QvkyPOEaOQpB69dBpwue2LhzskLQC+BHyobey6Lu2IGIXUfomekXSr7Td36F9m+41tfWuBR6mWW7YDHqvbL7Y9pR/xRjRRZurRS3/q0v9Mh74ptp8by2AiJqLsfoleelzS61o76ted7ii9oT8hRUwsmalHL50EXC3pJmA5sBtVca5DOozNLpeIMZA19egpSVOAd1Ad938QuHq4YmPbuN8CN3b6GraPHtMgIxosM/XoGUkLbC8BrpQ0tJEbj1YC5/YptIgJI0k9euloYEndvhHYbwNjn7B989iHFDGx5EFp9NJQl3Yn3xvLQCImqszUo5c250DRGZIOs321pBcCnwKeBr7QaQ0+IjZNHpRGz7Q8/ByiWnr5/weh7Q8/JZ0G7A4cAVwIPAX8CtjT9vv6FXNE02SmHr10REv7nI2MnWN7f0kvoNot83LbqyXdNnbhRTRfknr0zGY++Bw+Zfo3wF22V9evt+xtVBETS5J69Iykh1i/lj5Ut7cFtrY9uW34s5LeDhwDXFl/fh7wRH+ijWimJPXoGdvPuwxD0iLgROCEDsMXA5+nKtF7jqQDgC/y/CWciNhMeVAaPSdpJ+AC4Engw7YfKxxSxISRferRU5LeA9wCXGz7iG4JXdKZLe2/b2lfNfZRRjRXll+iZyRdCewNfBx4rF4zB8D29W3DZ7e0j2X9naUvGtMgIxouST166Ung+8C+bf3rgPak3u30adYDI0YhST16xvYxmzE819lFjIEk9egZScvokqBtz23rmiXp81Sz9Nb2zLGNMqLZktSjl47ajLEnd2l/pkexRExI2dIYRUjapeXlOmCN7UdLxRPRFEnqUUTLUs3wQ9KpVCUC3mv7x8UCixhwSeoxbkiaBVxoe5/SsUQMqhw+inHD9nKyEyZiVJLUY9yQNBl4Yek4IgZZdr9EEZIWtnVtBRwKpExAxCgkqUcpO7a9XgOcbvuGEsFENEUelEZRkmYAuwIP2r5X0suBp22vKhtZxGDKTD2KkDQVWApsD/wGeIWkR6juKv2nkrFFDLIk9SjlNOBy2xcPd9TleGfZXlEsqogBl90vUcprWhN6bSYwvUQwEU2RpB6lDHXoO4zqgWlEjFCSepTyiKTXtfXNAVL/JWIUsqYepZwIXCPpBuC/gVnA24CDi0YVMeCypTGKkbQ11ZLLrlQ7YK6x/VTZqCIGW5J6FCHpINvfr9vbD19QLelDts8tG13E4MqaepRyUkv78pb2kf0OJKJJktSjlG4XT3faFRMRmyhJPUrpdvF01gMjRiG7X6KUbSTtTjWx2Lq1XTasiMGWpB6lrAbOq9tr2toRMULZ/RLjiqTpth8uHUfEoMpMPYqQ9FrgFOB/gRNtPyLpH4B/BnYuGVvEIEtSj1LOBz5BdfDoc5K2oUrmuXQ6YhSy+yVKecr29bbPBw4C7gPeWl8+HREjlJl6lPJMS3ul7U8ViySiQZLUo5RJkrag+m1xTd0eArC9tmhkEQMsST1K2RVw3R6q20NUh49mlgoqYtBlS2NERIPkQWlERIMkqUdENEiSekREg+RBaRQh6T/oUpHR9n59DieiMZLUo5RFba9fA5wJXFoglojGyO6XKErSEPBx4H3AIts3Fw4pYqBlph7F1DXULwZ+Duxl+w+FQ4oYeJmpRxGSjgNOqP+7tvW9nCiNGLkk9ShC0n0tL9ex/m7SdbZzojRihJLUIyIaJGvqUYykvwOOo6oDcz9wlu0rykYVMdhy+CiKkPRe4P3Ah4FXUiX3D0h6f9HAIgZcZupRygeB/W0/Xb++S9IRwA+AC8uFFTHYMlOPUp5pSegA1Fsany0UT0QjJKlHKZMlTW3tkLQtMLlQPBGNkOWXKOUs4CpJHwOWAzOAL9X9ETFC2dIYxUh6O3A81U1HDwBfs/3dslFFDLYk9YiIBsnySxQhaRndS+/O7XM4EY2RpB6lHFU6gIgmSlKPUk6wvbh0EBFNky2NUcrs0gFENFFm6lHKzpIWdnrD9nn9DiaiKZLUo5QtgemsL7k7LNuxIkYhST1KWWH7s6WDiGiarKlHKQ+WDiCiiXL4KMYNSYcA/2j7wNKxRAyqLL9EUZK2AxYAC6lqwCwpG1HEYEtSjyIkzaG6GGMu8B3gAdsHlI0qYvBlTT1KuR1YCcy2/WlgdeF4IhohST1K2Qd4CfALSacDUzcyPiI2QR6URlGStqGqA7OAapJxie3UVI8YoST1GDck7QEssP2R0rFEDKo8KI0iJJ3c5a3H+xpIRMMkqUcpxwNPAEupbj1qLxcQESOQpB6l7AgcCMwH9gSuBK6w/YeiUUUMuKypR3GSpgLvAt4NrLadCzQiRihbGmM8mAPsDexKtRQTESOUmXoUIWkvqqWX/YFlwKXAzbbzFzJiFJLUowhJzwF3A9cBa2mpo277k6Xiihh0eVAapXyAzhdiZJYRMQpJ6lGE7YskTQPW2H4KQNIQsKhsZBGDLcsvUYSkTwDHUk0sjgV+DXwb+F3qqUeMXGbqUcpRwKuAacBlwEuB021/s2hUEQMuST1Kedz2WmClpJ2Bw23fWTqoiEGXfepRSuu63/1J6BG9kZl6lLKzpIVUNV92qtsA2D6vXFgRgy1JPUq5lKr+S3s7IkYhu1+iKEkzgF2olmBWlI0mYvAlqUcRdRGvpcD2wApgd2AVMN/27wuGFjHQsvwSpZwGXG774uEOSQuALwEfKhZVxIDL7pco5TWtCR3A9hLg1YXiiWiEJPUo5U9d+p/paxQRDZOkHqU8Lul1rR3169xRGjEKWVOPUk4CrpZ0E7Ac2A2YBxxSMqiIQZfdL1GMpCnAO4CZwIPA1cMVGyNiZJLUowhJC+oHo0gayo1HEb2RNfUo5eiW9o3FoohomCT1KGWoSzsiRiFJPUpZ16UdEaOQNfUoQtJvqZZdhoD9aFmCsX10t89FxIZlS2OUckRL+5xiUUQ0TGbqERENkpl6FCHpIdavpQ/V7W2BrW1PLhZYxIBLUo8ibD/vUgxJi4ATgRPKRBTRDEnqUZSknYALgCeB19t+rHBIEQMtST2KkfQe4BTg07aXFg4nohHyoDSKkHQlsDfwcWBl63u2ry8SVEQDZKYepTwJfB/Yt61/HZCkHjFCmalHRDRIZupRhKRldCkPYHtun8OJaIwk9SjlqNIBRDRRll8iIhokVRojIhokST0iokGS1GNg2zE1EgAAAxpJREFUSFooaQtJb5F0Wel4RkLSFEkL6vYxkg4tHVM0Sx6UxiD5JHBx6SBGaTqwAFhi+6LCsUQD5UFp9I2kvwAuBHYFtgA+CiwEZgGTga/Y/rakm4CfAXsAfwkcDswDzgauA/4VOAN4FNge+IbtC+rPPQK8GDjA9rMdYtgT+BrwLPBH4IO275f0KeCdVBOdb9g+t70P+AFwme031F/rDqpdPMcArwR2qL/38bZvk3Qc8K76Z/1d3T4bOBL4MtVvyg/bPkfSGcCb6jAvtX2mpIuAp4EZwI7AMbbv3Ow/+JhQsvwS/bQIWGH7jVSJcF/g0Xpf+jzgXyRNq8f+2PY84IfAfNsXAA+zfivkFsAhwJuBj0l6Sd1/qe15nRJ67XzgONv7Al8HvlIn+oOA1wNzgVd16mPDd6mutr0f8B7gbEmTqP7BmWf7zXW8ewGfA35p+7PDH5R0MLAb8AaqxH60pNn127+xfQDVP0QLN/D9I4Ak9egvAcsAbN9FNfu8pX79JPBLqlk7wE/r//8PMKXD17rD9lrba+rPzaj7vZEYdrL9s7p9C/DXdVw/tv2s7dW2F3fpa/+1tjXJ/3v9c/wCmG77OWAtsFTSBcDLqBJ7J38F3Gp7ne0/AXdQ/SMCG/9ziHieJPXop7upZqtImgnMp5ppI2lbYDZwXz2207rgc6z/O7unpBdI2oYqKS5vGbMhKyW9um7vC/wKuAd4raRJ9YPYH9ZxtPetA3aQNFnSi6hm18Pm1D/HHsCD9fd4p+0jgePruIfafobWP5c31Z/fguo3g19v4M8hoqsk9einc4GZkm6meuB5ILC9pNuAm4BTba/awOdvBa6lSo5/pCoIdhNwiu3HNzGGDwJnSboVWAx8tJ65Xwf8CLgN+Jbt/+zQ9xuq5aCfAOcB97Z83T0l3Qgsqb/HvcBTkv6r/sxDwE7AKmBLSacPf9D2d4H76tIJdwBXZO08RioPSiNGSdIp1A88S8cSkS2N0TiSdqHz1sebbX+m3/FE9FNm6hERDZI19YiIBklSj4hokCT1iIgGSVKPiGiQJPWIiAb5P4TZBFbo3OK6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19c95710>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7.477194e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANKER</th>\n",
       "      <td>224084.40</td>\n",
       "      <td>1.259469e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS OWNER</th>\n",
       "      <td>449979.30</td>\n",
       "      <td>1.323342e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C.E.O.</th>\n",
       "      <td>1690.00</td>\n",
       "      <td>2.592983e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>2073284.79</td>\n",
       "      <td>1.618057e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAIRMAN</th>\n",
       "      <td>496547.00</td>\n",
       "      <td>1.296093e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSULTANT</th>\n",
       "      <td>2459912.71</td>\n",
       "      <td>2.544725e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGINEER</th>\n",
       "      <td>951525.55</td>\n",
       "      <td>1.818374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <td>1355161.05</td>\n",
       "      <td>4.138850e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANCE</th>\n",
       "      <td>296031.40</td>\n",
       "      <td>1.402515e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <td>4248875.80</td>\n",
       "      <td>1.363428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTMENTS</th>\n",
       "      <td>160480.00</td>\n",
       "      <td>1.018668e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTOR</th>\n",
       "      <td>884133.00</td>\n",
       "      <td>2.431769e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAWYER</th>\n",
       "      <td>3160478.87</td>\n",
       "      <td>3.912243e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>762883.22</td>\n",
       "      <td>1.444532e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <td>1709188.20</td>\n",
       "      <td>4.481973e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWNER</th>\n",
       "      <td>1001567.36</td>\n",
       "      <td>2.408287e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARTNER</th>\n",
       "      <td>395759.50</td>\n",
       "      <td>8.421771e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <td>3735124.94</td>\n",
       "      <td>3.594320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESIDENT</th>\n",
       "      <td>1878509.95</td>\n",
       "      <td>4.720924e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFESSOR</th>\n",
       "      <td>2165071.08</td>\n",
       "      <td>2.967027e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL ESTATE</th>\n",
       "      <td>528902.09</td>\n",
       "      <td>1.625902e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIRED</th>\n",
       "      <td>25305116.38</td>\n",
       "      <td>2.356124e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALES</th>\n",
       "      <td>392886.91</td>\n",
       "      <td>1.455465e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <td>672393.40</td>\n",
       "      <td>1.640253e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STUDENT</th>\n",
       "      <td>628099.75</td>\n",
       "      <td>1.073284e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEACHER</th>\n",
       "      <td>1250969.15</td>\n",
       "      <td>4.639849e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICE PRESIDENT</th>\n",
       "      <td>325647.15</td>\n",
       "      <td>8.801623e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WRITER</th>\n",
       "      <td>1084188.88</td>\n",
       "      <td>2.279284e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Party                 Democrat    Republican\n",
       "contbr_occupation                           \n",
       "ATTORNEY           11141982.97  7.477194e+06\n",
       "BANKER               224084.40  1.259469e+06\n",
       "BUSINESS OWNER       449979.30  1.323342e+06\n",
       "C.E.O.                 1690.00  2.592983e+06\n",
       "CEO                 2073284.79  1.618057e+06\n",
       "CHAIRMAN             496547.00  1.296093e+06\n",
       "CONSULTANT          2459912.71  2.544725e+06\n",
       "ENGINEER             951525.55  1.818374e+06\n",
       "EXECUTIVE           1355161.05  4.138850e+06\n",
       "FINANCE              296031.40  1.402515e+06\n",
       "HOMEMAKER           4248875.80  1.363428e+07\n",
       "INVESTMENTS          160480.00  1.018668e+06\n",
       "INVESTOR             884133.00  2.431769e+06\n",
       "LAWYER              3160478.87  3.912243e+05\n",
       "MANAGER              762883.22  1.444532e+06\n",
       "NOT EMPLOYED        1709188.20  4.481973e+04\n",
       "OWNER               1001567.36  2.408287e+06\n",
       "PARTNER              395759.50  8.421771e+05\n",
       "PHYSICIAN           3735124.94  3.594320e+06\n",
       "PRESIDENT           1878509.95  4.720924e+06\n",
       "PROFESSOR           2165071.08  2.967027e+05\n",
       "REAL ESTATE          528902.09  1.625902e+06\n",
       "RETIRED            25305116.38  2.356124e+07\n",
       "SALES                392886.91  1.455465e+06\n",
       "SELF-EMPLOYED        672393.40  1.640253e+06\n",
       "STUDENT              628099.75  1.073284e+06\n",
       "TEACHER             1250969.15  4.639849e+05\n",
       "VICE PRESIDENT       325647.15  8.801623e+05\n",
       "WRITER              1084188.88  2.279284e+05"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 無効回答を取り除く\n",
    "# occupation_df.drop(['INFORMATION REQUESTED PER BEST EFFORTS','INFORMATION REQUESTED'],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CEOをまとめる\n",
    "occupation_df.loc['CEO'] = occupation_df.loc['CEO'] + occupation_df.loc['C.E.O.']\n",
    "\n",
    "occupation_df.drop('C.E.O.',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19ff6940>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}