09_Election Analysis.ipynb

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      "#Election Data Project - Polls and Donors\n",
      "In this Data Project we will be looking at data from the 2012 election.\n",
      "\n",
      "In this project we will analyze two datasets. The first data set will be the results of political polls. We will analyze this aggregated poll data and answer some questions:\n",
      "\n",
      "1.) Who was being polled and what was their party affiliation?\n",
      "\n",
      "2.) Did the poll results favor Romney or Obama?\n",
      "\n",
      "3.) How do undecided voters effect the poll?\n",
      "\n",
      "4.) Can we account for the undecided voters?\n",
      "\n",
      "5.) How did voter sentiment change over time?\n",
      "\n",
      "6.) Can we see an effect in the polls from the debates?\n",
      "\n",
      "We'll discuss the second data set later on!\n",
      "\n",
      "Let's go ahead and start with our standard imports:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For data\n",
      "import pandas as pd\n",
      "from pandas import Series,DataFrame\n",
      "import numpy as np\n",
      "\n",
      "# For visualization\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "sns.set_style('whitegrid')\n",
      "%matplotlib inline\n",
      "\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data for the polls will be obtained from HuffPost Pollster. You can check their website http://elections.huffingtonpost.com/pollster. There are some pretty awesome politcal data stes to play with there so I encourage you to go and mess around with it yourself after completing this project.\n",
      "\n",
      "We're going to use the requests module to import some data from the web. For more information on requests, check out the documentation http://docs.python-requests.org/en/latest/.\n",
      "\n",
      "We will also be using StringIO to work with csv data we get from HuffPost. StringIO provides a convenient means of working with text in memory using the file API, find out more about it http://pymotw.com/2/StringIO/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use to grab data from the web(HTTP capabilities)\n",
      "import requests\n",
      "\n",
      "# We'll also use StringIO to work with the csv file, the DataFrame will require a .read() method\n",
      "from StringIO import StringIO"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This is the url link for the poll data in csv form\n",
      "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
      "\n",
      "# Use requests to get the information in text form\n",
      "source = requests.get(url).text\n",
      "\n",
      "# Use StringIO to avoid an IO error with pandas\n",
      "poll_data = StringIO(source) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have our data, we can set it as a DataFrame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set poll data as pandas DataFrame\n",
      "poll_df = pd.read_csv(poll_data)\n",
      "\n",
      "# Let's get a glimpse at the data\n",
      "poll_df.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 589 entries, 0 to 588\n",
        "Data columns (total 14 columns):\n",
        "Pollster                  589 non-null object\n",
        "Start Date                589 non-null object\n",
        "End Date                  589 non-null object\n",
        "Entry Date/Time (ET)      589 non-null object\n",
        "Number of Observations    567 non-null float64\n",
        "Population                589 non-null object\n",
        "Mode                      589 non-null object\n",
        "Obama                     589 non-null int64\n",
        "Romney                    589 non-null int64\n",
        "Undecided                 422 non-null float64\n",
        "Pollster URL              589 non-null object\n",
        "Source URL                587 non-null object\n",
        "Partisan                  589 non-null object\n",
        "Affiliation               589 non-null object\n",
        "dtypes: float64(2), int64(2), object(10)\n",
        "memory usage: 69.0+ KB\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Preview DataFrame\n",
      "poll_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Pollster</th>\n",
        "      <th>Start Date</th>\n",
        "      <th>End Date</th>\n",
        "      <th>Entry Date/Time (ET)</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Population</th>\n",
        "      <th>Mode</th>\n",
        "      <th>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Pollster URL</th>\n",
        "      <th>Source URL</th>\n",
        "      <th>Partisan</th>\n",
        "      <th>Affiliation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Politico/GWU/Battleground</td>\n",
        "      <td>2012-11-04</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 08:40:26 UTC</td>\n",
        "      <td>1000</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Live Phone</td>\n",
        "      <td>47</td>\n",
        "      <td>47</td>\n",
        "      <td>6</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>UPI/CVOTER</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-05 2000-01-01 18:30:15 UTC</td>\n",
        "      <td>3000</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Live Phone</td>\n",
        "      <td>49</td>\n",
        "      <td>48</td>\n",
        "      <td>NaN</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Gravis Marketing</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 09:22:02 UTC</td>\n",
        "      <td>872</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Automated Phone</td>\n",
        "      <td>48</td>\n",
        "      <td>48</td>\n",
        "      <td>4</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>JZ Analytics/Newsmax</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 07:38:41 UTC</td>\n",
        "      <td>1041</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Internet</td>\n",
        "      <td>47</td>\n",
        "      <td>47</td>\n",
        "      <td>6</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.jzanalytics.com/</td>\n",
        "      <td>Sponsor</td>\n",
        "      <td>Rep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Rasmussen</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 08:47:50 UTC</td>\n",
        "      <td>1500</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Automated Phone</td>\n",
        "      <td>48</td>\n",
        "      <td>49</td>\n",
        "      <td>NaN</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                    Pollster  Start Date    End Date  \\\n",
        "0  Politico/GWU/Battleground  2012-11-04  2012-11-05   \n",
        "1                 UPI/CVOTER  2012-11-03  2012-11-05   \n",
        "2           Gravis Marketing  2012-11-03  2012-11-05   \n",
        "3       JZ Analytics/Newsmax  2012-11-03  2012-11-05   \n",
        "4                  Rasmussen  2012-11-03  2012-11-05   \n",
        "\n",
        "                 Entry Date/Time (ET)  Number of Observations     Population  \\\n",
        "0  2012-11-06 2000-01-01 08:40:26 UTC                    1000  Likely Voters   \n",
        "1  2012-11-05 2000-01-01 18:30:15 UTC                    3000  Likely Voters   \n",
        "2  2012-11-06 2000-01-01 09:22:02 UTC                     872  Likely Voters   \n",
        "3  2012-11-06 2000-01-01 07:38:41 UTC                    1041  Likely Voters   \n",
        "4  2012-11-06 2000-01-01 08:47:50 UTC                    1500  Likely Voters   \n",
        "\n",
        "              Mode  Obama  Romney  Undecided  \\\n",
        "0       Live Phone     47      47          6   \n",
        "1       Live Phone     49      48        NaN   \n",
        "2  Automated Phone     48      48          4   \n",
        "3         Internet     47      47          6   \n",
        "4  Automated Phone     48      49        NaN   \n",
        "\n",
        "                                        Pollster URL  \\\n",
        "0  http://elections.huffingtonpost.com/pollster/p...   \n",
        "1  http://elections.huffingtonpost.com/pollster/p...   \n",
        "2  http://elections.huffingtonpost.com/pollster/p...   \n",
        "3  http://elections.huffingtonpost.com/pollster/p...   \n",
        "4  http://elections.huffingtonpost.com/pollster/p...   \n",
        "\n",
        "                                          Source URL     Partisan Affiliation  \n",
        "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None  \n",
        "1                                                NaN  Nonpartisan        None  \n",
        "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None  \n",
        "3                        http://www.jzanalytics.com/      Sponsor         Rep  \n",
        "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Factorplot the affiliation\n",
      "sns.factorplot('Affiliation',data=poll_df,kind=\"count\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
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       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x9c3ae10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like we are overall relatively neutral, but still leaning towards Democratic Affiliation, it will be good to keep this in mind. Let's see if sorting by the Population hue gives us any further insight into the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Factorplot the affiliation by Population\n",
      "sns.factorplot('Affiliation',data=poll_df,hue='Population',kind=\"count\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<seaborn.axisgrid.FacetGrid at 0x1697ca58>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Q9bGRQ4eyc4NLRZqZ9RdP9jAzs0JzkJmZWaE5yMzMrNAcZGZmVmgOMjMzKzQH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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xba47d30>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Looks like we have a strong showing of likely voters and Registered Voters, so the poll data should hopefully be a good reflection on the populations polled. Let's take another quick overview of the DataFrame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's look at the DataFrame again\n",
      "poll_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Pollster</th>\n",
        "      <th>Start Date</th>\n",
        "      <th>End Date</th>\n",
        "      <th>Entry Date/Time (ET)</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Population</th>\n",
        "      <th>Mode</th>\n",
        "      <th>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Pollster URL</th>\n",
        "      <th>Source URL</th>\n",
        "      <th>Partisan</th>\n",
        "      <th>Affiliation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Politico/GWU/Battleground</td>\n",
        "      <td>2012-11-04</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 08:40:26 UTC</td>\n",
        "      <td>1000</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Live Phone</td>\n",
        "      <td>47</td>\n",
        "      <td>47</td>\n",
        "      <td>6</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>UPI/CVOTER</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-05 2000-01-01 18:30:15 UTC</td>\n",
        "      <td>3000</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Live Phone</td>\n",
        "      <td>49</td>\n",
        "      <td>48</td>\n",
        "      <td>NaN</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Gravis Marketing</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 09:22:02 UTC</td>\n",
        "      <td>872</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Automated Phone</td>\n",
        "      <td>48</td>\n",
        "      <td>48</td>\n",
        "      <td>4</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>JZ Analytics/Newsmax</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 07:38:41 UTC</td>\n",
        "      <td>1041</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Internet</td>\n",
        "      <td>47</td>\n",
        "      <td>47</td>\n",
        "      <td>6</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.jzanalytics.com/</td>\n",
        "      <td>Sponsor</td>\n",
        "      <td>Rep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Rasmussen</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 08:47:50 UTC</td>\n",
        "      <td>1500</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Automated Phone</td>\n",
        "      <td>48</td>\n",
        "      <td>49</td>\n",
        "      <td>NaN</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                    Pollster  Start Date    End Date  \\\n",
        "0  Politico/GWU/Battleground  2012-11-04  2012-11-05   \n",
        "1                 UPI/CVOTER  2012-11-03  2012-11-05   \n",
        "2           Gravis Marketing  2012-11-03  2012-11-05   \n",
        "3       JZ Analytics/Newsmax  2012-11-03  2012-11-05   \n",
        "4                  Rasmussen  2012-11-03  2012-11-05   \n",
        "\n",
        "                 Entry Date/Time (ET)  Number of Observations     Population  \\\n",
        "0  2012-11-06 2000-01-01 08:40:26 UTC                    1000  Likely Voters   \n",
        "1  2012-11-05 2000-01-01 18:30:15 UTC                    3000  Likely Voters   \n",
        "2  2012-11-06 2000-01-01 09:22:02 UTC                     872  Likely Voters   \n",
        "3  2012-11-06 2000-01-01 07:38:41 UTC                    1041  Likely Voters   \n",
        "4  2012-11-06 2000-01-01 08:47:50 UTC                    1500  Likely Voters   \n",
        "\n",
        "              Mode  Obama  Romney  Undecided  \\\n",
        "0       Live Phone     47      47          6   \n",
        "1       Live Phone     49      48        NaN   \n",
        "2  Automated Phone     48      48          4   \n",
        "3         Internet     47      47          6   \n",
        "4  Automated Phone     48      49        NaN   \n",
        "\n",
        "                                        Pollster URL  \\\n",
        "0  http://elections.huffingtonpost.com/pollster/p...   \n",
        "1  http://elections.huffingtonpost.com/pollster/p...   \n",
        "2  http://elections.huffingtonpost.com/pollster/p...   \n",
        "3  http://elections.huffingtonpost.com/pollster/p...   \n",
        "4  http://elections.huffingtonpost.com/pollster/p...   \n",
        "\n",
        "                                          Source URL     Partisan Affiliation  \n",
        "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None  \n",
        "1                                                NaN  Nonpartisan        None  \n",
        "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None  \n",
        "3                        http://www.jzanalytics.com/      Sponsor         Rep  \n",
        "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None  "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's go ahead and take a look at the averages for Obama, Romney , and the polled people who remained undecided."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First we'll get the average\n",
      "avg = pd.DataFrame(poll_df.mean()) \n",
      "avg.drop('Number of Observations',axis=0,inplace=True)\n",
      "\n",
      "# After that let's get the error\n",
      "std = pd.DataFrame(poll_df.std())\n",
      "std.drop('Number of Observations',axis=0,inplace=True)\n",
      "#y error\n",
      "# now plot using pandas built-in plot, with kind='bar' and yerr='std'\n",
      "avg.plot(yerr=std,kind='bar',legend=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x16d9d668>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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euDpg3e4AjqeZ534U2FE2jjrwS+2vJ/H4uaV5e37JqZLetCLizcBPIuKTNFMmqsePMvMd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\ntAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1701cd68>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg = pd.DataFrame(poll_df.mean()) \n",
      "avg.drop('Number of Observations',axis=0,inplace=True)\n",
      "avg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Obama</th>\n",
        "      <td>46.772496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Romney</th>\n",
        "      <td>44.573854</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Undecided</th>\n",
        "      <td>6.549763</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                   0\n",
        "Obama      46.772496\n",
        "Romney     44.573854\n",
        "Undecided   6.549763"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interesting to see how close these polls seem to be, especially considering the undecided factor. Let's take a look at the numbers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Concatenate our Average and Std DataFrames\n",
      "poll_avg = pd.concat([avg,std],axis=1)\n",
      "\n",
      "#Rename columns\n",
      "poll_avg.columns = ['Average','STD']\n",
      "\n",
      "#Show\n",
      "poll_avg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\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>46.772496</td>\n",
        "      <td>2.448627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Romney</th>\n",
        "      <td>44.573854</td>\n",
        "      <td>2.927711</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Undecided</th>\n",
        "      <td>6.549763</td>\n",
        "      <td>3.702235</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "             Average       STD\n",
        "Obama      46.772496  2.448627\n",
        "Romney     44.573854  2.927711\n",
        "Undecided   6.549763  3.702235"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Looks like the polls indicate it as a fairly close race, but what about the undecided voters? Most of them will likely vote for one of the candidates once the election occurs. If we assume we split the undecided evenly between the two candidates the observed difference should be an unbiased estimate of the final difference."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Take a look at the DataFrame again\n",
      "poll_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Pollster</th>\n",
        "      <th>Start Date</th>\n",
        "      <th>End Date</th>\n",
        "      <th>Entry Date/Time (ET)</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Population</th>\n",
        "      <th>Mode</th>\n",
        "      <th>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Pollster URL</th>\n",
        "      <th>Source URL</th>\n",
        "      <th>Partisan</th>\n",
        "      <th>Affiliation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Politico/GWU/Battleground</td>\n",
        "      <td>2012-11-04</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 08:40:26 UTC</td>\n",
        "      <td>1000</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Live Phone</td>\n",
        "      <td>47</td>\n",
        "      <td>47</td>\n",
        "      <td>6</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>UPI/CVOTER</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-05 2000-01-01 18:30:15 UTC</td>\n",
        "      <td>3000</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Live Phone</td>\n",
        "      <td>49</td>\n",
        "      <td>48</td>\n",
        "      <td>NaN</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Gravis Marketing</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 09:22:02 UTC</td>\n",
        "      <td>872</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Automated Phone</td>\n",
        "      <td>48</td>\n",
        "      <td>48</td>\n",
        "      <td>4</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>JZ Analytics/Newsmax</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 07:38:41 UTC</td>\n",
        "      <td>1041</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Internet</td>\n",
        "      <td>47</td>\n",
        "      <td>47</td>\n",
        "      <td>6</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.jzanalytics.com/</td>\n",
        "      <td>Sponsor</td>\n",
        "      <td>Rep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Rasmussen</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 2000-01-01 08:47:50 UTC</td>\n",
        "      <td>1500</td>\n",
        "      <td>Likely Voters</td>\n",
        "      <td>Automated Phone</td>\n",
        "      <td>48</td>\n",
        "      <td>49</td>\n",
        "      <td>NaN</td>\n",
        "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
        "      <td>Nonpartisan</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                    Pollster  Start Date    End Date  \\\n",
        "0  Politico/GWU/Battleground  2012-11-04  2012-11-05   \n",
        "1                 UPI/CVOTER  2012-11-03  2012-11-05   \n",
        "2           Gravis Marketing  2012-11-03  2012-11-05   \n",
        "3       JZ Analytics/Newsmax  2012-11-03  2012-11-05   \n",
        "4                  Rasmussen  2012-11-03  2012-11-05   \n",
        "\n",
        "                 Entry Date/Time (ET)  Number of Observations     Population  \\\n",
        "0  2012-11-06 2000-01-01 08:40:26 UTC                    1000  Likely Voters   \n",
        "1  2012-11-05 2000-01-01 18:30:15 UTC                    3000  Likely Voters   \n",
        "2  2012-11-06 2000-01-01 09:22:02 UTC                     872  Likely Voters   \n",
        "3  2012-11-06 2000-01-01 07:38:41 UTC                    1041  Likely Voters   \n",
        "4  2012-11-06 2000-01-01 08:47:50 UTC                    1500  Likely Voters   \n",
        "\n",
        "              Mode  Obama  Romney  Undecided  \\\n",
        "0       Live Phone     47      47          6   \n",
        "1       Live Phone     49      48        NaN   \n",
        "2  Automated Phone     48      48          4   \n",
        "3         Internet     47      47          6   \n",
        "4  Automated Phone     48      49        NaN   \n",
        "\n",
        "                                        Pollster URL  \\\n",
        "0  http://elections.huffingtonpost.com/pollster/p...   \n",
        "1  http://elections.huffingtonpost.com/pollster/p...   \n",
        "2  http://elections.huffingtonpost.com/pollster/p...   \n",
        "3  http://elections.huffingtonpost.com/pollster/p...   \n",
        "4  http://elections.huffingtonpost.com/pollster/p...   \n",
        "\n",
        "                                          Source URL     Partisan Affiliation  \n",
        "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None  \n",
        "1                                                NaN  Nonpartisan        None  \n",
        "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None  \n",
        "3                        http://www.jzanalytics.com/      Sponsor         Rep  \n",
        "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None  "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "If we wanted to, we could also do a quick (and messy) time series analysis of the voter sentiment by plotting Obama/Romney favor versus the Poll End Dates. Let's take a look at how we could quickly do tht in pandas.\n",
      "\n",
      "Note: The time is in reverse chronological order. Also keep in mind the multiple polls per end date."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Quick plot of sentiment in the polls versus time.\n",
      "poll_df.plot(x='End Date',y=['Obama','Romney','Undecided'],marker='o',linestyle='')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x17169518>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lpF9dWpWK06muF0w/CVU7vq5k2ti3OoaBB1Y55u+JzUszXxzRi+82qHdE006w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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x16b876a0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "While this may give you a quick idea, go ahead and try creating a new DataFrame or editing poll_df to make a better visualization of the above idea!\n",
      "\n",
      "To lead you along the right path for plotting, we'll go ahead and answer another question related to plotting the sentiment versus time. Let's go ahead and plot out the difference between Obama and Romney and how it changes as time moves along. Remember from the last data project we used the datetime module to create timestamps, let's go ahead and use it now."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For timestamps\n",
      "from datetime import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we'll define a new column in our poll_df DataFrame to take into account the difference between Romney and Obama in the polls."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a new column for the difference between the two candidates\n",
      "poll_df['Difference'] = (poll_df.Obama - poll_df.Romney)/100\n",
      "# Preview the new column\n",
      "poll_df['Difference'].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "0    0.00\n",
        "1    0.01\n",
        "2    0.00\n",
        "3    0.00\n",
        "4   -0.01\n",
        "Name: Difference, dtype: float64"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great! Keep in mind that the Difference column is Obama minus Romney, thus a positive difference indicates a leaning towards Obama in the polls.\n",
      "\n",
      "Now let's go ahead and see if we can visualize how this sentiment in difference changes over time. We will start by using groupby to group the polls by their start data and then sorting it by that Start Date."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set as_index=Flase to keep the 0,1,2,... index. Then we'll take the mean of the polls on that day.\n",
      "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
      "\n",
      "# Let's go ahead and see what this looks like\n",
      "poll_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Start Date</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Difference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2009-03-13</td>\n",
        "      <td>1403</td>\n",
        "      <td>44</td>\n",
        "      <td>44</td>\n",
        "      <td>12</td>\n",
        "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2009-04-17</td>\n",
        "      <td>686</td>\n",
        "      <td>50</td>\n",
        "      <td>39</td>\n",
        "      <td>11</td>\n",
        "      <td>0.11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2009-05-14</td>\n",
        "      <td>1000</td>\n",
        "      <td>53</td>\n",
        "      <td>35</td>\n",
        "      <td>12</td>\n",
        "      <td>0.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2009-06-12</td>\n",
        "      <td>638</td>\n",
        "      <td>48</td>\n",
        "      <td>40</td>\n",
        "      <td>12</td>\n",
        "      <td>0.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2009-07-15</td>\n",
        "      <td>577</td>\n",
        "      <td>49</td>\n",
        "      <td>40</td>\n",
        "      <td>11</td>\n",
        "      <td>0.09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "   Start Date  Number of Observations  Obama  Romney  Undecided  Difference\n",
        "0  2009-03-13                    1403     44      44         12        0.00\n",
        "1  2009-04-17                     686     50      39         11        0.11\n",
        "2  2009-05-14                    1000     53      35         12        0.18\n",
        "3  2009-06-12                     638     48      40         12        0.08\n",
        "4  2009-07-15                     577     49      40         11        0.09"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great! Now plotting the Differencce versus time should be straight forward."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting the difference in polls between Obama and Romney\n",
      "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yEMF3gjDZTRjqH0JHR0eexSUc3nuYfX7/vfdxsuJkQdeZCIYPpIXYwx8dhrXDOullONBx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\ncc0AZssCbNavXM/Gx53p3XEZ0q6xZDKuBXkXky3JLGmHWZgEeOf11C+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IAAAc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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17130278>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "t would be very interesting to plot marker lines on the dates of the debates and see if there is any general insight to the poll results.\n",
      "\n",
      "The debate dates were Oct 3rd, Oct 11, and Oct 22nd. Let's plot some lines as markers and then zoom in on the month of October.\n",
      "\n",
      "In order to find where to set the x limits for the figure we need to find out where the index for the month of October in 2012 is. Here's a simple for loop to find that row. Note, the string format of the date makes this difficult to do without using a lambda expression or a map."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set row count and xlimit list\n",
      "row_in = 0\n",
      "xlimit = []\n",
      "\n",
      "# Cycle through dates until 2012-10 is found, then print row index\n",
      "for date in poll_df['Start Date']:\n",
      "    if date[0:7] == '2012-10':\n",
      "        xlimit.append(row_in)\n",
      "        row_in +=1\n",
      "    else:\n",
      "        row_in += 1\n",
      "        \n",
      "print min(xlimit)\n",
      "print max(xlimit)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "329\n",
        "356\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great now we know where to set our x limits for the month of October in our figure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Start with original figure\n",
      "fig = poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple',xlim=(329,356))\n",
      "\n",
      "# Now add the debate markers\n",
      "plt.axvline(x=329+2, linewidth=4, color='grey')\n",
      "plt.axvline(x=329+10, linewidth=4, color='grey')\n",
      "plt.axvline(x=329+21, linewidth=4, color='grey')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.lines.Line2D at 0x1850eb70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IiIiISN1SJwLmmhojuVGzRox7cRzFBcXMu2sexYXFYc9TRERERCJbxHfJSOyYyJS9U2os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\na12HtA6k/yKdNX9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       "text": [
        "<matplotlib.figure.Figure at 0x172fd668>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Surprisingly, thse polls reflect a dip for Obama after the second debate against Romney, even though memory serves that he performed much worse against Romney during the first debate.\n",
      "\n",
      "For all these polls it is important to remeber how geographical location can effect the value of a poll in predicting the outcomes of a national election."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Donor Data Set\n",
      "\n",
      "Let's go ahead and switch gears and take a look at a data set consisting of information on donations to the federal campaign.\n",
      "\n",
      "This is going to be the biggest data set we've looked at so far. You can download it here (https://www.dropbox.com/s/l29oppon2veaq4n/Election_Donor_Data.csv?dl=0) , then make sure to save it to the same folder your iPython Notebooks are in.\n",
      "\n",
      "The questions we will be trying to answer while looking at this Data Set is:\n",
      "\n",
      "1.) How much was donated and what was the average donation?\n",
      "\n",
      "2.) How did the donations differ between candidates?\n",
      "\n",
      "3.) How did the donations differ between Democrats and Republicans?\n",
      "\n",
      "4.) What were the demographics of the donors?\n",
      "\n",
      "5.) Is there a pattern to donation amounts?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set the DataFrame as the csv file\n",
      "donor_df = pd.read_csv('Election_Donor_Data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Users\\Fabio\\Anaconda\\lib\\site-packages\\pandas-0.16.2-py2.7-win-amd64.egg\\pandas\\io\\parsers.py:1170: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
        "  data = self._reader.read(nrows)\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get a quick overview\n",
      "donor_df.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1001731 entries, 0 to 1001730\n",
        "Data columns (total 16 columns):\n",
        "cmte_id              1001731 non-null object\n",
        "cand_id              1001731 non-null object\n",
        "cand_nm              1001731 non-null object\n",
        "contbr_nm            1001731 non-null object\n",
        "contbr_city          1001712 non-null object\n",
        "contbr_st            1001727 non-null object\n",
        "contbr_zip           1001620 non-null object\n",
        "contbr_employer      988002 non-null object\n",
        "contbr_occupation    993301 non-null object\n",
        "contb_receipt_amt    1001731 non-null float64\n",
        "contb_receipt_dt     1001731 non-null object\n",
        "receipt_desc         14166 non-null object\n",
        "memo_cd              92482 non-null object\n",
        "memo_text            97770 non-null object\n",
        "form_tp              1001731 non-null object\n",
        "file_num             1001731 non-null int64\n",
        "dtypes: float64(1), int64(1), object(14)\n",
        "memory usage: 129.9+ MB\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# let's also just take a glimpse\n",
      "donor_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cmte_id</th>\n",
        "      <th>cand_id</th>\n",
        "      <th>cand_nm</th>\n",
        "      <th>contbr_nm</th>\n",
        "      <th>contbr_city</th>\n",
        "      <th>contbr_st</th>\n",
        "      <th>contbr_zip</th>\n",
        "      <th>contbr_employer</th>\n",
        "      <th>contbr_occupation</th>\n",
        "      <th>contb_receipt_amt</th>\n",
        "      <th>contb_receipt_dt</th>\n",
        "      <th>receipt_desc</th>\n",
        "      <th>memo_cd</th>\n",
        "      <th>memo_text</th>\n",
        "      <th>form_tp</th>\n",
        "      <th>file_num</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>HARVEY, WILLIAM</td>\n",
        "      <td>MOBILE</td>\n",
        "      <td>AL</td>\n",
        "      <td>3.660103e+08</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>250</td>\n",
        "      <td>20-JUN-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>736166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>HARVEY, WILLIAM</td>\n",
        "      <td>MOBILE</td>\n",
        "      <td>AL</td>\n",
        "      <td>3.660103e+08</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>50</td>\n",
        "      <td>23-JUN-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>736166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>SMITH, LANIER</td>\n",
        "      <td>LANETT</td>\n",
        "      <td>AL</td>\n",
        "      <td>3.686334e+08</td>\n",
        "      <td>INFORMATION REQUESTED</td>\n",
        "      <td>INFORMATION REQUESTED</td>\n",
        "      <td>250</td>\n",
        "      <td>05-JUL-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>749073</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>BLEVINS, DARONDA</td>\n",
        "      <td>PIGGOTT</td>\n",
        "      <td>AR</td>\n",
        "      <td>7.245483e+08</td>\n",
        "      <td>NONE</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>250</td>\n",
        "      <td>01-AUG-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>749073</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>WARDENBURG, HAROLD</td>\n",
        "      <td>HOT SPRINGS NATION</td>\n",
        "      <td>AR</td>\n",
        "      <td>7.190165e+08</td>\n",
        "      <td>NONE</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>300</td>\n",
        "      <td>20-JUN-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>736166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
        "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
        "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
        "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
        "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
        "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
        "\n",
        "          contbr_city contbr_st    contbr_zip        contbr_employer  \\\n",
        "0              MOBILE        AL  3.660103e+08                RETIRED   \n",
        "1              MOBILE        AL  3.660103e+08                RETIRED   \n",
        "2              LANETT        AL  3.686334e+08  INFORMATION REQUESTED   \n",
        "3             PIGGOTT        AR  7.245483e+08                   NONE   \n",
        "4  HOT SPRINGS NATION        AR  7.190165e+08                   NONE   \n",
        "\n",
        "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
        "0                RETIRED                250        20-JUN-11          NaN   \n",
        "1                RETIRED                 50        23-JUN-11          NaN   \n",
        "2  INFORMATION REQUESTED                250        05-JUL-11          NaN   \n",
        "3                RETIRED                250        01-AUG-11          NaN   \n",
        "4                RETIRED                300        20-JUN-11          NaN   \n",
        "\n",
        "  memo_cd memo_text form_tp  file_num  \n",
        "0     NaN       NaN   SA17A    736166  \n",
        "1     NaN       NaN   SA17A    736166  \n",
        "2     NaN       NaN   SA17A    749073  \n",
        "3     NaN       NaN   SA17A    749073  \n",
        "4     NaN       NaN   SA17A    736166  "
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "donor_df['contb_receipt_amt'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        " 100.00     178188\n",
        " 50.00      137584\n",
        " 25.00      110345\n",
        " 250.00      91182\n",
        " 500.00      57984\n",
        " 2500.00     49005\n",
        " 35.00       37237\n",
        " 1000.00     36494\n",
        " 10.00       33986\n",
        " 200.00      27813\n",
        " 20.00       17565\n",
        " 15.00       16163\n",
        " 150.00      14600\n",
        " 75.00       13647\n",
        " 201.20      11718\n",
        " 30.00       11381\n",
        " 300.00      11204\n",
        " 20.12        9897\n",
        " 5.00         9024\n",
        " 40.00        5007\n",
        " 2000.00      4128\n",
        " 55.00        3760\n",
        " 1500.00      3705\n",
        " 3.00         3383\n",
        " 60.00        3084\n",
        " 400.00       3066\n",
        "-2500.00      2727\n",
        " 110.00       2554\n",
        " 125.00       2520\n",
        " 19.00        2474\n",
        "             ...  \n",
        " 180.95          1\n",
        " 223.10          1\n",
        "-1545.12         1\n",
        " 1179.88         1\n",
        "-226.20          1\n",
        " 178.05          1\n",
        " 122.14          1\n",
        " 79.95           1\n",
        " 130.80          1\n",
        " 314.60          1\n",
        " 121.58          1\n",
        "-195.32          1\n",
        " 201.24          1\n",
        " 152.05          1\n",
        " 59.49           1\n",
        " 0.88            1\n",
        " 19.35           1\n",
        " 58.18           1\n",
        " 71.20           1\n",
        " 70.68           1\n",
        " 163.90          1\n",
        " 14.97           1\n",
        " 264.39          1\n",
        " 162.60          1\n",
        " 81.15           1\n",
        " 45.15           1\n",
        " 106.56          1\n",
        " 62.20           1\n",
        " 58.82           1\n",
        " 73.83           1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "don_mean=donor_df['contb_receipt_amt'].mean()\n",
      "\n",
      "don_std=donor_df['contb_receipt_amt'].std()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'The average donation was %.2f with a std of %.2f' %(don_mean,don_std)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The average donation was 298.24 with a std of 3749.67\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Wow! That's a huge standard deviation! Let's see if there are any large donations or other factors messing with the distribution of the donations."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's make a Series from the DataFrame, use .copy() to avoid view errors\n",
      "top_donor = donor_df['contb_receipt_amt'].copy()\n",
      "\n",
      "# Now sort it\n",
      "top_donor.sort()\n",
      "\n",
      "# Then check the Series\n",
      "top_donor"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "114604     -30800.00\n",
        "226986     -25800.00\n",
        "101356      -7500.00\n",
        "398429      -5500.00\n",
        "250737      -5455.00\n",
        "33821       -5414.31\n",
        "908565      -5115.00\n",
        "456649      -5000.00\n",
        "574657      -5000.00\n",
        "30513       -5000.00\n",
        "562267      -5000.00\n",
        "30584       -5000.00\n",
        "86268       -5000.00\n",
        "708920      -5000.00\n",
        "665887      -5000.00\n",
        "708899      -5000.00\n",
        "708929      -5000.00\n",
        "21172       -5000.00\n",
        "21168       -5000.00\n",
        "21167       -5000.00\n",
        "262328      -5000.00\n",
        "946875      -5000.00\n",
        "7361        -5000.00\n",
        "416403      -5000.00\n",
        "21164       -5000.00\n",
        "707945      -5000.00\n",
        "615101      -5000.00\n",
        "7973        -5000.00\n",
        "54430       -5000.00\n",
        "54434       -5000.00\n",
        "             ...    \n",
        "708022      10000.00\n",
        "708898      10000.00\n",
        "710177      10000.00\n",
        "876244      10000.00\n",
        "709608      10000.00\n",
        "708919      10000.00\n",
        "709739      10000.00\n",
        "91145       10000.00\n",
        "708138      10000.00\n",
        "993178      10000.00\n",
        "709813      10000.00\n",
        "710730      10000.00\n",
        "708928      10000.00\n",
        "709268      10000.00\n",
        "99829       10000.00\n",
        "90076       10000.00\n",
        "709859      10000.00\n",
        "41888       10000.00\n",
        "65131       12700.00\n",
        "834301      25000.00\n",
        "823345      25000.00\n",
        "217891      25800.00\n",
        "114754      33300.00\n",
        "257270     451726.00\n",
        "335187     512710.91\n",
        "319478     526246.17\n",
        "344419    1511192.17\n",
        "344539    1679114.65\n",
        "326651    1944042.43\n",
        "325136    2014490.51\n",
        "Name: contb_receipt_amt, dtype: float64"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like we have some negative values, as well as some huge donation amounts! The negative values are due to the FEC recording refunds as well as donations, let's go ahead and only look at the positive contribution amounts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get rid of the negative values\n",
      "top_donor = top_donor[top_donor >0]\n",
      "\n",
      "# Sort the Series\n",
      "top_donor.sort()\n",
      "\n",
      "# Look at the top 10 most common donations value counts\n",
      "top_donor.value_counts().head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "100     178188\n",
        "50      137584\n",
        "25      110345\n",
        "250      91182\n",
        "500      57984\n",
        "2500     49005\n",
        "35       37237\n",
        "1000     36494\n",
        "10       33986\n",
        "200      27813\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we can see that the top 10 most common donations ranged from 10 to 2500 dollars.\n",
      "\n",
      "A quick question we could verify is if donations are usually made in round number amounts? (e.g. 10,20,50,100,500 etc.) We can quickly visualize this by making a histogram and checking for peaks at those values. Let's go ahead and do this for the most common amounts, up to 2500 dollars."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a Series of the common donations limited to 2500\n",
      "com_don = top_donor[top_donor < 2500]\n",
      "\n",
      "# Set a high number of bins to account for the non-round donations and check histogram for spikes.\n",
      "com_don.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1b1088d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r1Sa1D1OZYzHEsRjiWLTOeMPjSuqXn66LiN+i/qa/NSIuyMxngEuAHwPPA3dExDxgPnAG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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1b108208>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like our intuition was right, since we spikes at the round numbers.\n",
      "\n",
      "Let's dive deeper into the data and see if we can seperate donations by Party, in order to do this we'll have to figure out a way of creating a new 'Party' column. We can do this by starting with the candidates and their affliliation. Now let's go ahead and get a list of candidates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Grab the unique object from the candidate column\n",
      "candidates = donor_df.cand_nm.unique()\n",
      "#Show\n",
      "candidates"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
        "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
        "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick', 'Cain, Herman',\n",
        "       'Gingrich, Newt', 'McCotter, Thaddeus G', 'Huntsman, Jon',\n",
        "       'Perry, Rick'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's go ahead and seperate Obama from the Republican Candidates by adding a Party Affiliation column. We can do this by using map along a dictionary of party affiliations. Lecture 36 has a review of this topic."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Dictionary of party affiliation\n",
      "party_map = {'Bachmann, Michelle': 'Republican',\n",
      "           'Cain, Herman': 'Republican',\n",
      "           'Gingrich, Newt': 'Republican',\n",
      "           'Huntsman, Jon': 'Republican',\n",
      "           'Johnson, Gary Earl': 'Republican',\n",
      "           'McCotter, Thaddeus G': 'Republican',\n",
      "           'Obama, Barack': 'Democrat',\n",
      "           'Paul, Ron': 'Republican',\n",
      "           'Pawlenty, Timothy': 'Republican',\n",
      "           'Perry, Rick': 'Republican',\n",
      "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
      "           'Romney, Mitt': 'Republican',\n",
      "           'Santorum, Rick': 'Republican'}\n",
      "\n",
      "# Now map the party with candidate\n",
      "donor_df['Party'] = donor_df.cand_nm.map(party_map)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A quick note, we could have done this same operation manually using a for loop, however this operation would be much slower than using the map method.\n",
      "\n",
      "'''\n",
      "for i in xrange(0,len(donor_df)):\n",
      "    if donor_df['cand_nm'][i] == 'Obama,Barack':\n",
      "        donor_df['Party'][i] = 'Democrat'\n",
      "    else:\n",
      "        donor_df['Party'][i] = 'Republican'\n",
      "'''"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at our DataFrame and also make sure we clear refunds from the contribution amounts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Clear refunds\n",
      "donor_df = donor_df[donor_df.contb_receipt_amt >0]\n",
      "\n",
      "# Preview DataFrame\n",
      "donor_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cmte_id</th>\n",
        "      <th>cand_id</th>\n",
        "      <th>cand_nm</th>\n",
        "      <th>contbr_nm</th>\n",
        "      <th>contbr_city</th>\n",
        "      <th>contbr_st</th>\n",
        "      <th>contbr_zip</th>\n",
        "      <th>contbr_employer</th>\n",
        "      <th>contbr_occupation</th>\n",
        "      <th>contb_receipt_amt</th>\n",
        "      <th>contb_receipt_dt</th>\n",
        "      <th>receipt_desc</th>\n",
        "      <th>memo_cd</th>\n",
        "      <th>memo_text</th>\n",
        "      <th>form_tp</th>\n",
        "      <th>file_num</th>\n",
        "      <th>Party</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>HARVEY, WILLIAM</td>\n",
        "      <td>MOBILE</td>\n",
        "      <td>AL</td>\n",
        "      <td>3.660103e+08</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>250</td>\n",
        "      <td>20-JUN-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>736166</td>\n",
        "      <td>Republican</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>HARVEY, WILLIAM</td>\n",
        "      <td>MOBILE</td>\n",
        "      <td>AL</td>\n",
        "      <td>3.660103e+08</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>50</td>\n",
        "      <td>23-JUN-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>736166</td>\n",
        "      <td>Republican</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>SMITH, LANIER</td>\n",
        "      <td>LANETT</td>\n",
        "      <td>AL</td>\n",
        "      <td>3.686334e+08</td>\n",
        "      <td>INFORMATION REQUESTED</td>\n",
        "      <td>INFORMATION REQUESTED</td>\n",
        "      <td>250</td>\n",
        "      <td>05-JUL-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>749073</td>\n",
        "      <td>Republican</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>BLEVINS, DARONDA</td>\n",
        "      <td>PIGGOTT</td>\n",
        "      <td>AR</td>\n",
        "      <td>7.245483e+08</td>\n",
        "      <td>NONE</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>250</td>\n",
        "      <td>01-AUG-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>749073</td>\n",
        "      <td>Republican</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>C00410118</td>\n",
        "      <td>P20002978</td>\n",
        "      <td>Bachmann, Michelle</td>\n",
        "      <td>WARDENBURG, HAROLD</td>\n",
        "      <td>HOT SPRINGS NATION</td>\n",
        "      <td>AR</td>\n",
        "      <td>7.190165e+08</td>\n",
        "      <td>NONE</td>\n",
        "      <td>RETIRED</td>\n",
        "      <td>300</td>\n",
        "      <td>20-JUN-11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>SA17A</td>\n",
        "      <td>736166</td>\n",
        "      <td>Republican</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
        "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
        "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
        "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
        "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
        "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
        "\n",
        "          contbr_city contbr_st    contbr_zip        contbr_employer  \\\n",
        "0              MOBILE        AL  3.660103e+08                RETIRED   \n",
        "1              MOBILE        AL  3.660103e+08                RETIRED   \n",
        "2              LANETT        AL  3.686334e+08  INFORMATION REQUESTED   \n",
        "3             PIGGOTT        AR  7.245483e+08                   NONE   \n",
        "4  HOT SPRINGS NATION        AR  7.190165e+08                   NONE   \n",
        "\n",
        "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
        "0                RETIRED                250        20-JUN-11          NaN   \n",
        "1                RETIRED                 50        23-JUN-11          NaN   \n",
        "2  INFORMATION REQUESTED                250        05-JUL-11          NaN   \n",
        "3                RETIRED                250        01-AUG-11          NaN   \n",
        "4                RETIRED                300        20-JUN-11          NaN   \n",
        "\n",
        "  memo_cd memo_text form_tp  file_num       Party  \n",
        "0     NaN       NaN   SA17A    736166  Republican  \n",
        "1     NaN       NaN   SA17A    736166  Republican  \n",
        "2     NaN       NaN   SA17A    749073  Republican  \n",
        "3     NaN       NaN   SA17A    749073  Republican  \n",
        "4     NaN       NaN   SA17A    736166  Republican  "
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Let's start by aggregating the data by candidate. We'll take a quick look a the total amounts received by each candidate. First we will look a the total number of donations and then at the total amount."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "donor_df.groupby('cand_nm')['contb_receipt_amt'].count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Clearly Obama is the front-runner in number of people donating, which makes sense, since he is not competeing with any other democratic nominees. Let's take a look at the total dollar amounts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Groupby candidate and then displayt the total amount donated\n",
      "donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "This isn't super readable, and an important aspect of data science is to clearly present information. Let's go ahead and just print out these values in a clean for loop."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Start by setting the groupby as an object\n",
      "cand_amount = donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()\n",
      "\n",
      "# Our index tracker\n",
      "i = 0\n",
      "\n",
      "for don in cand_amount:\n",
      "    print \" The candidate %s raised %.0f dollars \" %(cand_amount.index[i],don)\n",
      "    print '\\n'\n",
      "    i += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The candidate Bachmann, Michelle raised 2711439 dollars \n",
        "\n",
        "\n",
        " The candidate Cain, Herman raised 7101082 dollars \n",
        "\n",
        "\n",
        " The candidate Gingrich, Newt raised 12832770 dollars \n",
        "\n",
        "\n",
        " The candidate Huntsman, Jon raised 3330373 dollars \n",
        "\n",
        "\n",
        " The candidate Johnson, Gary Earl raised 566962 dollars \n",
        "\n",
        "\n",
        " The candidate McCotter, Thaddeus G raised 39030 dollars \n",
        "\n",
        "\n",
        " The candidate Obama, Barack raised 135877427 dollars \n",
        "\n",
        "\n",
        " The candidate Paul, Ron raised 21009620 dollars \n",
        "\n",
        "\n",
        " The candidate Pawlenty, Timothy raised 6004819 dollars \n",
        "\n",
        "\n",
        " The candidate Perry, Rick raised 20305754 dollars \n",
        "\n",
        "\n",
        " The candidate Roemer, Charles E. 'Buddy' III raised 373010 dollars \n",
        "\n",
        "\n",
        " The candidate Romney, Mitt raised 88335908 dollars \n",
        "\n",
        "\n",
        " The candidate Santorum, Rick raised 11043159 dollars \n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# PLot out total donation amounts\n",
      "cand_amount.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1cb69550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BDkpeCvxTAxoo11ntgnib/6O/68WupJzhbCvQIK0+q07b1wH+Tt6KbP9X/d/J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\n1JiuUcPYmJRP39jUUlKhta9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5iJtZHT0q6XXAYpI2A5ylMgQHcTOrow8CewLPBT6NqxgO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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x20f04390>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now the comparison is very easy to see. As we saw berfore, clearly Obama is the front-runner in donation amounts, which makes sense, since he is not competeing with any other democratic nominees. How about we just compare Democrat versus Republican donations?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Groupby party and then count donations\n",
      "donor_df.groupby('Party')['contb_receipt_amt'].sum().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x20402438>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x213fceb8>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like Obama couldn't compete against all the republicans, but he certainly has the advantage of their funding being splintered across multiple candidates.\n",
      "\n",
      "Finally to start closing out the project, let's look at donations and who they came from (as far as occupation is concerned). We will start by grabing the occupation information from the dono_df DataFrame and then using pivot_table to make the index defined by the various occupations and then have the columns defined by the Party (Republican or Democrat). FInally we'll also pass an aggregation function in the pivot table, in this case a simple sum function will add up all the comntributions by anyone with the same profession."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use a pivot table to extract and organize the data by the donor occupation\n",
      "occupation_df = donor_df.pivot_table('contb_receipt_amt',\n",
      "                                index='contbr_occupation',\n",
      "                                columns='Party', aggfunc='sum')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's go ahead and check out the DataFrame\n",
      "occupation_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Party</th>\n",
        "      <th>Democrat</th>\n",
        "      <th>Republican</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contbr_occupation</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>MIXED-MEDIA ARTIST / STORYTELLER</th>\n",
        "      <td>100</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AREA VICE PRESIDENT</th>\n",
        "      <td>250</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RESEARCH ASSOCIATE</th>\n",
        "      <td>100</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TEACHER</th>\n",
        "      <td>500</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>THERAPIST</th>\n",
        "      <td>3900</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "Party                                Democrat  Republican\n",
        "contbr_occupation                                        \n",
        "   MIXED-MEDIA ARTIST / STORYTELLER       100         NaN\n",
        " AREA VICE PRESIDENT                      250         NaN\n",
        " RESEARCH ASSOCIATE                       100         NaN\n",
        " TEACHER                                  500         NaN\n",
        " THERAPIST                               3900         NaN"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Check size\n",
      "occupation_df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "(45067, 2)"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Wow! This is probably far too large to display effectively with a small, static visualization. What we should do is have a cut-off for total contribution amounts. Afterall, small donations of 20 dollars by one type of occupation won't give us too much insight. So let's set our cut off at 1 million dollars."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set a cut off point at 1 milllion dollars of sum contributions\n",
      "occupation_df = occupation_df[occupation_df.sum(1) > 1000000]\n",
      "# Now let's check the size!\n",
      "occupation_df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "(31, 2)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot out with pandas\n",
      "occupation_df.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1ef11cf8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lPYxpf6+u8r2+WPApKxeVai1bTd+rKzxfaa5tmJqmpqbEgyJyLfCiqk4J/n5P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\nknhERIcliUdEdFiSeEREh/1/StoCdWdrvZ4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x22f79860>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Horizontal plot, use a convienently colored cmap\n",
      "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x24ab79e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kSQKDkKQeTIkY8D7mAmdlMnr06AHvS5IkqYtBSFoFRMRk4L3AusAS4NTM/FVx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\n3GctAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x234db908>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like there are some occupations that are either mislabeled or aren't really occupations. Let's get rid of: Information Requested occupations and let's combine CEO and C.E.O."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Drop the unavailble occupations\n",
      "occupation_df.drop(['INFORMATION REQUESTED PER BEST EFFORTS','INFORMATION REQUESTED'],axis=0,inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set new ceo row as sum of the current two\n",
      "occupation_df.loc['CEO'] = occupation_df.loc['CEO'] + occupation_df.loc['C.E.O.']\n",
      "# Drop CEO\n",
      "occupation_df.drop('C.E.O.',inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Repeat previous plot!\n",
      "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1f937710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YCDxVUfyzzLwgInYBvkA513l94JbMvHQAH0mSJGmVZjDbTyJiO+CgzNyzuN4e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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2617f9e8>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Awesome! Looks like CEOs are a little more conservative leaning, this may be due to the tax philosphies of each party during the election."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There's still so much to discover in these rich datasets! Come up with your own political questions you want answered! Or just play around with different methods of visualizing the data!\n",
      "\n",
      "For more on general data analysis of politics, I highly suggest the 538 (http://fivethirtyeight.com/politics/) website!\n",
      "\n",
      "Again, great job on getting through the course this far! Go ahead and search the web for more data to discover!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}