nb_20131013_pew_data2.ipynb

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  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Analyze the Pew.txt file"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This time using separate function to read in the file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "%load_ext autoreload\n",
      "%autoreload 2\n",
      "from pew_data_analysis import *\n",
      "pd.set_printoptions(max_columns=0, max_rows=1000)\n",
      "import statsmodels\n",
      "from ggplot import *\n",
      "#import pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls data/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Merge_Codebook.csv            Pew Merge dataset for UMD.zip Pew_for_analysis.xlsx         ~$Merge_Codebook.xlsx\r\n",
        "Merge_Codebook.xlsx           Pew.txt                       README.txt                    ~$Pew_for_analysis.xlsx\r\n",
        "\u001b[34mPew Merge dataset for UMD\u001b[m\u001b[m     Pew_for_analysis.csv          US_FIPS_Codes.csv\r\n"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time pew = read_pew_txt('data/Pew.txt','data/Merge_Codebook.csv','data/US_FIPS_Codes.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 13.8 s, sys: 1.85 s, total: 15.6 s\n",
        "Wall time: 15.6 s\n"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#pew.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>rid</th>\n",
        "      <th>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>date</th>\n",
        "      <th>age</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>fipsco</th>\n",
        "      <th>density</th>\n",
        "      <th>regvoter</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 442262.000000</td>\n",
        "      <td>  389963.000000</td>\n",
        "      <td> 441892.000000</td>\n",
        "      <td> 442262.000000</td>\n",
        "      <td>   382454.000000</td>\n",
        "      <td> 442262.000000</td>\n",
        "      <td> 428020.000000</td>\n",
        "      <td> 319442.000000</td>\n",
        "      <td> 130895.000000</td>\n",
        "      <td> 305512</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 221131.500000</td>\n",
        "      <td>   54188.247245</td>\n",
        "      <td>      0.996082</td>\n",
        "      <td>   2002.931262</td>\n",
        "      <td>   408640.208807</td>\n",
        "      <td>     48.735786</td>\n",
        "      <td>     28.603262</td>\n",
        "      <td>     82.707014</td>\n",
        "      <td>      2.816555</td>\n",
        "      <td>      1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 127670.186713</td>\n",
        "      <td>  153832.541757</td>\n",
        "      <td>      0.500715</td>\n",
        "      <td>      6.359258</td>\n",
        "      <td>  2588051.467483</td>\n",
        "      <td>     18.667399</td>\n",
        "      <td>     15.723518</td>\n",
        "      <td>     97.583575</td>\n",
        "      <td>      1.393472</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      1.000000</td>\n",
        "      <td>       1.000000</td>\n",
        "      <td>      0.100000</td>\n",
        "      <td>   1990.000000</td>\n",
        "      <td>        0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 110566.250000</td>\n",
        "      <td>    1147.000000</td>\n",
        "      <td>      0.649572</td>\n",
        "      <td>   1998.000000</td>\n",
        "      <td>    40397.000000</td>\n",
        "      <td>     34.000000</td>\n",
        "      <td>     13.000000</td>\n",
        "      <td>     25.000000</td>\n",
        "      <td>      2.000000</td>\n",
        "      <td>      1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 221131.500000</td>\n",
        "      <td>    4409.000000</td>\n",
        "      <td>      0.881001</td>\n",
        "      <td>   2004.000000</td>\n",
        "      <td>    70212.000000</td>\n",
        "      <td>     48.000000</td>\n",
        "      <td>     29.000000</td>\n",
        "      <td>     61.000000</td>\n",
        "      <td>      3.000000</td>\n",
        "      <td>      1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 331696.750000</td>\n",
        "      <td>  100083.000000</td>\n",
        "      <td>      1.204123</td>\n",
        "      <td>   2008.000000</td>\n",
        "      <td>   100301.000000</td>\n",
        "      <td>     62.000000</td>\n",
        "      <td>     42.000000</td>\n",
        "      <td>    107.000000</td>\n",
        "      <td>      4.000000</td>\n",
        "      <td>      1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 442262.000000</td>\n",
        "      <td> 2020955.000000</td>\n",
        "      <td>      5.824109</td>\n",
        "      <td>   2013.000000</td>\n",
        "      <td> 20010528.000000</td>\n",
        "      <td>     99.000000</td>\n",
        "      <td>     78.000000</td>\n",
        "      <td>    840.000000</td>\n",
        "      <td>      9.000000</td>\n",
        "      <td>      1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Index: 8 entries, count to max\n",
        "Data columns (total 10 columns):\n",
        "id          8  non-null values\n",
        "rid         8  non-null values\n",
        "weight      8  non-null values\n",
        "year        8  non-null values\n",
        "date        8  non-null values\n",
        "age         8  non-null values\n",
        "fipsst      8  non-null values\n",
        "fipsco      8  non-null values\n",
        "density     8  non-null values\n",
        "regvoter    8  non-null values\n",
        "dtypes: float64(10)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>rid</th>\n",
        "      <th>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>date</th>\n",
        "      <th>survey</th>\n",
        "      <th>language</th>\n",
        "      <th>age</th>\n",
        "      <th>age2</th>\n",
        "      <th>sex</th>\n",
        "      <th>race</th>\n",
        "      <th>racethn</th>\n",
        "      <th>hisp</th>\n",
        "      <th>income</th>\n",
        "      <th>income2</th>\n",
        "      <th>educ</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>state_name</th>\n",
        "      <th>fipsco</th>\n",
        "      <th>county_name</th>\n",
        "      <th>usr</th>\n",
        "      <th>density</th>\n",
        "      <th>partyln</th>\n",
        "      <th>party</th>\n",
        "      <th>partysum</th>\n",
        "      <th>regvoter</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.941828</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 46</td>\n",
        "      <td> 30-49</td>\n",
        "      <td> Female</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td>      College graduate</td>\n",
        "      <td> 26</td>\n",
        "      <td> Michigan</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Independent</td>\n",
        "      <td> No leaning</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91640</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.465066</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 77</td>\n",
        "      <td>   65+</td>\n",
        "      <td>   Male</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td> Less than high school</td>\n",
        "      <td> 36</td>\n",
        "      <td> New York</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  Republican</td>\n",
        "      <td>   Rep/ln R</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 2 entries, 0 to 91640\n",
        "Data columns (total 26 columns):\n",
        "id             2  non-null values\n",
        "rid            2  non-null values\n",
        "weight         2  non-null values\n",
        "year           2  non-null values\n",
        "date           0  non-null values\n",
        "survey         2  non-null values\n",
        "language       2  non-null values\n",
        "age            2  non-null values\n",
        "age2           2  non-null values\n",
        "sex            2  non-null values\n",
        "race           2  non-null values\n",
        "racethn        0  non-null values\n",
        "hisp           0  non-null values\n",
        "income         0  non-null values\n",
        "income2        2  non-null values\n",
        "educ           2  non-null values\n",
        "fipsst         2  non-null values\n",
        "state_name     2  non-null values\n",
        "fipsco         0  non-null values\n",
        "county_name    0  non-null values\n",
        "usr            0  non-null values\n",
        "density        0  non-null values\n",
        "partyln        0  non-null values\n",
        "party          2  non-null values\n",
        "partysum       2  non-null values\n",
        "regvoter       0  non-null values\n",
        "dtypes: float64(7), int64(3), object(16)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Analyze each column"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Dates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.year.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x63f29f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fexsA+53/rwRQAsBzZ1u782+ybSPO15dq+27bcsVb7DbifF318ZYz/sVlyM/o\nbcT5+mK3LyqOH2978pdrPR6P/n8Ahmxrmobjx48DAOx2OwwTb55qfHxcnn/+eXnzzTf1turqajl/\n/ryIiHR3d0tNTY2IiHR0dEhDQ4P+vOLiYolGoyIikp+fr7c3NjbK0aNHRURkz549cvLkSRERGRoa\nkvXr18+aByw5t2nFPjO2deJaO/ZyMSrWnNNQIgK/348nn3wSr776qt7udrsRCAQwOjqKQCCAiooK\nAEB5eTk6OzvR398PTdOQmpqKjIwMAJPTVW1tbRgcHER7ezvcbrd+rBMnTmBkZAStra36sYiIyETm\nqiTvv/++pKSkSHFxsZSUlEhJSYm88847Eo1GZcuWLZKbmys+n09isZi+z5EjR2TNmjXidDrl7Nmz\nentPT4+UlpaK3W6X/fv36+3j4+Oyfft2yc3NlcrKSrl69eqsucCS70DuF/d0Evd5obGNHotE6beR\nY5HIfX6QseCVxULwdh/zi64o9v3iapi5trCcsZfDQmJrMHYsEqXfs9GwuLFI5D7fj4b4Y8HbfSzo\nOCwW84quKLYV+8zY1olr7diJVix4uw8iIoqLxSIhaaoTMBFNdQImoqlOwEQ01QkkHRYLIiKKi2sW\n84uuKLYV+8zY1olr7dhcsyAioqTDYpGQNNUJmIimOgET0VQnYCKa6gSSDosFERHFxTWL+UVXFNuK\nfWZs68S1dmyuWRARUdJhsUhImuoETERTnYCJaKoTMBFNdQJJh8WCiIji4prF/KIrim3FPjO2deJa\nOzbXLIiIKOmwWCQkTXUCJqKpTsBENNUJmIimOoGkw2JBRERxcc1iftEVxbZinxnbOnGtHZtrFkRE\nlHRYLBKSpjoBE9FUJ2AimuoETERTnUDSmbNY1NfXY/Xq1Xjqqaf0tlgsBp/PB5vNhqqqKgwPD+tf\na2pqQmFhIVwuF7q6uvT2SCSCsrIyFBQU4MCBA3r7xMQE/H4/8vLy4PF4cO3aNSP7RkREBpmzWGzf\nvh3vvvvujLaWlhbYbDZcvnwZOTk5OHbsGADg+vXraG5uxqlTp9DS0oKGhgZ9n71792Lfvn0Ih8M4\nc+YMuru7AQDt7e24desWIpEIvF4vDh48aHT/kpRHdQIm4lGdgIl4VCdgIh7VCSSdOYvFs88+i1Wr\nVs1oC4VC8Pv9SEtLQ319PYLBIAAgGAzC6/XCZrOhsrISIqJfdfT29qK2thZZWVmorq6esU9dXR3S\n09OxY8fWwsK2AAAJlElEQVQOvZ2IiMxlwWsW4XAYDocDAOBwOBAKhQBMvvA7nU79eUVFRQgGg+jr\n60N2drbe7nK5cO7cOQCThcflcgEAMjMzMTAwgLGxscX3xjI01QmYiKY6ARPRVCdgIprqBJLOQwvd\nYSEfwZr8uOu9+99tF5EZx4t/7G0A7Hf+vxJACaYuN7U7/ybbNuJ8fam277YtV7zFbiPO11Ufbznj\nX1yG/IzeRpyvL3b7ouL48bYBTdPg8Xj0/wMwZFvTNBw/fhwAYLfbYRiJ45NPPpEnn3xS366urpbz\n58+LiEh3d7fU1NSIiEhHR4c0NDTozysuLpZoNCoiIvn5+Xp7Y2OjHD16VERE9uzZIydPnhQRkaGh\nIVm/fv198wAggCh6qIptxT4ztnXiWjv2cjEq1oKnodxuNwKBAEZHRxEIBFBRUQEAKC8vR2dnJ/r7\n+6FpGlJTU5GRkQFgcrqqra0Ng4ODaG9vh9vt1o914sQJjIyMoLW1VT8WERGZzFyVZOvWrfLEE0/I\nV77yFcnJyZFAICDRaFS2bNkiubm54vP5JBaL6c8/cuSIrFmzRpxOp5w9e1Zv7+npkdLSUrHb7bJ/\n/369fXx8XLZv3y65ublSWVkpV69evW8usOQ7kPvFPZ3EfV5obKPHIlH6beRYJHKfH2QseGWxELzd\nx/yiK4p9v7galv6jgYky3hqMHYtE6fdsNCxuLBK5z/ejIf5Y8HYfCzoOi8W8oiuKbcU+M7Z14lo7\ndqIVC97ug4iI4mKxSEia6gRMRFOdgIloqhMwEU11AkmHxYKIiOLimsX8oiuKbcU+M7Z14lo7Ntcs\niIgo6bBYJCRNdQImoqlOwEQ01QmYiKY6gaTDYkFERHFxzWJ+0RXFtmKfGds6ca0dm2sWRESUdFgs\nEpKmOgET0VQnYCKa6gRMRFOdQNJhsSAiori4ZjG/6IpiW7HPjG2duNaOzTULIiJKOiwWCUlTnYCJ\naKoTMBFNdQImoqlOIOmwWBARUVxcs5hfdEWxrdhnxrZOXGvH5poFERElHRaLhKSpTsBENNUJmIim\nOgET0VQnkHRMUSzOnj0Lp9OJwsJC/NM//ZPqdBLARdUJmAjHYgrHYgrHwmimKBZ/8zd/gx/+8Id4\n77338M///M8YHBxUnZLJfa46ARPhWEzhWEzhWBhNebG4desWAOBP/uRPkJeXhxdeeAHBYFBxVkRE\nNJ3yYhEOh+FwOPRtl8uFc+fOKcwoEVxRnYCJXFGdgIlcUZ2AiVxRnUDSeUh1AguTYsHY94v7tsLY\ny2EhsY0ei0Tp92wWOxaJ3Of7mc9YqOv35K8DJA7lxWLDhg3427/9W327p6cHXq/3nuclyK+DEBEl\nJeXTUI8++iiAyU9EXblyBb/85S/hdrsVZ0VERNMpv7IAgCNHjuC73/0uJiYm0NDQgMcee0x1SkRE\nNI2yK4v6+nqsXr0aTz31FCorKxGJRPDzn/8cwWAQLpcLW7duxejoKIDJKajXX38dzzzzDEpKShAO\nh/XjRCIRlJWVoaCgAAcOHFDVnQcyfSzu6u3txZ/92Z8taCw8Hg8cDgdKS0tRWlqacB9B/uyzz/Dc\nc89h3bp18Hg8+PGPfwwAiMVi8Pl8sNlsqKqqwvDwsL5PU1MTCgsL4XK50NXVpbcn+nlh5FhY7by4\nefMmnnvuOWRkZOCv//qvZxzLaufFXGOx4PNCFDl79qycP39ennzySb3t29/+tvz0pz8VEZE33nhD\nmpqaRESks7NTqqqqZHx8XD755BP5wz/8Q32fl156Sdra2mRwcFA2btwo4XB4eTtiAKPGwuPxyIcf\nfri8yRvo6tWrcuHCBRERuXHjhuTn50s0GpVDhw7J7t275fbt27Jr1y45fPiwiIgMDAxIUVGRfPrp\np6JpmpSWlurHSvTzwsixsNp5MTIyIl1dXXLs2DHZvXv3jGNZ7byYaywWel4ou7J49tlnsWrVqhlt\nmqZh8+bNAIAtW7bggw8+AAD893//N7xeLx5++GHY7XakpKTgiy++ADD5Dry2thZZWVmorq5OyN/R\nMGosgMT+IMDjjz+OkpISAMBjjz2GdevWIRwOIxQKwe/3Iy0tDfX19fr3OBgMwuv1wmazobKyEiKi\nv6NK9PPCyLEArHVepKenY+PGjUhLS7vnWFY7L+YaC2Bh54XyBe7pnn/+eRw/fhxjY2N4++238T//\n8z8AgBdffBEnT57E559/jg8//BDhcBjBYBB9fX3Izs7W90+m39FY6Fjc9Z3vfAfPP/883n57OT5a\nu3T6+vrQ09OD8vLyGb+L43A4EAqFAEy+QDqdTn2foqKipDwvHmQs7rLSeXHX73801YrnxV33+5ju\nQs4LUyxw3/X3f//3aGxsREVFBb7xjW/gq1/9KoDJubWLFy/iT//0T5GVlYUNGzbMWikT+d3T71vM\nWPzbv/0bvva1r+HTTz/FK6+8gnXr1uGZZ55R2Y1FicViqK2txZtvvolHHnlkQd/X2X4oEvm8MGIs\neF5M+v3nW/W8uGuh54WprizsdjuOHj2KCxcu4Bvf+AZefPFFAJMn/WuvvYYPPvgAHR0dGBoaQkVF\nBdauXYuBgQF9/0uXLqGiokJV+oZa6FgAwNe+9jUAQF5eHurq6tDe3q4s/8WamJhATU0N/vzP/xw+\nnw/A5O/iRCIRAJMLlBs2bAAAuN1uXLp0Sd/3V7/6FTZs2JA054URYwFY77y4n8LCQsudF3NZ6Hlh\nqmJx48YNAMBvfvMbNDc36y+Qo6OjGBkZwZdffonm5mY89dRTSE2dTN3hcKCtrQ2Dg4Nob29Pmt/R\nWOhY/Pa3v9U/zRCNRtHe3o5vfetbyvJfDBGB3+/Hk08+iVdffVVvd7vdCAQCGB0dRSAQ0H/Ay8vL\n0dnZif7+fmiahtTUVGRkZABI/PPCqLGw4nkxfb/fZ7XzYvp+0y3qvFjkovwD27p1qzzxxBPy8MMP\nS05OjvzoRz+St956S77+9a9LYWGh/MM//IP+3CtXrkhRUZGsXbtWNm/eLNevX9e/1tPTI6WlpWK3\n22X//v0quvLAjBiL4eFhWb9+vTz99NPyx3/8x3Lo0CFV3Vm0999/X1JSUqS4uFhKSkqkpKRE3nnn\nHYlGo7JlyxbJzc0Vn88nsVhM3+fIkSOyZs0acTqdcvbsWb090c8Lo8bCqudFXl6eZGZmyiOPPCK5\nubkSiURExJrnxfSxyMnJkUgkIiMjIws+LxLmz6oSEZE6ppqGIiIic2KxICKiuFgsiIgoLhYLIiKK\ni8WCiIjiYrEgIqK4/j+RsyeSeNbx8gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9c4ff50>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are lots of surveys!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(pew.survey.value_counts())\n",
      "print pew.survey.value_counts().head(2)\n",
      "print pew.survey.value_counts().tail(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "276\n",
        "Decout00    5719\n",
        "Typo99      3973\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "AprNII04     790\n",
        "Janomni02    406\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We could parse these *date* strings.  They look like mddyy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.date.value_counts()[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "31901     1478\n",
        "80101     1277\n",
        "91299     1205\n",
        "0         1107\n",
        "101708     922\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "#pew['date_new'] = pew.date.apply(lambda d: "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Demographics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.language.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "English only           348561\n",
        "English and Spanish     93701\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These ages seem strange."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.age.value_counts()[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "50    11054\n",
        "40     9876\n",
        "18     9210\n",
        "45     9207\n",
        "42     9137\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are spikes at regular intervals, e.g., 45 and 50, and at ages with special significance, e.g. 18.  Perhaps some surveys were done on people with specific ages, or the people or the poll data collector was rounding down (or up)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.age.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x9bf5590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BzdCZUPA39WSu94i+eORAlBDBRy8Aj2DI7Fhz0JDuY8Cx3tdo7moOZl1ubmsOM717ru77\nG8WO1zkQpZjgs7syMhaEDFnN9HRTnoVE8WByMJlkjaea49x3oTqABAvULYI/2IG7Qb9P9UEvkhem\n5lhzSAzWHCii9H5mgyo864r0wZqDhnQYA56LZymw5pC4bcfyNLqp9iMd9jeaG6w5EBFRQjE5mIya\n8VTV5/BPRagOQCNCdQDaYM0hMZgc0kxsheZID9ohdXR4aBGlOtYcNDSXY8CxjkOrf5aCDjUC/dvE\nmgMFY82BFOK3WCKVQq+JSdyp50wOJqPfeKrKx14KBX9TV0J1ANrQ7z0yt0KviUncBY9MDmTgMxeI\naAJrDhpSVXOY2TMIor1mrvF59culRs0h+B5OQPT7OFHiTfccFdYcUta8hI8lEiXSXA1rkFpMDtoL\njOn7fP9Nu/HU6ITqADQiVAegDb5HEiPm5HDr1i289NJLePzxx2G32+F2u+Hz+VBZWQmLxYIdO3Zg\nbGzMWL65uRkFBQWw2+3o7e015g8MDGDdunVYvXo1Dh48GF9riIgoIWJODm+99RYsFgsuXLiACxcu\nwGq1wuVywWKx4PLly8jNzcWJEycAANevX0dLSwu6urrgcrlQX19vbKehoQH79++Hx+NBT08PvF5v\n/K1KYU6nU3UIGnGqDkAjTtUBaIPvkcSIOTl8+eWXePPNN/HAAw9g3rx5eOihh9DX14e6ujosXLgQ\ntbW1cLvdAAC3242KigpYLBaUl5dDSmkcVQwODqKmpgbZ2dmoqqoy1iEiInViSg7Xrl3D7du3sXfv\nXjgcDrzzzjsYHx+Hx+OB1WoFAFitVvT19QHwJwebzWasX1hYCLfbjaGhIeTk5Bjz7XY7zp07F097\nUl5ix1N1vWfSTAnVAWhEqA5AG6w5JEZMz3O4ffs2Ll26hCNHjmDLli149dVX8emnn87qlKlIH0bR\n1t+9ezfy8/MBAEuXLkVxcbFx+DixM6TKtJ9AYKhAINh06y9enIXx8UC9Z9GiB3Hq1P8N2v7E9iaK\n3RPTmyL+vfB4osc3/fRcb2/idd23F+v6kbb3deCVsA/H0Onp9rf491f/MqreP19//XVS/57qaT8B\nf//7Xwv+vIyZjJHVajV+P3XqlNy5c6esqqqS58+fl1JK6fV6ZXV1tZRSyo6ODllfX28sX1RUJEdH\nR6WUUq5atcqY39jYKI8fPx72t+II05QASEDe/wn9ffbrh6439baj/131y+kQg/5tirYfzXR/i29/\nTb/3q2rTvd9jFXPNoaCgAG63Gz///DM+//xzbNmyBQ6HA62trRgfH0drayvKysoAAKWlpejs7MTw\n8DCEEMjMzERWVhYA//BTW1sbRkZG0N7eDofDEWtIaWfmd1jl/Y+I18vQLMWaVQYHB6XD4ZBFRUWy\noaFBjo2NydHRUbl9+3aZl5cnKysrpc/nM5ZvamqSa9askTabTZ49e9aY39/fL0tKSmR+fr48cOBA\nxL8VR5imhCjfDLu7u6dcLvJ83b89x/Mtu1vTWFUcOXSH7APT7UfRlotvf1X/fg1+j6SDaP0fz/+C\nt8/QULTbWHR3dxtjjWoe16lquUjrdMM/zqpbrPG0KdblBIBNIe+TaPtRtOVm+16LdvsGFYLrHelg\nrm6fweSgoXjf1OmTHMy+XHLvrRT4fT78JyMES53kkG7mKjnEdLYSEZnZxFlqE1iLonC8t5KpmP26\nhEQTqgPQiFAdgDZ4nUNiMDmYyv/gH2e/X4MKwTOSiChxWHPQkN7PVVC1nA4x6N+mmRako22PNQdz\n4fMciGgaiR12nKtnE5M5MDmYjlAdgEaE6gA0IhD6PO/4v7mb9SE+rDkkBpMDERGFYc1BQ6w56BpD\nerQp2vUykd6HrDmoxZoDERElDZOD6QjVAWhEqA5AI0J1ANpgzSExmByIKEi062V4Z9d0wpqDhlhz\n0DUGtime2gTNDdYciIgoaZgcTEeoDkAjQnUAGhGqA9AGaw6JweRARERhWHPQEGsOusbANrHmoB/W\nHIiIKGmYHExHqA5AI0J1ABoRqgOIKPjmfck6/ZU1h8Tgk+CIaM4Ebt4H+HyRrp0gXbHmoCHWHHSN\ngW2abc1hquecT7ZkyXLjrq9ZWcswOnoj4nIUjs+QJqKUxSMM/bDmYDpCdQAaEaoD0IhI8t/T97G0\nrDkkBpMDEcUg+MFClIpYc9AQaw66xsA2zXS52dYcZrocheN1DkRElDRMDqYjVAegEaE6AI0I1QFo\ngzWHxGByICKiMDEnh3v37qGkpATbtm0DAPh8PlRWVsJisWDHjh0YGxszlm1ubkZBQQHsdjt6e3uN\n+QMDA1i3bh1Wr16NgwcPxtGMdOJUHYBGnKoD0IhTdQAzkJyHBTmdzjnbdjqJOTkcO3YMdrvdOJXN\n5XLBYrHg8uXLyM3NxYkTJwAA169fR0tLC7q6uuByuVBfX29so6GhAfv374fH40FPTw+8Xm+czSEi\nfQXOcJq44I30FVNyuHbtGk6dOoWXX37ZqIT39fWhrq4OCxcuRG1tLdxuNwDA7XajoqICFosF5eXl\nkFIaRxWDg4OoqalBdnY2qqqqjHUoGqE6AI0I1QFoRKgOQBusOSRGTMnhtddew5EjR5CZGVjd4/HA\narUCAKxWK/r6+gD4k4PNZjOWKywshNvtxtDQEHJycoz5drsd586di6kRRKQTfS+Qo5mb9e0zTp48\niZycHJSUlIRk6NmcSxtpp5lu/d27dyM/Px8AsHTpUhQXFxtjixNxpMq0n0BgHFkEzXci/Fvi5OnJ\n60fb3kymdd3edOvPdnuT58319mJdX/ft/Q9A9/3pjGnXj/RNXwgR1/snnvXNNn2/xQj+bAj+vIyZ\nnKU33nhD5ubmyvz8fLly5Uq5ePFiuWvXLllVVSXPnz8vpZTS6/XK6upqKaWUHR0dsr6+3li/qKhI\njo6OSimlXLVqlTG/sbFRHj9+POLfjCFMUwMgAXn/Z6rf0205HWJgmxLZ9mj7PM1cpP4Pfi1Wsx5W\nOnz4MK5evYpvv/0WbW1t2Lx5Mz766CM4HA60trZifHwcra2tKCsrAwCUlpais7MTw8PDEEIgMzMT\nWVlZAPzDT21tbRgZGUF7ezscDsdsw0lDQnUAGhGqA9CIUB2ANlhzSIy4r3OYGCLau3cvhoeHUVhY\niH/961/49a9/DQBYsWIF9u7di82bN+M3v/kNjh07Zqzb2NiIP//5z9iwYQM2btyI9evXxxsOEZlC\noC7B2oSeeG8lDfHeSrrGwDYlq+2zfb8HPw8CSK9nQvB5DkREUwh+HoR/mkcj8eLtM0xHqA5AI0J1\nABoRqgPQiFAdQEpgciAiojCsOWiINQddY2CbdK05RBt3T3V8ngMRpZ0lS5aHnNU0lzfso1BMDqYj\nVAegEaE6AI0I1QEkUOA010Ch2f8zsxv2iTmNLl0wORCRZvh8ah2w5qAh1hx0jYFt0qHtkT4LWHNg\nzYGIiJKAycF0hOoANCJUB6ARoToAjQjVAaQEJgciMpHkPGqUWHPQEmsOusbANunW9uCxddYcjDms\nORAR0dxgcjAdoToAjQjVAWhEqA5AI0J1ACmByYGIiMKw5qAh1hx0jYFt0q3trDmw5kBEREnE5GA6\nQnUAGhGqA9CIUB2ARoTqAFICkwMREYVhzUFDrDnoGgPbpFfb58N/k74JrDncn8OaAxGlM969dS4x\nOZiOUB2ARoTqADQiVAegEaE6gJTA5EBERGFYc9AQaw66xsA2mant6fKZwZoDERElDZOD6QjVAWhE\nqA5AI0J1ABoRIVNLliznbb5jME91AEREc8nn+y8mhl18vgy1wZgIaw4aYs1B1xjYJjO1PfJ9l1Lv\ns2Suag48ciCiFDTv/ocmxSqmmsPVq1exadMmrF27Fk6nE5988gkAwOfzobKyEhaLBTt27MDY2Jix\nTnNzMwoKCmC329Hb22vMHxgYwLp167B69WocPHgwzuakA6E6AI0I1QFoRKgOQCMCvEAufjElh/nz\n5+Po0aPo7+/HZ599hkOHDsHn88HlcsFiseDy5cvIzc3FiRMnAADXr19HS0sLurq64HK5UF9fb2yr\noaEB+/fvh8fjQU9PD7xeb2JaRkREMYspOaxcuRLFxcUAgIcffhhr166Fx+NBX18f6urqsHDhQtTW\n1sLtdgMA3G43KioqYLFYUF5eDimlcVQxODiImpoaZGdno6qqyliHpuJUHYBGnKoD0IhTdQAacaoO\nICXEfSrr0NAQ+vv7UVpaCo/HA6vVCgCwWq3o6+sD4E8ONpvNWKewsBButxtDQ0PIyckx5tvtdpw7\ndy7ekIiIKE5xFaR9Ph9qampw9OhRPPjgg7OqikcqFkVbf/fu3cjPzwcALF26FMXFxXA6nQAAIQQA\npMy0n0DgG5CYNH+yqebNZHszmdZ1e5PXmbx+ouKbq+3Fun6k7X2d4O1FWz/Z25t4fabba4qyvdBC\ndVbWMnR0/M2/dU3e//F9XvhfC/68jJmM0U8//SSfe+45efToUWNeVVWVPH/+vJRSSq/XK6urq6WU\nUnZ0dMj6+npjuaKiIjk6OiqllHLVqlXG/MbGRnn8+PGwvxVHmKYEQALy/s/k37ujvBbp91RZLtI6\n3Qnatk5tinW5bk3apKLtk3+f3BfRt2d20doUT/tiGlaSUqKurg5PPPEE9u3bZ8x3OBxobW3F+Pg4\nWltbUVZWBgAoLS1FZ2cnhoeHIYRAZmYmsrKyAPiHn9ra2jAyMoL29nY4HI5YQkojTtUBaMSpOgCN\nOFUHoBGn6gBSQywZ5auvvpIZGRmyqKhIFhcXy+LiYnn69Gk5Ojoqt2/fLvPy8mRlZaX0+XzGOk1N\nTXLNmjXSZrPJs2fPGvP7+/tlSUmJzM/PlwcOHIj492IM07Sg9Te+VPymyTalc9vNLlqb4mkfr5DW\nUPQrpLsR+Gak8xWwiV4u0joTfaFbrPG0KdblBIBNGrRppsvN5bYFQvsi2vYCT5PLylqG0dEbMBve\nlZWIKOECF8v5fD7eoC8Ijxw0xHsr6RoD25RObTfLZw6PHIiIKGmYHExHqA5AI0J1ABoRqgPQiFAd\nQEpgciAiojCsOWiINQddY2Cb0qntZvnMYc2BiIiShsnBdITqADQiVAegEaE6AI0I1QGkBCYHIiIK\nw5qDhlhz0DUGtil92h64chrQ++ppPkOaiChpJq6c9vP5MtSFogiHlUxHqA5AI0J1ABoRqgPQiFAd\nQEpgciAiojCsOWiINQddY2Cb0rft+t69lTUHIiJlAjWIdKk/cFjJdITqADQiVAegEaE6AI0I1QGk\nBCYHIqJZmZcWz31gzUFDrDnoGgPbxLaH/676s4n3ViIioqRhcjAdoToAjQjVAWhEqA5AI0J1ACmB\nyYGIKGaB+kOq1SBYc9AQaw66xsA2se3TLZf86yF4nQMRkfZS53oIDiuZjlAdgEaE6gA0IlQHoBGh\nOoCUwORARERhWHPQEGsOusbANrHts1suGZ9bvM6BiMhUzH0lNZOD6QjVAWhEqA5AI0J1ABoRqgO4\nb6I4LeHz+UyXKLRIDmfPnoXNZkNBQQH+8pe/qA5Hc1+rDkAj7IsA9kWAjn0RnCj+G9MWlixZntQE\no0Vy+O1vf4t3330XX375Jf76179iZGQkbJlUvdBk9v6f6gA0wr4IYF8E6N4XsV04508q8SWY2VCe\nHG7evAkAePbZZ/Hoo49i69atcLvdEZaUSGbHEBHNjcBRxOQhp4yMBdp8CVaeHDweD6xWqzFtt9tx\n7tw5hRHp7orqADRyRXUAGrmiOgCNXFEdwCwFJ4u7iJw0kn9BnYmukA7tHBWdlVwZU/z+f+7/TLfc\n5P5JheUmrxPcF7rFOtPldIhB1XI6xKBquZmuM1ngtdDPwMR/PipPDhs2bMDrr79uTPf396OioiJk\nmXS6xoGISAfKh5UeeughAP4zlq5cuYIvvvgCDodDcVREROlN+ZEDADQ1NeHVV1/F3bt3UV9fj4cf\nflh1SEREaU35kQMAlJeXY2BgAENDQ6ivrzfmp/P1D1evXsWmTZuwdu1aOJ1OfPLJJwAAn8+HyspK\nWCwW7NixA2NjY4ojTZ579+6hpKQE27ZtA5C+fXHr1i289NJLePzxx2G32+F2u9O2L95//308/fTT\neOqpp7AIcGDcAAAD70lEQVRv3z4A6bNf1NbWYsWKFXjyySeNedHa3tzcjIKCAtjtdvT29k67fS2S\nw1Rmcv1Dqpo/fz6OHj2K/v5+fPbZZzh06BB8Ph9cLhcsFgsuX76M3NxcnDhxQnWoSXPs2DHY7Xaj\n2JauffHWW2/BYrHgwoULuHDhAqxWa1r2xY0bN3D48GF88cUX8Hg8uHTpEjo7O9OmL/bs2YMzZ86E\nzJuq7devX0dLSwu6urrgcrlCvoRPRdvkMPPrH1LTypUrUVxcDAB4+OGHsXbtWng8HvT19aGurg4L\nFy5EbW1t2vTJtWvXcOrUKbz88svGCQrp2hdffvkl3nzzTTzwwAOYN28eHnroobTsi0WLFkFKiZs3\nb2J8fBw//vgjli5dmjZ9sXHjRixbtixk3lRtd7vdqKiogMViQXl5OaT0nyobjbbJgdc/BAwNDaG/\nvx+lpaUh/WK1WtHX16c4uuR47bXXcOTIEWRmBnbZdOyLa9eu4fbt29i7dy8cDgfeeecdjI+Pp2Vf\nLFq0CC6XC/n5+Vi5ciWeeeYZOByOtOyLCVO13e12w2azGcsVFhZO2y/aJgfy8/l8qKmpwdGjR/Hg\ngw+m5Wm9J0+eRE5ODkpKSkLan459cfv2bVy6dAnV1dUQQqC/vx+ffvppWvbFDz/8gL179+LixYu4\ncuUK/vnPf+LkyZNp2RcTZtP26a6F0DY5bNiwAd98840x3d/fj7KyMoURJd/du3dRXV2NF198EZWV\nlQD8/TIwMAAAGBgYwIYNG1SGmBT/+Mc/0NHRgVWrVuGFF17A3//+d7z44otp2RePPfYYCgsLsW3b\nNixatAgvvPACzpw5k5Z90dfXh7KyMjz22GPIzs7G888/j6+++iot+2LCVG13OBy4ePGisdw333wz\nbb9omxzS/foHKSXq6urwxBNPGGdhAP5/cmtrK8bHx9Ha2poWCfPw4cO4evUqvv32W7S1tWHz5s34\n6KOP0rIvAKCgoAButxs///wzPv/8c2zZsiUt+2Ljxo3wer24ceMG7ty5g9OnT2Pr1q1p2RcTpmp7\naWkpOjs7MTw8DCEEMjMzkZWVFX1jUmNCCGm1WuWaNWvksWPHVIeTVF999ZXMyMiQRUVFsri4WBYX\nF8vTp0/L0dFRuX37dpmXlycrKyulz+dTHWpSCSHktm3bpJQybfticHBQOhwOWVRUJBsaGuTY2Fja\n9sUHH3wgn332Wbl+/Xp56NAhee/evbTpi507d8pHHnlELliwQObm5srW1taobW9qapJr1qyRNptN\nnj17dtrtm+IxoURElFzaDisREZE6TA5ERBSGyYGIiMIwORARURgmByIiCsPkQEREYf4/ZErPN/kK\nzcQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9bed850>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.age2.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "30-49                159056\n",
        "50-64                111068\n",
        "65+                   88069\n",
        "18-29                 77350\n",
        "DK\\Refused             6563\n",
        "Missing\\not asked       156\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(pew)\n",
      "print len(pew[pew.age.notnull()])\n",
      "print len(pew[pew.age2.notnull()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262\n",
        "442262"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "442262"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.sex.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "Female    230365\n",
        "Male      211897\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg = pew[['id','year','age2','sex','racethn','income2']]\n",
      "dg.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>year</th>\n",
        "      <th>age2</th>\n",
        "      <th>sex</th>\n",
        "      <th>racethn</th>\n",
        "      <th>income2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1990</td>\n",
        "      <td> 30-49</td>\n",
        "      <td> Female</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91640</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1990</td>\n",
        "      <td>   65+</td>\n",
        "      <td>   Male</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "       id  year   age2     sex racethn            income2\n",
        "0       1  1990  30-49  Female     NaN  Missing\\not asked\n",
        "91640   2  1990    65+    Male     NaN  Missing\\not asked"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg2 = dg.groupby(['year','sex','racethn','age2','income2']).agg(np.count_nonzero)\n",
      "dg2 = dg2.rename(columns={'id':'count'})\n",
      "dg2.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>sex</th>\n",
        "      <th>racethn</th>\n",
        "      <th>age2</th>\n",
        "      <th>income2</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">1990</th>\n",
        "      <th rowspan=\"20\" valign=\"top\">Female</th>\n",
        "      <th rowspan=\"5\" valign=\"top\">Black non-Hisp</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">DK/Ref</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Hispanic</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Other</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "                                                                count\n",
        "year sex    racethn        age2              income2                 \n",
        "1990 Female Black non-Hisp 18-29             Missing\\not asked     41\n",
        "                           30-49             Missing\\not asked     71\n",
        "                           50-64             Missing\\not asked     39\n",
        "                           65+               Missing\\not asked     48\n",
        "                           DK\\Refused        Missing\\not asked      3\n",
        "            DK/Ref         18-29             Missing\\not asked      4\n",
        "                           30-49             Missing\\not asked     12\n",
        "                           50-64             Missing\\not asked      4\n",
        "                           65+               Missing\\not asked     10\n",
        "                           DK\\Refused        Missing\\not asked      4\n",
        "                           Missing\\not asked Missing\\not asked     24\n",
        "            Hispanic       18-29             Missing\\not asked     51\n",
        "                           30-49             Missing\\not asked     53\n",
        "                           50-64             Missing\\not asked     18\n",
        "                           65+               Missing\\not asked     13\n",
        "            Other          18-29             Missing\\not asked      7\n",
        "                           30-49             Missing\\not asked     10\n",
        "                           50-64             Missing\\not asked      4\n",
        "                           65+               Missing\\not asked      3\n",
        "                           DK\\Refused        Missing\\not asked      2"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg2.unstack('sex').unstack('racethn').head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th colspan=\"10\" halign=\"left\">count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>sex</th>\n",
        "      <th colspan=\"5\" halign=\"left\">Female</th>\n",
        "      <th colspan=\"5\" halign=\"left\">Male</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>racethn</th>\n",
        "      <th>Black non-Hisp</th>\n",
        "      <th>DK/Ref</th>\n",
        "      <th>Hispanic</th>\n",
        "      <th>Other</th>\n",
        "      <th>White non-Hisp</th>\n",
        "      <th>Black non-Hisp</th>\n",
        "      <th>DK/Ref</th>\n",
        "      <th>Hispanic</th>\n",
        "      <th>Other</th>\n",
        "      <th>White non-Hisp</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>age2</th>\n",
        "      <th>income2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">1990</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 41</td>\n",
        "      <td>  4</td>\n",
        "      <td> 51</td>\n",
        "      <td>  7</td>\n",
        "      <td> 212</td>\n",
        "      <td> 60</td>\n",
        "      <td> 12</td>\n",
        "      <td> 43</td>\n",
        "      <td> 10</td>\n",
        "      <td> 204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 71</td>\n",
        "      <td> 12</td>\n",
        "      <td> 53</td>\n",
        "      <td> 10</td>\n",
        "      <td> 417</td>\n",
        "      <td> 77</td>\n",
        "      <td> 11</td>\n",
        "      <td> 39</td>\n",
        "      <td> 10</td>\n",
        "      <td> 401</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 2 entries, (1990, 18-29, Missing\\not asked) to (1990, 30-49, Missing\\not asked)\n",
        "Data columns (total 10 columns):\n",
        "(count, Female, Black non-Hisp)    2  non-null values\n",
        "(count, Female, DK/Ref)            2  non-null values\n",
        "(count, Female, Hispanic)          2  non-null values\n",
        "(count, Female, Other)             2  non-null values\n",
        "(count, Female, White non-Hisp)    2  non-null values\n",
        "(count, Male, Black non-Hisp)      2  non-null values\n",
        "(count, Male, DK/Ref)              2  non-null values\n",
        "(count, Male, Hispanic)            2  non-null values\n",
        "(count, Male, Other)               2  non-null values\n",
        "(count, Male, White non-Hisp)      2  non-null values\n",
        "dtypes: float64(10)"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#dg2[dg2.age2 == '18-29']\n",
      "dg2.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>sex</th>\n",
        "      <th>racethn</th>\n",
        "      <th>age2</th>\n",
        "      <th>income2</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">1990</th>\n",
        "      <th rowspan=\"20\" valign=\"top\">Female</th>\n",
        "      <th rowspan=\"5\" valign=\"top\">Black non-Hisp</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">DK/Ref</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Hispanic</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Other</th>\n",
        "      <th>18-29</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30-49</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50-64</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65+</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "                                                                count\n",
        "year sex    racethn        age2              income2                 \n",
        "1990 Female Black non-Hisp 18-29             Missing\\not asked     41\n",
        "                           30-49             Missing\\not asked     71\n",
        "                           50-64             Missing\\not asked     39\n",
        "                           65+               Missing\\not asked     48\n",
        "                           DK\\Refused        Missing\\not asked      3\n",
        "            DK/Ref         18-29             Missing\\not asked      4\n",
        "                           30-49             Missing\\not asked     12\n",
        "                           50-64             Missing\\not asked      4\n",
        "                           65+               Missing\\not asked     10\n",
        "                           DK\\Refused        Missing\\not asked      4\n",
        "                           Missing\\not asked Missing\\not asked     24\n",
        "            Hispanic       18-29             Missing\\not asked     51\n",
        "                           30-49             Missing\\not asked     53\n",
        "                           50-64             Missing\\not asked     18\n",
        "                           65+               Missing\\not asked     13\n",
        "            Other          18-29             Missing\\not asked      7\n",
        "                           30-49             Missing\\not asked     10\n",
        "                           50-64             Missing\\not asked      4\n",
        "                           65+               Missing\\not asked      3\n",
        "                           DK\\Refused        Missing\\not asked      2"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg2.index[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "MultiIndex\n",
        "[(1990, u'Female', u'Black non-Hisp', u'18-29', u'Missing\\not asked'), (1990, u'Female', u'Black non-Hisp', u'30-49', u'Missing\\not asked'), (1990, u'Female', u'Black non-Hisp', u'50-64', u'Missing\\not asked'), (1990, u'Female', u'Black non-Hisp', u'65+', u'Missing\\not asked'), (1990, u'Female', u'Black non-Hisp', u'DK\\Refused', u'Missing\\not asked')]"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg2.xs('18-29', level='age2').head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>sex</th>\n",
        "      <th>racethn</th>\n",
        "      <th>income2</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">1990</th>\n",
        "      <th rowspan=\"5\" valign=\"top\">Female</th>\n",
        "      <th>Black non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK/Ref</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>   4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hispanic</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>   7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>White non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Male</th>\n",
        "      <th>Black non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK/Ref</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hispanic</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>White non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">1991</th>\n",
        "      <th rowspan=\"5\" valign=\"top\">Female</th>\n",
        "      <th>Black non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK/Ref</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>   4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hispanic</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>White non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Male</th>\n",
        "      <th>Black non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK/Ref</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hispanic</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td>  30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>White non-Hisp</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <td> 485</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "                                              count\n",
        "year sex    racethn        income2                 \n",
        "1990 Female Black non-Hisp Missing\\not asked     41\n",
        "            DK/Ref         Missing\\not asked      4\n",
        "            Hispanic       Missing\\not asked     51\n",
        "            Other          Missing\\not asked      7\n",
        "            White non-Hisp Missing\\not asked    212\n",
        "     Male   Black non-Hisp Missing\\not asked     60\n",
        "            DK/Ref         Missing\\not asked     12\n",
        "            Hispanic       Missing\\not asked     43\n",
        "            Other          Missing\\not asked     10\n",
        "            White non-Hisp Missing\\not asked    204\n",
        "1991 Female Black non-Hisp Missing\\not asked     43\n",
        "            DK/Ref         Missing\\not asked      4\n",
        "            Hispanic       Missing\\not asked     46\n",
        "            Other          Missing\\not asked     13\n",
        "            White non-Hisp Missing\\not asked    365\n",
        "     Male   Black non-Hisp Missing\\not asked     64\n",
        "            DK/Ref         Missing\\not asked      5\n",
        "            Hispanic       Missing\\not asked     57\n",
        "            Other          Missing\\not asked     30\n",
        "            White non-Hisp Missing\\not asked    485"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#ps5.plot(kind='bar', stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Females are *more* likely to answer DK\\Refused."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.age2 == 'DK\\Refused'].sex.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "Female    4293\n",
        "Male      2270\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.racethn.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "White non-Hisp    327168\n",
        "Black non-Hisp     39701\n",
        "Hispanic           28933\n",
        "Other              21586\n",
        "DK/Ref              5092\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.hisp.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "No            389830\n",
        "Yes            28933\n",
        "DK/Refused      3561\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.race.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "White          358296\n",
        "Black           43238\n",
        "Other/Mixed     22943\n",
        "Asian           10041\n",
        "DK/Refused       6457\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Why are there two *income* columns?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.income.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "$50,000 to $74,999    56115\n",
        "DK\\Refused            51980\n",
        "$20,000 to $29,999    44149\n",
        "$30,000 to $39,999    42308\n",
        "$75,000 to $99,999    37979\n",
        "$40,000 to $49,999    36696\n",
        "$10,000 to $19,999    36216\n",
        "$100k to $149,999     32736\n",
        "less than $10,000     22826\n",
        "$150,000+             15229\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.income2.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "$75,000+              106621\n",
        "$30,000 to $49,999     79004\n",
        "Missing\\not asked      77028\n",
        "less than $20,000      59042\n",
        "$50,000 to $74,999     56115\n",
        "$20,000 to $29,999     44149\n",
        "DK\\Refused             20303\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'all', len(pew)\n",
      "print 'income', len(pew[pew.income.notnull()])\n",
      "print 'income2', len(pew[pew.income2.notnull()])\n",
      "print 'income but not income2', len(pew[(pew.income2.isnull()) & (pew.income.notnull())])\n",
      "print 'income2 but not income', len(pew[(pew.income.isnull()) & (pew.income2.notnull())])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "all 442262\n",
        "income "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "376234\n",
        "income2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262\n",
        "income but not income2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "income2 but not income "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "66028\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.income.notnull()].year.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x6bac2b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iHdizoKjg5ymZGodzMS9GKA57FkREZDssFnGIPYuRlO4EDKJ0J2AQpTsB22GxICKiiNiz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\noYxRtiMLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6ad3d30>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.income2.notnull()].year.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x6baf670>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fexsA+53/rwRQAsBzZ1u782+ybSPO15dq+27bcsVb7DbifF318ZYz/sVlyM/o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\ni8WCiIjiYrEgIqK4/j+RsyeSeNbx8gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6bf25f0>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(pew.income, pew.income2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>income2</th>\n",
        "      <th>$20,000 to $29,999</th>\n",
        "      <th>$30,000 to $49,999</th>\n",
        "      <th>$50,000 to $74,999</th>\n",
        "      <th>$75,000+</th>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>Missing\\not asked</th>\n",
        "      <th>less than $20,000</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>income</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>$10,000 to $19,999</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 36216</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$100k to $149,999</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 32736</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$150,000+</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 15229</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$20,000 to $29,999</th>\n",
        "      <td> 44149</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$30,000 to $39,999</th>\n",
        "      <td>     0</td>\n",
        "      <td> 42308</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$40,000 to $49,999</th>\n",
        "      <td>     0</td>\n",
        "      <td> 36696</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$50,000 to $74,999</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 56115</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>$75,000 to $99,999</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 37979</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DK\\Refused</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 20677</td>\n",
        "      <td> 20303</td>\n",
        "      <td> 11000</td>\n",
        "      <td>     0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>less than $10,000</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 22826</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Index: 10 entries, $10,000 to $19,999 to less than $10,000\n",
        "Data columns (total 7 columns):\n",
        "$20,000 to $29,999    10  non-null values\n",
        "$30,000 to $49,999    10  non-null values\n",
        "$50,000 to $74,999    10  non-null values\n",
        "$75,000+              10  non-null values\n",
        "DK\\Refused            10  non-null values\n",
        "Missing\\not asked     10  non-null values\n",
        "less than $20,000     10  non-null values\n",
        "dtypes: int64(7)"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.educ.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "High school graduate          127171\n",
        "Some College                  109343\n",
        "College graduate               91640\n",
        "Post-graduate                  53238\n",
        "High school, incomplete        28952\n",
        "Business, Technical, Trade     14465\n",
        "Less than high school           8540\n",
        "Post-graduate degree            5036\n",
        "DK/Refused                      2594\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>rid</th>\n",
        "      <th>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>date</th>\n",
        "      <th>survey</th>\n",
        "      <th>language</th>\n",
        "      <th>age</th>\n",
        "      <th>age2</th>\n",
        "      <th>sex</th>\n",
        "      <th>race</th>\n",
        "      <th>racethn</th>\n",
        "      <th>hisp</th>\n",
        "      <th>income</th>\n",
        "      <th>income2</th>\n",
        "      <th>educ</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>state_name</th>\n",
        "      <th>fipsco</th>\n",
        "      <th>county_name</th>\n",
        "      <th>usr</th>\n",
        "      <th>density</th>\n",
        "      <th>partyln</th>\n",
        "      <th>party</th>\n",
        "      <th>partysum</th>\n",
        "      <th>regvoter</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.941828</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 46</td>\n",
        "      <td> 30-49</td>\n",
        "      <td> Female</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td>      College graduate</td>\n",
        "      <td> 26</td>\n",
        "      <td> Michigan</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Independent</td>\n",
        "      <td> No leaning</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91640</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.465066</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 77</td>\n",
        "      <td>   65+</td>\n",
        "      <td>   Male</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td> Less than high school</td>\n",
        "      <td> 36</td>\n",
        "      <td> New York</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  Republican</td>\n",
        "      <td>   Rep/ln R</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 2 entries, 0 to 91640\n",
        "Data columns (total 26 columns):\n",
        "id             2  non-null values\n",
        "rid            2  non-null values\n",
        "weight         2  non-null values\n",
        "year           2  non-null values\n",
        "date           0  non-null values\n",
        "survey         2  non-null values\n",
        "language       2  non-null values\n",
        "age            2  non-null values\n",
        "age2           2  non-null values\n",
        "sex            2  non-null values\n",
        "race           2  non-null values\n",
        "racethn        0  non-null values\n",
        "hisp           0  non-null values\n",
        "income         0  non-null values\n",
        "income2        2  non-null values\n",
        "educ           2  non-null values\n",
        "fipsst         2  non-null values\n",
        "state_name     2  non-null values\n",
        "fipsco         0  non-null values\n",
        "county_name    0  non-null values\n",
        "usr            0  non-null values\n",
        "density        0  non-null values\n",
        "partyln        0  non-null values\n",
        "party          2  non-null values\n",
        "partysum       2  non-null values\n",
        "regvoter       0  non-null values\n",
        "dtypes: float64(7), int64(3), object(16)"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Locations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "States, then counties, with most and least responses."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print pew.state_name.value_counts()[:3]\n",
      "print pew.state_name.value_counts()[-3:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "California    40684\n",
        "Texas         27152\n",
        "New York      24533\n",
        "dtype: int64\n",
        "District of Columbia    850\n",
        "Hawaii                  150\n",
        "Alaska                  124\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print pew.county_name.value_counts()[:3]\n",
      "print pew.county_name.value_counts()[-3:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Los Angeles    6718\n",
        "Cook           4083\n",
        "Jefferson      4060\n",
        "dtype: int64\n",
        "Hodgeman            1\n",
        "Northwest Arctic    1\n",
        "Stonewall           1\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mostly suburbanites."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.usr.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "Suburban     139778\n",
        "Urban         82713\n",
        "Rural         60954\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(pew), len(pew[pew.usr.notnull()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "283445\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.usr.notnull()].year.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x779f890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x777cf70>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "According to the Merge Codebook document, the scale goes from lowest population densith (1) to greatest (5).  Not sure what a *9* indicates; no data?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.density.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "1    30252\n",
        "2    28749\n",
        "3    27409\n",
        "4    24141\n",
        "5    20241\n",
        "9      103\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(pew), len(pew[pew.density.notnull()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "130895\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.density.notnull()].year.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "2008    29035\n",
        "2012    25051\n",
        "2010    24764\n",
        "2009    22159\n",
        "2011    19377\n",
        "2013    10509\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loc = pew[['id','year','density']]\n",
      "loc.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>year</th>\n",
        "      <th>density</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91640</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "       id  year  density\n",
        "0       1  1990      NaN\n",
        "91640   2  1990      NaN"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time l2 = pd.pivot_table(loc, values='id', rows='year', cols='density', aggfunc=np.count_nonzero)\n",
      "l2.head(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 80.5 ms, sys: 18.2 ms, total: 98.7 ms\n",
        "Wall time: 99.5 ms\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>density</th>\n",
        "      <th>1.0</th>\n",
        "      <th>2.0</th>\n",
        "      <th>3.0</th>\n",
        "      <th>4.0</th>\n",
        "      <th>5.0</th>\n",
        "      <th>9.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 6643</td>\n",
        "      <td> 6452</td>\n",
        "      <td> 6148</td>\n",
        "      <td> 5315</td>\n",
        "      <td> 4377</td>\n",
        "      <td> 100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 5056</td>\n",
        "      <td> 4895</td>\n",
        "      <td> 4720</td>\n",
        "      <td> 4100</td>\n",
        "      <td> 3386</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 5780</td>\n",
        "      <td> 5512</td>\n",
        "      <td> 5202</td>\n",
        "      <td> 4510</td>\n",
        "      <td> 3760</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "density     1     2     3     4     5    9\n",
        "year                                      \n",
        "2008     6643  6452  6148  5315  4377  100\n",
        "2009     5056  4895  4720  4100  3386    2\n",
        "2010     5780  5512  5202  4510  3760  NaN"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(l2.index, l2[1.0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "[<matplotlib.lines.Line2D at 0x6c1ecf0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1TqeTJ5980vv/27Zt827+O9sxrmvXrmXYsGF07NiRjh070r9/f3Jz\nc7nkkku4+uqr+cc//nHW9l500UWMHDmSN998k8mTJ9frPapnLyLyvTP1wj/44ANeeuklli1bdtYx\n8tpKvpztGNebb76ZDz74gMrKSkpKSti4cSMDBgzA6XRy4MAB7z9AR44cYefOnYCp/gpmzP6dd95h\n6NCh9X5vCnsRke+FhIScNkn72GOPUVZWxqBBg3A4HEyYMAGAL7/8kttvv937vJqSL4MGDWLChAne\nki/jx4+nuLiYmJgY9u/fz7hx4wAYNGgQPXv25Prrr2fYsGE8++yz3uNdlyxZQnp6Or169aJ///4U\nFhZiWRb3338/vXr1YuDAgfTo0YO0tLT6vzeVSxARaf7UsxcRCQIKexGRIKCwFxEJAgp7EZEgoLAX\nEQkCCnsRkSDw/wE3E6AuiROhlAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6cc5e10>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####How much coverage over counties do we have?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>rid</th>\n",
        "      <th>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>date</th>\n",
        "      <th>survey</th>\n",
        "      <th>language</th>\n",
        "      <th>age</th>\n",
        "      <th>age2</th>\n",
        "      <th>sex</th>\n",
        "      <th>race</th>\n",
        "      <th>racethn</th>\n",
        "      <th>hisp</th>\n",
        "      <th>income</th>\n",
        "      <th>income2</th>\n",
        "      <th>educ</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>state_name</th>\n",
        "      <th>fipsco</th>\n",
        "      <th>county_name</th>\n",
        "      <th>usr</th>\n",
        "      <th>density</th>\n",
        "      <th>partyln</th>\n",
        "      <th>party</th>\n",
        "      <th>partysum</th>\n",
        "      <th>regvoter</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0     </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.941828</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 46</td>\n",
        "      <td> 30-49</td>\n",
        "      <td> Female</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td>        College graduate</td>\n",
        "      <td> 26</td>\n",
        "      <td>   Michigan</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Independent</td>\n",
        "      <td> No leaning</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91640 </th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.465066</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 77</td>\n",
        "      <td>   65+</td>\n",
        "      <td>   Male</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td>   Less than high school</td>\n",
        "      <td> 36</td>\n",
        "      <td>   New York</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  Republican</td>\n",
        "      <td>   Rep/ln R</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1     </th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0.812558</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 29</td>\n",
        "      <td> 18-29</td>\n",
        "      <td>   Male</td>\n",
        "      <td> Black</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td>        College graduate</td>\n",
        "      <td> 24</td>\n",
        "      <td>   Maryland</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>    Democrat</td>\n",
        "      <td>   Dem/ln D</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100180</th>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1.126500</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 86</td>\n",
        "      <td>   65+</td>\n",
        "      <td>   Male</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td> High school, incomplete</td>\n",
        "      <td> 17</td>\n",
        "      <td>   Illinois</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>          DK</td>\n",
        "      <td> No leaning</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>129132</th>\n",
        "      <td> 5</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2.406894</td>\n",
        "      <td> 1990</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Jan90NII</td>\n",
        "      <td> English only</td>\n",
        "      <td> 39</td>\n",
        "      <td> 30-49</td>\n",
        "      <td>   Male</td>\n",
        "      <td> White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Missing\\not asked</td>\n",
        "      <td>            Some College</td>\n",
        "      <td> 34</td>\n",
        "      <td> New Jersey</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Independent</td>\n",
        "      <td> No leaning</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 5 entries, 0 to 129132\n",
        "Data columns (total 26 columns):\n",
        "id             5  non-null values\n",
        "rid            5  non-null values\n",
        "weight         5  non-null values\n",
        "year           5  non-null values\n",
        "date           0  non-null values\n",
        "survey         5  non-null values\n",
        "language       5  non-null values\n",
        "age            5  non-null values\n",
        "age2           5  non-null values\n",
        "sex            5  non-null values\n",
        "race           5  non-null values\n",
        "racethn        0  non-null values\n",
        "hisp           0  non-null values\n",
        "income         0  non-null values\n",
        "income2        5  non-null values\n",
        "educ           5  non-null values\n",
        "fipsst         5  non-null values\n",
        "state_name     5  non-null values\n",
        "fipsco         0  non-null values\n",
        "county_name    0  non-null values\n",
        "usr            0  non-null values\n",
        "density        0  non-null values\n",
        "partyln        0  non-null values\n",
        "party          5  non-null values\n",
        "partysum       5  non-null values\n",
        "regvoter       0  non-null values\n",
        "dtypes: float64(7), int64(3), object(16)"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fips = pd.read_csv('data/US_FIPS_Codes.csv')\n",
      "fips.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>County Name</th>\n",
        "      <th>FIPS State</th>\n",
        "      <th>FIPS County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Autauga</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Alabama</td>\n",
        "      <td> Baldwin</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "     State County Name  FIPS State  FIPS County\n",
        "0  Alabama     Autauga           1            1\n",
        "1  Alabama     Baldwin           1            3"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pewloc = pew[['id','year','fipsst','fipsco']]\n",
      "pewloc.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>year</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>fipsco</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1990</td>\n",
        "      <td> 26</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91640</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1990</td>\n",
        "      <td> 36</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "       id  year  fipsst  fipsco\n",
        "0       1  1990      26     NaN\n",
        "91640   2  1990      36     NaN"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl2 = pewloc.dropna()\n",
      "print len(pewloc), len(pl2)\n",
      "pl2.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262 319442\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>year</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>fipsco</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>395215</th>\n",
        "      <td> 122821</td>\n",
        "      <td> 1999</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>277000</th>\n",
        "      <td> 122822</td>\n",
        "      <td> 1999</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "            id  year  fipsst  fipsco\n",
        "395215  122821  1999       1       0\n",
        "277000  122822  1999       1       0"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl3 = pl2.merge(fips, left_on=['fipsst','fipsco'], right_on=['FIPS State','FIPS County'], how='right')\n",
      "print len(pl3)\n",
      "pl3.head(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "307738\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>year</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>fipsco</th>\n",
        "      <th>State</th>\n",
        "      <th>County Name</th>\n",
        "      <th>FIPS State</th>\n",
        "      <th>FIPS County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 123054</td>\n",
        "      <td> 1999</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Alabama</td>\n",
        "      <td> Autauga</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 123055</td>\n",
        "      <td> 1999</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Alabama</td>\n",
        "      <td> Autauga</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "       id  year  fipsst  fipsco    State County Name  FIPS State  FIPS County\n",
        "0  123054  1999       1       1  Alabama     Autauga           1            1\n",
        "1  123055  1999       1       1  Alabama     Autauga           1            1"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl3.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 307738 entries, 0 to 307737\n",
        "Data columns (total 8 columns):\n",
        "id             307698  non-null values\n",
        "year           307698  non-null values\n",
        "fipsst         307698  non-null values\n",
        "fipsco         307698  non-null values\n",
        "State          307738  non-null values\n",
        "County Name    307738  non-null values\n",
        "FIPS State     307738  non-null values\n",
        "FIPS County    307738  non-null values\n",
        "dtypes: float64(4), int64(2), object(2)"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are 40 counties from the FIPS 6-4 code set where we have no data.  This is over all the years of the surveys; within a year there may be more limited coverage."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl4 = pl3[pl3.id.isnull()].groupby(['State','County Name'])\n",
      "print len(pl4), 'counties with no data out of', len(fips), '(%.02f%%)'%(100.0*len(pl4)/len(fips))\n",
      "pl4.head(400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "40 counties with no data out of 3142 (1.27%)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>year</th>\n",
        "      <th>fipsst</th>\n",
        "      <th>fipsco</th>\n",
        "      <th>State</th>\n",
        "      <th>County Name</th>\n",
        "      <th>FIPS State</th>\n",
        "      <th>FIPS County</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>State</th>\n",
        "      <th>County Name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"12\" valign=\"top\">Alaska</th>\n",
        "      <th>Aleutians East</th>\n",
        "      <th>307698</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>        Aleutians East</td>\n",
        "      <td>  2</td>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aleutians West</th>\n",
        "      <th>307699</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>        Aleutians West</td>\n",
        "      <td>  2</td>\n",
        "      <td>  16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bristol Bay</th>\n",
        "      <th>307700</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>           Bristol Bay</td>\n",
        "      <td>  2</td>\n",
        "      <td>  60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dillingham</th>\n",
        "      <th>307701</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>            Dillingham</td>\n",
        "      <td>  2</td>\n",
        "      <td>  70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Haines</th>\n",
        "      <th>307702</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>                Haines</td>\n",
        "      <td>  2</td>\n",
        "      <td> 100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lake and Peninsula</th>\n",
        "      <th>307703</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>    Lake and Peninsula</td>\n",
        "      <td>  2</td>\n",
        "      <td> 164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>North Slope</th>\n",
        "      <th>307704</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>           North Slope</td>\n",
        "      <td>  2</td>\n",
        "      <td> 185</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sitka</th>\n",
        "      <th>307705</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>                 Sitka</td>\n",
        "      <td>  2</td>\n",
        "      <td> 220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Skagway Hoonah Angoon</th>\n",
        "      <th>307706</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td> Skagway Hoonah Angoon</td>\n",
        "      <td>  2</td>\n",
        "      <td> 232</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wrangell Petersburg</th>\n",
        "      <th>307707</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>   Wrangell Petersburg</td>\n",
        "      <td>  2</td>\n",
        "      <td> 280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Yakutat</th>\n",
        "      <th>307708</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>               Yakutat</td>\n",
        "      <td>  2</td>\n",
        "      <td> 282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Yukon Koyukuk</th>\n",
        "      <th>307709</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Alaska</td>\n",
        "      <td>         Yukon Koyukuk</td>\n",
        "      <td>  2</td>\n",
        "      <td> 290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <th>Mineral</th>\n",
        "      <th>307710</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Colorado</td>\n",
        "      <td>               Mineral</td>\n",
        "      <td>  8</td>\n",
        "      <td>  79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>District of Columbia</th>\n",
        "      <th>Montgomery</th>\n",
        "      <th>307711</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> District of Columbia</td>\n",
        "      <td>            Montgomery</td>\n",
        "      <td> 11</td>\n",
        "      <td>  31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kansas</th>\n",
        "      <th>Kearny</th>\n",
        "      <th>307712</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>               Kansas</td>\n",
        "      <td>                Kearny</td>\n",
        "      <td> 20</td>\n",
        "      <td>  93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Nebraska</th>\n",
        "      <th>Arthur</th>\n",
        "      <th>307713</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Nebraska</td>\n",
        "      <td>                Arthur</td>\n",
        "      <td> 31</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Banner</th>\n",
        "      <th>307714</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Nebraska</td>\n",
        "      <td>                Banner</td>\n",
        "      <td> 31</td>\n",
        "      <td>   7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Garden</th>\n",
        "      <th>307715</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Nebraska</td>\n",
        "      <td>                Garden</td>\n",
        "      <td> 31</td>\n",
        "      <td>  69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Loup</th>\n",
        "      <th>307716</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Nebraska</td>\n",
        "      <td>                  Loup</td>\n",
        "      <td> 31</td>\n",
        "      <td> 115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nuckolls</th>\n",
        "      <th>307717</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Nebraska</td>\n",
        "      <td>              Nuckolls</td>\n",
        "      <td> 31</td>\n",
        "      <td> 129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Mexico</th>\n",
        "      <th>Harding</th>\n",
        "      <th>307718</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>           New Mexico</td>\n",
        "      <td>               Harding</td>\n",
        "      <td> 35</td>\n",
        "      <td>  21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">North Dakota</th>\n",
        "      <th>Billings</th>\n",
        "      <th>307719</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>         North Dakota</td>\n",
        "      <td>              Billings</td>\n",
        "      <td> 38</td>\n",
        "      <td>   7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Burke</th>\n",
        "      <th>307720</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>         North Dakota</td>\n",
        "      <td>                 Burke</td>\n",
        "      <td> 38</td>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">South Dakota</th>\n",
        "      <th>Buffalo</th>\n",
        "      <th>307721</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>         South Dakota</td>\n",
        "      <td>               Buffalo</td>\n",
        "      <td> 46</td>\n",
        "      <td>  17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Campbell</th>\n",
        "      <th>307722</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>         South Dakota</td>\n",
        "      <td>              Campbell</td>\n",
        "      <td> 46</td>\n",
        "      <td>  21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jones</th>\n",
        "      <th>307723</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>         South Dakota</td>\n",
        "      <td>                 Jones</td>\n",
        "      <td> 46</td>\n",
        "      <td>  75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"12\" valign=\"top\">Texas</th>\n",
        "      <th>Borden</th>\n",
        "      <th>307724</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                Borden</td>\n",
        "      <td> 48</td>\n",
        "      <td>  33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Briscoe</th>\n",
        "      <th>307725</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>               Briscoe</td>\n",
        "      <td> 48</td>\n",
        "      <td>  45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Culberson</th>\n",
        "      <th>307726</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>             Culberson</td>\n",
        "      <td> 48</td>\n",
        "      <td> 109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kenedy</th>\n",
        "      <th>307727</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                Kenedy</td>\n",
        "      <td> 48</td>\n",
        "      <td> 261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kent</th>\n",
        "      <th>307728</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                  Kent</td>\n",
        "      <td> 48</td>\n",
        "      <td> 263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>King</th>\n",
        "      <th>307729</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                  King</td>\n",
        "      <td> 48</td>\n",
        "      <td> 269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Loving</th>\n",
        "      <th>307730</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                Loving</td>\n",
        "      <td> 48</td>\n",
        "      <td> 301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>McMullen</th>\n",
        "      <th>307731</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>              McMullen</td>\n",
        "      <td> 48</td>\n",
        "      <td> 311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Motley</th>\n",
        "      <th>307732</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                Motley</td>\n",
        "      <td> 48</td>\n",
        "      <td> 345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oldham</th>\n",
        "      <th>307733</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                Oldham</td>\n",
        "      <td> 48</td>\n",
        "      <td> 359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Reagan</th>\n",
        "      <th>307734</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>                Reagan</td>\n",
        "      <td> 48</td>\n",
        "      <td> 383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Throckmorton</th>\n",
        "      <th>307735</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>                Texas</td>\n",
        "      <td>          Throckmorton</td>\n",
        "      <td> 48</td>\n",
        "      <td> 447</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Virginia</th>\n",
        "      <th>Manassas Park City</th>\n",
        "      <th>307736</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Virginia</td>\n",
        "      <td>    Manassas Park City</td>\n",
        "      <td> 51</td>\n",
        "      <td> 685</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>South Boston City</th>\n",
        "      <th>307737</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>             Virginia</td>\n",
        "      <td>     South Boston City</td>\n",
        "      <td> 51</td>\n",
        "      <td> 780</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 40 entries, (Alaska, Aleutians East, 307698) to (Virginia, South Boston City, 307737)\n",
        "Data columns (total 8 columns):\n",
        "id             0  non-null values\n",
        "year           0  non-null values\n",
        "fipsst         0  non-null values\n",
        "fipsco         0  non-null values\n",
        "State          40  non-null values\n",
        "County Name    40  non-null values\n",
        "FIPS State     40  non-null values\n",
        "FIPS County    40  non-null values\n",
        "dtypes: float64(4), int64(2), object(2)"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Politics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.party.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "Independent      140659\n",
        "Democrat         140233\n",
        "Republican       127981\n",
        "No Preference     14636\n",
        "DK                11067\n",
        "Other              2312\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.partyln.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "Missing/not asked    262804\n",
        "Lean Democrat         59109\n",
        "Lean Republican       55178\n",
        "Other/DK              51246\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(pew.party, pew.partyln, dropna=False, margins=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>partyln</th>\n",
        "      <th>Lean Democrat</th>\n",
        "      <th>Lean Republican</th>\n",
        "      <th>Missing/not asked</th>\n",
        "      <th>Other/DK</th>\n",
        "      <th>All</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>party</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>DK</th>\n",
        "      <td>  1563</td>\n",
        "      <td>  1523</td>\n",
        "      <td>      1</td>\n",
        "      <td>  7664</td>\n",
        "      <td>  11067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Democrat</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 137482</td>\n",
        "      <td>     2</td>\n",
        "      <td> 140233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Independent</th>\n",
        "      <td> 53822</td>\n",
        "      <td> 49657</td>\n",
        "      <td>     22</td>\n",
        "      <td> 34719</td>\n",
        "      <td> 140659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>No Preference</th>\n",
        "      <td>  3255</td>\n",
        "      <td>  3314</td>\n",
        "      <td>     14</td>\n",
        "      <td>  8053</td>\n",
        "      <td>  14636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <td>   469</td>\n",
        "      <td>   684</td>\n",
        "      <td>    138</td>\n",
        "      <td>   808</td>\n",
        "      <td>   2312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Republican</th>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 125146</td>\n",
        "      <td>     0</td>\n",
        "      <td> 127981</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <td> 59109</td>\n",
        "      <td> 55178</td>\n",
        "      <td> 262804</td>\n",
        "      <td> 51246</td>\n",
        "      <td> 442262</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "partyln        Lean Democrat  Lean Republican  Missing/not asked  Other/DK     All\n",
        "party                                                                             \n",
        "DK                      1563             1523                  1      7664   11067\n",
        "Democrat                   0                0             137482         2  140233\n",
        "Independent            53822            49657                 22     34719  140659\n",
        "No Preference           3255             3314                 14      8053   14636\n",
        "Other                    469              684                138       808    2312\n",
        "Republican                 0                0             125146         0  127981\n",
        "All                    59109            55178             262804     51246  442262"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(pew)\n",
      "print len(pew[pew.party.notnull()])\n",
      "print len(pew[pew.partyln.notnull()])\n",
      "print len(pew[(pew.party.notnull()) & (pew.partyln.isnull())])\n",
      "print len(pew[(pew.partyln.notnull()) & (pew.party.isnull())])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262\n",
        "436888"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "428337"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "8552"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.party.notnull()].year.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x6c4cc90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BOJ1Ofd+8vDwEAgH09/cjPT1db3e5XOju7gYwXXhcLhcAIDU1FYODg5iYmDCoe/GJ87Ez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\nD16pJGNCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x746c470>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.partyln.notnull()].year.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x725a770>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XV7p9SXH8WNszv1zr8Xj0/wMwZFvTNJw6dQoAYLfbYZhY81RTU1PyzDPPyGuv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\niCgmFgsiIoqJxYKIiGJisSAiopj+H+xL1L6ifuJVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x72617f0>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fairly well balanced between those leaning Dem and those leaning Rep."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.partysum.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "Dem/ln D                          199342\n",
        "Rep/ln R                          183159\n",
        "No leaning                         54387\n",
        "Party or Partyln not available      5374"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "All are registered voters?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew.regvoter.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "1    305512"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(pew.regvoter)\n",
      "print len(pew[pew.regvoter.isnull()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "442262\n",
        "136750"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": []
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The number of responses with *regvoter* seems to be increasing over the years compared to the number with *regvoter* missing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.regvoter.isnull()].year.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x6c50910>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Qse07V2NbNW8jY1v3uRlSJF4bioiIYomTheUoRidgIorRCZiIYnQCJqIYnUBc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\nEBFRUJwsiIgoKE4WREQUFCcLIiIK6v8BK+y4AV3mkIEAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pew[pew.regvoter.notnull()].year.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x6b81710>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Write to CSV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%time pew.to_csv('data/Pew_for_analysis.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    }
   ],
   "metadata": {}
  }
 ]
}