nb_20131027_pew_data3.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Analyze the Pew.txt file: specific questions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Try to answer specific questions using the Pew data."
     ]
    },
    {
     "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 *"
     ],
     "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": 2
    },
    {
     "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 13.6 s, sys: 1.7 s, total: 15.3 s\n",
        "Wall time: 15.3 s\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "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": 4,
       "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": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Tracking a group"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Picking a group arbitrarily, let's see how ***republican women making at least 75k a year*** have changed over time in terms of *% below age 30* and *% urban*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk = pew[(pew.party=='Republican') & (pew.sex=='Female') & (pew.income2=='$75,000+')][['id','weight','year','age2','usr']]\n",
      "trk.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>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>age2</th>\n",
        "      <th>usr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10063 </th>\n",
        "      <td> 51988</td>\n",
        "      <td> 0.521003</td>\n",
        "      <td> 1994</td>\n",
        "      <td>   65+</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>140465</th>\n",
        "      <td> 52089</td>\n",
        "      <td> 1.218775</td>\n",
        "      <td> 1994</td>\n",
        "      <td> 18-29</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "           id    weight  year   age2  usr\n",
        "10063   51988  0.521003  1994    65+  NaN\n",
        "140465  52089  1.218775  1994  18-29  NaN"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate percent below age 30."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk2 = pd.crosstab(trk.year, trk.age2, margins=True)\n",
      "trk2['perc_below_30'] = 100.0 * trk2['18-29'] / trk2['All']\n",
      "trk2.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>age2</th>\n",
        "      <th>18-29</th>\n",
        "      <th>30-49</th>\n",
        "      <th>50-64</th>\n",
        "      <th>65+</th>\n",
        "      <th>DK\\Refused</th>\n",
        "      <th>All</th>\n",
        "      <th>perc_below_30</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",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1994</th>\n",
        "      <td> 30</td>\n",
        "      <td>  96</td>\n",
        "      <td> 36</td>\n",
        "      <td> 65</td>\n",
        "      <td> 19</td>\n",
        "      <td> 246</td>\n",
        "      <td> 12.195122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1995</th>\n",
        "      <td> 48</td>\n",
        "      <td> 169</td>\n",
        "      <td> 95</td>\n",
        "      <td> 97</td>\n",
        "      <td> 12</td>\n",
        "      <td> 421</td>\n",
        "      <td> 11.401425</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "age2  18-29  30-49  50-64  65+  DK\\Refused  All  perc_below_30\n",
        "year                                                          \n",
        "1994     30     96     36   65          19  246      12.195122\n",
        "1995     48    169     95   97          12  421      11.401425"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk3 = pd.crosstab(trk.year, trk.usr, margins=True)\n",
      "trk3['perc_urban'] = 100.0 * trk3['Urban '] / trk3['All']\n",
      "trk3.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>usr</th>\n",
        "      <th>Rural </th>\n",
        "      <th>Suburban </th>\n",
        "      <th>Urban </th>\n",
        "      <th>All</th>\n",
        "      <th>perc_urban</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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1996</th>\n",
        "      <td>   5</td>\n",
        "      <td>  26</td>\n",
        "      <td>   5</td>\n",
        "      <td>  429</td>\n",
        "      <td>  1.165501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td> 172</td>\n",
        "      <td> 501</td>\n",
        "      <td> 196</td>\n",
        "      <td> 1090</td>\n",
        "      <td> 17.981651</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "usr   Rural   Suburban   Urban    All  perc_urban\n",
        "year                                             \n",
        "1996       5         26       5   429    1.165501\n",
        "2000     172        501     196  1090   17.981651"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data for USR seems to be null for this group for years earlier than 2000, so for now remove those years."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk4 = trk2.merge(trk3, how='inner', left_index=True, right_index=True)\n",
      "trk4 = trk4[['perc_below_30','perc_urban']]\n",
      "trk4 = trk4[trk4.index != 1996]\n",
      "trk4 = trk4[trk4.index != 'All']\n",
      "trk4.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>perc_below_30</th>\n",
        "      <th>perc_urban</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td> 10.366972</td>\n",
        "      <td> 17.981651</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td>  9.148666</td>\n",
        "      <td> 17.916137</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "      perc_below_30  perc_urban\n",
        "year                           \n",
        "2000      10.366972   17.981651\n",
        "2001       9.148666   17.916137"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot this cohort on x-y plot with x the % below 30 and y the % that live in urban areas."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(2,2)\n",
      "plot(trk4.perc_below_30, trk4.perc_urban)\n",
      "xlabel('% younger than 30')\n",
      "ylabel('% urban')\n",
      "title('Republican women earning at least $75k a year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x65ecdb0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HR+PGjRsA2PfUokULYcC06MRGZSj+gyqNQYMG4dtvv8WTJ09w8uRJnDt3Tq7wvXjxApqa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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6601250>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The line crosses itself many times so it isn't easy to read."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add arrows so we can see the direction of time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array(trk4.perc_below_30.values)\n",
      "y = np.array(trk4.perc_urban.values)\n",
      "figsize(6,6)\n",
      "quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1)\n",
      "xlabel('% younger than 30')\n",
      "ylabel('% urban')\n",
      "title('Republican women earning at least $75k a year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.text.Text at 0x6b8fe10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9tG7dmvz9/YnP50v6bdUIjx8/Ji0tLQJA9erVo+fPn8s7JCIievv2LReXk5MT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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x65d2c30>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using this plot, it is hard to see a clear trend.  Perhaps the cohort is growing younger and more urban."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Changing the range of the axes helps make it clear that the changes here are not large."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array(trk4.perc_below_30.values)\n",
      "y = np.array(trk4.perc_urban.values)\n",
      "figsize(4,4)\n",
      "quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1)\n",
      "xlabel('% younger than 30')\n",
      "ylabel('% urban')\n",
      "title('Republican women earning at least $75k a year')\n",
      "plt.xlim(0,100)\n",
      "plt.ylim(0,100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(0, 100)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1b98eu3bteqeyXLduHWxsbITvVoaEhEAmkyEpKQlSqRQbNmx4p/kyxv6Zyi2w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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6b95e70>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Use weights"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So far we haven't taken weights into account.  We don't really know the trends without that information."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk5 = trk.copy()\n",
      "trk5.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>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>age2</th>\n",
        "      <th>usr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10063 </th>\n",
        "      <td> 51988</td>\n",
        "      <td> 0.521003</td>\n",
        "      <td> 1994</td>\n",
        "      <td>   65+</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>140465</th>\n",
        "      <td> 52089</td>\n",
        "      <td> 1.218775</td>\n",
        "      <td> 1994</td>\n",
        "      <td> 18-29</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "           id    weight  year   age2  usr\n",
        "10063   51988  0.521003  1994    65+  NaN\n",
        "140465  52089  1.218775  1994  18-29  NaN"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an educated guess for how to use the weights to make valid percentages in this case.  We already limited all records to our cohort (republican women earning more than 75k).  For these rows, we add a column called *below_30* which will receive a copy of the row's weight if the person is younger than 30 and 0 otherwise.  We do the equivalent for *urban*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk5['below_30'] = trk5.age2=='18-29'\n",
      "trk5['below_30'] = trk5.below_30.astype(int)\n",
      "trk5['below_30'] = trk5.below_30 * trk5.weight\n",
      "trk5['urban'] = trk5.usr=='Urban '\n",
      "trk5['urban'] = trk5.urban.astype(int)\n",
      "trk5['urban'] = trk5.urban * trk5.weight\n",
      "trk5.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>weight</th>\n",
        "      <th>year</th>\n",
        "      <th>age2</th>\n",
        "      <th>usr</th>\n",
        "      <th>below_30</th>\n",
        "      <th>urban</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10063 </th>\n",
        "      <td> 51988</td>\n",
        "      <td> 0.521003</td>\n",
        "      <td> 1994</td>\n",
        "      <td>   65+</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>140465</th>\n",
        "      <td> 52089</td>\n",
        "      <td> 1.218775</td>\n",
        "      <td> 1994</td>\n",
        "      <td> 18-29</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1.218775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "           id    weight  year   age2  usr  below_30  urban\n",
        "10063   51988  0.521003  1994    65+  NaN  0.000000      0\n",
        "140465  52089  1.218775  1994  18-29  NaN  1.218775      0"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(6,2)\n",
      "trk5[trk.usr.notnull()].year.hist(bins=len(list(set(trk5.year.values))))\n",
      "title('years with USR present')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "<matplotlib.text.Text at 0xbdccef0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WLlwIJyen5to0Y4wxPTRZDSEyMhLOzs4YMmQIACAkJARfffUVAgMDkZiYiBkz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb89ff10>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, *perc_below_30* is calculated for each year as the sum of all *below_30* values that year divided by the sum of all weights that year (times 100).  And again we do the equivalent for *perc_urban*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trk6 = trk5.groupby(['year'])\n",
      "trk7 = pd.DataFrame({'perc_below_30':100.0 * trk6.below_30.agg(np.sum) / trk6.weight.agg(np.sum)})\n",
      "trk7.head()\n",
      "trk7['perc_urban'] = 100.0 * trk6.urban.agg(np.sum) / trk6.weight.agg(np.sum)\n",
      "#print trk7.perc_urban\n",
      "trk7 = trk7[trk7.index > 1999]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array(trk7.perc_below_30.values)\n",
      "y = np.array(trk7.perc_urban.values)\n",
      "figsize(6,6)\n",
      "quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1)\n",
      "xlabel('% younger than 30')\n",
      "ylabel('% urban')\n",
      "title('Republican women earning at least $75k a year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.text.Text at 0x7786cf0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PH0qUx5s3bwgA/fbbb6y7zt7eXj6C5Uze6dTXr18nIip1tyr3BlYcFc74ExF9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\n/nwAwIsXL9C3b1+sX7++2DPtlJQUjBw5EjExMRgwYACAgiUfdHR0VBb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       "text": [
        "<matplotlib.figure.Figure at 0x7575330>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now with the weighted results it is even more difficult to see a trend.  The changes are small and may be explained by normal fluctuations within the party year-by-year and the challenges of phone polls."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}