ethnicolr/examples/ethnicolr_app_contrib20xx-census_ln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application: 2000/2010 Political Campaign Contributions by Race\n",
    "\n",
    "Using ethnicolr, we look to answer three basic questions:\n",
    "<ol>\n",
    "<li>What proportion of contributions were made by blacks, whites, Hispanics, and Asians? \n",
    "<li>What proportion of unique contributors were blacks, whites, Hispanics, and Asians?\n",
    "<li>What proportion of total donations were given by blacks, whites, Hispanics, and Asians?\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'cycle', u'transaction_id', u'transaction_type', u'amount', u'date',\n",
       "       u'bonica_cid', u'contributor_name', u'contributor_lname',\n",
       "       u'contributor_fname', u'contributor_mname', u'contributor_suffix',\n",
       "       u'contributor_title', u'contributor_ffname', u'contributor_type',\n",
       "       u'contributor_gender', u'contributor_address', u'contributor_city',\n",
       "       u'contributor_state', u'contributor_zipcode', u'contributor_occupation',\n",
       "       u'contributor_employer', u'contributor_category',\n",
       "       u'contributor_category_order', u'is_corp', u'organization_name',\n",
       "       u'parent_organization_name', u'recipient_name', u'bonica_rid',\n",
       "       u'recipient_party', u'recipient_type', u'recipient_state',\n",
       "       u'recipient_category', u'recipient_category_order',\n",
       "       u'recipient_district', u'seat', u'election_type',\n",
       "       u'contributor_cfscore', u'candidate_cfscore', u'latitude', u'longitude',\n",
       "       u'gis_confidence', u'contributor_district_90s',\n",
       "       u'contributor_district_00s', u'contributor_district_10s',\n",
       "       u'lname_frequency', u'efec_memo', u'efec_memo2',\n",
       "       u'efec_transaction_id_orig', u'efec_org_orig', u'efec_comid_orig',\n",
       "       u'efec_form_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/opt/names/fec_contrib/contribDB_2000.csv', nrows=100)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv/local/lib/python2.7/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from ethnicolr import census_ln"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load and Subset on Individual Contributors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv/local/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (8,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>contributor_name</th>\n",
       "      <th>contributor_lname</th>\n",
       "      <th>contributor_fname</th>\n",
       "      <th>contributor_type</th>\n",
       "      <th>pctwhite</th>\n",
       "      <th>pctblack</th>\n",
       "      <th>pctapi</th>\n",
       "      <th>pctaian</th>\n",
       "      <th>pct2prace</th>\n",
       "      <th>pcthispanic</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180.0</td>\n",
       "      <td>JOHNSON, KENYIE PAUL</td>\n",
       "      <td>johnson</td>\n",
       "      <td>kenyie</td>\n",
       "      <td>I</td>\n",
       "      <td>61.55</td>\n",
       "      <td>33.80</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>743.0</td>\n",
       "      <td>KIRSCH, STEVEN T</td>\n",
       "      <td>kirsch</td>\n",
       "      <td>steven</td>\n",
       "      <td>I</td>\n",
       "      <td>97.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180.0</td>\n",
       "      <td>MCCOY, TIMOTHY D</td>\n",
       "      <td>mccoy</td>\n",
       "      <td>timothy</td>\n",
       "      <td>I</td>\n",
       "      <td>69.00</td>\n",
       "      <td>26.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>188.0</td>\n",
       "      <td>WILLIAMS, VICTOR K</td>\n",
       "      <td>williams</td>\n",
       "      <td>victor</td>\n",
       "      <td>I</td>\n",
       "      <td>48.52</td>\n",
       "      <td>46.72</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>211.0</td>\n",
       "      <td>ELDER, CHESTER H</td>\n",
       "      <td>elder</td>\n",
       "      <td>chester</td>\n",
       "      <td>I</td>\n",
       "      <td>80.67</td>\n",
       "      <td>15.68</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13000.0</td>\n",
       "      <td>MACARTHUR, GREG</td>\n",
       "      <td>macarthur</td>\n",
       "      <td>greg</td>\n",
       "      <td>I</td>\n",
       "      <td>92.36</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13972.0</td>\n",
       "      <td>ABELE, CHRIS</td>\n",
       "      <td>abele</td>\n",
       "      <td>chris</td>\n",
       "      <td>I</td>\n",
       "      <td>97.03</td>\n",
       "      <td>0.58</td>\n",
       "      <td>(S)</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>PRICE, SOL</td>\n",
       "      <td>price</td>\n",
       "      <td>sol</td>\n",
       "      <td>I</td>\n",
       "      <td>76.11</td>\n",
       "      <td>19.79</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13600.0</td>\n",
       "      <td>KIRSCH, STEVEN T</td>\n",
       "      <td>kirsch</td>\n",
       "      <td>steven</td>\n",
       "      <td>I</td>\n",
       "      <td>97.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22146.0</td>\n",
       "      <td>KIRSCH, STEVEN T</td>\n",
       "      <td>kirsch</td>\n",
       "      <td>steven</td>\n",
       "      <td>I</td>\n",
       "      <td>97.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>813.0</td>\n",
       "      <td>SHAPP, RICHARD A</td>\n",
       "      <td>shapp</td>\n",
       "      <td>richard</td>\n",
       "      <td>I</td>\n",
       "      <td>72.12</td>\n",
       "      <td>14.42</td>\n",
       "      <td>(S)</td>\n",
       "      <td>9.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(S)</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>949.0</td>\n",
       "      <td>WILLIAMS, VICTOR K</td>\n",
       "      <td>williams</td>\n",
       "      <td>victor</td>\n",
       "      <td>I</td>\n",
       "      <td>48.52</td>\n",
       "      <td>46.72</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1717.0</td>\n",
       "      <td>HELFRICK, EDWARD W</td>\n",
       "      <td>helfrick</td>\n",
       "      <td>edward</td>\n",
       "      <td>I</td>\n",
       "      <td>97.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(S)</td>\n",
       "      <td>(S)</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.0</td>\n",
       "      <td>MCCOY, TIMOTHY D</td>\n",
       "      <td>mccoy</td>\n",
       "      <td>timothy</td>\n",
       "      <td>I</td>\n",
       "      <td>69.00</td>\n",
       "      <td>26.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24150.0</td>\n",
       "      <td>KIRSCH, STEVEN T</td>\n",
       "      <td>kirsch</td>\n",
       "      <td>steven</td>\n",
       "      <td>I</td>\n",
       "      <td>97.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>265.0</td>\n",
       "      <td>WILLIAMS, VICTOR K</td>\n",
       "      <td>williams</td>\n",
       "      <td>victor</td>\n",
       "      <td>I</td>\n",
       "      <td>48.52</td>\n",
       "      <td>46.72</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25750.0</td>\n",
       "      <td>SUTHERLAND, DWIGHT D JR</td>\n",
       "      <td>sutherland</td>\n",
       "      <td>dwight</td>\n",
       "      <td>I</td>\n",
       "      <td>86.49</td>\n",
       "      <td>8.37</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>45795.0</td>\n",
       "      <td>KIRSCH, STEVEN T</td>\n",
       "      <td>kirsch</td>\n",
       "      <td>steven</td>\n",
       "      <td>I</td>\n",
       "      <td>97.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28884.0</td>\n",
       "      <td>KIRSCH, STEVEN T</td>\n",
       "      <td>kirsch</td>\n",
       "      <td>steven</td>\n",
       "      <td>I</td>\n",
       "      <td>97.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>WOODS, FRED R</td>\n",
       "      <td>woods</td>\n",
       "      <td>fred</td>\n",
       "      <td>I</td>\n",
       "      <td>60.68</td>\n",
       "      <td>34.66</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     amount         contributor_name contributor_lname contributor_fname  \\\n",
       "0     180.0     JOHNSON, KENYIE PAUL           johnson            kenyie   \n",
       "1     743.0         KIRSCH, STEVEN T            kirsch            steven   \n",
       "2     180.0         MCCOY, TIMOTHY D             mccoy           timothy   \n",
       "3     188.0       WILLIAMS, VICTOR K          williams            victor   \n",
       "4     211.0         ELDER, CHESTER H             elder           chester   \n",
       "5   13000.0          MACARTHUR, GREG         macarthur              greg   \n",
       "6   13972.0             ABELE, CHRIS             abele             chris   \n",
       "7   15000.0               PRICE, SOL             price               sol   \n",
       "8   13600.0         KIRSCH, STEVEN T            kirsch            steven   \n",
       "9   22146.0         KIRSCH, STEVEN T            kirsch            steven   \n",
       "10    813.0         SHAPP, RICHARD A             shapp           richard   \n",
       "11    949.0       WILLIAMS, VICTOR K          williams            victor   \n",
       "12   1717.0       HELFRICK, EDWARD W          helfrick            edward   \n",
       "13      8.0         MCCOY, TIMOTHY D             mccoy           timothy   \n",
       "14  24150.0         KIRSCH, STEVEN T            kirsch            steven   \n",
       "15    265.0       WILLIAMS, VICTOR K          williams            victor   \n",
       "16  25750.0  SUTHERLAND, DWIGHT D JR        sutherland            dwight   \n",
       "17  45795.0         KIRSCH, STEVEN T            kirsch            steven   \n",
       "18  28884.0         KIRSCH, STEVEN T            kirsch            steven   \n",
       "19  30000.0            WOODS, FRED R             woods              fred   \n",
       "\n",
       "   contributor_type pctwhite pctblack pctapi pctaian pct2prace pcthispanic  \\\n",
       "0                 I    61.55    33.80   0.42    0.91      1.82        1.50   \n",
       "1                 I    97.32     0.16   0.34    0.32      0.63        1.23   \n",
       "2                 I    69.00    26.39   0.38    0.81      1.73        1.69   \n",
       "3                 I    48.52    46.72   0.37    0.78      2.01        1.60   \n",
       "4                 I    80.67    15.68   0.38    0.44      1.48        1.36   \n",
       "5                 I    92.36     2.32   0.62    1.11      1.83        1.76   \n",
       "6                 I    97.03     0.58    (S)     (S)      0.71        1.23   \n",
       "7                 I    76.11    19.79   0.42    0.64      1.58        1.46   \n",
       "8                 I    97.32     0.16   0.34    0.32      0.63        1.23   \n",
       "9                 I    97.32     0.16   0.34    0.32      0.63        1.23   \n",
       "10                I    72.12    14.42    (S)    9.62      0.00         (S)   \n",
       "11                I    48.52    46.72   0.37    0.78      2.01        1.60   \n",
       "12                I    97.73     0.00    (S)    0.00       (S)         (S)   \n",
       "13                I    69.00    26.39   0.38    0.81      1.73        1.69   \n",
       "14                I    97.32     0.16   0.34    0.32      0.63        1.23   \n",
       "15                I    48.52    46.72   0.37    0.78      2.01        1.60   \n",
       "16                I    86.49     8.37   0.56    0.83      1.68        2.07   \n",
       "17                I    97.32     0.16   0.34    0.32      0.63        1.23   \n",
       "18                I    97.32     0.16   0.34    0.32      0.63        1.23   \n",
       "19                I    60.68    34.66   0.35    0.84      1.92        1.55   \n",
       "\n",
       "    year  \n",
       "0   2000  \n",
       "1   2000  \n",
       "2   2000  \n",
       "3   2000  \n",
       "4   2000  \n",
       "5   2000  \n",
       "6   2000  \n",
       "7   2000  \n",
       "8   2000  \n",
       "9   2000  \n",
       "10  2000  \n",
       "11  2000  \n",
       "12  2000  \n",
       "13  2000  \n",
       "14  2000  \n",
       "15  2000  \n",
       "16  2000  \n",
       "17  2000  \n",
       "18  2000  \n",
       "19  2000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/opt/names/fec_contrib/contribDB_2000.csv', usecols=['amount', 'contributor_type', 'contributor_lname', 'contributor_fname', 'contributor_name'])\n",
    "sdf = df[df.contributor_type=='I'].copy()\n",
    "rdf2000 = census_ln(sdf, 'contributor_lname', 2000)\n",
    "rdf2000['year'] = 2000\n",
    "\n",
    "df = pd.read_csv('/opt/names/fec_contrib/contribDB_2010.csv.zip', usecols=['amount', 'contributor_type', 'contributor_lname', 'contributor_fname', 'contributor_name'])\n",
    "sdf = df[df.contributor_type=='I'].copy()\n",
    "rdf2010 = census_ln(sdf, 'contributor_lname', 2010)\n",
    "rdf2010['year'] = 2010\n",
    "\n",
    "rdf = pd.concat([rdf2000, rdf2010])\n",
    "rdf.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf.replace('(S)', 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf[['pctwhite', 'pctblack', 'pctapi', 'pctaian', 'pct2prace', 'pcthispanic']] = rdf[['pctwhite', 'pctblack', 'pctapi', 'pctaian', 'pct2prace', 'pcthispanic']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >hispanic</th> \n",
       "        <th class=\"col_heading level0 col1\" >white</th> \n",
       "        <th class=\"col_heading level0 col2\" >api</th> \n",
       "        <th class=\"col_heading level0 col3\" >black</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175level0_row0\" class=\"row_heading level0 row0\" >2000</th> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row0_col0\" class=\"data row0 col0\" >3.24%</td> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row0_col1\" class=\"data row0 col1\" >83.49%</td> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row0_col2\" class=\"data row0 col2\" >2.22%</td> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row0_col3\" class=\"data row0 col3\" >11.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175level0_row1\" class=\"row_heading level0 row1\" >2010</th> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row1_col0\" class=\"data row1 col0\" >4.32%</td> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row1_col1\" class=\"data row1 col1\" >82.71%</td> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row1_col2\" class=\"data row1 col2\" >2.74%</td> \n",
       "        <td id=\"T_6e99f2af_4c6b_11e8_8cdc_fa7917587175row1_col3\" class=\"data row1 col3\" >10.22%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f97d4837250>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.apply(lambda r: r / r.sum(), axis=1).style.format(\"{:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  What proportion of contributons were by blacks, whites, Hispanics, and Asians?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >hispanic</th> \n",
       "        <th class=\"col_heading level0 col1\" >white</th> \n",
       "        <th class=\"col_heading level0 col2\" >api</th> \n",
       "        <th class=\"col_heading level0 col3\" >black</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175level0_row0\" class=\"row_heading level0 row0\" >2000</th> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row0_col0\" class=\"data row0 col0\" >3.43%</td> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row0_col1\" class=\"data row0 col1\" >83.53%</td> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row0_col2\" class=\"data row0 col2\" >2.00%</td> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row0_col3\" class=\"data row0 col3\" >11.03%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175level0_row1\" class=\"row_heading level0 row1\" >2010</th> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row1_col0\" class=\"data row1 col0\" >5.49%</td> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row1_col1\" class=\"data row1 col1\" >80.43%</td> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row1_col2\" class=\"data row1 col2\" >2.75%</td> \n",
       "        <td id=\"T_6fa11380_4c6b_11e8_8cdc_fa7917587175row1_col3\" class=\"data row1 col3\" >11.33%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f97e787ead0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf['white'] = rdf.pctwhite / 100.0\n",
    "rdf['black'] = rdf.pctblack / 100.0\n",
    "rdf['api'] = rdf.pctapi / 100.0\n",
    "rdf['hispanic'] = rdf.pcthispanic / 100.0\n",
    "gdf = rdf.groupby(['year']).agg({'white': 'sum', 'black': 'sum', 'api': 'sum', 'hispanic': 'sum'})\n",
    "gdf.apply(lambda r: r / r.sum(), axis=1).style.format(\"{:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What proportion of the donors were blacks, whites, Hispanics, and Asians?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >hispanic</th> \n",
       "        <th class=\"col_heading level0 col1\" >white</th> \n",
       "        <th class=\"col_heading level0 col2\" >api</th> \n",
       "        <th class=\"col_heading level0 col3\" >black</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175level0_row0\" class=\"row_heading level0 row0\" >2000</th> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row0_col0\" class=\"data row0 col0\" >3.82%</td> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row0_col1\" class=\"data row0 col1\" >83.61%</td> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row0_col2\" class=\"data row0 col2\" >2.41%</td> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row0_col3\" class=\"data row0 col3\" >10.16%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175level0_row1\" class=\"row_heading level0 row1\" >2010</th> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row1_col0\" class=\"data row1 col0\" >6.12%</td> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row1_col1\" class=\"data row1 col1\" >80.43%</td> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row1_col2\" class=\"data row1 col2\" >3.69%</td> \n",
       "        <td id=\"T_76dd1ad6_4c6b_11e8_8cdc_fa7917587175row1_col3\" class=\"data row1 col3\" >9.76%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f97e787e8d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "udf = rdf.drop_duplicates(subset=['contributor_name']).copy()\n",
    "udf['white'] = udf.pctwhite / 100.0\n",
    "udf['black'] = udf.pctblack / 100.0\n",
    "udf['api'] = udf.pctapi / 100.0\n",
    "udf['hispanic'] = udf.pcthispanic / 100.0\n",
    "gdf = udf.groupby(['year']).agg({'white': 'sum', 'black': 'sum', 'api': 'sum', 'hispanic': 'sum'})\n",
    "gdf.apply(lambda r: r / r.sum(), axis=1).style.format(\"{:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What proportion of the total donation was given by blacks, whites, Hispanics, and Asians?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >hispanic</th> \n",
       "        <th class=\"col_heading level0 col1\" >white</th> \n",
       "        <th class=\"col_heading level0 col2\" >api</th> \n",
       "        <th class=\"col_heading level0 col3\" >black</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175level0_row0\" class=\"row_heading level0 row0\" >2000</th> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row0_col0\" class=\"data row0 col0\" >5.03</td> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row0_col1\" class=\"data row0 col1\" >129.64</td> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row0_col2\" class=\"data row0 col2\" >3.45</td> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row0_col3\" class=\"data row0 col3\" >17.15</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175level0_row1\" class=\"row_heading level0 row1\" >2010</th> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row1_col0\" class=\"data row1 col0\" >17.06</td> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row1_col1\" class=\"data row1 col1\" >326.51</td> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row1_col2\" class=\"data row1 col2\" >10.83</td> \n",
       "        <td id=\"T_3d1eb724_4c88_11e8_8cdc_fa7917587175row1_col3\" class=\"data row1 col3\" >40.35</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f97d4efe510>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf['white'] = rdf.amount * rdf.pctwhite / 100.0\n",
    "rdf['black'] = rdf.amount * rdf.pctblack / 100.0\n",
    "rdf['api'] = rdf.amount * rdf.pctapi / 100.0\n",
    "rdf['hispanic'] = rdf.amount * rdf.pcthispanic / 100.0\n",
    "gdf = rdf.groupby(['year']).agg({'white': 'sum', 'black': 'sum', 'api': 'sum', 'hispanic': 'sum'}) / 10e6\n",
    "gdf.style.format(\"{:0.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >hispanic</th> \n",
       "        <th class=\"col_heading level0 col1\" >white</th> \n",
       "        <th class=\"col_heading level0 col2\" >api</th> \n",
       "        <th class=\"col_heading level0 col3\" >black</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175level0_row0\" class=\"row_heading level0 row0\" >2000</th> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row0_col0\" class=\"data row0 col0\" >3.24%</td> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row0_col1\" class=\"data row0 col1\" >83.49%</td> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row0_col2\" class=\"data row0 col2\" >2.22%</td> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row0_col3\" class=\"data row0 col3\" >11.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175level0_row1\" class=\"row_heading level0 row1\" >2010</th> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row1_col0\" class=\"data row1 col0\" >4.32%</td> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row1_col1\" class=\"data row1 col1\" >82.71%</td> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row1_col2\" class=\"data row1 col2\" >2.74%</td> \n",
       "        <td id=\"T_3e0105a2_4c88_11e8_8cdc_fa7917587175row1_col3\" class=\"data row1 col3\" >10.22%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f97d4efe850>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.apply(lambda r: r / r.sum(), axis=1).style.format(\"{:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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 },
 "nbformat": 4,
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