notebooks/demographic-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demographic Analysis\n",
    "\n",
    "More details on this analysis are [available here](../README.md)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import lxml.html\n",
    "import cssselect\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load \"Voter Power Index\" scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "voter_power_index = pd.read_csv(\"../data/fivethirtyeight/voter-power-index.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abbrev</th>\n",
       "      <th>power</th>\n",
       "      <th>state_raw</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NH</td>\n",
       "      <td>5.6</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>NEW HAMPSHIRE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NV</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>NEVADA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NM</td>\n",
       "      <td>4.7</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>NEW MEXICO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MI</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>MICHIGAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>COLORADO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  abbrev  power      state_raw          state\n",
       "0     NH    5.6  New Hampshire  NEW HAMPSHIRE\n",
       "1     NV    4.9         Nevada         NEVADA\n",
       "2     NM    4.7     New Mexico     NEW MEXICO\n",
       "3     MI    3.1       Michigan       MICHIGAN\n",
       "4     CO    3.1       Colorado       COLORADO"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voter_power_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load state registered voter counts, by demographic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "demo_by_state = pd.read_csv(\n",
    "    \"../data/census/Table4b_2012.csv\",\n",
    "    skiprows=3,\n",
    "    skipfooter=10,\n",
    "    na_values=[\"-\"],\n",
    "    engine='python'\n",
    ").pipe(lambda x: x[x.columns[0:14]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Race and Hispanic origin</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Total Citizen Population</th>\n",
       "      <th>Total registered</th>\n",
       "      <th>Percent registered\n",
       "(Total)</th>\n",
       "      <th>Margin of Error1</th>\n",
       "      <th>Percent registered\n",
       "(Citizen)</th>\n",
       "      <th>Margin of Error</th>\n",
       "      <th>Total voted</th>\n",
       "      <th>Percent voted\n",
       "(Total)</th>\n",
       "      <th>Margin of Error.1</th>\n",
       "      <th>Percent voted\n",
       "(Citizen)</th>\n",
       "      <th>Margin of Error.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>Total</td>\n",
       "      <td>235,248</td>\n",
       "      <td>215,081</td>\n",
       "      <td>153,157</td>\n",
       "      <td>65.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>71.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>132,948</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>113,243</td>\n",
       "      <td>103,022</td>\n",
       "      <td>71,414</td>\n",
       "      <td>63.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>61,551</td>\n",
       "      <td>54.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>59.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>122,005</td>\n",
       "      <td>112,059</td>\n",
       "      <td>81,743</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>72.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>71,397</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>63.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White alone</td>\n",
       "      <td>187,084</td>\n",
       "      <td>173,466</td>\n",
       "      <td>124,697</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>71.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>107,846</td>\n",
       "      <td>57.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>.White non-Hispanic alone</td>\n",
       "      <td>155,615</td>\n",
       "      <td>152,862</td>\n",
       "      <td>112,706</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>73.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>98,041</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State   Race and Hispanic origin Total Population Total Citizen Population  \\\n",
       "0   All                      Total          235,248                  215,081   \n",
       "1   NaN                       Male          113,243                  103,022   \n",
       "2   NaN                     Female          122,005                  112,059   \n",
       "3   NaN                White alone          187,084                  173,466   \n",
       "4   NaN  .White non-Hispanic alone          155,615                  152,862   \n",
       "\n",
       "  Total registered Percent registered\\n(Total) Margin of Error1  \\\n",
       "0          153,157                        65.1              0.3   \n",
       "1           71,414                        63.1              0.4   \n",
       "2           81,743                        67.0              0.4   \n",
       "3          124,697                        66.7              0.3   \n",
       "4          112,706                        72.4              0.3   \n",
       "\n",
       "  Percent registered\\n(Citizen) Margin of Error Total voted  \\\n",
       "0                          71.2             0.3     132,948   \n",
       "1                          69.3             0.4      61,551   \n",
       "2                          72.9             0.4      71,397   \n",
       "3                          71.9             0.3     107,846   \n",
       "4                          73.7             0.3      98,041   \n",
       "\n",
       "  Percent voted\\n(Total) Margin of Error.1 Percent voted\\n(Citizen)  \\\n",
       "0                   56.5               0.3                     61.8   \n",
       "1                   54.4               0.4                     59.7   \n",
       "2                   58.5               0.4                     63.7   \n",
       "3                   57.6               0.3                     62.2   \n",
       "4                   63.0               0.3                     64.1   \n",
       "\n",
       "  Margin of Error.2  \n",
       "0               0.3  \n",
       "1               0.4  \n",
       "2               0.4  \n",
       "3               0.3  \n",
       "4               0.3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_by_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "demo_by_state[\"State\"] = demo_by_state[\"State\"].fillna(method=\"ffill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "demo_by_state.columns = [\n",
    "    \"state\", \"demographic\", \"total_pop\", \"total_citizen_pop\", \"total_registered\", \n",
    "   \"pct_registered\", \"moe_registered\", \"pct_registered_citizen\", \n",
    "   \"moe_registered_citizen\", \"total_voted\", \"pct_voted\", \"moe_voted\", \n",
    "   \"percent_voted_citizen\", \"moe_voted_citizen\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "demo_by_state[\"total_registered\"] = demo_by_state[\"total_registered\"].apply(lambda x: float(str(x).replace(\",\", \"\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>demographic</th>\n",
       "      <th>total_pop</th>\n",
       "      <th>total_citizen_pop</th>\n",
       "      <th>total_registered</th>\n",
       "      <th>pct_registered</th>\n",
       "      <th>moe_registered</th>\n",
       "      <th>pct_registered_citizen</th>\n",
       "      <th>moe_registered_citizen</th>\n",
       "      <th>total_voted</th>\n",
       "      <th>pct_voted</th>\n",
       "      <th>moe_voted</th>\n",
       "      <th>percent_voted_citizen</th>\n",
       "      <th>moe_voted_citizen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>Total</td>\n",
       "      <td>235,248</td>\n",
       "      <td>215,081</td>\n",
       "      <td>153157.0</td>\n",
       "      <td>65.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>71.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>132,948</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>Male</td>\n",
       "      <td>113,243</td>\n",
       "      <td>103,022</td>\n",
       "      <td>71414.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>61,551</td>\n",
       "      <td>54.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>59.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All</td>\n",
       "      <td>Female</td>\n",
       "      <td>122,005</td>\n",
       "      <td>112,059</td>\n",
       "      <td>81743.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>72.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>71,397</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>63.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All</td>\n",
       "      <td>White alone</td>\n",
       "      <td>187,084</td>\n",
       "      <td>173,466</td>\n",
       "      <td>124697.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>71.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>107,846</td>\n",
       "      <td>57.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>62.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All</td>\n",
       "      <td>.White non-Hispanic alone</td>\n",
       "      <td>155,615</td>\n",
       "      <td>152,862</td>\n",
       "      <td>112706.0</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>73.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>98,041</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state                demographic total_pop total_citizen_pop  \\\n",
       "0   All                      Total   235,248           215,081   \n",
       "1   All                       Male   113,243           103,022   \n",
       "2   All                     Female   122,005           112,059   \n",
       "3   All                White alone   187,084           173,466   \n",
       "4   All  .White non-Hispanic alone   155,615           152,862   \n",
       "\n",
       "   total_registered pct_registered moe_registered pct_registered_citizen  \\\n",
       "0          153157.0           65.1            0.3                   71.2   \n",
       "1           71414.0           63.1            0.4                   69.3   \n",
       "2           81743.0           67.0            0.4                   72.9   \n",
       "3          124697.0           66.7            0.3                   71.9   \n",
       "4          112706.0           72.4            0.3                   73.7   \n",
       "\n",
       "  moe_registered_citizen total_voted pct_voted moe_voted  \\\n",
       "0                    0.3     132,948      56.5       0.3   \n",
       "1                    0.4      61,551      54.4       0.4   \n",
       "2                    0.4      71,397      58.5       0.4   \n",
       "3                    0.3     107,846      57.6       0.3   \n",
       "4                    0.3      98,041      63.0       0.3   \n",
       "\n",
       "  percent_voted_citizen moe_voted_citizen  \n",
       "0                  61.8               0.3  \n",
       "1                  59.7               0.4  \n",
       "2                  63.7               0.4  \n",
       "3                  62.2               0.3  \n",
       "4                  64.1               0.3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_by_state.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join the two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "joined = pd.merge(\n",
    "    demo_by_state,\n",
    "    voter_power_index,\n",
    "    on=\"state\",\n",
    "    how=\"left\"\n",
    ").dropna(subset=[\"power\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>ALABAMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demographic</th>\n",
       "      <td>Total</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>White alone</td>\n",
       "      <td>.White non-Hispanic alone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pop</th>\n",
       "      <td>3,594</td>\n",
       "      <td>1,703</td>\n",
       "      <td>1,891</td>\n",
       "      <td>2,580</td>\n",
       "      <td>2,473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_citizen_pop</th>\n",
       "      <td>3,479</td>\n",
       "      <td>1,649</td>\n",
       "      <td>1,831</td>\n",
       "      <td>2,502</td>\n",
       "      <td>2,467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_registered</th>\n",
       "      <td>2556</td>\n",
       "      <td>1201</td>\n",
       "      <td>1354</td>\n",
       "      <td>1878</td>\n",
       "      <td>1866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_registered</th>\n",
       "      <td>71.1</td>\n",
       "      <td>70.5</td>\n",
       "      <td>71.6</td>\n",
       "      <td>72.8</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moe_registered</th>\n",
       "      <td>2.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_registered_citizen</th>\n",
       "      <td>73.5</td>\n",
       "      <td>72.9</td>\n",
       "      <td>74.0</td>\n",
       "      <td>75.1</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moe_registered_citizen</th>\n",
       "      <td>2.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_voted</th>\n",
       "      <td>2,154</td>\n",
       "      <td>1,009</td>\n",
       "      <td>1,145</td>\n",
       "      <td>1,530</td>\n",
       "      <td>1,530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_voted</th>\n",
       "      <td>59.9</td>\n",
       "      <td>59.2</td>\n",
       "      <td>60.6</td>\n",
       "      <td>59.3</td>\n",
       "      <td>61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moe_voted</th>\n",
       "      <td>2.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_voted_citizen</th>\n",
       "      <td>61.9</td>\n",
       "      <td>61.2</td>\n",
       "      <td>62.6</td>\n",
       "      <td>61.2</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moe_voted_citizen</th>\n",
       "      <td>2.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbrev</th>\n",
       "      <td>AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\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>state_raw</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             11       12       13           14  \\\n",
       "state                   ALABAMA  ALABAMA  ALABAMA      ALABAMA   \n",
       "demographic               Total     Male   Female  White alone   \n",
       "total_pop                 3,594    1,703    1,891        2,580   \n",
       "total_citizen_pop         3,479    1,649    1,831        2,502   \n",
       "total_registered           2556     1201     1354         1878   \n",
       "pct_registered             71.1     70.5     71.6         72.8   \n",
       "moe_registered              2.2      3.3      3.1          2.6   \n",
       "pct_registered_citizen     73.5     72.9     74.0         75.1   \n",
       "moe_registered_citizen      2.2      3.2      3.0          2.6   \n",
       "total_voted               2,154    1,009    1,145        1,530   \n",
       "pct_voted                  59.9     59.2     60.6         59.3   \n",
       "moe_voted                   2.4      3.5      3.3          2.9   \n",
       "percent_voted_citizen      61.9     61.2     62.6         61.2   \n",
       "moe_voted_citizen           2.4      3.5      3.3          2.9   \n",
       "abbrev                       AL       AL       AL           AL   \n",
       "power                         0        0        0            0   \n",
       "state_raw               Alabama  Alabama  Alabama      Alabama   \n",
       "\n",
       "                                               15  \n",
       "state                                     ALABAMA  \n",
       "demographic             .White non-Hispanic alone  \n",
       "total_pop                                   2,473  \n",
       "total_citizen_pop                           2,467  \n",
       "total_registered                             1866  \n",
       "pct_registered                               75.5  \n",
       "moe_registered                                2.6  \n",
       "pct_registered_citizen                       75.6  \n",
       "moe_registered_citizen                        2.5  \n",
       "total_voted                                 1,530  \n",
       "pct_voted                                    61.9  \n",
       "moe_voted                                     2.9  \n",
       "percent_voted_citizen                        62.0  \n",
       "moe_voted_citizen                             2.9  \n",
       "abbrev                                         AL  \n",
       "power                                           0  \n",
       "state_raw                                 Alabama  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "joined[\"demo_power\"] =  joined[\"total_registered\"] * joined[\"power\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "demo_grp = joined.groupby(\"demographic\")\n",
    "# Find the aggregate amount of voting power and total number of registered voters for each demographic\n",
    "demo_power = pd.DataFrame({\n",
    "    \"total_power\": demo_grp[\"demo_power\"].sum(),\n",
    "    \"total_registered\": demo_grp[\"total_registered\"].sum(),\n",
    "})\n",
    "# The \"power ratio\" is the total power for a demographic divided by the number of registered voters\n",
    "demo_power[\"power_ratio\"] = demo_power[\"total_power\"] / demo_power[\"total_registered\"]\n",
    "# Set all of the power ratios in line with the overall power ratio \n",
    "demo_power[\"power_ratio_norm\"] = demo_power[\"power_ratio\"] / demo_power.loc[\"Total\"][\"power_ratio\"]\n",
    "demo_power = demo_power.sort_values(\"power_ratio_norm\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gender = [\"Male\", \"Female\"]\n",
    "non_race = [\"Male\", \"Female\", \"Total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>power_ratio_norm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demographic</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             power_ratio_norm\n",
       "demographic                  \n",
       "Male                     1.01\n",
       "Female                   1.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_power.loc[gender][[\"power_ratio_norm\"]].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>power_ratio_norm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demographic</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>.White non-Hispanic alone</th>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White alone</th>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White alone or in combination</th>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black alone</th>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black alone or in combination</th>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic (of any race)</th>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian alone or in combination</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian alone</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               power_ratio_norm\n",
       "demographic                                    \n",
       ".White non-Hispanic alone                  1.05\n",
       "White alone                                1.03\n",
       "White alone or in combination              1.03\n",
       "Black alone                                0.91\n",
       "Black alone or in combination              0.91\n",
       "Hispanic (of any race)                     0.87\n",
       "Asian alone or in combination              0.58\n",
       "Asian alone                                0.58"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_power[\n",
    "    ~demo_power.index.isin(non_race)\n",
    "][[\"power_ratio_norm\"]].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kv/0cSetLaiNdsff7iGhcKPIz4ChJe0oaQxoEvhI9t1A9Aiwm6fDsb/s0+YPqZwE/l7Rl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\nZZvDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dfa2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = demo_power[\n",
    "    ~demo_power.index.isin(non_race)\n",
    "][[\"power_ratio_norm\"]].round(2).plot(kind=\"bar\", figsize=(10,5))\n",
    "ax.set_title(\"Power Ratio Of An Individual Voter For Each Demographic\", size=14)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Asian and Hispanic registered voters, by state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>demographic</th>\n",
       "      <th>total_registered</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>384.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>TEXAS</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>NEW JERSEY</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>243.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>ILLINOIS</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>HAWAII</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>FLORIDA</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>VIRGINIA</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>MASSACHUSETTS</td>\n",
       "      <td>Asian alone</td>\n",
       "      <td>119.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state  demographic  total_registered  power\n",
       "61      CALIFORNIA  Asian alone            1645.0    0.0\n",
       "369       NEW YORK  Asian alone             384.0    0.0\n",
       "490          TEXAS  Asian alone             299.0    0.0\n",
       "347     NEW JERSEY  Asian alone             243.0    0.3\n",
       "160       ILLINOIS  Asian alone             228.0    0.0\n",
       "138         HAWAII  Asian alone             227.0    0.1\n",
       "534     WASHINGTON  Asian alone             178.0    0.2\n",
       "116        FLORIDA  Asian alone             174.0    2.5\n",
       "523       VIRGINIA  Asian alone             136.0    2.3\n",
       "248  MASSACHUSETTS  Asian alone             119.0    0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[\n",
    "    joined[\"demographic\"] == \"Asian alone\"\n",
    "].sort_values(\"total_registered\", ascending=False).head(10)[[\"state\", \"demographic\", \"total_registered\", \"power\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>demographic</th>\n",
       "      <th>total_registered</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>3684.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>TEXAS</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>2652.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>FLORIDA</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>983.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ARIZONA</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>516.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>NEW JERSEY</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>ILLINOIS</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>415.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>NEW MEXICO</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>349.0</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>COLORADO</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>284.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>PENNSYLVANIA</td>\n",
       "      <td>Hispanic (of any race)</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state             demographic  total_registered  power\n",
       "62     CALIFORNIA  Hispanic (of any race)            3684.0    0.0\n",
       "491         TEXAS  Hispanic (of any race)            2652.0    0.0\n",
       "117       FLORIDA  Hispanic (of any race)            1622.0    2.5\n",
       "370      NEW YORK  Hispanic (of any race)             983.0    0.0\n",
       "40        ARIZONA  Hispanic (of any race)             516.0    1.6\n",
       "348    NEW JERSEY  Hispanic (of any race)             468.0    0.3\n",
       "161      ILLINOIS  Hispanic (of any race)             415.0    0.0\n",
       "359    NEW MEXICO  Hispanic (of any race)             349.0    4.7\n",
       "73       COLORADO  Hispanic (of any race)             284.0    3.1\n",
       "436  PENNSYLVANIA  Hispanic (of any race)             225.0    2.7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[\n",
    "    joined[\"demographic\"] == \"Hispanic (of any race)\"\n",
    "].sort_values(\"total_registered\", ascending=False).head(10)[[\"state\", \"demographic\", \"total_registered\", \"power\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Education Power\n",
    "\n",
    "*Note: This section uses data from the [Current Population Survey](http://www.census.gov/cps/data/cpstablecreator.html). Instead of looking at all **registered voters**, which is not possible through the survey, we are analyzing all **adult citizens**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Current Population Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cps_edu = pd.read_csv(\"../data/census/CPS Data - educational_attainment.csv\")\\\n",
    "    .dropna(subset=[\"Total\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cps_edu[\"State\"] = cps_edu[\"State\"].fillna(method=\"ffill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cps_education = cps_edu\\\n",
    "    .set_index([\"State\", \"Race\"])\\\n",
    "    .stack()\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns={\"level_2\": \"education\", 0: \"total_citizens_raw\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cps_education[\"total_citizens\"] = cps_education[\"total_citizens_raw\"].apply(lambda x: int(str(x).replace(\",\", \"\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def is_college_educated(education):\n",
    "    if education in [\"No high school diploma\", \"High school or equivalent\"]:\n",
    "        return False\n",
    "    elif education in [\"Some college less than 4-yr degree\", \"Bachelor's degree or higher\"]:\n",
    "        return True\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cps_education[\"is_college_educated\"] = cps_education[\"education\"].apply(is_college_educated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Race</th>\n",
       "      <th>education</th>\n",
       "      <th>total_citizens_raw</th>\n",
       "      <th>total_citizens</th>\n",
       "      <th>is_college_educated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Totals</td>\n",
       "      <td>Total</td>\n",
       "      <td>244,806,535</td>\n",
       "      <td>244806535</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Totals</td>\n",
       "      <td>No high school diploma</td>\n",
       "      <td>28,653,227</td>\n",
       "      <td>28653227</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Totals</td>\n",
       "      <td>High school or equivalent</td>\n",
       "      <td>70,882,009</td>\n",
       "      <td>70882009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Totals</td>\n",
       "      <td>Some college less than 4-yr degree</td>\n",
       "      <td>70,150,254</td>\n",
       "      <td>70150254</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Totals</td>\n",
       "      <td>Bachelor's degree or higher</td>\n",
       "      <td>75,121,045</td>\n",
       "      <td>75121045</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    State    Race                           education total_citizens_raw  \\\n",
       "0  Totals  Totals                               Total        244,806,535   \n",
       "1  Totals  Totals              No high school diploma         28,653,227   \n",
       "2  Totals  Totals           High school or equivalent         70,882,009   \n",
       "3  Totals  Totals  Some college less than 4-yr degree         70,150,254   \n",
       "4  Totals  Totals         Bachelor's degree or higher         75,121,045   \n",
       "\n",
       "   total_citizens is_college_educated  \n",
       "0       244806535                None  \n",
       "1        28653227               False  \n",
       "2        70882009               False  \n",
       "3        70150254                True  \n",
       "4        75121045                True  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_education.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge with \"Voter Power Index\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "joined_edu = pd.merge(\n",
    "    cps_education,\n",
    "    voter_power_index,\n",
    "    left_on=\"State\",\n",
    "    right_on=\"abbrev\",\n",
    "    how=\"left\"\n",
    ").dropna(subset=[\"power\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joined_edu[\"demo_power\"] = joined_edu[\"total_citizens\"] * joined_edu[\"power\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create groups by race and education level\n",
    "edu_grp = joined_edu.groupby([\"Race\", \"is_college_educated\"])\n",
    "# Find the total power and the total number of citizens for each race-plus-education group\n",
    "edu_power = pd.DataFrame({\n",
    "    \"total_power\": edu_grp[\"demo_power\"].sum(),\n",
    "    \"total_citizens\": edu_grp[\"total_citizens\"].sum(),\n",
    "})\n",
    "# Find the ratio of the total power to the total number of adult citizens\n",
    "edu_power[\"power_ratio\"] = edu_power[\"total_power\"] / edu_power[\"total_citizens\"]\n",
    "edu_power = edu_power.sort_values(\"power_ratio\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>is_college_educated</th>\n",
       "      <th>total_citizens</th>\n",
       "      <th>total_power</th>\n",
       "      <th>power_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>White alone</td>\n",
       "      <td>True</td>\n",
       "      <td>114703979</td>\n",
       "      <td>105893512.2</td>\n",
       "      <td>0.923190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White alone</td>\n",
       "      <td>False</td>\n",
       "      <td>77281676</td>\n",
       "      <td>69947423.3</td>\n",
       "      <td>0.905097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Race is_college_educated  total_citizens  total_power  power_ratio\n",
       "2  White alone                True       114703979  105893512.2     0.923190\n",
       "3  White alone               False        77281676   69947423.3     0.905097"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_reset = edu_power.reset_index()\n",
    "edu_reset[\n",
    "    edu_reset[\"Race\"] == \"White alone\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "---\n",
    "\n",
    "---"
   ]
  }
 ],
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