Blog_Binning_cat_features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.125179Z",
     "start_time": "2021-03-30T17:05:42.889030Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.447129Z",
     "start_time": "2021-03-30T17:05:44.127343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>voting_method</th>\n",
       "      <th>party_cd</th>\n",
       "      <th>birth_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABSENTEE ONESTOP</td>\n",
       "      <td>REP</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>UNA</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABSENTEE ONESTOP</td>\n",
       "      <td>DEM</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>REP</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABSENTEE ONESTOP</td>\n",
       "      <td>DEM</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      voting_method party_cd birth_state\n",
       "0  ABSENTEE ONESTOP      REP          NC\n",
       "1               NaN      UNA          MA\n",
       "2  ABSENTEE ONESTOP      DEM          MN\n",
       "3               NaN      REP          NY\n",
       "4  ABSENTEE ONESTOP      DEM          NC"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/UC_gen_2020.gz',\n",
    "                     dtype={'voting_method': str},\n",
    "                     usecols=['party_cd',\n",
    "                              'birth_state', \n",
    "                              'voting_method'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.473301Z",
     "start_time": "2021-03-30T17:05:44.449698Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168070 entries, 0 to 168069\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   voting_method  131483 non-null  object\n",
      " 1   party_cd       168070 non-null  object\n",
      " 2   birth_state    127298 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.487630Z",
     "start_time": "2021-03-30T17:05:44.477324Z"
    }
   },
   "outputs": [],
   "source": [
    "df['voting_method'].fillna(value='No Vote', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.498182Z",
     "start_time": "2021-03-30T17:05:44.489486Z"
    }
   },
   "outputs": [
    {
     "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>voting_method</th>\n",
       "      <th>party_cd</th>\n",
       "      <th>birth_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABSENTEE ONESTOP</td>\n",
       "      <td>REP</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Vote</td>\n",
       "      <td>UNA</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABSENTEE ONESTOP</td>\n",
       "      <td>DEM</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Vote</td>\n",
       "      <td>REP</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABSENTEE ONESTOP</td>\n",
       "      <td>DEM</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      voting_method party_cd birth_state\n",
       "0  ABSENTEE ONESTOP      REP          NC\n",
       "1           No Vote      UNA          MA\n",
       "2  ABSENTEE ONESTOP      DEM          MN\n",
       "3           No Vote      REP          NY\n",
       "4  ABSENTEE ONESTOP      DEM          NC"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.511298Z",
     "start_time": "2021-03-30T17:05:44.499781Z"
    }
   },
   "outputs": [],
   "source": [
    "df['birth_state'].fillna(value='Missing', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.539165Z",
     "start_time": "2021-03-30T17:05:44.514163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168070 entries, 0 to 168069\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   voting_method  168070 non-null  object\n",
      " 1   party_cd       168070 non-null  object\n",
      " 2   birth_state    168070 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.548930Z",
     "start_time": "2021-03-30T17:05:44.544921Z"
    }
   },
   "outputs": [],
   "source": [
    "def check_unique(col, df, dropna=False):\n",
    "\n",
    "    unique_vals = pd.DataFrame()\n",
    "    unique_vals['count'] = pd.Series(df[col].value_counts(dropna=dropna))\n",
    "    unique_vals['%'] = pd.Series(round(df[col].value_counts(normalize=True, dropna=dropna)*100, 2))\n",
    "    \n",
    "    return unique_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.558205Z",
     "start_time": "2021-03-30T17:05:44.552693Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_count(variable, data, rotation=0, ha='center', figsize=(8,6)):\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    ax = sns.countplot(x=data[variable],\n",
    "                       order=data[variable].value_counts().index,\n",
    "                       palette='nipy_spectral')\n",
    "    \n",
    "    ax.set_xticklabels(ax.get_xticklabels(),\n",
    "                       rotation=rotation,\n",
    "                       horizontalalignment=ha,\n",
    "                       fontsize=12, weight='bold')\n",
    "    ax.set_title(f'{variable.title()} Counts', \n",
    "                 fontsize=16,\n",
    "                 weight='bold')\n",
    "    ax.set_xlabel(f'{variable}',\n",
    "                  fontsize=14,\n",
    "                  weight='bold')\n",
    "    ax.set_ylabel('Count', fontsize=14, weight='bold')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:11:02.180573Z",
     "start_time": "2021-03-30T17:11:01.814457Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('party_cd', df)\n",
    "plt.savefig('Figures/blog_bin_cat_features/party_cd_count', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.843850Z",
     "start_time": "2021-03-30T17:05:44.810470Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REP</th>\n",
       "      <td>69079</td>\n",
       "      <td>41.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNA</th>\n",
       "      <td>57488</td>\n",
       "      <td>34.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>40340</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIB</th>\n",
       "      <td>995</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CST</th>\n",
       "      <td>96</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRE</th>\n",
       "      <td>72</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count      %\n",
       "REP  69079  41.10\n",
       "UNA  57488  34.20\n",
       "DEM  40340  24.00\n",
       "LIB    995   0.59\n",
       "CST     96   0.06\n",
       "GRE     72   0.04"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check distribution\n",
    "check_unique('party_cd', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:44.936023Z",
     "start_time": "2021-03-30T17:05:44.846493Z"
    }
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rep</th>\n",
       "      <td>69079</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>58651</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dem</th>\n",
       "      <td>40340</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count     %\n",
       "Rep    69079  41.1\n",
       "Other  58651  34.9\n",
       "Dem    40340  24.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create new column grouping most infrequent party categories\n",
    "  ## into same group as those who are unaffiliated\n",
    "df['party_grp'] = np.where(df['party_cd'].isin(['REP', 'DEM']),\n",
    "                           df['party_cd'].str.title(),\n",
    "                           'Other')\n",
    "\n",
    "## Check distribution\n",
    "check_unique('party_grp', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:45.141654Z",
     "start_time": "2021-03-30T17:05:44.937950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('party_grp', df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:41:43.330579Z",
     "start_time": "2021-03-30T17:41:42.943656Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('voting_method', df, rotation=45)\n",
    "plt.savefig('Figures/blog_bin_cat_features/voting_method_count', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:45.398614Z",
     "start_time": "2021-03-30T17:05:45.366476Z"
    }
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABSENTEE ONESTOP</th>\n",
       "      <td>88137</td>\n",
       "      <td>52.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Vote</th>\n",
       "      <td>36587</td>\n",
       "      <td>21.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN-PERSON</th>\n",
       "      <td>19813</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABSENTEE BY MAIL</th>\n",
       "      <td>19647</td>\n",
       "      <td>11.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABSENTEE CURBSIDE</th>\n",
       "      <td>2951</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROVISIONAL</th>\n",
       "      <td>380</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSFER</th>\n",
       "      <td>353</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURBSIDE</th>\n",
       "      <td>202</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      %\n",
       "ABSENTEE ONESTOP   88137  52.44\n",
       "No Vote            36587  21.77\n",
       "IN-PERSON          19813  11.79\n",
       "ABSENTEE BY MAIL   19647  11.69\n",
       "ABSENTEE CURBSIDE   2951   1.76\n",
       "PROVISIONAL          380   0.23\n",
       "TRANSFER             353   0.21\n",
       "CURBSIDE             202   0.12"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_unique('voting_method', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:45.418984Z",
     "start_time": "2021-03-30T17:05:45.400505Z"
    }
   },
   "outputs": [],
   "source": [
    "## Group original vote methods into more general categories in a new variable\n",
    " # Map the original categoriess to new ones\n",
    "vote_method_map = {'ABSENTEE ONESTOP': 'Early',\n",
    "                   'IN-PERSON': 'Election Day',\n",
    "                   'ABSENTEE BY MAIL': 'Early',\n",
    "                   'ABSENTEE CURBSIDE': 'Early',\n",
    "                   'TRANSFER': 'Other',\n",
    "                   'PROVISIONAL': 'Other',\n",
    "                   'CURBSIDE': 'Election Day',\n",
    "                   'No Vote': 'No Vote'}\n",
    "\n",
    "# Apply the map to create new vote_method_cat column\n",
    "df['vote_method_cat'] = df['voting_method'].map(vote_method_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:45.451702Z",
     "start_time": "2021-03-30T17:05:45.421119Z"
    }
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Early</th>\n",
       "      <td>110735</td>\n",
       "      <td>65.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Vote</th>\n",
       "      <td>36587</td>\n",
       "      <td>21.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Election Day</th>\n",
       "      <td>20015</td>\n",
       "      <td>11.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>733</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count      %\n",
       "Early         110735  65.89\n",
       "No Vote        36587  21.77\n",
       "Election Day   20015  11.91\n",
       "Other            733   0.44"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_unique('vote_method_cat', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:45.645785Z",
     "start_time": "2021-03-30T17:05:45.453920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('vote_method_cat', df, rotation=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T18:17:29.775706Z",
     "start_time": "2021-03-30T18:17:29.739271Z"
    }
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>46367</td>\n",
       "      <td>27.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>40772</td>\n",
       "      <td>24.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>13211</td>\n",
       "      <td>7.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>10042</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>4753</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>4492</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>4336</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>4334</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>3786</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>3351</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>3046</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>2430</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>2418</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>2336</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>1842</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count      %\n",
       "NC       46367  27.59\n",
       "Missing  40772  24.26\n",
       "NY       13211   7.86\n",
       "OC       10042   5.97\n",
       "SC        4753   2.83\n",
       "NJ        4492   2.67\n",
       "OH        4336   2.58\n",
       "PA        4334   2.58\n",
       "FL        3786   2.25\n",
       "CA        3351   1.99\n",
       "VA        3046   1.81\n",
       "MI        2430   1.45\n",
       "IL        2418   1.44\n",
       "GA        2336   1.39\n",
       "TX        1842   1.10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_unique('birth_state', df).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T18:19:21.834961Z",
     "start_time": "2021-03-30T18:19:21.819302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['birth_state'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T18:22:15.628581Z",
     "start_time": "2021-03-30T18:22:13.784169Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('birth_state', df)\n",
    "plt.savefig('Figures/blog_bin_cat_features/birth_state_count', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:46.728161Z",
     "start_time": "2021-03-30T17:05:46.722753Z"
    }
   },
   "outputs": [],
   "source": [
    "## Define function for grouping birth state/country into categories\n",
    "def get_birth_reg(state):\n",
    "    \n",
    "    # check if U.S. territory or out of country\n",
    "    if state in ['AS', 'GU', 'MP', 'PR', 'VI', 'OC']:\n",
    "        return 'Other'\n",
    "    \n",
    "    # the rest of the categories are based on U.S. Census Bureau regions\n",
    "    elif state in ['CT', 'ME', 'MA', 'NH', 'RI', 'VT',\n",
    "                     'NJ', 'NY', 'PA']:\n",
    "        return 'Northeast'\n",
    "    \n",
    "    elif state in ['DE', 'FL', 'GA', 'MD', 'NC', 'SC', 'VA', \n",
    "                     'DC', 'WV', 'AL', 'KY', 'MS', 'TN', 'AR',\n",
    "                     'LA', 'OK', 'TX']:\n",
    "        return 'South'\n",
    "    \n",
    "    elif state in ['IL', 'IN', 'MI', 'OH', 'WI',\n",
    "                     'IA', 'KS', 'MN', 'MO', 'NE', 'ND', 'SD']:\n",
    "        return 'Midwest'\n",
    "    \n",
    "    elif state in ['AZ', 'CO', 'ID', 'MT', 'NV', 'NM', 'UT',\n",
    "                     'WY', 'AK', 'CA', 'HI', 'OR', 'WA']:\n",
    "        return 'West'\n",
    "    \n",
    "    else:\n",
    "        return 'Missing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:46.838369Z",
     "start_time": "2021-03-30T17:05:46.730326Z"
    }
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>70810</td>\n",
       "      <td>42.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>40773</td>\n",
       "      <td>24.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northeast</th>\n",
       "      <td>26291</td>\n",
       "      <td>15.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Midwest</th>\n",
       "      <td>13749</td>\n",
       "      <td>8.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>10535</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>5912</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      %\n",
       "South      70810  42.13\n",
       "Missing    40773  24.26\n",
       "Northeast  26291  15.64\n",
       "Midwest    13749   8.18\n",
       "Other      10535   6.27\n",
       "West        5912   3.52"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a new column grouping birth_state into U.S. Census regions,\n",
    "  ## this time lumping territories and out of country into 'Other'\n",
    "df['birth_reg'] = df['birth_state'].apply(get_birth_reg)\n",
    "check_unique('birth_reg', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T17:05:47.079178Z",
     "start_time": "2021-03-30T17:05:46.840490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('birth_reg', df, rotation=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "learn-env",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}