scripts/04_nc_race_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ethnicolr: NC Voter Registration Name Model (Race only)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build a separate model that only predicts the race_code and takes out 'U', again assuming it to be 'missing at random.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NGRAMS = (2, 3)\n",
    "SAMPLE = 1000000\n",
    "EPOCHS = 15\n",
    "\n",
    "# Florida voter\n",
    "df = pd.read_csv('/opt/data/nc_voter/nc_voter_name_gender_race_ethnic_age.csv', usecols=['last_name', 'first_name', 'race_code'], nrows=SAMPLE * 2)\n",
    "df.dropna(subset=['first_name', 'last_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "https://s3.amazonaws.com/dl.ncsbe.gov/data/layout_ncvoter.txt\n",
    "\n",
    "/* ****************************************************************************\n",
    "Race codes\n",
    "race               description\n",
    "*******************************************************************************\n",
    "A                  ASIAN\n",
    "B                  BLACK or AFRICAN AMERICAN\n",
    "I                  AMERICAN INDIAN or ALASKA NATIVE\n",
    "M                  TWO or MORE RACES \n",
    "O                  OTHER\n",
    "P                  NATIVE HAWAIIAN or PACIFIC ISLANDER\n",
    "U                  UNDESIGNATED\n",
    "W                  WHITE\n",
    "**************************************************************************** */\n",
    "\n",
    "\n",
    "/* ****************************************************************************\n",
    "Ethnic codes\n",
    "ethnicity          description\n",
    "*******************************************************************************\n",
    "HL                 HISPANIC or LATINO\n",
    "NL                 NOT HISPANIC or NOT LATINO\n",
    "UN                 UNDESIGNATED\n",
    "**************************************************************************** */\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop U and UN from race and ethnic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AABEL</td>\n",
       "      <td>EVELYN</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AARON</td>\n",
       "      <td>RICHARD</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AARON</td>\n",
       "      <td>SANDRA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AARON</td>\n",
       "      <td>WILLIE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>GENA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ABAD</td>\n",
       "      <td>PRISCILLA</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>COLLEEN</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>JACK</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>MYRA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ABADILLA</td>\n",
       "      <td>STEVE</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ABASHIAN</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ABASHIAN</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>FALISA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>JENNIFER</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>RAFAT</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>CAMILLE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>RONALD</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>RONALD</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>TRACY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ABBEY</td>\n",
       "      <td>BRENT</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ABBEY</td>\n",
       "      <td>DEMETRA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>BEVERLY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>BRENDA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>DEREK</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999966</th>\n",
       "      <td>LEACH</td>\n",
       "      <td>WILLIAM</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999967</th>\n",
       "      <td>LEACH</td>\n",
       "      <td>YOLANDA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999968</th>\n",
       "      <td>LEACH SMITH</td>\n",
       "      <td>SARAH</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999969</th>\n",
       "      <td>LEACH-KEYES</td>\n",
       "      <td>DALFANEE</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999970</th>\n",
       "      <td>LEACHMAN</td>\n",
       "      <td>COLBY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999971</th>\n",
       "      <td>LEACHMAN</td>\n",
       "      <td>LORI</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999972</th>\n",
       "      <td>LEACOCK</td>\n",
       "      <td>DONNA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999973</th>\n",
       "      <td>LEACOCK</td>\n",
       "      <td>JAMIE</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999975</th>\n",
       "      <td>LEACOCK</td>\n",
       "      <td>JOSHUA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999977</th>\n",
       "      <td>LEADBETTER</td>\n",
       "      <td>DIANE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999978</th>\n",
       "      <td>LEADBETTER</td>\n",
       "      <td>LAURIE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999980</th>\n",
       "      <td>LEADEM</td>\n",
       "      <td>TIMOTHY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999981</th>\n",
       "      <td>LEADER</td>\n",
       "      <td>CHRISTOPHER</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999982</th>\n",
       "      <td>LEADER</td>\n",
       "      <td>MATTHEW</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999983</th>\n",
       "      <td>LEAF</td>\n",
       "      <td>CINDY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999984</th>\n",
       "      <td>LEAF</td>\n",
       "      <td>GREGORY</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999985</th>\n",
       "      <td>LEAF PRINCE</td>\n",
       "      <td>PATRICIA</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999986</th>\n",
       "      <td>LEAGUE</td>\n",
       "      <td>VICTOR</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999987</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>BROOKE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999988</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>KATHERINE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999989</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>MARGARET</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999990</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999991</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>PETER</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999992</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>WILLIAM</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999993</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>YVETTE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999995</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTHONY</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999996</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTOINETTE</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999997</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BETTY</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999998</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BILLY</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999999</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BRANDON</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1935862 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           last_name   first_name race_code\n",
       "0              AABEL       EVELYN         W\n",
       "1              AARON    CHRISTINA         W\n",
       "2              AARON      CLAUDIA         W\n",
       "3              AARON        JAMES         W\n",
       "4              AARON       NATHAN         W\n",
       "5              AARON      RICHARD         W\n",
       "6              AARON       SANDRA         W\n",
       "7              AARON       WILLIE         W\n",
       "8           AARONSON         GENA         W\n",
       "9           AARONSON      MICHAEL         W\n",
       "10              ABAD    PRISCILLA         O\n",
       "11            ABADIE      COLLEEN         M\n",
       "12            ABADIE         JACK         W\n",
       "13            ABADIE         MYRA         W\n",
       "14          ABADILLA        STEVE         O\n",
       "15          ABASHIAN       ASHLEY         W\n",
       "16          ABASHIAN      MICHAEL         W\n",
       "17             ABBAS       FALISA         B\n",
       "18             ABBAS     JENNIFER         W\n",
       "19             ABBAS        RAFAT         A\n",
       "20        ABBATECOLA      CAMILLE         W\n",
       "21        ABBATECOLA       RONALD         W\n",
       "22        ABBATECOLA       RONALD         W\n",
       "23        ABBATECOLA        TRACY         W\n",
       "24             ABBEY        BRENT         W\n",
       "25             ABBEY      DEMETRA         W\n",
       "26            ABBOTT       ASHLEY         W\n",
       "27            ABBOTT      BEVERLY         W\n",
       "28            ABBOTT       BRENDA         W\n",
       "29            ABBOTT        DEREK         W\n",
       "...              ...          ...       ...\n",
       "1999966        LEACH      WILLIAM         W\n",
       "1999967        LEACH      YOLANDA         B\n",
       "1999968  LEACH SMITH        SARAH         W\n",
       "1999969  LEACH-KEYES     DALFANEE         B\n",
       "1999970     LEACHMAN        COLBY         W\n",
       "1999971     LEACHMAN         LORI         W\n",
       "1999972      LEACOCK        DONNA         B\n",
       "1999973      LEACOCK        JAMIE         B\n",
       "1999975      LEACOCK       JOSHUA         B\n",
       "1999977   LEADBETTER        DIANE         W\n",
       "1999978   LEADBETTER       LAURIE         W\n",
       "1999980       LEADEM      TIMOTHY         W\n",
       "1999981       LEADER  CHRISTOPHER         W\n",
       "1999982       LEADER      MATTHEW         W\n",
       "1999983         LEAF        CINDY         W\n",
       "1999984         LEAF      GREGORY         W\n",
       "1999985  LEAF PRINCE     PATRICIA         W\n",
       "1999986       LEAGUE       VICTOR         W\n",
       "1999987        LEAHY       BROOKE         W\n",
       "1999988        LEAHY    KATHERINE         W\n",
       "1999989        LEAHY     MARGARET         W\n",
       "1999990        LEAHY      MICHAEL         W\n",
       "1999991        LEAHY        PETER         W\n",
       "1999992        LEAHY      WILLIAM         W\n",
       "1999993        LEAHY       YVETTE         W\n",
       "1999995         LEAK      ANTHONY         B\n",
       "1999996         LEAK   ANTOINETTE         B\n",
       "1999997         LEAK        BETTY         B\n",
       "1999998         LEAK        BILLY         B\n",
       "1999999         LEAK      BRANDON         B\n",
       "\n",
       "[1935862 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df.race_code=='U'].index, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race_code\n",
       "A      16176\n",
       "B     376291\n",
       "I       7761\n",
       "M      12004\n",
       "O      41580\n",
       "W    1482050\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race_code']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_code</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AABEL</td>\n",
       "      <td>EVELYN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AARON</td>\n",
       "      <td>RICHARD</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AARON</td>\n",
       "      <td>SANDRA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AARON</td>\n",
       "      <td>WILLIE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>GENA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ABAD</td>\n",
       "      <td>PRISCILLA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>COLLEEN</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>JACK</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>MYRA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ABADILLA</td>\n",
       "      <td>STEVE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ABASHIAN</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ABASHIAN</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>FALISA</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>JENNIFER</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>RAFAT</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>CAMILLE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>RONALD</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>RONALD</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>TRACY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ABBEY</td>\n",
       "      <td>BRENT</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ABBEY</td>\n",
       "      <td>DEMETRA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>BEVERLY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>BRENDA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>DEREK</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999966</th>\n",
       "      <td>LEACH</td>\n",
       "      <td>WILLIAM</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999967</th>\n",
       "      <td>LEACH</td>\n",
       "      <td>YOLANDA</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999968</th>\n",
       "      <td>LEACH SMITH</td>\n",
       "      <td>SARAH</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999969</th>\n",
       "      <td>LEACH-KEYES</td>\n",
       "      <td>DALFANEE</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999970</th>\n",
       "      <td>LEACHMAN</td>\n",
       "      <td>COLBY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999971</th>\n",
       "      <td>LEACHMAN</td>\n",
       "      <td>LORI</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999972</th>\n",
       "      <td>LEACOCK</td>\n",
       "      <td>DONNA</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999973</th>\n",
       "      <td>LEACOCK</td>\n",
       "      <td>JAMIE</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999975</th>\n",
       "      <td>LEACOCK</td>\n",
       "      <td>JOSHUA</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999977</th>\n",
       "      <td>LEADBETTER</td>\n",
       "      <td>DIANE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999978</th>\n",
       "      <td>LEADBETTER</td>\n",
       "      <td>LAURIE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999980</th>\n",
       "      <td>LEADEM</td>\n",
       "      <td>TIMOTHY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999981</th>\n",
       "      <td>LEADER</td>\n",
       "      <td>CHRISTOPHER</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999982</th>\n",
       "      <td>LEADER</td>\n",
       "      <td>MATTHEW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999983</th>\n",
       "      <td>LEAF</td>\n",
       "      <td>CINDY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999984</th>\n",
       "      <td>LEAF</td>\n",
       "      <td>GREGORY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999985</th>\n",
       "      <td>LEAF PRINCE</td>\n",
       "      <td>PATRICIA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999986</th>\n",
       "      <td>LEAGUE</td>\n",
       "      <td>VICTOR</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999987</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>BROOKE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999988</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>KATHERINE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999989</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>MARGARET</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999990</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999991</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>PETER</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999992</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>WILLIAM</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999993</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>YVETTE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999995</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTHONY</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999996</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTOINETTE</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999997</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BETTY</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999998</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BILLY</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999999</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BRANDON</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1935862 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           last_name   first_name race_code race\n",
       "0              AABEL       EVELYN         W    W\n",
       "1              AARON    CHRISTINA         W    W\n",
       "2              AARON      CLAUDIA         W    W\n",
       "3              AARON        JAMES         W    W\n",
       "4              AARON       NATHAN         W    W\n",
       "5              AARON      RICHARD         W    W\n",
       "6              AARON       SANDRA         W    W\n",
       "7              AARON       WILLIE         W    W\n",
       "8           AARONSON         GENA         W    W\n",
       "9           AARONSON      MICHAEL         W    W\n",
       "10              ABAD    PRISCILLA         O    O\n",
       "11            ABADIE      COLLEEN         M    M\n",
       "12            ABADIE         JACK         W    W\n",
       "13            ABADIE         MYRA         W    W\n",
       "14          ABADILLA        STEVE         O    O\n",
       "15          ABASHIAN       ASHLEY         W    W\n",
       "16          ABASHIAN      MICHAEL         W    W\n",
       "17             ABBAS       FALISA         B    B\n",
       "18             ABBAS     JENNIFER         W    W\n",
       "19             ABBAS        RAFAT         A    A\n",
       "20        ABBATECOLA      CAMILLE         W    W\n",
       "21        ABBATECOLA       RONALD         W    W\n",
       "22        ABBATECOLA       RONALD         W    W\n",
       "23        ABBATECOLA        TRACY         W    W\n",
       "24             ABBEY        BRENT         W    W\n",
       "25             ABBEY      DEMETRA         W    W\n",
       "26            ABBOTT       ASHLEY         W    W\n",
       "27            ABBOTT      BEVERLY         W    W\n",
       "28            ABBOTT       BRENDA         W    W\n",
       "29            ABBOTT        DEREK         W    W\n",
       "...              ...          ...       ...  ...\n",
       "1999966        LEACH      WILLIAM         W    W\n",
       "1999967        LEACH      YOLANDA         B    B\n",
       "1999968  LEACH SMITH        SARAH         W    W\n",
       "1999969  LEACH-KEYES     DALFANEE         B    B\n",
       "1999970     LEACHMAN        COLBY         W    W\n",
       "1999971     LEACHMAN         LORI         W    W\n",
       "1999972      LEACOCK        DONNA         B    B\n",
       "1999973      LEACOCK        JAMIE         B    B\n",
       "1999975      LEACOCK       JOSHUA         B    B\n",
       "1999977   LEADBETTER        DIANE         W    W\n",
       "1999978   LEADBETTER       LAURIE         W    W\n",
       "1999980       LEADEM      TIMOTHY         W    W\n",
       "1999981       LEADER  CHRISTOPHER         W    W\n",
       "1999982       LEADER      MATTHEW         W    W\n",
       "1999983         LEAF        CINDY         W    W\n",
       "1999984         LEAF      GREGORY         W    W\n",
       "1999985  LEAF PRINCE     PATRICIA         W    W\n",
       "1999986       LEAGUE       VICTOR         W    W\n",
       "1999987        LEAHY       BROOKE         W    W\n",
       "1999988        LEAHY    KATHERINE         W    W\n",
       "1999989        LEAHY     MARGARET         W    W\n",
       "1999990        LEAHY      MICHAEL         W    W\n",
       "1999991        LEAHY        PETER         W    W\n",
       "1999992        LEAHY      WILLIAM         W    W\n",
       "1999993        LEAHY       YVETTE         W    W\n",
       "1999995         LEAK      ANTHONY         B    B\n",
       "1999996         LEAK   ANTOINETTE         B    B\n",
       "1999997         LEAK        BETTY         B    B\n",
       "1999998         LEAK        BILLY         B    B\n",
       "1999999         LEAK      BRANDON         B    B\n",
       "\n",
       "[1935862 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'] = df['race_code']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race\n",
       "A     15262\n",
       "B    293173\n",
       "I      7356\n",
       "M     11844\n",
       "O     39305\n",
       "W    991209\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_code</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AABEL</td>\n",
       "      <td>EVELYN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AARON</td>\n",
       "      <td>RICHARD</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AARON</td>\n",
       "      <td>SANDRA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AARON</td>\n",
       "      <td>WILLIE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>GENA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ABAD</td>\n",
       "      <td>PRISCILLA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>COLLEEN</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>JACK</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ABADIE</td>\n",
       "      <td>MYRA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ABADILLA</td>\n",
       "      <td>STEVE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ABASHIAN</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ABASHIAN</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>FALISA</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>JENNIFER</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ABBAS</td>\n",
       "      <td>RAFAT</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>CAMILLE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>RONALD</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ABBATECOLA</td>\n",
       "      <td>TRACY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ABBEY</td>\n",
       "      <td>BRENT</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ABBEY</td>\n",
       "      <td>DEMETRA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>BEVERLY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>BRENDA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>DEREK</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>DWAYNE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688512</th>\n",
       "      <td>BAZEL</td>\n",
       "      <td>KIMBERLY</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763721</th>\n",
       "      <td>MENDOZA</td>\n",
       "      <td>VIRIDIANA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870258</th>\n",
       "      <td>ALLEN</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555767</th>\n",
       "      <td>PIERCE</td>\n",
       "      <td>JAQUELINE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117946</th>\n",
       "      <td>NAVARRETE</td>\n",
       "      <td>JOSE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992263</th>\n",
       "      <td>KHANANI</td>\n",
       "      <td>FALZA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428841</th>\n",
       "      <td>CRUZ HERRERA</td>\n",
       "      <td>MICHELLE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939177</th>\n",
       "      <td>SUTTON</td>\n",
       "      <td>BREANNA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919052</th>\n",
       "      <td>CUELLO</td>\n",
       "      <td>MARIELYS</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608449</th>\n",
       "      <td>VERLINGER</td>\n",
       "      <td>AMANDA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389827</th>\n",
       "      <td>ADAMS-CLARK</td>\n",
       "      <td>RYAN</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771688</th>\n",
       "      <td>RODRIGUEZ RODRIGUEZ</td>\n",
       "      <td>ENRIQUE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879002</th>\n",
       "      <td>BARBOSA</td>\n",
       "      <td>LILIANNA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70309</th>\n",
       "      <td>MORROW</td>\n",
       "      <td>NATASHA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509354</th>\n",
       "      <td>LLANES</td>\n",
       "      <td>JUAN</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840124</th>\n",
       "      <td>DABLO</td>\n",
       "      <td>STACEY</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150885</th>\n",
       "      <td>CHANG</td>\n",
       "      <td>NOU</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760228</th>\n",
       "      <td>NGUYEN</td>\n",
       "      <td>SPRING</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569850</th>\n",
       "      <td>SHOBE</td>\n",
       "      <td>KAREN</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256102</th>\n",
       "      <td>WOODS</td>\n",
       "      <td>PHARRAH</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506081</th>\n",
       "      <td>LEE</td>\n",
       "      <td>REBECCA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350596</th>\n",
       "      <td>MUKODA</td>\n",
       "      <td>RAY</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749626</th>\n",
       "      <td>KUMAR</td>\n",
       "      <td>ROMONA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538782</th>\n",
       "      <td>MORRISON</td>\n",
       "      <td>KIMBERLY</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263825</th>\n",
       "      <td>GONZALEZ-ROMERO</td>\n",
       "      <td>JAVIER</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339549</th>\n",
       "      <td>LOCKLEAR</td>\n",
       "      <td>PHILLIP</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139299</th>\n",
       "      <td>BANDY</td>\n",
       "      <td>MARY</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973921</th>\n",
       "      <td>BONAPARTE</td>\n",
       "      <td>DARRYLYN</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446959</th>\n",
       "      <td>ELY</td>\n",
       "      <td>CONSTANCE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678173</th>\n",
       "      <td>AMAYA</td>\n",
       "      <td>HUMBERTO</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5947254 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   last_name first_name race_code race\n",
       "0                      AABEL     EVELYN         W    W\n",
       "1                      AARON  CHRISTINA         W    W\n",
       "2                      AARON    CLAUDIA         W    W\n",
       "3                      AARON      JAMES         W    W\n",
       "4                      AARON     NATHAN         W    W\n",
       "5                      AARON    RICHARD         W    W\n",
       "6                      AARON     SANDRA         W    W\n",
       "7                      AARON     WILLIE         W    W\n",
       "8                   AARONSON       GENA         W    W\n",
       "9                   AARONSON    MICHAEL         W    W\n",
       "10                      ABAD  PRISCILLA         O    O\n",
       "11                    ABADIE    COLLEEN         M    M\n",
       "12                    ABADIE       JACK         W    W\n",
       "13                    ABADIE       MYRA         W    W\n",
       "14                  ABADILLA      STEVE         O    O\n",
       "15                  ABASHIAN     ASHLEY         W    W\n",
       "16                  ABASHIAN    MICHAEL         W    W\n",
       "17                     ABBAS     FALISA         B    B\n",
       "18                     ABBAS   JENNIFER         W    W\n",
       "19                     ABBAS      RAFAT         A    A\n",
       "20                ABBATECOLA    CAMILLE         W    W\n",
       "21                ABBATECOLA     RONALD         W    W\n",
       "23                ABBATECOLA      TRACY         W    W\n",
       "24                     ABBEY      BRENT         W    W\n",
       "25                     ABBEY    DEMETRA         W    W\n",
       "26                    ABBOTT     ASHLEY         W    W\n",
       "27                    ABBOTT    BEVERLY         W    W\n",
       "28                    ABBOTT     BRENDA         W    W\n",
       "29                    ABBOTT      DEREK         W    W\n",
       "30                    ABBOTT     DWAYNE         W    W\n",
       "...                      ...        ...       ...  ...\n",
       "1688512                BAZEL   KIMBERLY         O    O\n",
       "763721               MENDOZA  VIRIDIANA         O    O\n",
       "1870258                ALLEN      JAMES         O    O\n",
       "1555767               PIERCE  JAQUELINE         O    O\n",
       "1117946            NAVARRETE       JOSE         O    O\n",
       "1992263              KHANANI      FALZA         O    O\n",
       "1428841         CRUZ HERRERA   MICHELLE         O    O\n",
       "939177                SUTTON    BREANNA         O    O\n",
       "1919052               CUELLO   MARIELYS         O    O\n",
       "1608449            VERLINGER     AMANDA         O    O\n",
       "389827           ADAMS-CLARK       RYAN         O    O\n",
       "1771688  RODRIGUEZ RODRIGUEZ    ENRIQUE         O    O\n",
       "1879002              BARBOSA   LILIANNA         O    O\n",
       "70309                 MORROW    NATASHA         O    O\n",
       "1509354               LLANES       JUAN         O    O\n",
       "1840124                DABLO     STACEY         O    O\n",
       "150885                 CHANG        NOU         O    O\n",
       "1760228               NGUYEN     SPRING         O    O\n",
       "569850                 SHOBE      KAREN         O    O\n",
       "1256102                WOODS    PHARRAH         O    O\n",
       "1506081                  LEE    REBECCA         O    O\n",
       "350596                MUKODA        RAY         O    O\n",
       "749626                 KUMAR     ROMONA         O    O\n",
       "1538782             MORRISON   KIMBERLY         O    O\n",
       "263825       GONZALEZ-ROMERO     JAVIER         O    O\n",
       "339549              LOCKLEAR    PHILLIP         O    O\n",
       "1139299                BANDY       MARY         O    O\n",
       "973921             BONAPARTE   DARRYLYN         O    O\n",
       "446959                   ELY  CONSTANCE         O    O\n",
       "678173                 AMAYA   HUMBERTO         O    O\n",
       "\n",
       "[5947254 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_size = df['race'].value_counts().max()\n",
    "lst = [df]\n",
    "for class_index, group in df.groupby('race'):\n",
    "    lst.append(group.sample(max_size-len(group), replace=True))\n",
    "odf = pd.concat(lst)\n",
    "odf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>991209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>991209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>991209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>991209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>991209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>991209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      first_name\n",
       "race            \n",
       "A         991209\n",
       "B         991209\n",
       "I         991209\n",
       "M         991209\n",
       "O         991209\n",
       "W         991209"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf.groupby('race').agg({'first_name': 'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_code</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>244479</th>\n",
       "      <td>GAY</td>\n",
       "      <td>GEORGE</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>1988842</th>\n",
       "      <td>JUNG</td>\n",
       "      <td>KATHERINE</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877347</th>\n",
       "      <td>BAKER</td>\n",
       "      <td>CHRISTAIN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905838</th>\n",
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       "    <tr>\n",
       "      <th>674689</th>\n",
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       "      <td>BLIA</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285432</th>\n",
       "      <td>PATRICK</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461253</th>\n",
       "      <td>GRADY</td>\n",
       "      <td>ERIC</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224561</th>\n",
       "      <td>LONDON</td>\n",
       "      <td>TAMMY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746879</th>\n",
       "      <td>KESLER</td>\n",
       "      <td>ALLAN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672614</th>\n",
       "      <td>WILKERSON</td>\n",
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       "      <td>M</td>\n",
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       "      <th>1464220</th>\n",
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       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552094</th>\n",
       "      <td>REDROVAN</td>\n",
       "      <td>PAULINA</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>776865</th>\n",
       "      <td>PENDER</td>\n",
       "      <td>BRANDY</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509638</th>\n",
       "      <td>MACALISTER</td>\n",
       "      <td>ROBIN</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688010</th>\n",
       "      <td>BARRIOS</td>\n",
       "      <td>HENRY</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997150</th>\n",
       "      <td>LADINO</td>\n",
       "      <td>ESTEBAN</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834262</th>\n",
       "      <td>CRUZ</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281536</th>\n",
       "      <td>ACERA</td>\n",
       "      <td>LEONARDO</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323977</th>\n",
       "      <td>GALLARDO</td>\n",
       "      <td>LAURA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22628</th>\n",
       "      <td>CRUTCHFIELD</td>\n",
       "      <td>ASHLEY</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548737</th>\n",
       "      <td>PAGAN SALELLAS</td>\n",
       "      <td>VALERIE</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869061</th>\n",
       "      <td>AKTHAR</td>\n",
       "      <td>SHAHANAJ</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1017860</th>\n",
       "      <td>JONES</td>\n",
       "      <td>DESTINEE</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814225</th>\n",
       "      <td>WHITE</td>\n",
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       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376162</th>\n",
       "      <td>TAN</td>\n",
       "      <td>HOAN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859139</th>\n",
       "      <td>SAMS</td>\n",
       "      <td>BRIAN</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376073</th>\n",
       "      <td>WILLIAMS</td>\n",
       "      <td>ANGEL</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544848</th>\n",
       "      <td>NORTHRUP</td>\n",
       "      <td>SU</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781287</th>\n",
       "      <td>PROSPER</td>\n",
       "      <td>CARL</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423795</th>\n",
       "      <td>CONCEPCION</td>\n",
       "      <td>RAFAEL</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971997</th>\n",
       "      <td>HOHENSEE</td>\n",
       "      <td>RUBY</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131809</th>\n",
       "      <td>THOMAS</td>\n",
       "      <td>ARLINDA</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082120</th>\n",
       "      <td>APICE-SWENSEN</td>\n",
       "      <td>FRANCINE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015997</th>\n",
       "      <td>JAMES</td>\n",
       "      <td>ANGELA</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386001</th>\n",
       "      <td>ATKINS</td>\n",
       "      <td>TUA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241583</th>\n",
       "      <td>SAKADA</td>\n",
       "      <td>CHAD</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610488</th>\n",
       "      <td>ZAMBRANO</td>\n",
       "      <td>MARIA CRISTINA</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461222</th>\n",
       "      <td>GRACE</td>\n",
       "      <td>PENNY</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564926</th>\n",
       "      <td>REYNOLDS</td>\n",
       "      <td>AARON</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877937</th>\n",
       "      <td>WILLIS</td>\n",
       "      <td>ANNA</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66743</th>\n",
       "      <td>MENDOZA</td>\n",
       "      <td>KARLA</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595014</th>\n",
       "      <td>STURTZ</td>\n",
       "      <td>CANDACE</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544606</th>\n",
       "      <td>NORMAN</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448514</th>\n",
       "      <td>FISHER</td>\n",
       "      <td>HALEY</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540287</th>\n",
       "      <td>PAULSEN</td>\n",
       "      <td>HANA</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317987</th>\n",
       "      <td>DONBROWO</td>\n",
       "      <td>JOHN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548950</th>\n",
       "      <td>PAHE</td>\n",
       "      <td>CHARLES</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560655</th>\n",
       "      <td>QUICK</td>\n",
       "      <td>MARLENA</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483409</th>\n",
       "      <td>HUNT</td>\n",
       "      <td>SUZANNE</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920976</th>\n",
       "      <td>MARTELL</td>\n",
       "      <td>CHARLENE</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515545</th>\n",
       "      <td>MARINEZ</td>\n",
       "      <td>ROBERT</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547332</th>\n",
       "      <td>ORTIZ ZAYAS</td>\n",
       "      <td>JEAN</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107289</th>\n",
       "      <td>WHITMAN</td>\n",
       "      <td>IVA</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706409</th>\n",
       "      <td>CRISPINO</td>\n",
       "      <td>AUSTIN</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353676</th>\n",
       "      <td>OLMSTED</td>\n",
       "      <td>ALEXANDER</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518449</th>\n",
       "      <td>MATTOCK</td>\n",
       "      <td>DAVID PAUL</td>\n",
       "      <td>O</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903059</th>\n",
       "      <td>CAO-LUONG</td>\n",
       "      <td>Y NHA</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277166</th>\n",
       "      <td>JONES</td>\n",
       "      <td>HARRY</td>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669685</th>\n",
       "      <td>VANG</td>\n",
       "      <td>HOUA</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234568</th>\n",
       "      <td>PATTERSON</td>\n",
       "      <td>CHRISTIAN</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              last_name      first_name race_code race\n",
       "244479              GAY          GEORGE         B    B\n",
       "1988842            JUNG       KATHERINE         A    A\n",
       "1877347           BAKER       CHRISTAIN         W    W\n",
       "1905838           CATES            JEAN         B    B\n",
       "674689            XIONG            BLIA         A    A\n",
       "1285432         PATRICK         RAYMOND         I    I\n",
       "1461253           GRADY            ERIC         B    B\n",
       "1224561          LONDON           TAMMY         W    W\n",
       "746879           KESLER           ALLAN         W    W\n",
       "672614        WILKERSON         JONISHA         M    M\n",
       "1464220         GRIFFIN            SARA         I    I\n",
       "552094         REDROVAN         PAULINA         M    M\n",
       "776865           PENDER          BRANDY         M    M\n",
       "509638       MACALISTER           ROBIN         I    I\n",
       "1688010         BARRIOS           HENRY         O    O\n",
       "1997150          LADINO         ESTEBAN         O    O\n",
       "834262             CRUZ           DAVID         M    M\n",
       "281536            ACERA        LEONARDO         A    A\n",
       "1323977        GALLARDO           LAURA         O    O\n",
       "22628       CRUTCHFIELD          ASHLEY         W    W\n",
       "1548737  PAGAN SALELLAS         VALERIE         O    O\n",
       "1869061          AKTHAR        SHAHANAJ         A    A\n",
       "1017860           JONES        DESTINEE         I    I\n",
       "814225            WHITE            ANYA         M    M\n",
       "376162              TAN            HOAN         A    A\n",
       "1859139            SAMS           BRIAN         B    B\n",
       "1376073        WILLIAMS           ANGEL         B    B\n",
       "1544848        NORTHRUP              SU         A    A\n",
       "781287          PROSPER            CARL         M    M\n",
       "1423795      CONCEPCION          RAFAEL         O    O\n",
       "...                 ...             ...       ...  ...\n",
       "1971997        HOHENSEE            RUBY         M    M\n",
       "1131809          THOMAS         ARLINDA         B    B\n",
       "1082120   APICE-SWENSEN        FRANCINE         W    W\n",
       "1015997           JAMES          ANGELA         M    M\n",
       "1386001          ATKINS             TUA         O    O\n",
       "1241583          SAKADA            CHAD         A    A\n",
       "610488         ZAMBRANO  MARIA CRISTINA         A    A\n",
       "1461222           GRACE           PENNY         I    I\n",
       "1564926        REYNOLDS           AARON         M    M\n",
       "877937           WILLIS            ANNA         A    A\n",
       "66743           MENDOZA           KARLA         O    O\n",
       "1595014          STURTZ         CANDACE         W    W\n",
       "1544606          NORMAN         MICHAEL         I    I\n",
       "1448514          FISHER           HALEY         A    A\n",
       "540287          PAULSEN            HANA         A    A\n",
       "1317987        DONBROWO            JOHN         W    W\n",
       "1548950            PAHE         CHARLES         I    I\n",
       "1560655           QUICK         MARLENA         I    I\n",
       "1483409            HUNT         SUZANNE         I    I\n",
       "920976          MARTELL        CHARLENE         I    I\n",
       "1515545         MARINEZ          ROBERT         O    O\n",
       "1547332     ORTIZ ZAYAS            JEAN         O    O\n",
       "107289          WHITMAN             IVA         W    W\n",
       "1706409        CRISPINO          AUSTIN         W    W\n",
       "353676          OLMSTED       ALEXANDER         W    W\n",
       "1518449         MATTOCK      DAVID PAUL         O    O\n",
       "1903059       CAO-LUONG           Y NHA         A    A\n",
       "1277166           JONES           HARRY         I    I\n",
       "669685             VANG            HOUA         A    A\n",
       "1234568       PATTERSON       CHRISTIAN         M    M\n",
       "\n",
       "[1000000 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = odf.sample(SAMPLE, random_state=21)\n",
    "sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>first_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>166890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>166431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>166462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>166516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>166334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>167367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      first_name\n",
       "race            \n",
       "A         166890\n",
       "B         166431\n",
       "I         166462\n",
       "M         166516\n",
       "O         166334\n",
       "W         167367"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Additional features\n",
    "sdf['first_name'] = sdf.first_name.str.title()\n",
    "sdf['last_name'] = sdf.last_name.str.title()\n",
    "\n",
    "sdf.groupby('race').agg({'first_name': 'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_words = 12855\n",
      "CPU times: user 52min 15s, sys: 668 ms, total: 52min 16s\n",
      "Wall time: 52min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# concat last name and first name\n",
    "sdf['last_name_first_name'] = sdf['last_name'] + ' ' + sdf['first_name']\n",
    "\n",
    "# build n-gram list\n",
    "vect = CountVectorizer(analyzer='char', max_df=0.3, min_df=10, ngram_range=NGRAMS, lowercase=False) \n",
    "a = vect.fit_transform(sdf.last_name_first_name)\n",
    "vocab = vect.vocabulary_\n",
    "\n",
    "# sort n-gram by freq (highest -> lowest)\n",
    "words = []\n",
    "for b in vocab:\n",
    "    c = vocab[b]\n",
    "    #print(b, c, a[:, c].sum())\n",
    "    words.append((a[:, c].sum(), b))\n",
    "    #break\n",
    "words = sorted(words, reverse=True)\n",
    "words_list = [w[1] for w in words]\n",
    "num_words = len(words_list)\n",
    "print(\"num_words = %d\" % num_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ga': 1805,\n",
       " 'ay': 5130,\n",
       " 'y ': 12308,\n",
       " ' G': 103,\n",
       " 'Ge': 1828,\n",
       " 'eo': 6317,\n",
       " 'or': 9793,\n",
       " 'rg': 10402,\n",
       " 'ge': 6808,\n",
       " 'Gay': 1824,\n",
       " 'ay ': 5131,\n",
       " 'y G': 12316,\n",
       " ' Ge': 105,\n",
       " 'Geo': 1840,\n",
       " 'eor': 6334,\n",
       " 'org': 9802,\n",
       " 'rge': 10405,\n",
       " 'Ju': 2383,\n",
       " 'un': 11682,\n",
       " 'ng': 9140,\n",
       " 'g ': 6717,\n",
       " ' K': 169,\n",
       " 'Ka': 2414,\n",
       " 'at': 5029,\n",
       " 'th': 11178,\n",
       " 'he': 7121,\n",
       " 'er': 6362,\n",
       " 'ri': 10424,\n",
       " 'in': 7649,\n",
       " 'ne': 9104,\n",
       " 'Jun': 2396,\n",
       " 'ung': 11691,\n",
       " 'ng ': 9141,\n",
       " 'g K': 6728,\n",
       " ' Ka': 172,\n",
       " 'Kat': 2435,\n",
       " 'ath': 5036,\n",
       " 'the': 11184,\n",
       " 'her': 7140,\n",
       " 'eri': 6373,\n",
       " 'rin': 10440,\n",
       " 'ine': 7656,\n",
       " 'Ba': 988,\n",
       " 'ak': 4812,\n",
       " 'ke': 8086,\n",
       " 'r ': 10237,\n",
       " ' C': 43,\n",
       " 'Ch': 1184,\n",
       " 'hr': 7271,\n",
       " 'is': 7748,\n",
       " 'st': 10979,\n",
       " 'ta': 11095,\n",
       " 'ai': 4762,\n",
       " 'Bak': 999,\n",
       " 'ake': 4818,\n",
       " 'ker': 8102,\n",
       " 'er ': 6363,\n",
       " 'r C': 10240,\n",
       " ' Ch': 47,\n",
       " 'Chr': 1192,\n",
       " 'hri': 7276,\n",
       " 'ris': 10445,\n",
       " 'ist': 7766,\n",
       " 'sta': 10982,\n",
       " 'tai': 11106,\n",
       " 'ain': 4777,\n",
       " 'Ca': 1146,\n",
       " 'te': 11138,\n",
       " 'es': 6390,\n",
       " 's ': 10697,\n",
       " ' J': 154,\n",
       " 'Je': 2303,\n",
       " 'ea': 6028,\n",
       " 'an': 4888,\n",
       " 'Cat': 1164,\n",
       " 'ate': 5034,\n",
       " 'tes': 11160,\n",
       " 'es ': 6391,\n",
       " 's J': 10707,\n",
       " ' Je': 160,\n",
       " 'Jea': 2304,\n",
       " 'ean': 6042,\n",
       " 'Xi': 4285,\n",
       " 'io': 7678,\n",
       " 'on': 9717,\n",
       " ' B': 31,\n",
       " 'Bl': 1070,\n",
       " 'li': 8445,\n",
       " 'ia': 7416,\n",
       " 'Xio': 4291,\n",
       " 'ion': 7690,\n",
       " 'ong': 9727,\n",
       " 'g B': 6719,\n",
       " ' Bl': 37,\n",
       " 'Bli': 1073,\n",
       " 'lia': 8448,\n",
       " 'Pa': 3270,\n",
       " 'tr': 11289,\n",
       " 'ic': 7459,\n",
       " 'ck': 5529,\n",
       " 'k ': 8015,\n",
       " ' R': 261,\n",
       " 'Ra': 3436,\n",
       " 'ym': 12488,\n",
       " 'mo': 8862,\n",
       " 'nd': 9079,\n",
       " 'Pat': 3289,\n",
       " 'atr': 5045,\n",
       " 'tri': 11293,\n",
       " 'ric': 10429,\n",
       " 'ick': 7468,\n",
       " 'ck ': 5530,\n",
       " 'k R': 8033,\n",
       " ' Ra': 262,\n",
       " 'Ray': 3462,\n",
       " 'aym': 5146,\n",
       " 'ymo': 12494,\n",
       " 'mon': 8875,\n",
       " 'ond': 9724,\n",
       " 'Gr': 1909,\n",
       " 'ra': 10288,\n",
       " 'ad': 4648,\n",
       " 'dy': 5954,\n",
       " ' E': 68,\n",
       " 'Er': 1620,\n",
       " 'Gra': 1910,\n",
       " 'rad': 10295,\n",
       " 'ady': 4671,\n",
       " 'dy ': 5955,\n",
       " 'y E': 12314,\n",
       " ' Er': 86,\n",
       " 'Eri': 1628,\n",
       " 'Lo': 2683,\n",
       " 'do': 5864,\n",
       " 'n ': 8975,\n",
       " ' T': 289,\n",
       " 'Ta': 3816,\n",
       " 'am': 4861,\n",
       " 'mm': 8845,\n",
       " 'my': 8959,\n",
       " 'Lon': 2697,\n",
       " 'ndo': 9094,\n",
       " 'don': 5877,\n",
       " 'on ': 9718,\n",
       " 'n T': 8995,\n",
       " ' Ta': 291,\n",
       " 'Tam': 3831,\n",
       " 'amm': 4876,\n",
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       " 'out': 9887,\n",
       " 'uto': 11808,\n",
       " 'ton': 11274,\n",
       " 'n D': 8979,\n",
       " 'oo': 9746,\n",
       " 'ok': 9648,\n",
       " 'Coo': 1234,\n",
       " 'ook': 9759,\n",
       " 'ok ': 9649,\n",
       " 'Ras': 3456,\n",
       " 'ash': 5011,\n",
       " 'she': 10845,\n",
       " 'hee': 7128,\n",
       " 'eed': 6115,\n",
       " 'eda': 6087,\n",
       " 'Di': 1358,\n",
       " 'Dis': 1376,\n",
       " 'iso': 7762,\n",
       " 'som': 10935,\n",
       " 'omm': 9706,\n",
       " 'ma ': 8724,\n",
       " ' Ma': 197,\n",
       " 'Mar': 2776,\n",
       " 'ari': 4984,\n",
       " 'ki': 8132,\n",
       " 'rv': 10652,\n",
       " ' N': 209,\n",
       " 'Na': 2927,\n",
       " 'Mel': 2820,\n",
       " 'elk': 6258,\n",
       " 'lki': 8484,\n",
       " 'ki ': 8133,\n",
       " 'i E': 7371,\n",
       " 'Erv': 1637,\n",
       " 'rvi': 10655,\n",
       " 'vin': 11954,\n",
       " 'n N': 8989,\n",
       " ' Na': 210,\n",
       " 'Nat': 2948,\n",
       " 'ata': 5032,\n",
       " 'tal': 11109,\n",
       " 'lie': 8452,\n",
       " 'Ed': 1502,\n",
       " 'dw': 5948,\n",
       " 'ala': 4836,\n",
       " 's E': 10702,\n",
       " ' Ed': 73,\n",
       " 'Edw': 1515,\n",
       " 'dwi': 5951,\n",
       " 'win': 12124,\n",
       " 'iu': 7796,\n",
       " 'Hol': 2042,\n",
       " 'old': 9671,\n",
       " 'lde': 8375,\n",
       " 'den': 5765,\n",
       " 'Dak': 1305,\n",
       " 'kar': 8065,\n",
       " 'riu': 10447,\n",
       " 'ius': 7810,\n",
       " 'Du': 1425,\n",
       " 'Am': 817,\n",
       " 'mb': 8750,\n",
       " 'Dul': 1437,\n",
       " 'ula': 11633,\n",
       " 'la ': 8321,\n",
       " 'a A': 4518,\n",
       " ' Am': 17,\n",
       " 'Amb': 819,\n",
       " 'mbe': 8753,\n",
       " 'ber': 5258,\n",
       " 'Ni': 2993,\n",
       " 'Ty': 3980,\n",
       " 'Nic': 2997,\n",
       " 'cho': 5499,\n",
       " 'hol': 7252,\n",
       " 'ols': 9685,\n",
       " 's T': 10717,\n",
       " ' Ty': 304,\n",
       " 'Tyl': 3986,\n",
       " 'yle': 12479,\n",
       " 'Fl': 1759,\n",
       " 'Flo': 1763,\n",
       " 'ore': 9800,\n",
       " 'res': 10384,\n",
       " 's K': 10708,\n",
       " ' Ke': 174,\n",
       " 'Kev': 2465,\n",
       " 'evi': 6468,\n",
       " 'n C': 8978,\n",
       " 'tia': 11201,\n",
       " 'ef': 6136,\n",
       " 'fn': 6658,\n",
       " 'Vo': 4145,\n",
       " 'Hef': 1985,\n",
       " 'efn': 6143,\n",
       " 'fne': 6660,\n",
       " 'ner': 9124,\n",
       " 'r V': 10259,\n",
       " ' Vo': 326,\n",
       " 'Von': 4152,\n",
       " 'Tr': 3935,\n",
       " 'ux': 11837,\n",
       " 'x ': 12210,\n",
       " 'Tra': 3936,\n",
       " 'rau': 10312,\n",
       " 'aux': 5076,\n",
       " 'ux ': 11838,\n",
       " 'x A': 12211,\n",
       " 'vy': 12014,\n",
       " 'yc': 12391,\n",
       " 'h ': 7024,\n",
       " 'Bi': 1045,\n",
       " 'nt': 9339,\n",
       " 'Tha': 3877,\n",
       " 'hav': 7096,\n",
       " 'avy': 5093,\n",
       " 'vyc': 12017,\n",
       " 'ych': 12394,\n",
       " 'chi': 5494,\n",
       " 'th ': 11179,\n",
       " 'h B': 7026,\n",
       " ' Bi': 35,\n",
       " 'Bin': 1056,\n",
       " 'int': 7671,\n",
       " 'tj': 11226,\n",
       " 'je': 7916,\n",
       " 'Wir': 4228,\n",
       " 'irt': 7742,\n",
       " 'rtj': 10614,\n",
       " 'tje': 11228,\n",
       " 'jes': 7927,\n",
       " 'Ama': 818,\n",
       " 'man': 8738,\n",
       " 'Way': 4195,\n",
       " 'y A': 12310,\n",
       " 'Ane': 836,\n",
       " 'nec': 9109,\n",
       " 'ech': 6074,\n",
       " 'hil': 7176,\n",
       " 'Se': 3618,\n",
       " 'Seh': 3627,\n",
       " 'eh ': 6168,\n",
       " 'h D': 7028,\n",
       " 'Day': 1319,\n",
       " 'Ag': 727,\n",
       " 'gu': 6981,\n",
       " 'ud': 11491,\n",
       " 'ex': 6493,\n",
       " 'Agu': 737,\n",
       " 'gud': 6987,\n",
       " 'udo': 11504,\n",
       " 'o A': 9426,\n",
       " 'Ale': 796,\n",
       " 'lex': 8414,\n",
       " 'Fe': 1724,\n",
       " 'Fer': 1737,\n",
       " 'nan': 9044,\n",
       " 'dez': 5777,\n",
       " 'z V': 12642,\n",
       " 'od': 9529,\n",
       " 'wo': 12162,\n",
       " 'Blo': 1074,\n",
       " 'loo': 8548,\n",
       " 'ood': 9752,\n",
       " 'odw': 9550,\n",
       " 'dwo': 5952,\n",
       " 'wor': 12168,\n",
       " ' Do': 63,\n",
       " 'Don': 1403,\n",
       " 'nal': 9042,\n",
       " 'ald': 4839,\n",
       " 'Ton': 3922,\n",
       " 'g S': 6736,\n",
       " 'San': 3596,\n",
       " 'ui': 11579,\n",
       " 'Ne': 2956,\n",
       " 'wm': 12146,\n",
       " 'gui': 6991,\n",
       " 'uia': 11582,\n",
       " 'iar': 7436,\n",
       " 'r N': 10251,\n",
       " ' Ne': 211,\n",
       " ...}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def n_grams(seq, n=1):\n",
    "    \"\"\"Returns an itirator over the n-grams given a listTokens\"\"\"\n",
    "    shiftToken = lambda i: (el for j,el in enumerate(seq) if j>=i)\n",
    "    shiftedTokens = (shiftToken(i) for i in range(n))\n",
    "    tupleNGrams = zip(*shiftedTokens)\n",
    "    return tupleNGrams # if join in generator : (\" \".join(i) for i in tupleNGrams)\n",
    "\n",
    "def range_ngrams(listTokens, ngramRange=(1,2)):\n",
    "    \"\"\"Returns an itirator over all n-grams for n in range(ngramRange) given a listTokens.\"\"\"\n",
    "    ngrams = (ngramRange[0], ngramRange[1] + 1)\n",
    "    return chain(*(n_grams(listTokens, i) for i in range(*ngramRange)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max feature len = 37, Avg. feature len = 12\n",
      "CPU times: user 1min 46s, sys: 508 ms, total: 1min 46s\n",
      "Wall time: 1min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def find_ngrams(text, ngrams):\n",
    "    a = range_ngrams(text, ngrams)\n",
    "    wi = []\n",
    "    for i in a:\n",
    "        w = ''.join(i)\n",
    "        try:\n",
    "            idx = words_list.index(w)\n",
    "        except:\n",
    "            idx = 0\n",
    "        wi.append(idx)\n",
    "    return wi\n",
    "\n",
    "# build X from index of n-gram sequence\n",
    "X = np.array(sdf.last_name_first_name.apply(lambda c: find_ngrams(c, NGRAMS)))\n",
    "\n",
    "# check max/avg feature\n",
    "X_len = []\n",
    "for x in X:\n",
    "    X_len.append(len(x))\n",
    "\n",
    "max_feature_len = max(X_len)\n",
    "avg_feature_len = int(np.mean(X_len))\n",
    "\n",
    "print(\"Max feature len = %d, Avg. feature len = %d\" % (max_feature_len, avg_feature_len))\n",
    "y = np.array(sdf.race.astype('category').cat.codes)\n",
    "\n",
    "# Split train and test dataset\n",
    "X_train,  X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a LSTM model\n",
    "\n",
    "ref: http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800000 train sequences\n",
      "200000 test sequences\n",
      "Pad sequences (samples x time)\n",
      "X_train shape: (800000, 25)\n",
      "X_test shape: (200000, 25)\n",
      "6 classes\n",
      "Convert class vector to binary class matrix (for use with categorical_crossentropy)\n",
      "y_train shape: (800000, 6)\n",
      "y_test shape: (200000, 6)\n"
     ]
    }
   ],
   "source": [
    "'''The dataset is actually too small for LSTM to be of any advantage\n",
    "compared to simpler, much faster methods such as TF-IDF + LogReg.\n",
    "Notes:\n",
    "\n",
    "- RNNs are tricky. Choice of batch size is important,\n",
    "choice of loss and optimizer is critical, etc.\n",
    "Some configurations won't converge.\n",
    "\n",
    "- LSTM loss decrease patterns during training can be quite different\n",
    "from what you see with CNNs/MLPs/etc.\n",
    "'''\n",
    "import keras\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, Dropout, Activation\n",
    "from keras.layers import LSTM\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "from keras.models import load_model\n",
    "\n",
    "max_features = num_words # 20000\n",
    "feature_len = 25 # avg_feature_len # cut texts after this number of words (among top max_features most common words)\n",
    "batch_size = 32\n",
    "\n",
    "print(len(X_train), 'train sequences')\n",
    "print(len(X_test), 'test sequences')\n",
    "\n",
    "print('Pad sequences (samples x time)')\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=feature_len)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=feature_len)\n",
    "print('X_train shape:', X_train.shape)\n",
    "print('X_test shape:', X_test.shape)\n",
    "\n",
    "num_classes = np.max(y_train) + 1\n",
    "print(num_classes, 'classes')\n",
    "\n",
    "print('Convert class vector to binary class matrix '\n",
    "      '(for use with categorical_crossentropy)')\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "print('y_train shape:', y_train.shape)\n",
    "print('y_test shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build model...\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 25, 50)            642750    \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 25, 32)            4832      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 12, 32)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100)               53200     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6)                 606       \n",
      "=================================================================\n",
      "Total params: 701,388\n",
      "Trainable params: 701,388\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('Build model...')\n",
    "\n",
    "if False:\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(num_words, 32, input_length=feature_len))\n",
    "    model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "if True:\n",
    "    embedding_vecor_length = 50\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(num_words, embedding_vecor_length, input_length=feature_len))\n",
    "    model.add(Conv1D(activation=\"relu\", padding=\"same\", filters=32, kernel_size=3))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(LSTM(100))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "# try using different optimizers and different optimizer configs\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Train on 720000 samples, validate on 80000 samples\n",
      "Epoch 1/15\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "720000/720000 [==============================] - 584s 812us/step - loss: 1.3442 - acc: 0.4720 - val_loss: 1.2440 - val_acc: 0.5135\n",
      "Epoch 2/15\n",
      "720000/720000 [==============================] - 574s 798us/step - loss: 1.1483 - acc: 0.5572 - val_loss: 1.0726 - val_acc: 0.5884\n",
      "Epoch 3/15\n",
      "720000/720000 [==============================] - 519s 721us/step - loss: 0.9765 - acc: 0.6293 - val_loss: 0.9428 - val_acc: 0.6432\n",
      "Epoch 4/15\n",
      "720000/720000 [==============================] - 531s 737us/step - loss: 0.8691 - acc: 0.6733 - val_loss: 0.8727 - val_acc: 0.6717\n",
      "Epoch 5/15\n",
      "720000/720000 [==============================] - 535s 744us/step - loss: 0.8091 - acc: 0.6956 - val_loss: 0.8265 - val_acc: 0.6893\n",
      "Epoch 6/15\n",
      "720000/720000 [==============================] - 505s 701us/step - loss: 0.7731 - acc: 0.7093 - val_loss: 0.8118 - val_acc: 0.6964\n",
      "Epoch 7/15\n",
      "720000/720000 [==============================] - 517s 718us/step - loss: 0.7528 - acc: 0.7165 - val_loss: 0.8015 - val_acc: 0.6996\n",
      "Epoch 8/15\n",
      "720000/720000 [==============================] - 526s 730us/step - loss: 0.7361 - acc: 0.7227 - val_loss: 0.7810 - val_acc: 0.7064\n",
      "Epoch 9/15\n",
      "720000/720000 [==============================] - 539s 749us/step - loss: 0.7261 - acc: 0.7265 - val_loss: 0.7705 - val_acc: 0.7094\n",
      "Epoch 10/15\n",
      "720000/720000 [==============================] - 490s 681us/step - loss: 0.7166 - acc: 0.7303 - val_loss: 0.7684 - val_acc: 0.7120\n",
      "Epoch 11/15\n",
      "720000/720000 [==============================] - 491s 682us/step - loss: 0.7107 - acc: 0.7324 - val_loss: 0.7651 - val_acc: 0.7119\n",
      "Epoch 12/15\n",
      "720000/720000 [==============================] - 478s 664us/step - loss: 0.7070 - acc: 0.7330 - val_loss: 0.7581 - val_acc: 0.7138\n",
      "Epoch 13/15\n",
      "720000/720000 [==============================] - 466s 647us/step - loss: 0.7032 - acc: 0.7344 - val_loss: 0.7590 - val_acc: 0.7154\n",
      "Epoch 14/15\n",
      "720000/720000 [==============================] - 495s 688us/step - loss: 0.6996 - acc: 0.7355 - val_loss: 0.7630 - val_acc: 0.7117\n",
      "Epoch 15/15\n",
      "720000/720000 [==============================] - 501s 695us/step - loss: 0.6979 - acc: 0.7359 - val_loss: 0.7643 - val_acc: 0.7127\n",
      "200000/200000 [==============================] - 25s 123us/step\n",
      "Test score: 0.759171972079277\n",
      "Test accuracy: 0.71554\n",
      "CPU times: user 6h 43min 13s, sys: 54min 24s, total: 7h 37min 38s\n",
      "Wall time: 2h 9min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Train...')\n",
    "model.fit(X_train, y_train, batch_size=batch_size, epochs=EPOCHS,\n",
    "          validation_split=0.1, verbose=1)\n",
    "score, acc = model.evaluate(X_test, y_test,\n",
    "                            batch_size=batch_size, verbose=1)\n",
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.759171972079277\n",
      "Test accuracy: 0.71554\n"
     ]
    }
   ],
   "source": [
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.82      0.90      0.86     33378\n",
      "           B       0.56      0.58      0.57     33286\n",
      "           I       0.85      0.93      0.89     33292\n",
      "           M       0.75      0.82      0.78     33303\n",
      "           O       0.75      0.57      0.65     33267\n",
      "           W       0.54      0.50      0.52     33474\n",
      "\n",
      "    accuracy                           0.72    200000\n",
      "   macro avg       0.71      0.72      0.71    200000\n",
      "weighted avg       0.71      0.72      0.71    200000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict_classes(X_test, verbose=2)\n",
    "p = model.predict_proba(X_test, verbose=2) # to predict probability\n",
    "target_names = list(sdf.race.astype('category').cat.categories)\n",
    "print(classification_report(np.argmax(y_test, axis=1), y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[30080   843   249   289   994   923]\n",
      " [ 1089 19188  1823  3332  1273  6581]\n",
      " [  240   806 30833   365   186   862]\n",
      " [  270  2275   477 27274  1426  1581]\n",
      " [ 3402  3795   707  2006 18871  4486]\n",
      " [ 1685  7403  2002  3231  2291 16862]]\n"
     ]
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(np.argmax(y_test, axis=1), y_pred)\n",
    "print(conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import itertools\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(conf_mat, target_names, normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('../models/nc_voter_name_lstm.h5')\n",
    "model.save('/opt/github/ethnicolr/ethnicolr/models/nc_voter_reg/nc_voter_name_race_lstm_oversample.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_df = pd.DataFrame(words_list, columns=['vocab'])\n",
    "#words_df.to_csv('../models/nc_voter_name_vocab.csv', index=False, encoding='utf-8')\n",
    "words_df.to_csv('/opt/github/ethnicolr/ethnicolr/models/nc_voter_reg/nc_voter_name_race_vocab_oversample.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}