scripts/02_analyze_nc_voter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating Ethnicolr on North Carolina Voter Registration Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_id</th>\n",
       "      <th>county_desc</th>\n",
       "      <th>voter_reg_num</th>\n",
       "      <th>status_cd</th>\n",
       "      <th>voter_status_desc</th>\n",
       "      <th>reason_cd</th>\n",
       "      <th>voter_status_reason_desc</th>\n",
       "      <th>absent_ind</th>\n",
       "      <th>name_prefx_cd</th>\n",
       "      <th>last_name</th>\n",
       "      <th>...</th>\n",
       "      <th>munic_dist_desc</th>\n",
       "      <th>dist_1_abbrv</th>\n",
       "      <th>dist_1_desc</th>\n",
       "      <th>dist_2_abbrv</th>\n",
       "      <th>dist_2_desc</th>\n",
       "      <th>confidential_ind</th>\n",
       "      <th>age</th>\n",
       "      <th>ncid</th>\n",
       "      <th>vtd_abbrv</th>\n",
       "      <th>vtd_desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>9005990</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>AV</td>\n",
       "      <td>VERIFIED</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AABEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15A</td>\n",
       "      <td>15A PROSECUTORIAL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>Age Over 66</td>\n",
       "      <td>AA56273</td>\n",
       "      <td>08N</td>\n",
       "      <td>08N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>9048723</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>AV</td>\n",
       "      <td>VERIFIED</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AARON</td>\n",
       "      <td>...</td>\n",
       "      <td>BURLINGTON</td>\n",
       "      <td>15A</td>\n",
       "      <td>15A PROSECUTORIAL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>Age 26 - 40</td>\n",
       "      <td>AA98377</td>\n",
       "      <td>03S</td>\n",
       "      <td>03S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>9019674</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>AV</td>\n",
       "      <td>VERIFIED</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AARON</td>\n",
       "      <td>...</td>\n",
       "      <td>BURLINGTON</td>\n",
       "      <td>15A</td>\n",
       "      <td>15A PROSECUTORIAL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>Age Over 66</td>\n",
       "      <td>AA69747</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>9129589</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>AV</td>\n",
       "      <td>VERIFIED</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AARON</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15A</td>\n",
       "      <td>15A PROSECUTORIAL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>Age Over 66</td>\n",
       "      <td>AA170513</td>\n",
       "      <td>08N</td>\n",
       "      <td>08N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>9041748</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>AV</td>\n",
       "      <td>VERIFIED</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AARON</td>\n",
       "      <td>...</td>\n",
       "      <td>BURLINGTON</td>\n",
       "      <td>15A</td>\n",
       "      <td>15A PROSECUTORIAL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>Age 26 - 40</td>\n",
       "      <td>AA91549</td>\n",
       "      <td>03S</td>\n",
       "      <td>03S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   county_id county_desc  voter_reg_num status_cd voter_status_desc reason_cd  \\\n",
       "0          1    ALAMANCE        9005990         A            ACTIVE        AV   \n",
       "1          1    ALAMANCE        9048723         A            ACTIVE        AV   \n",
       "2          1    ALAMANCE        9019674         A            ACTIVE        AV   \n",
       "3          1    ALAMANCE        9129589         A            ACTIVE        AV   \n",
       "4          1    ALAMANCE        9041748         A            ACTIVE        AV   \n",
       "\n",
       "  voter_status_reason_desc absent_ind name_prefx_cd last_name  ...  \\\n",
       "0                 VERIFIED                              AABEL  ...   \n",
       "1                 VERIFIED                              AARON  ...   \n",
       "2                 VERIFIED                              AARON  ...   \n",
       "3                 VERIFIED                              AARON  ...   \n",
       "4                 VERIFIED                              AARON  ...   \n",
       "\n",
       "  munic_dist_desc dist_1_abbrv        dist_1_desc dist_2_abbrv dist_2_desc  \\\n",
       "0             NaN          15A  15A PROSECUTORIAL                            \n",
       "1      BURLINGTON          15A  15A PROSECUTORIAL                            \n",
       "2      BURLINGTON          15A  15A PROSECUTORIAL                            \n",
       "3             NaN          15A  15A PROSECUTORIAL                            \n",
       "4      BURLINGTON          15A  15A PROSECUTORIAL                            \n",
       "\n",
       "  confidential_ind          age      ncid  vtd_abbrv  vtd_desc  \n",
       "0                N  Age Over 66   AA56273        08N       08N  \n",
       "1                N  Age 26 - 40   AA98377        03S       03S  \n",
       "2                N  Age Over 66   AA69747        124       124  \n",
       "3                N  Age Over 66  AA170513        08N       08N  \n",
       "4                N  Age 26 - 40   AA91549        03S       03S  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ncvoter_Statewide.zip', delimiter='\\t', nrows=10)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['county_id', 'county_desc', 'voter_reg_num', 'status_cd',\n",
       "       'voter_status_desc', 'reason_cd', 'voter_status_reason_desc',\n",
       "       'absent_ind', 'name_prefx_cd', 'last_name', 'first_name', 'middle_name',\n",
       "       'name_suffix_lbl', 'res_street_address', 'res_city_desc', 'state_cd',\n",
       "       'zip_code', 'mail_addr1', 'mail_addr2', 'mail_addr3', 'mail_addr4',\n",
       "       'mail_city', 'mail_state', 'mail_zipcode', 'full_phone_number',\n",
       "       'race_code', 'ethnic_code', 'party_cd', 'gender_code', 'birth_age',\n",
       "       'birth_state', 'drivers_lic', 'registr_dt', 'precinct_abbrv',\n",
       "       'precinct_desc', 'municipality_abbrv', 'municipality_desc',\n",
       "       'ward_abbrv', 'ward_desc', 'cong_dist_abbrv', 'super_court_abbrv',\n",
       "       'judic_dist_abbrv', 'nc_senate_abbrv', 'nc_house_abbrv',\n",
       "       'county_commiss_abbrv', 'county_commiss_desc', 'township_abbrv',\n",
       "       'township_desc', 'school_dist_abbrv', 'school_dist_desc',\n",
       "       'fire_dist_abbrv', 'fire_dist_desc', 'water_dist_abbrv',\n",
       "       'water_dist_desc', 'sewer_dist_abbrv', 'sewer_dist_desc',\n",
       "       'sanit_dist_abbrv', 'sanit_dist_desc', 'rescue_dist_abbrv',\n",
       "       'rescue_dist_desc', 'munic_dist_abbrv', 'munic_dist_desc',\n",
       "       'dist_1_abbrv', 'dist_1_desc', 'dist_2_abbrv', 'dist_2_desc',\n",
       "       'confidential_ind', 'age', 'ncid', 'vtd_abbrv', 'vtd_desc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>ethnic_code</th>\n",
       "      <th>gender_code</th>\n",
       "      <th>birth_age</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>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  last_name first_name race_code ethnic_code gender_code  birth_age\n",
       "0     AABEL     EVELYN         W          NL           F         81\n",
       "1     AARON  CHRISTINA         W          UN           F         40\n",
       "2     AARON    CLAUDIA         W          NL           F         72\n",
       "3     AARON      JAMES         W          UN           M         69\n",
       "4     AARON     NATHAN         W          UN           M         40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ncvoter_Statewide.zip', delimiter='\\t', usecols=['last_name', 'first_name', 'race_code', 'gender_code', 'ethnic_code', 'birth_age'])\n",
    "df.head()"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ethnic_code  race_code\n",
       "HL           A                721\n",
       "             B               6288\n",
       "             I                719\n",
       "             M              12158\n",
       "             O              79737\n",
       "             U              36007\n",
       "             W              47255\n",
       "NL                              1\n",
       "             A              56906\n",
       "             B            1273955\n",
       "             I              51815\n",
       "             M              23159\n",
       "             O              83157\n",
       "             U              18397\n",
       "             W            4511174\n",
       "UN                              2\n",
       "             A              30438\n",
       "             B             450583\n",
       "             I               9608\n",
       "             M              17402\n",
       "             O              19847\n",
       "             U             204858\n",
       "             W             804727\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['ethnic_code', 'race_code']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('/opt/data/nc_voter/nc_voter_name_gender_race_ethnic_age.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_code</th>\n",
       "      <th>ethnic_code</th>\n",
       "      <th>gender_code</th>\n",
       "      <th>birth_age</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>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  last_name first_name race_code ethnic_code gender_code  birth_age\n",
       "0     AABEL     EVELYN         W          NL           F         81\n",
       "1     AARON  CHRISTINA         W          UN           F         40\n",
       "2     AARON    CLAUDIA         W          NL           F         72\n",
       "3     AARON      JAMES         W          UN           M         69\n",
       "4     AARON     NATHAN         W          UN           M         40"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/opt/data/nc_voter/nc_voter_name_gender_race_ethnic_age.csv')\n",
    "df.head()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from ethnicolr import pred_fl_reg_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\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:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session 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",
      "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",
      "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"
     ]
    },
    {
     "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>ethnic_code</th>\n",
       "      <th>gender_code</th>\n",
       "      <th>birth_age</th>\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</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>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>81</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.026478</td>\n",
       "      <td>0.074987</td>\n",
       "      <td>0.023811</td>\n",
       "      <td>0.874724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.017828</td>\n",
       "      <td>0.162495</td>\n",
       "      <td>0.812943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>72</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.061856</td>\n",
       "      <td>0.177148</td>\n",
       "      <td>0.758328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.001917</td>\n",
       "      <td>0.183722</td>\n",
       "      <td>0.811515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>40</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.011188</td>\n",
       "      <td>0.012027</td>\n",
       "      <td>0.144476</td>\n",
       "      <td>0.832309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  last_name first_name race_code ethnic_code gender_code  birth_age      race  \\\n",
       "0     AABEL     EVELYN         W          NL           F         81  nh_white   \n",
       "1     AARON  CHRISTINA         W          UN           F         40  nh_white   \n",
       "2     AARON    CLAUDIA         W          NL           F         72  nh_white   \n",
       "3     AARON      JAMES         W          UN           M         69  nh_white   \n",
       "4     AARON     NATHAN         W          UN           M         40  nh_white   \n",
       "\n",
       "      asian  hispanic  nh_black  nh_white  \n",
       "0  0.026478  0.074987  0.023811  0.874724  \n",
       "1  0.006734  0.017828  0.162495  0.812943  \n",
       "2  0.002667  0.061856  0.177148  0.758328  \n",
       "3  0.002847  0.001917  0.183722  0.811515  \n",
       "4  0.011188  0.012027  0.144476  0.832309  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf = pred_fl_reg_name(df, lname_col='last_name', fname_col='first_name')\n",
    "odf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#odf.to_csv('/opt/data/nc_voter/nc_voter_name_gender_race_ethnic_age+pred_fl_reg_name.csv.gz', index=False, compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>52537</td>\n",
       "      <td>4425</td>\n",
       "      <td>4045</td>\n",
       "      <td>27023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>7055</td>\n",
       "      <td>14387</td>\n",
       "      <td>581251</td>\n",
       "      <td>1128110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1341</td>\n",
       "      <td>1337</td>\n",
       "      <td>7303</td>\n",
       "      <td>52161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1135</td>\n",
       "      <td>7802</td>\n",
       "      <td>8732</td>\n",
       "      <td>35048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>21049</td>\n",
       "      <td>82400</td>\n",
       "      <td>11080</td>\n",
       "      <td>68202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>5869</td>\n",
       "      <td>39904</td>\n",
       "      <td>32842</td>\n",
       "      <td>180626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>8490</td>\n",
       "      <td>79923</td>\n",
       "      <td>180621</td>\n",
       "      <td>5093931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race       asian  hispanic  nh_black  nh_white\n",
       "race_code                                     \n",
       "               0         0         0         3\n",
       "A          52537      4425      4045     27023\n",
       "B           7055     14387    581251   1128110\n",
       "I           1341      1337      7303     52161\n",
       "M           1135      7802      8732     35048\n",
       "O          21049     82400     11080     68202\n",
       "U           5869     39904     32842    180626\n",
       "W           8490     79923    180621   5093931"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(odf['race_code'], odf['race'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnic_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HL</th>\n",
       "      <td>652</td>\n",
       "      <td>130373</td>\n",
       "      <td>6381</td>\n",
       "      <td>45474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>66277</td>\n",
       "      <td>71899</td>\n",
       "      <td>596955</td>\n",
       "      <td>5283236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN</th>\n",
       "      <td>30547</td>\n",
       "      <td>27906</td>\n",
       "      <td>222538</td>\n",
       "      <td>1256394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race         asian  hispanic  nh_black  nh_white\n",
       "ethnic_code                                     \n",
       "HL             652    130373      6381     45474\n",
       "NL           66277     71899    596955   5283236\n",
       "UN           30547     27906    222538   1256394"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(odf['ethnic_code'], odf['race'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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>ethnic_code</th>\n",
       "      <th>gender_code</th>\n",
       "      <th>birth_age</th>\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "      <th>ethnic_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>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>81</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.026478</td>\n",
       "      <td>0.074987</td>\n",
       "      <td>0.023811</td>\n",
       "      <td>0.874724</td>\n",
       "      <td>NL+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CHRISTINA</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.017828</td>\n",
       "      <td>0.162495</td>\n",
       "      <td>0.812943</td>\n",
       "      <td>UN+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>72</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.061856</td>\n",
       "      <td>0.177148</td>\n",
       "      <td>0.758328</td>\n",
       "      <td>NL+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.001917</td>\n",
       "      <td>0.183722</td>\n",
       "      <td>0.811515</td>\n",
       "      <td>UN+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARON</td>\n",
       "      <td>NATHAN</td>\n",
       "      <td>W</td>\n",
       "      <td>UN</td>\n",
       "      <td>M</td>\n",
       "      <td>40</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.011188</td>\n",
       "      <td>0.012027</td>\n",
       "      <td>0.144476</td>\n",
       "      <td>0.832309</td>\n",
       "      <td>UN+W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  last_name first_name race_code ethnic_code gender_code  birth_age      race  \\\n",
       "0     AABEL     EVELYN         W          NL           F         81  nh_white   \n",
       "1     AARON  CHRISTINA         W          UN           F         40  nh_white   \n",
       "2     AARON    CLAUDIA         W          NL           F         72  nh_white   \n",
       "3     AARON      JAMES         W          UN           M         69  nh_white   \n",
       "4     AARON     NATHAN         W          UN           M         40  nh_white   \n",
       "\n",
       "      asian  hispanic  nh_black  nh_white ethnic_race  \n",
       "0  0.026478  0.074987  0.023811  0.874724        NL+W  \n",
       "1  0.006734  0.017828  0.162495  0.812943        UN+W  \n",
       "2  0.002667  0.061856  0.177148  0.758328        NL+W  \n",
       "3  0.002847  0.001917  0.183722  0.811515        UN+W  \n",
       "4  0.011188  0.012027  0.144476  0.832309        UN+W  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf['ethnic_race'] = odf['ethnic_code'] + '+' + odf['race_code']\n",
    "odf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
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       "    <tr>\n",
       "      <th>ethnic_race</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HL+A</th>\n",
       "      <td>101</td>\n",
       "      <td>377</td>\n",
       "      <td>38</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+B</th>\n",
       "      <td>23</td>\n",
       "      <td>1713</td>\n",
       "      <td>1766</td>\n",
       "      <td>2786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+I</th>\n",
       "      <td>8</td>\n",
       "      <td>406</td>\n",
       "      <td>43</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+M</th>\n",
       "      <td>94</td>\n",
       "      <td>5949</td>\n",
       "      <td>1053</td>\n",
       "      <td>5061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+O</th>\n",
       "      <td>227</td>\n",
       "      <td>63702</td>\n",
       "      <td>1662</td>\n",
       "      <td>14144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+U</th>\n",
       "      <td>93</td>\n",
       "      <td>28279</td>\n",
       "      <td>773</td>\n",
       "      <td>6860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+W</th>\n",
       "      <td>106</td>\n",
       "      <td>29947</td>\n",
       "      <td>1046</td>\n",
       "      <td>16156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+A</th>\n",
       "      <td>33998</td>\n",
       "      <td>2681</td>\n",
       "      <td>2504</td>\n",
       "      <td>17699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+B</th>\n",
       "      <td>4654</td>\n",
       "      <td>9368</td>\n",
       "      <td>420805</td>\n",
       "      <td>839118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+I</th>\n",
       "      <td>1170</td>\n",
       "      <td>735</td>\n",
       "      <td>6084</td>\n",
       "      <td>43826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+M</th>\n",
       "      <td>683</td>\n",
       "      <td>745</td>\n",
       "      <td>4370</td>\n",
       "      <td>17360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+O</th>\n",
       "      <td>18452</td>\n",
       "      <td>15943</td>\n",
       "      <td>6842</td>\n",
       "      <td>41914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+U</th>\n",
       "      <td>422</td>\n",
       "      <td>720</td>\n",
       "      <td>2349</td>\n",
       "      <td>14905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+W</th>\n",
       "      <td>6898</td>\n",
       "      <td>41707</td>\n",
       "      <td>154001</td>\n",
       "      <td>4308413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+A</th>\n",
       "      <td>18438</td>\n",
       "      <td>1367</td>\n",
       "      <td>1503</td>\n",
       "      <td>9119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+B</th>\n",
       "      <td>2378</td>\n",
       "      <td>3306</td>\n",
       "      <td>158680</td>\n",
       "      <td>286206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+I</th>\n",
       "      <td>163</td>\n",
       "      <td>196</td>\n",
       "      <td>1176</td>\n",
       "      <td>8073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+M</th>\n",
       "      <td>358</td>\n",
       "      <td>1108</td>\n",
       "      <td>3309</td>\n",
       "      <td>12627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+O</th>\n",
       "      <td>2370</td>\n",
       "      <td>2755</td>\n",
       "      <td>2576</td>\n",
       "      <td>12144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+U</th>\n",
       "      <td>5354</td>\n",
       "      <td>10905</td>\n",
       "      <td>29720</td>\n",
       "      <td>158861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UN+W</th>\n",
       "      <td>1486</td>\n",
       "      <td>8269</td>\n",
       "      <td>25574</td>\n",
       "      <td>769362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race         asian  hispanic  nh_black  nh_white\n",
       "ethnic_race                                     \n",
       "HL+A           101       377        38       205\n",
       "HL+B            23      1713      1766      2786\n",
       "HL+I             8       406        43       262\n",
       "HL+M            94      5949      1053      5061\n",
       "HL+O           227     63702      1662     14144\n",
       "HL+U            93     28279       773      6860\n",
       "HL+W           106     29947      1046     16156\n",
       "NL+              0         0         0         1\n",
       "NL+A         33998      2681      2504     17699\n",
       "NL+B          4654      9368    420805    839118\n",
       "NL+I          1170       735      6084     43826\n",
       "NL+M           683       745      4370     17360\n",
       "NL+O         18452     15943      6842     41914\n",
       "NL+U           422       720      2349     14905\n",
       "NL+W          6898     41707    154001   4308413\n",
       "UN+              0         0         0         2\n",
       "UN+A         18438      1367      1503      9119\n",
       "UN+B          2378      3306    158680    286206\n",
       "UN+I           163       196      1176      8073\n",
       "UN+M           358      1108      3309     12627\n",
       "UN+O          2370      2755      2576     12144\n",
       "UN+U          5354     10905     29720    158861\n",
       "UN+W          1486      8269     25574    769362"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(odf['ethnic_race'], odf['race'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison # 1:\n",
    "---------------\n",
    "\n",
    "Accuracy using Known-Known:\n",
    "\n",
    "1. (race_code == 'B') & (ethnic_code == 'NL') ==> nh_black\n",
    "2. (race_code == 'W') & (ethnic_code == 'NL') ==> nh_white\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5785129, 13)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf['nc_race'] = odf.ethnic_race\n",
    "odf.dropna(subset=['nc_race']) # remove missing\n",
    "odf.loc[(odf.race_code=='B') & (odf.ethnic_code=='NL'), 'nc_race'] = 'nh_black'\n",
    "odf.loc[(odf.race_code=='W') & (odf.ethnic_code=='NL'), 'nc_race'] = 'nh_white'\n",
    "\n",
    "comp_1 = odf[odf.nc_race.isin([\"nh_black\", \"nh_white\"])]\n",
    "comp_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race      asian  hispanic  nh_black  nh_white\n",
      "nc_race                                      \n",
      "nh_black   4654      9368    420805    839118\n",
      "nh_white   6898     41707    154001   4308413\n",
      "Total N 5784964\n",
      "NH Black: 0.33\n",
      "NH White: 0.96\n",
      "Accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "cf = pd.crosstab(comp_1['nc_race'], comp_1['race'])\n",
    "print(cf)\n",
    "\n",
    "# Total\n",
    "print(\"Total N\", cf.values.sum())\n",
    "\n",
    "# TP/Total \n",
    "print(\"NH Black:\",(cf.values[0,2]/cf.values[0,].sum()).round(2))\n",
    "print(\"NH White:\", (cf.values[1,3]/cf.values[1,].sum()).round(2))\n",
    "print(\"Accuracy:\", ((cf.values[0,2] + cf.values[1,3])/cf.values.sum()).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison #2: Low FP\n",
    "------------------------\n",
    "\n",
    "1. (race_code == 'B') & (ethnic_code == 'NL') ==> nh_black\n",
    "2. (race_code == 'W') & (ethnic_code == 'NL') ==> nh_white\n",
    "3. ((race_code == 'W') & (ethnic_code == 'HL')) | ((race_code == 'B') & (ethnic_code == 'HL')) ==> hispanic\n",
    "4. (race_code == 'A') & (ethnic_code == 'NL') ==> asian\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5895578, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf['nc_race_low_fp'] = odf.ethnic_race\n",
    "odf.loc[(odf.race_code=='B') & (odf.ethnic_code=='NL'), 'nc_race_low_fp'] = 'nh_black'\n",
    "odf.loc[(odf.race_code=='W') & (odf.ethnic_code=='NL'), 'nc_race_low_fp'] = 'nh_white'\n",
    "odf.loc[((odf.race_code == 'W') & (odf.ethnic_code == 'HL')) |\n",
    "        ((odf.race_code == 'B') & (odf.ethnic_code == 'HL')), 'nc_race_low_fp'] = 'hispanic'\n",
    "odf.loc[(odf.race_code == 'A') & (odf.ethnic_code == 'NL'), 'nc_race_low_fp'] = 'asian'\n",
    "odf\n",
    "\n",
    "comp_2 = odf[odf.nc_race_low_fp.isin([\"nh_black\", \"nh_white\", \"hispanic\", \"asian\"])]\n",
    "comp_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race            asian  hispanic  nh_black  nh_white\n",
      "nc_race_low_fp                                     \n",
      "asian           33998      2681      2504     17699\n",
      "hispanic          129     31660      2812     18942\n",
      "nh_black         4654      9368    420805    839118\n",
      "nh_white         6898     41707    154001   4308413\n",
      "Total N 5895389\n",
      "Accuracy 0.8133264827817129\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nc_race_low_fp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>asian</th>\n",
       "      <td>0.597693</td>\n",
       "      <td>0.047133</td>\n",
       "      <td>0.044021</td>\n",
       "      <td>0.311153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.591300</td>\n",
       "      <td>0.052519</td>\n",
       "      <td>0.353772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_black</th>\n",
       "      <td>0.003653</td>\n",
       "      <td>0.007354</td>\n",
       "      <td>0.330316</td>\n",
       "      <td>0.658677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_white</th>\n",
       "      <td>0.001529</td>\n",
       "      <td>0.009246</td>\n",
       "      <td>0.034139</td>\n",
       "      <td>0.955086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race               asian  hispanic  nh_black  nh_white\n",
       "nc_race_low_fp                                        \n",
       "asian           0.597693  0.047133  0.044021  0.311153\n",
       "hispanic        0.002409  0.591300  0.052519  0.353772\n",
       "nh_black        0.003653  0.007354  0.330316  0.658677\n",
       "nh_white        0.001529  0.009246  0.034139  0.955086"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = pd.crosstab(comp_2['nc_race_low_fp'], comp_2['race'])\n",
    "print(cf)\n",
    "\n",
    "print(\"Total N\", cf.values.sum())\n",
    "\n",
    "# accuracy\n",
    "print(\"Accuracy\", np.diag(cf.values).sum()/cf.values.sum())\n",
    "\n",
    "# tp/total\n",
    "cf[\"sum\"] = cf.values.sum(axis = 1)\n",
    "cf.loc[:,\"asian\":\"nh_white\"].div(cf[\"sum\"], axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison #3: Low FN\n",
    "-----------------------\n",
    "1. (race_code == 'B') & (ethnic_code == 'NL') ==> nh_black\n",
    "2. (race_code == 'W') & (ethnic_code == 'NL') ==> nh_white\n",
    "3. ethnic_code == 'HL' ==> hispanic\n",
    "4. (race_code == 'A') & (ethnic_code == 'NL') ==> asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6024920, 15)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf['nc_race_low_fn'] = odf.ethnic_race\n",
    "odf.loc[(odf.race_code=='B') & (odf.ethnic_code=='NL'), 'nc_race_low_fn'] = 'nh_black'\n",
    "odf.loc[(odf.race_code=='W') & (odf.ethnic_code=='NL'), 'nc_race_low_fn'] = 'nh_white'\n",
    "odf.loc[(odf.ethnic_code=='HL'), 'nc_race_low_fn'] = 'nc_hispanic'\n",
    "odf.loc[(odf.race_code=='A') & (odf.ethnic_code=='NL'), 'nc_race_low_fn'] = 'nc_asian'\n",
    "odf\n",
    "\n",
    "comp_3 = odf[odf.nc_race_low_fn.isin([\"nh_black\", \"nh_white\", \"nc_hispanic\", \"nc_asian\"])]\n",
    "comp_3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race            asian  hispanic  nh_black  nh_white\n",
      "nc_race_low_fn                                     \n",
      "nc_asian        33998      2681      2504     17699\n",
      "nc_hispanic       652    130373      6381     45474\n",
      "nh_black         4654      9368    420805    839118\n",
      "nh_white         6898     41707    154001   4308413\n",
      "Total N 6024726\n",
      "Accuracy 0.8122508807869436\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nc_race_low_fn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nc_asian</th>\n",
       "      <td>0.597693</td>\n",
       "      <td>0.047133</td>\n",
       "      <td>0.044021</td>\n",
       "      <td>0.311153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nc_hispanic</th>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.712888</td>\n",
       "      <td>0.034892</td>\n",
       "      <td>0.248655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_black</th>\n",
       "      <td>0.003653</td>\n",
       "      <td>0.007354</td>\n",
       "      <td>0.330316</td>\n",
       "      <td>0.658677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_white</th>\n",
       "      <td>0.001529</td>\n",
       "      <td>0.009246</td>\n",
       "      <td>0.034139</td>\n",
       "      <td>0.955086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race               asian  hispanic  nh_black  nh_white\n",
       "nc_race_low_fn                                        \n",
       "nc_asian        0.597693  0.047133  0.044021  0.311153\n",
       "nc_hispanic     0.003565  0.712888  0.034892  0.248655\n",
       "nh_black        0.003653  0.007354  0.330316  0.658677\n",
       "nh_white        0.001529  0.009246  0.034139  0.955086"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = pd.crosstab(comp_3['nc_race_low_fn'], comp_3['race'])\n",
    "print(cf)\n",
    "\n",
    "print(\"Total N\", cf.values.sum())\n",
    "\n",
    "\n",
    "# accuracy\n",
    "print(\"Accuracy\", np.diag(cf.values).sum()/cf.values.sum())\n",
    "\n",
    "# tp/total\n",
    "cf[\"sum\"] = cf.values.sum(axis = 1)\n",
    "cf.loc[:,\"asian\":\"nh_white\"].div(cf[\"sum\"], axis = 0)\n",
    "\n",
    "# accuracy\n",
    "#(np.diag(cf.values).sum())/cf.values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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