03-Data-Preparation-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03 Data Preparation\n",
    "\n",
    "The next part should show whether NamSor performs equally well in all groups. For that we need to create two different data sets out of the data set we already have: One for each API endpoint. Then in each set we set the score column to NamSor's prediction probability and the label_value to whether NamSor's prediction was correct or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing necessary libraries... \n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "# >>> Import Libraries\n",
    "\n",
    "print(\"Importing necessary libraries... \")\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "print(\"Libraries imported.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing COMPAS data set... \n",
      "Data set imported. It is has 7214 entries and looks like this:\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>entity_id</th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>score</th>\n",
       "      <th>label_value</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>sex_pred</th>\n",
       "      <th>sex_pred_prob</th>\n",
       "      <th>race_pred</th>\n",
       "      <th>race_pred_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>miguel</td>\n",
       "      <td>hernandez</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>Greater than 45</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.999286</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.975499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>kevon</td>\n",
       "      <td>dixon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.956720</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.857965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>ed</td>\n",
       "      <td>philo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less than 25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.968813</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0.611053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>marcu</td>\n",
       "      <td>brown</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less than 25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.622665</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.764072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>bouthy</td>\n",
       "      <td>pierrelouis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.509131</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.800832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  entity_id  level_0  index   first         last  score  \\\n",
       "0           0          1        0      0  miguel    hernandez    0.0   \n",
       "1           1          3        1      1   kevon        dixon    0.0   \n",
       "2           2          4        2      2      ed        philo    0.0   \n",
       "3           3          5        3      3   marcu        brown    1.0   \n",
       "4           4          6        4      4  bouthy  pierrelouis    0.0   \n",
       "\n",
       "   label_value              race   sex          age_cat sex_pred  \\\n",
       "0            0             Other  Male  Greater than 45     Male   \n",
       "1            1  African-American  Male          25 - 45     Male   \n",
       "2            1  African-American  Male     Less than 25     Male   \n",
       "3            0  African-American  Male     Less than 25     Male   \n",
       "4            0             Other  Male          25 - 45     Male   \n",
       "\n",
       "   sex_pred_prob         race_pred  race_pred_prob  \n",
       "0       0.999286          Hispanic        0.975499  \n",
       "1       0.956720  African-American        0.857965  \n",
       "2       0.968813             Asian        0.611053  \n",
       "3       0.622665  African-American        0.764072  \n",
       "4       0.509131  African-American        0.800832  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# >>> Import COMPAS data set\n",
    "\n",
    "print(\"Importing COMPAS data set... \")\n",
    "\n",
    "df = pd.read_csv(\"data/compas_with_predictions_cleaned.csv\")\n",
    "\n",
    "print(\"Data set imported. It is has {} entries and looks like this:\".format(df.shape[0]))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mapToNumber(b):\n",
    "    if(b):\n",
    "        return 1.0\n",
    "    else:\n",
    "        return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program_Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Program_Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Program_Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
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       "      <th>4</th>\n",
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       "      <td>Other</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>marsha</td>\n",
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       "      <td>Other</td>\n",
       "      <td>0.001543</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>edward</td>\n",
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       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>steven</td>\n",
       "      <td>stewart</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.008355</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>elizabeth</td>\n",
       "      <td>thieme</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "      <td>bo</td>\n",
       "      <td>bradac</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.071431</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>benjamin</td>\n",
       "      <td>franc</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.010254</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>ellyaher</td>\n",
       "      <td>lanza</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.496973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>kortney</td>\n",
       "      <td>coleman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.021928</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18</td>\n",
       "      <td>jarrod</td>\n",
       "      <td>turbe</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19</td>\n",
       "      <td>craig</td>\n",
       "      <td>gilbert</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20</td>\n",
       "      <td>samuel</td>\n",
       "      <td>seraphin</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.005015</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>mario</td>\n",
       "      <td>hernandez</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.032220</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22</td>\n",
       "      <td>darrious</td>\n",
       "      <td>davis</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.023343</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23</td>\n",
       "      <td>neil</td>\n",
       "      <td>heckart</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.009151</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24</td>\n",
       "      <td>michael</td>\n",
       "      <td>lux</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.008046</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25</td>\n",
       "      <td>columbus</td>\n",
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       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.010852</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>vandivuiet</td>\n",
       "      <td>williams</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.343373</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27</td>\n",
       "      <td>nelson</td>\n",
       "      <td>avalo</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.013228</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28</td>\n",
       "      <td>janel</td>\n",
       "      <td>denicola</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.036407</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30</td>\n",
       "      <td>dominic</td>\n",
       "      <td>pabon</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.011273</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>32</td>\n",
       "      <td>russell</td>\n",
       "      <td>sottile</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.008110</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>33</td>\n",
       "      <td>andre</td>\n",
       "      <td>ashley</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.016623</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>37</td>\n",
       "      <td>deandrae</td>\n",
       "      <td>counts</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.078910</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>38</td>\n",
       "      <td>victoria</td>\n",
       "      <td>soltau</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.004600</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39</td>\n",
       "      <td>najee</td>\n",
       "      <td>sapp</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.326974</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7184</th>\n",
       "      <td>10962</td>\n",
       "      <td>yolani</td>\n",
       "      <td>moratz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.010579</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7185</th>\n",
       "      <td>10963</td>\n",
       "      <td>paul</td>\n",
       "      <td>wyatt</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.005889</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7186</th>\n",
       "      <td>10964</td>\n",
       "      <td>terrence</td>\n",
       "      <td>brown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.159760</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7187</th>\n",
       "      <td>10965</td>\n",
       "      <td>anthony</td>\n",
       "      <td>fields</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.009133</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7188</th>\n",
       "      <td>10966</td>\n",
       "      <td>shameel</td>\n",
       "      <td>koya</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.075597</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7189</th>\n",
       "      <td>10967</td>\n",
       "      <td>george</td>\n",
       "      <td>bedward</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.004979</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7190</th>\n",
       "      <td>10969</td>\n",
       "      <td>eric</td>\n",
       "      <td>sparks</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7191</th>\n",
       "      <td>10971</td>\n",
       "      <td>eugene</td>\n",
       "      <td>drogus</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.124951</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7192</th>\n",
       "      <td>10972</td>\n",
       "      <td>matt</td>\n",
       "      <td>munoz</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.015363</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7193</th>\n",
       "      <td>10975</td>\n",
       "      <td>warren</td>\n",
       "      <td>aiken</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7194</th>\n",
       "      <td>10976</td>\n",
       "      <td>arleen</td>\n",
       "      <td>martin</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.004799</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7195</th>\n",
       "      <td>10977</td>\n",
       "      <td>adrian</td>\n",
       "      <td>williams</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.032812</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7196</th>\n",
       "      <td>10979</td>\n",
       "      <td>angelita</td>\n",
       "      <td>diaz</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7197</th>\n",
       "      <td>10980</td>\n",
       "      <td>jarvis</td>\n",
       "      <td>yates</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.010895</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7198</th>\n",
       "      <td>10981</td>\n",
       "      <td>orett</td>\n",
       "      <td>harrison</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.479505</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7199</th>\n",
       "      <td>10982</td>\n",
       "      <td>austin</td>\n",
       "      <td>harris</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.011124</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7200</th>\n",
       "      <td>10984</td>\n",
       "      <td>shantrina</td>\n",
       "      <td>stfort</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.059085</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7201</th>\n",
       "      <td>10985</td>\n",
       "      <td>kyle</td>\n",
       "      <td>miller</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.019499</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7202</th>\n",
       "      <td>10987</td>\n",
       "      <td>ceasar</td>\n",
       "      <td>gomez</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.014571</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7203</th>\n",
       "      <td>10988</td>\n",
       "      <td>luis</td>\n",
       "      <td>fernandez</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7204</th>\n",
       "      <td>10989</td>\n",
       "      <td>rodney</td>\n",
       "      <td>montgomery</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.010307</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7205</th>\n",
       "      <td>10990</td>\n",
       "      <td>christopher</td>\n",
       "      <td>tun</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.018242</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7206</th>\n",
       "      <td>10992</td>\n",
       "      <td>alexander</td>\n",
       "      <td>vega</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0.013417</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7207</th>\n",
       "      <td>10994</td>\n",
       "      <td>jarred</td>\n",
       "      <td>payne</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.005528</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7208</th>\n",
       "      <td>10995</td>\n",
       "      <td>raheem</td>\n",
       "      <td>smith</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.067109</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7209</th>\n",
       "      <td>10996</td>\n",
       "      <td>steven</td>\n",
       "      <td>butler</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.007007</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7210</th>\n",
       "      <td>10997</td>\n",
       "      <td>malcolm</td>\n",
       "      <td>simmons</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7211</th>\n",
       "      <td>10999</td>\n",
       "      <td>winston</td>\n",
       "      <td>gregory</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.008722</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7212</th>\n",
       "      <td>11000</td>\n",
       "      <td>farrah</td>\n",
       "      <td>jean</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0.009026</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7213</th>\n",
       "      <td>11001</td>\n",
       "      <td>florencia</td>\n",
       "      <td>sanmartin</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7214 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      entity_id        first         last     sex sex_pred              race  \\\n",
       "0             1       miguel    hernandez    Male     Male             Other   \n",
       "1             3        kevon        dixon    Male     Male  African-American   \n",
       "2             4           ed        philo    Male     Male  African-American   \n",
       "3             5        marcu        brown    Male     Male  African-American   \n",
       "4             6       bouthy  pierrelouis    Male     Male             Other   \n",
       "5             7       marsha        miles    Male   Female             Other   \n",
       "6             8       edward       riddle    Male     Male         Caucasian   \n",
       "7             9       steven      stewart    Male     Male             Other   \n",
       "8            10    elizabeth       thieme  Female   Female         Caucasian   \n",
       "9            13           bo       bradac    Male     Male         Caucasian   \n",
       "10           14     benjamin        franc    Male     Male         Caucasian   \n",
       "11           15     ellyaher        lanza    Male   Female  African-American   \n",
       "12           16      kortney      coleman  Female   Female         Caucasian   \n",
       "13           18       jarrod        turbe    Male     Male  African-American   \n",
       "14           19        craig      gilbert  Female     Male         Caucasian   \n",
       "15           20       samuel     seraphin    Male     Male  African-American   \n",
       "16           21        mario    hernandez    Male     Male          Hispanic   \n",
       "17           22     darrious        davis    Male     Male  African-American   \n",
       "18           23         neil      heckart    Male     Male         Caucasian   \n",
       "19           24      michael          lux    Male     Male         Caucasian   \n",
       "20           25     columbus       wilson    Male     Male  African-American   \n",
       "21           26   vandivuiet     williams    Male   Female  African-American   \n",
       "22           27       nelson        avalo    Male     Male         Caucasian   \n",
       "23           28        janel     denicola  Female   Female         Caucasian   \n",
       "24           30      dominic        pabon    Male     Male          Hispanic   \n",
       "25           32      russell      sottile    Male     Male         Caucasian   \n",
       "26           33        andre       ashley    Male     Male             Other   \n",
       "27           37     deandrae       counts    Male     Male  African-American   \n",
       "28           38     victoria       soltau  Female   Female         Caucasian   \n",
       "29           39        najee         sapp    Male     Male  African-American   \n",
       "...         ...          ...          ...     ...      ...               ...   \n",
       "7184      10962       yolani       moratz  Female   Female         Caucasian   \n",
       "7185      10963         paul        wyatt    Male     Male         Caucasian   \n",
       "7186      10964     terrence        brown    Male     Male  African-American   \n",
       "7187      10965      anthony       fields    Male     Male         Caucasian   \n",
       "7188      10966      shameel         koya    Male     Male         Caucasian   \n",
       "7189      10967       george      bedward    Male     Male  African-American   \n",
       "7190      10969         eric       sparks    Male     Male  African-American   \n",
       "7191      10971       eugene       drogus    Male     Male         Caucasian   \n",
       "7192      10972         matt        munoz    Male     Male         Caucasian   \n",
       "7193      10975       warren        aiken    Male     Male  African-American   \n",
       "7194      10976       arleen       martin  Female   Female         Caucasian   \n",
       "7195      10977       adrian     williams    Male     Male  African-American   \n",
       "7196      10979     angelita         diaz    Male   Female  African-American   \n",
       "7197      10980       jarvis        yates    Male     Male  African-American   \n",
       "7198      10981        orett     harrison    Male   Female  African-American   \n",
       "7199      10982       austin       harris    Male     Male         Caucasian   \n",
       "7200      10984    shantrina       stfort  Female   Female  African-American   \n",
       "7201      10985         kyle       miller    Male     Male  African-American   \n",
       "7202      10987       ceasar        gomez    Male     Male          Hispanic   \n",
       "7203      10988         luis    fernandez    Male     Male          Hispanic   \n",
       "7204      10989       rodney   montgomery    Male     Male  African-American   \n",
       "7205      10990  christopher          tun    Male     Male         Caucasian   \n",
       "7206      10992    alexander         vega    Male     Male         Caucasian   \n",
       "7207      10994       jarred        payne    Male     Male  African-American   \n",
       "7208      10995       raheem        smith    Male     Male  African-American   \n",
       "7209      10996       steven       butler    Male     Male  African-American   \n",
       "7210      10997      malcolm      simmons    Male     Male  African-American   \n",
       "7211      10999      winston      gregory    Male     Male             Other   \n",
       "7212      11000       farrah         jean  Female   Female  African-American   \n",
       "7213      11001    florencia    sanmartin  Female   Female          Hispanic   \n",
       "\n",
       "         score  label_value  \n",
       "0     0.000714          1.0  \n",
       "1     0.043280          1.0  \n",
       "2     0.031187          1.0  \n",
       "3     0.377335          1.0  \n",
       "4     0.490869          1.0  \n",
       "5     0.001543          0.0  \n",
       "6     0.000248          1.0  \n",
       "7     0.008355          1.0  \n",
       "8     0.001108          1.0  \n",
       "9     0.071431          1.0  \n",
       "10    0.010254          1.0  \n",
       "11    0.496973          0.0  \n",
       "12    0.021928          1.0  \n",
       "13    0.000041          1.0  \n",
       "14    0.000000          0.0  \n",
       "15    0.005015          1.0  \n",
       "16    0.032220          1.0  \n",
       "17    0.023343          1.0  \n",
       "18    0.009151          1.0  \n",
       "19    0.008046          1.0  \n",
       "20    0.010852          1.0  \n",
       "21    0.343373          0.0  \n",
       "22    0.013228          1.0  \n",
       "23    0.036407          1.0  \n",
       "24    0.011273          1.0  \n",
       "25    0.008110          1.0  \n",
       "26    0.016623          1.0  \n",
       "27    0.078910          1.0  \n",
       "28    0.004600          1.0  \n",
       "29    0.326974          1.0  \n",
       "...        ...          ...  \n",
       "7184  0.010579          1.0  \n",
       "7185  0.005889          1.0  \n",
       "7186  0.159760          1.0  \n",
       "7187  0.009133          1.0  \n",
       "7188  0.075597          1.0  \n",
       "7189  0.004979          1.0  \n",
       "7190  0.004627          1.0  \n",
       "7191  0.124951          1.0  \n",
       "7192  0.015363          1.0  \n",
       "7193  0.007866          1.0  \n",
       "7194  0.004799          1.0  \n",
       "7195  0.032812          1.0  \n",
       "7196  0.000466          0.0  \n",
       "7197  0.010895          1.0  \n",
       "7198  0.479505          0.0  \n",
       "7199  0.011124          1.0  \n",
       "7200  0.059085          1.0  \n",
       "7201  0.019499          1.0  \n",
       "7202  0.014571          1.0  \n",
       "7203  0.000559          1.0  \n",
       "7204  0.010307          1.0  \n",
       "7205  0.018242          1.0  \n",
       "7206  0.013417          1.0  \n",
       "7207  0.005528          1.0  \n",
       "7208  0.067109          1.0  \n",
       "7209  0.007007          1.0  \n",
       "7210  0.000041          1.0  \n",
       "7211  0.008722          1.0  \n",
       "7212  0.009026          1.0  \n",
       "7213  0.000021          1.0  \n",
       "\n",
       "[7214 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# >>> create a data set for gender\n",
    "df_gender = df[['entity_id', 'first', 'last', 'sex', 'sex_pred', 'race']];\n",
    "df_gender['score'] = 1 - df['sex_pred_prob']\n",
    "df_gender['label_value'] = (df['sex'] == df['sex_pred'])\n",
    "\n",
    "df_gender['label_value'] = df_gender['label_value'].apply(lambda x: mapToNumber(x)) \n",
    "\n",
    "df_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program_Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Program_Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Program_Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>entity_id</th>\n",
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       "      <th>last</th>\n",
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       "      <th>race_pred</th>\n",
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       "      <td>Hispanic</td>\n",
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       "      <th>25</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7197</th>\n",
       "      <td>10980</td>\n",
       "      <td>jarvis</td>\n",
       "      <td>yates</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.175836</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7198</th>\n",
       "      <td>10981</td>\n",
       "      <td>orett</td>\n",
       "      <td>harrison</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.293892</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7199</th>\n",
       "      <td>10982</td>\n",
       "      <td>austin</td>\n",
       "      <td>harris</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.486816</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7200</th>\n",
       "      <td>10984</td>\n",
       "      <td>shantrina</td>\n",
       "      <td>stfort</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.234030</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7201</th>\n",
       "      <td>10985</td>\n",
       "      <td>kyle</td>\n",
       "      <td>miller</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.221754</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7202</th>\n",
       "      <td>10987</td>\n",
       "      <td>ceasar</td>\n",
       "      <td>gomez</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.033071</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7203</th>\n",
       "      <td>10988</td>\n",
       "      <td>luis</td>\n",
       "      <td>fernandez</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.024501</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7204</th>\n",
       "      <td>10989</td>\n",
       "      <td>rodney</td>\n",
       "      <td>montgomery</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.366494</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7205</th>\n",
       "      <td>10990</td>\n",
       "      <td>christopher</td>\n",
       "      <td>tun</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.107803</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7206</th>\n",
       "      <td>10992</td>\n",
       "      <td>alexander</td>\n",
       "      <td>vega</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.059294</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7207</th>\n",
       "      <td>10994</td>\n",
       "      <td>jarred</td>\n",
       "      <td>payne</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.408834</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7208</th>\n",
       "      <td>10995</td>\n",
       "      <td>raheem</td>\n",
       "      <td>smith</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.130172</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7209</th>\n",
       "      <td>10996</td>\n",
       "      <td>steven</td>\n",
       "      <td>butler</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.409484</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7210</th>\n",
       "      <td>10997</td>\n",
       "      <td>malcolm</td>\n",
       "      <td>simmons</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.190121</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7211</th>\n",
       "      <td>10999</td>\n",
       "      <td>winston</td>\n",
       "      <td>gregory</td>\n",
       "      <td>Other</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.196530</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7212</th>\n",
       "      <td>11000</td>\n",
       "      <td>farrah</td>\n",
       "      <td>jean</td>\n",
       "      <td>African-American</td>\n",
       "      <td>African-American</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.124396</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7213</th>\n",
       "      <td>11001</td>\n",
       "      <td>florencia</td>\n",
       "      <td>sanmartin</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.145185</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7214 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      entity_id        first         last              race         race_pred  \\\n",
       "0             1       miguel    hernandez             Other          Hispanic   \n",
       "1             3        kevon        dixon  African-American  African-American   \n",
       "2             4           ed        philo  African-American             Asian   \n",
       "3             5        marcu        brown  African-American  African-American   \n",
       "4             6       bouthy  pierrelouis             Other  African-American   \n",
       "5             7       marsha        miles             Other  African-American   \n",
       "6             8       edward       riddle         Caucasian         Caucasian   \n",
       "7             9       steven      stewart             Other         Caucasian   \n",
       "8            10    elizabeth       thieme         Caucasian         Caucasian   \n",
       "9            13           bo       bradac         Caucasian             Asian   \n",
       "10           14     benjamin        franc         Caucasian         Caucasian   \n",
       "11           15     ellyaher        lanza  African-American          Hispanic   \n",
       "12           16      kortney      coleman         Caucasian  African-American   \n",
       "13           18       jarrod        turbe  African-American  African-American   \n",
       "14           19        craig      gilbert         Caucasian         Caucasian   \n",
       "15           20       samuel     seraphin  African-American  African-American   \n",
       "16           21        mario    hernandez          Hispanic          Hispanic   \n",
       "17           22     darrious        davis  African-American  African-American   \n",
       "18           23         neil      heckart         Caucasian         Caucasian   \n",
       "19           24      michael          lux         Caucasian         Caucasian   \n",
       "20           25     columbus       wilson  African-American  African-American   \n",
       "21           26   vandivuiet     williams  African-American  African-American   \n",
       "22           27       nelson        avalo         Caucasian          Hispanic   \n",
       "23           28        janel     denicola         Caucasian         Caucasian   \n",
       "24           30      dominic        pabon          Hispanic          Hispanic   \n",
       "25           32      russell      sottile         Caucasian         Caucasian   \n",
       "26           33        andre       ashley             Other  African-American   \n",
       "27           37     deandrae       counts  African-American  African-American   \n",
       "28           38     victoria       soltau         Caucasian         Caucasian   \n",
       "29           39        najee         sapp  African-American  African-American   \n",
       "...         ...          ...          ...               ...               ...   \n",
       "7184      10962       yolani       moratz         Caucasian          Hispanic   \n",
       "7185      10963         paul        wyatt         Caucasian         Caucasian   \n",
       "7186      10964     terrence        brown  African-American  African-American   \n",
       "7187      10965      anthony       fields         Caucasian  African-American   \n",
       "7188      10966      shameel         koya         Caucasian             Asian   \n",
       "7189      10967       george      bedward  African-American  African-American   \n",
       "7190      10969         eric       sparks  African-American         Caucasian   \n",
       "7191      10971       eugene       drogus         Caucasian         Caucasian   \n",
       "7192      10972         matt        munoz         Caucasian          Hispanic   \n",
       "7193      10975       warren        aiken  African-American  African-American   \n",
       "7194      10976       arleen       martin         Caucasian         Caucasian   \n",
       "7195      10977       adrian     williams  African-American  African-American   \n",
       "7196      10979     angelita         diaz  African-American          Hispanic   \n",
       "7197      10980       jarvis        yates  African-American  African-American   \n",
       "7198      10981        orett     harrison  African-American  African-American   \n",
       "7199      10982       austin       harris         Caucasian  African-American   \n",
       "7200      10984    shantrina       stfort  African-American  African-American   \n",
       "7201      10985         kyle       miller  African-American         Caucasian   \n",
       "7202      10987       ceasar        gomez          Hispanic          Hispanic   \n",
       "7203      10988         luis    fernandez          Hispanic          Hispanic   \n",
       "7204      10989       rodney   montgomery  African-American  African-American   \n",
       "7205      10990  christopher          tun         Caucasian             Asian   \n",
       "7206      10992    alexander         vega         Caucasian          Hispanic   \n",
       "7207      10994       jarred        payne  African-American  African-American   \n",
       "7208      10995       raheem        smith  African-American  African-American   \n",
       "7209      10996       steven       butler  African-American         Caucasian   \n",
       "7210      10997      malcolm      simmons  African-American  African-American   \n",
       "7211      10999      winston      gregory             Other  African-American   \n",
       "7212      11000       farrah         jean  African-American  African-American   \n",
       "7213      11001    florencia    sanmartin          Hispanic          Hispanic   \n",
       "\n",
       "         sex     score  label_value  \n",
       "0       Male  0.024501          0.0  \n",
       "1       Male  0.142035          1.0  \n",
       "2       Male  0.388947          0.0  \n",
       "3       Male  0.235928          1.0  \n",
       "4       Male  0.199168          0.0  \n",
       "5       Male  0.278000          0.0  \n",
       "6       Male  0.238652          1.0  \n",
       "7       Male  0.345223          0.0  \n",
       "8     Female  0.233335          1.0  \n",
       "9       Male  0.141849          0.0  \n",
       "10      Male  0.482025          1.0  \n",
       "11      Male  0.475519          0.0  \n",
       "12    Female  0.362972          0.0  \n",
       "13      Male  0.484628          1.0  \n",
       "14    Female  0.358345          1.0  \n",
       "15      Male  0.200377          1.0  \n",
       "16      Male  0.024501          1.0  \n",
       "17      Male  0.093806          1.0  \n",
       "18      Male  0.228843          1.0  \n",
       "19      Male  0.137798          1.0  \n",
       "20      Male  0.276768          1.0  \n",
       "21      Male  0.129849          1.0  \n",
       "22      Male  0.058880          0.0  \n",
       "23    Female  0.379063          1.0  \n",
       "24      Male  0.202453          1.0  \n",
       "25      Male  0.135417          1.0  \n",
       "26      Male  0.200903          0.0  \n",
       "27      Male  0.331227          1.0  \n",
       "28    Female  0.501288          1.0  \n",
       "29      Male  0.242778          1.0  \n",
       "...      ...       ...          ...  \n",
       "7184  Female  0.438093          0.0  \n",
       "7185    Male  0.217587          1.0  \n",
       "7186    Male  0.210884          1.0  \n",
       "7187    Male  0.276662          0.0  \n",
       "7188    Male  0.059344          0.0  \n",
       "7189    Male  0.405802          1.0  \n",
       "7190    Male  0.481765          0.0  \n",
       "7191    Male  0.540909          1.0  \n",
       "7192    Male  0.212097          0.0  \n",
       "7193    Male  0.519171          1.0  \n",
       "7194  Female  0.404640          1.0  \n",
       "7195    Male  0.124982          1.0  \n",
       "7196    Male  0.067734          0.0  \n",
       "7197    Male  0.175836          1.0  \n",
       "7198    Male  0.293892          1.0  \n",
       "7199    Male  0.486816          0.0  \n",
       "7200  Female  0.234030          1.0  \n",
       "7201    Male  0.221754          0.0  \n",
       "7202    Male  0.033071          1.0  \n",
       "7203    Male  0.024501          1.0  \n",
       "7204    Male  0.366494          1.0  \n",
       "7205    Male  0.107803          0.0  \n",
       "7206    Male  0.059294          0.0  \n",
       "7207    Male  0.408834          1.0  \n",
       "7208    Male  0.130172          1.0  \n",
       "7209    Male  0.409484          0.0  \n",
       "7210    Male  0.190121          1.0  \n",
       "7211    Male  0.196530          0.0  \n",
       "7212  Female  0.124396          1.0  \n",
       "7213  Female  0.145185          1.0  \n",
       "\n",
       "[7214 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# >>> create a data set for ethnicity\n",
    "df_ethnicity = df[['entity_id', 'first', 'last', 'race', 'race_pred', 'sex']];\n",
    "df_ethnicity['score'] = 1 - df['race_pred_prob']\n",
    "df_ethnicity['label_value'] = (df['race'] == df['race_pred'])\n",
    "\n",
    "df_ethnicity['label_value'] = df_ethnicity['label_value'].apply(lambda x: mapToNumber(x)) \n",
    "\n",
    "df_ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving compas dataframe with predictions for gender to CSV... \n",
      "CSV saved!\n"
     ]
    }
   ],
   "source": [
    "# Saving results \n",
    "print(\"Saving compas dataframe with predictions for gender to CSV... \")\n",
    "df_gender.to_csv(\"data/compas_gender_predictions.csv\")\n",
    "print(\"CSV saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving compas dataframe with predictions for ethnicity to CSV... \n",
      "CSV saved!\n"
     ]
    }
   ],
   "source": [
    "print(\"Saving compas dataframe with predictions for ethnicity to CSV... \")\n",
    "df_ethnicity.to_csv(\"data/compas_ethnicity_predictions.csv\")\n",
    "print(\"CSV saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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