4_3_Logistic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "\n",
    "## Dataset\n",
    "\n",
    "Статистика по внебрачным романам\n",
    "http://statsmodels.sourceforge.net/stable/datasets/generated/fair.html\n",
    "\n",
    "Собрана опросом среди читательниц журнала.\n",
    "\n",
    "Данные содержат 6366 ответа по 9 параметрам:\n",
    "\n",
    "* `rate_marriage`: оценка брака женщиной по 5-балльной шкале\n",
    "* `age`: возраст\n",
    "* `yrs_married`: лет в браке\n",
    "* `children`: количество детей\n",
    "* `religious`: религиозность по 4-балльной шкале\n",
    "* `educ`: уровень образования (максимум – 20)\n",
    "* `occupation`: род занятий\n",
    "* `occupation_husb`: род занятий мужа\n",
    "* `affairs`: время, затраченное на романы\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Импорты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from patsy import dmatrices\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "      <th>affair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.230769</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.852174</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.799999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.266665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.041666</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.266665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.361111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.041666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.839996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.545454</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.532609</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.799999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.187878</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.199999</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6336</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6342</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6343</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6348</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6349</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6350</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6353</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6354</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6355</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6356</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6358</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6359</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6361</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6366 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
       "0               3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
       "1               3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
       "2               4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
       "3               4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
       "4               5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
       "5               4.0  27.0          9.0       0.0        2.0  14.0         3.0   \n",
       "6               5.0  37.0         23.0       5.5        2.0  12.0         5.0   \n",
       "7               5.0  37.0         23.0       5.5        2.0  12.0         2.0   \n",
       "8               3.0  22.0          2.5       0.0        2.0  12.0         3.0   \n",
       "9               3.0  27.0          6.0       0.0        1.0  16.0         3.0   \n",
       "10              2.0  27.0          6.0       2.0        1.0  16.0         3.0   \n",
       "11              5.0  27.0          6.0       2.0        3.0  14.0         3.0   \n",
       "12              3.0  37.0         16.5       5.5        1.0  12.0         2.0   \n",
       "13              5.0  27.0          6.0       0.0        2.0  14.0         3.0   \n",
       "14              4.0  22.0          6.0       1.0        1.0  14.0         4.0   \n",
       "15              4.0  37.0          9.0       2.0        2.0  14.0         3.0   \n",
       "16              4.0  27.0          6.0       1.0        1.0  12.0         3.0   \n",
       "17              1.0  37.0         23.0       5.5        4.0  14.0         5.0   \n",
       "18              2.0  42.0         23.0       2.0        2.0  20.0         4.0   \n",
       "19              4.0  37.0          6.0       0.0        2.0  16.0         5.0   \n",
       "20              5.0  22.0          2.5       0.0        2.0  14.0         3.0   \n",
       "21              3.0  37.0         16.5       5.5        2.0   9.0         3.0   \n",
       "22              3.0  42.0         23.0       5.5        3.0  12.0         5.0   \n",
       "23              2.0  27.0          9.0       2.0        4.0  20.0         3.0   \n",
       "24              4.0  27.0          6.0       1.0        2.0  12.0         5.0   \n",
       "25              5.0  27.0          2.5       0.0        3.0  16.0         4.0   \n",
       "26              2.0  27.0          6.0       2.0        2.0  12.0         2.0   \n",
       "27              5.0  37.0         13.0       1.0        3.0  12.0         3.0   \n",
       "28              2.0  32.0         16.5       2.0        2.0  12.0         4.0   \n",
       "29              3.0  27.0          6.0       1.0        1.0  14.0         3.0   \n",
       "...             ...   ...          ...       ...        ...   ...         ...   \n",
       "6336            5.0  42.0         23.0       4.0        3.0  14.0         5.0   \n",
       "6337            5.0  27.0          6.0       0.0        4.0  14.0         4.0   \n",
       "6338            5.0  42.0         23.0       2.0        3.0  12.0         2.0   \n",
       "6339            4.0  32.0         13.0       3.0        3.0  16.0         4.0   \n",
       "6340            5.0  27.0         13.0       3.0        3.0  16.0         4.0   \n",
       "6341            5.0  27.0          9.0       1.0        2.0  14.0         4.0   \n",
       "6342            4.0  22.0          2.5       0.0        2.0  16.0         4.0   \n",
       "6343            5.0  17.5          2.5       0.0        4.0  12.0         3.0   \n",
       "6344            4.0  32.0         16.5       2.0        2.0  12.0         3.0   \n",
       "6345            5.0  27.0          9.0       1.0        3.0  12.0         3.0   \n",
       "6346            4.0  22.0          2.5       0.0        4.0  14.0         4.0   \n",
       "6347            5.0  22.0          2.5       1.0        2.0  12.0         3.0   \n",
       "6348            5.0  27.0          0.5       0.0        4.0  20.0         4.0   \n",
       "6349            5.0  37.0         16.5       3.0        3.0  14.0         5.0   \n",
       "6350            5.0  32.0         13.0       2.0        4.0  14.0         3.0   \n",
       "6351            4.0  22.0          0.5       0.0        2.0  16.0         3.0   \n",
       "6352            5.0  42.0         23.0       2.0        4.0  12.0         3.0   \n",
       "6353            5.0  22.0          2.5       2.0        2.0  14.0         3.0   \n",
       "6354            5.0  42.0         23.0       4.0        4.0  12.0         3.0   \n",
       "6355            4.0  27.0          6.0       0.0        3.0  12.0         3.0   \n",
       "6356            5.0  32.0         13.0       3.0        3.0  12.0         3.0   \n",
       "6357            5.0  32.0         13.0       4.0        2.0  14.0         4.0   \n",
       "6358            3.0  27.0          6.0       2.0        4.0  14.0         3.0   \n",
       "6359            4.0  22.0          2.5       0.0        3.0  16.0         5.0   \n",
       "6360            5.0  22.0          2.5       0.0        2.0  14.0         3.0   \n",
       "6361            5.0  32.0         13.0       2.0        3.0  17.0         4.0   \n",
       "6362            4.0  32.0         13.0       1.0        1.0  16.0         5.0   \n",
       "6363            5.0  22.0          2.5       0.0        2.0  14.0         3.0   \n",
       "6364            5.0  32.0          6.0       1.0        3.0  14.0         3.0   \n",
       "6365            4.0  22.0          2.5       0.0        2.0  16.0         2.0   \n",
       "\n",
       "      occupation_husb    affairs  affair  \n",
       "0                 5.0   0.111111       1  \n",
       "1                 4.0   3.230769       1  \n",
       "2                 5.0   1.400000       1  \n",
       "3                 5.0   0.727273       1  \n",
       "4                 4.0   4.666666       1  \n",
       "5                 4.0   4.666666       1  \n",
       "6                 4.0   0.852174       1  \n",
       "7                 3.0   1.826086       1  \n",
       "8                 3.0   4.799999       1  \n",
       "9                 5.0   1.333333       1  \n",
       "10                5.0   3.266665       1  \n",
       "11                5.0   2.041666       1  \n",
       "12                3.0   0.484848       1  \n",
       "13                2.0   2.000000       1  \n",
       "14                4.0   3.266665       1  \n",
       "15                6.0   1.361111       1  \n",
       "16                5.0   2.000000       1  \n",
       "17                2.0   1.826086       1  \n",
       "18                4.0   1.826086       1  \n",
       "19                4.0   2.041666       1  \n",
       "20                4.0   7.839996       1  \n",
       "21                2.0   2.545454       1  \n",
       "22                4.0   0.532609       1  \n",
       "23                4.0   0.622222       1  \n",
       "24                4.0   0.583333       1  \n",
       "25                1.0   4.799999       1  \n",
       "26                5.0   0.166667       1  \n",
       "27                4.0   0.615385       1  \n",
       "28                2.0   1.187878       1  \n",
       "29                6.0  11.199999       1  \n",
       "...               ...        ...     ...  \n",
       "6336              4.0   0.000000       0  \n",
       "6337              4.0   0.000000       0  \n",
       "6338              2.0   0.000000       0  \n",
       "6339              2.0   0.000000       0  \n",
       "6340              2.0   0.000000       0  \n",
       "6341              5.0   0.000000       0  \n",
       "6342              1.0   0.000000       0  \n",
       "6343              5.0   0.000000       0  \n",
       "6344              4.0   0.000000       0  \n",
       "6345              5.0   0.000000       0  \n",
       "6346              2.0   0.000000       0  \n",
       "6347              2.0   0.000000       0  \n",
       "6348              4.0   0.000000       0  \n",
       "6349              5.0   0.000000       0  \n",
       "6350              6.0   0.000000       0  \n",
       "6351              1.0   0.000000       0  \n",
       "6352              2.0   0.000000       0  \n",
       "6353              5.0   0.000000       0  \n",
       "6354              5.0   0.000000       0  \n",
       "6355              4.0   0.000000       0  \n",
       "6356              5.0   0.000000       0  \n",
       "6357              4.0   0.000000       0  \n",
       "6358              1.0   0.000000       0  \n",
       "6359              5.0   0.000000       0  \n",
       "6360              3.0   0.000000       0  \n",
       "6361              3.0   0.000000       0  \n",
       "6362              5.0   0.000000       0  \n",
       "6363              1.0   0.000000       0  \n",
       "6364              4.0   0.000000       0  \n",
       "6365              4.0   0.000000       0  \n",
       "\n",
       "[6366 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "dta = sm.datasets.fair.load_pandas().data\n",
    "\n",
    "# add \"affair\" column: 1 represents having affairs, 0 represents not\n",
    "dta['affair'] = (dta.affairs > 0).astype(int)\n",
    "\n",
    "dta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Исследование данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affair</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.329701</td>\n",
       "      <td>28.390679</td>\n",
       "      <td>7.989335</td>\n",
       "      <td>1.238813</td>\n",
       "      <td>2.504521</td>\n",
       "      <td>14.322977</td>\n",
       "      <td>3.405286</td>\n",
       "      <td>3.833758</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.647345</td>\n",
       "      <td>30.537019</td>\n",
       "      <td>11.152460</td>\n",
       "      <td>1.728933</td>\n",
       "      <td>2.261568</td>\n",
       "      <td>13.972236</td>\n",
       "      <td>3.463712</td>\n",
       "      <td>3.884559</td>\n",
       "      <td>2.187243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rate_marriage        age  yrs_married  children  religious       educ  \\\n",
       "affair                                                                          \n",
       "0            4.329701  28.390679     7.989335  1.238813   2.504521  14.322977   \n",
       "1            3.647345  30.537019    11.152460  1.728933   2.261568  13.972236   \n",
       "\n",
       "        occupation  occupation_husb   affairs  \n",
       "affair                                         \n",
       "0         3.405286         3.833758  0.000000  \n",
       "1         3.463712         3.884559  2.187243  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta.groupby('affair').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "      <th>affair</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_marriage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>33.823232</td>\n",
       "      <td>13.914141</td>\n",
       "      <td>2.308081</td>\n",
       "      <td>2.343434</td>\n",
       "      <td>13.848485</td>\n",
       "      <td>3.232323</td>\n",
       "      <td>3.838384</td>\n",
       "      <td>1.201671</td>\n",
       "      <td>0.747475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>30.471264</td>\n",
       "      <td>10.727011</td>\n",
       "      <td>1.735632</td>\n",
       "      <td>2.330460</td>\n",
       "      <td>13.864943</td>\n",
       "      <td>3.327586</td>\n",
       "      <td>3.764368</td>\n",
       "      <td>1.615745</td>\n",
       "      <td>0.635057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>30.008056</td>\n",
       "      <td>10.239174</td>\n",
       "      <td>1.638469</td>\n",
       "      <td>2.308157</td>\n",
       "      <td>14.001007</td>\n",
       "      <td>3.402820</td>\n",
       "      <td>3.798590</td>\n",
       "      <td>1.371281</td>\n",
       "      <td>0.550856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>28.856601</td>\n",
       "      <td>8.816905</td>\n",
       "      <td>1.369536</td>\n",
       "      <td>2.400981</td>\n",
       "      <td>14.144514</td>\n",
       "      <td>3.420161</td>\n",
       "      <td>3.835861</td>\n",
       "      <td>0.674837</td>\n",
       "      <td>0.322926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>28.574702</td>\n",
       "      <td>8.311662</td>\n",
       "      <td>1.252794</td>\n",
       "      <td>2.506334</td>\n",
       "      <td>14.399776</td>\n",
       "      <td>3.454918</td>\n",
       "      <td>3.892697</td>\n",
       "      <td>0.348174</td>\n",
       "      <td>0.181446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age  yrs_married  children  religious       educ  \\\n",
       "rate_marriage                                                           \n",
       "1.0            33.823232    13.914141  2.308081   2.343434  13.848485   \n",
       "2.0            30.471264    10.727011  1.735632   2.330460  13.864943   \n",
       "3.0            30.008056    10.239174  1.638469   2.308157  14.001007   \n",
       "4.0            28.856601     8.816905  1.369536   2.400981  14.144514   \n",
       "5.0            28.574702     8.311662  1.252794   2.506334  14.399776   \n",
       "\n",
       "               occupation  occupation_husb   affairs    affair  \n",
       "rate_marriage                                                   \n",
       "1.0              3.232323         3.838384  1.201671  0.747475  \n",
       "2.0              3.327586         3.764368  1.615745  0.635057  \n",
       "3.0              3.402820         3.798590  1.371281  0.550856  \n",
       "4.0              3.420161         3.835861  0.674837  0.322926  \n",
       "5.0              3.454918         3.892697  0.348174  0.181446  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta.groupby('rate_marriage').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with histograms of education and marriage rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114f6dd30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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21cwGBwcZGhrq2f0vXbqUU089dUrua+bMmdNiEuWu+lrrNeeWj5sV64k1a9bkLqFxdsXM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\nxczMzGrNzYqZmZnV2v8Pfdt9lqgU/BQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f55be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of education\n",
    "dta.educ.hist()\n",
    "plt.title('Histogram of Education')\n",
    "plt.xlabel('Education Level')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115040400>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FtKT3X0XEtZLmkirvW9tecivpUCKk0z/3thYtHdrsTbq764OKJbbvaGljZmZm09y0KFxI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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f5aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of marriage rating\n",
    "dta.rate_marriage.hist()\n",
    "plt.title('Histogram of Marriage Rating')\n",
    "plt.xlabel('Marriage Rating')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115076e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33XRTm+vOnj17mderrroqm266KUuWLGHRokX07NmTr3zlK9x+++1MnTp1ufXz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115040128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot of marriage rating grouped by affair (True or False)\n",
    "pd.crosstab(dta.rate_marriage, dta.affair.astype(bool)).plot(kind='bar')\n",
    "plt.title('Marriage Rating Distribution by Affair Status')\n",
    "plt.xlabel('Marriage Rating')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114e822b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EDADGA49FRGWe72pmZrmZOXOmn/3RDrSKpCIibsnmpDiDdNljKjA8It7JqvQH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f507b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "affair_yrs_married = pd.crosstab(dta.yrs_married, dta.affair.astype(bool))\n",
    "affair_yrs_married.div(affair_yrs_married.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Affair Percentage by Years Married')\n",
    "plt.xlabel('Years Married')\n",
    "plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Дополнительная обработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>affair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6336</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6342</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6343</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6348</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6349</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6350</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6353</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6354</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6355</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6356</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6358</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6359</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6361</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6366 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      affair\n",
       "0        1.0\n",
       "1        1.0\n",
       "2        1.0\n",
       "3        1.0\n",
       "4        1.0\n",
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       "7        1.0\n",
       "8        1.0\n",
       "9        1.0\n",
       "10       1.0\n",
       "11       1.0\n",
       "12       1.0\n",
       "13       1.0\n",
       "14       1.0\n",
       "15       1.0\n",
       "16       1.0\n",
       "17       1.0\n",
       "18       1.0\n",
       "19       1.0\n",
       "20       1.0\n",
       "21       1.0\n",
       "22       1.0\n",
       "23       1.0\n",
       "24       1.0\n",
       "25       1.0\n",
       "26       1.0\n",
       "27       1.0\n",
       "28       1.0\n",
       "29       1.0\n",
       "...      ...\n",
       "6336     0.0\n",
       "6337     0.0\n",
       "6338     0.0\n",
       "6339     0.0\n",
       "6340     0.0\n",
       "6341     0.0\n",
       "6342     0.0\n",
       "6343     0.0\n",
       "6344     0.0\n",
       "6345     0.0\n",
       "6346     0.0\n",
       "6347     0.0\n",
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       "6349     0.0\n",
       "6350     0.0\n",
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       "6352     0.0\n",
       "6353     0.0\n",
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       "6355     0.0\n",
       "6356     0.0\n",
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       "6359     0.0\n",
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       "6364     0.0\n",
       "6365     0.0\n",
       "\n",
       "[6366 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dataframes with an intercept column and dummy variables for\n",
    "# occupation and occupation_husb\n",
    "y, X = dmatrices('affair ~ rate_marriage + age + yrs_married + children + \\\n",
    "                  religious + educ + C(occupation) + C(occupation_husb)',\n",
    "                  dta, return_type=\"dataframe\")\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intercept</th>\n",
       "      <th>C(occupation)[T.2.0]</th>\n",
       "      <th>C(occupation)[T.3.0]</th>\n",
       "      <th>C(occupation)[T.4.0]</th>\n",
       "      <th>C(occupation)[T.5.0]</th>\n",
       "      <th>C(occupation)[T.6.0]</th>\n",
       "      <th>C(occupation_husb)[T.2.0]</th>\n",
       "      <th>C(occupation_husb)[T.3.0]</th>\n",
       "      <th>C(occupation_husb)[T.4.0]</th>\n",
       "      <th>C(occupation_husb)[T.5.0]</th>\n",
       "      <th>C(occupation_husb)[T.6.0]</th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
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       "      <td>3.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</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",
       "      <td>...</td>\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>6336</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6342</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6343</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6348</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6349</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6350</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6353</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6354</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6355</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6356</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6358</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6359</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6361</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6366 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Intercept  C(occupation)[T.2.0]  C(occupation)[T.3.0]  \\\n",
       "0           1.0                   1.0                   0.0   \n",
       "1           1.0                   0.0                   1.0   \n",
       "2           1.0                   0.0                   1.0   \n",
       "3           1.0                   0.0                   0.0   \n",
       "4           1.0                   0.0                   1.0   \n",
       "5           1.0                   0.0                   1.0   \n",
       "6           1.0                   0.0                   0.0   \n",
       "7           1.0                   1.0                   0.0   \n",
       "8           1.0                   0.0                   1.0   \n",
       "9           1.0                   0.0                   1.0   \n",
       "10          1.0                   0.0                   1.0   \n",
       "11          1.0                   0.0                   1.0   \n",
       "12          1.0                   1.0                   0.0   \n",
       "13          1.0                   0.0                   1.0   \n",
       "14          1.0                   0.0                   0.0   \n",
       "15          1.0                   0.0                   1.0   \n",
       "16          1.0                   0.0                   1.0   \n",
       "17          1.0                   0.0                   0.0   \n",
       "18          1.0                   0.0                   0.0   \n",
       "19          1.0                   0.0                   0.0   \n",
       "20          1.0                   0.0                   1.0   \n",
       "21          1.0                   0.0                   1.0   \n",
       "22          1.0                   0.0                   0.0   \n",
       "23          1.0                   0.0                   1.0   \n",
       "24          1.0                   0.0                   0.0   \n",
       "25          1.0                   0.0                   0.0   \n",
       "26          1.0                   1.0                   0.0   \n",
       "27          1.0                   0.0                   1.0   \n",
       "28          1.0                   0.0                   0.0   \n",
       "29          1.0                   0.0                   1.0   \n",
       "...         ...                   ...                   ...   \n",
       "6336        1.0                   0.0                   0.0   \n",
       "6337        1.0                   0.0                   0.0   \n",
       "6338        1.0                   1.0                   0.0   \n",
       "6339        1.0                   0.0                   0.0   \n",
       "6340        1.0                   0.0                   0.0   \n",
       "6341        1.0                   0.0                   0.0   \n",
       "6342        1.0                   0.0                   0.0   \n",
       "6343        1.0                   0.0                   1.0   \n",
       "6344        1.0                   0.0                   1.0   \n",
       "6345        1.0                   0.0                   1.0   \n",
       "6346        1.0                   0.0                   0.0   \n",
       "6347        1.0                   0.0                   1.0   \n",
       "6348        1.0                   0.0                   0.0   \n",
       "6349        1.0                   0.0                   0.0   \n",
       "6350        1.0                   0.0                   1.0   \n",
       "6351        1.0                   0.0                   1.0   \n",
       "6352        1.0                   0.0                   1.0   \n",
       "6353        1.0                   0.0                   1.0   \n",
       "6354        1.0                   0.0                   1.0   \n",
       "6355        1.0                   0.0                   1.0   \n",
       "6356        1.0                   0.0                   1.0   \n",
       "6357        1.0                   0.0                   0.0   \n",
       "6358        1.0                   0.0                   1.0   \n",
       "6359        1.0                   0.0                   0.0   \n",
       "6360        1.0                   0.0                   1.0   \n",
       "6361        1.0                   0.0                   0.0   \n",
       "6362        1.0                   0.0                   0.0   \n",
       "6363        1.0                   0.0                   1.0   \n",
       "6364        1.0                   0.0                   1.0   \n",
       "6365        1.0                   1.0                   0.0   \n",
       "\n",
       "      C(occupation)[T.4.0]  C(occupation)[T.5.0]  C(occupation)[T.6.0]  \\\n",
       "0                      0.0                   0.0                   0.0   \n",
       "1                      0.0                   0.0                   0.0   \n",
       "2                      0.0                   0.0                   0.0   \n",
       "3                      0.0                   1.0                   0.0   \n",
       "4                      0.0                   0.0                   0.0   \n",
       "5                      0.0                   0.0                   0.0   \n",
       "6                      0.0                   1.0                   0.0   \n",
       "7                      0.0                   0.0                   0.0   \n",
       "8                      0.0                   0.0                   0.0   \n",
       "9                      0.0                   0.0                   0.0   \n",
       "10                     0.0                   0.0                   0.0   \n",
       "11                     0.0                   0.0                   0.0   \n",
       "12                     0.0                   0.0                   0.0   \n",
       "13                     0.0                   0.0                   0.0   \n",
       "14                     1.0                   0.0                   0.0   \n",
       "15                     0.0                   0.0                   0.0   \n",
       "16                     0.0                   0.0                   0.0   \n",
       "17                     0.0                   1.0                   0.0   \n",
       "18                     1.0                   0.0                   0.0   \n",
       "19                     0.0                   1.0                   0.0   \n",
       "20                     0.0                   0.0                   0.0   \n",
       "21                     0.0                   0.0                   0.0   \n",
       "22                     0.0                   1.0                   0.0   \n",
       "23                     0.0                   0.0                   0.0   \n",
       "24                     0.0                   1.0                   0.0   \n",
       "25                     1.0                   0.0                   0.0   \n",
       "26                     0.0                   0.0                   0.0   \n",
       "27                     0.0                   0.0                   0.0   \n",
       "28                     1.0                   0.0                   0.0   \n",
       "29                     0.0                   0.0                   0.0   \n",
       "...                    ...                   ...                   ...   \n",
       "6336                   0.0                   1.0                   0.0   \n",
       "6337                   1.0                   0.0                   0.0   \n",
       "6338                   0.0                   0.0                   0.0   \n",
       "6339                   1.0                   0.0                   0.0   \n",
       "6340                   1.0                   0.0                   0.0   \n",
       "6341                   1.0                   0.0                   0.0   \n",
       "6342                   1.0                   0.0                   0.0   \n",
       "6343                   0.0                   0.0                   0.0   \n",
       "6344                   0.0                   0.0                   0.0   \n",
       "6345                   0.0                   0.0                   0.0   \n",
       "6346                   1.0                   0.0                   0.0   \n",
       "6347                   0.0                   0.0                   0.0   \n",
       "6348                   1.0                   0.0                   0.0   \n",
       "6349                   0.0                   1.0                   0.0   \n",
       "6350                   0.0                   0.0                   0.0   \n",
       "6351                   0.0                   0.0                   0.0   \n",
       "6352                   0.0                   0.0                   0.0   \n",
       "6353                   0.0                   0.0                   0.0   \n",
       "6354                   0.0                   0.0                   0.0   \n",
       "6355                   0.0                   0.0                   0.0   \n",
       "6356                   0.0                   0.0                   0.0   \n",
       "6357                   1.0                   0.0                   0.0   \n",
       "6358                   0.0                   0.0                   0.0   \n",
       "6359                   0.0                   1.0                   0.0   \n",
       "6360                   0.0                   0.0                   0.0   \n",
       "6361                   1.0                   0.0                   0.0   \n",
       "6362                   0.0                   1.0                   0.0   \n",
       "6363                   0.0                   0.0                   0.0   \n",
       "6364                   0.0                   0.0                   0.0   \n",
       "6365                   0.0                   0.0                   0.0   \n",
       "\n",
       "      C(occupation_husb)[T.2.0]  C(occupation_husb)[T.3.0]  \\\n",
       "0                           0.0                        0.0   \n",
       "1                           0.0                        0.0   \n",
       "2                           0.0                        0.0   \n",
       "3                           0.0                        0.0   \n",
       "4                           0.0                        0.0   \n",
       "5                           0.0                        0.0   \n",
       "6                           0.0                        0.0   \n",
       "7                           0.0                        1.0   \n",
       "8                           0.0                        1.0   \n",
       "9                           0.0                        0.0   \n",
       "10                          0.0                        0.0   \n",
       "11                          0.0                        0.0   \n",
       "12                          0.0                        1.0   \n",
       "13                          1.0                        0.0   \n",
       "14                          0.0                        0.0   \n",
       "15                          0.0                        0.0   \n",
       "16                          0.0                        0.0   \n",
       "17                          1.0                        0.0   \n",
       "18                          0.0                        0.0   \n",
       "19                          0.0                        0.0   \n",
       "20                          0.0                        0.0   \n",
       "21                          1.0                        0.0   \n",
       "22                          0.0                        0.0   \n",
       "23                          0.0                        0.0   \n",
       "24                          0.0                        0.0   \n",
       "25                          0.0                        0.0   \n",
       "26                          0.0                        0.0   \n",
       "27                          0.0                        0.0   \n",
       "28                          1.0                        0.0   \n",
       "29                          0.0                        0.0   \n",
       "...                         ...                        ...   \n",
       "6336                        0.0                        0.0   \n",
       "6337                        0.0                        0.0   \n",
       "6338                        1.0                        0.0   \n",
       "6339                        1.0                        0.0   \n",
       "6340                        1.0                        0.0   \n",
       "6341                        0.0                        0.0   \n",
       "6342                        0.0                        0.0   \n",
       "6343                        0.0                        0.0   \n",
       "6344                        0.0                        0.0   \n",
       "6345                        0.0                        0.0   \n",
       "6346                        1.0                        0.0   \n",
       "6347                        1.0                        0.0   \n",
       "6348                        0.0                        0.0   \n",
       "6349                        0.0                        0.0   \n",
       "6350                        0.0                        0.0   \n",
       "6351                        0.0                        0.0   \n",
       "6352                        1.0                        0.0   \n",
       "6353                        0.0                        0.0   \n",
       "6354                        0.0                        0.0   \n",
       "6355                        0.0                        0.0   \n",
       "6356                        0.0                        0.0   \n",
       "6357                        0.0                        0.0   \n",
       "6358                        0.0                        0.0   \n",
       "6359                        0.0                        0.0   \n",
       "6360                        0.0                        1.0   \n",
       "6361                        0.0                        1.0   \n",
       "6362                        0.0                        0.0   \n",
       "6363                        0.0                        0.0   \n",
       "6364                        0.0                        0.0   \n",
       "6365                        0.0                        0.0   \n",
       "\n",
       "      C(occupation_husb)[T.4.0]  C(occupation_husb)[T.5.0]  \\\n",
       "0                           0.0                        1.0   \n",
       "1                           1.0                        0.0   \n",
       "2                           0.0                        1.0   \n",
       "3                           0.0                        1.0   \n",
       "4                           1.0                        0.0   \n",
       "5                           1.0                        0.0   \n",
       "6                           1.0                        0.0   \n",
       "7                           0.0                        0.0   \n",
       "8                           0.0                        0.0   \n",
       "9                           0.0                        1.0   \n",
       "10                          0.0                        1.0   \n",
       "11                          0.0                        1.0   \n",
       "12                          0.0                        0.0   \n",
       "13                          0.0                        0.0   \n",
       "14                          1.0                        0.0   \n",
       "15                          0.0                        0.0   \n",
       "16                          0.0                        1.0   \n",
       "17                          0.0                        0.0   \n",
       "18                          1.0                        0.0   \n",
       "19                          1.0                        0.0   \n",
       "20                          1.0                        0.0   \n",
       "21                          0.0                        0.0   \n",
       "22                          1.0                        0.0   \n",
       "23                          1.0                        0.0   \n",
       "24                          1.0                        0.0   \n",
       "25                          0.0                        0.0   \n",
       "26                          0.0                        1.0   \n",
       "27                          1.0                        0.0   \n",
       "28                          0.0                        0.0   \n",
       "29                          0.0                        0.0   \n",
       "...                         ...                        ...   \n",
       "6336                        1.0                        0.0   \n",
       "6337                        1.0                        0.0   \n",
       "6338                        0.0                        0.0   \n",
       "6339                        0.0                        0.0   \n",
       "6340                        0.0                        0.0   \n",
       "6341                        0.0                        1.0   \n",
       "6342                        0.0                        0.0   \n",
       "6343                        0.0                        1.0   \n",
       "6344                        1.0                        0.0   \n",
       "6345                        0.0                        1.0   \n",
       "6346                        0.0                        0.0   \n",
       "6347                        0.0                        0.0   \n",
       "6348                        1.0                        0.0   \n",
       "6349                        0.0                        1.0   \n",
       "6350                        0.0                        0.0   \n",
       "6351                        0.0                        0.0   \n",
       "6352                        0.0                        0.0   \n",
       "6353                        0.0                        1.0   \n",
       "6354                        0.0                        1.0   \n",
       "6355                        1.0                        0.0   \n",
       "6356                        0.0                        1.0   \n",
       "6357                        1.0                        0.0   \n",
       "6358                        0.0                        0.0   \n",
       "6359                        0.0                        1.0   \n",
       "6360                        0.0                        0.0   \n",
       "6361                        0.0                        0.0   \n",
       "6362                        0.0                        1.0   \n",
       "6363                        0.0                        0.0   \n",
       "6364                        1.0                        0.0   \n",
       "6365                        1.0                        0.0   \n",
       "\n",
       "      C(occupation_husb)[T.6.0]  rate_marriage   age  yrs_married  children  \\\n",
       "0                           0.0            3.0  32.0          9.0       3.0   \n",
       "1                           0.0            3.0  27.0         13.0       3.0   \n",
       "2                           0.0            4.0  22.0          2.5       0.0   \n",
       "3                           0.0            4.0  37.0         16.5       4.0   \n",
       "4                           0.0            5.0  27.0          9.0       1.0   \n",
       "5                           0.0            4.0  27.0          9.0       0.0   \n",
       "6                           0.0            5.0  37.0         23.0       5.5   \n",
       "7                           0.0            5.0  37.0         23.0       5.5   \n",
       "8                           0.0            3.0  22.0          2.5       0.0   \n",
       "9                           0.0            3.0  27.0          6.0       0.0   \n",
       "10                          0.0            2.0  27.0          6.0       2.0   \n",
       "11                          0.0            5.0  27.0          6.0       2.0   \n",
       "12                          0.0            3.0  37.0         16.5       5.5   \n",
       "13                          0.0            5.0  27.0          6.0       0.0   \n",
       "14                          0.0            4.0  22.0          6.0       1.0   \n",
       "15                          1.0            4.0  37.0          9.0       2.0   \n",
       "16                          0.0            4.0  27.0          6.0       1.0   \n",
       "17                          0.0            1.0  37.0         23.0       5.5   \n",
       "18                          0.0            2.0  42.0         23.0       2.0   \n",
       "19                          0.0            4.0  37.0          6.0       0.0   \n",
       "20                          0.0            5.0  22.0          2.5       0.0   \n",
       "21                          0.0            3.0  37.0         16.5       5.5   \n",
       "22                          0.0            3.0  42.0         23.0       5.5   \n",
       "23                          0.0            2.0  27.0          9.0       2.0   \n",
       "24                          0.0            4.0  27.0          6.0       1.0   \n",
       "25                          0.0            5.0  27.0          2.5       0.0   \n",
       "26                          0.0            2.0  27.0          6.0       2.0   \n",
       "27                          0.0            5.0  37.0         13.0       1.0   \n",
       "28                          0.0            2.0  32.0         16.5       2.0   \n",
       "29                          1.0            3.0  27.0          6.0       1.0   \n",
       "...                         ...            ...   ...          ...       ...   \n",
       "6336                        0.0            5.0  42.0         23.0       4.0   \n",
       "6337                        0.0            5.0  27.0          6.0       0.0   \n",
       "6338                        0.0            5.0  42.0         23.0       2.0   \n",
       "6339                        0.0            4.0  32.0         13.0       3.0   \n",
       "6340                        0.0            5.0  27.0         13.0       3.0   \n",
       "6341                        0.0            5.0  27.0          9.0       1.0   \n",
       "6342                        0.0            4.0  22.0          2.5       0.0   \n",
       "6343                        0.0            5.0  17.5          2.5       0.0   \n",
       "6344                        0.0            4.0  32.0         16.5       2.0   \n",
       "6345                        0.0            5.0  27.0          9.0       1.0   \n",
       "6346                        0.0            4.0  22.0          2.5       0.0   \n",
       "6347                        0.0            5.0  22.0          2.5       1.0   \n",
       "6348                        0.0            5.0  27.0          0.5       0.0   \n",
       "6349                        0.0            5.0  37.0         16.5       3.0   \n",
       "6350                        1.0            5.0  32.0         13.0       2.0   \n",
       "6351                        0.0            4.0  22.0          0.5       0.0   \n",
       "6352                        0.0            5.0  42.0         23.0       2.0   \n",
       "6353                        0.0            5.0  22.0          2.5       2.0   \n",
       "6354                        0.0            5.0  42.0         23.0       4.0   \n",
       "6355                        0.0            4.0  27.0          6.0       0.0   \n",
       "6356                        0.0            5.0  32.0         13.0       3.0   \n",
       "6357                        0.0            5.0  32.0         13.0       4.0   \n",
       "6358                        0.0            3.0  27.0          6.0       2.0   \n",
       "6359                        0.0            4.0  22.0          2.5       0.0   \n",
       "6360                        0.0            5.0  22.0          2.5       0.0   \n",
       "6361                        0.0            5.0  32.0         13.0       2.0   \n",
       "6362                        0.0            4.0  32.0         13.0       1.0   \n",
       "6363                        0.0            5.0  22.0          2.5       0.0   \n",
       "6364                        0.0            5.0  32.0          6.0       1.0   \n",
       "6365                        0.0            4.0  22.0          2.5       0.0   \n",
       "\n",
       "      religious  educ  \n",
       "0           3.0  17.0  \n",
       "1           1.0  14.0  \n",
       "2           1.0  16.0  \n",
       "3           3.0  16.0  \n",
       "4           1.0  14.0  \n",
       "5           2.0  14.0  \n",
       "6           2.0  12.0  \n",
       "7           2.0  12.0  \n",
       "8           2.0  12.0  \n",
       "9           1.0  16.0  \n",
       "10          1.0  16.0  \n",
       "11          3.0  14.0  \n",
       "12          1.0  12.0  \n",
       "13          2.0  14.0  \n",
       "14          1.0  14.0  \n",
       "15          2.0  14.0  \n",
       "16          1.0  12.0  \n",
       "17          4.0  14.0  \n",
       "18          2.0  20.0  \n",
       "19          2.0  16.0  \n",
       "20          2.0  14.0  \n",
       "21          2.0   9.0  \n",
       "22          3.0  12.0  \n",
       "23          4.0  20.0  \n",
       "24          2.0  12.0  \n",
       "25          3.0  16.0  \n",
       "26          2.0  12.0  \n",
       "27          3.0  12.0  \n",
       "28          2.0  12.0  \n",
       "29          1.0  14.0  \n",
       "...         ...   ...  \n",
       "6336        3.0  14.0  \n",
       "6337        4.0  14.0  \n",
       "6338        3.0  12.0  \n",
       "6339        3.0  16.0  \n",
       "6340        3.0  16.0  \n",
       "6341        2.0  14.0  \n",
       "6342        2.0  16.0  \n",
       "6343        4.0  12.0  \n",
       "6344        2.0  12.0  \n",
       "6345        3.0  12.0  \n",
       "6346        4.0  14.0  \n",
       "6347        2.0  12.0  \n",
       "6348        4.0  20.0  \n",
       "6349        3.0  14.0  \n",
       "6350        4.0  14.0  \n",
       "6351        2.0  16.0  \n",
       "6352        4.0  12.0  \n",
       "6353        2.0  14.0  \n",
       "6354        4.0  12.0  \n",
       "6355        3.0  12.0  \n",
       "6356        3.0  12.0  \n",
       "6357        2.0  14.0  \n",
       "6358        4.0  14.0  \n",
       "6359        3.0  16.0  \n",
       "6360        2.0  14.0  \n",
       "6361        3.0  17.0  \n",
       "6362        1.0  16.0  \n",
       "6363        2.0  14.0  \n",
       "6364        3.0  14.0  \n",
       "6365        2.0  16.0  \n",
       "\n",
       "[6366 rows x 17 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.rename(columns = {'C(occupation)[T.2.0]':'occ_2',\n",
    "                        'C(occupation)[T.3.0]':'occ_3',\n",
    "                        'C(occupation)[T.4.0]':'occ_4',\n",
    "                        'C(occupation)[T.5.0]':'occ_5',\n",
    "                        'C(occupation)[T.6.0]':'occ_6',\n",
    "                        'C(occupation_husb)[T.2.0]':'occ_husb_2',\n",
    "                        'C(occupation_husb)[T.3.0]':'occ_husb_3',\n",
    "                        'C(occupation_husb)[T.4.0]':'occ_husb_4',\n",
    "                        'C(occupation_husb)[T.5.0]':'occ_husb_5',\n",
    "                        'C(occupation_husb)[T.6.0]':'occ_husb_6'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1., ...,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flatten y into a 1-D array\n",
    "y = np.ravel(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72588752748978946"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate a logistic regression model, and fit with X and y\n",
    "model = LogisticRegression()\n",
    "model = model.fit(X, y)\n",
    "\n",
    "# check the accuracy on the training set\n",
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32249450204209867"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what percentage had affairs?\n",
    "y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.48983597,  0.18806653,  0.49894811,  0.25066854,  0.83900818,\n",
       "         0.83390852,  0.19063586,  0.29783259,  0.16140877,  0.18777078,\n",
       "         0.1940162 , -0.70312196, -0.05841791,  0.1056766 ,  0.01691949,\n",
       "        -0.37113582,  0.00401625]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.48983597],\n",
       "       [ 0.18806653],\n",
       "       [ 0.49894811],\n",
       "       [ 0.25066854],\n",
       "       [ 0.83900818],\n",
       "       [ 0.83390852],\n",
       "       [ 0.19063586],\n",
       "       [ 0.29783259],\n",
       "       [ 0.16140877],\n",
       "       [ 0.18777078],\n",
       "       [ 0.1940162 ],\n",
       "       [-0.70312196],\n",
       "       [-0.05841791],\n",
       "       [ 0.1056766 ],\n",
       "       [ 0.01691949],\n",
       "       [-0.37113582],\n",
       "       [ 0.00401625]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Intercept', array([ 1.48983597])),\n",
       " ('occ_2', array([ 0.18806653])),\n",
       " ('occ_3', array([ 0.49894811])),\n",
       " ('occ_4', array([ 0.25066854])),\n",
       " ('occ_5', array([ 0.83900818])),\n",
       " ('occ_6', array([ 0.83390852])),\n",
       " ('occ_husb_2', array([ 0.19063586])),\n",
       " ('occ_husb_3', array([ 0.29783259])),\n",
       " ('occ_husb_4', array([ 0.16140877])),\n",
       " ('occ_husb_5', array([ 0.18777078])),\n",
       " ('occ_husb_6', array([ 0.1940162])),\n",
       " ('rate_marriage', array([-0.70312196])),\n",
       " ('age', array([-0.05841791])),\n",
       " ('yrs_married', array([ 0.1056766])),\n",
       " ('children', array([ 0.01691949])),\n",
       " ('religious', array([-0.37113582])),\n",
       " ('educ', array([ 0.00401625]))]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X.columns, np.transpose(model.coef_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>[1.48983596947]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>occ_2</td>\n",
       "      <td>[0.188066529506]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>occ_3</td>\n",
       "      <td>[0.498948106888]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>occ_4</td>\n",
       "      <td>[0.250668538162]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>occ_5</td>\n",
       "      <td>[0.839008182281]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>occ_6</td>\n",
       "      <td>[0.833908524592]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>occ_husb_2</td>\n",
       "      <td>[0.190635862523]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>occ_husb_3</td>\n",
       "      <td>[0.29783259112]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>occ_husb_4</td>\n",
       "      <td>[0.161408768431]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>occ_husb_5</td>\n",
       "      <td>[0.187770784705]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>occ_husb_6</td>\n",
       "      <td>[0.19401619855]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rate_marriage</td>\n",
       "      <td>[-0.703121957357]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age</td>\n",
       "      <td>[-0.0584179111267]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>yrs_married</td>\n",
       "      <td>[0.1056765983]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>children</td>\n",
       "      <td>[0.0169194854071]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>religious</td>\n",
       "      <td>[-0.37113582114]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>educ</td>\n",
       "      <td>[0.00401624682195]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0                   1\n",
       "0       Intercept     [1.48983596947]\n",
       "1           occ_2    [0.188066529506]\n",
       "2           occ_3    [0.498948106888]\n",
       "3           occ_4    [0.250668538162]\n",
       "4           occ_5    [0.839008182281]\n",
       "5           occ_6    [0.833908524592]\n",
       "6      occ_husb_2    [0.190635862523]\n",
       "7      occ_husb_3     [0.29783259112]\n",
       "8      occ_husb_4    [0.161408768431]\n",
       "9      occ_husb_5    [0.187770784705]\n",
       "10     occ_husb_6     [0.19401619855]\n",
       "11  rate_marriage   [-0.703121957357]\n",
       "12            age  [-0.0584179111267]\n",
       "13    yrs_married      [0.1056765983]\n",
       "14       children   [0.0169194854071]\n",
       "15      religious    [-0.37113582114]\n",
       "16           educ  [0.00401624682195]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(X.columns, np.transpose(model.coef_))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Прогноз вероятности\n",
    "\n",
    "Например, 25-летняя женщина, учитель, закончила колледж, замужем 3 года, имеет 1 ребенка, очень религиозна, средне довольно браком, а ее муж бизнесмен."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashvets/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.77522204,  0.22477796]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rate_marriage\tage\tyrs_married\tchildren\treligious\teduc\toccupation\toccupation_husb\taffairs\taffair\n",
    "model.predict_proba(np.array([1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 3, 25, 3, 1, 4, 16]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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