KarenJewell.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "### Contents\n",
    "[Environment Setup](#environment)\n",
    "\n",
    "[Data Preparation](#preparation)\n",
    "\n",
    "Competition Data\n",
    "\n",
    "Match Data\n",
    "\n",
    "Events Data\n",
    "\n",
    "[Analysis: Competitions & Seasons](#analysis_comp)\n",
    "\n",
    "Competitions & Seasons\n",
    "\n",
    "Gender\n",
    "\n",
    "Home-Away\n",
    "\n",
    "\n",
    "[Analysis: Teams, Lineup & Manager](#analysis_team)\n",
    "\n",
    "[Analysis: Events](#analysis_events)\n",
    "\n",
    "Events frequency per match\n",
    "\n",
    "Event types\n",
    "\n",
    "[Attempting a Predictive Model](#predictive_model)\n",
    "\n",
    "Feature selection\n",
    "\n",
    "Model build & evaluation\n",
    "\n",
    "[Conclusion](#conclusion)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='environment'></a>\n",
    "# Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Setting environment\n",
    "import glob\n",
    "import json\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "### Allow multi-line results\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "### See all dataframe output\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1818 files found.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>format</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>json</th>\n",
       "      <td>1818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        filename\n",
       "format          \n",
       "json        1818"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Identifying all files in directory\n",
    "path = r'open-data-master/data/**'\n",
    "listing = glob.glob(path, recursive=True)\n",
    "\n",
    "### Listing the files\n",
    "filelist = pd.DataFrame()\n",
    "filelist['filename'] = [f for f in listing if '.' in f] \n",
    "filelist['format'] = filelist.apply(lambda x: x['filename'].rsplit(\".\",1)[1], axis=1)\n",
    "\n",
    "### Check number of files and file types\n",
    "print(len(filelist), \"files found.\")\n",
    "filelist.groupby(['format']).count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='preparation'></a>\n",
    "# Data Preparation\n",
    "### Competition Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competition_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>competition_name</th>\n",
       "      <th>competition_gender</th>\n",
       "      <th>season_name</th>\n",
       "      <th>match_updated</th>\n",
       "      <th>match_available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2017/2018</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2016/2017</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2006/2007</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2004/2005</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>1999/2000</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>37</td>\n",
       "      <td>90</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2020/2021</td>\n",
       "      <td>2020-09-01T11:56:49.987109</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>2020-08-17T00:16:15.196468</td>\n",
       "      <td>2020-08-17T00:16:15.196468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2020-08-24T14:34:34.401523</td>\n",
       "      <td>2020-08-24T14:34:34.401523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>International</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>male</td>\n",
       "      <td>2018</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>2020-09-04T15:17:40.862314</td>\n",
       "      <td>2020-09-04T15:17:40.862314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2017/2018</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2016/2017</td>\n",
       "      <td>2020-09-04T15:17:40.862314</td>\n",
       "      <td>2020-09-04T15:17:40.862314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2007/2008</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2006/2007</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2005/2006</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2004/2005</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NWSL</td>\n",
       "      <td>female</td>\n",
       "      <td>2018</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>England</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>male</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>2020-08-31T20:40:28.969635</td>\n",
       "      <td>2020-08-31T20:40:28.969635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>International</td>\n",
       "      <td>Women's World Cup</td>\n",
       "      <td>female</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    competition_id  season_id              country_name  \\\n",
       "0               16          4                    Europe   \n",
       "1               16          1                    Europe   \n",
       "2               16          2                    Europe   \n",
       "3               16         27                    Europe   \n",
       "4               16         26                    Europe   \n",
       "5               16         25                    Europe   \n",
       "6               16         24                    Europe   \n",
       "7               16         23                    Europe   \n",
       "8               16         22                    Europe   \n",
       "9               16         21                    Europe   \n",
       "10              16         41                    Europe   \n",
       "11              16         39                    Europe   \n",
       "12              16         37                    Europe   \n",
       "13              16         44                    Europe   \n",
       "14              16         76                    Europe   \n",
       "15              37         90                   England   \n",
       "16              37         42                   England   \n",
       "17              37          4                   England   \n",
       "18              43          3             International   \n",
       "19              11         42                     Spain   \n",
       "20              11          4                     Spain   \n",
       "21              11          1                     Spain   \n",
       "22              11          2                     Spain   \n",
       "23              11         27                     Spain   \n",
       "24              11         26                     Spain   \n",
       "25              11         25                     Spain   \n",
       "26              11         24                     Spain   \n",
       "27              11         23                     Spain   \n",
       "28              11         22                     Spain   \n",
       "29              11         21                     Spain   \n",
       "30              11         41                     Spain   \n",
       "31              11         40                     Spain   \n",
       "32              11         39                     Spain   \n",
       "33              11         38                     Spain   \n",
       "34              11         37                     Spain   \n",
       "35              49          3  United States of America   \n",
       "36               2         44                   England   \n",
       "37              72         30             International   \n",
       "\n",
       "           competition_name competition_gender season_name  \\\n",
       "0          Champions League               male   2018/2019   \n",
       "1          Champions League               male   2017/2018   \n",
       "2          Champions League               male   2016/2017   \n",
       "3          Champions League               male   2015/2016   \n",
       "4          Champions League               male   2014/2015   \n",
       "5          Champions League               male   2013/2014   \n",
       "6          Champions League               male   2012/2013   \n",
       "7          Champions League               male   2011/2012   \n",
       "8          Champions League               male   2010/2011   \n",
       "9          Champions League               male   2009/2010   \n",
       "10         Champions League               male   2008/2009   \n",
       "11         Champions League               male   2006/2007   \n",
       "12         Champions League               male   2004/2005   \n",
       "13         Champions League               male   2003/2004   \n",
       "14         Champions League               male   1999/2000   \n",
       "15  FA Women's Super League             female   2020/2021   \n",
       "16  FA Women's Super League             female   2019/2020   \n",
       "17  FA Women's Super League             female   2018/2019   \n",
       "18           FIFA World Cup               male        2018   \n",
       "19                  La Liga               male   2019/2020   \n",
       "20                  La Liga               male   2018/2019   \n",
       "21                  La Liga               male   2017/2018   \n",
       "22                  La Liga               male   2016/2017   \n",
       "23                  La Liga               male   2015/2016   \n",
       "24                  La Liga               male   2014/2015   \n",
       "25                  La Liga               male   2013/2014   \n",
       "26                  La Liga               male   2012/2013   \n",
       "27                  La Liga               male   2011/2012   \n",
       "28                  La Liga               male   2010/2011   \n",
       "29                  La Liga               male   2009/2010   \n",
       "30                  La Liga               male   2008/2009   \n",
       "31                  La Liga               male   2007/2008   \n",
       "32                  La Liga               male   2006/2007   \n",
       "33                  La Liga               male   2005/2006   \n",
       "34                  La Liga               male   2004/2005   \n",
       "35                     NWSL             female        2018   \n",
       "36           Premier League               male   2003/2004   \n",
       "37        Women's World Cup             female        2019   \n",
       "\n",
       "                 match_updated             match_available  \n",
       "0             2020-07-29T05:00            2020-07-29T05:00  \n",
       "1             2020-07-29T05:00            2020-07-29T05:00  \n",
       "2   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "3   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "4   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "5   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "6   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "7   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "8             2020-07-29T05:00            2020-07-29T05:00  \n",
       "9             2020-07-29T05:00            2020-07-29T05:00  \n",
       "10  2020-08-30T10:18:39.435424  2020-08-30T10:18:39.435424  \n",
       "11            2020-07-29T05:00            2020-07-29T05:00  \n",
       "12            2020-07-29T05:00            2020-07-29T05:00  \n",
       "13  2020-08-30T10:18:39.435424  2020-08-30T10:18:39.435424  \n",
       "14            2020-07-29T05:00            2020-07-29T05:00  \n",
       "15  2020-09-01T11:56:49.987109                        None  \n",
       "16  2020-08-17T00:16:15.196468  2020-08-17T00:16:15.196468  \n",
       "17  2020-08-24T14:34:34.401523  2020-08-24T14:34:34.401523  \n",
       "18            2020-07-29T05:00            2020-07-29T05:00  \n",
       "19  2020-09-04T15:17:40.862314  2020-09-04T15:17:40.862314  \n",
       "20            2020-07-29T05:00            2020-07-29T05:00  \n",
       "21            2020-07-29T05:00            2020-07-29T05:00  \n",
       "22  2020-09-04T15:17:40.862314  2020-09-04T15:17:40.862314  \n",
       "23            2020-07-29T05:00            2020-07-29T05:00  \n",
       "24            2020-07-29T05:00            2020-07-29T05:00  \n",
       "25            2020-07-29T05:00            2020-07-29T05:00  \n",
       "26            2020-07-29T05:00            2020-07-29T05:00  \n",
       "27            2020-07-29T05:00            2020-07-29T05:00  \n",
       "28            2020-07-29T05:00            2020-07-29T05:00  \n",
       "29            2020-07-29T05:00            2020-07-29T05:00  \n",
       "30            2020-07-29T05:00            2020-07-29T05:00  \n",
       "31            2020-07-29T05:00            2020-07-29T05:00  \n",
       "32            2020-07-29T05:00            2020-07-29T05:00  \n",
       "33            2020-07-29T05:00            2020-07-29T05:00  \n",
       "34            2020-07-29T05:00            2020-07-29T05:00  \n",
       "35            2020-07-29T05:00            2020-07-29T05:00  \n",
       "36  2020-08-31T20:40:28.969635  2020-08-31T20:40:28.969635  \n",
       "37            2020-07-29T05:00            2020-07-29T05:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Loading Competitions data (single file)\n",
    "competitions = filelist[filelist['filename'].str.contains(\"competition\")]\n",
    "comp = pd.read_json(competitions['filename'].iloc[0])\n",
    "comp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>match_date</th>\n",
       "      <th>kick_off</th>\n",
       "      <th>competition</th>\n",
       "      <th>season</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>match_status</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>metadata</th>\n",
       "      <th>match_week</th>\n",
       "      <th>competition_stage</th>\n",
       "      <th>stadium</th>\n",
       "      <th>referee</th>\n",
       "      <th>competition_id</th>\n",
       "      <th>season_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69225</td>\n",
       "      <td>2008-12-13</td>\n",
       "      <td>22:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 220, 'away_team_name': 'Real ...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 1011, 'name': 'Luis Medina Cantalejo'}</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69212</td>\n",
       "      <td>2008-10-04</td>\n",
       "      <td>22:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 212, 'away_team_name': 'Atlét...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 1070, 'name': 'Iturralde '}</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69235</td>\n",
       "      <td>2008-11-01</td>\n",
       "      <td>20:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 223, 'home_team_name': 'Málag...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 346, 'name': 'Estadio La Rosaleda', 'co...</td>\n",
       "      <td>{'id': 407, 'name': 'Carlos Velasco Carballo',...</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69232</td>\n",
       "      <td>2008-10-25</td>\n",
       "      <td>22:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 403, 'away_team_name': 'Almer...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 1007, 'name': 'Alfonso Perez Burrull'}</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69216</td>\n",
       "      <td>2009-03-07</td>\n",
       "      <td>20:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 215, 'away_team_name': 'Athle...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 221, 'name': 'Alberto Undiano', 'countr...</td>\n",
       "      <td>11</td>\n",
       "      <td>41</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>3749278</td>\n",
       "      <td>2003-12-20</td>\n",
       "      <td>16:00:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 328, 'home_team_name': 'Bolto...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-30T07:13:01.092744</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>3749526</td>\n",
       "      <td>2003-10-26</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 75, 'home_team_name': 'Charlt...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>3749052</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>16:00:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 46, 'home_team_name': 'Wolver...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>3749603</td>\n",
       "      <td>2004-05-09</td>\n",
       "      <td>17:05:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 55, 'home_team_name': 'Fulham...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>3749431</td>\n",
       "      <td>2004-04-11</td>\n",
       "      <td>17:05:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 37, 'home_team_name': 'Newcas...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>878 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  match_date      kick_off  \\\n",
       "0      69225  2008-12-13  22:00:00.000   \n",
       "1      69212  2008-10-04  22:00:00.000   \n",
       "2      69235  2008-11-01  20:00:00.000   \n",
       "3      69232  2008-10-25  22:00:00.000   \n",
       "4      69216  2009-03-07  20:00:00.000   \n",
       "..       ...         ...           ...   \n",
       "873  3749278  2003-12-20  16:00:00.000   \n",
       "874  3749526  2003-10-26  15:00:00.000   \n",
       "875  3749052  2004-02-07  16:00:00.000   \n",
       "876  3749603  2004-05-09  17:05:00.000   \n",
       "877  3749431  2004-04-11  17:05:00.000   \n",
       "\n",
       "                                           competition  \\\n",
       "0    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "1    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "2    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "3    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "4    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "..                                                 ...   \n",
       "873  {'competition_id': 2, 'country_name': 'England...   \n",
       "874  {'competition_id': 2, 'country_name': 'England...   \n",
       "875  {'competition_id': 2, 'country_name': 'England...   \n",
       "876  {'competition_id': 2, 'country_name': 'England...   \n",
       "877  {'competition_id': 2, 'country_name': 'England...   \n",
       "\n",
       "                                            season  \\\n",
       "0    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "1    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "2    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "3    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "4    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "..                                             ...   \n",
       "873  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "874  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "875  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "876  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "877  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "\n",
       "                                             home_team  \\\n",
       "0    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "1    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "2    {'home_team_id': 223, 'home_team_name': 'Málag...   \n",
       "3    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "4    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "..                                                 ...   \n",
       "873  {'home_team_id': 328, 'home_team_name': 'Bolto...   \n",
       "874  {'home_team_id': 75, 'home_team_name': 'Charlt...   \n",
       "875  {'home_team_id': 46, 'home_team_name': 'Wolver...   \n",
       "876  {'home_team_id': 55, 'home_team_name': 'Fulham...   \n",
       "877  {'home_team_id': 37, 'home_team_name': 'Newcas...   \n",
       "\n",
       "                                             away_team  home_score  \\\n",
       "0    {'away_team_id': 220, 'away_team_name': 'Real ...         2.0   \n",
       "1    {'away_team_id': 212, 'away_team_name': 'Atlét...         6.0   \n",
       "2    {'away_team_id': 217, 'away_team_name': 'Barce...         1.0   \n",
       "3    {'away_team_id': 403, 'away_team_name': 'Almer...         5.0   \n",
       "4    {'away_team_id': 215, 'away_team_name': 'Athle...         2.0   \n",
       "..                                                 ...         ...   \n",
       "873  {'away_team_id': 1, 'away_team_name': 'Arsenal...         1.0   \n",
       "874  {'away_team_id': 1, 'away_team_name': 'Arsenal...         1.0   \n",
       "875  {'away_team_id': 1, 'away_team_name': 'Arsenal...         1.0   \n",
       "876  {'away_team_id': 1, 'away_team_name': 'Arsenal...         0.0   \n",
       "877  {'away_team_id': 1, 'away_team_name': 'Arsenal...         0.0   \n",
       "\n",
       "     away_score match_status                last_updated  \\\n",
       "0           0.0    available            2020-07-29T05:00   \n",
       "1           1.0    available            2020-07-29T05:00   \n",
       "2           4.0    available            2020-07-29T05:00   \n",
       "3           0.0    available            2020-07-29T05:00   \n",
       "4           0.0    available            2020-07-29T05:00   \n",
       "..          ...          ...                         ...   \n",
       "873         1.0    available  2020-08-30T07:13:01.092744   \n",
       "874         1.0    available            2020-07-29T05:00   \n",
       "875         3.0    available            2020-07-29T05:00   \n",
       "876         1.0    available            2020-07-29T05:00   \n",
       "877         0.0    available            2020-07-29T05:00   \n",
       "\n",
       "                                              metadata  match_week  \\\n",
       "0    {'data_version': '1.1.0', 'shot_fidelity_versi...        15.0   \n",
       "1    {'data_version': '1.1.0', 'shot_fidelity_versi...         6.0   \n",
       "2    {'data_version': '1.1.0', 'shot_fidelity_versi...         9.0   \n",
       "3    {'data_version': '1.1.0', 'shot_fidelity_versi...         8.0   \n",
       "4    {'data_version': '1.1.0', 'shot_fidelity_versi...        26.0   \n",
       "..                                                 ...         ...   \n",
       "873  {'data_version': '1.1.0', 'shot_fidelity_versi...         1.0   \n",
       "874  {'data_version': '1.1.0', 'shot_fidelity_versi...        10.0   \n",
       "875  {'data_version': '1.1.0', 'shot_fidelity_versi...        24.0   \n",
       "876  {'data_version': '1.1.0', 'shot_fidelity_versi...        37.0   \n",
       "877  {'data_version': '1.1.0', 'shot_fidelity_versi...        32.0   \n",
       "\n",
       "                       competition_stage  \\\n",
       "0    {'id': 1, 'name': 'Regular Season'}   \n",
       "1    {'id': 1, 'name': 'Regular Season'}   \n",
       "2    {'id': 1, 'name': 'Regular Season'}   \n",
       "3    {'id': 1, 'name': 'Regular Season'}   \n",
       "4    {'id': 1, 'name': 'Regular Season'}   \n",
       "..                                   ...   \n",
       "873  {'id': 1, 'name': 'Regular Season'}   \n",
       "874  {'id': 1, 'name': 'Regular Season'}   \n",
       "875  {'id': 1, 'name': 'Regular Season'}   \n",
       "876  {'id': 1, 'name': 'Regular Season'}   \n",
       "877  {'id': 1, 'name': 'Regular Season'}   \n",
       "\n",
       "                                               stadium  \\\n",
       "0    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "1    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "2    {'id': 346, 'name': 'Estadio La Rosaleda', 'co...   \n",
       "3    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "4    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "..                                                 ...   \n",
       "873                                                NaN   \n",
       "874                                                NaN   \n",
       "875                                                NaN   \n",
       "876                                                NaN   \n",
       "877                                                NaN   \n",
       "\n",
       "                                               referee competition_id  \\\n",
       "0        {'id': 1011, 'name': 'Luis Medina Cantalejo'}             11   \n",
       "1                   {'id': 1070, 'name': 'Iturralde '}             11   \n",
       "2    {'id': 407, 'name': 'Carlos Velasco Carballo',...             11   \n",
       "3        {'id': 1007, 'name': 'Alfonso Perez Burrull'}             11   \n",
       "4    {'id': 221, 'name': 'Alberto Undiano', 'countr...             11   \n",
       "..                                                 ...            ...   \n",
       "873                       {'id': 1279, 'name': 'None'}              2   \n",
       "874                       {'id': 1279, 'name': 'None'}              2   \n",
       "875                       {'id': 1279, 'name': 'None'}              2   \n",
       "876                       {'id': 1279, 'name': 'None'}              2   \n",
       "877                       {'id': 1279, 'name': 'None'}              2   \n",
       "\n",
       "    season_id  \n",
       "0          41  \n",
       "1          41  \n",
       "2          41  \n",
       "3          41  \n",
       "4          41  \n",
       "..        ...  \n",
       "873        44  \n",
       "874        44  \n",
       "875        44  \n",
       "876        44  \n",
       "877        44  \n",
       "\n",
       "[878 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Preparation of match data\n",
    "matchlist = filelist[filelist['filename'].str.contains('matches')]\n",
    "\n",
    "matches = pd.DataFrame()\n",
    "for m in matchlist['filename']: \n",
    "    ma = pd.DataFrame()\n",
    "    ma = pd.read_json(m, dtype = {\"match_id\": str})\n",
    "    ma['competition_id'], ma['season_id'] = (m.split(\"/\")[3],m.split(\"/\")[4].split(\".\")[0])\n",
    "    matches = matches.append(ma,ignore_index=True)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Function to extract values from nested JSON objects\n",
    "def extract (dfcol,key):\n",
    "    if isinstance(dfcol,dict):\n",
    "        return dfcol.get(key)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Function to identify which team won the match\n",
    "def win(df):\n",
    "    if df['home_score']>df['away_score']:\n",
    "        result = 'home'\n",
    "        winteam = df['home_team_id']\n",
    "    elif df['home_score']<df['away_score']:\n",
    "        result = 'away'\n",
    "        winteam = df['away_team_id']\n",
    "    elif df['home_score']==df['away_score']:\n",
    "        result = 'draw'\n",
    "        winteam = 0\n",
    "    else:\n",
    "        result = 'err'\n",
    "        winteam = 'err'\n",
    "    return result, winteam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>match_date</th>\n",
       "      <th>kick_off</th>\n",
       "      <th>competition</th>\n",
       "      <th>season</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>match_status</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>metadata</th>\n",
       "      <th>match_week</th>\n",
       "      <th>competition_stage</th>\n",
       "      <th>stadium</th>\n",
       "      <th>referee</th>\n",
       "      <th>competition_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>home_team_id</th>\n",
       "      <th>away_team_id</th>\n",
       "      <th>competition_stage_id</th>\n",
       "      <th>competition_name</th>\n",
       "      <th>season_name</th>\n",
       "      <th>stadium_id</th>\n",
       "      <th>referee_id</th>\n",
       "      <th>match_gender</th>\n",
       "      <th>win</th>\n",
       "      <th>win_team_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69225</td>\n",
       "      <td>2008-12-13</td>\n",
       "      <td>22:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 220, 'away_team_name': 'Real ...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 1011, 'name': 'Luis Medina Cantalejo'}</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>217</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>342.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>male</td>\n",
       "      <td>home</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69212</td>\n",
       "      <td>2008-10-04</td>\n",
       "      <td>22:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 212, 'away_team_name': 'Atlét...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 1070, 'name': 'Iturralde '}</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>217</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>342.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>male</td>\n",
       "      <td>home</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69235</td>\n",
       "      <td>2008-11-01</td>\n",
       "      <td>20:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 223, 'home_team_name': 'Málag...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 346, 'name': 'Estadio La Rosaleda', 'co...</td>\n",
       "      <td>{'id': 407, 'name': 'Carlos Velasco Carballo',...</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>223</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>346.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>male</td>\n",
       "      <td>away</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69232</td>\n",
       "      <td>2008-10-25</td>\n",
       "      <td>22:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 403, 'away_team_name': 'Almer...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 1007, 'name': 'Alfonso Perez Burrull'}</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>217</td>\n",
       "      <td>403</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>342.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>male</td>\n",
       "      <td>home</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69216</td>\n",
       "      <td>2009-03-07</td>\n",
       "      <td>20:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 41, 'season_name': '2008/2009'}</td>\n",
       "      <td>{'home_team_id': 217, 'home_team_name': 'Barce...</td>\n",
       "      <td>{'away_team_id': 215, 'away_team_name': 'Athle...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 342, 'name': 'Camp Nou', 'country': {'i...</td>\n",
       "      <td>{'id': 221, 'name': 'Alberto Undiano', 'countr...</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>217</td>\n",
       "      <td>215</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>342.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>male</td>\n",
       "      <td>home</td>\n",
       "      <td>217</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",
       "      <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>873</th>\n",
       "      <td>3749278</td>\n",
       "      <td>2003-12-20</td>\n",
       "      <td>16:00:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 328, 'home_team_name': 'Bolto...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-30T07:13:01.092744</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>328</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>3749526</td>\n",
       "      <td>2003-10-26</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 75, 'home_team_name': 'Charlt...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>3749052</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>16:00:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 46, 'home_team_name': 'Wolver...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>male</td>\n",
       "      <td>away</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>3749603</td>\n",
       "      <td>2004-05-09</td>\n",
       "      <td>17:05:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 55, 'home_team_name': 'Fulham...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>male</td>\n",
       "      <td>away</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>3749431</td>\n",
       "      <td>2004-04-11</td>\n",
       "      <td>17:05:00.000</td>\n",
       "      <td>{'competition_id': 2, 'country_name': 'England...</td>\n",
       "      <td>{'season_id': 44, 'season_name': '2003/2004'}</td>\n",
       "      <td>{'home_team_id': 37, 'home_team_name': 'Newcas...</td>\n",
       "      <td>{'away_team_id': 1, 'away_team_name': 'Arsenal...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'id': 1279, 'name': 'None'}</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>878 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  match_date      kick_off  \\\n",
       "0      69225  2008-12-13  22:00:00.000   \n",
       "1      69212  2008-10-04  22:00:00.000   \n",
       "2      69235  2008-11-01  20:00:00.000   \n",
       "3      69232  2008-10-25  22:00:00.000   \n",
       "4      69216  2009-03-07  20:00:00.000   \n",
       "..       ...         ...           ...   \n",
       "873  3749278  2003-12-20  16:00:00.000   \n",
       "874  3749526  2003-10-26  15:00:00.000   \n",
       "875  3749052  2004-02-07  16:00:00.000   \n",
       "876  3749603  2004-05-09  17:05:00.000   \n",
       "877  3749431  2004-04-11  17:05:00.000   \n",
       "\n",
       "                                           competition  \\\n",
       "0    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "1    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "2    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "3    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "4    {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "..                                                 ...   \n",
       "873  {'competition_id': 2, 'country_name': 'England...   \n",
       "874  {'competition_id': 2, 'country_name': 'England...   \n",
       "875  {'competition_id': 2, 'country_name': 'England...   \n",
       "876  {'competition_id': 2, 'country_name': 'England...   \n",
       "877  {'competition_id': 2, 'country_name': 'England...   \n",
       "\n",
       "                                            season  \\\n",
       "0    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "1    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "2    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "3    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "4    {'season_id': 41, 'season_name': '2008/2009'}   \n",
       "..                                             ...   \n",
       "873  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "874  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "875  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "876  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "877  {'season_id': 44, 'season_name': '2003/2004'}   \n",
       "\n",
       "                                             home_team  \\\n",
       "0    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "1    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "2    {'home_team_id': 223, 'home_team_name': 'Málag...   \n",
       "3    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "4    {'home_team_id': 217, 'home_team_name': 'Barce...   \n",
       "..                                                 ...   \n",
       "873  {'home_team_id': 328, 'home_team_name': 'Bolto...   \n",
       "874  {'home_team_id': 75, 'home_team_name': 'Charlt...   \n",
       "875  {'home_team_id': 46, 'home_team_name': 'Wolver...   \n",
       "876  {'home_team_id': 55, 'home_team_name': 'Fulham...   \n",
       "877  {'home_team_id': 37, 'home_team_name': 'Newcas...   \n",
       "\n",
       "                                             away_team  home_score  \\\n",
       "0    {'away_team_id': 220, 'away_team_name': 'Real ...         2.0   \n",
       "1    {'away_team_id': 212, 'away_team_name': 'Atlét...         6.0   \n",
       "2    {'away_team_id': 217, 'away_team_name': 'Barce...         1.0   \n",
       "3    {'away_team_id': 403, 'away_team_name': 'Almer...         5.0   \n",
       "4    {'away_team_id': 215, 'away_team_name': 'Athle...         2.0   \n",
       "..                                                 ...         ...   \n",
       "873  {'away_team_id': 1, 'away_team_name': 'Arsenal...         1.0   \n",
       "874  {'away_team_id': 1, 'away_team_name': 'Arsenal...         1.0   \n",
       "875  {'away_team_id': 1, 'away_team_name': 'Arsenal...         1.0   \n",
       "876  {'away_team_id': 1, 'away_team_name': 'Arsenal...         0.0   \n",
       "877  {'away_team_id': 1, 'away_team_name': 'Arsenal...         0.0   \n",
       "\n",
       "     away_score match_status                last_updated  \\\n",
       "0           0.0    available            2020-07-29T05:00   \n",
       "1           1.0    available            2020-07-29T05:00   \n",
       "2           4.0    available            2020-07-29T05:00   \n",
       "3           0.0    available            2020-07-29T05:00   \n",
       "4           0.0    available            2020-07-29T05:00   \n",
       "..          ...          ...                         ...   \n",
       "873         1.0    available  2020-08-30T07:13:01.092744   \n",
       "874         1.0    available            2020-07-29T05:00   \n",
       "875         3.0    available            2020-07-29T05:00   \n",
       "876         1.0    available            2020-07-29T05:00   \n",
       "877         0.0    available            2020-07-29T05:00   \n",
       "\n",
       "                                              metadata  match_week  \\\n",
       "0    {'data_version': '1.1.0', 'shot_fidelity_versi...        15.0   \n",
       "1    {'data_version': '1.1.0', 'shot_fidelity_versi...         6.0   \n",
       "2    {'data_version': '1.1.0', 'shot_fidelity_versi...         9.0   \n",
       "3    {'data_version': '1.1.0', 'shot_fidelity_versi...         8.0   \n",
       "4    {'data_version': '1.1.0', 'shot_fidelity_versi...        26.0   \n",
       "..                                                 ...         ...   \n",
       "873  {'data_version': '1.1.0', 'shot_fidelity_versi...         1.0   \n",
       "874  {'data_version': '1.1.0', 'shot_fidelity_versi...        10.0   \n",
       "875  {'data_version': '1.1.0', 'shot_fidelity_versi...        24.0   \n",
       "876  {'data_version': '1.1.0', 'shot_fidelity_versi...        37.0   \n",
       "877  {'data_version': '1.1.0', 'shot_fidelity_versi...        32.0   \n",
       "\n",
       "                       competition_stage  \\\n",
       "0    {'id': 1, 'name': 'Regular Season'}   \n",
       "1    {'id': 1, 'name': 'Regular Season'}   \n",
       "2    {'id': 1, 'name': 'Regular Season'}   \n",
       "3    {'id': 1, 'name': 'Regular Season'}   \n",
       "4    {'id': 1, 'name': 'Regular Season'}   \n",
       "..                                   ...   \n",
       "873  {'id': 1, 'name': 'Regular Season'}   \n",
       "874  {'id': 1, 'name': 'Regular Season'}   \n",
       "875  {'id': 1, 'name': 'Regular Season'}   \n",
       "876  {'id': 1, 'name': 'Regular Season'}   \n",
       "877  {'id': 1, 'name': 'Regular Season'}   \n",
       "\n",
       "                                               stadium  \\\n",
       "0    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "1    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "2    {'id': 346, 'name': 'Estadio La Rosaleda', 'co...   \n",
       "3    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "4    {'id': 342, 'name': 'Camp Nou', 'country': {'i...   \n",
       "..                                                 ...   \n",
       "873                                                NaN   \n",
       "874                                                NaN   \n",
       "875                                                NaN   \n",
       "876                                                NaN   \n",
       "877                                                NaN   \n",
       "\n",
       "                                               referee competition_id  \\\n",
       "0        {'id': 1011, 'name': 'Luis Medina Cantalejo'}             11   \n",
       "1                   {'id': 1070, 'name': 'Iturralde '}             11   \n",
       "2    {'id': 407, 'name': 'Carlos Velasco Carballo',...             11   \n",
       "3        {'id': 1007, 'name': 'Alfonso Perez Burrull'}             11   \n",
       "4    {'id': 221, 'name': 'Alberto Undiano', 'countr...             11   \n",
       "..                                                 ...            ...   \n",
       "873                       {'id': 1279, 'name': 'None'}              2   \n",
       "874                       {'id': 1279, 'name': 'None'}              2   \n",
       "875                       {'id': 1279, 'name': 'None'}              2   \n",
       "876                       {'id': 1279, 'name': 'None'}              2   \n",
       "877                       {'id': 1279, 'name': 'None'}              2   \n",
       "\n",
       "    season_id  home_team_id  away_team_id  competition_stage_id  \\\n",
       "0          41           217           220                     1   \n",
       "1          41           217           212                     1   \n",
       "2          41           223           217                     1   \n",
       "3          41           217           403                     1   \n",
       "4          41           217           215                     1   \n",
       "..        ...           ...           ...                   ...   \n",
       "873        44           328             1                     1   \n",
       "874        44            75             1                     1   \n",
       "875        44            46             1                     1   \n",
       "876        44            55             1                     1   \n",
       "877        44            37             1                     1   \n",
       "\n",
       "    competition_name season_name  stadium_id  referee_id match_gender   win  \\\n",
       "0            La Liga   2008/2009       342.0      1011.0         male  home   \n",
       "1            La Liga   2008/2009       342.0      1070.0         male  home   \n",
       "2            La Liga   2008/2009       346.0       407.0         male  away   \n",
       "3            La Liga   2008/2009       342.0      1007.0         male  home   \n",
       "4            La Liga   2008/2009       342.0       221.0         male  home   \n",
       "..               ...         ...         ...         ...          ...   ...   \n",
       "873   Premier League   2003/2004         NaN      1279.0         male  draw   \n",
       "874   Premier League   2003/2004         NaN      1279.0         male  draw   \n",
       "875   Premier League   2003/2004         NaN      1279.0         male  away   \n",
       "876   Premier League   2003/2004         NaN      1279.0         male  away   \n",
       "877   Premier League   2003/2004         NaN      1279.0         male  draw   \n",
       "\n",
       "     win_team_id  \n",
       "0            217  \n",
       "1            217  \n",
       "2            217  \n",
       "3            217  \n",
       "4            217  \n",
       "..           ...  \n",
       "873            0  \n",
       "874            0  \n",
       "875            1  \n",
       "876            1  \n",
       "877            0  \n",
       "\n",
       "[878 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Expansion of match data\n",
    "matches['home_team_id'] = matches.apply(lambda x: extract(x['home_team'],'home_team_id'), axis=1)\n",
    "matches['away_team_id'] = matches.apply(lambda x: extract(x['away_team'],'away_team_id'), axis=1)\n",
    "matches['competition_stage_id'] = matches.apply(lambda x: extract(x['competition_stage'],'id'), axis=1)\n",
    "matches['competition_name'] = matches.apply(lambda x: extract(x['competition'],'competition_name'), axis=1)\n",
    "matches['season_name'] = matches.apply(lambda x: extract(x['season'],'season_name'), axis=1)\n",
    "matches['stadium_id'] = matches.apply(lambda x: extract(x['stadium'],'id'), axis=1)\n",
    "matches['referee_id'] = matches.apply(lambda x: extract(x['referee'],'id'), axis=1)\n",
    "matches['match_gender'] = matches.apply(lambda x: extract(x['home_team'],'home_team_gender'), axis=1)\n",
    "matches[['win','win_team_id']] = matches.apply(lambda x: win(x),axis=1, result_type='expand')\n",
    "matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Events Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nJoin on inner to remove events with no match entry and thus win/loss information, \\nbut all appear to relate so no rows are removed.\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>index</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>type</th>\n",
       "      <th>possession</th>\n",
       "      <th>possession_team</th>\n",
       "      <th>play_pattern</th>\n",
       "      <th>team</th>\n",
       "      <th>duration</th>\n",
       "      <th>tactics</th>\n",
       "      <th>related_events</th>\n",
       "      <th>player</th>\n",
       "      <th>position</th>\n",
       "      <th>location</th>\n",
       "      <th>pass</th>\n",
       "      <th>under_pressure</th>\n",
       "      <th>carry</th>\n",
       "      <th>out</th>\n",
       "      <th>counterpress</th>\n",
       "      <th>ball_receipt</th>\n",
       "      <th>clearance</th>\n",
       "      <th>duel</th>\n",
       "      <th>foul_committed</th>\n",
       "      <th>dribble</th>\n",
       "      <th>goalkeeper</th>\n",
       "      <th>shot</th>\n",
       "      <th>interception</th>\n",
       "      <th>50_50</th>\n",
       "      <th>block</th>\n",
       "      <th>ball_recovery</th>\n",
       "      <th>miscontrol</th>\n",
       "      <th>foul_won</th>\n",
       "      <th>off_camera</th>\n",
       "      <th>substitution</th>\n",
       "      <th>match_id</th>\n",
       "      <th>injury_stoppage</th>\n",
       "      <th>bad_behaviour</th>\n",
       "      <th>half_start</th>\n",
       "      <th>match_date</th>\n",
       "      <th>kick_off</th>\n",
       "      <th>competition</th>\n",
       "      <th>season</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>match_status</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>metadata</th>\n",
       "      <th>match_week</th>\n",
       "      <th>competition_stage</th>\n",
       "      <th>stadium</th>\n",
       "      <th>referee</th>\n",
       "      <th>competition_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>home_team_id</th>\n",
       "      <th>away_team_id</th>\n",
       "      <th>competition_stage_id</th>\n",
       "      <th>competition_name</th>\n",
       "      <th>season_name</th>\n",
       "      <th>stadium_id</th>\n",
       "      <th>referee_id</th>\n",
       "      <th>match_gender</th>\n",
       "      <th>win</th>\n",
       "      <th>win_team_id</th>\n",
       "      <th>type_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82b19aed-b4e8-4f38-a387-4e12f29e7b3b</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-09-14 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 35, 'name': 'Starting XI'}</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 974, 'name': 'Reading WFC'}</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Play'}</td>\n",
       "      <td>{'id': 974, 'name': 'Reading WFC'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>{'formation': 41212, 'lineup': [{'player': {'i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2275050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 37, 'country_name': 'Englan...</td>\n",
       "      <td>{'season_id': 42, 'season_name': '2019/2020'}</td>\n",
       "      <td>{'home_team_id': 974, 'home_team_name': 'Readi...</td>\n",
       "      <td>{'away_team_id': 969, 'away_team_name': 'Birmi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-11T11:12:29.925337</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 577, 'name': 'Adams Park', 'country': {...</td>\n",
       "      <td>{'id': 932, 'name': 'T. Bishop'}</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>974</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>577.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>female</td>\n",
       "      <td>home</td>\n",
       "      <td>974</td>\n",
       "      <td>Starting XI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42871193-e087-43bf-aacf-524313dff9e9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-09-14 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 35, 'name': 'Starting XI'}</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 974, 'name': 'Reading WFC'}</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Play'}</td>\n",
       "      <td>{'id': 969, 'name': 'Birmingham City WFC'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>{'formation': 4231, 'lineup': [{'player': {'id...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2275050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 37, 'country_name': 'Englan...</td>\n",
       "      <td>{'season_id': 42, 'season_name': '2019/2020'}</td>\n",
       "      <td>{'home_team_id': 974, 'home_team_name': 'Readi...</td>\n",
       "      <td>{'away_team_id': 969, 'away_team_name': 'Birmi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-11T11:12:29.925337</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 577, 'name': 'Adams Park', 'country': {...</td>\n",
       "      <td>{'id': 932, 'name': 'T. Bishop'}</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>974</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>577.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>female</td>\n",
       "      <td>home</td>\n",
       "      <td>974</td>\n",
       "      <td>Starting XI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1c806345-22e6-4815-aa08-806d3b4504a7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-09-14 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 18, 'name': 'Half Start'}</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 974, 'name': 'Reading WFC'}</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Play'}</td>\n",
       "      <td>{'id': 969, 'name': 'Birmingham City WFC'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[465b2c31-8769-41b6-ae08-eb0dae446e74]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2275050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 37, 'country_name': 'Englan...</td>\n",
       "      <td>{'season_id': 42, 'season_name': '2019/2020'}</td>\n",
       "      <td>{'home_team_id': 974, 'home_team_name': 'Readi...</td>\n",
       "      <td>{'away_team_id': 969, 'away_team_name': 'Birmi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-11T11:12:29.925337</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 577, 'name': 'Adams Park', 'country': {...</td>\n",
       "      <td>{'id': 932, 'name': 'T. Bishop'}</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>974</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>577.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>female</td>\n",
       "      <td>home</td>\n",
       "      <td>974</td>\n",
       "      <td>Half Start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>465b2c31-8769-41b6-ae08-eb0dae446e74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-09-14 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 18, 'name': 'Half Start'}</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 974, 'name': 'Reading WFC'}</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Play'}</td>\n",
       "      <td>{'id': 974, 'name': 'Reading WFC'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1c806345-22e6-4815-aa08-806d3b4504a7]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2275050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 37, 'country_name': 'Englan...</td>\n",
       "      <td>{'season_id': 42, 'season_name': '2019/2020'}</td>\n",
       "      <td>{'home_team_id': 974, 'home_team_name': 'Readi...</td>\n",
       "      <td>{'away_team_id': 969, 'away_team_name': 'Birmi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-11T11:12:29.925337</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 577, 'name': 'Adams Park', 'country': {...</td>\n",
       "      <td>{'id': 932, 'name': 'T. Bishop'}</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>974</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>577.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>female</td>\n",
       "      <td>home</td>\n",
       "      <td>974</td>\n",
       "      <td>Half Start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09f718ca-1a4d-4559-a18a-e35074609109</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-09-14 00:00:00.443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 30, 'name': 'Pass'}</td>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 969, 'name': 'Birmingham City WFC'}</td>\n",
       "      <td>{'id': 9, 'name': 'From Kick Off'}</td>\n",
       "      <td>{'id': 969, 'name': 'Birmingham City WFC'}</td>\n",
       "      <td>0.85577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8a899249-71ef-45a0-9b4c-bfd54458b9ca]</td>\n",
       "      <td>{'id': 31563, 'name': 'Rachel Williams'}</td>\n",
       "      <td>{'id': 23, 'name': 'Center Forward'}</td>\n",
       "      <td>[61.0, 40.1]</td>\n",
       "      <td>{'recipient': {'id': 15562, 'name': 'Lucy Stan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2275050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>15:00:00.000</td>\n",
       "      <td>{'competition_id': 37, 'country_name': 'Englan...</td>\n",
       "      <td>{'season_id': 42, 'season_name': '2019/2020'}</td>\n",
       "      <td>{'home_team_id': 974, 'home_team_name': 'Readi...</td>\n",
       "      <td>{'away_team_id': 969, 'away_team_name': 'Birmi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-08-11T11:12:29.925337</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 577, 'name': 'Adams Park', 'country': {...</td>\n",
       "      <td>{'id': 932, 'name': 'T. Bishop'}</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>974</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>577.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>female</td>\n",
       "      <td>home</td>\n",
       "      <td>974</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>353977</th>\n",
       "      <td>39f0bba2-0ab8-426c-8681-6c55684e8bbc</td>\n",
       "      <td>4192</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-09-14 00:47:57.324</td>\n",
       "      <td>92</td>\n",
       "      <td>57</td>\n",
       "      <td>{'id': 43, 'name': 'Carry'}</td>\n",
       "      <td>215</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>{'id': 3, 'name': 'From Free Kick'}</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>3.46720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[800bb431-2024-4334-a85c-1c2fb4e6b02e, a0171eb...</td>\n",
       "      <td>{'id': 5560, 'name': 'Carlos Alberto Vela Garr...</td>\n",
       "      <td>{'id': 23, 'name': 'Center Forward'}</td>\n",
       "      <td>[107.5, 67.1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'end_location': [113.9, 77.9]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-09-10</td>\n",
       "      <td>18:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 23, 'season_name': '2011/2012'}</td>\n",
       "      <td>{'home_team_id': 210, 'home_team_name': 'Real ...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 5043, 'name': 'Reale Arena', 'country':...</td>\n",
       "      <td>{'id': 180, 'name': 'Antonio Mateu', 'country'...</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>210</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>5043.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "      <td>Carry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353978</th>\n",
       "      <td>d502e2d6-57d5-4a57-8a4b-2d214f1d148f</td>\n",
       "      <td>4193</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-09-14 00:48:00.792</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 3, 'name': 'Dispossessed'}</td>\n",
       "      <td>215</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>{'id': 3, 'name': 'From Free Kick'}</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[800bb431-2024-4334-a85c-1c2fb4e6b02e, e3fd7b2...</td>\n",
       "      <td>{'id': 5560, 'name': 'Carlos Alberto Vela Garr...</td>\n",
       "      <td>{'id': 23, 'name': 'Center Forward'}</td>\n",
       "      <td>[113.9, 77.9]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-09-10</td>\n",
       "      <td>18:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 23, 'season_name': '2011/2012'}</td>\n",
       "      <td>{'home_team_id': 210, 'home_team_name': 'Real ...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 5043, 'name': 'Reale Arena', 'country':...</td>\n",
       "      <td>{'id': 180, 'name': 'Antonio Mateu', 'country'...</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>210</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>5043.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "      <td>Dispossessed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353979</th>\n",
       "      <td>e3fd7b21-ca28-416f-b705-c54c61380cd8</td>\n",
       "      <td>4194</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-09-14 00:48:00.792</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 4, 'name': 'Duel'}</td>\n",
       "      <td>215</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>{'id': 3, 'name': 'From Free Kick'}</td>\n",
       "      <td>{'id': 217, 'name': 'Barcelona'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[d502e2d6-57d5-4a57-8a4b-2d214f1d148f]</td>\n",
       "      <td>{'id': 22102, 'name': 'Adriano Correia Claro'}</td>\n",
       "      <td>{'id': 6, 'name': 'Left Back'}</td>\n",
       "      <td>[7.1, 3.1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'outcome': {'id': 17, 'name': 'Success Out'},...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-09-10</td>\n",
       "      <td>18:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 23, 'season_name': '2011/2012'}</td>\n",
       "      <td>{'home_team_id': 210, 'home_team_name': 'Real ...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 5043, 'name': 'Reale Arena', 'country':...</td>\n",
       "      <td>{'id': 180, 'name': 'Antonio Mateu', 'country'...</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>210</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>5043.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "      <td>Duel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353980</th>\n",
       "      <td>d2c83105-e26d-43c5-b8bc-dcd7353d6ae4</td>\n",
       "      <td>4195</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-09-14 00:48:05.647</td>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "      <td>{'id': 34, 'name': 'Half End'}</td>\n",
       "      <td>215</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>{'id': 3, 'name': 'From Free Kick'}</td>\n",
       "      <td>{'id': 217, 'name': 'Barcelona'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[bc22e7c9-90c7-4946-8582-56ffc10df7ea]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-09-10</td>\n",
       "      <td>18:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 23, 'season_name': '2011/2012'}</td>\n",
       "      <td>{'home_team_id': 210, 'home_team_name': 'Real ...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 5043, 'name': 'Reale Arena', 'country':...</td>\n",
       "      <td>{'id': 180, 'name': 'Antonio Mateu', 'country'...</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>210</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>5043.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "      <td>Half End</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353981</th>\n",
       "      <td>bc22e7c9-90c7-4946-8582-56ffc10df7ea</td>\n",
       "      <td>4196</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-09-14 00:48:05.647</td>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "      <td>{'id': 34, 'name': 'Half End'}</td>\n",
       "      <td>215</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>{'id': 3, 'name': 'From Free Kick'}</td>\n",
       "      <td>{'id': 210, 'name': 'Real Sociedad'}</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[d2c83105-e26d-43c5-b8bc-dcd7353d6ae4]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-09-10</td>\n",
       "      <td>18:00:00.000</td>\n",
       "      <td>{'competition_id': 11, 'country_name': 'Spain'...</td>\n",
       "      <td>{'season_id': 23, 'season_name': '2011/2012'}</td>\n",
       "      <td>{'home_team_id': 210, 'home_team_name': 'Real ...</td>\n",
       "      <td>{'away_team_id': 217, 'away_team_name': 'Barce...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>available</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>{'data_version': '1.1.0', 'shot_fidelity_versi...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Season'}</td>\n",
       "      <td>{'id': 5043, 'name': 'Reale Arena', 'country':...</td>\n",
       "      <td>{'id': 180, 'name': 'Antonio Mateu', 'country'...</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>210</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>5043.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>male</td>\n",
       "      <td>draw</td>\n",
       "      <td>0</td>\n",
       "      <td>Half End</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>353982 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          id  index  period  \\\n",
       "0       82b19aed-b4e8-4f38-a387-4e12f29e7b3b      1       1   \n",
       "1       42871193-e087-43bf-aacf-524313dff9e9      2       1   \n",
       "2       1c806345-22e6-4815-aa08-806d3b4504a7      3       1   \n",
       "3       465b2c31-8769-41b6-ae08-eb0dae446e74      4       1   \n",
       "4       09f718ca-1a4d-4559-a18a-e35074609109      5       1   \n",
       "...                                      ...    ...     ...   \n",
       "353977  39f0bba2-0ab8-426c-8681-6c55684e8bbc   4192       2   \n",
       "353978  d502e2d6-57d5-4a57-8a4b-2d214f1d148f   4193       2   \n",
       "353979  e3fd7b21-ca28-416f-b705-c54c61380cd8   4194       2   \n",
       "353980  d2c83105-e26d-43c5-b8bc-dcd7353d6ae4   4195       2   \n",
       "353981  bc22e7c9-90c7-4946-8582-56ffc10df7ea   4196       2   \n",
       "\n",
       "                     timestamp  minute  second  \\\n",
       "0      2020-09-14 00:00:00.000       0       0   \n",
       "1      2020-09-14 00:00:00.000       0       0   \n",
       "2      2020-09-14 00:00:00.000       0       0   \n",
       "3      2020-09-14 00:00:00.000       0       0   \n",
       "4      2020-09-14 00:00:00.443       0       0   \n",
       "...                        ...     ...     ...   \n",
       "353977 2020-09-14 00:47:57.324      92      57   \n",
       "353978 2020-09-14 00:48:00.792      93       0   \n",
       "353979 2020-09-14 00:48:00.792      93       0   \n",
       "353980 2020-09-14 00:48:05.647      93       5   \n",
       "353981 2020-09-14 00:48:05.647      93       5   \n",
       "\n",
       "                                     type  possession  \\\n",
       "0       {'id': 35, 'name': 'Starting XI'}           1   \n",
       "1       {'id': 35, 'name': 'Starting XI'}           1   \n",
       "2        {'id': 18, 'name': 'Half Start'}           1   \n",
       "3        {'id': 18, 'name': 'Half Start'}           1   \n",
       "4              {'id': 30, 'name': 'Pass'}           2   \n",
       "...                                   ...         ...   \n",
       "353977        {'id': 43, 'name': 'Carry'}         215   \n",
       "353978  {'id': 3, 'name': 'Dispossessed'}         215   \n",
       "353979          {'id': 4, 'name': 'Duel'}         215   \n",
       "353980     {'id': 34, 'name': 'Half End'}         215   \n",
       "353981     {'id': 34, 'name': 'Half End'}         215   \n",
       "\n",
       "                                   possession_team  \\\n",
       "0               {'id': 974, 'name': 'Reading WFC'}   \n",
       "1               {'id': 974, 'name': 'Reading WFC'}   \n",
       "2               {'id': 974, 'name': 'Reading WFC'}   \n",
       "3               {'id': 974, 'name': 'Reading WFC'}   \n",
       "4       {'id': 969, 'name': 'Birmingham City WFC'}   \n",
       "...                                            ...   \n",
       "353977        {'id': 210, 'name': 'Real Sociedad'}   \n",
       "353978        {'id': 210, 'name': 'Real Sociedad'}   \n",
       "353979        {'id': 210, 'name': 'Real Sociedad'}   \n",
       "353980        {'id': 210, 'name': 'Real Sociedad'}   \n",
       "353981        {'id': 210, 'name': 'Real Sociedad'}   \n",
       "\n",
       "                               play_pattern  \\\n",
       "0         {'id': 1, 'name': 'Regular Play'}   \n",
       "1         {'id': 1, 'name': 'Regular Play'}   \n",
       "2         {'id': 1, 'name': 'Regular Play'}   \n",
       "3         {'id': 1, 'name': 'Regular Play'}   \n",
       "4        {'id': 9, 'name': 'From Kick Off'}   \n",
       "...                                     ...   \n",
       "353977  {'id': 3, 'name': 'From Free Kick'}   \n",
       "353978  {'id': 3, 'name': 'From Free Kick'}   \n",
       "353979  {'id': 3, 'name': 'From Free Kick'}   \n",
       "353980  {'id': 3, 'name': 'From Free Kick'}   \n",
       "353981  {'id': 3, 'name': 'From Free Kick'}   \n",
       "\n",
       "                                              team  duration  \\\n",
       "0               {'id': 974, 'name': 'Reading WFC'}   0.00000   \n",
       "1       {'id': 969, 'name': 'Birmingham City WFC'}   0.00000   \n",
       "2       {'id': 969, 'name': 'Birmingham City WFC'}   0.00000   \n",
       "3               {'id': 974, 'name': 'Reading WFC'}   0.00000   \n",
       "4       {'id': 969, 'name': 'Birmingham City WFC'}   0.85577   \n",
       "...                                            ...       ...   \n",
       "353977        {'id': 210, 'name': 'Real Sociedad'}   3.46720   \n",
       "353978        {'id': 210, 'name': 'Real Sociedad'}   0.00000   \n",
       "353979            {'id': 217, 'name': 'Barcelona'}   0.00000   \n",
       "353980            {'id': 217, 'name': 'Barcelona'}   0.00000   \n",
       "353981        {'id': 210, 'name': 'Real Sociedad'}   0.00000   \n",
       "\n",
       "                                                  tactics  \\\n",
       "0       {'formation': 41212, 'lineup': [{'player': {'i...   \n",
       "1       {'formation': 4231, 'lineup': [{'player': {'id...   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "353977                                                NaN   \n",
       "353978                                                NaN   \n",
       "353979                                                NaN   \n",
       "353980                                                NaN   \n",
       "353981                                                NaN   \n",
       "\n",
       "                                           related_events  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                  [465b2c31-8769-41b6-ae08-eb0dae446e74]   \n",
       "3                  [1c806345-22e6-4815-aa08-806d3b4504a7]   \n",
       "4                  [8a899249-71ef-45a0-9b4c-bfd54458b9ca]   \n",
       "...                                                   ...   \n",
       "353977  [800bb431-2024-4334-a85c-1c2fb4e6b02e, a0171eb...   \n",
       "353978  [800bb431-2024-4334-a85c-1c2fb4e6b02e, e3fd7b2...   \n",
       "353979             [d502e2d6-57d5-4a57-8a4b-2d214f1d148f]   \n",
       "353980             [bc22e7c9-90c7-4946-8582-56ffc10df7ea]   \n",
       "353981             [d2c83105-e26d-43c5-b8bc-dcd7353d6ae4]   \n",
       "\n",
       "                                                   player  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                {'id': 31563, 'name': 'Rachel Williams'}   \n",
       "...                                                   ...   \n",
       "353977  {'id': 5560, 'name': 'Carlos Alberto Vela Garr...   \n",
       "353978  {'id': 5560, 'name': 'Carlos Alberto Vela Garr...   \n",
       "353979     {'id': 22102, 'name': 'Adriano Correia Claro'}   \n",
       "353980                                                NaN   \n",
       "353981                                                NaN   \n",
       "\n",
       "                                    position       location  \\\n",
       "0                                        NaN            NaN   \n",
       "1                                        NaN            NaN   \n",
       "2                                        NaN            NaN   \n",
       "3                                        NaN            NaN   \n",
       "4       {'id': 23, 'name': 'Center Forward'}   [61.0, 40.1]   \n",
       "...                                      ...            ...   \n",
       "353977  {'id': 23, 'name': 'Center Forward'}  [107.5, 67.1]   \n",
       "353978  {'id': 23, 'name': 'Center Forward'}  [113.9, 77.9]   \n",
       "353979        {'id': 6, 'name': 'Left Back'}     [7.1, 3.1]   \n",
       "353980                                   NaN            NaN   \n",
       "353981                                   NaN            NaN   \n",
       "\n",
       "                                                     pass  under_pressure  \\\n",
       "0                                                     NaN             NaN   \n",
       "1                                                     NaN             NaN   \n",
       "2                                                     NaN             NaN   \n",
       "3                                                     NaN             NaN   \n",
       "4       {'recipient': {'id': 15562, 'name': 'Lucy Stan...             NaN   \n",
       "...                                                   ...             ...   \n",
       "353977                                                NaN             1.0   \n",
       "353978                                                NaN             1.0   \n",
       "353979                                                NaN             1.0   \n",
       "353980                                                NaN             NaN   \n",
       "353981                                                NaN             NaN   \n",
       "\n",
       "                                  carry  out  counterpress ball_receipt  \\\n",
       "0                                   NaN  NaN           NaN          NaN   \n",
       "1                                   NaN  NaN           NaN          NaN   \n",
       "2                                   NaN  NaN           NaN          NaN   \n",
       "3                                   NaN  NaN           NaN          NaN   \n",
       "4                                   NaN  NaN           NaN          NaN   \n",
       "...                                 ...  ...           ...          ...   \n",
       "353977  {'end_location': [113.9, 77.9]}  NaN           NaN          NaN   \n",
       "353978                              NaN  NaN           NaN          NaN   \n",
       "353979                              NaN  NaN           NaN          NaN   \n",
       "353980                              NaN  NaN           NaN          NaN   \n",
       "353981                              NaN  NaN           NaN          NaN   \n",
       "\n",
       "       clearance                                               duel  \\\n",
       "0            NaN                                                NaN   \n",
       "1            NaN                                                NaN   \n",
       "2            NaN                                                NaN   \n",
       "3            NaN                                                NaN   \n",
       "4            NaN                                                NaN   \n",
       "...          ...                                                ...   \n",
       "353977       NaN                                                NaN   \n",
       "353978       NaN                                                NaN   \n",
       "353979       NaN  {'outcome': {'id': 17, 'name': 'Success Out'},...   \n",
       "353980       NaN                                                NaN   \n",
       "353981       NaN                                                NaN   \n",
       "\n",
       "       foul_committed dribble goalkeeper shot interception 50_50 block  \\\n",
       "0                 NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "1                 NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "2                 NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "3                 NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "4                 NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "...               ...     ...        ...  ...          ...   ...   ...   \n",
       "353977            NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "353978            NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "353979            NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "353980            NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "353981            NaN     NaN        NaN  NaN          NaN   NaN   NaN   \n",
       "\n",
       "       ball_recovery miscontrol foul_won  off_camera substitution match_id  \\\n",
       "0                NaN        NaN      NaN         NaN          NaN  2275050   \n",
       "1                NaN        NaN      NaN         NaN          NaN  2275050   \n",
       "2                NaN        NaN      NaN         NaN          NaN  2275050   \n",
       "3                NaN        NaN      NaN         NaN          NaN  2275050   \n",
       "4                NaN        NaN      NaN         NaN          NaN  2275050   \n",
       "...              ...        ...      ...         ...          ...      ...   \n",
       "353977           NaN        NaN      NaN         NaN          NaN    69264   \n",
       "353978           NaN        NaN      NaN         NaN          NaN    69264   \n",
       "353979           NaN        NaN      NaN         NaN          NaN    69264   \n",
       "353980           NaN        NaN      NaN         NaN          NaN    69264   \n",
       "353981           NaN        NaN      NaN         NaN          NaN    69264   \n",
       "\n",
       "       injury_stoppage bad_behaviour half_start  match_date      kick_off  \\\n",
       "0                  NaN           NaN        NaN  2020-01-12  15:00:00.000   \n",
       "1                  NaN           NaN        NaN  2020-01-12  15:00:00.000   \n",
       "2                  NaN           NaN        NaN  2020-01-12  15:00:00.000   \n",
       "3                  NaN           NaN        NaN  2020-01-12  15:00:00.000   \n",
       "4                  NaN           NaN        NaN  2020-01-12  15:00:00.000   \n",
       "...                ...           ...        ...         ...           ...   \n",
       "353977             NaN           NaN        NaN  2011-09-10  18:00:00.000   \n",
       "353978             NaN           NaN        NaN  2011-09-10  18:00:00.000   \n",
       "353979             NaN           NaN        NaN  2011-09-10  18:00:00.000   \n",
       "353980             NaN           NaN        NaN  2011-09-10  18:00:00.000   \n",
       "353981             NaN           NaN        NaN  2011-09-10  18:00:00.000   \n",
       "\n",
       "                                              competition  \\\n",
       "0       {'competition_id': 37, 'country_name': 'Englan...   \n",
       "1       {'competition_id': 37, 'country_name': 'Englan...   \n",
       "2       {'competition_id': 37, 'country_name': 'Englan...   \n",
       "3       {'competition_id': 37, 'country_name': 'Englan...   \n",
       "4       {'competition_id': 37, 'country_name': 'Englan...   \n",
       "...                                                   ...   \n",
       "353977  {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "353978  {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "353979  {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "353980  {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "353981  {'competition_id': 11, 'country_name': 'Spain'...   \n",
       "\n",
       "                                               season  \\\n",
       "0       {'season_id': 42, 'season_name': '2019/2020'}   \n",
       "1       {'season_id': 42, 'season_name': '2019/2020'}   \n",
       "2       {'season_id': 42, 'season_name': '2019/2020'}   \n",
       "3       {'season_id': 42, 'season_name': '2019/2020'}   \n",
       "4       {'season_id': 42, 'season_name': '2019/2020'}   \n",
       "...                                               ...   \n",
       "353977  {'season_id': 23, 'season_name': '2011/2012'}   \n",
       "353978  {'season_id': 23, 'season_name': '2011/2012'}   \n",
       "353979  {'season_id': 23, 'season_name': '2011/2012'}   \n",
       "353980  {'season_id': 23, 'season_name': '2011/2012'}   \n",
       "353981  {'season_id': 23, 'season_name': '2011/2012'}   \n",
       "\n",
       "                                                home_team  \\\n",
       "0       {'home_team_id': 974, 'home_team_name': 'Readi...   \n",
       "1       {'home_team_id': 974, 'home_team_name': 'Readi...   \n",
       "2       {'home_team_id': 974, 'home_team_name': 'Readi...   \n",
       "3       {'home_team_id': 974, 'home_team_name': 'Readi...   \n",
       "4       {'home_team_id': 974, 'home_team_name': 'Readi...   \n",
       "...                                                   ...   \n",
       "353977  {'home_team_id': 210, 'home_team_name': 'Real ...   \n",
       "353978  {'home_team_id': 210, 'home_team_name': 'Real ...   \n",
       "353979  {'home_team_id': 210, 'home_team_name': 'Real ...   \n",
       "353980  {'home_team_id': 210, 'home_team_name': 'Real ...   \n",
       "353981  {'home_team_id': 210, 'home_team_name': 'Real ...   \n",
       "\n",
       "                                                away_team  home_score  \\\n",
       "0       {'away_team_id': 969, 'away_team_name': 'Birmi...         1.0   \n",
       "1       {'away_team_id': 969, 'away_team_name': 'Birmi...         1.0   \n",
       "2       {'away_team_id': 969, 'away_team_name': 'Birmi...         1.0   \n",
       "3       {'away_team_id': 969, 'away_team_name': 'Birmi...         1.0   \n",
       "4       {'away_team_id': 969, 'away_team_name': 'Birmi...         1.0   \n",
       "...                                                   ...         ...   \n",
       "353977  {'away_team_id': 217, 'away_team_name': 'Barce...         2.0   \n",
       "353978  {'away_team_id': 217, 'away_team_name': 'Barce...         2.0   \n",
       "353979  {'away_team_id': 217, 'away_team_name': 'Barce...         2.0   \n",
       "353980  {'away_team_id': 217, 'away_team_name': 'Barce...         2.0   \n",
       "353981  {'away_team_id': 217, 'away_team_name': 'Barce...         2.0   \n",
       "\n",
       "        away_score match_status                last_updated  \\\n",
       "0              0.0    available  2020-08-11T11:12:29.925337   \n",
       "1              0.0    available  2020-08-11T11:12:29.925337   \n",
       "2              0.0    available  2020-08-11T11:12:29.925337   \n",
       "3              0.0    available  2020-08-11T11:12:29.925337   \n",
       "4              0.0    available  2020-08-11T11:12:29.925337   \n",
       "...            ...          ...                         ...   \n",
       "353977         2.0    available            2020-07-29T05:00   \n",
       "353978         2.0    available            2020-07-29T05:00   \n",
       "353979         2.0    available            2020-07-29T05:00   \n",
       "353980         2.0    available            2020-07-29T05:00   \n",
       "353981         2.0    available            2020-07-29T05:00   \n",
       "\n",
       "                                                 metadata  match_week  \\\n",
       "0       {'data_version': '1.1.0', 'shot_fidelity_versi...        12.0   \n",
       "1       {'data_version': '1.1.0', 'shot_fidelity_versi...        12.0   \n",
       "2       {'data_version': '1.1.0', 'shot_fidelity_versi...        12.0   \n",
       "3       {'data_version': '1.1.0', 'shot_fidelity_versi...        12.0   \n",
       "4       {'data_version': '1.1.0', 'shot_fidelity_versi...        12.0   \n",
       "...                                                   ...         ...   \n",
       "353977  {'data_version': '1.1.0', 'shot_fidelity_versi...         3.0   \n",
       "353978  {'data_version': '1.1.0', 'shot_fidelity_versi...         3.0   \n",
       "353979  {'data_version': '1.1.0', 'shot_fidelity_versi...         3.0   \n",
       "353980  {'data_version': '1.1.0', 'shot_fidelity_versi...         3.0   \n",
       "353981  {'data_version': '1.1.0', 'shot_fidelity_versi...         3.0   \n",
       "\n",
       "                          competition_stage  \\\n",
       "0       {'id': 1, 'name': 'Regular Season'}   \n",
       "1       {'id': 1, 'name': 'Regular Season'}   \n",
       "2       {'id': 1, 'name': 'Regular Season'}   \n",
       "3       {'id': 1, 'name': 'Regular Season'}   \n",
       "4       {'id': 1, 'name': 'Regular Season'}   \n",
       "...                                     ...   \n",
       "353977  {'id': 1, 'name': 'Regular Season'}   \n",
       "353978  {'id': 1, 'name': 'Regular Season'}   \n",
       "353979  {'id': 1, 'name': 'Regular Season'}   \n",
       "353980  {'id': 1, 'name': 'Regular Season'}   \n",
       "353981  {'id': 1, 'name': 'Regular Season'}   \n",
       "\n",
       "                                                  stadium  \\\n",
       "0       {'id': 577, 'name': 'Adams Park', 'country': {...   \n",
       "1       {'id': 577, 'name': 'Adams Park', 'country': {...   \n",
       "2       {'id': 577, 'name': 'Adams Park', 'country': {...   \n",
       "3       {'id': 577, 'name': 'Adams Park', 'country': {...   \n",
       "4       {'id': 577, 'name': 'Adams Park', 'country': {...   \n",
       "...                                                   ...   \n",
       "353977  {'id': 5043, 'name': 'Reale Arena', 'country':...   \n",
       "353978  {'id': 5043, 'name': 'Reale Arena', 'country':...   \n",
       "353979  {'id': 5043, 'name': 'Reale Arena', 'country':...   \n",
       "353980  {'id': 5043, 'name': 'Reale Arena', 'country':...   \n",
       "353981  {'id': 5043, 'name': 'Reale Arena', 'country':...   \n",
       "\n",
       "                                                  referee competition_id  \\\n",
       "0                        {'id': 932, 'name': 'T. Bishop'}             37   \n",
       "1                        {'id': 932, 'name': 'T. Bishop'}             37   \n",
       "2                        {'id': 932, 'name': 'T. Bishop'}             37   \n",
       "3                        {'id': 932, 'name': 'T. Bishop'}             37   \n",
       "4                        {'id': 932, 'name': 'T. Bishop'}             37   \n",
       "...                                                   ...            ...   \n",
       "353977  {'id': 180, 'name': 'Antonio Mateu', 'country'...             11   \n",
       "353978  {'id': 180, 'name': 'Antonio Mateu', 'country'...             11   \n",
       "353979  {'id': 180, 'name': 'Antonio Mateu', 'country'...             11   \n",
       "353980  {'id': 180, 'name': 'Antonio Mateu', 'country'...             11   \n",
       "353981  {'id': 180, 'name': 'Antonio Mateu', 'country'...             11   \n",
       "\n",
       "       season_id  home_team_id  away_team_id  competition_stage_id  \\\n",
       "0             42           974           969                     1   \n",
       "1             42           974           969                     1   \n",
       "2             42           974           969                     1   \n",
       "3             42           974           969                     1   \n",
       "4             42           974           969                     1   \n",
       "...          ...           ...           ...                   ...   \n",
       "353977        23           210           217                     1   \n",
       "353978        23           210           217                     1   \n",
       "353979        23           210           217                     1   \n",
       "353980        23           210           217                     1   \n",
       "353981        23           210           217                     1   \n",
       "\n",
       "               competition_name season_name  stadium_id  referee_id  \\\n",
       "0       FA Women's Super League   2019/2020       577.0       932.0   \n",
       "1       FA Women's Super League   2019/2020       577.0       932.0   \n",
       "2       FA Women's Super League   2019/2020       577.0       932.0   \n",
       "3       FA Women's Super League   2019/2020       577.0       932.0   \n",
       "4       FA Women's Super League   2019/2020       577.0       932.0   \n",
       "...                         ...         ...         ...         ...   \n",
       "353977                  La Liga   2011/2012      5043.0       180.0   \n",
       "353978                  La Liga   2011/2012      5043.0       180.0   \n",
       "353979                  La Liga   2011/2012      5043.0       180.0   \n",
       "353980                  La Liga   2011/2012      5043.0       180.0   \n",
       "353981                  La Liga   2011/2012      5043.0       180.0   \n",
       "\n",
       "       match_gender   win  win_team_id     type_name  \n",
       "0            female  home          974   Starting XI  \n",
       "1            female  home          974   Starting XI  \n",
       "2            female  home          974    Half Start  \n",
       "3            female  home          974    Half Start  \n",
       "4            female  home          974          Pass  \n",
       "...             ...   ...          ...           ...  \n",
       "353977         male  draw            0         Carry  \n",
       "353978         male  draw            0  Dispossessed  \n",
       "353979         male  draw            0          Duel  \n",
       "353980         male  draw            0      Half End  \n",
       "353981         male  draw            0      Half End  \n",
       "\n",
       "[353982 rows x 69 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Preparation and expansion of match events data\n",
    "eventslist = filelist[filelist['filename'].str.contains('events')]\n",
    "\n",
    "events = pd.DataFrame()\n",
    "for e in eventslist['filename'][0:100]: \n",
    "    ev = pd.DataFrame()\n",
    "    ev = pd.read_json(e)\n",
    "    ev['match_id'] = str((e.split(\"/\")[3].split(\".\")[0]))\n",
    "    events = events.append(ev,ignore_index=True)\n",
    "\n",
    "### Combining match and event data\n",
    "events = events.merge(matches, left_on='match_id', right_on='match_id', how='inner')\n",
    "'''\n",
    "Join on inner to remove events with no match entry and thus win/loss information, \n",
    "but all appear to relate so no rows are removed.\n",
    "'''\n",
    "### Identifying event type by common name\n",
    "events['type_name'] = events.apply(lambda x: extract(x['type'],'name'), axis=1)\n",
    "events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analysis_comp'></a>\n",
    "## Analysis: Competitions & Seasons\n",
    "### Competition & Seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More than half of the matches in this dataset are from the La Liga tournament (55%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='competition_name', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]),\n",
       " [Text(0, 0, 'La Liga'),\n",
       "  Text(1, 0, \"FA Women's Super League\"),\n",
       "  Text(2, 0, 'FIFA World Cup'),\n",
       "  Text(3, 0, \"Women's World Cup\"),\n",
       "  Text(4, 0, 'NWSL'),\n",
       "  Text(5, 0, 'Premier League'),\n",
       "  Text(6, 0, 'Champions League')])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "La Liga                    0.55\n",
       "FA Women's Super League    0.22\n",
       "FIFA World Cup             0.07\n",
       "Women's World Cup          0.06\n",
       "NWSL                       0.04\n",
       "Premier League             0.04\n",
       "Champions League           0.02\n",
       "Name: competition_name, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Match distribution by competition\n",
    "sns.countplot(x='competition_name', \n",
    "              data=matches, \n",
    "              order = matches['competition_name'].value_counts().index)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n",
    "round(matches['competition_name'].value_counts() / len(matches),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking into the seasons by competitions, this dataset contains matches \n",
    "for 16 seasons of the La Liga, 14 of the Champions League, 2 for the FA Women's Super League\n",
    "and 1 season each for the reamining 4 leagues.\n",
    "\n",
    "Yet a further exploration shows that despite the 14 Champion League seaons, only 1 match is covered in each season. The seasons best covered are actually the FA Women's Super League (107 and 87), the FIFA World Cup (64) and the Women's World Cup (52). Each of the La Liga seasons contain a range of 7 to 38 matches, and considering each La Liga season contains 380 matches (20 teams play each other twice)  [source: https://en.wikipedia.org/wiki/La_Liga], the best covered season contains only 10% of the season's matches. It is fair to say this dataset while still useful, is incomplete and caution should be exercised in interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "competition_name\n",
       "Champions League           14\n",
       "FA Women's Super League     2\n",
       "FIFA World Cup              1\n",
       "La Liga                    16\n",
       "NWSL                        1\n",
       "Premier League              1\n",
       "Women's World Cup           1\n",
       "Name: season_name, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "competition_name         season_name\n",
       "Champions League         2003/2004        1\n",
       "                         2004/2005        1\n",
       "                         2006/2007        1\n",
       "                         2008/2009        1\n",
       "                         2009/2010        1\n",
       "                         2010/2011        1\n",
       "                         2011/2012        1\n",
       "                         2012/2013        1\n",
       "                         2013/2014        1\n",
       "                         2014/2015        1\n",
       "                         2015/2016        1\n",
       "                         2016/2017        1\n",
       "                         2017/2018        1\n",
       "                         2018/2019        1\n",
       "FA Women's Super League  2018/2019      107\n",
       "                         2019/2020       87\n",
       "FIFA World Cup           2018            64\n",
       "La Liga                  2004/2005        7\n",
       "                         2005/2006       17\n",
       "                         2006/2007       26\n",
       "                         2007/2008       28\n",
       "                         2008/2009       31\n",
       "                         2009/2010       35\n",
       "                         2010/2011       33\n",
       "                         2011/2012       37\n",
       "                         2012/2013       32\n",
       "                         2013/2014       31\n",
       "                         2014/2015       38\n",
       "                         2015/2016       33\n",
       "                         2016/2017       34\n",
       "                         2017/2018       36\n",
       "                         2018/2019       34\n",
       "                         2019/2020       33\n",
       "NWSL                     2018            36\n",
       "Premier League           2003/2004       33\n",
       "Women's World Cup        2019            52\n",
       "Name: match_id, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Match and season distribution by competition\n",
    "matches.groupby(['competition_name'])['season_name'].nunique()\n",
    "matches.groupby(['competition_name','season_name'])['match_id'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender\n",
    "Assuming there are no mix-gendered competitions, we extract the competition genders from the home team gender. 68% of our matches are for male competitions, largely made of the La Liga matches (55% of total matches), despite our 2 womens' competitions having the best covered seasons.\n",
    "\n",
    "Also interesting to note is our dataset contains matches from 3 female competitions and 4 male competitions, thus in looking at the count of competitions the distribution seems even, but not at match level where 68% of the matches are from the 4 male competitions and the remaining 32% from the 3 female competitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_gender  competition_name       \n",
       "female        FA Women's Super League    194\n",
       "              NWSL                        36\n",
       "              Women's World Cup           52\n",
       "male          Champions League            14\n",
       "              FIFA World Cup              64\n",
       "              La Liga                    485\n",
       "              Premier League              33\n",
       "Name: match_id, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Competition by gender\n",
    "matches.groupby(['match_gender','competition_name'])['match_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='match_gender', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "male      0.68\n",
       "female    0.32\n",
       "Name: match_gender, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Matches by gender\n",
    "sns.countplot(x='match_gender', data=matches)\n",
    "plt.show()\n",
    "round(matches['match_gender'].value_counts() / len(matches),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home-Away\n",
    "In our dataset, the majority (47%) of matches are won by the home team, 37% are won by the away team and 16% are a tie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='win', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "home    0.47\n",
       "away    0.37\n",
       "draw    0.16\n",
       "Name: win, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Match distribution won at home or away\n",
    "sns.countplot(x='win', data=matches)\n",
    "plt.show()\n",
    "round(matches['win'].value_counts() / len(matches),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we split the matches by gender, we see there is a slight difference in the distribution\n",
    "of matches won at home, away and drawn. While both male and female gendered matches contain\n",
    "more matches won at home than away, the distribution is less distinct in the female matches\n",
    "than in the male matches. Because we know this dataset is incomplete, we cannot say that \n",
    "more male matches are won at home, this is only true for the limits of this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd8116be190>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "match_gender  win \n",
       "female        away    111\n",
       "              draw     37\n",
       "              home    134\n",
       "male          away    210\n",
       "              draw    105\n",
       "              home    281\n",
       "Name: match_id, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Match distribution won at home or away by gender\n",
    "g = sns.FacetGrid(matches, col=\"match_gender\")\n",
    "g.map_dataframe(sns.countplot, x=\"win\")\n",
    "matches.groupby(['match_gender','win'])['match_id'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at La Liga stats for 2017/2018 [source: https://www.soccerstats.com/latest.asp?league=spain_2018], for the full season Home wins were 47%, Away wins 30% and Draws 23%. Our sample of 36 matches for the same season is slightly unrepresentative at 49%, 35% and 16% respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='win', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "home    0.49\n",
       "away    0.35\n",
       "draw    0.16\n",
       "Name: win, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'\\n\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Match distribution won at home or away for La Liga only\n",
    "sns.countplot(x='win', data=matches[matches['competition_name']=='La Liga'])\n",
    "plt.show()\n",
    "round(matches[matches['competition_name']=='La Liga'].win.value_counts() / \n",
    "      len(matches[matches['competition_name']=='La Liga']),2)\n",
    "'''\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the chart below we see that generally the home-away win patterns are generally followed throughout all the competitions with the exception of the Champions League. This variation might be to do with the fact that unlike the other competitions using league tables, the Champions League has a knockout format, although it seems more plausible it is simply due to the sample as there is only 1 match per season for the Champions League in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x432 with 0 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='competition_name', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]),\n",
       " [Text(0, 0, 'La Liga'),\n",
       "  Text(1, 0, 'Champions League'),\n",
       "  Text(2, 0, \"Women's World Cup\"),\n",
       "  Text(3, 0, 'FIFA World Cup'),\n",
       "  Text(4, 0, \"FA Women's Super League\"),\n",
       "  Text(5, 0, 'NWSL'),\n",
       "  Text(6, 0, 'Premier League')])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Match distribution won at home or away across all competitions\n",
    "plt.figure(figsize=(18,6))\n",
    "sns.countplot(x='competition_name', data=matches, hue='win' )\n",
    "plt.xticks(rotation=90)\n",
    "#matches.groupby(['competition_name','win'])['match_id'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analysis_team'></a>\n",
    "# Analysis: Teams, Lineup & Manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because there is not a complete dataset, especially of a series, I have chosen not to consider player lineup or manager as a match winning feature. We would typically expect the football team and manager to remain consistent over a series (noting the starting lineup will change), and some analysis could have been made here to see if starting lineups, the use of a player, or the manager would have had any significant effect on match performance. However, as seen in the distribution of teams below, (i.e. the occurence of a team's match data as either a Home or Away team), there is a heavily skewed distribution of data in favour of the Barcelona team. Of 878 match data across all the competitions, Barcelona plays in 488 of them (56%).\n",
    "\n",
    "It might not be farfetched to suggest the original creator of the dataset is a Barcelona fan. Yet, it could also be possible that Barcelona just have the most extensive collection of match data made accessible and available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total matches:  878\n",
      "total teams:  145\n",
      "team matches:\n",
      " Barcelona                     488\n",
      "Manchester City WFC            36\n",
      "Arsenal WFC                    35\n",
      "Brighton & Hove Albion WFC     35\n",
      "Bristol City WFC               34\n",
      "West Ham United LFC            34\n",
      "Chelsea FCW                    34\n",
      "Reading WFC                    34\n",
      "Arsenal                        33\n",
      "Liverpool WFC                  33\n",
      "Everton LFC                    33\n",
      "Birmingham City WFC            32\n",
      "Real Madrid                    31\n",
      "Atlético Madrid                30\n",
      "Espanyol                       27\n",
      "Valencia                       27\n",
      "Sevilla                        27\n",
      "Athletic Bilbao                25\n",
      "Villarreal                     22\n",
      "Getafe                         22\n",
      "Real Sociedad                  21\n",
      "Real Betis                     20\n",
      "Yeovil Town LFC                19\n",
      "Levante                        19\n",
      "Manchester United              18\n",
      "Osasuna                        18\n",
      "Málaga                         17\n",
      "Deportivo La Coruna            17\n",
      "Celta Vigo                     16\n",
      "Tottenham Hotspur Women        15\n",
      "North Carolina Courage         13\n",
      "Real Zaragoza                  13\n",
      "Racing Santander               13\n",
      "Mallorca                       12\n",
      "Granada                        12\n",
      "Real Valladolid                11\n",
      "Eibar                          11\n",
      "Almería                        11\n",
      "Rayo Vallecano                 10\n",
      "Deportivo Alavés                9\n",
      "Utah Royals                     9\n",
      "Washington Spirit               9\n",
      "Sporting Gijón                  8\n",
      "Leganés                         8\n",
      "Orlando Pride                   8\n",
      "England Women's                 7\n",
      "Sweden Women's                  7\n",
      "France                          7\n",
      "Croatia                         7\n",
      "Seattle Reign                   7\n",
      "Sky Blue FC                     7\n",
      "England                         7\n",
      "United States Women's           7\n",
      "Portland Thorns                 7\n",
      "Chicago Red Stars               7\n",
      "Belgium                         7\n",
      "Netherlands Women's             7\n",
      "Las Palmas                      6\n",
      "Liverpool                       6\n",
      "Russia                          5\n",
      "France Women's                  5\n",
      "Uruguay                         5\n",
      "Germany Women's                 5\n",
      "Recreativo Huelva               5\n",
      "Italy Women's                   5\n",
      "Brazil                          5\n",
      "Houston Dash                    5\n",
      "Norway Women's                  5\n",
      "Sweden                          5\n",
      "Australia Women's               4\n",
      "Colombia                        4\n",
      "Portugal                        4\n",
      "Spain                           4\n",
      "Argentina                       4\n",
      "China PR Women's                4\n",
      "Canada Women's                  4\n",
      "Cameroon Women's                4\n",
      "Nigeria Women's                 4\n",
      "Switzerland                     4\n",
      "Girona                          4\n",
      "Brazil Women's                  4\n",
      "Japan Women's                   4\n",
      "Spain Women's                   4\n",
      "Mexico                          4\n",
      "Japan                           4\n",
      "Denmark                         4\n",
      "Morocco                         3\n",
      "Serbia                          3\n",
      "Argentina Women's               3\n",
      "South Korea                     3\n",
      "South Africa Women's            3\n",
      "Costa Rica                      3\n",
      "Tottenham Hotspur               3\n",
      "Nigeria                         3\n",
      "Scotland Women's                3\n",
      "Bayern Munich                   3\n",
      "Thailand Women's                3\n",
      "Senegal                         3\n",
      "Germany                         3\n",
      "Korea Republic Women's          3\n",
      "Egypt                           3\n",
      "Panama                          3\n",
      "Chelsea                         3\n",
      "Iran                            3\n",
      "Tunisia                         3\n",
      "Elche                           3\n",
      "Jamaica Women's                 3\n",
      "Iceland                         3\n",
      "Saudi Arabia                    3\n",
      "New Zealand Women's             3\n",
      "Poland                          3\n",
      "Chile Women's                   3\n",
      "Australia                       3\n",
      "Peru                            3\n",
      "Newcastle United                2\n",
      "Leeds United                    2\n",
      "Bolton Wanderers                2\n",
      "Manchester City                 2\n",
      "Birmingham City                 2\n",
      "Hércules                        2\n",
      "Blackburn Rovers                2\n",
      "Tenerife                        2\n",
      "Numancia                        2\n",
      "Xerez                           2\n",
      "Wolverhampton Wanderers         2\n",
      "Córdoba                         2\n",
      "Fulham                          2\n",
      "Middlesbrough                   2\n",
      "Charlton Athletic               2\n",
      "AC Milan                        2\n",
      "Juventus                        2\n",
      "Albacete                        2\n",
      "Real Murcia                     1\n",
      "Inter Milan                     1\n",
      "AS Monaco                       1\n",
      "Southampton                     1\n",
      "Borussia Dortmund               1\n",
      "Gimnàstic Tarragona             1\n",
      "Aston Villa                     1\n",
      "Everton                         1\n",
      "Leicester City                  1\n",
      "Huesca                          1\n",
      "FC Porto                        1\n",
      "Cádiz                           1\n",
      "Portsmouth                      1\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/football/lib/python3.7/site-packages/ipykernel_launcher.py:2: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "### Team distribution in dataset\n",
    "teamdist = pd.Series()\n",
    "teamdist = matches.apply(lambda x: extract(x['home_team'],'home_team_name'), axis=1)\n",
    "teamdist = teamdist.append(matches.apply(lambda x: extract(x['away_team'],'away_team_name'), axis=1))\n",
    "print('total matches: ',len(matches))\n",
    "print('total teams: ', teamdist.nunique())\n",
    "print('team matches:\\n',teamdist.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analysis_events'></a>\n",
    "# Analysis: Events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Barring the use of lineup and manager data to build a model to predict a winning team, we look at match event data. This is a really extensive dataset and the limits of my personal computer meant I was not able to extract events data for all matches, I have thus limited event analysis to the first 100 matches ordered by match ID (~11% of total matches).\n",
    "\n",
    "With this random sampling, by luck most of the distribution by competition is retained as shown in the table below showing match distribution for event data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "competition_name\n",
       "Champions League            1\n",
       "FA Women's Super League    19\n",
       "FIFA World Cup              8\n",
       "La Liga                    55\n",
       "NWSL                        5\n",
       "Premier League              1\n",
       "Women's World Cup           9\n",
       "Name: match_id, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Matches in reduced dataset by competition\n",
    "eventdist = pd.DataFrame()\n",
    "eventdist['count'] = events.groupby(['competition_name','match_id'])['id'].count()\n",
    "eventdist = eventdist.reset_index()\n",
    "events.groupby(['competition_name'])['match_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we look at the number of events recorded by competition to identify if there is some bias in the data.\n",
    "In looking at the number of events recorded per match, we see there is some slight variation per competition (where we have >1 match per competition). In the boxplot below, La Liga matches appear to have more events recorded than the FA Women's Super League. In fact, the 3 female competitions appear to have fewer events recorded than the male competitions, although it is possibly arbitary as 2 of our 4 male competitions are only represented by a single match. Further analysis using the wider dataset would be needed to come to a significant conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='competition_name', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]),\n",
       " [Text(0, 0, 'Champions League'),\n",
       "  Text(1, 0, \"FA Women's Super League\"),\n",
       "  Text(2, 0, 'FIFA World Cup'),\n",
       "  Text(3, 0, 'La Liga'),\n",
       "  Text(4, 0, 'NWSL'),\n",
       "  Text(5, 0, 'Premier League'),\n",
       "  Text(6, 0, \"Women's World Cup\")])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Event distribution per match in reduced dataset by competition\n",
    "sns.boxplot(x = 'competition_name', y='count', data=eventdist)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking deeper, La Liga matches have more events recorded and less variance than the FA Women's Super League (standard deviation of 311 and 414 respectively). This does indicate that not only do La Liga matches contain more events, they do so with greater consistency. Yet there could be multiple ways of interpreting this. Does this mean the La Liga is more \"eventful\" than the FA Women's Super League, or is it a reflection that La Liga matches are more carefully observed than the FA Women's Super League's matches?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3612.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>402.495279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2655.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3364.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3632.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3903.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4598.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count\n",
       "count    98.000000\n",
       "mean   3612.061224\n",
       "std     402.495279\n",
       "min    2655.000000\n",
       "25%    3364.750000\n",
       "50%    3632.000000\n",
       "75%    3903.750000\n",
       "max    4598.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competition_name</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>Champions League</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3433.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>3433.00</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>3433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FA Women's Super League</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3317.578947</td>\n",
       "      <td>414.407786</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>3030.50</td>\n",
       "      <td>3247.0</td>\n",
       "      <td>3558.0</td>\n",
       "      <td>4143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIFA World Cup</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3591.875000</td>\n",
       "      <td>248.300186</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>3500.25</td>\n",
       "      <td>3655.0</td>\n",
       "      <td>3768.0</td>\n",
       "      <td>3832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>La Liga</th>\n",
       "      <td>55.0</td>\n",
       "      <td>3792.490909</td>\n",
       "      <td>311.654003</td>\n",
       "      <td>2916.0</td>\n",
       "      <td>3570.00</td>\n",
       "      <td>3776.0</td>\n",
       "      <td>3989.0</td>\n",
       "      <td>4525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NWSL</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3284.400000</td>\n",
       "      <td>257.835800</td>\n",
       "      <td>2937.0</td>\n",
       "      <td>3092.00</td>\n",
       "      <td>3384.0</td>\n",
       "      <td>3478.0</td>\n",
       "      <td>3531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premier League</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3237.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3237.0</td>\n",
       "      <td>3237.00</td>\n",
       "      <td>3237.0</td>\n",
       "      <td>3237.0</td>\n",
       "      <td>3237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Women's World Cup</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3392.666667</td>\n",
       "      <td>511.909904</td>\n",
       "      <td>2945.0</td>\n",
       "      <td>3055.00</td>\n",
       "      <td>3291.0</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>4598.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count         mean         std     min      25%  \\\n",
       "competition_name                                                           \n",
       "Champions League           1.0  3433.000000         NaN  3433.0  3433.00   \n",
       "FA Women's Super League   19.0  3317.578947  414.407786  2655.0  3030.50   \n",
       "FIFA World Cup             8.0  3591.875000  248.300186  3118.0  3500.25   \n",
       "La Liga                   55.0  3792.490909  311.654003  2916.0  3570.00   \n",
       "NWSL                       5.0  3284.400000  257.835800  2937.0  3092.00   \n",
       "Premier League             1.0  3237.000000         NaN  3237.0  3237.00   \n",
       "Women's World Cup          9.0  3392.666667  511.909904  2945.0  3055.00   \n",
       "\n",
       "                            50%     75%     max  \n",
       "competition_name                                 \n",
       "Champions League         3433.0  3433.0  3433.0  \n",
       "FA Women's Super League  3247.0  3558.0  4143.0  \n",
       "FIFA World Cup           3655.0  3768.0  3832.0  \n",
       "La Liga                  3776.0  3989.0  4525.0  \n",
       "NWSL                     3384.0  3478.0  3531.0  \n",
       "Premier League           3237.0  3237.0  3237.0  \n",
       "Women's World Cup        3291.0  3521.0  4598.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Event distribution per match in reduced dataset by competition\n",
    "eventdist.describe()\n",
    "eventdist.groupby(['competition_name'])['count'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the distribution of event types in our dataset, we see in the chart below that passes and the ball being received are the two most populous events, which we would expect is normal in a game of football. \n",
    "\n",
    "The presence of a pass on its own is relatively meaningless, but the number of passes made by a winning/losing team in a match could be interesting. Another interesting feature to infer here would be the number of passes failed to be received by a winning or losing team, likely calculated as Passes - Ball Receipt. If given more time, I would have explored this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x432 with 0 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type_name', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34]),\n",
       " [Text(0, 0, 'Starting XI'),\n",
       "  Text(1, 0, 'Half Start'),\n",
       "  Text(2, 0, 'Pass'),\n",
       "  Text(3, 0, 'Ball Receipt*'),\n",
       "  Text(4, 0, 'Carry'),\n",
       "  Text(5, 0, 'Pressure'),\n",
       "  Text(6, 0, 'Miscontrol'),\n",
       "  Text(7, 0, 'Ball Recovery'),\n",
       "  Text(8, 0, 'Clearance'),\n",
       "  Text(9, 0, 'Duel'),\n",
       "  Text(10, 0, 'Foul Committed'),\n",
       "  Text(11, 0, 'Dispossessed'),\n",
       "  Text(12, 0, 'Dribbled Past'),\n",
       "  Text(13, 0, 'Dribble'),\n",
       "  Text(14, 0, 'Block'),\n",
       "  Text(15, 0, 'Foul Won'),\n",
       "  Text(16, 0, 'Goal Keeper'),\n",
       "  Text(17, 0, 'Shot'),\n",
       "  Text(18, 0, 'Interception'),\n",
       "  Text(19, 0, '50/50'),\n",
       "  Text(20, 0, 'Injury Stoppage'),\n",
       "  Text(21, 0, 'Referee Ball-Drop'),\n",
       "  Text(22, 0, 'Shield'),\n",
       "  Text(23, 0, 'Player Off'),\n",
       "  Text(24, 0, 'Player On'),\n",
       "  Text(25, 0, 'Half End'),\n",
       "  Text(26, 0, 'Offside'),\n",
       "  Text(27, 0, 'Substitution'),\n",
       "  Text(28, 0, 'Tactical Shift'),\n",
       "  Text(29, 0, 'Bad Behaviour'),\n",
       "  Text(30, 0, 'Error'),\n",
       "  Text(31, 0, 'Own Goal Against'),\n",
       "  Text(32, 0, 'Own Goal For'),\n",
       "  Text(33, 0, 'Camera On'),\n",
       "  Text(34, 0, 'Camera off')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Event type distribution in reduced dataset\n",
    "plt.figure(figsize=(18,6))\n",
    "sns.countplot(x='type_name', data=events)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n",
    "#round(events['type_name'].value_counts() / len(events),2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='predictive_model'></a>\n",
    "# Attempting a Predictive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ideally, with more time I would have extracted more features from the events data and identified the best features to use. But for the purposes of demonstrating a built model I have picked some features, considerably at random, but largely influenced by an article [source: https://www.espn.co.uk/football/blog/name/93/post/2297787/headline] suggesting that a team's confidence in playing various formations is a critical factor in match performance. This led me to wonder if a team's starting formation might be indicative of success.\n",
    "\n",
    "Along with starting formation, I also included if the team was playing at home (team_home = True) or not (team_home = False); and whether the match was played by males or females. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_home</th>\n",
       "      <th>team_won</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>343</th>\n",
       "      <th>352</th>\n",
       "      <th>433</th>\n",
       "      <th>442</th>\n",
       "      <th>451</th>\n",
       "      <th>3142</th>\n",
       "      <th>3412</th>\n",
       "      <th>3511</th>\n",
       "      <th>4141</th>\n",
       "      <th>4222</th>\n",
       "      <th>4231</th>\n",
       "      <th>4321</th>\n",
       "      <th>4411</th>\n",
       "      <th>41212</th>\n",
       "      <th>41221</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3561</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3562</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6584</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6585</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9944</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9945</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13478</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13479</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16911</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16912</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20202</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20203</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24034</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24035</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28022</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28023</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31269</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31270</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35217</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35218</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39049</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39050</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42291</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42292</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45245</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45246</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49332</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49333</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52302</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52303</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55460</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55461</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58814</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58815</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63199</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63200</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66877</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66878</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69980</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69981</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73110</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73111</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77147</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81151</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81152</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84919</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84920</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88835</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88836</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92347</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92348</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96610</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96611</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100199</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100200</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103750</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103751</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107565</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107566</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111170</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111171</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114208</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114209</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117686</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117687</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121241</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121242</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124393</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124394</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127860</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127861</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131624</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131625</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135295</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135296</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138773</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138774</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142478</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142479</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146451</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146452</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150227</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150228</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153935</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153936</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157508</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157509</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160226</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160227</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162881</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162882</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166829</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166830</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170937</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170938</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174974</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174975</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178715</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178716</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182099</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182100</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186219</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186220</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190086</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190087</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194684</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194685</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198498</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198499</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202029</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202030</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205990</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205991</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208906</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208907</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212654</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212655</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216192</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216193</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220311</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220312</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223366</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223367</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227344</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227345</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231183</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231184</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234730</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234731</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238144</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238145</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241894</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241895</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246103</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246104</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249607</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249608</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253849</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253850</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257671</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257672</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260898</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260899</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264657</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264658</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268321</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268322</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271725</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271726</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275662</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275663</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278607</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278608</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281537</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281538</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285601</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285602</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288538</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288539</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292205</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292206</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295864</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295865</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299317</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299318</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302554</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302555</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305818</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305819</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309339</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309340</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313093</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313094</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316472</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316473</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320201</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320202</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323750</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323751</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327488</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327489</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332013</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332014</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336003</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336004</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339095</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339096</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342213</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342214</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346356</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346357</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349786</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349787</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        team_home  team_won  female  male  343  352  433  442  451  3142  \\\n",
       "0            True      True       1     0    0    0    0    0    0     0   \n",
       "1           False     False       1     0    0    0    0    0    0     0   \n",
       "3561         True     False       1     0    0    0    0    0    0     0   \n",
       "3562        False      True       1     0    0    0    0    0    0     0   \n",
       "6584         True      True       0     1    0    0    1    0    0     0   \n",
       "6585        False     False       0     1    0    0    1    0    0     0   \n",
       "9944         True     False       0     1    0    0    0    1    0     0   \n",
       "9945        False      True       0     1    0    0    1    0    0     0   \n",
       "13478        True     False       0     1    0    0    0    0    0     0   \n",
       "13479       False      True       0     1    0    0    0    0    0     0   \n",
       "16911        True     False       1     0    0    0    0    0    0     0   \n",
       "16912       False      True       1     0    0    0    0    0    0     0   \n",
       "20202        True     False       0     1    0    0    0    0    0     0   \n",
       "20203       False     False       0     1    0    0    0    1    0     0   \n",
       "24034        True     False       0     1    0    0    0    1    0     0   \n",
       "24035       False      True       0     1    0    0    1    0    0     0   \n",
       "28022        True     False       1     0    0    0    0    0    0     0   \n",
       "28023       False      True       1     0    0    0    0    0    0     0   \n",
       "31269        True      True       0     1    0    0    1    0    0     0   \n",
       "31270       False     False       0     1    0    1    0    0    0     0   \n",
       "35217        True      True       0     1    0    0    0    0    0     0   \n",
       "35218       False     False       0     1    0    0    0    0    0     0   \n",
       "39049        True     False       0     1    0    0    0    0    0     0   \n",
       "39050       False     False       0     1    0    0    1    0    0     0   \n",
       "42291        True      True       1     0    0    0    0    1    0     0   \n",
       "42292       False     False       1     0    0    0    0    0    0     0   \n",
       "45245        True      True       0     1    0    0    1    0    0     0   \n",
       "45246       False     False       0     1    0    0    0    0    0     0   \n",
       "49332        True     False       1     0    0    0    0    1    0     0   \n",
       "49333       False      True       1     0    0    0    1    0    0     0   \n",
       "52302        True     False       1     0    0    0    0    0    0     0   \n",
       "52303       False      True       1     0    0    0    0    0    0     0   \n",
       "55460        True      True       0     1    0    0    0    0    0     0   \n",
       "55461       False     False       0     1    0    0    1    0    0     0   \n",
       "58814        True     False       0     1    0    0    0    1    0     0   \n",
       "58815       False      True       0     1    0    0    1    0    0     0   \n",
       "63199        True      True       1     0    0    0    0    0    0     0   \n",
       "63200       False     False       1     0    0    0    0    0    0     0   \n",
       "66877        True     False       1     0    0    0    1    0    0     0   \n",
       "66878       False      True       1     0    0    0    0    1    0     0   \n",
       "69980        True     False       1     0    0    0    0    1    0     0   \n",
       "69981       False     False       1     0    0    1    0    0    0     0   \n",
       "73110        True     False       0     1    0    0    0    0    0     0   \n",
       "73111       False      True       0     1    0    0    0    0    0     0   \n",
       "77147        True      True       0     1    0    0    0    1    0     0   \n",
       "77148       False     False       0     1    0    0    0    0    0     0   \n",
       "81151        True     False       0     1    0    0    0    0    0     0   \n",
       "81152       False     False       0     1    0    0    1    0    0     0   \n",
       "84919        True     False       0     1    0    0    0    0    0     0   \n",
       "84920       False     False       0     1    0    0    1    0    0     0   \n",
       "88835        True      True       1     0    0    0    0    0    0     0   \n",
       "88836       False     False       1     0    0    0    0    0    0     0   \n",
       "92347        True     False       0     1    0    0    0    1    0     0   \n",
       "92348       False      True       0     1    0    0    1    0    0     0   \n",
       "96610        True      True       0     1    0    0    0    1    0     0   \n",
       "96611       False     False       0     1    0    0    0    1    0     0   \n",
       "100199       True     False       0     1    0    0    0    0    0     0   \n",
       "100200      False      True       0     1    0    0    1    0    0     0   \n",
       "103750       True      True       0     1    0    0    1    0    0     0   \n",
       "103751      False     False       0     1    0    1    0    0    0     0   \n",
       "107565       True     False       0     1    0    0    0    0    0     0   \n",
       "107566      False     False       0     1    0    0    0    0    0     0   \n",
       "111170       True     False       1     0    0    0    0    1    0     0   \n",
       "111171      False      True       1     0    0    0    0    0    0     0   \n",
       "114208       True      True       1     0    0    0    1    0    0     0   \n",
       "114209      False     False       1     0    0    0    0    0    0     0   \n",
       "117686       True     False       1     0    0    0    0    1    0     0   \n",
       "117687      False      True       1     0    0    0    0    1    0     0   \n",
       "121241       True      True       0     1    0    0    1    0    0     0   \n",
       "121242      False     False       0     1    0    0    0    1    0     0   \n",
       "124393       True      True       0     1    0    0    1    0    0     0   \n",
       "124394      False     False       0     1    0    0    0    0    1     0   \n",
       "127860       True      True       0     1    0    0    1    0    0     0   \n",
       "127861      False     False       0     1    0    0    0    0    0     0   \n",
       "131624       True      True       0     1    0    0    0    0    0     0   \n",
       "131625      False     False       0     1    1    0    0    0    0     0   \n",
       "135295       True     False       1     0    0    0    0    1    0     0   \n",
       "135296      False      True       1     0    0    0    0    1    0     0   \n",
       "138773       True     False       0     1    0    0    0    0    0     0   \n",
       "138774      False      True       0     1    0    0    0    0    0     0   \n",
       "142478       True     False       0     1    0    0    0    0    0     0   \n",
       "142479      False     False       0     1    0    0    1    0    0     0   \n",
       "146451       True      True       0     1    0    0    1    0    0     0   \n",
       "146452      False     False       0     1    0    0    0    0    0     0   \n",
       "150227       True     False       1     0    0    0    0    0    0     0   \n",
       "150228      False      True       1     0    0    0    1    0    0     0   \n",
       "153935       True      True       1     0    0    0    1    0    0     0   \n",
       "153936      False     False       1     0    0    0    0    0    0     0   \n",
       "157508       True     False       1     0    0    0    0    0    0     0   \n",
       "157509      False      True       1     0    0    0    0    0    0     0   \n",
       "160226       True     False       1     0    0    0    0    0    0     0   \n",
       "160227      False      True       1     0    0    0    0    0    0     0   \n",
       "162881       True      True       0     1    0    0    1    0    0     0   \n",
       "162882      False     False       0     1    0    0    0    0    0     0   \n",
       "166829       True      True       0     1    0    0    0    1    0     0   \n",
       "166830      False     False       0     1    0    0    0    1    0     0   \n",
       "170937       True      True       0     1    1    0    0    0    0     0   \n",
       "170938      False     False       0     1    0    0    0    1    0     0   \n",
       "174974       True     False       0     1    0    0    1    0    0     0   \n",
       "174975      False      True       0     1    0    0    0    0    0     0   \n",
       "178715       True     False       1     0    0    0    0    0    0     0   \n",
       "178716      False     False       1     0    0    0    0    1    0     0   \n",
       "182099       True     False       0     1    0    0    0    1    0     0   \n",
       "182100      False     False       0     1    0    0    0    0    1     0   \n",
       "186219       True      True       0     1    0    0    0    0    0     0   \n",
       "186220      False     False       0     1    0    0    1    0    0     0   \n",
       "190086       True     False       1     0    0    0    0    0    0     0   \n",
       "190087      False     False       1     0    0    0    1    0    0     0   \n",
       "194684       True     False       0     1    0    0    0    0    0     0   \n",
       "194685      False      True       0     1    0    0    1    0    0     0   \n",
       "198498       True     False       1     0    0    0    1    0    0     0   \n",
       "198499      False      True       1     0    0    0    0    1    0     0   \n",
       "202029       True      True       0     1    0    0    1    0    0     0   \n",
       "202030      False     False       0     1    0    0    1    0    0     0   \n",
       "205990       True     False       0     1    0    0    0    1    0     0   \n",
       "205991      False      True       0     1    0    0    1    0    0     0   \n",
       "208906       True     False       0     1    0    0    0    0    0     0   \n",
       "208907      False     False       0     1    0    0    1    0    0     0   \n",
       "212654       True     False       0     1    0    0    0    0    0     0   \n",
       "212655      False      True       0     1    0    0    1    0    0     0   \n",
       "216192       True     False       1     0    0    0    0    0    0     0   \n",
       "216193      False      True       1     0    0    0    0    0    0     0   \n",
       "220311       True     False       1     0    0    0    1    0    0     0   \n",
       "220312      False      True       1     0    0    0    1    0    0     0   \n",
       "223366       True     False       0     1    0    0    1    0    0     0   \n",
       "223367      False      True       0     1    0    0    1    0    0     0   \n",
       "227344       True      True       0     1    0    0    0    1    0     0   \n",
       "227345      False     False       0     1    0    0    1    0    0     0   \n",
       "231183       True      True       0     1    0    1    0    0    0     0   \n",
       "231184      False     False       0     1    0    0    1    0    0     0   \n",
       "234730       True     False       1     0    0    0    0    0    0     0   \n",
       "234731      False      True       1     0    0    0    0    0    0     0   \n",
       "238144       True     False       0     1    1    0    0    0    0     0   \n",
       "238145      False      True       0     1    0    0    0    0    0     0   \n",
       "241894       True     False       0     1    0    0    0    0    0     0   \n",
       "241895      False      True       0     1    0    0    0    0    0     0   \n",
       "246103       True      True       0     1    1    0    0    0    0     0   \n",
       "246104      False     False       0     1    0    0    1    0    0     0   \n",
       "249607       True     False       0     1    0    0    1    0    0     0   \n",
       "249608      False     False       0     1    0    0    0    0    0     0   \n",
       "253849       True     False       0     1    0    0    0    0    0     0   \n",
       "253850      False      True       0     1    0    0    0    0    0     0   \n",
       "257671       True     False       1     0    0    0    0    0    0     0   \n",
       "257672      False      True       1     0    0    0    0    1    0     0   \n",
       "260898       True     False       0     1    0    0    1    0    0     0   \n",
       "260899      False      True       0     1    0    1    0    0    0     0   \n",
       "264657       True     False       0     1    0    0    0    0    0     0   \n",
       "264658      False      True       0     1    0    0    1    0    0     0   \n",
       "268321       True      True       0     1    0    0    0    0    0     0   \n",
       "268322      False     False       0     1    0    0    1    0    0     0   \n",
       "271725       True      True       0     1    0    0    1    0    0     0   \n",
       "271726      False     False       0     1    0    0    0    0    0     0   \n",
       "275662       True      True       1     0    0    0    0    1    0     0   \n",
       "275663      False     False       1     0    0    0    1    0    0     0   \n",
       "278607       True      True       1     0    0    0    0    0    0     0   \n",
       "278608      False     False       1     0    0    0    0    0    0     0   \n",
       "281537       True      True       0     1    0    0    1    0    0     0   \n",
       "281538      False     False       0     1    0    0    0    1    0     0   \n",
       "285601       True      True       1     0    0    0    0    1    0     0   \n",
       "285602      False     False       1     0    0    0    1    0    0     0   \n",
       "288538       True     False       0     1    0    0    1    0    0     0   \n",
       "288539      False      True       0     1    0    0    0    0    0     0   \n",
       "292205       True     False       0     1    0    0    0    1    0     0   \n",
       "292206      False      True       0     1    0    0    1    0    0     0   \n",
       "295864       True     False       0     1    0    0    1    0    0     0   \n",
       "295865      False      True       0     1    0    0    1    0    0     0   \n",
       "299317       True      True       0     1    0    0    0    1    0     0   \n",
       "299318      False     False       0     1    0    0    0    1    0     0   \n",
       "302554       True      True       1     0    0    0    0    0    0     0   \n",
       "302555      False     False       1     0    0    0    0    0    0     0   \n",
       "305818       True     False       1     0    0    0    0    1    0     0   \n",
       "305819      False      True       1     0    0    0    0    0    0     0   \n",
       "309339       True     False       0     1    0    0    0    0    0     0   \n",
       "309340      False      True       0     1    0    0    1    0    0     0   \n",
       "313093       True      True       0     1    0    0    1    0    0     0   \n",
       "313094      False     False       0     1    0    0    0    1    0     0   \n",
       "316472       True     False       0     1    0    0    0    0    0     0   \n",
       "316473      False      True       0     1    0    0    1    0    0     0   \n",
       "320201       True      True       0     1    0    0    0    0    0     0   \n",
       "320202      False     False       0     1    0    0    1    0    0     0   \n",
       "323750       True      True       0     1    0    0    0    0    0     0   \n",
       "323751      False     False       0     1    0    0    1    0    0     0   \n",
       "327488       True     False       0     1    0    0    1    0    0     0   \n",
       "327489      False     False       0     1    0    0    1    0    0     0   \n",
       "332013       True     False       0     1    0    0    0    1    0     0   \n",
       "332014      False      True       0     1    0    0    1    0    0     0   \n",
       "336003       True     False       1     0    0    0    0    0    0     1   \n",
       "336004      False     False       1     0    0    0    0    1    0     0   \n",
       "339095       True      True       0     1    0    0    0    1    0     0   \n",
       "339096      False     False       0     1    0    0    1    0    0     0   \n",
       "342213       True      True       1     0    0    0    1    0    0     0   \n",
       "342214      False     False       1     0    0    0    0    0    0     0   \n",
       "346356       True      True       0     1    0    0    1    0    0     0   \n",
       "346357      False     False       0     1    0    0    0    0    0     0   \n",
       "349786       True     False       0     1    0    0    0    0    0     0   \n",
       "349787      False     False       0     1    0    0    1    0    0     0   \n",
       "\n",
       "        3412  3511  4141  4222  4231  4321  4411  41212  41221  \n",
       "0          0     0     0     0     0     0     0      1      0  \n",
       "1          0     0     0     0     1     0     0      0      0  \n",
       "3561       0     0     0     1     0     0     0      0      0  \n",
       "3562       0     0     0     0     0     0     0      0      1  \n",
       "6584       0     0     0     0     0     0     0      0      0  \n",
       "6585       0     0     0     0     0     0     0      0      0  \n",
       "9944       0     0     0     0     0     0     0      0      0  \n",
       "9945       0     0     0     0     0     0     0      0      0  \n",
       "13478      0     0     0     0     0     0     0      1      0  \n",
       "13479      0     0     0     0     0     0     0      0      1  \n",
       "16911      0     0     1     0     0     0     0      0      0  \n",
       "16912      0     0     0     0     1     0     0      0      0  \n",
       "20202      0     0     0     0     0     0     0      1      0  \n",
       "20203      0     0     0     0     0     0     0      0      0  \n",
       "24034      0     0     0     0     0     0     0      0      0  \n",
       "24035      0     0     0     0     0     0     0      0      0  \n",
       "28022      0     0     0     0     1     0     0      0      0  \n",
       "28023      0     0     1     0     0     0     0      0      0  \n",
       "31269      0     0     0     0     0     0     0      0      0  \n",
       "31270      0     0     0     0     0     0     0      0      0  \n",
       "35217      0     0     1     0     0     0     0      0      0  \n",
       "35218      0     0     0     0     1     0     0      0      0  \n",
       "39049      0     0     0     0     1     0     0      0      0  \n",
       "39050      0     0     0     0     0     0     0      0      0  \n",
       "42291      0     0     0     0     0     0     0      0      0  \n",
       "42292      0     0     0     0     1     0     0      0      0  \n",
       "45245      0     0     0     0     0     0     0      0      0  \n",
       "45246      0     0     1     0     0     0     0      0      0  \n",
       "49332      0     0     0     0     0     0     0      0      0  \n",
       "49333      0     0     0     0     0     0     0      0      0  \n",
       "52302      0     0     0     0     1     0     0      0      0  \n",
       "52303      0     0     1     0     0     0     0      0      0  \n",
       "55460      0     0     0     0     1     0     0      0      0  \n",
       "55461      0     0     0     0     0     0     0      0      0  \n",
       "58814      0     0     0     0     0     0     0      0      0  \n",
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      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Extracting features from reduced dataset for predictive model\n",
    "tactics = events[['team','tactics','type_name', 'home_team_id','match_gender','win_team_id']]\n",
    "tactics = tactics[(tactics['tactics'].notnull()) & (tactics['type_name']=='Starting XI')]\n",
    "tactics['formation'] = tactics.apply(lambda x: extract(x['tactics'],'formation'), axis=1)\n",
    "tactics['team_id'] = tactics.apply(lambda x: extract(x['team'],'id'), axis=1)\n",
    "tactics['team_home'] = tactics['team_id']==tactics['home_team_id']\n",
    "tactics['team_won'] = tactics['team_id']==tactics['win_team_id']\n",
    "tactics = tactics.drop(columns={'team','tactics','type_name','win_team_id','team_id','home_team_id'})\n",
    "\n",
    "### One-hot encoding gender and formation\n",
    "tactics = pd.concat([tactics.drop('match_gender', axis=1), pd.get_dummies(tactics['match_gender'])], axis=1)\n",
    "tactics = pd.concat([tactics.drop('formation', axis=1), pd.get_dummies(tactics['formation'])], axis=1)\n",
    "tactics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the preparation of our feature set, we see in the chart below that our dataset is generally balanced about 60-40 team losing and team winning. It is worth remembering our dataset has 100 matches, but the starting formations of both sides doubles the dataset to 200 as each match is represented by both the home and away team. One would expect then that team wins will equal team losses, but in the data preprocessing we considered only clear wins, and draws are in this case classified as a loss. Approximately 10 of our 100 matches have resulted in a draw, and in the case of our binary classifier, considered a loss.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='team_won', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Distribution of target class\n",
    "sns.countplot(x='team_won', data=tactics)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logreg accuracy:  52.307692307692314\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBC accuracy:  58.46153846153847\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DT accuracy:  55.38461538461539\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF accuracy:  55.38461538461539\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB accuracy:  43.07692307692308\n"
     ]
    }
   ],
   "source": [
    "### Split train-test data\n",
    "x = tactics.copy().drop(columns={'team_won'})\n",
    "y = tactics['team_won']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)\n",
    "xy_train = list(zip(x_train, y_train))\n",
    "xy_test = list(zip(x_test, y_test))\n",
    "\n",
    "### Classifier models\n",
    "dict_classifiers = {\n",
    "    \"Logreg\": sklearn.linear_model.LogisticRegression(solver='lbfgs'),\n",
    "    \"GBC\": sklearn.ensemble.GradientBoostingClassifier(),\n",
    "    \"DT\": sklearn.tree.DecisionTreeClassifier(),\n",
    "    \"RF\": sklearn.ensemble.RandomForestClassifier(max_depth=None, random_state=0),\n",
    "    \"NB\": sklearn.naive_bayes.GaussianNB(),\n",
    "}\n",
    "\n",
    "for model, model_inst in dict_classifiers.items():\n",
    "    model_inst.fit(x_train, y_train)\n",
    "    pred = np.array(model_inst.predict(x_test))\n",
    "    print(model, \"accuracy: \",metrics.accuracy_score(y_test, pred)*100)\n",
    "    #print(metrics.classification_report(y_test, pred, digits=3))\n",
    "    if model == 'RF':\n",
    "        feat_importances = pd.Series(model_inst.feature_importances_, index=x.columns)\n",
    "        feat_importances.nlargest(10).plot(kind='barh')\n",
    "        plt.show()\n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, none of the models have performed extraordinarily well, the best accuracy achieved is 55% for the Decision Tree and Random Forest models. This will likely be due to the relevance of the features selected for this exercise. In the bar chart above (feature importance from the Random Forest model), we see that whether the team is playing at home or away is the most prominent feature, which does align with the home-away analysis earlier in this report.\n",
    "\n",
    "The heatmap below demonstrates that all the features have weak correlations with each other, save for the exception of Male/Female which we know to be exclusive. Perhaps the most interesting insight from the correlation map is that male more than female matches favour a 433 formation. But at 0.23 that is not a strong correlation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Correlations of features\n",
    "corrmat = tactics.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "g=sns.heatmap(tactics[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusion'></a>\n",
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall from this dataset we find that although the data covers 7 competitions, 4 male competitions and 3 female competitions, the proportion of matches are 68% male and 32% female. We find that none of the competitions' data is complete for any season, but the best covered season is that beloning to the FA Women's Super League 2018/2019 and 2019/2020 seasons. Yet the competition with the most number of seasons and matches included in this dataset is the La Liga. The Barcelona football team is the most prominent team and appears in 56% of the matches in this dataset as either a home or away team.\n",
    "\n",
    "In looking at factors which impact a win, matches are most likely to be won at home games, and this appears to be consistent across competitions, seasons and genders. We did build a model to attempt to predict match wins, but the results are affected by a rather poor feature set. More time would have allowed a deeper exploration into the rich match events data, possibly identifying better features there. Although it was also noted that match events tended to be observed more in male competitions than female competitions, and thus if using event data as a predictor of match wins, this should be taken into consideration."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
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