{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Engineering of StrataBet Data\n",
    "##### Notebook to engineer the event data provided by [StrataBet]( http://www.stratagem.co/)\n",
    "\n",
    "### By [Edd Webster](https://www.twitter.com/eddwebster)\n",
    "Notebook first written: 13/12/2020<br>\n",
    "Notebook last updated: 26/12/2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](../../img/stratabet_logo.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Click [here](#section5) to jump straight to the Exploratory Data Analysis section and skip the [Task Brief](#section2), [Data Sources](#section3), and [Data Engineering](#section4) sections. Or click [here](#section6) to jump straight to the Conclusion.\n",
    "\n",
    "<b>This article was written with the aid of StrataData, which is property of [Stratagem Technologies](http://www.stratagem.co/). StrataData powers the [StrataBet Sports Trading Platform](http://www.stratabet.com/), in addition to [StrataBet Premium Recommendations](http://app.stratabet.com/recommendations).</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a id='sectionintro'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='import_libraries'>Introduction</a>\n",
    "This notebook engineers  [StrataBet]( http://www.stratagem.co/) data for football matches in a variety of European leagues during the 16/17 and 17/18 seasons, using [pandas](http://pandas.pydata.org/) for data manipulation through DataFrames.\n",
    "\n",
    "For more information about this notebook and the author, I'm available through all the following channels:\n",
    "*    [eddwebster.com](https://www.eddwebster.com/);\n",
    "*    edd.j.webster@gmail.com;\n",
    "*    [@eddwebster](https://www.twitter.com/eddwebster);\n",
    "*    [linkedin.com/in/eddwebster](https://www.linkedin.com/in/eddwebster/);\n",
    "*    [github/eddwebster](https://github.com/eddwebster/);\n",
    "*    [public.tableau.com/profile/edd.webster](https://public.tableau.com/profile/edd.webster);\n",
    "*    [kaggle.com/eddwebster](https://www.kaggle.com/eddwebster); and\n",
    "*    [hackerrank.com/eddwebster](https://www.hackerrank.com/eddwebster).\n",
    "\n",
    "![title](../../img/fifa21eddwebsterbanner.png)\n",
    "\n",
    "The accompanying GitHub repository for this notebook can be found [here](https://github.com/eddwebster/fifa-league) and a static version of this notebook can be found [here](https://nbviewer.jupyter.org/github/eddwebster/football_analytics/blob/master/notebooks/B%29%20Data%20Engineering/Opta%20%23mcfcanalytics%20PL%202011-2012.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a id='sectioncontents'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='notebook_contents'>Notebook Contents</a>\n",
    "1.    [Notebook Dependencies](#section1)<br>\n",
    "2.    [Project Brief](#section2)<br>\n",
    "3.    [Data Sources](#section3)<br>\n",
    "      1.    [Introduction](#section3.1)<br>\n",
    "      2.    [Data Dictionary](#section3.2)<br>\n",
    "      3.    [Creating the DataFrame](#section3.3)<br>\n",
    "      4.    [Initial Data Handling](#section3.4)<br>\n",
    "      5.    [Export the Raw DataFrame](#section3.5)<br>         \n",
    "4.    [Data Engineering](#section4)<br>\n",
    "      1.    [Introduction](#section4.1)<br>\n",
    "      2.    [Columns of Interest](#section4.2)<br>\n",
    "      3.    [String Cleaning](#section4.3)<br>\n",
    "      4.    [Converting Data Types](#section4.4)<br>\n",
    "      5.    [Export the Engineered DataFrame](#section4.5)<br>\n",
    "5.    [Exploratory Data Analysis (EDA)](#section5)<br>\n",
    "      1.    [...](#section5.1)<br>\n",
    "      2.    [...](#section5.2)<br>\n",
    "      3.    [...](#section5.3)<br>\n",
    "6.    [Summary](#section6)<br>\n",
    "7.    [Next Steps](#section7)<br>\n",
    "8.    [Bibliography](#section8)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a id='section1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section1'>1. Notebook Dependencies</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was written using [Python 3](https://docs.python.org/3.7/) and requires the following libraries:\n",
    "*    [`Jupyter notebooks`](https://jupyter.org/) for this notebook environment with which this project is presented;\n",
    "*    [`NumPy`](http://www.numpy.org/) for multidimensional array computing;\n",
    "*    [`pandas`](http://pandas.pydata.org/) for data analysis and manipulation; and\n",
    "*    [`matplotlib`](https://matplotlib.org/contents.html?v=20200411155018) for data visualisations;\n",
    "\n",
    "All packages used for this notebook except for BeautifulSoup can be obtained by downloading and installing the [Conda](https://anaconda.org/anaconda/conda) distribution, available on all platforms (Windows, Linux and Mac OSX). Step-by-step guides on how to install Anaconda can be found for Windows [here](https://medium.com/@GalarnykMichael/install-python-on-windows-anaconda-c63c7c3d1444) and Mac [here](https://medium.com/@GalarnykMichael/install-python-on-mac-anaconda-ccd9f2014072), as well as in the Anaconda documentation itself [here](https://docs.anaconda.com/anaconda/install/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "# Python ≥3.5 (ideally)\n",
    "import platform\n",
    "import sys, getopt\n",
    "assert sys.version_info >= (3, 5)\n",
    "import csv\n",
    "\n",
    "# Import Dependencies\n",
    "%matplotlib inline\n",
    "\n",
    "# Math Operations\n",
    "import numpy as np\n",
    "from math import pi\n",
    "\n",
    "# Datetime\n",
    "import datetime\n",
    "from datetime import date\n",
    "import time\n",
    "\n",
    "# Data Preprocessing\n",
    "import pandas as pd    # version 1.0.3\n",
    "import os    #  used to read the csv filenames\n",
    "import re\n",
    "import random\n",
    "from io import BytesIO\n",
    "from pathlib import Path\n",
    "\n",
    "# Reading directories\n",
    "import glob\n",
    "import os\n",
    "\n",
    "# Working with JSON\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "from ast import literal_eval\n",
    "\n",
    "# Data Visualisation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import missingno as msno    # visually display missing data\n",
    "\n",
    "# Display in Jupyter\n",
    "from IPython.display import Image, YouTubeVideo\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "# Ignore Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")\n",
    "\n",
    "print('Setup Complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.7.6\n",
      "NumPy: 1.18.1\n",
      "pandas: 1.0.1\n",
      "matplotlib: 3.1.3\n",
      "Seaborn: 0.10.0\n"
     ]
    }
   ],
   "source": [
    "# Python / module versions used here for reference\n",
    "print('Python: {}'.format(platform.python_version()))\n",
    "print('NumPy: {}'.format(np.__version__))\n",
    "print('pandas: {}'.format(pd.__version__))\n",
    "print('matplotlib: {}'.format(mpl.__version__))\n",
    "print('Seaborn: {}'.format(sns.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defined Filepaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up initial paths to subfolders\n",
    "base_dir = os.path.join('..', '..', )\n",
    "data_dir = os.path.join(base_dir, 'data')\n",
    "data_dir_fbref = os.path.join(base_dir, 'data', 'fbref')\n",
    "data_dir_stratabet = os.path.join(base_dir, 'data', 'stratabet')\n",
    "img_dir = os.path.join(base_dir, 'img')\n",
    "fig_dir = os.path.join(base_dir, 'img', 'fig')\n",
    "video_dir = os.path.join(base_dir, 'video')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='section2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section2'>2. Project Brief</a>\n",
    "This Jupyter notebook explores how to engineer [StrataBet]( http://www.stratagem.co/) Events data using [pandas](http://pandas.pydata.org/) for data manipulation through DataFrames.\n",
    "\n",
    "The resulting engineered Datarame is exported as a CSV file. This data can be further analysed in Python, joined to other datasets, or explored using Tableau, PowerBI, Microsoft Excel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='section3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section3'>3. Data Sources</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.1'>3.1. Introduction</a>\n",
    "This StrataData has been made availble by [Stratagem Technologies](http://www.stratagem.co/). StrataData powers the [StrataBet Sports Trading Platform](http://www.stratabet.com/), in addition to [StrataBet Premium Recommendations](http://app.stratabet.com/recommendations).</b>\n",
    "\n",
    "Before conducting our EDA, the data needs to be imported as a DataFrame in the Data Sources section [Section 3](#section3) and cleaned in the Data Engineering section [Section 4](#section4).\n",
    "\n",
    "We'll be using the [pandas](http://pandas.pydata.org/) library to import our data to this workbook as a DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.2'>3.2. Chances</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.2.1'>3.2.1. Data Dictionary</a>\n",
    "The [StrataBet]( http://www.stratagem.co/) Events dataset has twelve features (columns) with the following definitions and data types:\n",
    "\n",
    "| Feature     | Data type    |\n",
    "|------|-----|\n",
    "| `eventId`    | int64     |\n",
    "| `subEventName`    | object     |\n",
    "| `tags`    | object     |\n",
    "| `playerId`    | int64     |\n",
    "| `positions`    | object     |\n",
    "| `matchId`    | int64     |\n",
    "| `eventName`    | object     |\n",
    "| `teamId`    | int64     |\n",
    "| `matchPeriod`    | object     |\n",
    "| `eventSec`    | float64     |\n",
    "| `subEventId`    | object     |\n",
    "| `id`    | int64     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.2.2'>3.2.2. Import Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine individual csv files to form one DataFrame, df_raw, using glob\n",
    "lst_files_chances = glob.glob(data_dir_stratabet + '/raw/chances/individual_competitions' + \"/*.csv\")    # Creates a list of all csv files\n",
    "\n",
    "li = []    # pd.concat takes a list of DataFrames as an argument\n",
    "\n",
    "for filename in lst_files_chances:\n",
    "    df_raw_temp = pd.read_csv(filename, index_col=None, header=0)\n",
    "    li.append(df_raw_temp)\n",
    "\n",
    "df_stratabet_chances_raw = pd.concat(li, axis=0, ignore_index=True)    # ignore_index=True as we don't want pandas to try an align row indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.2.3'>3.2.3. Initial Data Handling</a>\n",
    "Let's quality of the dataset by looking first and last rows in pandas using the [head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) and [tail()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.tail.html) methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>icon</th>\n",
       "      <th>chanceRating</th>\n",
       "      <th>team</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "      <th>player</th>\n",
       "      <th>location_x</th>\n",
       "      <th>location_y</th>\n",
       "      <th>bodyPart</th>\n",
       "      <th>shotQuality</th>\n",
       "      <th>defPressure</th>\n",
       "      <th>numDefPlayers</th>\n",
       "      <th>numAttPlayers</th>\n",
       "      <th>outcome</th>\n",
       "      <th>primaryPlayer</th>\n",
       "      <th>primaryType</th>\n",
       "      <th>primaryLocation_x</th>\n",
       "      <th>primaryLocation_y</th>\n",
       "      <th>secondaryPlayer</th>\n",
       "      <th>secondaryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4684</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>24:43:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>81</td>\n",
       "      <td>48</td>\n",
       "      <td>Left</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saved</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>1</th>\n",
       "      <td>4685</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>45:29:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>27</td>\n",
       "      <td>60</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Defended</td>\n",
       "      <td>Thuram</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>-29</td>\n",
       "      <td>82</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4686</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>44:34:00</td>\n",
       "      <td>S. Siontis</td>\n",
       "      <td>23</td>\n",
       "      <td>117</td>\n",
       "      <td>Right</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Missed</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>3</th>\n",
       "      <td>4687</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>42:39:00</td>\n",
       "      <td>O. Gnjatic</td>\n",
       "      <td>-9</td>\n",
       "      <td>118</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Missed</td>\n",
       "      <td>G. Manousos</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4688</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>40:46:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0   4684       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "1   4685       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "2   4686       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "3   4687       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "4   4688       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "\n",
       "  awayteam_team2        icon chanceRating       team       type      time  \\\n",
       "0      Platanias  poorchance   poorchance    Kerkyra  Open Play  24:43:00   \n",
       "1      Platanias  goodchance   goodchance    Kerkyra  Open Play  45:29:00   \n",
       "2      Platanias  poorchance   poorchance    Kerkyra  Open Play  44:34:00   \n",
       "3      Platanias  poorchance   poorchance  Platanias  Open Play  42:39:00   \n",
       "4      Platanias  goodchance   goodchance    Kerkyra  Open Play  40:46:00   \n",
       "\n",
       "       player location_x location_y bodyPart shotQuality defPressure  \\\n",
       "0  D. Epstein         81         48     Left           3           5   \n",
       "1  D. Epstein         27         60     Left           2           2   \n",
       "2  S. Siontis         23        117    Right           2           1   \n",
       "3  O. Gnjatic         -9        118     Left           1           1   \n",
       "4  D. Epstein         42         15     Left           2           5   \n",
       "\n",
       "  numDefPlayers numAttPlayers   outcome primaryPlayer     primaryType  \\\n",
       "0             2             0     Saved             -               -   \n",
       "1             2             0  Defended        Thuram  Open Play Pass   \n",
       "2             4             1    Missed             -               -   \n",
       "3             3             1    Missed   G. Manousos  Open Play Pass   \n",
       "4             2             0     Saved             -               -   \n",
       "\n",
       "  primaryLocation_x primaryLocation_y secondaryPlayer secondaryType  \n",
       "0                 -                 -               -             -  \n",
       "1               -29                82               -             -  \n",
       "2                 -                 -               -             -  \n",
       "3                77                92               -             -  \n",
       "4                 -                 -               -             -  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 rows of the raw DataFrame, df_stratabet_chances_raw \n",
    "df_stratabet_chances_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>icon</th>\n",
       "      <th>chanceRating</th>\n",
       "      <th>team</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "      <th>player</th>\n",
       "      <th>location_x</th>\n",
       "      <th>location_y</th>\n",
       "      <th>bodyPart</th>\n",
       "      <th>shotQuality</th>\n",
       "      <th>defPressure</th>\n",
       "      <th>numDefPlayers</th>\n",
       "      <th>numAttPlayers</th>\n",
       "      <th>outcome</th>\n",
       "      <th>primaryPlayer</th>\n",
       "      <th>primaryType</th>\n",
       "      <th>primaryLocation_x</th>\n",
       "      <th>primaryLocation_y</th>\n",
       "      <th>secondaryPlayer</th>\n",
       "      <th>secondaryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119143</th>\n",
       "      <td>33422</td>\n",
       "      <td>EngPr</td>\n",
       "      <td>2463091</td>\n",
       "      <td>12/03/2018</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>41:13:00</td>\n",
       "      <td>L. Sane</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Missed</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Cross High</td>\n",
       "      <td>-69</td>\n",
       "      <td>48</td>\n",
       "      <td>Gabriel Jesus</td>\n",
       "      <td>Open Play Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119144</th>\n",
       "      <td>33424</td>\n",
       "      <td>EngPr</td>\n",
       "      <td>2463091</td>\n",
       "      <td>12/03/2018</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>greatchance</td>\n",
       "      <td>greatchance</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>56:58:00</td>\n",
       "      <td>R. Sterling</td>\n",
       "      <td>-18</td>\n",
       "      <td>13</td>\n",
       "      <td>Left</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>David Silva</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>-36</td>\n",
       "      <td>86</td>\n",
       "      <td>L. Sane</td>\n",
       "      <td>Open Play Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119145</th>\n",
       "      <td>33418</td>\n",
       "      <td>EngPr</td>\n",
       "      <td>2463091</td>\n",
       "      <td>12/03/2018</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>25:50:00</td>\n",
       "      <td>Fernandinho</td>\n",
       "      <td>3</td>\n",
       "      <td>97</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Missed</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>119146</th>\n",
       "      <td>33426</td>\n",
       "      <td>EngPr</td>\n",
       "      <td>2463091</td>\n",
       "      <td>12/03/2018</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>57:48:00</td>\n",
       "      <td>R. Sterling</td>\n",
       "      <td>31</td>\n",
       "      <td>92</td>\n",
       "      <td>Right</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Defended</td>\n",
       "      <td>L. Sane</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>-7</td>\n",
       "      <td>86</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Open Play Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119147</th>\n",
       "      <td>33427</td>\n",
       "      <td>EngPr</td>\n",
       "      <td>2463091</td>\n",
       "      <td>12/03/2018</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>58:29:00</td>\n",
       "      <td>L. Sane</td>\n",
       "      <td>-27</td>\n",
       "      <td>107</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Missed</td>\n",
       "      <td>R. Sterling</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>17</td>\n",
       "      <td>88</td>\n",
       "      <td>David Silva</td>\n",
       "      <td>Open Play Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "119143  33422       EngPr  2463091  12/03/2018    20:00:00     Stoke City   \n",
       "119144  33424       EngPr  2463091  12/03/2018    20:00:00     Stoke City   \n",
       "119145  33418       EngPr  2463091  12/03/2018    20:00:00     Stoke City   \n",
       "119146  33426       EngPr  2463091  12/03/2018    20:00:00     Stoke City   \n",
       "119147  33427       EngPr  2463091  12/03/2018    20:00:00     Stoke City   \n",
       "\n",
       "         awayteam_team2         icon chanceRating             team       type  \\\n",
       "119143  Manchester City   goodchance   goodchance  Manchester City  Open Play   \n",
       "119144  Manchester City  greatchance  greatchance  Manchester City  Open Play   \n",
       "119145  Manchester City   poorchance   poorchance  Manchester City  Open Play   \n",
       "119146  Manchester City   poorchance   poorchance  Manchester City  Open Play   \n",
       "119147  Manchester City   poorchance   poorchance  Manchester City  Open Play   \n",
       "\n",
       "            time       player location_x location_y bodyPart shotQuality  \\\n",
       "119143  41:13:00      L. Sane         45         42     Left           2   \n",
       "119144  56:58:00  R. Sterling        -18         13     Left           3   \n",
       "119145  25:50:00  Fernandinho          3         97     Left           2   \n",
       "119146  57:48:00  R. Sterling         31         92    Right           2   \n",
       "119147  58:29:00      L. Sane        -27        107     Left           2   \n",
       "\n",
       "       defPressure numDefPlayers numAttPlayers   outcome primaryPlayer  \\\n",
       "119143           2             2             0    Missed  K. De Bruyne   \n",
       "119144           4             2             0     Saved   David Silva   \n",
       "119145           1             3             2    Missed             -   \n",
       "119146           1             4             1  Defended       L. Sane   \n",
       "119147           1             5             3    Missed   R. Sterling   \n",
       "\n",
       "           primaryType primaryLocation_x primaryLocation_y secondaryPlayer  \\\n",
       "119143      Cross High               -69                48   Gabriel Jesus   \n",
       "119144  Open Play Pass               -36                86         L. Sane   \n",
       "119145               -                 -                 -               -   \n",
       "119146  Open Play Pass                -7                86    K. De Bruyne   \n",
       "119147  Open Play Pass                17                88     David Silva   \n",
       "\n",
       "         secondaryType  \n",
       "119143  Open Play Pass  \n",
       "119144  Open Play Pass  \n",
       "119145               -  \n",
       "119146  Open Play Pass  \n",
       "119147  Open Play Pass  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 5 rows of the raw DataFrame, df_stratabet_chances_raw \n",
    "df_stratabet_chances_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(119148, 27)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the raw DataFrame, df_stratabet_chances_raw \n",
    "print(df_stratabet_chances_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'competition', 'gsm_id', 'kickoffDate', 'kickoffTime',\n",
      "       'hometeam_team1', 'awayteam_team2', 'icon', 'chanceRating', 'team',\n",
      "       'type', 'time', 'player', 'location_x', 'location_y', 'bodyPart',\n",
      "       'shotQuality', 'defPressure', 'numDefPlayers', 'numAttPlayers',\n",
      "       'outcome', 'primaryPlayer', 'primaryType', 'primaryLocation_x',\n",
      "       'primaryLocation_y', 'secondaryPlayer', 'secondaryType'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the column names of the raw DataFrame, df_stratabet_chances_raw \n",
    "print(df_stratabet_chances_raw.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has six features (columns). Full details of these attributes can be found in the [Data Dictionary](section3.3.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                 int64\n",
       "competition          object\n",
       "gsm_id                int64\n",
       "kickoffDate          object\n",
       "kickoffTime          object\n",
       "hometeam_team1       object\n",
       "awayteam_team2       object\n",
       "icon                 object\n",
       "chanceRating         object\n",
       "team                 object\n",
       "type                 object\n",
       "time                 object\n",
       "player               object\n",
       "location_x           object\n",
       "location_y           object\n",
       "bodyPart             object\n",
       "shotQuality          object\n",
       "defPressure          object\n",
       "numDefPlayers        object\n",
       "numAttPlayers        object\n",
       "outcome              object\n",
       "primaryPlayer        object\n",
       "primaryType          object\n",
       "primaryLocation_x    object\n",
       "primaryLocation_y    object\n",
       "secondaryPlayer      object\n",
       "secondaryType        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data types of the features of the raw DataFrame, df_stratabet_chances_raw \n",
    "df_stratabet_chances_raw .dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All six of the columns have the object data type. Full details of these attributes and their data types can be found in the [Data Dictionary](section3.3.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119148 entries, 0 to 119147\n",
      "Data columns (total 27 columns):\n",
      " #   Column             Non-Null Count   Dtype \n",
      "---  ------             --------------   ----- \n",
      " 0   index              119148 non-null  int64 \n",
      " 1   competition        119148 non-null  object\n",
      " 2   gsm_id             119148 non-null  int64 \n",
      " 3   kickoffDate        119148 non-null  object\n",
      " 4   kickoffTime        119148 non-null  object\n",
      " 5   hometeam_team1     119148 non-null  object\n",
      " 6   awayteam_team2     119148 non-null  object\n",
      " 7   icon               119148 non-null  object\n",
      " 8   chanceRating       119148 non-null  object\n",
      " 9   team               119148 non-null  object\n",
      " 10  type               119148 non-null  object\n",
      " 11  time               119148 non-null  object\n",
      " 12  player             119148 non-null  object\n",
      " 13  location_x         119148 non-null  object\n",
      " 14  location_y         119148 non-null  object\n",
      " 15  bodyPart           119148 non-null  object\n",
      " 16  shotQuality        117618 non-null  object\n",
      " 17  defPressure        119148 non-null  object\n",
      " 18  numDefPlayers      119148 non-null  object\n",
      " 19  numAttPlayers      119148 non-null  object\n",
      " 20  outcome            119148 non-null  object\n",
      " 21  primaryPlayer      119148 non-null  object\n",
      " 22  primaryType        119148 non-null  object\n",
      " 23  primaryLocation_x  119148 non-null  object\n",
      " 24  primaryLocation_y  119148 non-null  object\n",
      " 25  secondaryPlayer    119148 non-null  object\n",
      " 26  secondaryType      119148 non-null  object\n",
      "dtypes: int64(2), object(25)\n",
      "memory usage: 24.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Info for the raw DataFrame, df_stratabet_chances_raw \n",
    "df_stratabet_chances_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>index</th>\n",
       "      <th>gsm_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>119148.000000</td>\n",
       "      <td>1.191480e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9941.664501</td>\n",
       "      <td>2.380177e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17678.010794</td>\n",
       "      <td>1.127321e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.237445e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1580.000000</td>\n",
       "      <td>2.247140e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3387.000000</td>\n",
       "      <td>2.404032e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6534.000000</td>\n",
       "      <td>2.467501e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80263.000000</td>\n",
       "      <td>2.701477e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index        gsm_id\n",
       "count  119148.000000  1.191480e+05\n",
       "mean     9941.664501  2.380177e+06\n",
       "std     17678.010794  1.127321e+05\n",
       "min         0.000000  2.237445e+06\n",
       "25%      1580.000000  2.247140e+06\n",
       "50%      3387.000000  2.404032e+06\n",
       "75%      6534.000000  2.467501e+06\n",
       "max     80263.000000  2.701477e+06"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the raw DataFrame, df_stratabet_raw, showing some summary statistics for each numberical column in the DataFrame\n",
    "df_stratabet_chances_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d73a810>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot visualisation of the missing values for each feature of the raw DataFrame, df_stratabet_chances_raw \n",
    "msno.matrix(df_stratabet_chances_raw, figsize = (30, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shotQuality    1530\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of missing values\n",
    "tm_null_value_stats = df_stratabet_chances_raw.isnull().sum(axis=0)\n",
    "tm_null_value_stats[tm_null_value_stats != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualisation shows us very quickly that there a few missing values in the `shotQuality` column, but otherwise the dataset is complete. This data is now ready for Data Engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.2.4'>3.2.4. Export Complete DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances_raw.to_csv(data_dir_stratabet + '/raw/chances/' + 'stratabet_chances_all.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.3'>3.3. Key Entries</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.3.1'>3.3.1. Data Dictionary</a>\n",
    "The [StrataBet]( http://www.stratagem.co/) Events dataset has twelve features (columns) with the following definitions and data types:\n",
    "\n",
    "| Feature     | Data type    |\n",
    "|------|-----|\n",
    "| `eventId`    | int64     |\n",
    "| `subEventName`    | object     |\n",
    "| `tags`    | object     |\n",
    "| `playerId`    | int64     |\n",
    "| `positions`    | object     |\n",
    "| `matchId`    | int64     |\n",
    "| `eventName`    | object     |\n",
    "| `teamId`    | int64     |\n",
    "| `matchPeriod`    | object     |\n",
    "| `eventSec`    | float64     |\n",
    "| `subEventId`    | object     |\n",
    "| `id`    | int64     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.3.2'>3.3.2. Import Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine individual csv files to form one DataFrame, df_raw, using glob\n",
    "lst_files_key_entries = glob.glob(data_dir_stratabet + '/raw/key_entries/individual_competitions' + \"/*.csv\")    # Creates a list of all csv files\n",
    "\n",
    "li = []    # pd.concat takes a list of DataFrames as an argument\n",
    "\n",
    "for filename in lst_files_key_entries:\n",
    "    df_raw_temp = pd.read_csv(filename, index_col=None, header=0)\n",
    "    li.append(df_raw_temp)\n",
    "\n",
    "df_stratabet_key_entries_raw = pd.concat(li, axis=0, ignore_index=True)    # ignore_index=True as we don't want pandas to try an align row indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.3.3'>3.3.3. Initial Data Handling</a>\n",
    "Let's quality of the dataset by looking first and last rows in pandas using the [head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) and [tail()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.tail.html) methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>team</th>\n",
       "      <th>keyentryArea</th>\n",
       "      <th>keyentryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153564</td>\n",
       "      <td>9570</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153565</td>\n",
       "      <td>9571</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Box</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153566</td>\n",
       "      <td>9572</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153567</td>\n",
       "      <td>9573</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153568</td>\n",
       "      <td>9574</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Box</td>\n",
       "      <td>Run</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     True  index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0  153564   9570       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "1  153565   9571       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "2  153566   9572       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "3  153567   9573       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "4  153568   9574       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "\n",
       "        awayteam_team2                 team keyentryArea keyentryType  \n",
       "0  Hamilton Academical              Rangers        Right         Pass  \n",
       "1  Hamilton Academical              Rangers          Box         Pass  \n",
       "2  Hamilton Academical              Rangers        Right         Pass  \n",
       "3  Hamilton Academical  Hamilton Academical        Right         Pass  \n",
       "4  Hamilton Academical              Rangers          Box          Run  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 rows of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>team</th>\n",
       "      <th>keyentryArea</th>\n",
       "      <th>keyentryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195682</th>\n",
       "      <td>111281</td>\n",
       "      <td>7814</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Right</td>\n",
       "      <td>Turnover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195683</th>\n",
       "      <td>111282</td>\n",
       "      <td>7815</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Left</td>\n",
       "      <td>Turnover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195684</th>\n",
       "      <td>111283</td>\n",
       "      <td>7816</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195685</th>\n",
       "      <td>111284</td>\n",
       "      <td>7817</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Box</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195686</th>\n",
       "      <td>111285</td>\n",
       "      <td>7818</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Box</td>\n",
       "      <td>Run</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          True  index competition   gsm_id kickoffDate kickoffTime  \\\n",
       "195682  111281   7814       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195683  111282   7815       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195684  111283   7816       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195685  111284   7817       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195686  111285   7818       GreSL  2701477  2018-03-11    15:15:00   \n",
       "\n",
       "       hometeam_team1 awayteam_team2        team keyentryArea keyentryType  \n",
       "195682          Lamia     Levadiakos  Levadiakos        Right     Turnover  \n",
       "195683          Lamia     Levadiakos       Lamia         Left     Turnover  \n",
       "195684          Lamia     Levadiakos       Lamia        Right         Pass  \n",
       "195685          Lamia     Levadiakos       Lamia          Box         Pass  \n",
       "195686          Lamia     Levadiakos  Levadiakos          Box          Run  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 5 rows of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195687, 11)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "print(df_stratabet_key_entries_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['True', 'index', 'competition', 'gsm_id', 'kickoffDate', 'kickoffTime',\n",
      "       'hometeam_team1', 'awayteam_team2', 'team', 'keyentryArea',\n",
      "       'keyentryType'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the column names of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "print(df_stratabet_key_entries_raw.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has six features (columns). Full details of these attributes can be found in the [Data Dictionary](section3.3.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195687 entries, 0 to 195686\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   True            195687 non-null  int64 \n",
      " 1   index           195687 non-null  int64 \n",
      " 2   competition     195687 non-null  object\n",
      " 3   gsm_id          195687 non-null  int64 \n",
      " 4   kickoffDate     195687 non-null  object\n",
      " 5   kickoffTime     195687 non-null  object\n",
      " 6   hometeam_team1  195687 non-null  object\n",
      " 7   awayteam_team2  195687 non-null  object\n",
      " 8   team            195687 non-null  object\n",
      " 9   keyentryArea    195687 non-null  object\n",
      " 10  keyentryType    195687 non-null  object\n",
      "dtypes: int64(3), object(8)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Info for the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>gsm_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195687.000000</td>\n",
       "      <td>195687.000000</td>\n",
       "      <td>1.956870e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97843.000000</td>\n",
       "      <td>6453.847414</td>\n",
       "      <td>2.354363e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56490.115401</td>\n",
       "      <td>5040.288373</td>\n",
       "      <td>1.125099e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.237445e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48921.500000</td>\n",
       "      <td>2675.000000</td>\n",
       "      <td>2.246823e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97843.000000</td>\n",
       "      <td>5401.000000</td>\n",
       "      <td>2.360808e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>146764.500000</td>\n",
       "      <td>8928.000000</td>\n",
       "      <td>2.467223e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>195686.000000</td>\n",
       "      <td>25384.000000</td>\n",
       "      <td>2.701477e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                True          index        gsm_id\n",
       "count  195687.000000  195687.000000  1.956870e+05\n",
       "mean    97843.000000    6453.847414  2.354363e+06\n",
       "std     56490.115401    5040.288373  1.125099e+05\n",
       "min         0.000000       0.000000  2.237445e+06\n",
       "25%     48921.500000    2675.000000  2.246823e+06\n",
       "50%     97843.000000    5401.000000  2.360808e+06\n",
       "75%    146764.500000    8928.000000  2.467223e+06\n",
       "max    195686.000000   25384.000000  2.701477e+06"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the raw DataFrame, df_stratabet_key_entries_raw, showing some summary statistics for each numberical column in the DataFrame\n",
    "df_stratabet_key_entries_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e4a8390>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot visualisation of the missing values for each feature of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "msno.matrix(df_stratabet_key_entries_raw, figsize = (30, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of missing values\n",
    "tm_null_value_stats = df_stratabet_key_entries_raw.isnull().sum(axis=0)\n",
    "tm_null_value_stats[tm_null_value_stats != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualisation shows us very quickly that there a few missing values in the `shotQuality` column, but otherwise the dataset is complete. This data is now ready for Data Engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.3.4'>3.3.4. Export Complete DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_key_entries_raw.to_csv(data_dir_stratabet + '/raw/key_entries/' + 'stratabet_key_entries_all.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.4'>3.4. Match Info</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.4.1'>3.4.1. Data Dictionary</a>\n",
    "The [StrataBet]( http://www.stratagem.co/) Events dataset has twelve features (columns) with the following definitions and data types:\n",
    "\n",
    "| Feature     | Data type    |\n",
    "|------|-----|\n",
    "| `eventId`    | int64     |\n",
    "| `subEventName`    | object     |\n",
    "| `tags`    | object     |\n",
    "| `playerId`    | int64     |\n",
    "| `positions`    | object     |\n",
    "| `matchId`    | int64     |\n",
    "| `eventName`    | object     |\n",
    "| `teamId`    | int64     |\n",
    "| `matchPeriod`    | object     |\n",
    "| `eventSec`    | float64     |\n",
    "| `subEventId`    | object     |\n",
    "| `id`    | int64     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.4.2'>3.4.2. Import Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine individual csv files to form one DataFrame, df_raw, using glob\n",
    "lst_files_key_entries = glob.glob(data_dir_stratabet + '/raw/key_entries/individual_competitions' + \"/*.csv\")    # Creates a list of all csv files\n",
    "\n",
    "li = []    # pd.concat takes a list of DataFrames as an argument\n",
    "\n",
    "for filename in lst_files_key_entries:\n",
    "    df_raw_temp = pd.read_csv(filename, index_col=None, header=0)\n",
    "    li.append(df_raw_temp)\n",
    "\n",
    "df_stratabet_key_entries_raw = pd.concat(li, axis=0, ignore_index=True)    # ignore_index=True as we don't want pandas to try an align row indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.4.3'>3.4.3. Initial Data Handling</a>\n",
    "Let's quality of the dataset by looking first and last rows in pandas using the [head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) and [tail()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.tail.html) methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>team</th>\n",
       "      <th>keyentryArea</th>\n",
       "      <th>keyentryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153564</td>\n",
       "      <td>9570</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153565</td>\n",
       "      <td>9571</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Box</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153566</td>\n",
       "      <td>9572</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153567</td>\n",
       "      <td>9573</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153568</td>\n",
       "      <td>9574</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Box</td>\n",
       "      <td>Run</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     True  index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0  153564   9570       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "1  153565   9571       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "2  153566   9572       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "3  153567   9573       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "4  153568   9574       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "\n",
       "        awayteam_team2                 team keyentryArea keyentryType  \n",
       "0  Hamilton Academical              Rangers        Right         Pass  \n",
       "1  Hamilton Academical              Rangers          Box         Pass  \n",
       "2  Hamilton Academical              Rangers        Right         Pass  \n",
       "3  Hamilton Academical  Hamilton Academical        Right         Pass  \n",
       "4  Hamilton Academical              Rangers          Box          Run  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 rows of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>team</th>\n",
       "      <th>keyentryArea</th>\n",
       "      <th>keyentryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195682</th>\n",
       "      <td>111281</td>\n",
       "      <td>7814</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Right</td>\n",
       "      <td>Turnover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195683</th>\n",
       "      <td>111282</td>\n",
       "      <td>7815</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Left</td>\n",
       "      <td>Turnover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195684</th>\n",
       "      <td>111283</td>\n",
       "      <td>7816</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195685</th>\n",
       "      <td>111284</td>\n",
       "      <td>7817</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Box</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195686</th>\n",
       "      <td>111285</td>\n",
       "      <td>7818</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Box</td>\n",
       "      <td>Run</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          True  index competition   gsm_id kickoffDate kickoffTime  \\\n",
       "195682  111281   7814       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195683  111282   7815       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195684  111283   7816       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195685  111284   7817       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195686  111285   7818       GreSL  2701477  2018-03-11    15:15:00   \n",
       "\n",
       "       hometeam_team1 awayteam_team2        team keyentryArea keyentryType  \n",
       "195682          Lamia     Levadiakos  Levadiakos        Right     Turnover  \n",
       "195683          Lamia     Levadiakos       Lamia         Left     Turnover  \n",
       "195684          Lamia     Levadiakos       Lamia        Right         Pass  \n",
       "195685          Lamia     Levadiakos       Lamia          Box         Pass  \n",
       "195686          Lamia     Levadiakos  Levadiakos          Box          Run  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 5 rows of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195687, 11)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "print(df_stratabet_key_entries_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['True', 'index', 'competition', 'gsm_id', 'kickoffDate', 'kickoffTime',\n",
      "       'hometeam_team1', 'awayteam_team2', 'team', 'keyentryArea',\n",
      "       'keyentryType'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the column names of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "print(df_stratabet_key_entries_raw.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has six features (columns). Full details of these attributes can be found in the [Data Dictionary](section3.3.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195687 entries, 0 to 195686\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   True            195687 non-null  int64 \n",
      " 1   index           195687 non-null  int64 \n",
      " 2   competition     195687 non-null  object\n",
      " 3   gsm_id          195687 non-null  int64 \n",
      " 4   kickoffDate     195687 non-null  object\n",
      " 5   kickoffTime     195687 non-null  object\n",
      " 6   hometeam_team1  195687 non-null  object\n",
      " 7   awayteam_team2  195687 non-null  object\n",
      " 8   team            195687 non-null  object\n",
      " 9   keyentryArea    195687 non-null  object\n",
      " 10  keyentryType    195687 non-null  object\n",
      "dtypes: int64(3), object(8)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Info for the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>gsm_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195687.000000</td>\n",
       "      <td>195687.000000</td>\n",
       "      <td>1.956870e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97843.000000</td>\n",
       "      <td>6453.847414</td>\n",
       "      <td>2.354363e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56490.115401</td>\n",
       "      <td>5040.288373</td>\n",
       "      <td>1.125099e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.237445e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48921.500000</td>\n",
       "      <td>2675.000000</td>\n",
       "      <td>2.246823e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97843.000000</td>\n",
       "      <td>5401.000000</td>\n",
       "      <td>2.360808e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>146764.500000</td>\n",
       "      <td>8928.000000</td>\n",
       "      <td>2.467223e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>195686.000000</td>\n",
       "      <td>25384.000000</td>\n",
       "      <td>2.701477e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                True          index        gsm_id\n",
       "count  195687.000000  195687.000000  1.956870e+05\n",
       "mean    97843.000000    6453.847414  2.354363e+06\n",
       "std     56490.115401    5040.288373  1.125099e+05\n",
       "min         0.000000       0.000000  2.237445e+06\n",
       "25%     48921.500000    2675.000000  2.246823e+06\n",
       "50%     97843.000000    5401.000000  2.360808e+06\n",
       "75%    146764.500000    8928.000000  2.467223e+06\n",
       "max    195686.000000   25384.000000  2.701477e+06"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the raw DataFrame, df_stratabet_key_entries_raw, showing some summary statistics for each numberical column in the DataFrame\n",
    "df_stratabet_key_entries_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108796a90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot visualisation of the missing values for each feature of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "msno.matrix(df_stratabet_key_entries_raw, figsize = (30, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of missing values\n",
    "tm_null_value_stats = df_stratabet_key_entries_raw.isnull().sum(axis=0)\n",
    "tm_null_value_stats[tm_null_value_stats != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualisation shows us very quickly that there a few missing values in the `shotQuality` column, but otherwise the dataset is complete. This data is now ready for Data Engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.4.4'>3.2.4. Export Complete DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_key_entries_raw.to_csv(data_dir_stratabet + '/raw/key_entries/' + 'stratabet_key_entries_all.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.5'>3.5. Minutes Played</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.5.1'>3.5.1. Data Dictionary</a>\n",
    "The [StrataBet]( http://www.stratagem.co/) Events dataset has twelve features (columns) with the following definitions and data types:\n",
    "\n",
    "| Feature     | Data type    |\n",
    "|------|-----|\n",
    "| `eventId`    | int64     |\n",
    "| `subEventName`    | object     |\n",
    "| `tags`    | object     |\n",
    "| `playerId`    | int64     |\n",
    "| `positions`    | object     |\n",
    "| `matchId`    | int64     |\n",
    "| `eventName`    | object     |\n",
    "| `teamId`    | int64     |\n",
    "| `matchPeriod`    | object     |\n",
    "| `eventSec`    | float64     |\n",
    "| `subEventId`    | object     |\n",
    "| `id`    | int64     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.5.2'>3.5.2. Import Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine individual csv files to form one DataFrame, df_raw, using glob\n",
    "lst_files_key_entries = glob.glob(data_dir_stratabet + '/raw/key_entries/individual_competitions' + \"/*.csv\")    # Creates a list of all csv files\n",
    "\n",
    "li = []    # pd.concat takes a list of DataFrames as an argument\n",
    "\n",
    "for filename in lst_files_key_entries:\n",
    "    df_raw_temp = pd.read_csv(filename, index_col=None, header=0)\n",
    "    li.append(df_raw_temp)\n",
    "\n",
    "df_stratabet_key_entries_raw = pd.concat(li, axis=0, ignore_index=True)    # ignore_index=True as we don't want pandas to try an align row indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.5.3'>3.5.3. Initial Data Handling</a>\n",
    "Let's quality of the dataset by looking first and last rows in pandas using the [head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) and [tail()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.tail.html) methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>team</th>\n",
       "      <th>keyentryArea</th>\n",
       "      <th>keyentryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153564</td>\n",
       "      <td>9570</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153565</td>\n",
       "      <td>9571</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Box</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153566</td>\n",
       "      <td>9572</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153567</td>\n",
       "      <td>9573</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153568</td>\n",
       "      <td>9574</td>\n",
       "      <td>ScoPr</td>\n",
       "      <td>2242424</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Hamilton Academical</td>\n",
       "      <td>Rangers</td>\n",
       "      <td>Box</td>\n",
       "      <td>Run</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     True  index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0  153564   9570       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "1  153565   9571       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "2  153566   9572       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "3  153567   9573       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "4  153568   9574       ScoPr  2242424  2016-08-06    11:30:00        Rangers   \n",
       "\n",
       "        awayteam_team2                 team keyentryArea keyentryType  \n",
       "0  Hamilton Academical              Rangers        Right         Pass  \n",
       "1  Hamilton Academical              Rangers          Box         Pass  \n",
       "2  Hamilton Academical              Rangers        Right         Pass  \n",
       "3  Hamilton Academical  Hamilton Academical        Right         Pass  \n",
       "4  Hamilton Academical              Rangers          Box          Run  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 rows of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>team</th>\n",
       "      <th>keyentryArea</th>\n",
       "      <th>keyentryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195682</th>\n",
       "      <td>111281</td>\n",
       "      <td>7814</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Right</td>\n",
       "      <td>Turnover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195683</th>\n",
       "      <td>111282</td>\n",
       "      <td>7815</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Left</td>\n",
       "      <td>Turnover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195684</th>\n",
       "      <td>111283</td>\n",
       "      <td>7816</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Right</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195685</th>\n",
       "      <td>111284</td>\n",
       "      <td>7817</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Box</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195686</th>\n",
       "      <td>111285</td>\n",
       "      <td>7818</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2701477</td>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Lamia</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Levadiakos</td>\n",
       "      <td>Box</td>\n",
       "      <td>Run</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          True  index competition   gsm_id kickoffDate kickoffTime  \\\n",
       "195682  111281   7814       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195683  111282   7815       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195684  111283   7816       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195685  111284   7817       GreSL  2701477  2018-03-11    15:15:00   \n",
       "195686  111285   7818       GreSL  2701477  2018-03-11    15:15:00   \n",
       "\n",
       "       hometeam_team1 awayteam_team2        team keyentryArea keyentryType  \n",
       "195682          Lamia     Levadiakos  Levadiakos        Right     Turnover  \n",
       "195683          Lamia     Levadiakos       Lamia         Left     Turnover  \n",
       "195684          Lamia     Levadiakos       Lamia        Right         Pass  \n",
       "195685          Lamia     Levadiakos       Lamia          Box         Pass  \n",
       "195686          Lamia     Levadiakos  Levadiakos          Box          Run  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 5 rows of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195687, 11)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "print(df_stratabet_key_entries_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['True', 'index', 'competition', 'gsm_id', 'kickoffDate', 'kickoffTime',\n",
      "       'hometeam_team1', 'awayteam_team2', 'team', 'keyentryArea',\n",
      "       'keyentryType'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the column names of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "print(df_stratabet_key_entries_raw.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has six features (columns). Full details of these attributes can be found in the [Data Dictionary](section3.3.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195687 entries, 0 to 195686\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   True            195687 non-null  int64 \n",
      " 1   index           195687 non-null  int64 \n",
      " 2   competition     195687 non-null  object\n",
      " 3   gsm_id          195687 non-null  int64 \n",
      " 4   kickoffDate     195687 non-null  object\n",
      " 5   kickoffTime     195687 non-null  object\n",
      " 6   hometeam_team1  195687 non-null  object\n",
      " 7   awayteam_team2  195687 non-null  object\n",
      " 8   team            195687 non-null  object\n",
      " 9   keyentryArea    195687 non-null  object\n",
      " 10  keyentryType    195687 non-null  object\n",
      "dtypes: int64(3), object(8)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Info for the raw DataFrame, df_stratabet_key_entries_raw \n",
    "df_stratabet_key_entries_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>True</th>\n",
       "      <th>index</th>\n",
       "      <th>gsm_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195687.000000</td>\n",
       "      <td>195687.000000</td>\n",
       "      <td>1.956870e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97843.000000</td>\n",
       "      <td>6453.847414</td>\n",
       "      <td>2.354363e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56490.115401</td>\n",
       "      <td>5040.288373</td>\n",
       "      <td>1.125099e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.237445e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48921.500000</td>\n",
       "      <td>2675.000000</td>\n",
       "      <td>2.246823e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97843.000000</td>\n",
       "      <td>5401.000000</td>\n",
       "      <td>2.360808e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>146764.500000</td>\n",
       "      <td>8928.000000</td>\n",
       "      <td>2.467223e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>195686.000000</td>\n",
       "      <td>25384.000000</td>\n",
       "      <td>2.701477e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                True          index        gsm_id\n",
       "count  195687.000000  195687.000000  1.956870e+05\n",
       "mean    97843.000000    6453.847414  2.354363e+06\n",
       "std     56490.115401    5040.288373  1.125099e+05\n",
       "min         0.000000       0.000000  2.237445e+06\n",
       "25%     48921.500000    2675.000000  2.246823e+06\n",
       "50%     97843.000000    5401.000000  2.360808e+06\n",
       "75%    146764.500000    8928.000000  2.467223e+06\n",
       "max    195686.000000   25384.000000  2.701477e+06"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the raw DataFrame, df_stratabet_key_entries_raw, showing some summary statistics for each numberical column in the DataFrame\n",
    "df_stratabet_key_entries_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d9a0fd0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot visualisation of the missing values for each feature of the raw DataFrame, df_stratabet_key_entries_raw \n",
    "msno.matrix(df_stratabet_key_entries_raw, figsize = (30, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of missing values\n",
    "tm_null_value_stats = df_stratabet_key_entries_raw.isnull().sum(axis=0)\n",
    "tm_null_value_stats[tm_null_value_stats != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualisation shows us very quickly that there a few missing values in the `shotQuality` column, but otherwise the dataset is complete. This data is now ready for Data Engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.5.4'>3.5.4. Export Complete DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_key_entries_raw.to_csv(data_dir_stratabet + '/raw/key_entries/' + 'stratabet_key_entries_all.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section4'>4. Data Engineering</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.1'>4.1. Chances</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.1'>4.1.1. Assign Raw DataFrame to Engineered DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign Raw DataFrame to Engineered DataFrame\n",
    "df_stratabet_chances = df_stratabet_chances_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.2'>4.1.2. Create `Full_Fixture_Date` Attribute</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>icon</th>\n",
       "      <th>chanceRating</th>\n",
       "      <th>team</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "      <th>player</th>\n",
       "      <th>location_x</th>\n",
       "      <th>location_y</th>\n",
       "      <th>bodyPart</th>\n",
       "      <th>shotQuality</th>\n",
       "      <th>defPressure</th>\n",
       "      <th>numDefPlayers</th>\n",
       "      <th>numAttPlayers</th>\n",
       "      <th>outcome</th>\n",
       "      <th>primaryPlayer</th>\n",
       "      <th>primaryType</th>\n",
       "      <th>primaryLocation_x</th>\n",
       "      <th>primaryLocation_y</th>\n",
       "      <th>secondaryPlayer</th>\n",
       "      <th>secondaryType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4684</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>24:43:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>81</td>\n",
       "      <td>48</td>\n",
       "      <td>Left</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saved</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>1</th>\n",
       "      <td>4685</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>45:29:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>27</td>\n",
       "      <td>60</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Defended</td>\n",
       "      <td>Thuram</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>-29</td>\n",
       "      <td>82</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4686</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>44:34:00</td>\n",
       "      <td>S. Siontis</td>\n",
       "      <td>23</td>\n",
       "      <td>117</td>\n",
       "      <td>Right</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Missed</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>3</th>\n",
       "      <td>4687</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>42:39:00</td>\n",
       "      <td>O. Gnjatic</td>\n",
       "      <td>-9</td>\n",
       "      <td>118</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Missed</td>\n",
       "      <td>G. Manousos</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4688</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>40:46:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0   4684       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "1   4685       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "2   4686       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "3   4687       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "4   4688       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "\n",
       "  awayteam_team2        icon chanceRating       team       type      time  \\\n",
       "0      Platanias  poorchance   poorchance    Kerkyra  Open Play  24:43:00   \n",
       "1      Platanias  goodchance   goodchance    Kerkyra  Open Play  45:29:00   \n",
       "2      Platanias  poorchance   poorchance    Kerkyra  Open Play  44:34:00   \n",
       "3      Platanias  poorchance   poorchance  Platanias  Open Play  42:39:00   \n",
       "4      Platanias  goodchance   goodchance    Kerkyra  Open Play  40:46:00   \n",
       "\n",
       "       player location_x location_y bodyPart shotQuality defPressure  \\\n",
       "0  D. Epstein         81         48     Left           3           5   \n",
       "1  D. Epstein         27         60     Left           2           2   \n",
       "2  S. Siontis         23        117    Right           2           1   \n",
       "3  O. Gnjatic         -9        118     Left           1           1   \n",
       "4  D. Epstein         42         15     Left           2           5   \n",
       "\n",
       "  numDefPlayers numAttPlayers   outcome primaryPlayer     primaryType  \\\n",
       "0             2             0     Saved             -               -   \n",
       "1             2             0  Defended        Thuram  Open Play Pass   \n",
       "2             4             1    Missed             -               -   \n",
       "3             3             1    Missed   G. Manousos  Open Play Pass   \n",
       "4             2             0     Saved             -               -   \n",
       "\n",
       "  primaryLocation_x primaryLocation_y secondaryPlayer secondaryType  \n",
       "0                 -                 -               -             -  \n",
       "1               -29                82               -             -  \n",
       "2                 -                 -               -             -  \n",
       "3                77                92               -             -  \n",
       "4                 -                 -               -             -  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stratabet_chances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances['Full_Fixture_Date'] = df_stratabet_chances['kickoffDate'].astype(str) + ' ' + df_stratabet_chances['hometeam_team1'].astype(str) + ' vs. ' + df_stratabet_chances['awayteam_team2'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.3'>4.1.3. Convert Data Types</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances['location_x'] = df_stratabet_chances['location_x'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['location_y'] = df_stratabet_chances['location_y'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['primaryLocation_x'] = df_stratabet_chances['primaryLocation_x'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['primaryLocation_y'] = df_stratabet_chances['primaryLocation_y'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['shotQuality'] = df_stratabet_chances['shotQuality'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['defPressure'] = df_stratabet_chances['defPressure'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['numDefPlayers'] = df_stratabet_chances['numDefPlayers'].apply(pd.to_numeric, errors='coerce')\n",
    "df_stratabet_chances['numAttPlayers'] = df_stratabet_chances['numAttPlayers'].apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.4'>4.1.4. Convert X, Y Coordinates to Standardised Coordiantes</a>\n",
    "\n",
    "From the documenation, the XY coordinates in addition to grid locations, with (0,0) representing the absolute centre of the defended goal line. The pitch length runs from 0 to 420, while the width runs from 136 to -136 (left to right).\n",
    "\n",
    "Some key reference points of note:\n",
    "*    Left Goalpost (15, 0)\n",
    "*    Right Goalpost (-15, 0)\n",
    "*    6-Yard Box Left Corner: (37, 22)\n",
    "*    6-Yard Box Right Corner: (-37, 22)\n",
    "*    Penalty Spot: (0, 44)\n",
    "*    18-Yard Box Left Corner: (81, 66)\n",
    "*    18-Yard Box Right Corner: (-81, 66)\n",
    "*    Centre Spot: (0, 210) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances['location_y_120'] = ((df_stratabet_chances['location_y'] / 480) * 120).round(2)\n",
    "df_stratabet_chances['location_x_80'] = (((df_stratabet_chances['location_x'] + 136) / 272) * 80).round(2)\n",
    "df_stratabet_chances['primaryLocation_y_120'] = ((df_stratabet_chances['primaryLocation_y'] / 480) * 120).round(2)\n",
    "df_stratabet_chances['primaryLocation_x_80'] = (((df_stratabet_chances['primaryLocation_x'] + 136) / 272) * 80).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>index</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>icon</th>\n",
       "      <th>chanceRating</th>\n",
       "      <th>team</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "      <th>player</th>\n",
       "      <th>location_x</th>\n",
       "      <th>location_y</th>\n",
       "      <th>bodyPart</th>\n",
       "      <th>shotQuality</th>\n",
       "      <th>defPressure</th>\n",
       "      <th>numDefPlayers</th>\n",
       "      <th>numAttPlayers</th>\n",
       "      <th>outcome</th>\n",
       "      <th>primaryPlayer</th>\n",
       "      <th>primaryType</th>\n",
       "      <th>primaryLocation_x</th>\n",
       "      <th>primaryLocation_y</th>\n",
       "      <th>secondaryPlayer</th>\n",
       "      <th>secondaryType</th>\n",
       "      <th>Full_Fixture_Date</th>\n",
       "      <th>location_y_120</th>\n",
       "      <th>location_x_80</th>\n",
       "      <th>primaryLocation_y_120</th>\n",
       "      <th>primaryLocation_x_80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4684</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>24:43:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>81.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>12.00</td>\n",
       "      <td>63.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4685</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>45:29:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>27.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Defended</td>\n",
       "      <td>Thuram</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>15.00</td>\n",
       "      <td>47.94</td>\n",
       "      <td>20.5</td>\n",
       "      <td>31.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4686</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>44:34:00</td>\n",
       "      <td>S. Siontis</td>\n",
       "      <td>23.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Missed</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>29.25</td>\n",
       "      <td>46.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4687</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>42:39:00</td>\n",
       "      <td>O. Gnjatic</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Missed</td>\n",
       "      <td>G. Manousos</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>77.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>29.50</td>\n",
       "      <td>37.35</td>\n",
       "      <td>23.0</td>\n",
       "      <td>62.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4688</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>40:46:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>3.75</td>\n",
       "      <td>52.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0   4684       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "1   4685       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "2   4686       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "3   4687       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "4   4688       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "\n",
       "  awayteam_team2        icon chanceRating       team       type      time  \\\n",
       "0      Platanias  poorchance   poorchance    Kerkyra  Open Play  24:43:00   \n",
       "1      Platanias  goodchance   goodchance    Kerkyra  Open Play  45:29:00   \n",
       "2      Platanias  poorchance   poorchance    Kerkyra  Open Play  44:34:00   \n",
       "3      Platanias  poorchance   poorchance  Platanias  Open Play  42:39:00   \n",
       "4      Platanias  goodchance   goodchance    Kerkyra  Open Play  40:46:00   \n",
       "\n",
       "       player  location_x  location_y bodyPart  shotQuality  defPressure  \\\n",
       "0  D. Epstein        81.0        48.0     Left          3.0          5.0   \n",
       "1  D. Epstein        27.0        60.0     Left          2.0          2.0   \n",
       "2  S. Siontis        23.0       117.0    Right          2.0          1.0   \n",
       "3  O. Gnjatic        -9.0       118.0     Left          1.0          1.0   \n",
       "4  D. Epstein        42.0        15.0     Left          2.0          5.0   \n",
       "\n",
       "   numDefPlayers  numAttPlayers   outcome primaryPlayer     primaryType  \\\n",
       "0            2.0            0.0     Saved             -               -   \n",
       "1            2.0            0.0  Defended        Thuram  Open Play Pass   \n",
       "2            4.0            1.0    Missed             -               -   \n",
       "3            3.0            1.0    Missed   G. Manousos  Open Play Pass   \n",
       "4            2.0            0.0     Saved             -               -   \n",
       "\n",
       "   primaryLocation_x  primaryLocation_y secondaryPlayer secondaryType  \\\n",
       "0                NaN                NaN               -             -   \n",
       "1              -29.0               82.0               -             -   \n",
       "2                NaN                NaN               -             -   \n",
       "3               77.0               92.0               -             -   \n",
       "4                NaN                NaN               -             -   \n",
       "\n",
       "                  Full_Fixture_Date  location_y_120  location_x_80  \\\n",
       "0  10/09/2016 Kerkyra vs. Platanias           12.00          63.82   \n",
       "1  10/09/2016 Kerkyra vs. Platanias           15.00          47.94   \n",
       "2  10/09/2016 Kerkyra vs. Platanias           29.25          46.76   \n",
       "3  10/09/2016 Kerkyra vs. Platanias           29.50          37.35   \n",
       "4  10/09/2016 Kerkyra vs. Platanias            3.75          52.35   \n",
       "\n",
       "   primaryLocation_y_120  primaryLocation_x_80  \n",
       "0                    NaN                   NaN  \n",
       "1                   20.5                 31.47  \n",
       "2                    NaN                   NaN  \n",
       "3                   23.0                 62.65  \n",
       "4                    NaN                   NaN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stratabet_chances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances['location_y_120_inv'] = 120 - ((df_stratabet_chances['location_y'] / 480) * 120).round(2)\n",
    "df_stratabet_chances['location_x_80_inv'] = 80 - (((df_stratabet_chances['location_x'] + 136) / 272) * 80).round(2)\n",
    "df_stratabet_chances['primaryLocation_y_120_inv'] = 120 -  ((df_stratabet_chances['primaryLocation_y'] / 480) * 120).round(2)\n",
    "df_stratabet_chances['primaryLocation_x_80_inv'] = 80 - (((df_stratabet_chances['primaryLocation_x'] + 136) / 272) * 80).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.5'>4.1.5. Renaming</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances = df_stratabet_chances.rename(columns = {'index':'id'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.6'>4.1.6. Assign New Attributes</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances['Season'] = 'TO ADD'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code here to add seasons for each match per 'competition' and 'kickoffDate' - varies per league"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.7'>4.1.7. Create DataFrame of Teams and Leagues</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>competition</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77137</th>\n",
       "      <td>AusAL</td>\n",
       "      <td>Adelaide United</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77053</th>\n",
       "      <td>AusAL</td>\n",
       "      <td>Brisbane Roar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77104</th>\n",
       "      <td>AusAL</td>\n",
       "      <td>Central Coast Mariners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77066</th>\n",
       "      <td>AusAL</td>\n",
       "      <td>Melbourne City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77052</th>\n",
       "      <td>AusAL</td>\n",
       "      <td>Melbourne Victory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      competition                    team\n",
       "77137       AusAL         Adelaide United\n",
       "77053       AusAL           Brisbane Roar\n",
       "77104       AusAL  Central Coast Mariners\n",
       "77066       AusAL          Melbourne City\n",
       "77052       AusAL       Melbourne Victory"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrame of Teams and Leagues\n",
    "\n",
    "## Select columns of interest\n",
    "cols = ['competition', 'team']\n",
    "df_stratabet_teams_leagues = df_stratabet_chances[cols]\n",
    "\n",
    "## Drop duplicates\n",
    "df_stratabet_teams_leagues = df_stratabet_teams_leagues.drop_duplicates()\n",
    "\n",
    "## Order columns by league and team\n",
    "df_stratabet_teams_leagues = df_stratabet_teams_leagues.sort_values(['competition', 'team'], ascending=[True, True])\n",
    "\n",
    "## Display DataFrame\n",
    "df_stratabet_teams_leagues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export DataFrame\n",
    "df_stratabet_teams_leagues.to_csv(data_dir_stratabet + '/reference/teams_leagues.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.1.8'>4.1.8. Export DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances.to_csv(data_dir_stratabet + '/engineered/chances/stratabet_chances_all.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_chances.to_csv(data_dir + '/export/stratabet_events_chances.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>id</th>\n",
       "      <th>competition</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>kickoffDate</th>\n",
       "      <th>kickoffTime</th>\n",
       "      <th>hometeam_team1</th>\n",
       "      <th>awayteam_team2</th>\n",
       "      <th>icon</th>\n",
       "      <th>chanceRating</th>\n",
       "      <th>team</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "      <th>player</th>\n",
       "      <th>location_x</th>\n",
       "      <th>location_y</th>\n",
       "      <th>bodyPart</th>\n",
       "      <th>shotQuality</th>\n",
       "      <th>defPressure</th>\n",
       "      <th>numDefPlayers</th>\n",
       "      <th>numAttPlayers</th>\n",
       "      <th>outcome</th>\n",
       "      <th>primaryPlayer</th>\n",
       "      <th>primaryType</th>\n",
       "      <th>primaryLocation_x</th>\n",
       "      <th>primaryLocation_y</th>\n",
       "      <th>secondaryPlayer</th>\n",
       "      <th>secondaryType</th>\n",
       "      <th>Full_Fixture_Date</th>\n",
       "      <th>location_y_120</th>\n",
       "      <th>location_x_80</th>\n",
       "      <th>primaryLocation_y_120</th>\n",
       "      <th>primaryLocation_x_80</th>\n",
       "      <th>location_y_120_inv</th>\n",
       "      <th>location_x_80_inv</th>\n",
       "      <th>primaryLocation_y_120_inv</th>\n",
       "      <th>primaryLocation_x_80_inv</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4684</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>24:43:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>81.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>12.00</td>\n",
       "      <td>63.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.00</td>\n",
       "      <td>16.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TO ADD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4685</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>45:29:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>27.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Defended</td>\n",
       "      <td>Thuram</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>15.00</td>\n",
       "      <td>47.94</td>\n",
       "      <td>20.5</td>\n",
       "      <td>31.47</td>\n",
       "      <td>105.00</td>\n",
       "      <td>32.06</td>\n",
       "      <td>99.5</td>\n",
       "      <td>48.53</td>\n",
       "      <td>TO ADD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4686</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>44:34:00</td>\n",
       "      <td>S. Siontis</td>\n",
       "      <td>23.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Missed</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>29.25</td>\n",
       "      <td>46.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.75</td>\n",
       "      <td>33.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TO ADD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4687</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>poorchance</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>42:39:00</td>\n",
       "      <td>O. Gnjatic</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Missed</td>\n",
       "      <td>G. Manousos</td>\n",
       "      <td>Open Play Pass</td>\n",
       "      <td>77.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>29.50</td>\n",
       "      <td>37.35</td>\n",
       "      <td>23.0</td>\n",
       "      <td>62.65</td>\n",
       "      <td>90.50</td>\n",
       "      <td>42.65</td>\n",
       "      <td>97.0</td>\n",
       "      <td>17.35</td>\n",
       "      <td>TO ADD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4688</td>\n",
       "      <td>GreSL</td>\n",
       "      <td>2355591</td>\n",
       "      <td>10/09/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Platanias</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>goodchance</td>\n",
       "      <td>Kerkyra</td>\n",
       "      <td>Open Play</td>\n",
       "      <td>40:46:00</td>\n",
       "      <td>D. Epstein</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saved</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>10/09/2016 Kerkyra vs. Platanias</td>\n",
       "      <td>3.75</td>\n",
       "      <td>52.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.25</td>\n",
       "      <td>27.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TO ADD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id competition   gsm_id kickoffDate kickoffTime hometeam_team1  \\\n",
       "0  4684       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "1  4685       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "2  4686       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "3  4687       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "4  4688       GreSL  2355591  10/09/2016    13:00:00        Kerkyra   \n",
       "\n",
       "  awayteam_team2        icon chanceRating       team       type      time  \\\n",
       "0      Platanias  poorchance   poorchance    Kerkyra  Open Play  24:43:00   \n",
       "1      Platanias  goodchance   goodchance    Kerkyra  Open Play  45:29:00   \n",
       "2      Platanias  poorchance   poorchance    Kerkyra  Open Play  44:34:00   \n",
       "3      Platanias  poorchance   poorchance  Platanias  Open Play  42:39:00   \n",
       "4      Platanias  goodchance   goodchance    Kerkyra  Open Play  40:46:00   \n",
       "\n",
       "       player  location_x  location_y bodyPart  shotQuality  defPressure  \\\n",
       "0  D. Epstein        81.0        48.0     Left          3.0          5.0   \n",
       "1  D. Epstein        27.0        60.0     Left          2.0          2.0   \n",
       "2  S. Siontis        23.0       117.0    Right          2.0          1.0   \n",
       "3  O. Gnjatic        -9.0       118.0     Left          1.0          1.0   \n",
       "4  D. Epstein        42.0        15.0     Left          2.0          5.0   \n",
       "\n",
       "   numDefPlayers  numAttPlayers   outcome primaryPlayer     primaryType  \\\n",
       "0            2.0            0.0     Saved             -               -   \n",
       "1            2.0            0.0  Defended        Thuram  Open Play Pass   \n",
       "2            4.0            1.0    Missed             -               -   \n",
       "3            3.0            1.0    Missed   G. Manousos  Open Play Pass   \n",
       "4            2.0            0.0     Saved             -               -   \n",
       "\n",
       "   primaryLocation_x  primaryLocation_y secondaryPlayer secondaryType  \\\n",
       "0                NaN                NaN               -             -   \n",
       "1              -29.0               82.0               -             -   \n",
       "2                NaN                NaN               -             -   \n",
       "3               77.0               92.0               -             -   \n",
       "4                NaN                NaN               -             -   \n",
       "\n",
       "                  Full_Fixture_Date  location_y_120  location_x_80  \\\n",
       "0  10/09/2016 Kerkyra vs. Platanias           12.00          63.82   \n",
       "1  10/09/2016 Kerkyra vs. Platanias           15.00          47.94   \n",
       "2  10/09/2016 Kerkyra vs. Platanias           29.25          46.76   \n",
       "3  10/09/2016 Kerkyra vs. Platanias           29.50          37.35   \n",
       "4  10/09/2016 Kerkyra vs. Platanias            3.75          52.35   \n",
       "\n",
       "   primaryLocation_y_120  primaryLocation_x_80  location_y_120_inv  \\\n",
       "0                    NaN                   NaN              108.00   \n",
       "1                   20.5                 31.47              105.00   \n",
       "2                    NaN                   NaN               90.75   \n",
       "3                   23.0                 62.65               90.50   \n",
       "4                    NaN                   NaN              116.25   \n",
       "\n",
       "   location_x_80_inv  primaryLocation_y_120_inv  primaryLocation_x_80_inv  \\\n",
       "0              16.18                        NaN                       NaN   \n",
       "1              32.06                       99.5                     48.53   \n",
       "2              33.24                        NaN                       NaN   \n",
       "3              42.65                       97.0                     17.35   \n",
       "4              27.65                        NaN                       NaN   \n",
       "\n",
       "   Season  \n",
       "0  TO ADD  \n",
       "1  TO ADD  \n",
       "2  TO ADD  \n",
       "3  TO ADD  \n",
       "4  TO ADD  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stratabet_chances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119148, 37)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stratabet_chances.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All code below here is old and needs to be sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.2'>4.2. Key Entries</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.3'>4.3. Match Info</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.4'>4.4. Minutes Played</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Old code from here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.3'>4.3. String Cleaning</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split `Label` column into seperate `Fixture` and `Score` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Break down 'Label' column into constituent parts - Fixtures, Score, Date, Home Goals, Away Goals, etc.\n",
    "df_stratabet['fixture'] = df_stratabet['label'].str.split(', ').str[0]\n",
    "df_stratabet['score_home_away'] = df_stratabet['label'].str.split(', ').str[1]\n",
    "df_stratabet['team_home'] = df_stratabet['fixture'].str.split(' - ').str[0]\n",
    "df_stratabet['team_away'] = df_stratabet['fixture'].str.split(' - ').str[1]\n",
    "df_stratabet['goals_home'] = df_stratabet['score_home_away'].str.split(' - ').str[0]\n",
    "df_stratabet['goals_away'] = df_stratabet['score_home_away'].str.split(' - ').str[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split `date` column into seperate `date_isolated` and `time_isolated` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet['date_isolated'] = df_stratabet['date'].str.split(' at').str[0]\n",
    "df_stratabet['time_isolated'] = df_stratabet['date'].str.split(' at ').str[1]\n",
    "df_stratabet['date_time_isolated'] = df_stratabet['date'].str.split(' GMT').str[0].str.replace(' at ', ' ', regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.4'>4.4. Drop columns</a>\n",
    "As this is a large dataset with >3mil rows, we will remove every column that is not required at this stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display columns\n",
    "df_stratabet.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df_stratabet = df_stratabet.drop(['tags', 'dateutc', 'wyId_x', 'label', 'date', 'referees', 'wyId_y', 'date_isolated', 'time_isolated', 'date_time_isolated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.5'>4.5. Create New Attributes</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create full fixture data from broken down attributes created in section 4.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet['full_fixture_date'] = df_stratabet['date_date'].astype(str) + ' ' + df_stratabet['team_home'].astype(str)  + ' ' + df_stratabet['goals_home'].astype(str) + ' ' + ' v ' + ' ' + df_stratabet['goals_away'].astype(str) + ' ' + df_stratabet['team_away'].astype(str) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create 'season' attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet['season'] = '17/18'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.5'>4.5. Reorder DataFrame</a>\n",
    "Correctly order all the rows in the DataFrame by date, time, country, fixture, half, and time in the match. Important when looking at events and the following event e.g. is possession retains? Which player receives the pass, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_stratabet = df_stratabet.sort_values(['date_date', 'time_time', 'country', 'league_name', 'full_fixture_date', 'matchPeriod', 'eventSec'], ascending=[True, True, True, True, True, True, True])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.6'>4.6. Create New Attributes</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `followingPossession` column\n",
    "'teamIdNext' = following 'teamId'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet['teamIdNext'] = df_stratabet['teamId'].shift(-1)\n",
    "df_stratabet['teamNameNext'] = df_stratabet['teamName'].shift(-1)\n",
    "df_stratabet['fullNameNext'] = df_stratabet['fullName'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `player2player` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet['player2player'] = df_stratabet['fullName'] + ' - ' + df_stratabet['fullNameNext']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `isPossessionRetained` column\n",
    "When `teamId` is not followed by the same `teamId` in the following row, possession is lost. We want to creat a column that stats this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet['isPossessionRetained'] = np.where(df_stratabet['teamId'] == df_stratabet['teamIdNext'], True, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Clean Positions data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.7'>4.7. Export DataFrame</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Break down data into individual matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_results = list(df_stratabet['full_fixture_date'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, g in df_stratabet.groupby('full_fixture_date'):\n",
    "    g.to_csv(data_dir_wyscout + '/engineered/individual_matches/{}.csv'.format(i), header=True, index_label=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Complete dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet.to_csv(data_dir_wyscout + '/engineered/combined/wyscout_events_big5_1718.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.8'>4.8. Aggregate Data</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.8.1'>4.8.1. Fixture Level</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns of interest\n",
    "\n",
    "## Define columns\n",
    "cols = ['season',\n",
    "        'date_time_timestamp',\n",
    "        'fixture',\n",
    "        'team_home',\n",
    "        'team_away',\n",
    "        'teamName',\n",
    "        'goals_home',\n",
    "        'goals_away',\n",
    "        'eventName',\n",
    "        'subEventName'\n",
    "       ]\n",
    "\n",
    "## Streamline DataFrame with columns of interest\n",
    "df_stratabet_select = df_stratabet[cols]\n",
    "\n",
    "## \n",
    "df_stratabet_select['Opponent'] = np.where(df_stratabet_select['team_home'] == df_stratabet_select['teamName'], df_stratabet_select['team_away'], df_stratabet_select['team_home'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "\n",
    "## Group DataFrame and Aggregate on 'eventName'\n",
    "df_stratabet_fixture_grouped = (df_stratabet_select\n",
    "                                  .groupby(['season', 'date_time_timestamp', 'fixture', 'teamName', 'Opponent', 'goals_home', 'goals_away', 'eventName'])\n",
    "                                  .agg({'eventName': ['count']})\n",
    "                             )\n",
    "\n",
    "## Drop level\n",
    "df_stratabet_fixture_grouped.columns = df_stratabet_fixture_grouped.columns.droplevel(level=0)\n",
    "\n",
    "\n",
    "## Reset index\n",
    "df_stratabet_fixture_grouped = df_stratabet_fixture_grouped.reset_index()\n",
    "\n",
    "## Rename columns\n",
    "df_stratabet_fixture_grouped = df_stratabet_fixture_grouped.rename(columns={'season': 'Season',\n",
    "                                                                        'date_time_timestamp': 'Date',\n",
    "                                                                        'fixture': 'Fixture',\n",
    "                                                                        'teamName': 'Team',\n",
    "                                                                        'Opponent': 'Opponent',\n",
    "                                                                        'goals_home': 'Goals_Home',\n",
    "                                                                        'goals_away': 'Goals_Away',\n",
    "                                                                        'eventName': 'Event',\n",
    "                                                                        'count': 'Team_Value'\n",
    "                                                                       }\n",
    "                                                              )\n",
    "\n",
    "## Display DataFrame\n",
    "df_stratabet_fixture_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns of interest\n",
    "\n",
    "## Define columns\n",
    "cols = ['Season',\n",
    "        'Date',\n",
    "        'Fixture',\n",
    "        'Team',\n",
    "        'Opponent',\n",
    "        'Event',\n",
    "        'Team_Value'\n",
    "       ]\n",
    "\n",
    "## Streamline DataFrame with columns of interest\n",
    "df_stratabet_fixture_grouped_select = df_stratabet_fixture_grouped[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join DataFrame to itself on 'Date', 'Fixture', 'Team'/'Opponent', and 'Event', to join Team and Opponent together\n",
    "df_stratabet_fixture_grouped = pd.merge(df_stratabet_fixture_grouped, df_stratabet_fixture_grouped,  how='left', left_on=['Season', 'Date', 'Fixture', 'Opponent', 'Event'], right_on = ['Season', 'Date', 'Fixture', 'Team', 'Event'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean Data\n",
    "\n",
    "## Drop columns\n",
    "df_stratabet_fixture_grouped = df_stratabet_fixture_grouped.drop(columns=['Team_y', 'Opponent_y', 'Goals_Home_y', 'Goals_Away_y'])\n",
    "\n",
    "## Rename columns\n",
    "df_stratabet_fixture_grouped = df_stratabet_fixture_grouped.rename(columns={'Season_x': 'Season',\n",
    "                                                                        'Team_x': 'Team',\n",
    "                                                                        'Opponent_x': 'Opponent',\n",
    "                                                                        'Goals_Home_x': 'Goals_Home',\n",
    "                                                                        'Goals_Away_x': 'Goals_Away',\n",
    "                                                                        'Team_Value_x': 'Team_Value',\n",
    "                                                                        'Team_Value_y': 'Opponent_Value',\n",
    "                                                                       }\n",
    "                                                                  )\n",
    "\n",
    "## Replace null values with zeros\n",
    "df_stratabet_fixture_grouped['Team_Value'] = df_stratabet_fixture_grouped['Team_Value'].replace(np.nan, 0)\n",
    "df_stratabet_fixture_grouped['Opponent_Value'] = df_stratabet_fixture_grouped['Opponent_Value'].replace(np.nan, 0)\n",
    "\n",
    "## Convert Opponent_Value' from Float64 to Int64 type\n",
    "df_stratabet_fixture_grouped['Opponent_Value'] = df_stratabet_fixture_grouped['Opponent_Value'].astype('Int64')\n",
    "\n",
    "## Display DataFrame\n",
    "df_stratabet_fixture_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "\n",
    "##\n",
    "df_fixture_gw = (df_stratabet_fixture_grouped\n",
    "                     .groupby(['Date', 'Team'])\n",
    "                     .agg({'Team': ['nunique']})\n",
    "                )\n",
    "\n",
    "##\n",
    "df_fixture_gw.columns = df_fixture_gw.columns.droplevel(level=0)\n",
    "\n",
    "\n",
    "##\n",
    "df_fixture_gw = df_fixture_gw.reset_index()\n",
    "\n",
    "##\n",
    "df_fixture_gw = df_fixture_gw.rename(columns={'Date': 'Date',\n",
    "                                              'nunique': 'Gameweek',\n",
    "                                             }\n",
    "                                    )\n",
    "\n",
    "## Groupby. See: https://stackoverflow.com/questions/18554920/pandas-aggregate-count-distinct\n",
    "df_fixture_gw = (df_fixture_gw.groupby(['Team', 'Date']).sum()\n",
    "                              .groupby(level=0).cumsum().reset_index()\n",
    "                )\n",
    "\n",
    "## Display DataFrame\n",
    "df_fixture_gw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join DataFrame \n",
    "df_stratabet_fixture_grouped = pd.merge(df_stratabet_fixture_grouped, df_fixture_gw, how='left', left_on=['Date', 'Team'], right_on = ['Date', 'Team'])\n",
    "\n",
    "# Display DataFrame\n",
    "df_stratabet_fixture_grouped.head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Export DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_fixture_grouped.to_csv(data_dir_wyscout + '/engineered/combined/wyscout_aggregated_fixtures_big5_1718.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.8.2'>4.8.2. Team Level</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group DataFrame by Team\n",
    "\n",
    "##\n",
    "df_stratabet_team_grouped = (df_stratabet_fixture_grouped\n",
    "                               .groupby(['Team', 'Event'])\n",
    "                               .agg({'Team_Value': ['sum'],\n",
    "                                     'Opponent_Value': ['sum']\n",
    "                                    }\n",
    "                                   )\n",
    "                          )\n",
    "\n",
    "##\n",
    "df_stratabet_team_grouped.columns = df_stratabet_team_grouped.columns.droplevel(level=0)\n",
    "\n",
    "##\n",
    "df_stratabet_team_grouped = df_stratabet_team_grouped.reset_index()\n",
    "\n",
    "## Rename columns\n",
    "df_stratabet_team_grouped.columns = ['Team', 'Event', 'Team_Value', 'Opponent_Value']\n",
    "\n",
    "## Display columns\n",
    "df_stratabet_team_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Export DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stratabet_team_grouped.to_csv(data_dir_wyscout + '/engineered/combined/wyscout_aggregated_team_big5_1718.csv', index=None, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section5'>5. Exploratory Data Analysis</a>\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section6'>6. Summary</a>\n",
    "This notebook scrapes data for player valuations using [Beautifulsoup](https://pypi.org/project/beautifulsoup4/) from [TransferMarkt](https://www.transfermarkt.co.uk/) using [pandas](http://pandas.pydata.org/) for data maniuplation through DataFrames and [Beautifulsoup](https://pypi.org/project/beautifulsoup4/) for webscraping."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='#section7'>7. Next Steps</a>\n",
    "..."
   ]
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    "## <a id='#section8'>8. References</a>\n",
    "..."
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    "---\n",
    "<b>This article was written with the aid of StrataData, which is property of [Stratagem Technologies](http://www.stratagem.co/). StrataData powers the [StrataBet Sports Trading Platform](http://www.stratabet.com/), in addition to [StrataBet Premium Recommendations](http://app.stratabet.com/recommendations).</b>\n",
    "\n",
    "***Visit my website [EddWebster.com](https://www.eddwebster.com) or my [GitHub Repository](https://github.com/eddwebster) for more projects. If you'd like to get in contact, my Twitter handle is [@eddwebster](http://www.twitter.com/eddwebster) and my email is: edd.j.webster@gmail.com.***"
   ]
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   "source": [
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