{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wyscout Data Engineering\n",
    "##### Notebook to engineered previously parsed Event data from [Wyscout](https://wyscout.com/).\n",
    "\n",
    "### By [Edd Webster](https://www.twitter.com/eddwebster)\n",
    "Notebook first written: 26/01/2021<br>\n",
    "Notebook last updated: 12/02/2021\n",
    "\n",
    "![title](../../img/wyscout_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='import_libraries'>Introduction</a>\n",
    "This notebook parses pubicly available [Wyscout](https://wyscout.com/) football match data for the Big 5 European leagues for the 17/18 season, 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/edd_webster/fifa21eddwebsterbanner.png)\n",
    "\n",
    "The accompanying GitHub repository for this notebook can be found [here](https://github.com/eddwebster/football_analytics) and a static version of this notebook can be found [here](https://nbviewer.jupyter.org/github/eddwebster/football_analytics/blob/master/notebooks/3_data_engineering/Wyscout%20Data%20Engineering.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "## <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.    [Competitions](#section3.2)<br>\n",
    "      3.    [Events](#section3.3)<br>\n",
    "      4.    [Matches](#section3.4)<br>\n",
    "      5.    [Players](#section3.5)<br>\n",
    "      6.    [Teams](#section3.6)<br>\n",
    "4.    [Data Engineering](#section4)<br>\n",
    "      1.    [Join Datasets](#section4.1)<br>\n",
    "      5.    [Export 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'>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; and\n",
    "*    [`pandas`](http://pandas.pydata.org/) for data analysis and manipulation.\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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# 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",
    "import math\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\n",
    "import os\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",
    "from os.path import basename\n",
    "\n",
    "# Working with JSON\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "# Data Visualisation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import patches\n",
    "from matplotlib.patches import Arc\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import missingno as msno\n",
    "\n",
    "# Downloading data sources\n",
    "from urllib.parse import urlparse\n",
    "from urllib.request import urlopen, urlretrieve\n",
    "from zipfile import ZipFile, is_zipfile\n",
    "from tqdm import tqdm    # Progress Bar\n",
    "\n",
    "# Display in Jupyter\n",
    "from IPython.display import Image, Video, 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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.7.6\n",
      "NumPy: 1.18.0\n",
      "pandas: 1.2.0\n",
      "matplotlib: 3.3.2\n",
      "Seaborn: 0.11.1\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 Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define today's date\n",
    "today = datetime.datetime.now().strftime('%d/%m/%Y').replace('/', '')"
   ]
  },
  {
   "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_wyscout = os.path.join(base_dir, 'data', 'wyscout')\n",
    "scripts_dir = os.path.join(base_dir, 'scripts')\n",
    "scripts_dir_wyscout = os.path.join(base_dir, 'scripts', 'wyscout')\n",
    "img_dir = os.path.join(base_dir, 'img')\n",
    "fig_dir = os.path.join(base_dir, 'img', 'fig')\n",
    "fig_dir_wyscout = os.path.join(base_dir, 'img', 'fig', 'wyscout')\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": [
    "# Display all DataFrame columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section2'>2. Project Brief</a>\n",
    "This Jupyter notebook explores how to parse pubicly available [Wyscout](https://wyscout.com/) JSON data of football matches for the Big 5 European leagues for the 17/18 season using [pandas](http://pandas.pydata.org/) for data manipulation through DataFrames.\n",
    "\n",
    "The resulting five engineered DataFrames for each of the Big 5 European league are exported to CSV files. This data can be further analysed in Python, joined to other datasets, or explored using Tableau, PowerBI, Microsoft Excel.\n",
    "\n",
    "**Notebook Conventions**:<br>\n",
    "*    Variables that refer a `DataFrame` object are prefixed with `df_`.\n",
    "*    Variables that refer to a collection of `DataFrame` objects (e.g., a list, a set or a dict) are prefixed with `dfs_`.\n",
    "\n",
    "**References**:<br>\n",
    "This notebook uses Wyscout data, made publicly available in the following paper by Luca Pappalardo, Paolo Cintia, Alessio Rossi, Emanuele Massucco, Paolo Ferragina, Dino Pedreschi, and Fosca Giannotti. **[A Public Data Set of Spatio-Temporal Match Events in Soccer Competitions](https://www.nature.com/articles/s41597-019-0247-7)**. In *Scientific Data 6*, no. 1 (2019): 1-15."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section3'>3. Data Sources</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.1'>3.1. Introduction</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.1.1'>3.1.1. About Wyscout</a>\n",
    "[Wyscout](https://wyscout.com/) is an Italian company that supports football scouting, match analysis and transfer dynamics. The company was founded in Genoa, Italy in 2004 and provides video analysis tools and digital databases regarding performances and matches for coaches, teams and players dealing with football business.\n",
    "\n",
    "![title](../../img/wyscout_logo.png)\n",
    "\n",
    "The purpose is to allow them have a detailed sight of a large number of athletes about individual performances, patterns of play and tactical strategy.\n",
    "\n",
    "This notebook explores a complete dataset of event data for the Big 5 European leagues during the 17/18 season."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section3.1.2'>3.1.2. About the Wyscout publicly available data</a>\n",
    "A detailed description of the data can be found in the following paper:\n",
    "- Pappalardo, L., Cintia, P., Rossi, A. et al. **A public data set of spatio-temporal match events in soccer competitions**. Scientific Data 6, 236 (2019) doi:10.1038/s41597-019-0247-7, https://www.nature.com/articles/s41597-019-0247-7\n",
    "\n",
    "It is from this paper import the *matches*, *events*, *players*, *playerank*, *referees*, *coaches*, and *competition* data sets from the <a href=\"https://doi.org/10.6084/m9.figshare.c.4415000.v5\">figshare repository</a>. Data are stored in the `JSON` format. \n",
    "\n",
    "The complete data set contains:\n",
    "- 1,941 matches\n",
    "- 3,251,294 events\n",
    "- 4,299 players.\n",
    "\n",
    "The datasets we will be using are:\n",
    "- competitions;\n",
    "- events;\n",
    "- matches;\n",
    "- players; and\n",
    "- teams\n",
    "\n",
    "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)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section3.2'>3.2. Read in Data</a>\n",
    "The following cells read in the engineered CSV data prepared in the [Data Parsing](https://nbviewer.jupyter.org/github/eddwebster/football_analytics/blob/master/notebooks/2_data_parsing/Wyscout%20Parsing.ipynb) noteook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='#section3.3.1.'>3.3.1. Data Dictionary</a>\n",
    "\n",
    "The [Wyscout](https://wyscout.com/) 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     |\n",
    "\n",
    "Refer to the Wyscout [API docs](https://apidocs.wyscout.com/) and [Events Manual](https://footballdata.wyscout.com/wp-content/uploads/2018/03/Wyscout-Events-Manual.pdf) for further information about event and subevents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='#section3.3.2.'>3.3.2. Read in CSV as pandas DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3147: DtypeWarning: Columns (18,26) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# Read in preparsed Wyscout Events DataFrame\n",
    "df_wyscout_raw = pd.read_csv(os.path.join(data_dir_wyscout, 'raw', 'csv', 'combined', 'wyscout_big5_combined.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <a id='#section3.3.'>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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventId</th>\n",
       "      <th>subEventName</th>\n",
       "      <th>playerId</th>\n",
       "      <th>positions</th>\n",
       "      <th>matchId</th>\n",
       "      <th>eventName</th>\n",
       "      <th>teamId</th>\n",
       "      <th>matchPeriod</th>\n",
       "      <th>eventSec</th>\n",
       "      <th>subEventId</th>\n",
       "      <th>id</th>\n",
       "      <th>tags</th>\n",
       "      <th>status</th>\n",
       "      <th>roundId</th>\n",
       "      <th>gameweek</th>\n",
       "      <th>seasonId</th>\n",
       "      <th>dateutc</th>\n",
       "      <th>winner</th>\n",
       "      <th>venue</th>\n",
       "      <th>wyId_x</th>\n",
       "      <th>label</th>\n",
       "      <th>date</th>\n",
       "      <th>referees</th>\n",
       "      <th>duration</th>\n",
       "      <th>competitionId</th>\n",
       "      <th>wyId_y</th>\n",
       "      <th>area_name</th>\n",
       "      <th>weight</th>\n",
       "      <th>firstName</th>\n",
       "      <th>middleName</th>\n",
       "      <th>lastName</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>height</th>\n",
       "      <th>wyId_x.1</th>\n",
       "      <th>foot</th>\n",
       "      <th>shortName</th>\n",
       "      <th>passportArea_name</th>\n",
       "      <th>role_name</th>\n",
       "      <th>birthArea_name</th>\n",
       "      <th>name</th>\n",
       "      <th>wyId_y.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>Simple pass</td>\n",
       "      <td>26010</td>\n",
       "      <td>[{'y': 48, 'x': 50}, {'y': 50, 'x': 47}]</td>\n",
       "      <td>1694390</td>\n",
       "      <td>Pass</td>\n",
       "      <td>4418</td>\n",
       "      <td>1H</td>\n",
       "      <td>1.255990</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88178642</td>\n",
       "      <td>[{'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4165470</td>\n",
       "      <td>1</td>\n",
       "      <td>9291</td>\n",
       "      <td>2016-06-10 19:00:00</td>\n",
       "      <td>4418</td>\n",
       "      <td>Stade de France</td>\n",
       "      <td>1694390</td>\n",
       "      <td>France - Romania, 2 - 1</td>\n",
       "      <td>2016-06-10 21:00:00-02:00</td>\n",
       "      <td>[{'refereeId': 383185, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>Olivier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Giroud</td>\n",
       "      <td>1986-09-30</td>\n",
       "      <td>192</td>\n",
       "      <td>26010</td>\n",
       "      <td>left</td>\n",
       "      <td>O. Giroud</td>\n",
       "      <td>France</td>\n",
       "      <td>Forward</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>4418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>Simple pass</td>\n",
       "      <td>3682</td>\n",
       "      <td>[{'y': 50, 'x': 47}, {'y': 48, 'x': 41}]</td>\n",
       "      <td>1694390</td>\n",
       "      <td>Pass</td>\n",
       "      <td>4418</td>\n",
       "      <td>1H</td>\n",
       "      <td>2.351908</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88178643</td>\n",
       "      <td>[{'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4165470</td>\n",
       "      <td>1</td>\n",
       "      <td>9291</td>\n",
       "      <td>2016-06-10 19:00:00</td>\n",
       "      <td>4418</td>\n",
       "      <td>Stade de France</td>\n",
       "      <td>1694390</td>\n",
       "      <td>France - Romania, 2 - 1</td>\n",
       "      <td>2016-06-10 21:00:00-02:00</td>\n",
       "      <td>[{'refereeId': 383185, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>Antoine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Griezmann</td>\n",
       "      <td>1991-03-21</td>\n",
       "      <td>175</td>\n",
       "      <td>3682</td>\n",
       "      <td>left</td>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>France</td>\n",
       "      <td>Forward</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>4418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>Simple pass</td>\n",
       "      <td>31528</td>\n",
       "      <td>[{'y': 48, 'x': 41}, {'y': 35, 'x': 32}]</td>\n",
       "      <td>1694390</td>\n",
       "      <td>Pass</td>\n",
       "      <td>4418</td>\n",
       "      <td>1H</td>\n",
       "      <td>3.241028</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88178644</td>\n",
       "      <td>[{'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4165470</td>\n",
       "      <td>1</td>\n",
       "      <td>9291</td>\n",
       "      <td>2016-06-10 19:00:00</td>\n",
       "      <td>4418</td>\n",
       "      <td>Stade de France</td>\n",
       "      <td>1694390</td>\n",
       "      <td>France - Romania, 2 - 1</td>\n",
       "      <td>2016-06-10 21:00:00-02:00</td>\n",
       "      <td>[{'refereeId': 383185, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>N'Golo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kanté</td>\n",
       "      <td>1991-03-29</td>\n",
       "      <td>168</td>\n",
       "      <td>31528</td>\n",
       "      <td>right</td>\n",
       "      <td>N. Kanté</td>\n",
       "      <td>Mali</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>4418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>High pass</td>\n",
       "      <td>7855</td>\n",
       "      <td>[{'y': 35, 'x': 32}, {'y': 6, 'x': 89}]</td>\n",
       "      <td>1694390</td>\n",
       "      <td>Pass</td>\n",
       "      <td>4418</td>\n",
       "      <td>1H</td>\n",
       "      <td>6.033681</td>\n",
       "      <td>83.0</td>\n",
       "      <td>88178645</td>\n",
       "      <td>[{'id': 1802}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4165470</td>\n",
       "      <td>1</td>\n",
       "      <td>9291</td>\n",
       "      <td>2016-06-10 19:00:00</td>\n",
       "      <td>4418</td>\n",
       "      <td>Stade de France</td>\n",
       "      <td>1694390</td>\n",
       "      <td>France - Romania, 2 - 1</td>\n",
       "      <td>2016-06-10 21:00:00-02:00</td>\n",
       "      <td>[{'refereeId': 383185, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>Laurent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Koscielny</td>\n",
       "      <td>1985-09-10</td>\n",
       "      <td>186</td>\n",
       "      <td>7855</td>\n",
       "      <td>right</td>\n",
       "      <td>L. Koscielny</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Defender</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>4418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Ground defending duel</td>\n",
       "      <td>25437</td>\n",
       "      <td>[{'y': 6, 'x': 89}, {'y': 0, 'x': 85}]</td>\n",
       "      <td>1694390</td>\n",
       "      <td>Duel</td>\n",
       "      <td>4418</td>\n",
       "      <td>1H</td>\n",
       "      <td>13.143591</td>\n",
       "      <td>12.0</td>\n",
       "      <td>88178646</td>\n",
       "      <td>[{'id': 702}, {'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4165470</td>\n",
       "      <td>1</td>\n",
       "      <td>9291</td>\n",
       "      <td>2016-06-10 19:00:00</td>\n",
       "      <td>4418</td>\n",
       "      <td>Stade de France</td>\n",
       "      <td>1694390</td>\n",
       "      <td>France - Romania, 2 - 1</td>\n",
       "      <td>2016-06-10 21:00:00-02:00</td>\n",
       "      <td>[{'refereeId': 383185, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>Blaise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Matuidi</td>\n",
       "      <td>1987-04-09</td>\n",
       "      <td>175</td>\n",
       "      <td>25437</td>\n",
       "      <td>left</td>\n",
       "      <td>B. Matuidi</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>4418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eventId           subEventName  playerId  \\\n",
       "0        8            Simple pass     26010   \n",
       "1        8            Simple pass      3682   \n",
       "2        8            Simple pass     31528   \n",
       "3        8              High pass      7855   \n",
       "4        1  Ground defending duel     25437   \n",
       "\n",
       "                                  positions  matchId eventName  teamId  \\\n",
       "0  [{'y': 48, 'x': 50}, {'y': 50, 'x': 47}]  1694390      Pass    4418   \n",
       "1  [{'y': 50, 'x': 47}, {'y': 48, 'x': 41}]  1694390      Pass    4418   \n",
       "2  [{'y': 48, 'x': 41}, {'y': 35, 'x': 32}]  1694390      Pass    4418   \n",
       "3   [{'y': 35, 'x': 32}, {'y': 6, 'x': 89}]  1694390      Pass    4418   \n",
       "4    [{'y': 6, 'x': 89}, {'y': 0, 'x': 85}]  1694390      Duel    4418   \n",
       "\n",
       "  matchPeriod   eventSec  subEventId        id                         tags  \\\n",
       "0          1H   1.255990        85.0  88178642               [{'id': 1801}]   \n",
       "1          1H   2.351908        85.0  88178643               [{'id': 1801}]   \n",
       "2          1H   3.241028        85.0  88178644               [{'id': 1801}]   \n",
       "3          1H   6.033681        83.0  88178645               [{'id': 1802}]   \n",
       "4          1H  13.143591        12.0  88178646  [{'id': 702}, {'id': 1801}]   \n",
       "\n",
       "   status  roundId  gameweek  seasonId              dateutc  winner  \\\n",
       "0  Played  4165470         1      9291  2016-06-10 19:00:00    4418   \n",
       "1  Played  4165470         1      9291  2016-06-10 19:00:00    4418   \n",
       "2  Played  4165470         1      9291  2016-06-10 19:00:00    4418   \n",
       "3  Played  4165470         1      9291  2016-06-10 19:00:00    4418   \n",
       "4  Played  4165470         1      9291  2016-06-10 19:00:00    4418   \n",
       "\n",
       "             venue   wyId_x                    label  \\\n",
       "0  Stade de France  1694390  France - Romania, 2 - 1   \n",
       "1  Stade de France  1694390  France - Romania, 2 - 1   \n",
       "2  Stade de France  1694390  France - Romania, 2 - 1   \n",
       "3  Stade de France  1694390  France - Romania, 2 - 1   \n",
       "4  Stade de France  1694390  France - Romania, 2 - 1   \n",
       "\n",
       "                        date  \\\n",
       "0  2016-06-10 21:00:00-02:00   \n",
       "1  2016-06-10 21:00:00-02:00   \n",
       "2  2016-06-10 21:00:00-02:00   \n",
       "3  2016-06-10 21:00:00-02:00   \n",
       "4  2016-06-10 21:00:00-02:00   \n",
       "\n",
       "                                            referees duration  competitionId  \\\n",
       "0  [{'refereeId': 383185, 'role': 'referee'}, {'r...  Regular            102   \n",
       "1  [{'refereeId': 383185, 'role': 'referee'}, {'r...  Regular            102   \n",
       "2  [{'refereeId': 383185, 'role': 'referee'}, {'r...  Regular            102   \n",
       "3  [{'refereeId': 383185, 'role': 'referee'}, {'r...  Regular            102   \n",
       "4  [{'refereeId': 383185, 'role': 'referee'}, {'r...  Regular            102   \n",
       "\n",
       "   wyId_y area_name  weight firstName  middleName   lastName   birthDate  \\\n",
       "0     102       NaN      88   Olivier         NaN     Giroud  1986-09-30   \n",
       "1     102       NaN      71   Antoine         NaN  Griezmann  1991-03-21   \n",
       "2     102       NaN      68    N'Golo         NaN      Kanté  1991-03-29   \n",
       "3     102       NaN      75   Laurent         NaN  Koscielny  1985-09-10   \n",
       "4     102       NaN      70    Blaise         NaN    Matuidi  1987-04-09   \n",
       "\n",
       "   height  wyId_x.1   foot     shortName passportArea_name   role_name  \\\n",
       "0     192     26010   left     O. Giroud            France     Forward   \n",
       "1     175      3682   left  A. Griezmann            France     Forward   \n",
       "2     168     31528  right      N. Kanté              Mali  Midfielder   \n",
       "3     186      7855  right  L. Koscielny            Poland    Defender   \n",
       "4     175     25437   left    B. Matuidi            Angola  Midfielder   \n",
       "\n",
       "  birthArea_name    name  wyId_y.1  \n",
       "0         France  France      4418  \n",
       "1         France  France      4418  \n",
       "2         France  France      4418  \n",
       "3         France  France      4418  \n",
       "4         France  France      4418  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 rows of the raw DataFrame, df_wyscout_raw\n",
    "df_wyscout_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>eventId</th>\n",
       "      <th>subEventName</th>\n",
       "      <th>playerId</th>\n",
       "      <th>positions</th>\n",
       "      <th>matchId</th>\n",
       "      <th>eventName</th>\n",
       "      <th>teamId</th>\n",
       "      <th>matchPeriod</th>\n",
       "      <th>eventSec</th>\n",
       "      <th>subEventId</th>\n",
       "      <th>id</th>\n",
       "      <th>tags</th>\n",
       "      <th>status</th>\n",
       "      <th>roundId</th>\n",
       "      <th>gameweek</th>\n",
       "      <th>seasonId</th>\n",
       "      <th>dateutc</th>\n",
       "      <th>winner</th>\n",
       "      <th>venue</th>\n",
       "      <th>wyId_x</th>\n",
       "      <th>label</th>\n",
       "      <th>date</th>\n",
       "      <th>referees</th>\n",
       "      <th>duration</th>\n",
       "      <th>competitionId</th>\n",
       "      <th>wyId_y</th>\n",
       "      <th>area_name</th>\n",
       "      <th>weight</th>\n",
       "      <th>firstName</th>\n",
       "      <th>middleName</th>\n",
       "      <th>lastName</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>height</th>\n",
       "      <th>wyId_x.1</th>\n",
       "      <th>foot</th>\n",
       "      <th>shortName</th>\n",
       "      <th>passportArea_name</th>\n",
       "      <th>role_name</th>\n",
       "      <th>birthArea_name</th>\n",
       "      <th>name</th>\n",
       "      <th>wyId_y.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3025251</th>\n",
       "      <td>2</td>\n",
       "      <td>Foul</td>\n",
       "      <td>21234</td>\n",
       "      <td>[{'y': 87, 'x': 24}, {'y': 82, 'x': 26}]</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Foul</td>\n",
       "      <td>3185</td>\n",
       "      <td>2H</td>\n",
       "      <td>2824.741855</td>\n",
       "      <td>20.0</td>\n",
       "      <td>253567159</td>\n",
       "      <td>[]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4406278</td>\n",
       "      <td>38</td>\n",
       "      <td>181248</td>\n",
       "      <td>2018-05-20 13:00:00</td>\n",
       "      <td>3185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Genoa - Torino, 1 - 2</td>\n",
       "      <td>May 20, 2018 at 3:00:00 PM GMT+2</td>\n",
       "      <td>[{'refereeId': 393614, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>524</td>\n",
       "      <td>524</td>\n",
       "      <td>Italy</td>\n",
       "      <td>72</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belotti</td>\n",
       "      <td>1993-12-20</td>\n",
       "      <td>181</td>\n",
       "      <td>21234</td>\n",
       "      <td>right</td>\n",
       "      <td>A. Belotti</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Forward</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Torino</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025252</th>\n",
       "      <td>3</td>\n",
       "      <td>Free kick cross</td>\n",
       "      <td>70974</td>\n",
       "      <td>[{'y': 23, 'x': 75}, {'y': 65, 'x': 95}]</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>3193</td>\n",
       "      <td>2H</td>\n",
       "      <td>2870.982660</td>\n",
       "      <td>32.0</td>\n",
       "      <td>253567160</td>\n",
       "      <td>[{'id': 801}, {'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4406278</td>\n",
       "      <td>38</td>\n",
       "      <td>181248</td>\n",
       "      <td>2018-05-20 13:00:00</td>\n",
       "      <td>3185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Genoa - Torino, 1 - 2</td>\n",
       "      <td>May 20, 2018 at 3:00:00 PM GMT+2</td>\n",
       "      <td>[{'refereeId': 393614, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>524</td>\n",
       "      <td>524</td>\n",
       "      <td>Italy</td>\n",
       "      <td>64</td>\n",
       "      <td>Iuri José</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Picanço Medeiros</td>\n",
       "      <td>1994-07-10</td>\n",
       "      <td>174</td>\n",
       "      <td>70974</td>\n",
       "      <td>left</td>\n",
       "      <td>Iuri Medeiros</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Forward</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Genoa</td>\n",
       "      <td>3193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025253</th>\n",
       "      <td>1</td>\n",
       "      <td>Ground loose ball duel</td>\n",
       "      <td>14745</td>\n",
       "      <td>[{'y': 35, 'x': 5}, {'y': 36, 'x': 3}]</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Duel</td>\n",
       "      <td>3185</td>\n",
       "      <td>2H</td>\n",
       "      <td>2872.101142</td>\n",
       "      <td>13.0</td>\n",
       "      <td>253567161</td>\n",
       "      <td>[{'id': 702}, {'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4406278</td>\n",
       "      <td>38</td>\n",
       "      <td>181248</td>\n",
       "      <td>2018-05-20 13:00:00</td>\n",
       "      <td>3185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Genoa - Torino, 1 - 2</td>\n",
       "      <td>May 20, 2018 at 3:00:00 PM GMT+2</td>\n",
       "      <td>[{'refereeId': 393614, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>524</td>\n",
       "      <td>524</td>\n",
       "      <td>Italy</td>\n",
       "      <td>82</td>\n",
       "      <td>Cristian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Molinaro</td>\n",
       "      <td>1983-07-30</td>\n",
       "      <td>182</td>\n",
       "      <td>14745</td>\n",
       "      <td>left</td>\n",
       "      <td>C. Molinaro</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Torino</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025254</th>\n",
       "      <td>1</td>\n",
       "      <td>Ground loose ball duel</td>\n",
       "      <td>413041</td>\n",
       "      <td>[{'y': 65, 'x': 95}, {'y': 64, 'x': 97}]</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Duel</td>\n",
       "      <td>3193</td>\n",
       "      <td>2H</td>\n",
       "      <td>2872.990437</td>\n",
       "      <td>13.0</td>\n",
       "      <td>253567163</td>\n",
       "      <td>[{'id': 702}, {'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4406278</td>\n",
       "      <td>38</td>\n",
       "      <td>181248</td>\n",
       "      <td>2018-05-20 13:00:00</td>\n",
       "      <td>3185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Genoa - Torino, 1 - 2</td>\n",
       "      <td>May 20, 2018 at 3:00:00 PM GMT+2</td>\n",
       "      <td>[{'refereeId': 393614, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>524</td>\n",
       "      <td>524</td>\n",
       "      <td>Italy</td>\n",
       "      <td>83</td>\n",
       "      <td>Jawad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>El Yamiq</td>\n",
       "      <td>1992-02-29</td>\n",
       "      <td>190</td>\n",
       "      <td>413041</td>\n",
       "      <td>right</td>\n",
       "      <td>J. El Yamiq</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Genoa</td>\n",
       "      <td>3193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025255</th>\n",
       "      <td>1</td>\n",
       "      <td>Air duel</td>\n",
       "      <td>20927</td>\n",
       "      <td>[{'y': 36, 'x': 3}, {'y': 100, 'x': 100}]</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Duel</td>\n",
       "      <td>3185</td>\n",
       "      <td>2H</td>\n",
       "      <td>2874.049206</td>\n",
       "      <td>10.0</td>\n",
       "      <td>253567162</td>\n",
       "      <td>[{'id': 703}, {'id': 1801}]</td>\n",
       "      <td>Played</td>\n",
       "      <td>4406278</td>\n",
       "      <td>38</td>\n",
       "      <td>181248</td>\n",
       "      <td>2018-05-20 13:00:00</td>\n",
       "      <td>3185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2576338</td>\n",
       "      <td>Genoa - Torino, 1 - 2</td>\n",
       "      <td>May 20, 2018 at 3:00:00 PM GMT+2</td>\n",
       "      <td>[{'refereeId': 393614, 'role': 'referee'}, {'r...</td>\n",
       "      <td>Regular</td>\n",
       "      <td>524</td>\n",
       "      <td>524</td>\n",
       "      <td>Italy</td>\n",
       "      <td>84</td>\n",
       "      <td>Lorenzo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>De Silvestri</td>\n",
       "      <td>1988-05-23</td>\n",
       "      <td>186</td>\n",
       "      <td>20927</td>\n",
       "      <td>right</td>\n",
       "      <td>L. De Silvestri</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Torino</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         eventId            subEventName  playerId  \\\n",
       "3025251        2                    Foul     21234   \n",
       "3025252        3         Free kick cross     70974   \n",
       "3025253        1  Ground loose ball duel     14745   \n",
       "3025254        1  Ground loose ball duel    413041   \n",
       "3025255        1                Air duel     20927   \n",
       "\n",
       "                                         positions  matchId  eventName  \\\n",
       "3025251   [{'y': 87, 'x': 24}, {'y': 82, 'x': 26}]  2576338       Foul   \n",
       "3025252   [{'y': 23, 'x': 75}, {'y': 65, 'x': 95}]  2576338  Free Kick   \n",
       "3025253     [{'y': 35, 'x': 5}, {'y': 36, 'x': 3}]  2576338       Duel   \n",
       "3025254   [{'y': 65, 'x': 95}, {'y': 64, 'x': 97}]  2576338       Duel   \n",
       "3025255  [{'y': 36, 'x': 3}, {'y': 100, 'x': 100}]  2576338       Duel   \n",
       "\n",
       "         teamId matchPeriod     eventSec  subEventId         id  \\\n",
       "3025251    3185          2H  2824.741855        20.0  253567159   \n",
       "3025252    3193          2H  2870.982660        32.0  253567160   \n",
       "3025253    3185          2H  2872.101142        13.0  253567161   \n",
       "3025254    3193          2H  2872.990437        13.0  253567163   \n",
       "3025255    3185          2H  2874.049206        10.0  253567162   \n",
       "\n",
       "                                tags  status  roundId  gameweek  seasonId  \\\n",
       "3025251                           []  Played  4406278        38    181248   \n",
       "3025252  [{'id': 801}, {'id': 1801}]  Played  4406278        38    181248   \n",
       "3025253  [{'id': 702}, {'id': 1801}]  Played  4406278        38    181248   \n",
       "3025254  [{'id': 702}, {'id': 1801}]  Played  4406278        38    181248   \n",
       "3025255  [{'id': 703}, {'id': 1801}]  Played  4406278        38    181248   \n",
       "\n",
       "                     dateutc  winner venue   wyId_x                  label  \\\n",
       "3025251  2018-05-20 13:00:00    3185   NaN  2576338  Genoa - Torino, 1 - 2   \n",
       "3025252  2018-05-20 13:00:00    3185   NaN  2576338  Genoa - Torino, 1 - 2   \n",
       "3025253  2018-05-20 13:00:00    3185   NaN  2576338  Genoa - Torino, 1 - 2   \n",
       "3025254  2018-05-20 13:00:00    3185   NaN  2576338  Genoa - Torino, 1 - 2   \n",
       "3025255  2018-05-20 13:00:00    3185   NaN  2576338  Genoa - Torino, 1 - 2   \n",
       "\n",
       "                                     date  \\\n",
       "3025251  May 20, 2018 at 3:00:00 PM GMT+2   \n",
       "3025252  May 20, 2018 at 3:00:00 PM GMT+2   \n",
       "3025253  May 20, 2018 at 3:00:00 PM GMT+2   \n",
       "3025254  May 20, 2018 at 3:00:00 PM GMT+2   \n",
       "3025255  May 20, 2018 at 3:00:00 PM GMT+2   \n",
       "\n",
       "                                                  referees duration  \\\n",
       "3025251  [{'refereeId': 393614, 'role': 'referee'}, {'r...  Regular   \n",
       "3025252  [{'refereeId': 393614, 'role': 'referee'}, {'r...  Regular   \n",
       "3025253  [{'refereeId': 393614, 'role': 'referee'}, {'r...  Regular   \n",
       "3025254  [{'refereeId': 393614, 'role': 'referee'}, {'r...  Regular   \n",
       "3025255  [{'refereeId': 393614, 'role': 'referee'}, {'r...  Regular   \n",
       "\n",
       "         competitionId  wyId_y area_name  weight   firstName  middleName  \\\n",
       "3025251            524     524     Italy      72      Andrea         NaN   \n",
       "3025252            524     524     Italy      64  Iuri José          NaN   \n",
       "3025253            524     524     Italy      82    Cristian         NaN   \n",
       "3025254            524     524     Italy      83       Jawad         NaN   \n",
       "3025255            524     524     Italy      84     Lorenzo         NaN   \n",
       "\n",
       "                 lastName   birthDate  height  wyId_x.1   foot  \\\n",
       "3025251           Belotti  1993-12-20     181     21234  right   \n",
       "3025252  Picanço Medeiros  1994-07-10     174     70974   left   \n",
       "3025253          Molinaro  1983-07-30     182     14745   left   \n",
       "3025254          El Yamiq  1992-02-29     190    413041  right   \n",
       "3025255      De Silvestri  1988-05-23     186     20927  right   \n",
       "\n",
       "               shortName passportArea_name role_name birthArea_name    name  \\\n",
       "3025251       A. Belotti             Italy   Forward          Italy  Torino   \n",
       "3025252    Iuri Medeiros          Portugal   Forward       Portugal   Genoa   \n",
       "3025253      C. Molinaro             Italy  Defender          Italy  Torino   \n",
       "3025254      J. El Yamiq           Morocco  Defender        Morocco   Genoa   \n",
       "3025255  L. De Silvestri             Italy  Defender          Italy  Torino   \n",
       "\n",
       "         wyId_y.1  \n",
       "3025251      3185  \n",
       "3025252      3193  \n",
       "3025253      3185  \n",
       "3025254      3193  \n",
       "3025255      3185  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 5 rows of the raw DataFrame, df_wyscout_raw\n",
    "df_wyscout_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3025256, 41)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the raw DataFrame, ddf_wyscout_raw\n",
    "print(df_wyscout_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['eventId', 'subEventName', 'playerId', 'positions', 'matchId',\n",
      "       'eventName', 'teamId', 'matchPeriod', 'eventSec', 'subEventId', 'id',\n",
      "       'tags', 'status', 'roundId', 'gameweek', 'seasonId', 'dateutc',\n",
      "       'winner', 'venue', 'wyId_x', 'label', 'date', 'referees', 'duration',\n",
      "       'competitionId', 'wyId_y', 'area_name', 'weight', 'firstName',\n",
      "       'middleName', 'lastName', 'birthDate', 'height', 'wyId_x.1', 'foot',\n",
      "       'shortName', 'passportArea_name', 'role_name', 'birthArea_name', 'name',\n",
      "       'wyId_y.1'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the column names of the raw DataFrame, df_wyscout_raw\n",
    "print(df_wyscout_raw.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The joined dataset has forty 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": [
       "eventId                int64\n",
       "subEventName          object\n",
       "playerId               int64\n",
       "positions             object\n",
       "matchId                int64\n",
       "eventName             object\n",
       "teamId                 int64\n",
       "matchPeriod           object\n",
       "eventSec             float64\n",
       "subEventId           float64\n",
       "id                     int64\n",
       "tags                  object\n",
       "status                object\n",
       "roundId                int64\n",
       "gameweek               int64\n",
       "seasonId               int64\n",
       "dateutc               object\n",
       "winner                 int64\n",
       "venue                 object\n",
       "wyId_x                 int64\n",
       "label                 object\n",
       "date                  object\n",
       "referees              object\n",
       "duration              object\n",
       "competitionId          int64\n",
       "wyId_y                 int64\n",
       "area_name             object\n",
       "weight                 int64\n",
       "firstName             object\n",
       "middleName           float64\n",
       "lastName              object\n",
       "birthDate             object\n",
       "height                 int64\n",
       "wyId_x.1               int64\n",
       "foot                  object\n",
       "shortName             object\n",
       "passportArea_name     object\n",
       "role_name             object\n",
       "birthArea_name        object\n",
       "name                  object\n",
       "wyId_y.1               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data types of the features of the raw DataFrame, df_wyscout_raw\n",
    "df_wyscout_raw.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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: 3025256 entries, 0 to 3025255\n",
      "Data columns (total 41 columns):\n",
      " #   Column             Dtype  \n",
      "---  ------             -----  \n",
      " 0   eventId            int64  \n",
      " 1   subEventName       object \n",
      " 2   playerId           int64  \n",
      " 3   positions          object \n",
      " 4   matchId            int64  \n",
      " 5   eventName          object \n",
      " 6   teamId             int64  \n",
      " 7   matchPeriod        object \n",
      " 8   eventSec           float64\n",
      " 9   subEventId         float64\n",
      " 10  id                 int64  \n",
      " 11  tags               object \n",
      " 12  status             object \n",
      " 13  roundId            int64  \n",
      " 14  gameweek           int64  \n",
      " 15  seasonId           int64  \n",
      " 16  dateutc            object \n",
      " 17  winner             int64  \n",
      " 18  venue              object \n",
      " 19  wyId_x             int64  \n",
      " 20  label              object \n",
      " 21  date               object \n",
      " 22  referees           object \n",
      " 23  duration           object \n",
      " 24  competitionId      int64  \n",
      " 25  wyId_y             int64  \n",
      " 26  area_name          object \n",
      " 27  weight             int64  \n",
      " 28  firstName          object \n",
      " 29  middleName         float64\n",
      " 30  lastName           object \n",
      " 31  birthDate          object \n",
      " 32  height             int64  \n",
      " 33  wyId_x.1           int64  \n",
      " 34  foot               object \n",
      " 35  shortName          object \n",
      " 36  passportArea_name  object \n",
      " 37  role_name          object \n",
      " 38  birthArea_name     object \n",
      " 39  name               object \n",
      " 40  wyId_y.1           int64  \n",
      "dtypes: float64(3), int64(16), object(22)\n",
      "memory usage: 946.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Info for the raw DataFrame, df_wyscout_raw\n",
    "df_wyscout_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>eventId</th>\n",
       "      <th>playerId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>teamId</th>\n",
       "      <th>eventSec</th>\n",
       "      <th>subEventId</th>\n",
       "      <th>id</th>\n",
       "      <th>roundId</th>\n",
       "      <th>gameweek</th>\n",
       "      <th>seasonId</th>\n",
       "      <th>winner</th>\n",
       "      <th>wyId_x</th>\n",
       "      <th>competitionId</th>\n",
       "      <th>wyId_y</th>\n",
       "      <th>weight</th>\n",
       "      <th>middleName</th>\n",
       "      <th>height</th>\n",
       "      <th>wyId_x.1</th>\n",
       "      <th>wyId_y.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.017102e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "      <td>3.025256e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.694230e+00</td>\n",
       "      <td>8.953778e+04</td>\n",
       "      <td>2.494932e+06</td>\n",
       "      <td>3.004373e+03</td>\n",
       "      <td>1.390728e+03</td>\n",
       "      <td>6.013484e+01</td>\n",
       "      <td>2.158336e+08</td>\n",
       "      <td>4.391560e+06</td>\n",
       "      <td>1.815116e+01</td>\n",
       "      <td>1.709805e+05</td>\n",
       "      <td>2.317237e+03</td>\n",
       "      <td>2.494932e+06</td>\n",
       "      <td>4.797790e+02</td>\n",
       "      <td>4.797790e+02</td>\n",
       "      <td>7.622714e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.820490e+02</td>\n",
       "      <td>8.953778e+04</td>\n",
       "      <td>3.004373e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.141030e+00</td>\n",
       "      <td>1.169272e+05</td>\n",
       "      <td>1.591746e+05</td>\n",
       "      <td>2.925207e+03</td>\n",
       "      <td>8.347843e+02</td>\n",
       "      <td>3.257763e+01</td>\n",
       "      <td>3.023051e+07</td>\n",
       "      <td>5.686541e+04</td>\n",
       "      <td>1.126545e+01</td>\n",
       "      <td>4.054451e+04</td>\n",
       "      <td>3.021050e+03</td>\n",
       "      <td>1.591746e+05</td>\n",
       "      <td>1.845044e+02</td>\n",
       "      <td>1.845044e+02</td>\n",
       "      <td>7.149052e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.032542e+00</td>\n",
       "      <td>1.169272e+05</td>\n",
       "      <td>2.925207e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.694390e+06</td>\n",
       "      <td>6.740000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>8.817864e+07</td>\n",
       "      <td>4.165363e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.291000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.694390e+06</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>6.740000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.380000e+03</td>\n",
       "      <td>2.500086e+06</td>\n",
       "      <td>1.619000e+03</td>\n",
       "      <td>6.608105e+02</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.988861e+08</td>\n",
       "      <td>4.405654e+06</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.811440e+05</td>\n",
       "      <td>6.740000e+02</td>\n",
       "      <td>2.500086e+06</td>\n",
       "      <td>3.640000e+02</td>\n",
       "      <td>3.640000e+02</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.780000e+02</td>\n",
       "      <td>9.380000e+03</td>\n",
       "      <td>1.619000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.562300e+04</td>\n",
       "      <td>2.516836e+06</td>\n",
       "      <td>2.460000e+03</td>\n",
       "      <td>1.374896e+03</td>\n",
       "      <td>8.300000e+01</td>\n",
       "      <td>2.191298e+08</td>\n",
       "      <td>4.405655e+06</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.811500e+05</td>\n",
       "      <td>1.633000e+03</td>\n",
       "      <td>2.516836e+06</td>\n",
       "      <td>4.260000e+02</td>\n",
       "      <td>4.260000e+02</td>\n",
       "      <td>7.600000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.820000e+02</td>\n",
       "      <td>2.562300e+04</td>\n",
       "      <td>2.460000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.345420e+05</td>\n",
       "      <td>2.565827e+06</td>\n",
       "      <td>3.766000e+03</td>\n",
       "      <td>2.109554e+03</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>2.352463e+08</td>\n",
       "      <td>4.406122e+06</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>1.811890e+05</td>\n",
       "      <td>3.173000e+03</td>\n",
       "      <td>2.565827e+06</td>\n",
       "      <td>5.240000e+02</td>\n",
       "      <td>5.240000e+02</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>1.345420e+05</td>\n",
       "      <td>3.766000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.685830e+05</td>\n",
       "      <td>2.576338e+06</td>\n",
       "      <td>1.983000e+04</td>\n",
       "      <td>3.537356e+03</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.802175e+08</td>\n",
       "      <td>4.406278e+06</td>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>1.812480e+05</td>\n",
       "      <td>1.983000e+04</td>\n",
       "      <td>2.576338e+06</td>\n",
       "      <td>7.950000e+02</td>\n",
       "      <td>7.950000e+02</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.030000e+02</td>\n",
       "      <td>5.685830e+05</td>\n",
       "      <td>1.983000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eventId      playerId       matchId        teamId      eventSec  \\\n",
       "count  3.025256e+06  3.025256e+06  3.025256e+06  3.025256e+06  3.025256e+06   \n",
       "mean   5.694230e+00  8.953778e+04  2.494932e+06  3.004373e+03  1.390728e+03   \n",
       "std    3.141030e+00  1.169272e+05  1.591746e+05  2.925207e+03  8.347843e+02   \n",
       "min    1.000000e+00  1.200000e+01  1.694390e+06  6.740000e+02  0.000000e+00   \n",
       "25%    1.000000e+00  9.380000e+03  2.500086e+06  1.619000e+03  6.608105e+02   \n",
       "50%    8.000000e+00  2.562300e+04  2.516836e+06  2.460000e+03  1.374896e+03   \n",
       "75%    8.000000e+00  1.345420e+05  2.565827e+06  3.766000e+03  2.109554e+03   \n",
       "max    1.000000e+01  5.685830e+05  2.576338e+06  1.983000e+04  3.537356e+03   \n",
       "\n",
       "         subEventId            id       roundId      gameweek      seasonId  \\\n",
       "count  3.017102e+06  3.025256e+06  3.025256e+06  3.025256e+06  3.025256e+06   \n",
       "mean   6.013484e+01  2.158336e+08  4.391560e+06  1.815116e+01  1.709805e+05   \n",
       "std    3.257763e+01  3.023051e+07  5.686541e+04  1.126545e+01  4.054451e+04   \n",
       "min    1.000000e+01  8.817864e+07  4.165363e+06  0.000000e+00  9.291000e+03   \n",
       "25%    1.300000e+01  1.988861e+08  4.405654e+06  8.000000e+00  1.811440e+05   \n",
       "50%    8.300000e+01  2.191298e+08  4.405655e+06  1.800000e+01  1.811500e+05   \n",
       "75%    8.500000e+01  2.352463e+08  4.406122e+06  2.800000e+01  1.811890e+05   \n",
       "max    1.000000e+02  2.802175e+08  4.406278e+06  3.800000e+01  1.812480e+05   \n",
       "\n",
       "             winner        wyId_x  competitionId        wyId_y        weight  \\\n",
       "count  3.025256e+06  3.025256e+06   3.025256e+06  3.025256e+06  3.025256e+06   \n",
       "mean   2.317237e+03  2.494932e+06   4.797790e+02  4.797790e+02  7.622714e+01   \n",
       "std    3.021050e+03  1.591746e+05   1.845044e+02  1.845044e+02  7.149052e+00   \n",
       "min    0.000000e+00  1.694390e+06   2.800000e+01  2.800000e+01  0.000000e+00   \n",
       "25%    6.740000e+02  2.500086e+06   3.640000e+02  3.640000e+02  7.200000e+01   \n",
       "50%    1.633000e+03  2.516836e+06   4.260000e+02  4.260000e+02  7.600000e+01   \n",
       "75%    3.173000e+03  2.565827e+06   5.240000e+02  5.240000e+02  8.100000e+01   \n",
       "max    1.983000e+04  2.576338e+06   7.950000e+02  7.950000e+02  1.010000e+02   \n",
       "\n",
       "       middleName        height      wyId_x.1      wyId_y.1  \n",
       "count         0.0  3.025256e+06  3.025256e+06  3.025256e+06  \n",
       "mean          NaN  1.820490e+02  8.953778e+04  3.004373e+03  \n",
       "std           NaN  7.032542e+00  1.169272e+05  2.925207e+03  \n",
       "min           NaN  0.000000e+00  1.200000e+01  6.740000e+02  \n",
       "25%           NaN  1.780000e+02  9.380000e+03  1.619000e+03  \n",
       "50%           NaN  1.820000e+02  2.562300e+04  2.460000e+03  \n",
       "75%           NaN  1.870000e+02  1.345420e+05  3.766000e+03  \n",
       "max           NaN  2.030000e+02  5.685830e+05  1.983000e+04  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the raw DataFrame, df_wyscout_raw, showing some summary statistics for each numberical column in the DataFrame\n",
    "df_wyscout_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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_wyscout_raw\n",
    "msno.matrix(df_wyscout_raw, figsize = (30, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subEventName       8154\n",
       "subEventId         8154\n",
       "venue            166662\n",
       "area_name        179899\n",
       "middleName      3025256\n",
       "foot                609\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of missing values\n",
    "null_value_stats = df_wyscout_raw.isnull().sum(axis=0)\n",
    "null_value_stats[null_value_stats != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualisation shows us that there are no missing values in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section4'>4. Data Engineering</a>\n",
    "Before any Feature Engineering or Data Visualisation, we first need to clean and wrangle the datasets to a form that meet our needs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.1'>4.1. Assign Raw DataFrame to Engineered DataFrame</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign Raw DataFrame to Engineered DataFrame\n",
    "df_wyscout = df_wyscout_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.2'>4.2. String Cleaning</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split `Label` column into seperate `Fixture` and `Score` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Break down 'Label' column into constituent parts - Fixtures, Score, Date, Home Goals, Away Goals, etc.\n",
    "df_wyscout['fixture'] = df_wyscout['label'].str.split(', ').str[0]\n",
    "df_wyscout['score_home_away'] = df_wyscout['label'].str.split(', ').str[1]\n",
    "df_wyscout['team_home'] = df_wyscout['fixture'].str.split(' - ').str[0]\n",
    "df_wyscout['team_away'] = df_wyscout['fixture'].str.split(' - ').str[1]\n",
    "df_wyscout['goals_home'] = df_wyscout['score_home_away'].str.split(' - ').str[0]\n",
    "df_wyscout['goals_away'] = df_wyscout['score_home_away'].str.split(' - ').str[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split `date` column into separate `date_isolated` and `time_isolated` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['date_isolated'] = df_wyscout['date'].str.split(' at').str[0]\n",
    "df_wyscout['time_isolated'] = df_wyscout['date'].str.split(' at ').str[1]\n",
    "df_wyscout['date_time_isolated'] = df_wyscout['date'].str.split(' GMT').str[0].str.replace(' at ', ' ', regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.3'>4.3. Rename Columns</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout = df_wyscout.rename(columns={'name': 'teamName'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.4'>4.4. Convert Data Types</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['date_time_timestamp'] = pd.to_datetime(df_wyscout['dateutc'])\n",
    "df_wyscout['date_date'] = df_wyscout['date_time_timestamp'].dt.strftime('%d-%m-%Y')\n",
    "df_wyscout['time_time'] = df_wyscout['date_time_timestamp'].dt.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['full_fixture_date'] = df_wyscout['date_date'].astype(str) + ' ' + df_wyscout['team_home'].astype(str)  + ' ' + df_wyscout['goals_home'].astype(str) + ' ' + ' v ' + ' ' + df_wyscout['goals_away'].astype(str) + ' ' + df_wyscout['team_away'].astype(str) "
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout = df_wyscout.sort_values(['date_date', 'time_time', 'competitionId', 'full_fixture_date', 'matchPeriod', 'eventSec'], ascending=[True, True, True, True, True, True])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.6'>4.6. Create Features</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `competition_name` column derived from the `competitionId` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "\n",
    "## Define dictionary of competition names per competition ID\n",
    "dict_competition_name_wyscout = {28: 'FIFA World Cup',\n",
    "                                 102: 'UEFA EURO 2018',\n",
    "                                 364: 'Premier League',\n",
    "                                 412: 'Ligue 1',\n",
    "                                 426: 'Bundesliga',\n",
    "                                 524: 'Seria A',\n",
    "                                 795: 'La Liga'\n",
    "                                }\n",
    "\n",
    "## Apply dictionary of competition names per competition ID\n",
    "df_wyscout['competition_name'] = df_wyscout['competitionId'].map(dict_competition_name_wyscout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `season` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "\n",
    "## Define dictionary of seasons per competition ID\n",
    "dict_season_wyscout = {28: '2018',\n",
    "                       102: '2016',\n",
    "                       364: '17/18',\n",
    "                       412: '17/18',\n",
    "                       426: '17/18',\n",
    "                       524: '17/18',\n",
    "                       795: '17/18'\n",
    "                      }\n",
    "\n",
    "## Apply dictionary of seasons per competition ID\n",
    "df_wyscout['season'] = df_wyscout['competitionId'].map(dict_season_wyscout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `fullName` attribute \n",
    "Join together the `firstName` and `lastName` attribute to create a `fullName` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['fullName'] = df_wyscout['firstName'].astype(str) + ' ' + df_wyscout['lastName'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `previous_event` attribute "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['previous_event'] = df_wyscout['subEventName'].shift(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `followingPossession` column\n",
    "'teamIdNext' = following 'teamId'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['teamIdNext'] = df_wyscout['teamId'].shift(-1)\n",
    "df_wyscout['teamNameNext'] = df_wyscout['teamName'].shift(-1)\n",
    "df_wyscout['fullNameNext'] = df_wyscout['fullName'].shift(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `player2player` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['player2player'] = df_wyscout['fullName'] + ' - ' + df_wyscout['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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout['isPossessionRetained'] = np.where(df_wyscout['teamId'] == df_wyscout['teamIdNext'], True, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.7'>4.7. 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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout = df_wyscout.drop(['wyId_x', 'wyId_y', 'wyId_x.1'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.8'>4.8. Aggregate Data</a>\n",
    "Aggregated data used for Tableau dashboarding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.8.1'>4.8.1. Fixture Level</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "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_wyscout_select = df_wyscout[cols]\n",
    "\n",
    "## \n",
    "df_wyscout_select['Opponent'] = np.where(df_wyscout_select['team_home'] == df_wyscout_select['teamName'], df_wyscout_select['team_away'], df_wyscout_select['team_home'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Season</th>\n",
       "      <th>Date</th>\n",
       "      <th>Fixture</th>\n",
       "      <th>Team</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Goals_Home</th>\n",
       "      <th>Goals_Away</th>\n",
       "      <th>Event</th>\n",
       "      <th>Team_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Duel</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Foul</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Offside</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Season                Date            Fixture    Team  Opponent Goals_Home  \\\n",
       "0  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "1  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "2  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "3  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "4  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "\n",
       "  Goals_Away                    Event  Team_Value  \n",
       "0          2                     Duel         220  \n",
       "1          2                     Foul          12  \n",
       "2          2                Free Kick          58  \n",
       "3          2  Goalkeeper leaving line           2  \n",
       "4          2                  Offside           5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "\n",
    "## Group DataFrame and Aggregate on 'eventName'\n",
    "df_wyscout_fixture_grouped = (df_wyscout_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_wyscout_fixture_grouped.columns = df_wyscout_fixture_grouped.columns.droplevel(level=0)\n",
    "\n",
    "\n",
    "## Reset index\n",
    "df_wyscout_fixture_grouped = df_wyscout_fixture_grouped.reset_index()\n",
    "\n",
    "## Rename columns\n",
    "df_wyscout_fixture_grouped = df_wyscout_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_wyscout_fixture_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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_wyscout_fixture_grouped_select = df_wyscout_fixture_grouped[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join DataFrame to itself on 'Date', 'Fixture', 'Team'/'Opponent', and 'Event', to join Team and Opponent together\n",
    "df_wyscout_fixture_grouped = pd.merge(df_wyscout_fixture_grouped, df_wyscout_fixture_grouped,  how='left', left_on=['Season', 'Date', 'Fixture', 'Opponent', 'Event'], right_on = ['Season', 'Date', 'Fixture', 'Team', 'Event'])"
   ]
  },
  {
   "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>Season</th>\n",
       "      <th>Date</th>\n",
       "      <th>Fixture</th>\n",
       "      <th>Team</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Goals_Home</th>\n",
       "      <th>Goals_Away</th>\n",
       "      <th>Event</th>\n",
       "      <th>Team_Value</th>\n",
       "      <th>Opponent_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Duel</td>\n",
       "      <td>220</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Foul</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>58</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Offside</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Season                Date            Fixture    Team  Opponent Goals_Home  \\\n",
       "0  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "1  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "2  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "3  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "4  17/18 2017-08-04 18:45:00  Monaco - Toulouse  Monaco  Toulouse          3   \n",
       "\n",
       "  Goals_Away                    Event  Team_Value  Opponent_Value  \n",
       "0          2                     Duel         220             205  \n",
       "1          2                     Foul          12              23  \n",
       "2          2                Free Kick          58              47  \n",
       "3          2  Goalkeeper leaving line           2               0  \n",
       "4          2                  Offside           5               1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean Data\n",
    "\n",
    "## Drop columns\n",
    "df_wyscout_fixture_grouped = df_wyscout_fixture_grouped.drop(columns=['Team_y', 'Opponent_y', 'Goals_Home_y', 'Goals_Away_y'])\n",
    "\n",
    "## Rename columns\n",
    "df_wyscout_fixture_grouped = df_wyscout_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_wyscout_fixture_grouped['Team_Value'] = df_wyscout_fixture_grouped['Team_Value'].replace(np.nan, 0)\n",
    "df_wyscout_fixture_grouped['Opponent_Value'] = df_wyscout_fixture_grouped['Opponent_Value'].replace(np.nan, 0)\n",
    "\n",
    "## Convert Opponent_Value' from Float64 to Int64 type\n",
    "df_wyscout_fixture_grouped['Opponent_Value'] = df_wyscout_fixture_grouped['Opponent_Value'].astype('Int64')\n",
    "\n",
    "## Display DataFrame\n",
    "df_wyscout_fixture_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Team</th>\n",
       "      <th>Date</th>\n",
       "      <th>Gameweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>2017-08-12 14:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>2017-08-19 14:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>2017-08-26 11:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>2017-09-09 14:00:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>2017-09-15 19:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Team                Date  Gameweek\n",
       "0  AFC Bournemouth 2017-08-12 14:00:00         1\n",
       "1  AFC Bournemouth 2017-08-19 14:00:00         2\n",
       "2  AFC Bournemouth 2017-08-26 11:30:00         3\n",
       "3  AFC Bournemouth 2017-09-09 14:00:00         4\n",
       "4  AFC Bournemouth 2017-09-15 19:00:00         5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "\n",
    "##\n",
    "df_fixture_gw = (df_wyscout_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": 35,
   "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>Season</th>\n",
       "      <th>Date</th>\n",
       "      <th>Fixture</th>\n",
       "      <th>Team</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Goals_Home</th>\n",
       "      <th>Goals_Away</th>\n",
       "      <th>Event</th>\n",
       "      <th>Team_Value</th>\n",
       "      <th>Opponent_Value</th>\n",
       "      <th>Gameweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Duel</td>\n",
       "      <td>220</td>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Foul</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>58</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Offside</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Others on the ball</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Pass</td>\n",
       "      <td>467</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Save attempt</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Shot</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Duel</td>\n",
       "      <td>205</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Foul</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>47</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Offside</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Others on the ball</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Pass</td>\n",
       "      <td>170</td>\n",
       "      <td>467</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Save attempt</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-04 18:45:00</td>\n",
       "      <td>Monaco - Toulouse</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>Monaco</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Shot</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Duel</td>\n",
       "      <td>205</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Foul</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>33</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Others on the ball</td>\n",
       "      <td>82</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>328</td>\n",
       "      <td>724</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Save attempt</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Duel</td>\n",
       "      <td>192</td>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Foul</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Offside</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Others on the ball</td>\n",
       "      <td>55</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>724</td>\n",
       "      <td>328</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Save attempt</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 15:15:00</td>\n",
       "      <td>PSG - Amiens SC</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Amiens SC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Duel</td>\n",
       "      <td>160</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Foul</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Offside</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Others on the ball</td>\n",
       "      <td>46</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Pass</td>\n",
       "      <td>396</td>\n",
       "      <td>528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Save attempt</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Shot</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Duel</td>\n",
       "      <td>168</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Foul</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>51</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Offside</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Others on the ball</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>17/18</td>\n",
       "      <td>2017-08-05 18:00:00</td>\n",
       "      <td>Metz - Guingamp</td>\n",
       "      <td>Metz</td>\n",
       "      <td>Guingamp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Pass</td>\n",
       "      <td>528</td>\n",
       "      <td>396</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season                Date            Fixture       Team   Opponent  \\\n",
       "0   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "1   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "2   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "3   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "4   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "5   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "6   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "7   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "8   17/18 2017-08-04 18:45:00  Monaco - Toulouse     Monaco   Toulouse   \n",
       "9   17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "10  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "11  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "12  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "13  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "14  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "15  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "16  17/18 2017-08-04 18:45:00  Monaco - Toulouse   Toulouse     Monaco   \n",
       "17  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "18  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "19  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "20  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "21  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "22  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "23  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "24  17/18 2017-08-05 15:15:00    PSG - Amiens SC  Amiens SC        PSG   \n",
       "25  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "26  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "27  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "28  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "29  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "30  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "31  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "32  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "33  17/18 2017-08-05 15:15:00    PSG - Amiens SC        PSG  Amiens SC   \n",
       "34  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "35  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "36  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "37  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "38  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "39  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "40  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "41  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "42  17/18 2017-08-05 18:00:00    Metz - Guingamp   Guingamp       Metz   \n",
       "43  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "44  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "45  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "46  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "47  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "48  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "49  17/18 2017-08-05 18:00:00    Metz - Guingamp       Metz   Guingamp   \n",
       "\n",
       "   Goals_Home Goals_Away                    Event  Team_Value  Opponent_Value  \\\n",
       "0           3          2                     Duel         220             205   \n",
       "1           3          2                     Foul          12              23   \n",
       "2           3          2                Free Kick          58              47   \n",
       "3           3          2  Goalkeeper leaving line           2               0   \n",
       "4           3          2                  Offside           5               1   \n",
       "5           3          2       Others on the ball          46              49   \n",
       "6           3          2                     Pass         467             170   \n",
       "7           3          2             Save attempt           4               7   \n",
       "8           3          2                     Shot          12               4   \n",
       "9           3          2                     Duel         205             220   \n",
       "10          3          2                     Foul          23              12   \n",
       "11          3          2                Free Kick          47              58   \n",
       "12          3          2                  Offside           1               5   \n",
       "13          3          2       Others on the ball          49              46   \n",
       "14          3          2                     Pass         170             467   \n",
       "15          3          2             Save attempt           7               4   \n",
       "16          3          2                     Shot           4              12   \n",
       "17          2          0                     Duel         205             192   \n",
       "18          2          0                     Foul          12               9   \n",
       "19          2          0                Free Kick          33              41   \n",
       "20          2          0  Goalkeeper leaving line           1               3   \n",
       "21          2          0       Others on the ball          82              55   \n",
       "22          2          0                     Pass         328             724   \n",
       "23          2          0             Save attempt           5               1   \n",
       "24          2          0                     Shot           6              15   \n",
       "25          2          0                     Duel         192             205   \n",
       "26          2          0                     Foul           9              12   \n",
       "27          2          0                Free Kick          41              33   \n",
       "28          2          0  Goalkeeper leaving line           3               1   \n",
       "29          2          0                  Offside           3               0   \n",
       "30          2          0       Others on the ball          55              82   \n",
       "31          2          0                     Pass         724             328   \n",
       "32          2          0             Save attempt           1               5   \n",
       "33          2          0                     Shot          15               6   \n",
       "34          1          3                     Duel         160             168   \n",
       "35          1          3                     Foul          12               7   \n",
       "36          1          3                Free Kick          34              51   \n",
       "37          1          3  Goalkeeper leaving line           4               1   \n",
       "38          1          3                  Offside           1               3   \n",
       "39          1          3       Others on the ball          46              44   \n",
       "40          1          3                     Pass         396             528   \n",
       "41          1          3             Save attempt           4               5   \n",
       "42          1          3                     Shot           8               6   \n",
       "43          1          3                     Duel         168             160   \n",
       "44          1          3                     Foul           7              12   \n",
       "45          1          3                Free Kick          51              34   \n",
       "46          1          3  Goalkeeper leaving line           1               4   \n",
       "47          1          3                  Offside           3               1   \n",
       "48          1          3       Others on the ball          44              46   \n",
       "49          1          3                     Pass         528             396   \n",
       "\n",
       "    Gameweek  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "5          1  \n",
       "6          1  \n",
       "7          1  \n",
       "8          1  \n",
       "9          1  \n",
       "10         1  \n",
       "11         1  \n",
       "12         1  \n",
       "13         1  \n",
       "14         1  \n",
       "15         1  \n",
       "16         1  \n",
       "17         1  \n",
       "18         1  \n",
       "19         1  \n",
       "20         1  \n",
       "21         1  \n",
       "22         1  \n",
       "23         1  \n",
       "24         1  \n",
       "25         1  \n",
       "26         1  \n",
       "27         1  \n",
       "28         1  \n",
       "29         1  \n",
       "30         1  \n",
       "31         1  \n",
       "32         1  \n",
       "33         1  \n",
       "34         1  \n",
       "35         1  \n",
       "36         1  \n",
       "37         1  \n",
       "38         1  \n",
       "39         1  \n",
       "40         1  \n",
       "41         1  \n",
       "42         1  \n",
       "43         1  \n",
       "44         1  \n",
       "45         1  \n",
       "46         1  \n",
       "47         1  \n",
       "48         1  \n",
       "49         1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join DataFrame \n",
    "df_wyscout_fixture_grouped = pd.merge(df_wyscout_fixture_grouped, df_fixture_gw, how='left', left_on=['Date', 'Team'], right_on = ['Date', 'Team'])\n",
    "\n",
    "# Display DataFrame\n",
    "df_wyscout_fixture_grouped.head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='#section4.8.2'>4.8.2. Team Level</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Team</th>\n",
       "      <th>Event</th>\n",
       "      <th>Team_Value</th>\n",
       "      <th>Opponent_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>Duel</td>\n",
       "      <td>7977</td>\n",
       "      <td>8003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>Foul</td>\n",
       "      <td>350</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>Free Kick</td>\n",
       "      <td>1818</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>Goalkeeper leaving line</td>\n",
       "      <td>75</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>Offside</td>\n",
       "      <td>51</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Team                    Event  Team_Value  Opponent_Value\n",
       "0  AFC Bournemouth                     Duel        7977            8003\n",
       "1  AFC Bournemouth                     Foul         350             459\n",
       "2  AFC Bournemouth                Free Kick        1818            1631\n",
       "3  AFC Bournemouth  Goalkeeper leaving line          75              45\n",
       "4  AFC Bournemouth                  Offside          51              79"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group DataFrame by Team\n",
    "\n",
    "##\n",
    "df_wyscout_team_grouped = (df_wyscout_fixture_grouped\n",
    "                               .groupby(['Team', 'Event'])\n",
    "                               .agg({'Team_Value': ['sum'],\n",
    "                                     'Opponent_Value': ['sum']\n",
    "                                    }\n",
    "                                   )\n",
    "                          )\n",
    "\n",
    "##\n",
    "df_wyscout_team_grouped.columns = df_wyscout_team_grouped.columns.droplevel(level=0)\n",
    "\n",
    "##\n",
    "df_wyscout_team_grouped = df_wyscout_team_grouped.reset_index()\n",
    "\n",
    "## Rename columns\n",
    "df_wyscout_team_grouped.columns = ['Team', 'Event', 'Team_Value', 'Opponent_Value']\n",
    "\n",
    "## Display columns\n",
    "df_wyscout_team_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='#section4.9'>4.9. Filter Final DataFrames for 'Big 5' European Leagues Only</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_big5_leagues = [364, 412, 426, 524, 795]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Events DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout_big5 = df_wyscout[df_wyscout['competitionId'].isin(lst_big5_leagues)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aggregated DataFrame at fixture level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'competitionId'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'competitionId'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-8b32bfafd82e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_wyscout_fixture_grouped_big5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_wyscout_fixture_grouped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_wyscout_fixture_grouped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'competitionId'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst_big5_leagues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'competitionId'"
     ]
    }
   ],
   "source": [
    "df_wyscout_fixture_grouped_big5 = df_wyscout_fixture_grouped[df_wyscout_fixture_grouped['competitionId'].isin(lst_big5_leagues)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aggregated DataFrame at team level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyscout_team_grouped_big5 = df_wyscout_team_grouped[df_wyscout_team_grouped['competitionId'].isin(lst_big5_leagues)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section5'>5. Export Data</a>\n",
    "Export Data ready for building the Expected Goals models in the subsequent notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Events DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Events DataFrame as CSV\n",
    "if not os.path.exists(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_events_all_1718.csv')):\n",
    "    df_wyscout.to_csv(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_events_all_1718.csv'), index=None, header=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Events DataFrame as CSV\n",
    "if not os.path.exists(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_events_big5_1718.csv')):\n",
    "    df_wyscout_big5.to_csv(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_events_big5_1718.csv'), index=None, header=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aggregated DataFrames at fixture level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Aggregated DataFrame at the fixture Level as CSV\n",
    "if not os.path.exists(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_fixtures_all_1718.csv')):\n",
    "    df_wyscout_fixture_grouped.to_csv(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_fixtures_all_1718.csv'), index=None, header=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Aggregated DataFrame at the fixture Level as CSV\n",
    "if not os.path.exists(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_fixtures_big5_1718.csv')):\n",
    "    df_wyscout_fixture_grouped_big5.to_csv(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_fixtures_big5_1718.csv'), index=None, header=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aggregated DataFrames at team level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Aggregated DataFrame at the team Level as CSV\n",
    "if not os.path.exists(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_team_all_1718.csv')):\n",
    "    df_wyscout_team_grouped.to_csv(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_team_all_1718.csv'), index=None, header=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Aggregated DataFrame at the team Level as CSV\n",
    "if not os.path.exists(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_team_big5_1718.csv')):\n",
    "    df_wyscout_team_grouped_big5.to_csv(os.path.join(data_dir_wyscout, 'engineered', 'combined', 'wyscout_aggregated_team_big5_1718.csv'), index=None, header=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section6'>6. Summary</a>\n",
    "This notebook parses Wyscout data using [pandas](http://pandas.pydata.org/) for data manipulation through DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section7'>7. Next Steps</a>\n",
    "The step is to take the dataset created in this notebook and ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section8'>8. References</a>\n",
    "*    Data Parsing notebook: https://nbviewer.jupyter.org/github/eddwebster/football_analytics/blob/master/notebooks/2_data_parsing/Wyscout%20Parsing.ipynb\n",
    "*    Wyscout: https://wyscout.com/\n",
    "*    Wyscout Events data manual: https://footballdata.wyscout.com/events-manual/\n",
    "*    Pappalardo, Luca; Massucco, Emanuele (2019): Soccer match event dataset. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4415000.v5\n",
    "*    Pappalardo, L., Cintia, P., Rossi, A. et al. **A public data set of spatio-temporal match events in soccer competitions**. Scientific Data 6, 236 (2019) doi:10.1038/s41597-019-0247-7, https://www.nature.com/articles/s41597-019-0247-7\n",
    "*   Custom function to flatten pandas DataFrames with nested JSON column: https://stackoverflow.com/questions/39899005/how-to-flatten-a-pandas-dataframe-with-some-columns-as-json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a id='#section9'>9. Further Reading</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\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.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to the top](#top)"
   ]
  }
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