Teams_Cleaning&Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teams: Data Cleaning and Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we import the libraries that we will use during the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we import and explore the dataset that we created when scarping our source website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Game Week</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Home Team</th>\n",
       "      <th>Score</th>\n",
       "      <th>Away Team</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Referee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2015-07-24</td>\n",
       "      <td>17:30</td>\n",
       "      <td>Viktoria Plzeň</td>\n",
       "      <td>2–1</td>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>11,233</td>\n",
       "      <td>Doosan Arena</td>\n",
       "      <td>Pavel Franek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2015-07-24</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Vysočina Jihlava</td>\n",
       "      <td>0–0</td>\n",
       "      <td>Sparta Prague</td>\n",
       "      <td>3,894</td>\n",
       "      <td>Stadion v Jiráskově ulici</td>\n",
       "      <td>Tomas Kocourek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Sat</td>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>17:00</td>\n",
       "      <td>Příbram</td>\n",
       "      <td>2–3</td>\n",
       "      <td>Jablonec</td>\n",
       "      <td>4,182</td>\n",
       "      <td>Energon Aréna</td>\n",
       "      <td>Pavel Královec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Sat</td>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>17:00</td>\n",
       "      <td>Slovácko</td>\n",
       "      <td>4–3</td>\n",
       "      <td>Dukla Prague</td>\n",
       "      <td>3,726</td>\n",
       "      <td>Městský fotbalový stadion Miroslava Vale...</td>\n",
       "      <td>Zbyněk Proske</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Sat</td>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>17:00</td>\n",
       "      <td>Zbrojovka Brno</td>\n",
       "      <td>2–1</td>\n",
       "      <td>Baník Ostrava</td>\n",
       "      <td>5,326</td>\n",
       "      <td>Městský fotbalový stadion Srbská</td>\n",
       "      <td>Libor Kovařík</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mladá Boleslav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bohemians 1905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adidas Aréna</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Slovan Liberec</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sigma Olomouc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stadion u Nisy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Slovácko</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fastav Zlín</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Městský fotbalový stadion Miroslava Vale...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>NaN</td>\n",
       "      <td>České Budĕjov.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sinobo Stadium</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Fri</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FK Pardubice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jablonec</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stadion K Vinici</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1737 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Game Week Weekday        Date    Time         Home Team Score  \\\n",
       "0           1.0     Fri  2015-07-24  17:30     Viktoria Plzeň   2–1   \n",
       "1           1.0     Fri  2015-07-24  19:00   Vysočina Jihlava   0–0   \n",
       "2           1.0     Sat  2015-07-25  17:00            Příbram   2–3   \n",
       "3           1.0     Sat  2015-07-25  17:00           Slovácko   4–3   \n",
       "4           1.0     Sat  2015-07-25  17:00     Zbrojovka Brno   2–1   \n",
       "...         ...     ...         ...     ...               ...   ...   \n",
       "1732       34.0     Fri  2021-05-28     NaN    Mladá Boleslav   NaN   \n",
       "1733       34.0     Fri  2021-05-28     NaN    Slovan Liberec   NaN   \n",
       "1734       34.0     Fri  2021-05-28     NaN          Slovácko   NaN   \n",
       "1735       34.0     Fri  2021-05-28     NaN     Slavia Prague   NaN   \n",
       "1736       34.0     Fri  2021-05-28     NaN      FK Pardubice   NaN   \n",
       "\n",
       "           Away Team Attendance                                        Venue  \\\n",
       "0      Slavia Prague     11,233                                 Doosan Arena   \n",
       "1      Sparta Prague      3,894                    Stadion v Jiráskově ulici   \n",
       "2           Jablonec      4,182                                Energon Aréna   \n",
       "3       Dukla Prague      3,726  Městský fotbalový stadion Miroslava Vale...   \n",
       "4      Baník Ostrava      5,326             Městský fotbalový stadion Srbská   \n",
       "...              ...        ...                                          ...   \n",
       "1732  Bohemians 1905        NaN                                 Adidas Aréna   \n",
       "1733   Sigma Olomouc        NaN                               Stadion u Nisy   \n",
       "1734     Fastav Zlín        NaN  Městský fotbalový stadion Miroslava Vale...   \n",
       "1735  České Budĕjov.        NaN                               Sinobo Stadium   \n",
       "1736        Jablonec        NaN                             Stadion K Vinici   \n",
       "\n",
       "             Referee  \n",
       "0       Pavel Franek  \n",
       "1     Tomas Kocourek  \n",
       "2     Pavel Královec  \n",
       "3      Zbyněk Proske  \n",
       "4      Libor Kovařík  \n",
       "...              ...  \n",
       "1732             NaN  \n",
       "1733             NaN  \n",
       "1734             NaN  \n",
       "1735             NaN  \n",
       "1736             NaN  \n",
       "\n",
       "[1737 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = pd.read_csv(r\"C:\\Users\\Honza Stuchlík\\Documents\\IES\\Data Processing in Python\\Czech-Football-League\\scores.csv\")\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the data requires cleaning. We want to get rid of NaN values in the column \"Score\" so that we have only matches that have already been played. We also split the scores into two columns - home and away team goals to be able to do meaningful computations with the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Time</th>\n",
       "      <th>Home Team</th>\n",
       "      <th>Away Team</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Referee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-07-24</td>\n",
       "      <td>Fri</td>\n",
       "      <td>17:30</td>\n",
       "      <td>Viktoria Plzeň</td>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Doosan Arena</td>\n",
       "      <td>Pavel Franek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-07-24</td>\n",
       "      <td>Fri</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Vysočina Jihlava</td>\n",
       "      <td>Sparta Prague</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stadion v Jiráskově ulici</td>\n",
       "      <td>Tomas Kocourek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>Sat</td>\n",
       "      <td>17:00</td>\n",
       "      <td>Příbram</td>\n",
       "      <td>Jablonec</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Energon Aréna</td>\n",
       "      <td>Pavel Královec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>Sat</td>\n",
       "      <td>17:00</td>\n",
       "      <td>Slovácko</td>\n",
       "      <td>Dukla Prague</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Městský fotbalový stadion Miroslava Vale...</td>\n",
       "      <td>Zbyněk Proske</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>Sat</td>\n",
       "      <td>17:00</td>\n",
       "      <td>Zbrojovka Brno</td>\n",
       "      <td>Baník Ostrava</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Městský fotbalový stadion Srbská</td>\n",
       "      <td>Libor Kovařík</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>2021-01-27</td>\n",
       "      <td>Wed</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Fastav Zlín</td>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Stadion Letná</td>\n",
       "      <td>Pavel Franek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Sat</td>\n",
       "      <td>14:00</td>\n",
       "      <td>Teplice</td>\n",
       "      <td>Karviná</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Na Stínadlech</td>\n",
       "      <td>Ondřej Lerch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Sat</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Slovácko</td>\n",
       "      <td>České Budĕjov.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Městský fotbalový stadion Miroslava Vale...</td>\n",
       "      <td>Miroslav Zelinka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Sat</td>\n",
       "      <td>16:00</td>\n",
       "      <td>FK Pardubice</td>\n",
       "      <td>Příbram</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Stadion Ďolíček</td>\n",
       "      <td>Paval Julínek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Sat</td>\n",
       "      <td>18:30</td>\n",
       "      <td>Sparta Prague</td>\n",
       "      <td>Bohemians 1905</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Generali Arena</td>\n",
       "      <td>Karel Hrubes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Weekday    Time         Home Team       Away Team  \\\n",
       "0     2015-07-24     Fri  17:30     Viktoria Plzeň   Slavia Prague   \n",
       "1     2015-07-24     Fri  19:00   Vysočina Jihlava   Sparta Prague   \n",
       "2     2015-07-25     Sat  17:00            Příbram        Jablonec   \n",
       "3     2015-07-25     Sat  17:00           Slovácko    Dukla Prague   \n",
       "4     2015-07-25     Sat  17:00     Zbrojovka Brno   Baník Ostrava   \n",
       "...          ...     ...     ...               ...             ...   \n",
       "1556  2021-01-27     Wed  18:00        Fastav Zlín   Slavia Prague   \n",
       "1558  2021-01-30     Sat  14:00            Teplice         Karviná   \n",
       "1559  2021-01-30     Sat  16:00           Slovácko  České Budĕjov.   \n",
       "1560  2021-01-30     Sat  16:00       FK Pardubice         Příbram   \n",
       "1561  2021-01-30     Sat  18:30      Sparta Prague  Bohemians 1905   \n",
       "\n",
       "      Home Team Goals  Away Team Goals  \\\n",
       "0                   2                1   \n",
       "1                   0                0   \n",
       "2                   2                3   \n",
       "3                   4                3   \n",
       "4                   2                1   \n",
       "...               ...              ...   \n",
       "1556                2                6   \n",
       "1558                2                2   \n",
       "1559                0                0   \n",
       "1560                1                0   \n",
       "1561                0                1   \n",
       "\n",
       "                                            Venue           Referee  \n",
       "0                                    Doosan Arena      Pavel Franek  \n",
       "1                       Stadion v Jiráskově ulici    Tomas Kocourek  \n",
       "2                                   Energon Aréna    Pavel Královec  \n",
       "3     Městský fotbalový stadion Miroslava Vale...     Zbyněk Proske  \n",
       "4                Městský fotbalový stadion Srbská     Libor Kovařík  \n",
       "...                                           ...               ...  \n",
       "1556                                Stadion Letná      Pavel Franek  \n",
       "1558                                Na Stínadlech      Ondřej Lerch  \n",
       "1559  Městský fotbalový stadion Miroslava Vale...  Miroslav Zelinka  \n",
       "1560                              Stadion Ďolíček     Paval Julínek  \n",
       "1561                               Generali Arena      Karel Hrubes  \n",
       "\n",
       "[1365 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_nona = scores[scores['Score'].notna()].copy()\n",
    "scores_nona.loc[:, \"Home Team Goals\"] = scores_nona['Score'].astype(str).str[0].astype(int)\n",
    "scores_nona.loc[:, \"Away Team Goals\"] = scores_nona['Score'].astype(str).str[2].astype(int)\n",
    "scores_clean = scores_nona[[\"Date\", \"Weekday\", \"Time\", \"Home Team\", \"Away Team\", \"Home Team Goals\", \"Away Team Goals\", \"Venue\", \"Referee\"]]\n",
    "scores_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scores_clean.to_csv(r\"C:\\Users\\Honza Stuchlík\\Documents\\IES\\Data Processing in Python\\Czech-Football-League\\scores_clean.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we analayze **goals** and **match results**. Each of these has its own class with a set of functions that can be called to analyze different properties of our scores dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in the numbers of goals scored by teams playing on their home pitch and away and more specifically we want to compare the values to see whether teams are stronger when they play at home, i.e. if they score more goals at home and what is the difference between goals scored at home and away. For the purpose of analysing goals we wrote a class **Goals_summary**. The class takes three arguments: 1. **scores_df** (a dataframe with scores), 2. **start_date** (starting date of the time period that we want to analyze), 3. **end_date** (analogically, we specify the end of the time period). 7 functions can be called. The first function **overall** returns the numbers of goals in home and away games as well as the difference between home and away goals scored by all teams in the specified time period. The functions **home_pivot**, **away_pivot** and **total_pivot** return sorted pivot tables that show the distribution of goals scored at home, away or both together by each team. The function **ha_ratio_pivot** returns a pivot table where the home/away ratio represents the fraction of goals scored at home and away by each team. The functions **barplot** and **piechart** with the argument **stat** use Matplotlib to visualize numbers of goals scored by each team at home (stat = \"Home Team Goals\"), away (stat = \"Away Team Goals\") or in total (stat = \"Total Goals\"). Barplot can be displayed with horizontal bars or with vertical bars. This is determined by the logical argument **vertical**, which is set to True by default. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Goals_summary:\n",
    "    \n",
    "    def __init__(self, scores_df, start_date, end_date):\n",
    "        helping_data = scores_df.copy()\n",
    "        helping_data['Date'] = pd.to_datetime(helping_data['Date'])\n",
    "        mask = (helping_data['Date'] > start_date) & (helping_data['Date'] <= end_date)\n",
    "        scores_clean = helping_data.loc[mask]\n",
    "        self.scores_clean = scores_clean\n",
    "    \n",
    "    def overall(self):\n",
    "        total_home_goals = sum(self.scores_clean[\"Home Team Goals\"])\n",
    "        total_away_goals = sum(self.scores_clean[\"Away Team Goals\"])\n",
    "        goal_difference = total_home_goals - total_away_goals\n",
    "        total_goals = total_home_goals + total_away_goals\n",
    "        average_home_goals = sum(self.scores_clean[\"Home Team Goals\"]) / len(self.scores_clean[\"Home Team Goals\"])\n",
    "        average_away_goals = sum(self.scores_clean[\"Away Team Goals\"]) / len(self.scores_clean[\"Away Team Goals\"])\n",
    "        average_total_goals = (average_home_goals + average_away_goals) / 2\n",
    "        print(\"Total Home Goals: \" + str(total_home_goals))\n",
    "        print(\"Total Away Goals: \" + str(total_away_goals))\n",
    "        print(\"Home and Away Goals Difference: \" + str(goal_difference))\n",
    "        print(\"Total Goals: \" + str(total_goals))\n",
    "        print(\"Average Home Goals per Game: \" + str(round(average_home_goals,2)))\n",
    "        print(\"Average Away Goals per Game: \" + str(round(average_away_goals,2)))\n",
    "        print(\"Average Total Goals per Game: \" + str(round(average_total_goals,2)))\n",
    "        \n",
    "    \n",
    "    def home_pivot(self):\n",
    "        home_pivot_table = pd.pivot_table(self.scores_clean, values = \"Home Team Goals\", index = \"Home Team\", aggfunc = np.sum)\n",
    "        home_pivot_df = pd.DataFrame(home_pivot_table)\n",
    "        home_pivot_df = home_pivot_df.sort_values(by = \"Home Team Goals\", ascending = False)\n",
    "        return home_pivot_df\n",
    "    \n",
    "    def away_pivot(self):\n",
    "        away_pivot_table = pd.pivot_table(self.scores_clean, values = \"Away Team Goals\", index = \"Away Team\", aggfunc = np.sum)\n",
    "        away_pivot_df = pd.DataFrame(away_pivot_table)\n",
    "        away_pivot_df = away_pivot_df.sort_values(by = \"Away Team Goals\", ascending = False)\n",
    "        return away_pivot_df\n",
    "    \n",
    "    def total_pivot(self):\n",
    "        home_pivot_df = self.home_pivot()\n",
    "        away_pivot_df = self.away_pivot()\n",
    "        home_pivot_df[\"Team\"] = home_pivot_df.index\n",
    "        away_pivot_df[\"Team\"] = away_pivot_df.index\n",
    "        goals_df = pd.merge(home_pivot_df, away_pivot_df, on = \"Team\")\n",
    "        goals_df[\"Total Goals\"] = goals_df[\"Home Team Goals\"] + goals_df[\"Away Team Goals\"]\n",
    "        goals_df = goals_df.reindex(columns=[\"Team\", \"Home Team Goals\", \"Away Team Goals\", \"Total Goals\"])\n",
    "        goals_df_sortedby_total = goals_df.sort_values(by = \"Total Goals\", ascending = False)\n",
    "        goals_df_sortedby_total = goals_df_sortedby_total.set_index(\"Team\")\n",
    "        return goals_df_sortedby_total\n",
    "    \n",
    "    def ha_ratio_pivot(self):\n",
    "        home_pivot_df = self.home_pivot()\n",
    "        away_pivot_df = self.away_pivot()\n",
    "        home_pivot_df[\"Team\"] = home_pivot_df.index\n",
    "        away_pivot_df[\"Team\"] = away_pivot_df.index\n",
    "        goals_df = pd.merge(home_pivot_df, away_pivot_df, on = \"Team\")\n",
    "        goals_df[\"H/A Ratio\"] = goals_df[\"Home Team Goals\"] / goals_df[\"Away Team Goals\"]\n",
    "        goals_df_sortedby_haratio = goals_df.sort_values(by = \"H/A Ratio\", ascending = False)\n",
    "        goals_df_sortedby_haratio = goals_df_sortedby_haratio.reindex(columns=[\"Team\", \"Home Team Goals\", \"Away Team Goals\", \"H/A Ratio\"])\n",
    "        return goals_df_sortedby_haratio\n",
    "    \n",
    "    def barplot(self, stat, vertical = True):\n",
    "        if stat == \"Total Goals\":\n",
    "            df = self.total_pivot()\n",
    "        elif stat == \"Home Team Goals\":\n",
    "            df = self.home_pivot()\n",
    "        else:\n",
    "            df = self.away_pivot()\n",
    "        if vertical:\n",
    "            fig = plt.figure()\n",
    "            ax = fig.add_axes([0,0,4.5,1.5])\n",
    "            ax.bar(df.index, df[stat])\n",
    "            ax.set_ylabel('Number of Goals')\n",
    "            ax.set_title(f'{stat} Scored by Each Team')\n",
    "            plt.show()\n",
    "        else:\n",
    "            df = df.sort_values(by = stat, ascending = True)\n",
    "            fig = plt.figure()\n",
    "            ax = fig.add_axes([0,0,1.5,2])\n",
    "            ax.barh(df.index, df[stat])\n",
    "            ax.set_ylabel('Team')\n",
    "            ax.set_xlabel('Number of Goals')\n",
    "            ax.set_title(f'{stat} Scored by Each Team')\n",
    "            plt.show()\n",
    "        \n",
    "    def piechart(self, stat):\n",
    "        if stat == \"Total Goals\":\n",
    "            df = self.total_pivot()\n",
    "        elif stat == \"Home Team Goals\":\n",
    "            df = self.home_pivot()\n",
    "        else:\n",
    "            df = self.away_pivot()\n",
    "        plt.pie(df[stat], labels = df.index)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the class and its functions for different time periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "season2021 = [\"2020-08-20\", \"2021-05-28\"]\n",
    "season1920 = [\"2019-07-11\", \"2020-07-12\"]\n",
    "season1819 = [\"2018-07-19\", \"2019-05-26\"]\n",
    "season1718 = [\"2017-07-27\", \"2018-05-26\"]\n",
    "season1617 = [\"2016-07-29\", \"2017-05-27\"]\n",
    "season1516 = [\"2015-07-23\", \"2016-05-14\"]\n",
    "all_seasons = [\"2015-07-23\", \"2021-05-28\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Season 2019/2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we have a look at the previous season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Home Goals: 445\n",
      "Total Away Goals: 263\n",
      "Home and Away Goals Difference: 182\n",
      "Total Goals: 708\n",
      "Average Home Goals per Game: 1.64\n",
      "Average Away Goals per Game: 0.97\n",
      "Average Total Goals per Game: 1.31\n"
     ]
    }
   ],
   "source": [
    "overall1920 = Goals_summary(scores_clean, season1920[0], season1920[1]).overall()\n",
    "overall1920"
   ]
  },
  {
   "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>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Total Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>40</td>\n",
       "      <td>29</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>44</td>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Home Team Goals  Away Team Goals  Total Goals\n",
       "Team                                                         \n",
       "Slavia Prague                40               29           69\n",
       "Viktoria Plzeň               46               22           68\n",
       "Sparta Prague                44               22           66\n",
       "Slovan Liberec               39               18           57\n",
       "Mladá Boleslav               37               19           56\n",
       "Jablonec                     31               17           48\n",
       "Baník Ostrava                28               19           47\n",
       "České Budĕjov.               23               24           47\n",
       "Bohemians 1905               27               17           44\n",
       "Sigma Olomouc                21               18           39\n",
       "Teplice                      28                9           37\n",
       "Slovácko                     21               16           37\n",
       "Fastav Zlín                  20               10           30\n",
       "Karviná                      14               11           25\n",
       "Příbram                      16                5           21\n",
       "Opava                        10                7           17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalpivot1920 = Goals_summary(scores_clean, season1920[0], season1920[1]).total_pivot()\n",
    "totalpivot1920"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Games</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Draws</th>\n",
       "      <th>Losses</th>\n",
       "      <th>Goals for</th>\n",
       "      <th>Goals against</th>\n",
       "      <th>Goal Difference</th>\n",
       "      <th>Points</th>\n",
       "      <th>Last 5 Games</th>\n",
       "      <th>Attendance per Game</th>\n",
       "      <th>Top Team Scorers</th>\n",
       "      <th>Top Goalkeeper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>W W W W W</td>\n",
       "      <td>2,019</td>\n",
       "      <td>Abdallah Sima - 9</td>\n",
       "      <td>Ondřej Kolář</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Sparta Prague</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>D D W W L</td>\n",
       "      <td>1,445</td>\n",
       "      <td>Lukáš Juliš - 11</td>\n",
       "      <td>Florin Niță</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jablonec</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>D W W W W</td>\n",
       "      <td>722</td>\n",
       "      <td>Ivan Schranz - 6</td>\n",
       "      <td>Jan Hanuš</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Slovácko</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>W W W W D</td>\n",
       "      <td>442</td>\n",
       "      <td>Jan Kliment - 6</td>\n",
       "      <td>Vít Nemrava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Baník Ostrava</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>W D L D W</td>\n",
       "      <td>1,382</td>\n",
       "      <td>Dyjan Carlos De Azevedo - 6</td>\n",
       "      <td>Jan Laštůvka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Sigma Olomouc</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>D D L L W</td>\n",
       "      <td>819</td>\n",
       "      <td>David Houska - 4</td>\n",
       "      <td>Aleš Mandous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>FK Pardubice</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>-3</td>\n",
       "      <td>25</td>\n",
       "      <td>D W L L W</td>\n",
       "      <td>523</td>\n",
       "      <td>David Huf - 5</td>\n",
       "      <td>Marek Boháč</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>České Budĕjov.</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>L W W W D</td>\n",
       "      <td>471</td>\n",
       "      <td>Benjamin Čolić, Patrik Brandner - 5</td>\n",
       "      <td>Jaroslav Drobný</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Slovan Liberec</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>W D D W L</td>\n",
       "      <td>984</td>\n",
       "      <td>Michael Rabušic - 7</td>\n",
       "      <td>Filip Nguyen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Viktoria Plzeň</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>W L L D W</td>\n",
       "      <td>1,333</td>\n",
       "      <td>Aleš Čermák, Jean-David Beauguel - 6</td>\n",
       "      <td>Aleš Hruška</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Karviná</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>-8</td>\n",
       "      <td>21</td>\n",
       "      <td>W L L L D</td>\n",
       "      <td>738</td>\n",
       "      <td>Kristi Qose - 4</td>\n",
       "      <td>Petr Bolek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Bohemians 1905</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>-3</td>\n",
       "      <td>19</td>\n",
       "      <td>L D L D W</td>\n",
       "      <td>806</td>\n",
       "      <td>Petr Hronek - 3</td>\n",
       "      <td>Patrik Le Giang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Fastav Zlín</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>-6</td>\n",
       "      <td>18</td>\n",
       "      <td>D L D D L</td>\n",
       "      <td>737</td>\n",
       "      <td>Tomáš Poznar - 7</td>\n",
       "      <td>Stanislav Dostál</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Teplice</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>-19</td>\n",
       "      <td>17</td>\n",
       "      <td>W W L L D</td>\n",
       "      <td>712</td>\n",
       "      <td>Jakub Mareš - 6</td>\n",
       "      <td>Tomáš Grigar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Příbram</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>-21</td>\n",
       "      <td>10</td>\n",
       "      <td>L D L L L</td>\n",
       "      <td>552</td>\n",
       "      <td>Tomáš Pilík, Emmanuel Antwi... - 2</td>\n",
       "      <td>Ondřej Kočí</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Zbrojovka Brno</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>-18</td>\n",
       "      <td>10</td>\n",
       "      <td>W L L D L</td>\n",
       "      <td>1,233</td>\n",
       "      <td>Peter Štepanovský, Jan Hladík... - 2</td>\n",
       "      <td>Martin Šustr, Jiří Floder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Mladá Boleslav</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>-13</td>\n",
       "      <td>10</td>\n",
       "      <td>L L L D L</td>\n",
       "      <td>461</td>\n",
       "      <td>Jiří Klíma - 4</td>\n",
       "      <td>Petr Mikulec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Opava</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>-23</td>\n",
       "      <td>8</td>\n",
       "      <td>L D L L D</td>\n",
       "      <td>744</td>\n",
       "      <td>Jan Zidek, Karol Mondek - 2</td>\n",
       "      <td>Vilem Fendrich</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank             Team  Games  Wins  Draws  Losses  Goals for  \\\n",
       "0      1    Slavia Prague     16    14      2       0         48   \n",
       "1      2    Sparta Prague     17    11      2       4         33   \n",
       "2      3         Jablonec     16    11      2       3         33   \n",
       "3      4         Slovácko     17     9      4       4         30   \n",
       "4      5    Baník Ostrava     16     7      5       4         21   \n",
       "5      6    Sigma Olomouc     16     6      7       3         25   \n",
       "6      7     FK Pardubice     17     7      4       6         16   \n",
       "7      8   České Budĕjov.     17     6      7       4         23   \n",
       "8      9   Slovan Liberec     16     7      4       5         24   \n",
       "9     10   Viktoria Plzeň     16     7      3       6         30   \n",
       "10    11          Karviná     17     5      6       6         17   \n",
       "11    12   Bohemians 1905     17     5      4       8         19   \n",
       "12    13      Fastav Zlín     16     5      3       8         18   \n",
       "13    14          Teplice     17     5      2      10         18   \n",
       "14    15          Příbram     17     2      4      11         12   \n",
       "15    16   Zbrojovka Brno     16     2      4      10         13   \n",
       "16    17   Mladá Boleslav     16     2      4      10         17   \n",
       "17    18            Opava     16     1      5      10         11   \n",
       "\n",
       "    Goals against  Goal Difference  Points Last 5 Games Attendance per Game  \\\n",
       "0               8               40      44    W W W W W               2,019   \n",
       "1              19               14      35    D D W W L               1,445   \n",
       "2              16               17      35    D W W W W                 722   \n",
       "3              17               13      31    W W W W D                 442   \n",
       "4              13                8      26    W D L D W               1,382   \n",
       "5              19                6      25    D D L L W                 819   \n",
       "6              19               -3      25    D W L L W                 523   \n",
       "7              23                0      25    L W W W D                 471   \n",
       "8              17                7      25    W D D W L                 984   \n",
       "9              21                9      24    W L L D W               1,333   \n",
       "10             25               -8      21    W L L L D                 738   \n",
       "11             22               -3      19    L D L D W                 806   \n",
       "12             24               -6      18    D L D D L                 737   \n",
       "13             37              -19      17    W W L L D                 712   \n",
       "14             33              -21      10    L D L L L                 552   \n",
       "15             31              -18      10    W L L D L               1,233   \n",
       "16             30              -13      10    L L L D L                 461   \n",
       "17             34              -23       8    L D L L D                 744   \n",
       "\n",
       "                        Top Team Scorers             Top Goalkeeper  \n",
       "0                      Abdallah Sima - 9               Ondřej Kolář  \n",
       "1                       Lukáš Juliš - 11                Florin Niță  \n",
       "2                       Ivan Schranz - 6                  Jan Hanuš  \n",
       "3                        Jan Kliment - 6                Vít Nemrava  \n",
       "4            Dyjan Carlos De Azevedo - 6               Jan Laštůvka  \n",
       "5                       David Houska - 4               Aleš Mandous  \n",
       "6                          David Huf - 5                Marek Boháč  \n",
       "7    Benjamin Čolić, Patrik Brandner - 5            Jaroslav Drobný  \n",
       "8                    Michael Rabušic - 7               Filip Nguyen  \n",
       "9   Aleš Čermák, Jean-David Beauguel - 6                Aleš Hruška  \n",
       "10                       Kristi Qose - 4                 Petr Bolek  \n",
       "11                       Petr Hronek - 3            Patrik Le Giang  \n",
       "12                      Tomáš Poznar - 7           Stanislav Dostál  \n",
       "13                       Jakub Mareš - 6               Tomáš Grigar  \n",
       "14    Tomáš Pilík, Emmanuel Antwi... - 2                Ondřej Kočí  \n",
       "15  Peter Štepanovský, Jan Hladík... - 2  Martin Šustr, Jiří Floder  \n",
       "16                        Jiří Klíma - 4               Petr Mikulec  \n",
       "17           Jan Zidek, Karol Mondek - 2             Vilem Fendrich  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "league_table = pd.read_csv(r\"C:\\Users\\Honza Stuchlík\\Documents\\IES\\Data Processing in Python\\Czech-Football-League\\league_table.csv\")\n",
    "league_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the ranking based on total goals scored approximately corresponds to the rankings in the league table. This supports the famous hypothesis from the TV series Okresní přebor: \"The more goals we score, the more points we have.\" (Hovorka L., 2010)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statistics can be visualized in multiple ways through the prewritten functions. We use different styles to show all possibilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "homebarplot1920 = Goals_summary(scores_clean, season1920[0], season1920[1]).barplot(\"Home Team Goals\", vertical = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "totalbarplot1920 = Goals_summary(scores_clean, season1920[0], season1920[1]).barplot(\"Total Goals\")\n",
    "totalbarplot1920"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "totalpiechart1920 = Goals_summary(scores_clean, season1920[0], season1920[1]).piechart(\"Total Goals\")\n",
    "totalpiechart1920"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Season 2015/2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the oldest season in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Home Goals: 414\n",
      "Total Away Goals: 262\n",
      "Home and Away Goals Difference: 152\n",
      "Total Goals: 676\n",
      "Average Home Goals per Game: 1.73\n",
      "Average Away Goals per Game: 1.09\n",
      "Average Total Goals per Game: 1.41\n"
     ]
    }
   ],
   "source": [
    "overall1516 = Goals_summary(scores_clean, season1516[0], season1516[1]).overall()\n",
    "overall1516"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice a lower amount of total goals scored in this season than in 2019/2020, but the average number of goals per game is 0.1 higher. This can be caused by a different number of games played in each season. In 2019/2020 there were those \"irregular\" games in championship and relegation rounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Total Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dukla Prague</th>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vysočina Jihlava</th>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Home Team Goals  Away Team Goals  Total Goals\n",
       "Team                                                           \n",
       "Mladá Boleslav                 44               19           63\n",
       "Sparta Prague                  41               20           61\n",
       "Viktoria Plzeň                 32               25           57\n",
       "Slovan Liberec                 33               18           51\n",
       "Slavia Prague                  31               17           48\n",
       "Jablonec                       25               21           46\n",
       "Dukla Prague                   25               19           44\n",
       "Slovácko                       26               11           37\n",
       "Zbrojovka Brno                 23               14           37\n",
       "Teplice                        17               20           37\n",
       "Sigma Olomouc                  26                9           35\n",
       "Bohemians 1905                 18               17           35\n",
       "Fastav Zlín                    17               17           34\n",
       "Příbram                        20               13           33\n",
       "Vysočina Jihlava               21               10           31\n",
       "Baník Ostrava                  15               12           27"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_pivot1516 = Goals_summary(scores_clean, season1516[0], season1516[1]).total_pivot()\n",
    "total_pivot1516"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>H/A Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sigma Olomouc</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>2.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Slovácko</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>2.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mladá Boleslav</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>2.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vysočina Jihlava</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sparta Prague</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>2.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Slovan Liberec</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>1.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Zbrojovka Brno</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>1.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Příbram</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>1.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dukla Prague</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>1.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Viktoria Plzeň</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>1.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Baník Ostrava</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jablonec</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>1.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bohemians 1905</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>1.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Fastav Zlín</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Teplice</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Team  Home Team Goals  Away Team Goals  H/A Ratio\n",
       "5      Sigma Olomouc               26                9   2.888889\n",
       "6           Slovácko               26               11   2.363636\n",
       "0     Mladá Boleslav               44               19   2.315789\n",
       "10  Vysočina Jihlava               21               10   2.100000\n",
       "1      Sparta Prague               41               20   2.050000\n",
       "2     Slovan Liberec               33               18   1.833333\n",
       "4      Slavia Prague               31               17   1.823529\n",
       "9     Zbrojovka Brno               23               14   1.642857\n",
       "11           Příbram               20               13   1.538462\n",
       "7       Dukla Prague               25               19   1.315789\n",
       "3     Viktoria Plzeň               32               25   1.280000\n",
       "15     Baník Ostrava               15               12   1.250000\n",
       "8           Jablonec               25               21   1.190476\n",
       "12    Bohemians 1905               18               17   1.058824\n",
       "13       Fastav Zlín               17               17   1.000000\n",
       "14           Teplice               17               20   0.850000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ha_ratio_pivot1516 = Goals_summary(scores_clean, season1516[0], season1516[1]).ha_ratio_pivot()\n",
    "ha_ratio_pivot1516"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All Seasons (2015/2016 - 2020/2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Home Goals: 2107\n",
      "Total Away Goals: 1481\n",
      "Home and Away Goals Difference: 626\n",
      "Total Goals: 3588\n",
      "Average Home Goals per Game: 1.54\n",
      "Average Away Goals per Game: 1.08\n",
      "Average Total Goals per Game: 1.31\n"
     ]
    }
   ],
   "source": [
    "overall_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).overall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The long-term average number of goals scored at home is 0.46 higher than for goals scored away, which proves the advantage of playing on the home pitch that we included in the predicting function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Home Team Goals</th>\n",
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       "    <tr>\n",
       "      <th>Home Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>151</td>\n",
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       "      <th>Slovácko</th>\n",
       "      <td>117</td>\n",
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       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dukla Prague</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vysočina Jihlava</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hradec Králové</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Home Team Goals\n",
       "Home Team                        \n",
       "Slavia Prague                 194\n",
       "Viktoria Plzeň                189\n",
       "Sparta Prague                 189\n",
       "Jablonec                      161\n",
       "Mladá Boleslav                158\n",
       "Slovan Liberec                151\n",
       "Slovácko                      117\n",
       "Sigma Olomouc                 105\n",
       "Fastav Zlín                   101\n",
       "Bohemians 1905                101\n",
       "Baník Ostrava                  92\n",
       "Dukla Prague                   89\n",
       "Karviná                        86\n",
       "Příbram                        85\n",
       "Teplice                        77\n",
       "Zbrojovka Brno                 57\n",
       "Vysočina Jihlava               55\n",
       "Opava                          47\n",
       "České Budĕjov.                 31\n",
       "Hradec Králové                 11\n",
       "FK Pardubice                   11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_pivot_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).home_pivot()\n",
    "home_pivot_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Away Team Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Away Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
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       "      <th>Jablonec</th>\n",
       "      <td>113</td>\n",
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       "      <th>Fastav Zlín</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dukla Prague</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vysočina Jihlava</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hradec Králové</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Away Team Goals\n",
       "Away Team                        \n",
       "Slavia Prague                 157\n",
       "Viktoria Plzeň                120\n",
       "Mladá Boleslav                115\n",
       "Sparta Prague                 114\n",
       "Jablonec                      113\n",
       "Fastav Zlín                    81\n",
       "Bohemians 1905                 80\n",
       "Slovácko                       79\n",
       "Slovan Liberec                 78\n",
       "Baník Ostrava                  75\n",
       "Sigma Olomouc                  72\n",
       "Karviná                        67\n",
       "Teplice                        59\n",
       "Dukla Prague                   54\n",
       "Příbram                        50\n",
       "Zbrojovka Brno                 45\n",
       "České Budĕjov.                 39\n",
       "Vysočina Jihlava               32\n",
       "Opava                          28\n",
       "Hradec Králové                 18\n",
       "FK Pardubice                    5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "away_pivot_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).away_pivot()\n",
    "away_pivot_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
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       "      <th>Slavia Prague</th>\n",
       "      <td>194</td>\n",
       "      <td>157</td>\n",
       "      <td>351</td>\n",
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       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>189</td>\n",
       "      <td>120</td>\n",
       "      <td>309</td>\n",
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       "      <th>Sparta Prague</th>\n",
       "      <td>189</td>\n",
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       "      <td>273</td>\n",
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       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>151</td>\n",
       "      <td>78</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>117</td>\n",
       "      <td>79</td>\n",
       "      <td>196</td>\n",
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       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>101</td>\n",
       "      <td>81</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>101</td>\n",
       "      <td>80</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>105</td>\n",
       "      <td>72</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>92</td>\n",
       "      <td>75</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dukla Prague</th>\n",
       "      <td>89</td>\n",
       "      <td>54</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>77</td>\n",
       "      <td>59</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>85</td>\n",
       "      <td>50</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vysočina Jihlava</th>\n",
       "      <td>55</td>\n",
       "      <td>32</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>47</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hradec Králové</th>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Home Team Goals  Away Team Goals  Total Goals\n",
       "Team                                                           \n",
       "Slavia Prague                 194              157          351\n",
       "Viktoria Plzeň                189              120          309\n",
       "Sparta Prague                 189              114          303\n",
       "Jablonec                      161              113          274\n",
       "Mladá Boleslav                158              115          273\n",
       "Slovan Liberec                151               78          229\n",
       "Slovácko                      117               79          196\n",
       "Fastav Zlín                   101               81          182\n",
       "Bohemians 1905                101               80          181\n",
       "Sigma Olomouc                 105               72          177\n",
       "Baník Ostrava                  92               75          167\n",
       "Karviná                        86               67          153\n",
       "Dukla Prague                   89               54          143\n",
       "Teplice                        77               59          136\n",
       "Příbram                        85               50          135\n",
       "Zbrojovka Brno                 57               45          102\n",
       "Vysočina Jihlava               55               32           87\n",
       "Opava                          47               28           75\n",
       "České Budĕjov.                 31               39           70\n",
       "Hradec Králové                 11               18           29\n",
       "FK Pardubice                   11                5           16"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_pivot_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).total_pivot()\n",
    "total_pivot_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slavia Prague obviously dominates the Czech league in the last seasons as they have most goals scored both home and away out of all teams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>H/A Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FK Pardubice</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Slovan Liberec</td>\n",
       "      <td>151</td>\n",
       "      <td>78</td>\n",
       "      <td>1.935897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Vysočina Jihlava</td>\n",
       "      <td>55</td>\n",
       "      <td>32</td>\n",
       "      <td>1.718750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Příbram</td>\n",
       "      <td>85</td>\n",
       "      <td>50</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Opava</td>\n",
       "      <td>47</td>\n",
       "      <td>28</td>\n",
       "      <td>1.678571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sparta Prague</td>\n",
       "      <td>189</td>\n",
       "      <td>114</td>\n",
       "      <td>1.657895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dukla Prague</td>\n",
       "      <td>89</td>\n",
       "      <td>54</td>\n",
       "      <td>1.648148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Viktoria Plzeň</td>\n",
       "      <td>189</td>\n",
       "      <td>120</td>\n",
       "      <td>1.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Slovácko</td>\n",
       "      <td>117</td>\n",
       "      <td>79</td>\n",
       "      <td>1.481013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sigma Olomouc</td>\n",
       "      <td>105</td>\n",
       "      <td>72</td>\n",
       "      <td>1.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jablonec</td>\n",
       "      <td>161</td>\n",
       "      <td>113</td>\n",
       "      <td>1.424779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mladá Boleslav</td>\n",
       "      <td>158</td>\n",
       "      <td>115</td>\n",
       "      <td>1.373913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Teplice</td>\n",
       "      <td>77</td>\n",
       "      <td>59</td>\n",
       "      <td>1.305085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Karviná</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>1.283582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Zbrojovka Brno</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>1.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bohemians 1905</td>\n",
       "      <td>101</td>\n",
       "      <td>80</td>\n",
       "      <td>1.262500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fastav Zlín</td>\n",
       "      <td>101</td>\n",
       "      <td>81</td>\n",
       "      <td>1.246914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Slavia Prague</td>\n",
       "      <td>194</td>\n",
       "      <td>157</td>\n",
       "      <td>1.235669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Baník Ostrava</td>\n",
       "      <td>92</td>\n",
       "      <td>75</td>\n",
       "      <td>1.226667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>České Budĕjov.</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>0.794872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hradec Králové</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Team  Home Team Goals  Away Team Goals  H/A Ratio\n",
       "20      FK Pardubice               11                5   2.200000\n",
       "5     Slovan Liberec              151               78   1.935897\n",
       "16  Vysočina Jihlava               55               32   1.718750\n",
       "13           Příbram               85               50   1.700000\n",
       "17             Opava               47               28   1.678571\n",
       "2      Sparta Prague              189              114   1.657895\n",
       "11      Dukla Prague               89               54   1.648148\n",
       "1     Viktoria Plzeň              189              120   1.575000\n",
       "6           Slovácko              117               79   1.481013\n",
       "7      Sigma Olomouc              105               72   1.458333\n",
       "3           Jablonec              161              113   1.424779\n",
       "4     Mladá Boleslav              158              115   1.373913\n",
       "14           Teplice               77               59   1.305085\n",
       "12           Karviná               86               67   1.283582\n",
       "15    Zbrojovka Brno               57               45   1.266667\n",
       "9     Bohemians 1905              101               80   1.262500\n",
       "8        Fastav Zlín              101               81   1.246914\n",
       "0      Slavia Prague              194              157   1.235669\n",
       "10     Baník Ostrava               92               75   1.226667\n",
       "18    České Budĕjov.               31               39   0.794872\n",
       "19    Hradec Králové               11               18   0.611111"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ha_ratio_pivot_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).ha_ratio_pivot()\n",
    "ha_ratio_pivot_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The home/away ratio shows that some teams are more sensitive on where they play. Pardubice have scored 2.2 times more goals on their home pitch tahn what they scored away. This is a little bit of a paradox since Pardubice are new in the first league this season, and their home stadium is under construction, so they use the pitch from Bohemians 1905 as their home venue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "homebarh_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).barplot(stat = \"Home Team Goals\", vertical=False)\n",
    "homebarh_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "awaybar_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).barplot(stat = \"Away Team Goals\")\n",
    "awaybar_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "totalpie_all = Goals_summary(scores_clean, all_seasons[0], all_seasons[1]).piechart(\"Total Goals\")\n",
    "totalpie_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the total numbers of goals scored in each season:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seasons_list = [season1516, season1617, season1718, season1819, season1920, season2021]\n",
    "goals_per_season = []\n",
    "for i in seasons_list:\n",
    "    total_pivot = Goals_summary(scores_clean, i[0], i[1]).total_pivot()\n",
    "    total_season_goals = sum(total_pivot[\"Total Goals\"])\n",
    "    goals_per_season.append(total_season_goals)\n",
    "seasons_names = [\"15/16\", \"16/17\", \"17/18\", \"18/19\", \"19/20\", \"20/21\"]\n",
    "plt.plot(seasons_names,goals_per_season)\n",
    "plt.title('Total Goals Scored per Season')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Goals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the mean numbers of goals scored in each season:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_mean_goals = []\n",
    "for i in seasons_list:\n",
    "    home_pivot = Goals_summary(scores_clean, i[0], i[1]).home_pivot()\n",
    "    h_mean_season_goals = sum(home_pivot[\"Home Team Goals\"]) / len(home_pivot[\"Home Team Goals\"])\n",
    "    h_mean_goals.append(h_mean_season_goals)\n",
    "plt.plot(seasons_names,h_mean_goals)\n",
    "plt.title('Average Goals Scored by Home Team each Season')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Goals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyze match results we built a class **Matches_summary** that takes a dataframe with scores and two dates like the first class. This class has a function **overall** which returns the number of games played and teams that participated in those games. The function **results_pivot** takes a logical argument **wins** indicating whether we want to receive a pivot table with wins or losses of all teams and returns a Pandas dataframe with the relevant values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Matches_summary:\n",
    "    \n",
    "    def __init__(self, scores_df, start_date, end_date):\n",
    "        helping_data = scores_df.copy()\n",
    "        helping_data['Date'] = pd.to_datetime(helping_data['Date'])\n",
    "        mask = (helping_data['Date'] > start_date) & (helping_data['Date'] <= end_date)\n",
    "        scores_clean = helping_data.loc[mask]\n",
    "        winners = []\n",
    "        losers = []\n",
    "        for idx in range(0,scores_clean.shape[0]):\n",
    "            if scores_clean.iloc[idx,:]['Home Team Goals'] > scores_clean.iloc[idx,:]['Away Team Goals']:\n",
    "                winners.append(scores_clean.iloc[idx,:]['Home Team'])\n",
    "                losers.append(scores_clean.iloc[idx,:]['Away Team'])\n",
    "            elif scores_clean.iloc[idx,:]['Home Team Goals'] == scores_clean.iloc[idx,:]['Away Team Goals']:\n",
    "                winners.append('Draw')\n",
    "                losers.append('Draw')\n",
    "            else:\n",
    "                winners.append(scores_clean.iloc[idx,:]['Away Team'])\n",
    "                losers.append(scores_clean.iloc[idx,:]['Home Team'])\n",
    "        scores_win = scores_clean.copy()        \n",
    "        scores_win['Winner'] = winners\n",
    "        scores_win['Loser'] = losers\n",
    "        self.scores_win = scores_win\n",
    "       \n",
    "    def overall(self):\n",
    "        games = self.scores_win.shape[0]\n",
    "        array = np.array(self.scores_win[\"Home Team\"])\n",
    "        unique = len(np.unique(array))\n",
    "        print(f\"{games} games were played in your specified time period.\")\n",
    "        print(f\"{unique} different teams participated in those games.\")\n",
    "        \n",
    "    def results_pivot(self, wins = True):\n",
    "        if wins:\n",
    "            index = 'Winner'\n",
    "            by = 'Wins'\n",
    "        else:\n",
    "            index = 'Loser'\n",
    "            by = 'Losses'\n",
    "        r_pivot = self.scores_win.pivot_table(index=index, values='Home Team', aggfunc='count')\n",
    "        r_pivot = pd.DataFrame(r_pivot)\n",
    "        r_pivot['Team'] = r_pivot.index\n",
    "        r_pivot[by] = r_pivot['Home Team']\n",
    "        r_pivot = r_pivot.set_index('Team')\n",
    "        r_pivot = r_pivot.sort_values(by = by, ascending = False)\n",
    "        r_pivot_nice = pd.DataFrame(r_pivot.iloc[1:][by])\n",
    "        return r_pivot_nice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Current Season (2020/2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148 games were played in your specified time period.\n",
      "18 different teams participated in those games.\n"
     ]
    }
   ],
   "source": [
    "matches2021_overall = Matches_summary(scores_clean, season2021[0], season2021[1]).overall()\n",
    "matches2021_overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Wins\n",
       "Team                \n",
       "Slavia Prague     14\n",
       "Jablonec          11\n",
       "Sparta Prague     11\n",
       "Slovácko           9\n",
       "Baník Ostrava      7\n",
       "FK Pardubice       7\n",
       "Viktoria Plzeň     7\n",
       "Slovan Liberec     7\n",
       "Sigma Olomouc      6\n",
       "České Budĕjov.     6\n",
       "Bohemians 1905     5\n",
       "Karviná            5\n",
       "Teplice            5\n",
       "Fastav Zlín        5\n",
       "Mladá Boleslav     2\n",
       "Zbrojovka Brno     2\n",
       "Příbram            2\n",
       "Opava              1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches2021_wins = Matches_summary(scores_clean, season2021[0], season2021[1]).results_pivot()\n",
    "matches2021_wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Losses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Losses\n",
       "Team                  \n",
       "Příbram             11\n",
       "Zbrojovka Brno      10\n",
       "Mladá Boleslav      10\n",
       "Opava               10\n",
       "Teplice             10\n",
       "Fastav Zlín          8\n",
       "Bohemians 1905       8\n",
       "FK Pardubice         6\n",
       "Karviná              6\n",
       "Viktoria Plzeň       6\n",
       "Slovan Liberec       5\n",
       "Sparta Prague        4\n",
       "Baník Ostrava        4\n",
       "Slovácko             4\n",
       "České Budĕjov.       4\n",
       "Sigma Olomouc        3\n",
       "Jablonec             3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches2021_losses = Matches_summary(scores_clean, season2021[0], season2021[1]).results_pivot(wins = False)\n",
    "matches2021_losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slavia Prague is not included in the pivot table above because they have not lost a single game this season."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All Seasons (2015/2016 - 2020/2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1365 games were played in your specified time period.\n",
      "21 different teams participated in those games.\n"
     ]
    }
   ],
   "source": [
    "all_matches_overall = Matches_summary(scores_clean, all_seasons[0], all_seasons[1]).overall()\n",
    "all_matches_overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dukla Prague</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vysočina Jihlava</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hradec Králové</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Wins\n",
       "Team                  \n",
       "Viktoria Plzeň     115\n",
       "Slavia Prague      114\n",
       "Sparta Prague       96\n",
       "Jablonec            76\n",
       "Slovan Liberec      74\n",
       "Mladá Boleslav      66\n",
       "Slovácko            57\n",
       "Fastav Zlín         53\n",
       "Bohemians 1905      52\n",
       "Sigma Olomouc       47\n",
       "Baník Ostrava       43\n",
       "Teplice             35\n",
       "Karviná             35\n",
       "Dukla Prague        32\n",
       "Příbram             31\n",
       "Zbrojovka Brno      28\n",
       "Vysočina Jihlava    22\n",
       "České Budĕjov.      19\n",
       "Opava               18\n",
       "Hradec Králové       8\n",
       "FK Pardubice         7"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winner_pivot = Matches_summary(scores_clean, all_seasons[0], all_seasons[1]).results_pivot()\n",
    "winner_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, Viktoria Plzeň has the highest number of wins in all analyzed seasons together, not Slavia Prague as we expected based on the rankings and numbers of goals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Losses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Příbram</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastav Zlín</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karviná</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemians 1905</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baník Ostrava</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovácko</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mladá Boleslav</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dukla Prague</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovan Liberec</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teplice</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jablonec</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma Olomouc</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbrojovka Brno</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vysočina Jihlava</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opava</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparta Prague</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktoria Plzeň</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hradec Králové</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>České Budĕjov.</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slavia Prague</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FK Pardubice</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Losses\n",
       "Team                    \n",
       "Příbram               84\n",
       "Fastav Zlín           78\n",
       "Karviná               68\n",
       "Bohemians 1905        67\n",
       "Baník Ostrava         67\n",
       "Slovácko              66\n",
       "Mladá Boleslav        61\n",
       "Dukla Prague          59\n",
       "Slovan Liberec        56\n",
       "Teplice               56\n",
       "Jablonec              50\n",
       "Sigma Olomouc         49\n",
       "Zbrojovka Brno        49\n",
       "Vysočina Jihlava      46\n",
       "Opava                 44\n",
       "Sparta Prague         38\n",
       "Viktoria Plzeň        28\n",
       "Hradec Králové        19\n",
       "České Budĕjov.        19\n",
       "Slavia Prague         18\n",
       "FK Pardubice           6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loser_pivot = Matches_summary(scores_clean, all_seasons[0], all_seasons[1]).results_pivot(wins = False)\n",
    "loser_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the pivot tables with wins and losses using a Matplotlib bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = winner_pivot.shape[0]\n",
    "blue_bar = winner_pivot[\"Wins\"]\n",
    "orange_bar = loser_pivot[\"Losses\"]\n",
    "ind = np.arange(N)\n",
    "plt.figure(figsize=(10,7))\n",
    "width = 0.3       \n",
    "plt.barh(ind, blue_bar, width, label='Wins')\n",
    "plt.barh(ind + width, orange_bar, width, label='Losses')\n",
    "plt.ylabel('Team')\n",
    "plt.xlabel('Number of Games')\n",
    "plt.title('Number of Wins and Losses by Team')\n",
    "plt.yticks(ind + width / 2, (winner_pivot.index))\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}