Week_3/Day_2/dataviz_challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the Compass, we will be using data from international football (soccer :)) matches that took place between 1872 and 2019 (148 years). You can download the dataset from [**this link**](https://drive.google.com/file/d/1cCn5botBKzh1XZOvrxpcLle-Ua7Fh9BR/view?usp=sharing) and find more information about it on [**Kaggle**](https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017).\n",
    "\n",
    "We need to make sure we understand all variables and information they store before we start working on the task. It's very important to understand the dataset to create meaningful visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import os\n",
    "import datetime as dt\n",
    "import calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/d/lighthouse/lighthouse_data_notes/Week_3/Day_2'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()\n",
    "os.chdir('/mnt/d/lighthouse/lighthouse_data_notes/Week_3/Day_2')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('results.csv', header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
       "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
       "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  \n",
       "3   England    False  \n",
       "4  Scotland    False  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = pd.to_datetime(data['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>date</th>\n",
       "      <th>team</th>\n",
       "      <th>score</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>0</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>4</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>1874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>3</td>\n",
       "      <td>1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41581</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41582</th>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41583</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41584</th>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41585</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83172 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        team  score  year\n",
       "0     1872-11-30    Scotland      0  1872\n",
       "1     1873-03-08     England      4  1873\n",
       "2     1874-03-07    Scotland      2  1874\n",
       "3     1875-03-06     England      2  1875\n",
       "4     1876-03-04    Scotland      3  1876\n",
       "...          ...         ...    ...   ...\n",
       "41581 2020-01-10      Canada      4  2020\n",
       "41582 2020-01-12      Sweden      1  2020\n",
       "41583 2020-01-15     Iceland      1  2020\n",
       "41584 2020-01-19     Iceland      1  2020\n",
       "41585 2020-02-01  Costa Rica      0  2020\n",
       "\n",
       "[83172 rows x 4 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home = data[['date','home_team','home_score','year']]\n",
    "away = data[['date','away_team','away_score','year']]\n",
    "home.columns =['date','team','score','year']\n",
    "away.columns =['date','team','score','year']\n",
    "all_teams = pd.concat([home,away])\n",
    "all_teams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### Instruction\n",
    "> Use visualizations to answer following questions. Try different Python packages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "Which teams scored the largest number of goals in FIFA World Cup?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-81-030826ad99c3>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  fifa_world['date'] = pd.to_datetime(fifa_world['date'])\n",
      "<ipython-input-81-030826ad99c3>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  fifa_world['year'] = fifa_world['date'].dt.year\n"
     ]
    }
   ],
   "source": [
    "# Fifa games\n",
    "fifa_world = data[data['tournament']=='FIFA World Cup']\n",
    "fifa_world['date'] = pd.to_datetime(fifa_world['date'])\n",
    "fifa_world['year'] = fifa_world['date'].dt.year\n",
    "\n",
    "# All teams\n",
    "home = fifa_world[['date','home_team','home_score','year']]\n",
    "away = fifa_world[['date','away_team','away_score','year']]\n",
    "home.columns =['date','team','score','year']\n",
    "away.columns =['date','team','score','year']\n",
    "teams = pd.concat([home,away])\n",
    "teams = teams.groupby('team').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>score</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>229</td>\n",
       "      <td>215974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>226</td>\n",
       "      <td>216178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>137</td>\n",
       "      <td>160862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>128</td>\n",
       "      <td>164374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>120</td>\n",
       "      <td>131124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0</td>\n",
       "      <td>5958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>0</td>\n",
       "      <td>6018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China PR</th>\n",
       "      <td>0</td>\n",
       "      <td>6006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DR Congo</th>\n",
       "      <td>0</td>\n",
       "      <td>5922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     score    year\n",
       "team                              \n",
       "Brazil                 229  215974\n",
       "Germany                226  216178\n",
       "Argentina              137  160862\n",
       "Italy                  128  164374\n",
       "France                 120  131124\n",
       "...                    ...     ...\n",
       "Canada                   0    5958\n",
       "Trinidad and Tobago      0    6018\n",
       "China PR                 0    6006\n",
       "Indonesia                0    1938\n",
       "DR Congo                 0    5922\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test = fifa_world[fifa_world['year']==1930]\n",
    "#home = test[['date','home_team','home_score','year']]\n",
    "#away = test[['date','away_team','away_score','year']]\n",
    "#home.columns =['date','team','score','year']\n",
    "#away.columns =['date','team','score','year']\n",
    "#teams = pd.concat([home,away])\n",
    "#teams = teams.groupby('team').sum()\n",
    "teams = teams.sort_values(by ='score', ascending = False)\n",
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'goals')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "ax.bar(teams.head(15).index,teams.head(15).score)\n",
    "ax.set_title('Overall FIFA goals', size= 25)\n",
    "ax.set_xlabel('Teams')\n",
    "ax.set_ylabel('goals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "What is the number of matches played in each tournament throughout history?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tournament</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friendly</th>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "      <td>17029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIFA World Cup qualification</th>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "      <td>7236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UEFA Euro qualification</th>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "      <td>2582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>African Cup of Nations qualification</th>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIFA World Cup</th>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       date  home_team  away_team  home_score  \\\n",
       "tournament                                                                      \n",
       "Friendly                              17029      17029      17029       17029   \n",
       "FIFA World Cup qualification           7236       7236       7236        7236   \n",
       "UEFA Euro qualification                2582       2582       2582        2582   \n",
       "African Cup of Nations qualification   1672       1672       1672        1672   \n",
       "FIFA World Cup                          900        900        900         900   \n",
       "\n",
       "                                      away_score   city  country  neutral  \n",
       "tournament                                                                 \n",
       "Friendly                                   17029  17029    17029    17029  \n",
       "FIFA World Cup qualification                7236   7236     7236     7236  \n",
       "UEFA Euro qualification                     2582   2582     2582     2582  \n",
       "African Cup of Nations qualification        1672   1672     1672     1672  \n",
       "FIFA World Cup                               900    900      900      900  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = data.groupby('tournament').count()\n",
    "matches = matches.sort_values(by ='date', ascending = False)\n",
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Friendly', 'FIFA World Cup qualification', 'UEFA Euro qualification',\n",
       "       'African Cup of Nations qualification', 'FIFA World Cup'],\n",
       "      dtype='object', name='tournament')"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head(5).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'games')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "ax.bar(matches.head(5).index, matches.head(5).date)\n",
    "ax.set_title('Overall games played', size= 25)\n",
    "ax.set_xlabel('Tournament Style')\n",
    "ax.set_ylabel('games')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task \n",
    "Show the trend in number of matches per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      date  home_team  away_team  home_score  away_score  tournament  city  \\\n",
       "year                                                                         \n",
       "1872     1          1          1           1           1           1     1   \n",
       "1873     1          1          1           1           1           1     1   \n",
       "1874     1          1          1           1           1           1     1   \n",
       "1875     1          1          1           1           1           1     1   \n",
       "1876     2          2          2           2           2           2     2   \n",
       "\n",
       "      country  neutral  \n",
       "year                    \n",
       "1872        1        1  \n",
       "1873        1        1  \n",
       "1874        1        1  \n",
       "1875        1        1  \n",
       "1876        2        2  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_year = data\n",
    "by_year['year'] = by_year['date'].dt.year\n",
    "by_year = by_year.groupby(by='year').count()\n",
    "by_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2019, 2008, 2011, 2004, 2000, 2001, 2012, 2015, 2007, 2013,\n",
       "            ...\n",
       "            1881, 1880, 1879, 1878, 1877, 1876, 1874, 1873, 1875, 1872],\n",
       "           dtype='int64', name='year', length=149)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_year.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1880, 0, '1880'),\n",
       " Text(1885, 0, '1885'),\n",
       " Text(1890, 0, '1890'),\n",
       " Text(1895, 0, '1895'),\n",
       " Text(1900, 0, '1900'),\n",
       " Text(1905, 0, '1905'),\n",
       " Text(1910, 0, '1910'),\n",
       " Text(1915, 0, '1915'),\n",
       " Text(1920, 0, '1920'),\n",
       " Text(1925, 0, '1925'),\n",
       " Text(1930, 0, '1930'),\n",
       " Text(1935, 0, '1935'),\n",
       " Text(1940, 0, '1940'),\n",
       " Text(1945, 0, '1945'),\n",
       " Text(1950, 0, '1950'),\n",
       " Text(1955, 0, '1955'),\n",
       " Text(1960, 0, '1960'),\n",
       " Text(1965, 0, '1965'),\n",
       " Text(1970, 0, '1970'),\n",
       " Text(1975, 0, '1975'),\n",
       " Text(1980, 0, '1980'),\n",
       " Text(1985, 0, '1985'),\n",
       " Text(1990, 0, '1990'),\n",
       " Text(1995, 0, '1995'),\n",
       " Text(2000, 0, '2000'),\n",
       " Text(2005, 0, '2005'),\n",
       " Text(2010, 0, '2010'),\n",
       " Text(2015, 0, '2015')]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.plot(by_year.index, by_year['date'])\n",
    "ax.set_title('games per year', size= 25)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('games')\n",
    "\n",
    "ax.set_xlim([1870, 2019])\n",
    "xticklabels = np.arange(1880, 2020, 5) \n",
    "ax.xaxis.set_ticks(xticklabels)\n",
    "ax.set_xticklabels(xticklabels, fontsize=10, rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "Which teams are the most successful ones? (winning percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "winnings = data.iloc[:,:5]\n",
    "winnings['year'] = data['year']\n",
    "winnings['winning_team'] = \"\"\n",
    "winnings['winning_team'].loc[winnings['home_score'] > winnings['away_score']] = winnings['home_team']\n",
    "winnings['winning_team'].loc[winnings['home_score'] < winnings['away_score']] = winnings['away_team']\n",
    "winnings['winning_team'].loc[winnings['home_score'] == winnings['away_score']] = 'tie'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "alls = all_teams.groupby(by='team').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winning_team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abkhazia</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alderney</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zanzibar</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tie</th>\n",
       "      <td>9578</td>\n",
       "      <td>9578</td>\n",
       "      <td>9578</td>\n",
       "      <td>9578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Åland Islands</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               home_team  away_team  home_score  away_score\n",
       "winning_team                                               \n",
       "Abkhazia              12         12          12          12\n",
       "Afghanistan           30         30          30          30\n",
       "Albania               84         84          84          84\n",
       "Alderney               3          3           3           3\n",
       "Algeria              214        214         214         214\n",
       "...                  ...        ...         ...         ...\n",
       "Zambia               328        328         328         328\n",
       "Zanzibar              41         41          41          41\n",
       "Zimbabwe             189        189         189         189\n",
       "tie                 9578       9578        9578        9578\n",
       "Åland Islands         21         21          21          21\n",
       "\n",
       "[301 rows x 4 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winnings = winnings.drop(['date','year'], axis = 1)\n",
    "winnings = winnings.groupby('winning_team').count()\n",
    "winnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = alls.join(winnings, lsuffix=alls.index, rsuffix=winnings.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = result.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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>total</th>\n",
       "      <th>won</th>\n",
       "      <th>lost</th>\n",
       "      <th>win_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Surrey</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asturias</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canary Islands</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire</th>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Padania</th>\n",
       "      <td>43</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.674419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liechtenstein</th>\n",
       "      <td>198</td>\n",
       "      <td>15.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>0.075758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.049180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Djibouti</th>\n",
       "      <td>88</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>164</td>\n",
       "      <td>6.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.036585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Marino</th>\n",
       "      <td>163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.006135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                total   won   lost  win_percent\n",
       "team                                           \n",
       "Surrey              1   1.0    0.0     1.000000\n",
       "Asturias            1   1.0    0.0     1.000000\n",
       "Canary Islands      4   3.0    1.0     0.750000\n",
       "Yorkshire           7   5.0    2.0     0.714286\n",
       "Padania            43  29.0   14.0     0.674419\n",
       "...               ...   ...    ...          ...\n",
       "Liechtenstein     198  15.0  183.0     0.075758\n",
       "Anguilla           61   3.0   58.0     0.049180\n",
       "Djibouti           88   4.0   84.0     0.045455\n",
       "Andorra           164   6.0  158.0     0.036585\n",
       "San Marino        163   1.0  162.0     0.006135\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['win_percent'] = result['home_score']/result['date']\n",
    "result = result.sort_values(by='win_percent', ascending = False)\n",
    "result['total'] = result['date']\n",
    "result['lost'] = result['date'] - result['away_team']\n",
    "result['won'] = result['away_team']\n",
    "res = result[['total','won','lost','win_percent']]\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'games played')"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mNfY85ruZbPsXvehFuf7663P44Yfn9a9/fTbffPO85CUvyWGHHZb11ltvbZcMAGM3rl/lXaj8GjEAs0FLpXXIE57whCTJiSeemFNPPTW77757HvrQh2ajjTbK8ccfnzPPPPP2UGnLLbfMz372s7ss48orr8xmm222VuteaGay7RctWpRXvOIV+e53v5sf//jHedWrXpU3velNOeKII9Z2uQAAALBKhErrkE033TQPe9jD8va3vz3rrbdedtppp1RVdtttt7z1rW/Nrbfeenuo9NjHPjbHHXdcrr/++tvn/9a3vpVLLrkku+2227iewoIwdNvf7373y2te85pst912Of/885N0rcWSaDEGAADAnCVUWscsW7YsJ510Uh7/+MfffpnUxLDtt98+W2yxRZLkla98ZZJkv/32yzHHHJOjjjoqz3zmM/Pwhz88z3rWs8ZW/0Iwk23/p3/6p3nta1+bY445JieeeGIOOeSQXHjhhXniE5+Y5I6Out/73vfmm9/8Zr7zne+M58kAAADANBZ0n0rr4rXky5Yty3/8x39k9913v9OwJHdqBbNkyZKccMIJ+eu//usccMABWbx4cZ761Kfm7W9/++2tYOa0tu7tmwkz2fa77rprjjjiiLz3ve/NzTffnO222y5HHHFE9t9//yTJAx7wgLztbW/LO9/5zrzrXe/K1ltvnUsuuWR8TwoAAAAmqbYO//M+apdddmmnn376lOPOOOOM7Lzzzmu5Ilh7zjjjjJxyyik58MADs/nmm4+7HAAYTEfda9e6+OUqAONRVWe01naZapzL3wAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMNiCCZWWL18+7hJgjfDaBgAAYBwWRKi0ePHi3HTTTeMuA9aIm266SbAEAADAWrcgQqWtttoqF110UW644Qb/fDNvLF++PDfccEMuuOCCXHHFFUmSRYsWxCENAADAHLD+uAtYGzbbbLPcdtttOf/887PeeuuNuxyYNcuXL88VV1yRq6++Oq21bLTRRuMuCQAAgAViQYRKSbJkyZL8/Oc/z7HHHpuNNtooixcvTlWNuyxYLa213HLLLbn++uuz3377ZYMNNhh3SQAAACwQCyZUSpIHP/jBWbRoUc4888xcd9114y4HZsW97nWv7Lbbbnnwgx887lIAAABYQBZUqJQkO+ywQ3bYYYdxlwEAAACwTtOrLwAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAg60/7gKq6pIk1ye5LcmtrbVdqmqzJB9LsjTJJUme21r75bhqBAAAAODO5kpLpb1aazu21nbpH78myfGtte2THN8/BgAAAGCOmCuh0mRPT/KB/v4Hkuw/vlIAAAAAmGwuhEotyZeq6oyqOrgftkVr7fL+/hVJtphqxqo6uKpOr6rTr7rqqrVRKwAAAACZA30qJdmttXZZVf1Wki9X1fdGR7bWWlW1qWZsrR2e5PAk2WWXXaacBgAAAIDZN/aWSq21y/rbnyX5dJLHJLmyqrZMkv72Z+OrEAAAAIDJxhoqVdU9q+peE/eT7Jvk3CSfTfKCfrIXJDlmPBUCAAAAMJVxX/62RZJPV9VELR9urR1bVd9K8vGqenGSHyV57hhrBAAAAGCSsYZKrbUfJnnkFMN/kWTvtV8RAAAAADMx9j6VAAAAAFj3CJUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhsToRKVbVeVZ1VVZ/vH29TVd+sqh9U1ceqavG4awQAAADgDnMiVErysiTfHXn8liRvb61tl+SXSV48lqoAAAAAmNLYQ6Wq2jrJ05K8r39cSZ6Y5JP9JB9Isv9YigMAAABgSmMPlZK8I8mrkyzvH2+e5JrW2q3940uTbDWGugAAAACYxlhDpar63SQ/a62dsYrzH1xVp1fV6VddddUsVwcAAADAdMbdUukJSf5PVV2S5KPpLnv7tySbVNX6/TRbJ7lsqplba4e31nZpre2yZMmStVEvAAAAABlzqNRae21rbevW2tIkv5/kK621A5OckOTZ/WQvSHLMmEoEAAAAYArjbqk0nb9N8sqq+kG6Ppb+a8z1AAAAADBi/ZVPsna01k5McmJ//4dJHjPOegAAAACY3lxtqQQAAADAHCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYLD1pxtRVe9fxWW21tqLV3FeAAAAANYB04ZKSQ6aZnhLUisY3pIIlQAAAADmsRWFSttMerwoyduTLEvyziQnJrkiyX2T7JXkL5OclOSVs14lAAAAAHPKtKFSa+1Ho4+r6hXpAqVHTRr3/SRfraoPJDkjydOTvGP2SwUAAABgrhjSUffBST4+OWya0Fq7OMkn+ukAAAAAmMeGhEpLk1yzkml+2U8HAAAAwDw2JFT6eZL9phtZVdWP/8XqFgUAAADA3Laijron+0SSl1fVx5P8bX+5W5KkqrZJ8pYkj0jXmTcAAAAwR9RhU/2IO2tKO6SNu4S1Ykio9A9Jdkvy7CTPqKrLklyZZIskWyVZL8m3khw6yzUCAAAAMMfM+PK31toN6UKlv0tySZL7J3l0f3txktclWdZPBwAAAMA8NqSlUlprtyR5Y5I3VtVGSTZOcq0gCQAAAGBhGRQqjeqDJGESAAAAwAI0OFSqqiVJnpXkwUnu2Vr745Hh2yT5TmvtV7NaJQAAAABzyqBQqapenOSdSe6epJK0JH/cj94iyTeSHJzkv2axRgAAAADmmBl31F1V+yQ5PMkFSZ6R5D2j41tr5yY5L8n+s1gfAAAAAHPQkJZKf5vk8iR7tNauq6qdppjmnCS7zkplAAAAAMxZM26plGSXJJ9vrV23gmkuTXLf1SsJAAAAgLluSKi0OMmNK5lmkyS3rXI1AAAAAKwThoRKlyTZeSXTPDbJ91e5GgAAAADWCUNCpWOSLKuq50w1sqpemOQRSY6ejcIAAAAAmLuGdNT91iS/n+QjVfXsJBsnSVX9RZJlSZ6Z5MIk75rtIgEAAACYW2YcKrXWfllVeyT5YJLR1krv7G9PTvIHrbWV9bsEAAAAwDpuSEultNZ+nGTPqnpEkl2TbJ7k2iSnttbOWAP1AQAAADAHDQqVJrTWzklyzizXAgAAAMA6YsYddVfVO6vqwWuyGAAAAADWDUN+/e0vkpxbVSdV1YFVtXhNFQUAAADA3DYkVHpOkuOTPCFdZ90/raq3VdUOa6QyAAAAAOasGYdKrbWjW2v7JtkuyVuT3JLklUnOr6qvVNVzq2qDNVQnAAAAAHPIkJZKSZLW2sWttdcmuX/uaL20R5KPJLm0qt5cVdvObpkAAAAAzCWDQ6UJrbVbR1ov7Zrkp0mWJHl1ku9X1eeraudZqhMAAACAOWSVQ6Ukqao9qurDSb6aZKskVyV5R5KvJXlqklOr6nmrWyQAAAAAc8v6Q2eoqk2THJTk4CQPTFJJTknyniSfaK39pp/uMUk+leTQJB+bnXIBAAAAmAtmHCpV1bJ0QdKzktw9yQ1J3pvkPa2170yevrV2WlX9d5K/naVaAQAAAJgjhrRU+mp/e166VkkfbK3dsJJ5Luv/AAAAAJhHhvSp9NEke7TWHt5ae/cMAqW01v6ztbbNqpcHAAAAwFw045ZKrbU/WJOFAAAAALDuWK1ffwMAAABgYVqVX397dJL9kmyV5G5TTNJaay9e3cIAAAAAmLuG/PpbJTkyyfOTVJLW305oI8OFSgAAAADz2JDL3/4iyR8m+VCSXdIFSO9I8vgk/zfJ9ek68/6d2S0RAAAAgLlmyOVvL0jy/dbaQUnSNVzKNa21U5OcWlXHJTk1yZeT/Pcs1wkAAADAHDKkpdKDknxl0rDbQ6nW2llJPp/kpbNQFwAAAABz2NBff7t25P6NSTabNP7CdOETAAAAAPPYkFDpsnS/+Dbhh0l2njTN9unCJgAAAADmsSGh0mm5c4j0v0keU1V/X1UPrao/T/L0dP0qAQAAADCPDQmVjk6yXlVt0z9+a5IfJTksyTlJ3pXkmiSvmc0CAQAAAJh7Zvzrb621zyT5zMjjq6tqpyR/kmTbJJck+WBr7fLZLREAAACAuWbGodJUWmvXJnnbLNUCAAAAwDpi6K+/AQAAAMD0LZWqavdVXWhr7aRVnRcAAACAuW9Fl7+dmKSt4nLXW8X5AAAAAFgHrChUen1WPVQCAAAAYB6bNlRqrR26FusAAAAAYB2io24AAAAABlvR5W/TqqplSXZKsnGSa5Oc1Vo7eTYLAwAAAGDuGhQqVdUTkrw/yXYTg9L3u1RVFyZ5UWvt67NaIQAAAABzzoxDparaOcmXk9w9yVfT/TrcFUnum2SvJLsn+XJVLWutnTnDZd49yUlJ7tbX8snW2iFVtU2SjybZPMkZSf6wtXbLTGsFAAAAYM0a0qfSP6ULfp7eWturtXZYa+29/e2eSZ6RZHE/3Uz9OskTW2uPTLJjkidX1eOSvCXJ21tr2yX5ZZIXD1gmAAAAAGvYkFDp8Uk+1Vr73FQjW2vHJPl0P92MtM4N/cMN+r+W5IlJPtkP/0CS/QfUCQAAAMAaNiRUWp7kByuZ5sL0fSzNVFWtV1VnJ/lZusvrLkpyTWvt1n6SS5NsNWSZAAAAAKxZQ0Kl05M8ciXTPDLJaUMKaK3d1lrbMcnWSR6T5EEznbeqDq6q06vq9KuuumrIagEAAABYDUNCpb9Lsk9V/dlUI6vqz5PsneTvV6WQ1to1SU5IsmuSTapqohPxrZNcNs08h7fWdmmt7bJkyZJVWS0AAAAAq2DGv/6WZN8kX0ny71X18iQnJ7kyyRZJdkuyfZJjk+xXVfuNzNdaa/841QKrakmS37TWrqmqeyTZJ10n3SckeXa6X4B7QZJjhjwpAAAAANasIaHSoSP3t+//JntK/zeqJZkyVEqyZZIPVNV66VpNfby19vmqOj/JR6vqDUnOSvJfA+oEAAAAYA0bEirtNdsrb62dk2SnKYb/MF3/SgAAAADMQTMOlVprX12ThQAAAACw7hjSUTcAAAAAJBl2+RsAwJxSh9W4S1hQ2iFt3CUAAHOIlkoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYKv9629V9aAkT0lyU5KPttauXe2qAAAAAJjTZtxSqar+oaour6rNRoY9KclZSd6W5N1JzqyqzWe/TAAAAADmkiGXvz0lyfdaa1ePDHtTkpbkkCTvSbJNkpfNXnkAAAAAzEVDQqWlSb478aCqtkqyc5J3t9be0Fr7iyRfSbL/bBYIAAAAwNwzJFTaNMloK6UnpGul9PmRYWckuf8s1AUAAADAHDYkVLoqyVYjj/dK8psk3xwZtnjgMgEAAABYBw359bezk/yfqnpYkpuTPC/J11prvxqZZmmSy2etOgAAAADmpCGtit6aZOMk307y/f7+v0yMrKr10l0Sd/psFggAAADA3DPjlkqttZOr6neT/Em6vpSOaq3978gkj09yWZJPz26JAAAAAMw1Qy5/S2vt2CTHTjPu5CQ7zUZRAAAsLO3QcVewwBwy7gIAmA90qg0AAADAYINCpapaVFV/WVWnVtW1VXXryLidqurdVfXA2S8TAAAAgLlkxqFSVS1O8uUk70iybZLrk9TIJBcneVGSA2exPgAAAADmoCEtlf4myV5JDkuyRZL3jY5srV2T5KQk+81WcQAAAADMTUNCpQOTnNJae31rbXm6X4Cb7OIk95+VygAAAACYs4aEStskOXUl01ydZLNVLwcAAACAdcGQUOnmJJusZJr7J7lmVYsBAAAAYN0wJFQ6O8m+fYfdd1FVG6frT+m0WagLAAAAgDlsSKh0eJL7JTmqqu49OqKqNklyZJJNk/znbBUHAAAAwNy0/kwnbK19pKr2SXJQkv+T5JdJUlWnJ3lokrsl+Y/W2hfXQJ0AAAAAzCFDWiqltfaiJC9Kcn6SJUkqyaOS/CDJi1trfznrFQIAAAAw58y4pdKE1tqRSY6sqnuku9zt2tbajbNdGAAAAABz1+BQaUJr7VdJfjWLtQAAAACwjhh0+RsAAAAAJANDpap6TFUdXVUXVdWvq+q2Kf5uXVPFAgAAADA3zPjyt6p6dpKPpguiLklyWhIBEgAAAMACNKRPpUOT3Jjkaa21r62ZcgAAAABYFwy5/G27JB8RKAEAAAAwJFS6Islv1lQhAAAAAKw7hoRKn0iyT1UtXlPFAAAAALBuGBIqHZLkmiQfr6oHrJlyAAAAAFgXzLij7tbaTVV1cJITkvywqq5Jcu3Uk7ZtZ6k+AAAAAOagGbdUqqrdknw9yaZJbktyU5Ka4m9I6ycAAAAA1kEzbqmU5C1JNkjyR0k+3FpbvmZKAgAAAGCuGxIqPTLJR1pr/7OmigEAAABg3TDkUrUbkly9pgoBAAAAYN0xJFT6YpI91lQhAAAAAKw7hoRKr0ly76r6j6q655oqCAAAAIC5b0ifSh9Ncn2SlyT5o6q6IMm1U0zXWmt7z0ZxAAAAAMxNQ0KlPUfu3zPJTtNM11a5GgAAAADWCTMOlVprQy6VAwAAAGAeExQBAAAAMJhQCQAAAIDBhvSpdLuq2jrJVknuNtX41tpJq1MUAAAAAHPboFCpqvZN8vYkD1rJpOutckUAAAAAzHkzvvytqh6X5PNJNkny70kqyUlJjkjyvf7x55K8ftarBAAAAGBOGdKn0muT3Jzk0a21l/XDTmitvSTJw5K8IcmTknxydksEAAAAYK4ZEirtmuSzrbWfTp6/df4hyXeTHDaL9QEAAAAwBw0JlTZO8uORx7ckueekaU5JsvvqFgUAAADA3DYkVPpZkk0nPd520jQbJLnH6hYFAAAAwNw2JFS6IHcOkU5Nsk9VPTBJquq+SZ6V5MLZKw8AAACAuWhIqHRskj2qarP+8b+la5V0VlV9K90vwC1J8o5ZrRAAAACAOWdIqPTedP0l/SZJWmunJHlOkovT/frb5Un+rLX2wdkuEgAAAIC5Zf2ZTthauy7JNycN+3SST892UQAAAADMbUNaKgEAAABAEqESAAAAAKtgxpe/VdUPZzDZ8iTXJflukk+11o5e1cIAAAAAmLtmHCqla9W0fpLf7h/fmuQXSTYfWc5Pk/xWkh2T/H5VfTHJ/q2122alWgAAAADmhCGXvz0iyWVJTk6yW5K7t9a2THL3JMv64Zcm2SrJDkmOTfLUJC+bboFVdb+qOqGqzq+q86rqZf3wzarqy1V1YX+76ao8OQAAAADWjCGh0j8l2TjJ3q21r7fWlidJa215a+2UJPsk2STJP7XWLkzynHQh1IErWOatSf66tfaQJI9L8udV9ZAkr0lyfGtt+yTH948BAAAAmCOGhErPSPLZ1tqtU41srd2S5HNJntk/vildIPTA6RbYWru8tXZmf//6dH0xbZXk6Uk+0E/2gST7D6gTAAAAgDVsSKi0eZLFK5lmg366CVdkhv02VdXSJDsl+WaSLVprl48sY4sBdQIAAACwhg3pqPuHSZ5VVX/ftyq6k6q6d5JnJbl4ZPCWSa5e2YKraqMkRyd5eWvtuqq6fVxrrVVVm2a+g5McnCT3v//9BzwVAAAAWDjaoeOuYIE5ZNwFrB1DWiodnu7StG9W1YFVtbSq7tHfPj9dC6PfTvLeJKkuGdozydkrWmhVbZAuUDqqtfapfvCVVbVlP37LJD+bat7W2uGttV1aa7ssWbJkwFMBAAAAYHXMuKVSa+3fqmqHJC9J8sEpJqkkh7fW/q1//FtJPpLky9Mtsw+e/ivJd1tr/zoy6rNJXpDkzf3tMTOtEwAAAIA1b8jlb2mtvbSqPpzkoCQ7pvs1uOuSnJXkg621k0amvTLJa1eyyCck+cMk36mqs/th/zddmPTxqnpxkh8lee6QOgEAAABYswaFSknSWvtakq/Nxsr7ZdU0o/eejXUAAAAAMPuG9KkEAAAAAEmESgAAAACsAqESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMHWH3cBAACrqh067goWmEPGXQAAMJdoqQQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYba6hUVe+vqp9V1bkjwzarqi9X1YX97abjrBEAAACAuxp3S6Ujkzx50rDXJDm+tbZ9kuP7xwAAAADMIWMNlVprJyW5etLgpyf5QH//A0n2X5s1AQAAALBy426pNJUtWmuX9/evSLLFdBNW1cFVdXpVnX7VVVetneoAAAAAmJOh0u1aay1JW8H4w1tru7TWdlmyZMlarAwAAABgYZuLodKVVbVlkvS3PxtzPQAAAABMMhdDpc8meUF//wVJjhljLQAAAABMYayhUlV9JMk3kuxQVZdW1YuTvDnJPlV1YZIn9Y8BAAAAmEPWH+fKW2sHTDNq77VaCAAAAACDzMXL3wAAAACY44RKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwdYfdwEAAADMTXVYjbuEBaUd0sZdAgyipRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMHWH3cBALA2+Wnktc/PIwMAzE9aKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYbP1xFwCwLqjDatwlLCjtkDbuEgCAJO3QcVewwBwy7gJgGC2VAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYLD1x10AAKxN7dBxV7AAHTLuAoC1qQ6rcZew4LRD2rhLABYoLZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAxWrc2Pn5/cZZdd2umnnz7uMmaFn2Fdu9bkT7Dal2vXGv053bIv16o1+dlkX6599uf8YV/OH/bl/GJ/zh/25fwxT7KWJKmqM1pru0w1TkslAAAAAAYTKgEAAAAwmFAJAAAAgMHmbKhUVU+uqu9X1Q+q6jXjrgcAAACAO8zJUKmq1kvyH0mekuQhSQ6oqoeMtyoAAAAAJszJUCnJY5L8oLX2w9baLUk+muTpY64JAAAAgN5cDZW2SvKTkceX9sMAAAAAmAPWH3cBq6OqDk5ycP/whqr6/jjrIfdJ8vNxFzFUHVrjLmEusi/nj3VyX6bsy2nYn/OHfTl/2Jfzx7q5LxP7c2rr5v60L6diX47fA6YbMVdDpcuS3G/k8db9sDtprR2e5PC1VRQrVlWnt9Z2GXcdrD77cv6wL+cX+3P+sC/nD/ty/rAv5xf7c/6wL+e2uXr527eSbF9V21TV4iS/n+SzY64JAAAAgN6cbKnUWru1qv4iyXFJ1kvy/tbaeWMuCwAAAIDenAyVkqS19sUkXxx3HQziUsT5w76cP+zL+cX+nD/sy/nDvpw/7Mv5xf6cP+zLOaxaa+OuAQAAAIB1zFztUwkAAACAOUyoxJ1U1euq6ryqOqeqzq6qx467JmauqvavqlZVD1rJdP93FZf/kqr6o1WrbmGoqvtW1Uer6qKqOqOqvlhVD5wDdR1UVf++gvF7VtXn18a65rvqfK2qnjIy7DlVdewM5p3xtlvRtP3rbpMZF82MVNVt/WfjuVX1iaracMC8l1TVfVZxvd5714KqumHcNcxHq3nc7FJV71zB+Fn77JphPR/pz5FfMWn4oVX1qrVVx1y1Ns6B+n3++Fla1pKq+mZVnVVVyyaNO7GqTh95vEtVnThyf9rX5bpq5Fj9dlWdOVvbebZU1QZV9eaqurCv7xuj51qztI6lVfUHs7nMhUCoxO2qatckv5vkUa21RyR5UpKfzHDe9Vf0mLXmgCRf629XZHCoVFXrt9b+s7X2wVWqbAGoqkry6SQntta2ba3tnOS1SbZYg+t0rM0xrbuu/CVJ/rWq7l5VGyV5Y5I/X9F8s7kvW2tPba1dM2n5VVU+91fPr1prO7bWHpbklnT7eY3z3ju3OJYGW6Xjpj/vOL219ldrtryZqar7Jnl0a+0RrbW3r+ay5t1n91o8B9ozyWyFHXsn+U5rbafW2slTjP+tqUKLufS6nGUTx+oj0+27N427oEn+McmWSR7WWntUkv2T3GuW17E0yZSh0nw8bmeLD0RGbZnk5621XydJa+3nrbWfjn67OimlP7SqPlRVpyT50BSPl1TV0VX1rf7vCVW1qE+Xl/TLWFRVP5h4zKrr/3HdLcmLk/x+P2zLqjpp5BvCZVX15iT36Icd1Sfy544s51VVdWh//8Sqekf/Tc3LRr+Jq6o/6ffrt/v9vGE//Dn9ur5dVSet3a0wdnsl+U1r7T8nBrTWvt1aO7mqNqqq4/tvVr5TVU9Pbv9G5LtVdUR1rQS/VFX36MdNt42PrKr/rKpvJnnr0GNqZfuoqh7Tf/tzVlV9vap26IcfVFWfqqpj+3W+dWSeF1bVBVV1WpInzHRd81Vr7dwkn0vyt0n+Icn/JPmX6r7hPrWqHpHc9X10dBlV9bR+P9xnBdvxt6fZH5f08y2tqu9X1QeTnJvkflX1N/3r6pyqOmwNb4r57OQk21XV79Ud33T/v6raIkmqavP+eD6vqt6XpCZmrKrPVPct/nlVdfDI8Buq6p/6/XzqyLJW+t7L7JnqGJnmWDqyPy6/U33Llaratj8mz6iqk6vqQVV1r6q6uKo26Ke59+jjBWZlx83kc8nbWyJV1R7Vnbuc3c838c/kRlX1yar6XnXnNdVPv3NVfbXfF8dV1Zb98L+qqvP7/fvRyQVW92XAf/f79ayq2qsf9aUkW/XrXzZ5vpH57/Ia6IdP/uye8vlM8/p7fVW9fGQd/1RVL1uN/bAmrOgcqKrqn0eOl+cld21pVlX/XlUH9fcvqarD6o7zpgdV1dJ0oeQrJvbDTI6t/vj9Sr9Nj6+q+1fVjknemuTp/bLuMcVz+uckr5s8cNLrcqOR18s5VfWsfvi+1X2Gn1ldC72NVn3TjsW9k/wyuf05TnUOe8+q+kJ1n0fnjuzXJ/fH45lV9c6RbXWnFn39PEv7+8+vqtP6ffHeqlpvtJjqPuv+JMlfjvyvemVr7eP9+AP62s6tqreMzHfDyP1nV9WR/f0j+9q+XlU/rKpn95O9Ocmyvo5XVHfu+9mq+kqS46vqg1W1/8gyj5rYHgtaa82fv7TWkmSjJGcnuSDJu5Ps0Q+/JMl9+vu7pPsGIkkOTXJGkntM8/jDSXbr798/yXf7+4ckeXl/f98kR4/7uc+HvyQHJvmv/v7Xk+yc5K+TvK4ftl6Se/X3bxiZb2mSc0cevyrJof39E5O8e2TcoUle1d/ffGT4G9K9ySfJd5Js1d/fZNzbZS3vg79K8vZpxq2f5N79/fsk+UG6fzKXJrk1yY79uI8nef5KtvGRST6fZL3+8UqPqSQHJfn36fZRum/+Pt/fv3eS9fv7T5pYXr+MHybZOMndk/woyf3SBdI/TrIkyeIkp6xoXQvlL8k9k3y/3wbvSXJIP/yJSc7u7x+aO79vHpTk35M8I90/X5uuYJ9NuT/6cZf0r7OlSZYnedzI6+Pw/rW3qH8d7T7ubbWu/KV/7+yP52OS/FmSTXPHD5/8cZJ/6e+/M8k/9PeflqTljs/Szfrbe6QLKDbvH7ckv9fff2uSvxt5nazwvdff7Ozf6Y6RKY6lnZN8eWTeTfrb45Ns399/bJKv9Pf/O8n+/f2DJ14nC+Fv4HEz+T1xz9zx2fS5JE/o72/UL2/PJNcm2brfX99I9wXbBunOhZb00z8vyfv7+z9NcrfR/Tap3r8emfZB6T7f7p5J50uT5hk9Rqd7DRyZO392T/V8VvT6O7OfdlGSi0bfC+bCX1Z8DvSsJF9Ody66Rb9Ntxzdv/10/57koP7+JbnjvOelSd43eVv3j1d6bPXb+gX9/Rcl+Ux//6D05ytTzHNiuv97vpIuMBv9H2j0dfmWJO8YmW/TdJ+/JyW5Zz/sb9N/HszlvyS3pftf8Hvpjqud++HTncM+K8kRI/NPnI/8JMn2/TQfH9lWk/fduf1r+8H9PtqgH/7uJH80qbZHJDlrmrp/O3ech67f77OJ18To/zzPTnJku+N4/ER/PD0kyQ8m79uR18ilueNze4+R18/GSS5Of868kP804eJ2rbUbqmrnJMvSvXl+rKpes5LZPtta+9U0j5+U5CFVt385e+8+pX9/upOKd6R7Y//vWXoKC90BSf6tv//R/vFnk7y//8bmM621s1dhuR+bZvjDquoNSTZJdzJ0XD/8lCRHVtXHk3xqFdY3X1WSN1bV7un+MdkqdzQJv3hk35yR7gM2mX4bJ8knWmu39feHHlMr20cbJ/lAVW2f7p/c0W/8jm+tXZskVXV+kgekO8E4sbV2VT/8Y0km+lBYsK+H1tqN/ba4Id3x+Kx++Feqa8Vy737Sye+jT0x38rpva+26fth023Gq/TH5suUftdZO7e/v2/+d1T/eKN2J34JpRbaa7lFVZ/f3T07yX0l2SPd5uWW6UPXifvzuSZ6ZJK21L1TVL0eW81dV9Yz+/v3S7YNfpLs0aOJb+zOS7DNFDSt6X2D1TXeM/Dh3PpZ+mOR3qupdSb6Q5Ev9Oc7jk3xi5Nznbv3t+5K8Oslnkrww3TfuC8WQ4ya563vihFPSXVZ8VJJPtdYu7bfzaa21S5OkX8/SJNckeViSL/fTrJfk8n455yQ5qqo+k25/TLZbknclSWvte1X1o3SfaddNMe2drOQ1kNz5s3uq5zPl66+1dlJV/aKqdkp37nBWa+0XK6tnDtktyUf6535lVX01yaOz8m068Xl3Rvr30ynM5NjadWT+D6UL7WfqDUn+Ll0wNJUnpb9CIElaa7+sqt9NF1Sc0r8OFqcLPOe6X7XWdkxu7xblg1X1sEx/DvuddK2w35IuiDm5bwF2cWvtwn45/5Mu7FuRvdMF9d/qt9c9kvxsQN2Pzp3PQ49K9xn8mZXM95nW2vIk51ffWnIaX26tXZ0krbWvVtW7q7si4Fnpvni9dUCt85JQiTvp3+xPTHJiVX0nyQvStaKYuFTy7pNmuXEFjxel+0bv5knT3FBVV1bVE5M8Jl0LG1ZDVW2W7h/Rh1dVS3fy1JL8Tbo31ael+4f0X9td++UY3b/JyvfxhCPTfQvw7eqaKu+ZJK21l1TXwfvTkpxRVTuvYyc+q+O8dN+CTOXAdN+g7Nxa+01VXZI7tvWvR6a7Ld2HaTLNNu7dvl9aaz8ZckxNtY8mTfKPSU5orT2jb5Z84si4ybWu8HNkgb8eku7ka/lKppl8jF2U5HfS/RNzerLCfTaT/TG6/Eryptbae2dWPpPcfsI9oQ8V/rW19tmq2jPdN7HT6qd5UpJdW2s3VXdJ+cR7wW9a//Vnpt+fR2b69wVW35THSP9eOPq++8uqemSS/dJdjvPcJC9Pcs3k10g//SnVXYKzZ7qWKudOnmYeG3rcTHne0Vp7c1V9IclT0/2zvl8/aqr3wUpyXmtt1ykW9bR050a/l+R1VfXwWfyncFGmeQ30Rl9DUz2fFb1Hvy9dq4n7pvsyaa5Z0TnQdFZ2Djqxb6c931jTx1b/RdAbkjxuwGyVLohYWR+nc1Zr7RvVdX+yJN1r9C7nsK21C6rqUf34N1TV8em+0J7OdPu7knygtfbaFcz7gyT3r6p7j3zhNqOnMsX6Joy+d1SmN/k96YNJnp8uTHzhgFrmLX0qcbuq2qFvmTBhx3SXU1ySLj1O+m/aZ+hLSf5yZPk7jox7X7o+Rka/sWHVPTvJh1prD2itLW2t3S/dt367J7mytXZEum3+qH7639Qd15tfma4jws2r6m7pOmufiXslubxfzu0hRlVt21r7ZmvtH5Jcle5b+IXiK0nuVnfuI+UR1fW9sHGSn/Ufxnula1GyMlNu42nM+JiawT7aOMll/f2DZlDnN5Ps0b+GNkjynAHrWihOTr8P+xPfn6/gpOhH6d5rP1hVD+3nma3teFySF/Xfpqeqtqqq31rFZdEZPV5eMDL8pPSdfVbX0eumI9P/sg+UHpRh/6gkw94XGG5Gx0j/z9ai1trR6VoxPKo/pi+uquf001QfPE34YLquAbTQnv64mVb/Pvid1tpbknwr3aVp0/l+kiV9a4tU96tRD62ug/X7tdZOSNfyZON0rYFGjb5fPzBdFw7fn0mNM3gNrOz5rOj19+kkT07XKmMutlBc0TnQyUmeV1Xr9S08dk9yWrrPu4dU1d2q+8XSvWewnutz186ZV3ZsfT13tCY6sK9niDekaw01lS9n5Ic4qmrTJKcmeUJVbdcPu2fNgV8CHqL/fFovXSvaKc9hq+q3k9zUWvufdP1PPSrdpXNLq2rbflGjwdol/TTpw6ht+uHHJ3n2xGu9qjarqjudJ7fWbkrXyvHfqmpxP92S/lg7Ld156H2q64vpgCRf7We9sqoe3B/7z8jKTfX6muzIdF8ipLV2/gyWOe8JlRi1UbpLXs6vqnPSNds8NMlh6Q7g09N9UzBTf5Vkl+o6rTs/d/61j8/263NiNTsOSHeyMerodG96366qs9L1JzBxedzhSc6pqqNaa79J8vp0b8hfTvdhMBN/ny5MOGXSPP9cfUd56T7Evz386ayb+hYGz0jypOp+Tve8dL+ccUWSo9IdD99J8keZ2XaebhtPZcgxtbJ99NYkb+pfNytt0dpauzzde8U3+lq/O2BdC8WhSXbu31vfnJX8E9Va+166E99P9Cdms7IdW2tfSnfi/Y3+tfjJzP4vpyw0h6bbT2ck+fnI8MOS7N6/Dzwz3eVTSXJskvWr6rvpXgunZpgh7wvMUHW/6vPrAcfIVuladZ+dLtCf+Ib9wCQvrqpvp2u58fSReY5KFy5+ZI08iXXLoZn6uFmRl1fXCe85SX6T5H+nm7C1dku6L9ze0u+Ls9NdlrZekv/p9+1ZSd7ZJv1SZrr+XBb103wsXR8/v86KrZ87Wj2s6DWwwuezotdf/5xOSPLxufiF7ErOgT6d7rLDb6cLn17dWruitfaTdH3unNvfnjXlwu/sc0meUXfuMH1lx9ZfJnlhv63/MMnLBj63L6b7Qmcqb0iyab8vv51kr/4yrIOSfKRf5zey4hB0rpj4IZ+z0732X9C/1qY7h314ktP66Q9J8ob+CpWDk3yhqs7MnS9jOzrJZv1r4y/S9eM7Ecz8XbrLiM9J9//IllPU93fp9sP5/fnQ55Nc15+Hvibd8fHtJGe01o7p53lNP93Xc8clsCtyTpLbqut8/BVTTdBauzLdua7/Y3sTHeTBWlVVu6TrzG/aX88AZs4xBbDq+tYkR7TWHrMG1/HsJE9vrf3hmloH41FVn073+vniGlzHoiRnJnnORH81dBxbc1d1rbNf1Vqb6ZUQc151v0T3nXQtVK8ddz1zgT6VWOuq6/z7z6LZPswKxxTAqquql6RrXf3yNbiOdyV5Srq+R5hH+tYbF6Tr9mFNreMh6VpbfFqgdGeOLdamqnpSusvw3i5QuoOWSgAAAAAMpk8lAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCABasqlpaVa2qjhx3LQAA6xqhEgAAAACDCZUAAAAAGEyoBAAsSFV1aJKL+4cv6C+Dm/g7aGS6/arqi1X186r6dVVdVFX/XFWbTLHMvarq8Ko6v6quq6pfVdW5VXVIVd19qhr69e1ZVQdU1RlVdVNV/bSq/rWq7tZP98SqOrFf5i+r6kNVtfkUy3tEVX2kqi7pa72qqs6sqndU1QaztOkAAJIk1Vobdw0AAGtdVe2ZZP8kL0vy7SSfGRn9mdba2VV1SJJDk1yd5PNJfpbkEUn2TXJ+kl1ba9eNLPPYJA9K8vUklyW5e5InJNkpyYlJntRau21k+kOTHJLkU0me0tdweb/8hyX5QJJjknw0yReS/DDJ45PsmuTY1tpTRpb1iCTfTNKSfDZdYHbvJNsl2SvJZq21G1ZlWwEATEWoBAAsWFW1NF348oHW2kGTxu2V5CtJvpHkqa21a0bGHZTkv5O8o7X2ipHhv5Pk4jbpBKuq/jHJ3yX5/dbax0aGH5ouVLouyeNaa9/th98tyZnpAqprkjyztfbVftyiJMcleVKSnVprZ/fD/yXJK5Ps31o7ZtL6N01ybWtt+bAtBAAwPZe/AQBM7a/62z8ZDZSSpLV2ZJKzkxw4afgPJwdKvbf3t/tNs653TgRK/XJ+neRj6c7VvjARKPXjlif5n/7hI6dY1q8mD2it/VKgBADMtvXHXQAAwBy1a5LfJHlOVT1nivGLkyypqs1ba79Ikqq6Z7rL6Z6R5IFJ7pWkRubZapp1nT7FsJ/2t2dMMe6y/nbrkWEf69f9mar6ZJL/l+SU1tpF06wTAGC1CJUAAKa2ebpzpUNWMt1GSX7Rd4T9lSSPSXJuupDnqnTBVPrl3G2aZVw7xbBbZzDu9s63W2unVdWyJK9L8uwkf5gkVfX9JIe11j6ykucBADCIUAkAYGrXJlnUWttshtM/PV2gdGRr7YWjI6pqy6w8nFptrbVvJPndvk+mnZM8OclfJvlwVV3VWvt/a7oGAGDh0KcSALCQTfwS23pTjDs1yaZV9dAZLmu7/vZTU4zbY2hhq6O19uvW2tdba/+QO/qGevrarAEAmP+ESgDAQvbLJC3J/acYN9G59hFV9duTR1bVPavqcSODLulv95w03e8kectqV7oSVfX4qrrHFKO26G9vWtM1AAALi8vfAIAFq7V2Q1V9M8myqjoqyQXpWi99trV2fFW9JsmbklxYVV9McnG6PpQekK710dfSXWKWJJ9L8oMkr6yqhyc5K11Y9btJvpCpg6vZ9OokT6yqk/s6b0jy0CRPSReeHb6G1w8ALDBCJQBgofvDdK2SnpzkgHS/1nZpknNaa2+pqlPSXUK2W7pLyK5N9+trhyf58MRCWms3VtUTk7w5XWulZUl+mOQfk/xrkuet4efx7nTh0WP7Wtfvn8e7k/xLa+1Ha3j9AMACU621cdcAAAAAwDpGn0oAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYLD/D/8YU/7h5BUPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.bar(res.head(9).index, res['won'].head(9), color = 'green', label = 'won')\n",
    "ax.bar(res.head(9).index, res['lost'].head(9), color = 'red', label = 'lost')\n",
    "\n",
    "leg = ax.legend(loc=2, ncol=2, numpoints=1, fontsize=15, shadow=True)\n",
    "\n",
    "ax.set_title('win percentage', size= 20)\n",
    "ax.set_xlabel('teams',fontsize=20)\n",
    "ax.set_ylabel('games played',fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "Which teams are the least successful ones? (winning percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'games played')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.bar(res.tail(9).index, res['won'].tail(9), color = 'green', label = 'won')\n",
    "ax.bar(res.tail(9).index, res['lost'].tail(9), color = 'red', label = 'lost')\n",
    "\n",
    "leg = ax.legend(loc=2, ncol=2, numpoints=1, fontsize=15, shadow=True)\n",
    "\n",
    "ax.set_title('win percentage', size= 25)\n",
    "ax.set_xlabel('teams')\n",
    "ax.set_ylabel('games played')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "Which months through the history had more matches? Is it June, July, or others? Does the number of matches change from month to month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "      <td>2218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "      <td>2564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "      <td>4039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "      <td>2770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "      <td>2752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "      <td>4296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "      <td>4642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  home_team  away_team  home_score  away_score  tournament  city  \\\n",
       "month                                                                         \n",
       "Jan    2218       2218       2218        2218        2218        2218  2218   \n",
       "Feb    2564       2564       2564        2564        2564        2564  2564   \n",
       "Mar    4039       4039       4039        4039        4039        4039  4039   \n",
       "Apr    2538       2538       2538        2538        2538        2538  2538   \n",
       "May    3231       3231       3231        3231        3231        3231  3231   \n",
       "Jun    5969       5969       5969        5969        5969        5969  5969   \n",
       "Jul    2770       2770       2770        2770        2770        2770  2770   \n",
       "Aug    2752       2752       2752        2752        2752        2752  2752   \n",
       "Sep    4296       4296       4296        4296        4296        4296  4296   \n",
       "Oct    4642       4642       4642        4642        4642        4642  4642   \n",
       "Nov    4293       4293       4293        4293        4293        4293  4293   \n",
       "Dec    2274       2274       2274        2274        2274        2274  2274   \n",
       "\n",
       "       country  neutral  year  \n",
       "month                          \n",
       "Jan       2218     2218  2218  \n",
       "Feb       2564     2564  2564  \n",
       "Mar       4039     4039  4039  \n",
       "Apr       2538     2538  2538  \n",
       "May       3231     3231  3231  \n",
       "Jun       5969     5969  5969  \n",
       "Jul       2770     2770  2770  \n",
       "Aug       2752     2752  2752  \n",
       "Sep       4296     4296  4296  \n",
       "Oct       4642     4642  4642  \n",
       "Nov       4293     4293  4293  \n",
       "Dec       2274     2274  2274  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = data\n",
    "months['month'] = data['date'].dt.month\n",
    "months = months.groupby('month').count()\n",
    "months = months.rename(index = lambda x: calendar.month_abbr[x])\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'games')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.plot(months.index, months['date'])\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "\n",
    "ax.set_title('games each month', size = 20)\n",
    "ax.set_xlabel('months', size = 20)\n",
    "ax.set_ylabel('games', size = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "Which teams played against each other the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "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></th>\n",
       "      <th>date</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Abkhazia</th>\n",
       "      <th>Artsakh</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chagos Islands</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chameria</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kárpátalja</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern Cyprus</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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 rowspan=\"5\" valign=\"top\">Åland Islands</th>\n",
       "      <th>Jersey</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Menorca</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saare County</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shetland</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ynys Môn</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10976 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               date  home_score  away_score  tournament  city  \\\n",
       "home_team     away_team                                                         \n",
       "Abkhazia      Artsakh             1           1           1           1     1   \n",
       "              Chagos Islands      1           1           1           1     1   \n",
       "              Chameria            2           2           2           2     2   \n",
       "              Kárpátalja          1           1           1           1     1   \n",
       "              Northern Cyprus     2           2           2           2     2   \n",
       "...                             ...         ...         ...         ...   ...   \n",
       "Åland Islands Jersey              2           2           2           2     2   \n",
       "              Menorca             2           2           2           2     2   \n",
       "              Saare County        2           2           2           2     2   \n",
       "              Shetland            4           4           4           4     4   \n",
       "              Ynys Môn            2           2           2           2     2   \n",
       "\n",
       "                               country  neutral  year  month  \n",
       "home_team     away_team                                       \n",
       "Abkhazia      Artsakh                1        1     1      1  \n",
       "              Chagos Islands         1        1     1      1  \n",
       "              Chameria               2        2     2      2  \n",
       "              Kárpátalja             1        1     1      1  \n",
       "              Northern Cyprus        2        2     2      2  \n",
       "...                                ...      ...   ...    ...  \n",
       "Åland Islands Jersey                 2        2     2      2  \n",
       "              Menorca                2        2     2      2  \n",
       "              Saare County           2        2     2      2  \n",
       "              Shetland               4        4     4      4  \n",
       "              Ynys Môn               2        2     2      2  \n",
       "\n",
       "[10976 rows x 9 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faced = data\n",
    "faced = faced.groupby(['home_team','away_team']).count()\n",
    "faced.sort_values('date', ascending = False).sort_index()\n",
    "#faced.groupby(level=0).head(1)\n",
    "#df.groupby(level=0).head(1)\n",
    "faced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "faced = faced.sort_values('date', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "Apply your creativity to show some additional insights from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_score = data['home_score']\n",
    "away_score = data['away_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82918f8d90>"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.hist(home_score, bins = 10, label = 'home score',align='left', alpha = 0.5)\n",
    "ax.hist(away_score, bins = 10, label = 'away score',align='left', alpha = 0.5)\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task (Stretch)\n",
    "Create these graphs in Tableau as well."
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}