European Soccer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sunrise-syntax",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "viral-florida",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>sqlite_sequence</td>\n",
       "      <td>sqlite_sequence</td>\n",
       "      <td>4</td>\n",
       "      <td>CREATE TABLE sqlite_sequence(name,seq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>Player_Attributes</td>\n",
       "      <td>Player_Attributes</td>\n",
       "      <td>11</td>\n",
       "      <td>CREATE TABLE \"Player_Attributes\" (\\n\\t`id`\\tIN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>Player</td>\n",
       "      <td>Player</td>\n",
       "      <td>14</td>\n",
       "      <td>CREATE TABLE `Player` (\\n\\t`id`\\tINTEGER PRIMA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>table</td>\n",
       "      <td>Match</td>\n",
       "      <td>Match</td>\n",
       "      <td>18</td>\n",
       "      <td>CREATE TABLE `Match` (\\n\\t`id`\\tINTEGER PRIMAR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>table</td>\n",
       "      <td>League</td>\n",
       "      <td>League</td>\n",
       "      <td>24</td>\n",
       "      <td>CREATE TABLE `League` (\\n\\t`id`\\tINTEGER PRIMA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>table</td>\n",
       "      <td>Country</td>\n",
       "      <td>Country</td>\n",
       "      <td>26</td>\n",
       "      <td>CREATE TABLE `Country` (\\n\\t`id`\\tINTEGER PRIM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>table</td>\n",
       "      <td>Team</td>\n",
       "      <td>Team</td>\n",
       "      <td>29</td>\n",
       "      <td>CREATE TABLE \"Team\" (\\n\\t`id`\\tINTEGER PRIMARY...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>table</td>\n",
       "      <td>Team_Attributes</td>\n",
       "      <td>Team_Attributes</td>\n",
       "      <td>2</td>\n",
       "      <td>CREATE TABLE `Team_Attributes` (\\n\\t`id`\\tINTE...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type               name           tbl_name  rootpage  \\\n",
       "0  table    sqlite_sequence    sqlite_sequence         4   \n",
       "1  table  Player_Attributes  Player_Attributes        11   \n",
       "2  table             Player             Player        14   \n",
       "3  table              Match              Match        18   \n",
       "4  table             League             League        24   \n",
       "5  table            Country            Country        26   \n",
       "6  table               Team               Team        29   \n",
       "7  table    Team_Attributes    Team_Attributes         2   \n",
       "\n",
       "                                                 sql  \n",
       "0             CREATE TABLE sqlite_sequence(name,seq)  \n",
       "1  CREATE TABLE \"Player_Attributes\" (\\n\\t`id`\\tIN...  \n",
       "2  CREATE TABLE `Player` (\\n\\t`id`\\tINTEGER PRIMA...  \n",
       "3  CREATE TABLE `Match` (\\n\\t`id`\\tINTEGER PRIMAR...  \n",
       "4  CREATE TABLE `League` (\\n\\t`id`\\tINTEGER PRIMA...  \n",
       "5  CREATE TABLE `Country` (\\n\\t`id`\\tINTEGER PRIM...  \n",
       "6  CREATE TABLE \"Team\" (\\n\\t`id`\\tINTEGER PRIMARY...  \n",
       "7  CREATE TABLE `Team_Attributes` (\\n\\t`id`\\tINTE...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = sqlite3.connect('database.sqlite')\n",
    "\n",
    "tables = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM sqlite_master\n",
    "                        WHERE type='table';\"\"\", conn)\n",
    "tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "internal-dakota",
   "metadata": {},
   "source": [
    "### List of Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "funded-wound",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       id         name\n",
       "0       1      Belgium\n",
       "1    1729      England\n",
       "2    4769       France\n",
       "3    7809      Germany\n",
       "4   10257        Italy\n",
       "5   13274  Netherlands\n",
       "6   15722       Poland\n",
       "7   17642     Portugal\n",
       "8   19694     Scotland\n",
       "9   21518        Spain\n",
       "10  24558  Switzerland"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM Country;\"\"\", conn)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "front-vinyl",
   "metadata": {},
   "source": [
    "### List of Leagues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "polyphonic-conditioning",
   "metadata": {},
   "outputs": [
    {
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       "      <th>country_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  country_id                      name\n",
       "0       1           1    Belgium Jupiler League\n",
       "1    1729        1729    England Premier League\n",
       "2    4769        4769            France Ligue 1\n",
       "3    7809        7809     Germany 1. Bundesliga\n",
       "4   10257       10257             Italy Serie A\n",
       "5   13274       13274    Netherlands Eredivisie\n",
       "6   15722       15722        Poland Ekstraklasa\n",
       "7   17642       17642  Portugal Liga ZON Sagres\n",
       "8   19694       19694   Scotland Premier League\n",
       "9   21518       21518           Spain LIGA BBVA\n",
       "10  24558       24558  Switzerland Super League"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leagues = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM League;\"\"\", conn)\n",
    "leagues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limiting-pillow",
   "metadata": {},
   "source": [
    "### List of leagues and their country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "positive-daughter",
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>name</th>\n",
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       "      <th>name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>4769</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  country_id                      name     id         name\n",
       "0       1           1    Belgium Jupiler League      1      Belgium\n",
       "1    1729        1729    England Premier League   1729      England\n",
       "2    4769        4769            France Ligue 1   4769       France\n",
       "3    7809        7809     Germany 1. Bundesliga   7809      Germany\n",
       "4   10257       10257             Italy Serie A  10257        Italy\n",
       "5   13274       13274    Netherlands Eredivisie  13274  Netherlands\n",
       "6   15722       15722        Poland Ekstraklasa  15722       Poland\n",
       "7   17642       17642  Portugal Liga ZON Sagres  17642     Portugal\n",
       "8   19694       19694   Scotland Premier League  19694     Scotland\n",
       "9   21518       21518           Spain LIGA BBVA  21518        Spain\n",
       "10  24558       24558  Switzerland Super League  24558  Switzerland"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leagues_cont = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM League\n",
    "                        JOIN Country ON Country.id = League.country_id;\"\"\", conn)\n",
    "leagues_cont"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perfect-keyboard",
   "metadata": {},
   "source": [
    "### List of teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "classified-hopkins",
   "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>id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16848</td>\n",
       "      <td>8350</td>\n",
       "      <td>29</td>\n",
       "      <td>1. FC Kaiserslautern</td>\n",
       "      <td>KAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15624</td>\n",
       "      <td>8722</td>\n",
       "      <td>31</td>\n",
       "      <td>1. FC Köln</td>\n",
       "      <td>FCK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16239</td>\n",
       "      <td>8165</td>\n",
       "      <td>171</td>\n",
       "      <td>1. FC Nürnberg</td>\n",
       "      <td>NUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16243</td>\n",
       "      <td>9905</td>\n",
       "      <td>169</td>\n",
       "      <td>1. FSV Mainz 05</td>\n",
       "      <td>MAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11817</td>\n",
       "      <td>8576</td>\n",
       "      <td>614</td>\n",
       "      <td>AC Ajaccio</td>\n",
       "      <td>AJA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11074</td>\n",
       "      <td>108893</td>\n",
       "      <td>111989</td>\n",
       "      <td>AC Arles-Avignon</td>\n",
       "      <td>ARL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49116</td>\n",
       "      <td>6493</td>\n",
       "      <td>1714</td>\n",
       "      <td>AC Bellinzona</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26560</td>\n",
       "      <td>10217</td>\n",
       "      <td>650</td>\n",
       "      <td>ADO Den Haag</td>\n",
       "      <td>HAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9537</td>\n",
       "      <td>8583</td>\n",
       "      <td>57</td>\n",
       "      <td>AJ Auxerre</td>\n",
       "      <td>AUX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9547</td>\n",
       "      <td>9829</td>\n",
       "      <td>69</td>\n",
       "      <td>AS Monaco</td>\n",
       "      <td>MON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  team_api_id  team_fifa_api_id        team_long_name team_short_name\n",
       "0  16848         8350                29  1. FC Kaiserslautern             KAI\n",
       "1  15624         8722                31            1. FC Köln             FCK\n",
       "2  16239         8165               171        1. FC Nürnberg             NUR\n",
       "3  16243         9905               169       1. FSV Mainz 05             MAI\n",
       "4  11817         8576               614            AC Ajaccio             AJA\n",
       "5  11074       108893            111989      AC Arles-Avignon             ARL\n",
       "6  49116         6493              1714         AC Bellinzona             BEL\n",
       "7  26560        10217               650          ADO Den Haag             HAA\n",
       "8   9537         8583                57            AJ Auxerre             AUX\n",
       "9   9547         9829                69             AS Monaco             MON"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM Team\n",
    "                        ORDER BY team_long_name\n",
    "                        LIMIT 10;\"\"\", conn)\n",
    "teams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "first-generator",
   "metadata": {},
   "source": [
    "### List of players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "subsequent-riverside",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>505942</td>\n",
       "      <td>Aaron Appindangoye</td>\n",
       "      <td>218353</td>\n",
       "      <td>1992-02-29 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>189615</td>\n",
       "      <td>1989-12-15 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>186170</td>\n",
       "      <td>1991-05-13 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>30572</td>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>140161</td>\n",
       "      <td>1982-05-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>17725</td>\n",
       "      <td>1979-11-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>154</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11055</th>\n",
       "      <td>11071</td>\n",
       "      <td>26357</td>\n",
       "      <td>Zoumana Camara</td>\n",
       "      <td>2488</td>\n",
       "      <td>1979-04-03 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11056</th>\n",
       "      <td>11072</td>\n",
       "      <td>111182</td>\n",
       "      <td>Zsolt Laczko</td>\n",
       "      <td>164680</td>\n",
       "      <td>1986-12-18 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11057</th>\n",
       "      <td>11073</td>\n",
       "      <td>36491</td>\n",
       "      <td>Zsolt Low</td>\n",
       "      <td>111191</td>\n",
       "      <td>1979-04-29 00:00:00</td>\n",
       "      <td>180.34</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11058</th>\n",
       "      <td>11074</td>\n",
       "      <td>35506</td>\n",
       "      <td>Zurab Khizanishvili</td>\n",
       "      <td>47058</td>\n",
       "      <td>1981-10-06 00:00:00</td>\n",
       "      <td>185.42</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11059</th>\n",
       "      <td>11075</td>\n",
       "      <td>39902</td>\n",
       "      <td>Zvjezdan Misimovic</td>\n",
       "      <td>102359</td>\n",
       "      <td>1982-06-05 00:00:00</td>\n",
       "      <td>180.34</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11060 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  player_api_id          player_name  player_fifa_api_id  \\\n",
       "0          1         505942   Aaron Appindangoye              218353   \n",
       "1          2         155782      Aaron Cresswell              189615   \n",
       "2          3         162549          Aaron Doran              186170   \n",
       "3          4          30572        Aaron Galindo              140161   \n",
       "4          5          23780         Aaron Hughes               17725   \n",
       "...      ...            ...                  ...                 ...   \n",
       "11055  11071          26357       Zoumana Camara                2488   \n",
       "11056  11072         111182         Zsolt Laczko              164680   \n",
       "11057  11073          36491            Zsolt Low              111191   \n",
       "11058  11074          35506  Zurab Khizanishvili               47058   \n",
       "11059  11075          39902   Zvjezdan Misimovic              102359   \n",
       "\n",
       "                  birthday  height  weight  \n",
       "0      1992-02-29 00:00:00  182.88     187  \n",
       "1      1989-12-15 00:00:00  170.18     146  \n",
       "2      1991-05-13 00:00:00  170.18     163  \n",
       "3      1982-05-08 00:00:00  182.88     198  \n",
       "4      1979-11-08 00:00:00  182.88     154  \n",
       "...                    ...     ...     ...  \n",
       "11055  1979-04-03 00:00:00  182.88     168  \n",
       "11056  1986-12-18 00:00:00  182.88     176  \n",
       "11057  1979-04-29 00:00:00  180.34     154  \n",
       "11058  1981-10-06 00:00:00  185.42     172  \n",
       "11059  1982-06-05 00:00:00  180.34     176  \n",
       "\n",
       "[11060 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM Player;\"\"\", conn)\n",
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "reasonable-conducting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      int64\n",
       "player_api_id           int64\n",
       "player_name            object\n",
       "player_fifa_api_id      int64\n",
       "birthday               object\n",
       "height                float64\n",
       "weight                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "canadian-ceremony",
   "metadata": {},
   "source": [
    "### Calculate the age of players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "opening-remains",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>505942</td>\n",
       "      <td>Aaron Appindangoye</td>\n",
       "      <td>218353</td>\n",
       "      <td>1992-02-29 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>189615</td>\n",
       "      <td>1989-12-15 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>146</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>186170</td>\n",
       "      <td>1991-05-13 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>163</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>30572</td>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>140161</td>\n",
       "      <td>1982-05-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>198</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>17725</td>\n",
       "      <td>1979-11-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>154</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_api_id         player_name  player_fifa_api_id  \\\n",
       "0   1         505942  Aaron Appindangoye              218353   \n",
       "1   2         155782     Aaron Cresswell              189615   \n",
       "2   3         162549         Aaron Doran              186170   \n",
       "3   4          30572       Aaron Galindo              140161   \n",
       "4   5          23780        Aaron Hughes               17725   \n",
       "\n",
       "              birthday  height  weight  birth_year  \n",
       "0  1992-02-29 00:00:00  182.88     187        1992  \n",
       "1  1989-12-15 00:00:00  170.18     146        1989  \n",
       "2  1991-05-13 00:00:00  170.18     163        1991  \n",
       "3  1982-05-08 00:00:00  182.88     198        1982  \n",
       "4  1979-11-08 00:00:00  182.88     154        1979  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players['birth_year'] = pd.to_datetime(players['birthday'])\n",
    "players['birth_year'] =players['birth_year'].dt.year\n",
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "tired-plymouth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>155782</td>\n",
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       "      <td>189615</td>\n",
       "      <td>1989-12-15 00:00:00</td>\n",
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       "      <td>1989</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>186170</td>\n",
       "      <td>1991-05-13 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>163</td>\n",
       "      <td>1991</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>30572</td>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>140161</td>\n",
       "      <td>1982-05-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>198</td>\n",
       "      <td>1982</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>17725</td>\n",
       "      <td>1979-11-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>154</td>\n",
       "      <td>1979</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_api_id         player_name  player_fifa_api_id  \\\n",
       "0   1         505942  Aaron Appindangoye              218353   \n",
       "1   2         155782     Aaron Cresswell              189615   \n",
       "2   3         162549         Aaron Doran              186170   \n",
       "3   4          30572       Aaron Galindo              140161   \n",
       "4   5          23780        Aaron Hughes               17725   \n",
       "\n",
       "              birthday  height  weight  birth_year  age  \n",
       "0  1992-02-29 00:00:00  182.88     187        1992   25  \n",
       "1  1989-12-15 00:00:00  170.18     146        1989   28  \n",
       "2  1991-05-13 00:00:00  170.18     163        1991   26  \n",
       "3  1982-05-08 00:00:00  182.88     198        1982   35  \n",
       "4  1979-11-08 00:00:00  182.88     154        1979   38  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data set is for 4 years ago \n",
    "players['age'] = players['birth_year'].apply(lambda x: 2017-x)\n",
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "typical-expression",
   "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": [
    "sns.histplot(data= players, x= 'age',bins = 30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "hundred-minute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data = players,x='height',kind=\"kde\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "electrical-blues",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data = players,x='weight',kind=\"kde\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automatic-street",
   "metadata": {},
   "source": [
    "### How many players are taller than 2.00 meters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "activated-globe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT(Player.id)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNT(Player.id)\n",
       "0                22"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.read_sql(\"\"\"SELECT COUNT(Player.id)\n",
    "                    FROM Player\n",
    "                    WHERE Player.height >= 200\"\"\",conn)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "straight-bargain",
   "metadata": {},
   "source": [
    "### Who are the 10 heaviest players?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "passive-porcelain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5908</td>\n",
       "      <td>148325</td>\n",
       "      <td>Kristof van Hout</td>\n",
       "      <td>185306</td>\n",
       "      <td>1987-02-09 00:00:00</td>\n",
       "      <td>208.28</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10313</td>\n",
       "      <td>27313</td>\n",
       "      <td>Tim Wiese</td>\n",
       "      <td>53012</td>\n",
       "      <td>1981-12-17 00:00:00</td>\n",
       "      <td>193.04</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4884</td>\n",
       "      <td>5044</td>\n",
       "      <td>Jeroen Verhoeven</td>\n",
       "      <td>137692</td>\n",
       "      <td>1980-04-30 00:00:00</td>\n",
       "      <td>198.12</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4412</td>\n",
       "      <td>27267</td>\n",
       "      <td>Ishmael Miller</td>\n",
       "      <td>169725</td>\n",
       "      <td>1987-03-05 00:00:00</td>\n",
       "      <td>193.04</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1598</td>\n",
       "      <td>101584</td>\n",
       "      <td>Cassio</td>\n",
       "      <td>167958</td>\n",
       "      <td>1987-06-06 00:00:00</td>\n",
       "      <td>195.58</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1822</td>\n",
       "      <td>19020</td>\n",
       "      <td>Christopher Samba</td>\n",
       "      <td>46590</td>\n",
       "      <td>1984-03-28 00:00:00</td>\n",
       "      <td>193.04</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5976</td>\n",
       "      <td>210822</td>\n",
       "      <td>Lars Unnerstall</td>\n",
       "      <td>199833</td>\n",
       "      <td>1990-07-20 00:00:00</td>\n",
       "      <td>198.12</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6772</td>\n",
       "      <td>30669</td>\n",
       "      <td>Marcus Hahnemann</td>\n",
       "      <td>53785</td>\n",
       "      <td>1972-06-15 00:00:00</td>\n",
       "      <td>190.50</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>40005</td>\n",
       "      <td>Abdoulaye Faye</td>\n",
       "      <td>100329</td>\n",
       "      <td>1978-02-26 00:00:00</td>\n",
       "      <td>187.96</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3030</td>\n",
       "      <td>33060</td>\n",
       "      <td>Enoch Showunmi</td>\n",
       "      <td>157589</td>\n",
       "      <td>1982-04-21 00:00:00</td>\n",
       "      <td>195.58</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  player_api_id        player_name  player_fifa_api_id  \\\n",
       "0   5908         148325   Kristof van Hout              185306   \n",
       "1  10313          27313          Tim Wiese               53012   \n",
       "2   4884           5044   Jeroen Verhoeven              137692   \n",
       "3   4412          27267     Ishmael Miller              169725   \n",
       "4   1598         101584             Cassio              167958   \n",
       "5   1822          19020  Christopher Samba               46590   \n",
       "6   5976         210822    Lars Unnerstall              199833   \n",
       "7   6772          30669   Marcus Hahnemann               53785   \n",
       "8     45          40005     Abdoulaye Faye              100329   \n",
       "9   3030          33060     Enoch Showunmi              157589   \n",
       "\n",
       "              birthday  height  weight  \n",
       "0  1987-02-09 00:00:00  208.28     243  \n",
       "1  1981-12-17 00:00:00  193.04     243  \n",
       "2  1980-04-30 00:00:00  198.12     227  \n",
       "3  1987-03-05 00:00:00  193.04     225  \n",
       "4  1987-06-06 00:00:00  195.58     220  \n",
       "5  1984-03-28 00:00:00  193.04     220  \n",
       "6  1990-07-20 00:00:00  198.12     220  \n",
       "7  1972-06-15 00:00:00  190.50     220  \n",
       "8  1978-02-26 00:00:00  187.96     218  \n",
       "9  1982-04-21 00:00:00  195.58     218  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_heaviest = pd.read_sql('''SELECT *\n",
    "            FROM Player\n",
    "            ORDER BY Player.weight DESC\n",
    "            LIMIT 10''',conn)\n",
    "df_heaviest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "driving-assets",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>...</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
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       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>2015-11-19 00:00:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-09-21 00:00:00</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
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       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-03-20 00:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_fifa_api_id  player_api_id                 date  overall_rating  \\\n",
       "0   1              218353         505942  2016-02-18 00:00:00            67.0   \n",
       "1   2              218353         505942  2015-11-19 00:00:00            67.0   \n",
       "2   3              218353         505942  2015-09-21 00:00:00            62.0   \n",
       "3   4              218353         505942  2015-03-20 00:00:00            61.0   \n",
       "4   5              218353         505942  2007-02-22 00:00:00            61.0   \n",
       "\n",
       "   potential preferred_foot attacking_work_rate defensive_work_rate  crossing  \\\n",
       "0       71.0          right              medium              medium      49.0   \n",
       "1       71.0          right              medium              medium      49.0   \n",
       "2       66.0          right              medium              medium      49.0   \n",
       "3       65.0          right              medium              medium      48.0   \n",
       "4       65.0          right              medium              medium      48.0   \n",
       "\n",
       "   ...  vision  penalties  marking  standing_tackle  sliding_tackle  \\\n",
       "0  ...    54.0       48.0     65.0             69.0            69.0   \n",
       "1  ...    54.0       48.0     65.0             69.0            69.0   \n",
       "2  ...    54.0       48.0     65.0             66.0            69.0   \n",
       "3  ...    53.0       47.0     62.0             63.0            66.0   \n",
       "4  ...    53.0       47.0     62.0             63.0            66.0   \n",
       "\n",
       "   gk_diving  gk_handling  gk_kicking  gk_positioning  gk_reflexes  \n",
       "0        6.0         11.0        10.0             8.0          8.0  \n",
       "1        6.0         11.0        10.0             8.0          8.0  \n",
       "2        6.0         11.0        10.0             8.0          8.0  \n",
       "3        5.0         10.0         9.0             7.0          7.0  \n",
       "4        5.0         10.0         9.0             7.0          7.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_att = pd.read_sql('''SELECT *  FROM Player_Attributes ''',conn)\n",
    "player_att.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "perfect-stock",
   "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>player_name</th>\n",
       "      <th>last_report</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>preferred_foot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron Appindangoye</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187</td>\n",
       "      <td>67</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>2016-04-21 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>146</td>\n",
       "      <td>74</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>2016-01-07 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>163</td>\n",
       "      <td>65</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>2016-04-21 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>198</td>\n",
       "      <td>69</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>2015-12-24 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>154</td>\n",
       "      <td>70</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player_name          last_report  height  weight  overall_rating  \\\n",
       "0  Aaron Appindangoye  2016-02-18 00:00:00  182.88     187              67   \n",
       "1     Aaron Cresswell  2016-04-21 00:00:00  170.18     146              74   \n",
       "2         Aaron Doran  2016-01-07 00:00:00  170.18     163              65   \n",
       "3       Aaron Galindo  2016-04-21 00:00:00  182.88     198              69   \n",
       "4        Aaron Hughes  2015-12-24 00:00:00  182.88     154              70   \n",
       "\n",
       "  preferred_foot  \n",
       "0          right  \n",
       "1           left  \n",
       "2          right  \n",
       "3          right  \n",
       "4          right  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_rate = pd.read_sql('''SELECT Player.player_name,MAX(date) as last_report ,Player.height,\n",
    "Player.weight,overall_rating,preferred_foot \n",
    "FROM Player_Attributes \n",
    "INNER JOIN Player ON Player_Attributes.player_fifa_api_id = Player.player_fifa_api_id \n",
    "GROUP BY Player.player_name''',conn)\n",
    "player_rate.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modified-carry",
   "metadata": {},
   "source": [
    "### Select players that their overall rating is over 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "natural-henry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>player_name</th>\n",
       "      <th>last_report</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>preferred_foot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Andres Iniesta</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>150</td>\n",
       "      <td>88</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>Arjen Robben</td>\n",
       "      <td>2016-01-28 00:00:00</td>\n",
       "      <td>180.34</td>\n",
       "      <td>176</td>\n",
       "      <td>89</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>185.42</td>\n",
       "      <td>176</td>\n",
       "      <td>93</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>David De Gea</td>\n",
       "      <td>2016-04-21 00:00:00</td>\n",
       "      <td>193.04</td>\n",
       "      <td>181</td>\n",
       "      <td>87</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2782</th>\n",
       "      <td>Eden Hazard</td>\n",
       "      <td>2016-01-28 00:00:00</td>\n",
       "      <td>172.72</td>\n",
       "      <td>163</td>\n",
       "      <td>88</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>Gareth Bale</td>\n",
       "      <td>2016-04-28 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>163</td>\n",
       "      <td>87</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4510</th>\n",
       "      <td>James Rodriguez</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>180.34</td>\n",
       "      <td>165</td>\n",
       "      <td>87</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4796</th>\n",
       "      <td>Jerome Boateng</td>\n",
       "      <td>2016-01-14 00:00:00</td>\n",
       "      <td>193.04</td>\n",
       "      <td>198</td>\n",
       "      <td>87</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6053</th>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>2015-12-17 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>159</td>\n",
       "      <td>94</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6245</th>\n",
       "      <td>Luis Suarez</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187</td>\n",
       "      <td>90</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6255</th>\n",
       "      <td>Luka Modric</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>175.26</td>\n",
       "      <td>143</td>\n",
       "      <td>87</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6417</th>\n",
       "      <td>Manuel Neuer</td>\n",
       "      <td>2016-04-21 00:00:00</td>\n",
       "      <td>193.04</td>\n",
       "      <td>203</td>\n",
       "      <td>90</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7181</th>\n",
       "      <td>Mesut Oezil</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>168</td>\n",
       "      <td>88</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7709</th>\n",
       "      <td>Neymar</td>\n",
       "      <td>2016-02-04 00:00:00</td>\n",
       "      <td>175.26</td>\n",
       "      <td>150</td>\n",
       "      <td>90</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8422</th>\n",
       "      <td>Philipp Lahm</td>\n",
       "      <td>2016-01-28 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>146</td>\n",
       "      <td>87</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8844</th>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>2016-01-28 00:00:00</td>\n",
       "      <td>185.42</td>\n",
       "      <td>174</td>\n",
       "      <td>88</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9461</th>\n",
       "      <td>Sergio Aguero</td>\n",
       "      <td>2016-03-10 00:00:00</td>\n",
       "      <td>172.72</td>\n",
       "      <td>163</td>\n",
       "      <td>88</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9485</th>\n",
       "      <td>Sergio Ramos</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>165</td>\n",
       "      <td>87</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>Thiago Silva</td>\n",
       "      <td>2015-09-21 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>174</td>\n",
       "      <td>88</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10235</th>\n",
       "      <td>Toni Kroos</td>\n",
       "      <td>2016-01-21 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>172</td>\n",
       "      <td>87</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10827</th>\n",
       "      <td>Zlatan Ibrahimovic</td>\n",
       "      <td>2015-09-21 00:00:00</td>\n",
       "      <td>195.58</td>\n",
       "      <td>209</td>\n",
       "      <td>89</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              player_name          last_report  height  weight  \\\n",
       "723        Andres Iniesta  2015-10-16 00:00:00  170.18     150   \n",
       "929          Arjen Robben  2016-01-28 00:00:00  180.34     176   \n",
       "1958    Cristiano Ronaldo  2015-10-16 00:00:00  185.42     176   \n",
       "2295         David De Gea  2016-04-21 00:00:00  193.04     181   \n",
       "2782          Eden Hazard  2016-01-28 00:00:00  172.72     163   \n",
       "3582          Gareth Bale  2016-04-28 00:00:00  182.88     163   \n",
       "4510      James Rodriguez  2016-02-18 00:00:00  180.34     165   \n",
       "4796       Jerome Boateng  2016-01-14 00:00:00  193.04     198   \n",
       "6053         Lionel Messi  2015-12-17 00:00:00  170.18     159   \n",
       "6245          Luis Suarez  2015-10-16 00:00:00  182.88     187   \n",
       "6255          Luka Modric  2015-10-16 00:00:00  175.26     143   \n",
       "6417         Manuel Neuer  2016-04-21 00:00:00  193.04     203   \n",
       "7181          Mesut Oezil  2016-02-18 00:00:00  182.88     168   \n",
       "7709               Neymar  2016-02-04 00:00:00  175.26     150   \n",
       "8422         Philipp Lahm  2016-01-28 00:00:00  170.18     146   \n",
       "8844   Robert Lewandowski  2016-01-28 00:00:00  185.42     174   \n",
       "9461        Sergio Aguero  2016-03-10 00:00:00  172.72     163   \n",
       "9485         Sergio Ramos  2016-03-31 00:00:00  182.88     165   \n",
       "9969         Thiago Silva  2015-09-21 00:00:00  182.88     174   \n",
       "10235          Toni Kroos  2016-01-21 00:00:00  182.88     172   \n",
       "10827  Zlatan Ibrahimovic  2015-09-21 00:00:00  195.58     209   \n",
       "\n",
       "       overall_rating preferred_foot  \n",
       "723                88          right  \n",
       "929                89           left  \n",
       "1958               93          right  \n",
       "2295               87          right  \n",
       "2782               88          right  \n",
       "3582               87           left  \n",
       "4510               87           left  \n",
       "4796               87          right  \n",
       "6053               94           left  \n",
       "6245               90          right  \n",
       "6255               87          right  \n",
       "6417               90          right  \n",
       "7181               88           left  \n",
       "7709               90          right  \n",
       "8422               87          right  \n",
       "8844               88          right  \n",
       "9461               88          right  \n",
       "9485               87          right  \n",
       "9969               88          right  \n",
       "10235              87          right  \n",
       "10827              89          right  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_player = player_rate[player_rate['overall_rating'] > 86]\n",
    "top_player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "accessory-magnet",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "visible-family",
   "metadata": {},
   "outputs": [
    {
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font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">93</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" transform=\"translate(0,89.28999999999999)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">94</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-8461c8\"><g class=\"clips\"/><clipPath id=\"legend8461c8\"><rect width=\"92\" height=\"64\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(596.12,60)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" width=\"92\" height=\"64\" x=\"0\" y=\"0\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url('#legend8461c8')\"><text class=\"legendtitletext\" text-anchor=\"start\" x=\"2\" y=\"15.600000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">preferred_foot</text><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,30.1)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">right</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M6,0A6,6 0 1,1 0,-6A6,6 0 0,1 6,0Z\" style=\"opacity: 0.7; 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fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" x=\"0\" y=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\"/></g><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"333\" y=\"410.8\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">height</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,38.934375,215)\" x=\"38.934375\" y=\"215\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">overall_rating</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(top_player, x=\"height\", y=\"overall_rating\",size='weight', hover_data=['player_name'],\n",
    "                 color='preferred_foot')\n",
    "fig.show(\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constant-steam",
   "metadata": {},
   "source": [
    "### List of matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "arabic-scholar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>...</th>\n",
       "      <th>SJA</th>\n",
       "      <th>VCH</th>\n",
       "      <th>VCD</th>\n",
       "      <th>VCA</th>\n",
       "      <th>GBH</th>\n",
       "      <th>GBD</th>\n",
       "      <th>GBA</th>\n",
       "      <th>BSH</th>\n",
       "      <th>BSD</th>\n",
       "      <th>BSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>492473</td>\n",
       "      <td>9987</td>\n",
       "      <td>9993</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.73</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492474</td>\n",
       "      <td>10000</td>\n",
       "      <td>9994</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492475</td>\n",
       "      <td>9984</td>\n",
       "      <td>8635</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>492476</td>\n",
       "      <td>9991</td>\n",
       "      <td>9998</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.50</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492477</td>\n",
       "      <td>7947</td>\n",
       "      <td>9985</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.73</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  country_id  league_id     season  stage                 date  \\\n",
       "0   1           1          1  2008/2009      1  2008-08-17 00:00:00   \n",
       "1   2           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "2   3           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "3   4           1          1  2008/2009      1  2008-08-17 00:00:00   \n",
       "4   5           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "\n",
       "   match_api_id  home_team_api_id  away_team_api_id  home_team_goal  ...  \\\n",
       "0        492473              9987              9993               1  ...   \n",
       "1        492474             10000              9994               0  ...   \n",
       "2        492475              9984              8635               0  ...   \n",
       "3        492476              9991              9998               5  ...   \n",
       "4        492477              7947              9985               1  ...   \n",
       "\n",
       "    SJA   VCH   VCD   VCA   GBH   GBD   GBA   BSH   BSD   BSA  \n",
       "0  4.00  1.65  3.40  4.50  1.78  3.25  4.00  1.73  3.40  4.20  \n",
       "1  3.80  2.00  3.25  3.25  1.85  3.25  3.75  1.91  3.25  3.60  \n",
       "2  2.50  2.35  3.25  2.65  2.50  3.20  2.50  2.30  3.20  2.75  \n",
       "3  7.50  1.45  3.75  6.50  1.50  3.75  5.50  1.44  3.75  6.50  \n",
       "4  1.73  4.50  3.40  1.65  4.50  3.50  1.65  4.75  3.30  1.67  \n",
       "\n",
       "[5 rows x 115 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = pd.read_sql('''SELECT * FROM Match;''',conn)\n",
    "matches.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-annual",
   "metadata": {},
   "source": [
    "### How many matches were\n",
    "\n",
    "    1.won by the home team\n",
    "    2.won by the away team\n",
    "    3.finished with a draw\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "medium-banner",
   "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>outcome_count</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11917</td>\n",
       "      <td>home_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7466</td>\n",
       "      <td>away_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6596</td>\n",
       "      <td>draw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   outcome_count   outcome\n",
       "0          11917  home_win\n",
       "1           7466  away_win\n",
       "2           6596      draw"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_won = pd.read_sql(\"\"\"SELECT \n",
    "    COUNT(matches.id) AS outcome_count,\n",
    "    CASE \n",
    "        WHEN matches.home_team_goal > matches.away_team_goal\n",
    "            THEN 'home_win'\n",
    "        WHEN matches.home_team_goal = matches.away_team_goal\n",
    "            THEN 'draw'\n",
    "        ELSE 'away_win'\n",
    "    END AS outcome\n",
    "    FROM \"Match\" AS matches\n",
    "    GROUP BY outcome\n",
    "    ORDER BY outcome_count DESC\"\"\",conn)\n",
    "df_won"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legal-capitol",
   "metadata": {},
   "source": [
    "### How many matches where played in each league"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "impossible-footwear",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>league_name</th>\n",
       "      <th>match_count</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>3040</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>3040</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>3040</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>3017</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2448</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "      <td>2448</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "      <td>2052</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "      <td>1920</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "      <td>1824</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>1728</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "      <td>1422</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id               league_name  match_count country_name\n",
       "0    1729    England Premier League         3040      England\n",
       "1    4769            France Ligue 1         3040       France\n",
       "2   21518           Spain LIGA BBVA         3040        Spain\n",
       "3   10257             Italy Serie A         3017        Italy\n",
       "4    7809     Germany 1. Bundesliga         2448      Germany\n",
       "5   13274    Netherlands Eredivisie         2448  Netherlands\n",
       "6   17642  Portugal Liga ZON Sagres         2052     Portugal\n",
       "7   15722        Poland Ekstraklasa         1920       Poland\n",
       "8   19694   Scotland Premier League         1824     Scotland\n",
       "9       1    Belgium Jupiler League         1728      Belgium\n",
       "10  24558  Switzerland Super League         1422  Switzerland"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_play = pd.read_sql(\"\"\"SELECT\n",
    "                        League.id,\n",
    "                        League.name AS league_name,\n",
    "                        COUNT(matches.id) AS match_count,\n",
    "                        Country.name AS country_name\n",
    "                        FROM \"Match\" AS matches\n",
    "                        JOIN League ON matches.league_id = League.id\n",
    "                        JOIN Country ON League.country_id = Country.id\n",
    "                        GROUP BY League.id\n",
    "                        ORDER BY\n",
    "                        match_count DESC,country_name ASC\"\"\",conn)\n",
    "df_play"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "altered-geometry",
   "metadata": {},
   "source": [
    "### How many matches were played on a per-country basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "desperate-wallace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>league_name</th>\n",
       "      <th>match_count</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>3040</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>3040</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>3040</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>3017</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2448</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "      <td>2448</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "      <td>2052</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "      <td>1920</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "      <td>1824</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>1728</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "      <td>1422</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id               league_name  match_count country_name\n",
       "0    1729    England Premier League         3040      England\n",
       "1    4769            France Ligue 1         3040       France\n",
       "2   21518           Spain LIGA BBVA         3040        Spain\n",
       "3   10257             Italy Serie A         3017        Italy\n",
       "4    7809     Germany 1. Bundesliga         2448      Germany\n",
       "5   13274    Netherlands Eredivisie         2448  Netherlands\n",
       "6   17642  Portugal Liga ZON Sagres         2052     Portugal\n",
       "7   15722        Poland Ekstraklasa         1920       Poland\n",
       "8   19694   Scotland Premier League         1824     Scotland\n",
       "9       1    Belgium Jupiler League         1728      Belgium\n",
       "10  24558  Switzerland Super League         1422  Switzerland"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_count = pd.read_sql('''SELECT\n",
    "    League.id,\n",
    "    League.name AS league_name,\n",
    "    COUNT(matches.id) AS match_count,\n",
    "    Country.name AS country_name\n",
    "    FROM \"Match\" AS matches\n",
    "    JOIN League ON matches.league_id = League.id\n",
    "    JOIN Country ON League.country_id = Country.id\n",
    "    GROUP BY League.id\n",
    "    ORDER BY\n",
    "    match_count DESC,\n",
    "    country_name ASC''',conn)\n",
    "match_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "victorian-external",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(match_count,x='country_name',y ='match_count',color = 'match_count',height=400)\n",
    "fig.show('svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "maritime-wright",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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