{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instalacja pakietów i przygotowanie datasetu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in c:\\programdata\\anaconda3\\lib\\site-packages (1.5.12)\n",
      "Requirement already satisfied: six>=1.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (1.15.0)\n",
      "Requirement already satisfied: requests in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (2.24.0)\n",
      "Requirement already satisfied: python-slugify in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (4.0.1)\n",
      "Requirement already satisfied: urllib3 in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (1.25.11)\n",
      "Requirement already satisfied: python-dateutil in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (2.8.1)\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (4.50.2)\n",
      "Requirement already satisfied: certifi in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle) (2020.6.20)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->kaggle) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->kaggle) (2.10)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.1.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2020.1)\n",
      "Requirement already satisfied: numpy>=1.15.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.19.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (1.19.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install kaggle\n",
    "!pip install pandas\n",
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading fifa19.zip to C:\\Users\\Ania\\Desktop\\AITECH\\[IUM] Inżynieria uczenia maszynowego\\ium_434760\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0.00/2.18M [00:00<?, ?B/s]\n",
      " 46%|####5     | 1.00M/2.18M [00:00<00:00, 4.74MB/s]\n",
      " 92%|#########1| 2.00M/2.18M [00:00<00:00, 5.31MB/s]\n",
      "100%|##########| 2.18M/2.18M [00:00<00:00, 5.93MB/s]\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d karangadiya/fifa19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "\n",
    "with zipfile.ZipFile('fifa19.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizacja i usuwanie artefaktów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Usuwanie artefaktów\n",
    "df=pd.read_csv('data.csv')\n",
    "df = df[df[\"Release Clause\"].notna()]\n",
    "df = df[df[\"Release Clause\"].notnull()]\n",
    "df.to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizacja\n",
    "df=pd.read_csv('data.csv')\n",
    "if df[\"Overall\"].mean() > 1:\n",
    "    df[\"Overall\"]= df[\"Overall\"]/100 \n",
    "df[\"Release Clause\"] = df[\"Release Clause\"].str.replace(\"€\", \"\")\n",
    "\n",
    "df[\"Release Clause\"] = (df[\"Release Clause\"].replace(r'[KM]+$', '', regex=True).astype(float) * \n",
    "          df[\"Release Clause\"].str.extract(r'[\\d\\.]+([KM]+)', expand=False)\n",
    "          .replace(['K','M'], [1000, 1000000]).astype(int))\n",
    "df.to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Podział na train/dev/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df=pd.read_csv('data.csv')\n",
    "train, dev = train_test_split(df, train_size=0.6, test_size=0.4, shuffle=True)\n",
    "dev, test = train_test_split(dev,  train_size=0.5, test_size=0.5, shuffle=False)\n",
    "\n",
    "test.to_csv('test.csv') \n",
    "dev.to_csv('dev.csv') \n",
    "train.to_csv('train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Odczyt danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test dataset length: 3329\n",
      "Dev dataset length: 3329\n",
      "Train dataset length: 9985\n",
      "Whole dataset length: 16643\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('data.csv')\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "dev = pd.read_csv('dev.csv')\n",
    "\n",
    "print(f\"Test dataset length: {len(test)}\")\n",
    "print(f\"Dev dataset length: {len(dev)}\")\n",
    "print(f\"Train dataset length: {len(train)}\")\n",
    "print(f\"Whole dataset length: {len(data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0.1.1</th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Photo</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Flag</th>\n",
       "      <th>Overall</th>\n",
       "      <th>...</th>\n",
       "      <th>Composure</th>\n",
       "      <th>Marking</th>\n",
       "      <th>StandingTackle</th>\n",
       "      <th>SlidingTackle</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKHandling</th>\n",
       "      <th>GKKicking</th>\n",
       "      <th>GKPositioning</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/158023.png</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>https://cdn.sofifa.org/flags/52.png</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>226500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/20801.png</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>https://cdn.sofifa.org/flags/38.png</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>127100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/190871.png</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>https://cdn.sofifa.org/flags/54.png</td>\n",
       "      <td>0.92</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>228100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
       "      <td>Spain</td>\n",
       "      <td>https://cdn.sofifa.org/flags/45.png</td>\n",
       "      <td>0.91</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>138600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/192985.png</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>https://cdn.sofifa.org/flags/7.png</td>\n",
       "      <td>0.91</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>196400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16638</th>\n",
       "      <td>16638</td>\n",
       "      <td>18202</td>\n",
       "      <td>18202</td>\n",
       "      <td>238813</td>\n",
       "      <td>J. Lundstram</td>\n",
       "      <td>19</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/238813.png</td>\n",
       "      <td>England</td>\n",
       "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>143000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16639</th>\n",
       "      <td>16639</td>\n",
       "      <td>18203</td>\n",
       "      <td>18203</td>\n",
       "      <td>243165</td>\n",
       "      <td>N. Christoffersson</td>\n",
       "      <td>19</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/243165.png</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>https://cdn.sofifa.org/flags/46.png</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>113000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16640</th>\n",
       "      <td>16640</td>\n",
       "      <td>18204</td>\n",
       "      <td>18204</td>\n",
       "      <td>241638</td>\n",
       "      <td>B. Worman</td>\n",
       "      <td>16</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/241638.png</td>\n",
       "      <td>England</td>\n",
       "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>165000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16641</th>\n",
       "      <td>16641</td>\n",
       "      <td>18205</td>\n",
       "      <td>18205</td>\n",
       "      <td>246268</td>\n",
       "      <td>D. Walker-Rice</td>\n",
       "      <td>17</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/246268.png</td>\n",
       "      <td>England</td>\n",
       "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>143000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16642</th>\n",
       "      <td>16642</td>\n",
       "      <td>18206</td>\n",
       "      <td>18206</td>\n",
       "      <td>246269</td>\n",
       "      <td>G. Nugent</td>\n",
       "      <td>16</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/246269.png</td>\n",
       "      <td>England</td>\n",
       "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>165000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16643 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1      ID                Name  \\\n",
       "0               0             0               0  158023            L. Messi   \n",
       "1               1             1               1   20801   Cristiano Ronaldo   \n",
       "2               2             2               2  190871           Neymar Jr   \n",
       "3               3             3               3  193080              De Gea   \n",
       "4               4             4               4  192985        K. De Bruyne   \n",
       "...           ...           ...             ...     ...                 ...   \n",
       "16638       16638         18202           18202  238813        J. Lundstram   \n",
       "16639       16639         18203           18203  243165  N. Christoffersson   \n",
       "16640       16640         18204           18204  241638           B. Worman   \n",
       "16641       16641         18205           18205  246268      D. Walker-Rice   \n",
       "16642       16642         18206           18206  246269           G. Nugent   \n",
       "\n",
       "       Age                                           Photo Nationality  \\\n",
       "0       31  https://cdn.sofifa.org/players/4/19/158023.png   Argentina   \n",
       "1       33   https://cdn.sofifa.org/players/4/19/20801.png    Portugal   \n",
       "2       26  https://cdn.sofifa.org/players/4/19/190871.png      Brazil   \n",
       "3       27  https://cdn.sofifa.org/players/4/19/193080.png       Spain   \n",
       "4       27  https://cdn.sofifa.org/players/4/19/192985.png     Belgium   \n",
       "...    ...                                             ...         ...   \n",
       "16638   19  https://cdn.sofifa.org/players/4/19/238813.png     England   \n",
       "16639   19  https://cdn.sofifa.org/players/4/19/243165.png      Sweden   \n",
       "16640   16  https://cdn.sofifa.org/players/4/19/241638.png     England   \n",
       "16641   17  https://cdn.sofifa.org/players/4/19/246268.png     England   \n",
       "16642   16  https://cdn.sofifa.org/players/4/19/246269.png     England   \n",
       "\n",
       "                                      Flag  Overall  ...  Composure Marking  \\\n",
       "0      https://cdn.sofifa.org/flags/52.png     0.94  ...       96.0    33.0   \n",
       "1      https://cdn.sofifa.org/flags/38.png     0.94  ...       95.0    28.0   \n",
       "2      https://cdn.sofifa.org/flags/54.png     0.92  ...       94.0    27.0   \n",
       "3      https://cdn.sofifa.org/flags/45.png     0.91  ...       68.0    15.0   \n",
       "4       https://cdn.sofifa.org/flags/7.png     0.91  ...       88.0    68.0   \n",
       "...                                    ...      ...  ...        ...     ...   \n",
       "16638  https://cdn.sofifa.org/flags/14.png     0.47  ...       45.0    40.0   \n",
       "16639  https://cdn.sofifa.org/flags/46.png     0.47  ...       42.0    22.0   \n",
       "16640  https://cdn.sofifa.org/flags/14.png     0.47  ...       41.0    32.0   \n",
       "16641  https://cdn.sofifa.org/flags/14.png     0.47  ...       46.0    20.0   \n",
       "16642  https://cdn.sofifa.org/flags/14.png     0.46  ...       43.0    40.0   \n",
       "\n",
       "      StandingTackle SlidingTackle GKDiving  GKHandling GKKicking  \\\n",
       "0               28.0          26.0      6.0        11.0      15.0   \n",
       "1               31.0          23.0      7.0        11.0      15.0   \n",
       "2               24.0          33.0      9.0         9.0      15.0   \n",
       "3               21.0          13.0     90.0        85.0      87.0   \n",
       "4               58.0          51.0     15.0        13.0       5.0   \n",
       "...              ...           ...      ...         ...       ...   \n",
       "16638           48.0          47.0     10.0        13.0       7.0   \n",
       "16639           15.0          19.0     10.0         9.0       9.0   \n",
       "16640           13.0          11.0      6.0         5.0      10.0   \n",
       "16641           25.0          27.0     14.0         6.0      14.0   \n",
       "16642           43.0          50.0     10.0        15.0       9.0   \n",
       "\n",
       "       GKPositioning  GKReflexes  Release Clause  \n",
       "0               14.0         8.0     226500000.0  \n",
       "1               14.0        11.0     127100000.0  \n",
       "2               15.0        11.0     228100000.0  \n",
       "3               88.0        94.0     138600000.0  \n",
       "4               10.0        13.0     196400000.0  \n",
       "...              ...         ...             ...  \n",
       "16638            8.0         9.0        143000.0  \n",
       "16639            5.0        12.0        113000.0  \n",
       "16640            6.0        13.0        165000.0  \n",
       "16641            8.0         9.0        143000.0  \n",
       "16642           12.0         9.0        165000.0  \n",
       "\n",
       "[16643 rows x 91 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimum, maksimum, średnia, mediana, odchylenie standardowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall zawodnika (0-1):\n",
      "Minimum: 0.46\n",
      "Maksimum: 0.94\n",
      "Średnia: 0.6616277113501784\n",
      "Mediana: 0.66\n",
      "Odchylenie standardowe: 0.07008236149926617\n"
     ]
    }
   ],
   "source": [
    "overall = data[\"Overall\"]\n",
    "print(\"Overall zawodnika (0-1):\")\n",
    "print(f\"Minimum: {overall.min()}\")\n",
    "print(f\"Maksimum: {overall.max()}\")\n",
    "\n",
    "print(f\"Średnia: {overall.mean()}\")\n",
    "print(f\"Mediana: {overall.median()}\")\n",
    "print(f\"Odchylenie standardowe: {overall.std()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiek zawodnika:\n",
      "Minimum: 16\n",
      "Maksimum: 45\n",
      "Średnia: 25.226221234152497\n",
      "Mediana: 25.0\n",
      "Odchylenie standardowe: 4.71658785571582\n"
     ]
    }
   ],
   "source": [
    "age = data[\"Age\"]\n",
    "print(\"Wiek zawodnika:\")\n",
    "print(f\"Minimum: {age.min()}\")\n",
    "print(f\"Maksimum: {age.max()}\")\n",
    "\n",
    "print(f\"Średnia: {age.mean()}\")\n",
    "print(f\"Mediana: {age.median()}\")\n",
    "print(f\"Odchylenie standardowe: {age.std()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liczba zawodników dla poszczególnych narodowości (top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g1+vr3F4fNjbWDY361JTKNkYf42lIIYcUMgDSyCGFDIA4OaTwXKWQAZBGDmNmaHD5v/DCC3ByckK7du0AAG+99RZSU1Nhbm5ueIxWq4VKpYKtrS20Wq1he0FBAVQqVb3Gu3mzGHq98Mj7m+KLpNUWNerzm+obJYUcUsjQ2BxSyCClHE+iVLYx+hh/hQxSydHYDGZmisfuNDf4mP+wYcNw+PBhFBYWQqfT4dChQ3Bzc8Ply5eRm5sLnU6H5ORkODs7w87ODpaWlsjMzAQAJCQkwNnZuaFDExFRIzV4z793796YPn06Jk2ahMrKSgwaNAgTJ05Et27dMHfuXNy7dw9DhgyBm5sbACAqKgrBwcEoLi5Gz5494efn12RPgoger03bVrCyfPLL/XF/pZTfq0JRYVlTxiITanD5A8CYMWMwZsyYGtucnJyQmJhY67H29vaIjY1tzHBE1EBWlhbw/DShUf9G0iovNPZAyNP8EnrSYTL+EmoajSp/IqL6kMovIeLyDkREssTyJyKSIZY/EZEMsfyJiGSI5U9EJEMsfyIiGWL5ExHJEMufiEiGWP5ERDLE8icikiGWPxGRDLH8iYhkiOVPRCRDLH8iIhniks5EJCu8sM19LH8ikhVeU+A+HvYhIpIhlj8RkQw1yWGfFStW4Pbt24iIiMCRI0cQHh6Oe/fuYeTIkfD39wcAZGVlISgoCCUlJXB0dMSSJUtgYcGjTkQkT6a+nnGj2/fo0aPYu3cvhg4divLycgQGBmLbtm3o0KEDPvjgA2RkZGDIkCEICAjA8uXL4eDggMDAQMTExGDSpEmNHZ6I6C/J1O89NOqwz507d7BmzRrMnDkTAHD27Fl07twZnTp1goWFBTw9PZGamoq8vDyUl5fDwcEBAKBWq5GamtqYoYmIqBEaVf4hISHw9/dH27ZtAQD5+flQKpWG+1UqFTQaTa3tSqUSGo2mMUMTEVEjNPiwz549e9ChQwc4OTkhLi4OAKDX66FQKAyPEQQBCoXikdvrw8bGuqFRn9qTjq+JRQo5pJABkEYOKWQApJFDChkAaeSQQgag4TkaXP4pKSnQarXw8vLC3bt3UVpairy8PJibmxseo9VqoVKpYGtrC61Wa9heUFAAlUpVr/Fu3iyGXi888v6m+EZotY2budtUPwxSyCGFDI3NIYUMUskhhQxSySGFDGLkMDNTPHanucHlv3nzZsPHcXFxOH78OJYsWQIXFxfk5ubixRdfRHJyMnx9fWFnZwdLS0tkZmaiT58+SEhIgLOzc0OHJiKiRmrSuZaWlpaIiIjA3Llzce/ePQwZMgRubm4AgKioKAQHB6O4uBg9e/aEn59fUw5NRET10CTlr1aroVarAQBOTk5ITEys9Rh7e3vExsY2xXBERNRIPMOXiEiGWP5ERDLE8icikiGWPxGRDLH8iYhkiOVPRCRDLH8iIhli+RMRyRDLn4hIhlj+REQyxPInIpIhlj8RkQyx/ImIZIjlT0QkQyx/IiIZYvkTEckQy5+ISIZY/kREMsTyJyKSIZY/EZEMNar8v/zyS4waNQqjRo3CypUrAQBHjhyBp6cnXFxcsGbNGsNjs7KyoFar4erqiqCgIFRVVTUuORERNViDy//IkSM4fPgw9u7di/j4eJw/fx7JyckIDAxEdHQ0UlJScO7cOWRkZAAAAgICEBISgrS0NAiCgJiYmCZ7EkREVD8NLn+lUolFixahZcuWaNGiBbp3746cnBx07twZnTp1goWFBTw9PZGamoq8vDyUl5fDwcEBAKBWq5GamtpUz4GIiOrJoqGf2KNHD8PHOTk52LdvH95++20olUrDdpVKBY1Gg/z8/BrblUolNBpNvcazsbFuaNSnplS2MfoYT0MKOaSQAZBGDilkAKSRQwoZAGnkkEIGoOE5Glz+1S5duoQPPvgACxYsgLm5OXJycgz3CYIAhUIBvV4PhUJRa3t93LxZDL1eeOT9TfGN0GqLGvX5TfXDIIUcUsjQ2BxSyCCVHFLIIJUcUsggRg4zM8Vjd5ob9YZvZmYm3n33XXz66afw8fGBra0ttFrtA6G0UKlUtbYXFBRApVI1ZmgiImqEBpf/jRs38OGHHyIqKgqjRo0CAPTu3RuXL19Gbm4udDodkpOT4ezsDDs7O1haWiIzMxMAkJCQAGdn56Z5BkREVG8NPuyzceNG3Lt3DxEREYZtEyZMQEREBObOnYt79+5hyJAhcHNzAwBERUUhODgYxcXF6NmzJ/z8/BqfnoiIGqTB5R8cHIzg4OA670tMTKy1zd7eHrGxsQ0djoiImhDP8CUikiGWPxGRDLH8iYhkiOVPRCRDLH8iIhli+RMRyRDLn4hIhlj+REQyxPInIpIhlj8RkQyx/ImIZIjlT0QkQyx/IiIZYvkTEckQy5+ISIZY/kREMsTyJyKSIZY/EZEMsfyJiGRI1PJPSkqCu7s7XFxcsGPHDjGHJiKiBzT4Au71pdFosGbNGsTFxaFly5aYMGEC+vfvj5dfflmsCERE9P+JVv5HjhzBgAED8NxzzwEAXF1dkZqaijlz5jzV55uZKZ74GNXzrRoT8anGMHYGqeSQQoamyCGFDFLJIYUMUskhhQzGzvGkfApBEIRGj/4UNmzYgNLSUvj7+wMA9uzZg7Nnz2LZsmViDE9ERA8Q7Zi/Xq+HQvHf30SCINS4TURE4hGt/G1tbaHVag23tVotVCqVWMMTEdEDRCv/gQMH4ujRo7h16xbKysqwf/9+ODs7izU8ERE9QLQ3fNu3bw9/f3/4+fmhsrISY8aMwd///nexhiciogeI9oYvERFJB8/wJSKSIZY/EZEMsfyJiGSI5U9EJEMsfyIiGWL5E8lYRUWFqSOQiXCqp5GdPXvW5OczFBYWIikpCXfu3MGD3+6nXVSvqVRUVGDjxo24fPkyQkJCsGXLFsyYMQMtW7YULUNOTg62b9+O0tJSCIIAvV6Pa9euyXaJ8aFDh2LYsGHw8fEx6c/pnTt3UFZWBkEQoNPpcO3aNTg5OYma4dKlS7h7926N10jfvn1FzfCw4uJiWFtbG+XfFu0kLzEtXrz4sfeHh4eLlASIjIzEnTt34OXlBS8vLyiVStHGrvbxxx+jTZs26NGjh0nXU1q6dCnatWuHCxcuwNzcHFeuXEFgYCCioqJEy/DJJ59g6NChyMzMhI+PDw4cOIAePXqINr69vT0UCgXq2udSKBTIysoSLQsA7Nu3D2lpaVi9ejVu3rwJb29vjB49WtSf03Xr1mHr1q2oqqrCc889h/z8fPTq1Qt79uwRLcOSJUtw8OBBdOrUybBNoVDgm2++ES0DABw8eBAnT57E7NmzMWbMGNy6dQsLFy6EWq1u+sGEZiguLk6Ii4sT5s6dK0ybNk2Ij48XkpOThdmzZwsLFy4UPc+1a9eEf/7zn4KHh4cwY8YMYd++fUJFRYVo43t4eIg21uN4e3sLgiAIXl5egiAIgl6vF0aNGiVqhuqvxapVq4QTJ04IZWVlgru7u6gZpGr//v2Cs7Oz4ODgIMyaNUvIyckRZdxhw4YJRUVFwqJFi4Tc3Fzh4MGDwvvvvy/K2NVGjBghlJWViTpmXdRqtZCVlSXExMQICxYsEIqLiwUfHx+jjNUs9/x9fHwAADt37sTu3bthZnb/rY2RI0di3Lhxouexs7ODt7c3LCwssGvXLmzbtg1r1qzB/PnzMWLECKOP/z//8z/Izs6Gvb290cd6HIVCgYqKCsNfH7dv3xb9L5FWrVqhoqICXbp0wfnz5+Ho6Cjq+Lt378b48ePx5Zdf1nm/2IficnNzkZiYiOTkZHTs2BHz58+Hi4sLfv31V7z//vvYv3+/0TOoVCpYW1ujR48eyM7OhouLC1atWmX0cR/UqVOnOv8aMwV7e3usX78eo0ePxjPPPIPKykqjjNMsy79aUVER7ty5g3bt2gEACgoKUFpaKmqGPXv2ICEhAVqtFt7e3ti5cydsbW2h0Wjg4+MjSvlfunQJPj4+sLGxgaWlpWE57R9//NHoYz/Iz88PU6dOhVarRVhYGA4cOCB62Y0ePRozZ85EVFQUxo8fj0OHDqF9+/aijS+Vgqk2depUqNVqbNq0CXZ2dobtQ4YMwS+//CJKBmtra8THx6Nnz57Yvn07VCoVysvLRRm72rPPPotRo0bh9ddfr/EelJiHiAHghRdewLJly3Du3DlERkYiIiICHTt2NMpYzfoN3/j4eERFReGNN96AIAg4ffo0goOD4erqKlqGBQsWwNfXF/379691X1pamihZ8vLy6tz+4ItdLL///juOHTsGnU6H/v37429/+5voGarfRPvzzz/x22+/YfDgwWjVqvFXVKqP/fv3w8XFpca2rVu34p133hE1hxRoNBp8//33mDZtGiIiInDkyBF88MEHGDVqlGgZ9u7dW+f26qMIYikuLsYPP/yA119/HZ07d8aOHTvg7e2NZ555psnHatblDwD5+fn497//DYVCgT59+sDGxkb0DKaaRXDw4EEMGzYM8fHxdd7v7e1t9AwPunjxIv71r39hzZo1+OOPPxASEoJly5ahW7duomWQysynV199FUOHDkVkZKThhe3j4/PIEmpq1W88V3vwa2GKN56lwJQzjh71Gq1mjNdqsz7sU1hYiAMHDhhe6P/5z38AiPtCX7p0KdLT000yi+C3337DsGHDcOzYsTrvF7v8P/vsM8PXvnv37pg9ezaCgoLw7bffipZBKjOfXnnlFfTr1w/jx4/H+vXr0bVrV1EPCWVnZ4s21qNU/7J7cAbUg/8X8xfQ+vXrsWXLFlRVVeH555+HRqMRdcZR9Wv0ypUryM3NxZAhQ2Bubo7Dhw/j5ZdfZvnXlxRe6IcPH0ZqaiqsrKxEH/ujjz4CUPdxS7GPqQJAWVlZjQv4DBo0CJGRkaJmKCgowObNm0Udsy4KhQLvvvsuevTogffeew/BwcFo0aKF6Dlu3bqFxMRElJSU1DjvYeXKlUYfu/qvHCn8Itq7dy8yMjIQFhaGWbNm4f/+7/+wc+dO0cavfo1OmTIFiYmJhvcp7969iw8//NAoYzbr8pfCC10KswjS09Oxdu3aGic2lZeX4+jRo6LmaNeuHb799luMHj0aAJCSkiL6YTipzHyq/pkYNGgQNm3ahDlz5uDGjRui55g3bx46dOiA06dP46233sJPP/2E1157TdQMlZWV2LVrF44fPw4LCwsMHDgQY8aMEXWHTQozjoD7h6mfe+45w+1WrVrVuPxtU2rW5S+FF7oUZhGEh4dj2bJl2Lx5M2bOnIkffvgBZWVloo3/YI4lS5Zg5cqVaNGiBfr27YuwsDBRM0hl5lNoaKjh4y5dumDXrl0mOcs4Pz8f33zzDVasWAEXFxdMnz5d9Dedly5diuLiYvj4+ECv1yMhIQEXL15EcHCwaBmkMOMIuH/G9dSpU+Hi4gJBELBv3z6MHDnSKGM16/KXwgv9zTffxJtvvinaeHVp06YNBgwYgFOnTqGoqAgBAQFwd3cXPUfHjh2xYcMG0cd90KPm14utc+fO2LJlS63DLWJ79tlnAQBdu3ZFdnY2evfuLXqG06dPIykpyXB7+PDh8PLyEjVDWFgYvv/+e3h7e+Onn35CSEgI5s2bJ2oG4P7qBGlpaTh+/DgUCgWmTZuGf/zjH0YZq1mXvxRe6D4+PnXOIhCTlZUVLl++jO7du+P48eMYMGCA0U4ceZxDhw5h7dq1tWY+ifHLuHrm04kTJ+q8X+xpr1I43AIAAwYMwEcffYSFCxdi2rRpOH/+vOjvT7Vv3x5Xr141TIrIz88XfRmU9u3bw8vLC/v374ejoyNmzpxp+MUotm7dusHGxsbwGjlx4oRRZgc266meFRUVyMjIQElJCQAYivfjjz8WLYOpZxEAwPHjx7Fjxw5ERkZi4sSJuHLlCsaMGYOFCxeKlgEAXF1dsWjRolpvwItRvOvWrcNHH330yHWfxD6Zx83NDampqVixYgXc3Nzw0ksv4Z133kFiYqKoOYD7M0xeeuklnDt3DidPnoS7uztUKpXRx50yZQoUCgVu376Na9euoW/fvjAzM8OpU6fQo0cPUQ+DJSQkYOXKlejTpw90Oh3Onj2L5cuXY8iQIaJlAMRdY6hZ7/l/8sknuHv3Lq5cuQJHR0ccO3YMb7zxhqgZTD2LAAD69euHfv364c6dO9iyZQv0er1J9mqef/55DBs2TPRxgf/OfPLw8MCgQYNq3CfGEgYPk8LhFgCYO3cu1q9fDwDo1asXevXqhXfeeQdbt24VZey6TJs2zehjP+yrr75CXFyc4WzvvLw8zJw5U/Ty/+WXX0SbHdisy//ixYvYv38/wsLC4Ovri3nz5ol+HE8Kswiys7OxYMECaDQaCIKAbt26YeXKlXjppZdEzdGnTx+Eh4fjzTffhKWlpWG7GCe8paSkoKKiwvAXQLWqqips2LCh1tm2xmbqwy1z5sxBVlYWNBpNjWPKOp0Otra2omTo16+f4eOLFy+isLBQlHHr8swzz9Q41GRnZ2eSqbdizg5s1uVvY2MDhUKBrl274uLFi/D29hb9WLcUZhEEBgbC39/fsNd94MABLFq0SPS/QM6ePQsAuHDhgmGbWCe8lZSU4NSpUygpKalx0pu5uTn8/f2NPv7D3nnnHRQXF8POzg6rV6/GiRMnjDafuy4RERG4c+cOwsLCasyqsbCwEH367SeffILz58/XONQk9nLKr732Gt5//334+vrC3Nwc+/btg0qlMpx5K9YJkWLODmzWx/w/++wztGzZEhMnTsT8+fPh7u6OpKSkGjMLjE0K65bUtWyAt7f3E08pb46OHj0q+kVC6jJy5Ejs27fP1DEkYeTIkUhOToa5ubnJMkjlGiBirjHUrMtfp9Ph3//+NxwdHZGeno4jR45g3LhxeOWVV0wdTVTV68eMGzcO5ubmSElJwalTp/Dpp58CgNFWDXzY6dOnsWHDhhonm12/fh3p6emijA/c/6vjX//6V60ZR2JftMPf3x9DhgzB3//+9xqHe8T6Xjy8tk81UyytsGjRIsyYMUPUNZ6eRnl5uUnOzBdrjaFmWf6Pms5XTcxLs23ZsgXR0dEoKiqqsV3MF9fw4cMBwPBif3gRL7HOe3B3d8d7772HvXv3YsqUKdi/fz9sbGwQGBgoyvgA4OnpifHjx9eacfTg8WcxPPw9qSb2yWZSEB8fj8DAQKhUKpibm5vkfJy6zoIvKyvDr7/+KloGQNzZgc3ymP+6deseeZ/YxxK/+eYbxMfHi7ZH97CDBw9iy5YteOmll3DgwAHExsbi1VdfxezZs0V/Q6tly5bw9fVFXl4e2rZti5UrV8LT01PUDFZWVnj77bdFHfNhf/zxB3bt2gWVSoWvv/4ap06dQs+ePTF9+nST5jKVDRs2YOvWrSZ7jQDSOQtezNmBzbL8t23bZuoIBt26dcMLL7xgkrE3btyIlJQUrFixAtnZ2QgICEBQUBCysrIQGRkp6h43AFhaWuLOnTvo2rUrzpw5AycnJ+h0OlEzDB48GNu2bcPgwYNrzDgSq3i++eYbbNq0Cebm5ujXrx8uX74Md3d3HD9+HKGhoaIsqCY1zz//PBwdHU26yqpUzoIXc3Zgsyz/an5+fjVuKxQKWFlZoVu3bqKdwefn5wdPT0/07t27xhtaYryBlJCQgN27d6NVq1aIiorC8OHDMXbsWAiCYJIf7KlTp8Lf3x/r16/H2LFjkZSUhF69eomaISEhAQBqLPgn5iGG3bt3IyUlBWVlZXjrrbdw+PBhPPPMM5g8ebLoS2xLRZcuXTBu3DgMHDiwxl+jYi69LpWz4MWcHdisy7979+6wsLCAr68vACA5ORl//vkn2rdvj6CgIFGWf1i1ahU8PT1NctUshUJhuELVsWPHMGnSJMN2U7CyssKmTZugUCjw3XffIScnR/RF98R8c7kuFhYWaN26NVq3bo1OnToZLuRibm4OC4tm/XJ8pI4dO5r0kA9w/w34tWvXIjIyEv/7v/+L3bt3Y8yYMaLneHCNoYMHDxp1jaFm/dN25swZxMXFGW7b29vD19cXUVFRok1zbNmypehXiapmbm6OwsJClJaWIisry3Bma15enkmKJjIyEkOHDgUAtG7dGq+++qroGe7evYvIyEhcuXIF69atw4oVK7B48WK0bdtWlPHNzMwMH5tyaqOUzJkzB7du3cKZM2eg0+ng4OAg2qHSB6d4tm7dGqGhobC3t8eIESMMO0tiat++veEM50WLFhl1rGZd/pWVlbh06RJ69OgB4P4qn9Vr2Yv1J12fPn0QEREBZ2fnGn/SijHjaMaMGfD29kZVVRXGjBkDlUqFlJQUrFmzRtQTiqp16tQJixcvRu/evWtMoRPzcMdnn32GQYMG4ezZs2jdujVUKhXmz5+Pr7/+WpTxc3JyDIcjH/xYEATk5uaKkkFqDh06hMDAQDg4OECv1yMkJARhYWGiLAVS1ywvQRBw8eJFzJs3Dxs3bjR6BuDRU2+rGWN2YLOc6lnt2LFjWLhwIWxsbKDX61FYWIiVK1ciPT0dzz77LGbMmGH0DFOmTKm1TcwZRxqNBrdv3zYcXsnIyICVlVWdF5Q3Zob27dtLYlE1tVqNuLi4Gie5jR49WrQF1Y4fP/7Y+8WecioFarUaX3zxhWExs6tXr2LOnDmG92dMZdSoUfj+++9FHVPM64806z3//v3744cffsB//vMfmJmZoXv37mjRogXeeOMN0Y57jxo1ChMmTBBlrLq0b9/esFgVANEXqgKAmTNnYu/evQgPD8emTZtMsnBXNXNzcxQVFRm+/zk5OTUOxRibHMv9SaqqqmqsYtmpUyfo9XqT5dFqtfjhhx8M78eIyd/fX7Qzv5t1+efl5WH79u21zuYUc09z+/btJi1/KXjwa5+UlGTS8p87dy6mTJmCGzduYPbs2Th9+jQ+//xzk+Wh+2/4btmyxfAGa2xsrEkmSFTLzc3F2bNnTTLt9uWXX8aXX35Z69Ao1/Ovp7Fjx8LR0bHW2ZzGWCfjUaZPn46Kigr07t27xrxyU70JbAoPri0khTWFbt26hbNnz0Kn06F3794mOw+D7rt58yaWLVuGX3/9FYIgYMCAAQgKChLlmgJSI+Zh4ma9519VVSX6BUse5uDgYNLxpcaUJ/IAta/ulp2dDSsrK3Tv3t0wE4nEZWNjg7Vr15o6hiSIeYJqs97zX758OQYOHIjBgwfXWB5VbKWlpbhy5QpeeeUVlJeXo3Xr1ibLYgq9evUyvO9Q/eYvAJOs4bJgwQLk5uYaVlXdv38/rK2tYWZmhi5dumDBggWiZZG74cOHP3ZnQI7rHOXl5SE4OBh5eXnYsWMHPv30U3z++ed48cUXm3ysZl3+gwcPRkFBQY0FzcResfDo0aMICQmBTqfD7t274eHhgVWrVmHw4MGiZTC1vLy8x94v5vHdsWPHYseOHYadgYqKCkyZMgW7d+8WddYPSevnQiree+89TJ06FVFRUdi7dy/27NmDhIQEo1zSUrxpDiKqXgjp8OHDSExMRFZWFrKyspCdnY3JkyeLmmX16tXYuXMn2rZtC6VSiR07dshu/RY7O7vH/iemwsJCVFVVGW5XVlaitLQUAES7ghLdV/3979ixIzIyMrBixQqEhYXhxx9/RIcOHUwdzyRu375t2DFUKBQYN24ciouLjTJWszzmv2fPHsPZeQsXLqxxgYTMzExRs+j1+hqXh3v55ZdFHZ9qmjx5Mnx9fTF06FAIgoCMjAy8/fbb2LJli+yu8yAVK1euRG5uLnx9fSEIAuLi4nD16lUEBQWZOprorKys8OeffxqOVpw8edJoh6ybZfk/uAf38N6c2Ht3tra2OHjwIBQKBQoLC7Fjxw6Tr2MiZx4eHujfvz+OHj0KMzMzrFu3Dj169EBOTo5JTuen+xctj4+PN5xvMXToUNGX+paKxYsX44MPPsCVK1fg5eWFu3fvGu3N8GZZ/g96+A0lsWebLF26FGFhYbhx4wZGjBiB/v37Y+nSpaJmoP+aPHky9u3bh7/97W81tnfp0sU0gQg6nQ5VVVWGPVydTifbdY9ee+01xMbGIicnBzqdDt26deOef32YejphtZ07d0KpVGL16tUYM2YMbt26hezsbJNcJILus7e3R3x8vMkun0i1eXp6ws/PzzAD6/vvv4eHh4eJU5nG1atXsWvXLty+fdvoJ6Y2y9k+j5taqNVq8dtvvxk9w4YNG3D06FGEhoaia9euGD16NL766iscPHgQFy5c4FmlJlJ9+UTgvzsJFRUVOHTokKkiydrdu3eh0+lw7tw5HD16FMeOHYOfn59sr23g7e0NJycnUU5MbZZ7/mlpaaaOgPj4eMTGxhrWBzEzM4OdnR0mTJgANzc3E6eTr+r1/CsrK3HgwAF8++23ouwMUG0XLlzAjBkz8Pnnn8PZ2RnOzs5YvXo1Vq1aBXt7e9Gv9SAFgiCIdmJqsyx/KcwPNjc3r7Ew1KxZswDcv5iHKRaMovuuXr2KmJgYxMXF4e7du5g5cya++OILU8eSpRUrVmDVqlU1Vpj95JNP0LdvX0RERGDLli2mC2cir7/+Og4cOIB//OMfRl9wsFmWvxTo9XoUFxfD2toaAODq6goAKCoqEnUVSbrvwIED2LVrF86fP48RI0Zg5cqV+Oyzz2S1xpLUFBYW1rm0+JtvvomoqCgTJDKd6vX8BUHArl27RDkxleVvJJ6enli4cCFWrFhh+AVQUlKCwMBAjB492sTp5Gfu3LkYOXIkdu/ejc6dOwOQzsQAuaqqqoJer6+1M6TX601y/VxTys7OfuR9FRUVRhmTu6BGMmPGDLRr1w5vvvkmxowZg7Fjx2Lw4MGwsbHB1KlTTR1PdhITE9G+fXtMmjQJ48aNw9atW6HT6UwdS9b69u1b53W0o6Oj0atXLxMkMr3x48fXuK3X6w3XIG9qzXK2j5RoNBqcPXsWwP1ZSHI9bV0qqqqq8NNPPyEuLg4///wzBg4ciMmTJ5vkIjdyV1xcjBkzZuDPP/+Evb09LC0tceHCBbRr1w5fffUVnnvuOVNHFI2fn1+dV3mzsLDA8OHDsW7duiYfk+VPsnXr1i3Ex8cjPj6eC7qZiCAI+PXXX5GVlQUzMzP06tULjo6Opo5lMsuXL0dwcLAoY7H8iYgkJCkpCb///jtmzpyJtLQ0o53zwGP+REQSERUVhYyMDOzfvx9VVVX47rvvEBERYZSxWP5ERBJx+PBhREZGwtLSEm3atMHmzZvx888/G2Uslj8RkURUT3t9cOkRY50XxHn+REQS4ebmhnnz5uHu3bvYsmULEhMTjbbIHd/wJSIysevXrxs+/vnnn3HkyBEIgoABAwZg2LBhRll1luVPRGRi1Rezf7iOCwoKUFlZaZTlHVj+REQSU1JSghUrVuDw4cNYtmwZBg0a1ORj8A1fIiIJOXr0qGH9r8TERKMUP8A3fImIJKG0tBQRERFG3dt/EPf8iYhM7OjRo4aL1iclJRm9+AEe8yciMjl7e3tYWFhApVLVWGq8ej3/H3/8scnHZPkTEZlYXl7eY+83xtUJWf5ERDLEY/5ERDLE8icikiGWPxGRDLH8iYhk6P8BPwiHWekvnNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"Nationality\"].value_counts().head(10).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 10 najlepszych i najgorszych drużyn względem średniego Overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>Overall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Club</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Juventus</th>\n",
       "      <td>0.822800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Napoli</th>\n",
       "      <td>0.800417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inter</th>\n",
       "      <td>0.796190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Madrid</th>\n",
       "      <td>0.782424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FC Barcelona</th>\n",
       "      <td>0.780303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milan</th>\n",
       "      <td>0.775417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris Saint-Germain</th>\n",
       "      <td>0.774333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roma</th>\n",
       "      <td>0.774000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manchester United</th>\n",
       "      <td>0.772424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SL Benfica</th>\n",
       "      <td>0.770741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Overall\n",
       "Club                         \n",
       "Juventus             0.822800\n",
       "Napoli               0.800417\n",
       "Inter                0.796190\n",
       "Real Madrid          0.782424\n",
       "FC Barcelona         0.780303\n",
       "Milan                0.775417\n",
       "Paris Saint-Germain  0.774333\n",
       "Roma                 0.774000\n",
       "Manchester United    0.772424\n",
       "SL Benfica           0.770741"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"Club\", \"Overall\"]].groupby(\"Club\").mean().sort_values(\"Overall\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>Overall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Club</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>St. Patrick's Athletic</th>\n",
       "      <td>0.577826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambridge United</th>\n",
       "      <td>0.572593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waterford FC</th>\n",
       "      <td>0.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morecambe</th>\n",
       "      <td>0.569600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crewe Alexandra</th>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sligo Rovers</th>\n",
       "      <td>0.566316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derry City</th>\n",
       "      <td>0.555882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bohemian FC</th>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Limerick FC</th>\n",
       "      <td>0.545263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bray Wanderers</th>\n",
       "      <td>0.536522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Overall\n",
       "Club                            \n",
       "St. Patrick's Athletic  0.577826\n",
       "Cambridge United        0.572593\n",
       "Waterford FC            0.570000\n",
       "Morecambe               0.569600\n",
       "Crewe Alexandra         0.566667\n",
       "Sligo Rovers            0.566316\n",
       "Derry City              0.555882\n",
       "Bohemian FC             0.550000\n",
       "Limerick FC             0.545263\n",
       "Bray Wanderers          0.536522"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"Club\", \"Overall\"]].groupby(\"Club\").mean().sort_values(\"Overall\", ascending=False).tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top 10 klauzul uwolnienia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29            Isco\n",
       "11        T. Kroos\n",
       "16         H. Kane\n",
       "7        L. Suárez\n",
       "17    A. Griezmann\n",
       "25       K. Mbappé\n",
       "5        E. Hazard\n",
       "4     K. De Bruyne\n",
       "0         L. Messi\n",
       "2        Neymar Jr\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(\"Release Clause\").tail(10)[\"Name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zależność między wiekiem a overall zawodników dla top 10 klubów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x202e9f658b0>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 499.225x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "#Wyświetlenie danych tylko dla top 10 klubów względem overall\n",
    "clubs = data[[\"Club\", \"Overall\"]].groupby(\"Club\", as_index=False).mean().sort_values(\"Overall\", ascending=False).head(10)[\"Club\"]\n",
    "\n",
    "data[data[\"Club\"].isin(clubs)]\n",
    "sns.relplot(data=data[data[\"Club\"].isin(clubs)], x=\"Overall\", y=\"Age\", hue=\"Club\")"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}