notebooks/prep_playbook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# retrieve local copies of raw and prepared data from dvc\n",
    "# checkout the readme for instructions about how to gain access\n",
    "\n",
    "# os.system(\"dvc pull\")\n",
    "\n",
    "home = os.path.expanduser(\"~/transfermarkt-datasets\")\n",
    "# when running from codespaces set the home directory as below instead\n",
    "# home = os.path.expanduser(\"/workspaces/transfermarkt-datasets/\")\n",
    "os.chdir(home)\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    import warnings\n",
    "    warnings.simplefilter(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the asset runner\n",
    "\n",
    "import pandas as pd\n",
    "from transfermarkt_datasets.core.dataset import Dataset\n",
    "\n",
    "td = Dataset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data using the \"load_assets\" method\n",
    "td.load_assets()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asset(name=cur_players)\n"
     ]
    }
   ],
   "source": [
    "# get a reference to one asset\n",
    "\n",
    "players = td.assets[\"cur_players\"]\n",
    "print(players)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>club_id</th>\n",
       "      <th>total_market_value</th>\n",
       "      <th>squad_size</th>\n",
       "      <th>average_age</th>\n",
       "      <th>foreigners_number</th>\n",
       "      <th>foreigners_percentage</th>\n",
       "      <th>national_team_players</th>\n",
       "      <th>stadium_seats</th>\n",
       "      <th>coach_name</th>\n",
       "      <th>last_season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>426.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>379.000000</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5314.525822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.403756</td>\n",
       "      <td>25.704639</td>\n",
       "      <td>10.983568</td>\n",
       "      <td>45.227968</td>\n",
       "      <td>4.788732</td>\n",
       "      <td>24284.410798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.830986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11752.013818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.817703</td>\n",
       "      <td>1.596598</td>\n",
       "      <td>6.726748</td>\n",
       "      <td>19.941673</td>\n",
       "      <td>5.083071</td>\n",
       "      <td>17132.848095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.207283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>421.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>30.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11024.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1139.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20046.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3415.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>32798.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83678.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>81365.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            club_id  total_market_value  squad_size  average_age  \\\n",
       "count    426.000000                 0.0  426.000000   388.000000   \n",
       "mean    5314.525822                 NaN   24.403756    25.704639   \n",
       "std    11752.013818                 NaN    8.817703     1.596598   \n",
       "min        3.000000                 NaN    0.000000    18.300000   \n",
       "25%      421.000000                 NaN   24.000000    24.700000   \n",
       "50%     1139.500000                 NaN   27.000000    25.700000   \n",
       "75%     3415.750000                 NaN   29.000000    26.900000   \n",
       "max    83678.000000                 NaN   41.000000    29.700000   \n",
       "\n",
       "       foreigners_number  foreigners_percentage  national_team_players  \\\n",
       "count         426.000000             379.000000             426.000000   \n",
       "mean           10.983568              45.227968               4.788732   \n",
       "std             6.726748              19.941673               5.083071   \n",
       "min             0.000000               2.400000               0.000000   \n",
       "25%             6.000000              30.800000               1.000000   \n",
       "50%            12.000000              46.700000               3.000000   \n",
       "75%            16.000000              58.000000               7.000000   \n",
       "max            31.000000             100.000000              22.000000   \n",
       "\n",
       "       stadium_seats  coach_name  last_season  \n",
       "count     426.000000         0.0   426.000000  \n",
       "mean    24284.410798         NaN  2020.830986  \n",
       "std     17132.848095         NaN     3.207283  \n",
       "min         0.000000         NaN  2012.000000  \n",
       "25%     11024.500000         NaN  2019.000000  \n",
       "50%     20046.000000         NaN  2023.000000  \n",
       "75%     32798.000000         NaN  2023.000000  \n",
       "max     81365.000000         NaN  2023.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a reference to the pandas dataframes containing prepared data\n",
    "\n",
    "clubs = td.assets[\"cur_clubs\"]\n",
    "\n",
    "prep = clubs.prep_df\n",
    "prep.describe()\n"
   ]
  }
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