notebooks/viz_playbook.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datadotworld as dw\n",
    "import pandas as pd\n",
    "\n",
    "dataset = dw.load_dataset('dcereijo/player-scores', force_update=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "appearances = dataset.dataframes['appearances']\n",
    "players = dataset.dataframes['players']\n",
    "games = dataset.dataframes['games']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "appearances_players = pd.merge(\n",
    "    appearances,\n",
    "    players,\n",
    "    on=\"player_id\"\n",
    ")\n",
    "\n",
    "merged = pd.merge(\n",
    "    appearances_players,\n",
    "    games,\n",
    "    on=\"game_id\"\n",
    ")\n",
    "\n",
    "scored_goals = merged.groupby(\n",
    "    by=[\"competition_code\", \"season\", \"player_id\"]\n",
    ").sum()[\"goals\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/q2/hh_dptgj26x8ntwrn4lnpp580000gn/T/ipykernel_17226/1211105413.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"all\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplayers_by_foot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"foot\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"N/a\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"position\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"foot\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"player_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplayers_by_foot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.close(\"all\")\n",
    "\n",
    "players_by_foot = players[players[\"foot\"] != \"N/a\"].groupby(by=[\"position\", \"foot\"]).count()[\"player_id\"]\n",
    "players_by_foot.unstack().transpose().plot.pie(subplots=True, figsize=(18, 18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.savefig('foot_proportion_by_postition.png')"
   ]
  }
 ]
}