stat_playing_cards.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_list = df.player_id.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility functions\n",
    "def get_winning_rate(df, player):\n",
    "    '''calculate the winning rate of the specified player'''\n",
    "    df_player = df[df.player_id == player]\n",
    "    df_player_win = df_player[df_player.player_win_lose == 'win']\n",
    "    winning_rate = len(df_player_win) / len(df_player)\n",
    "    return winning_rate\n",
    "\n",
    "def get_n_games(df, player):\n",
    "    '''calculate the number of games played by the specified player'''\n",
    "    df_player = df[df.player_id == player]\n",
    "    return len(df_player)\n",
    "\n",
    "def get_dealer_winning_rate_at_n_decks(df, n_decks):\n",
    "    '''calculate the dealer's winning rate of the specified n_decks'''\n",
    "    df_n_decks = df[df.n_decks == n_decks]\n",
    "    if len(df_n_decks) > 0:\n",
    "        df_selected = df[(df.n_decks == n_decks) & (df.dealer_win_lose == 'win')]\n",
    "        winning_rate = df_selected.game_id.nunique() / df_n_decks.game_id.nunique()\n",
    "        return winning_rate\n",
    "    else:\n",
    "        return 'na'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_decks_list = list(range(3, df.n_decks.max()+1))\n",
    "# n_decks\n",
    "dealer_winning_rates = []\n",
    "for n_decks in n_decks_list:\n",
    "    dealer_winning_rates.append(get_dealer_winning_rate_at_n_decks(df, n_decks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4, 0.42105263157894735, 0.42857142857142855, 0.42857142857142855, 0.75]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dealer_winning_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatterplot(n_decks_list, dealer_winning_rates)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}