graphWinrateVsGameDuration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a005a019",
   "metadata": {},
   "outputs": [],
   "source": [
    "from RiotParser import RiotParser\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "def get_winRate_list(df, parser):\n",
    "\n",
    "    # The list is represented as follows:\n",
    "    # winRates[0] : 0 - 15 mins\n",
    "    # winRates[1] : 15 - 20 mins\n",
    "    # winRates[2] : 20 - 25 mins\n",
    "    # winRates[3] : 25 - 30 mins\n",
    "    # winRates[4] : 30 - 35 mins\n",
    "    # winRates[5] : 35 - 40 mins\n",
    "    # winRates[6]: 40+ mins\n",
    "    winRates = []\n",
    "\n",
    "    # This is for winrate 0 - 15 mins\n",
    "    wins = df.loc[(df[parser.GAME_DURATION] >= 0) & \n",
    "                    (df[parser.GAME_DURATION] < 60 * 15) &\n",
    "                    (df[parser.MATCH_RESULT] == 'Victory')].shape[0]\n",
    "    totalGames = df.loc[(df[parser.GAME_DURATION] >= 0) & \n",
    "                         (df[parser.GAME_DURATION] < 60 * 15)].shape[0]\n",
    "\n",
    "    winrate = int(wins / totalGames * 100)\n",
    "    winRates.append(winrate)\n",
    "\n",
    "    # This is for the intervals between 15 and 40 mins\n",
    "    for i in range(3, 8):\n",
    "        \n",
    "        # winrate calculated by wins/total games within a certain timeframe\n",
    "        wins = df.loc[(df[parser.GAME_DURATION] >= 60 * (i * 5)) &\n",
    "                       (df[parser.GAME_DURATION] < 60 * ((i + 1) * 5)) &\n",
    "                       (df[parser.MATCH_RESULT] == 'Victory')].shape[0]\n",
    "        totalGames = df.loc[(df[parser.GAME_DURATION] >= 60 * (i * 5)) &\n",
    "                            (df[parser.GAME_DURATION] < 60 * ((i + 1) * 5))].shape[0]\n",
    "\n",
    "        winrate = int(wins / totalGames * 100)\n",
    "\n",
    "        winRates.append(winrate)\n",
    "\n",
    "    # This is for winrate 40+ mins\n",
    "    wins = df.loc[(df[parser.GAME_DURATION] >= 60 * 40) &\n",
    "                  (df[parser.MATCH_RESULT] == 'Victory')].shape[0]\n",
    "    totalGames = df.loc[(df[parser.GAME_DURATION] >= 60 * 40)].shape[0]\n",
    "\n",
    "    winrate = int(wins / totalGames * 100)\n",
    "    winRates.append(winrate)\n",
    "\n",
    "    return winRates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1dc09ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in the files\n",
    "df1 = pd.read_csv('Summoner Data\\Controleed Freak.csv')\n",
    "df2 = pd.read_csv('Summoner Data\\Ender Dragon 3.csv')\n",
    "df3 = pd.read_csv('Summoner Data\\cyrs7.csv')\n",
    "df4 = pd.read_csv('Summoner Data\\Belox.csv')\n",
    "df5 = pd.read_csv('Summoner Data\\TF Blade.csv')\n",
    "df6 = pd.read_csv('Summoner Data\\Adrian Riven.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c9ef522",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Axis Labels:\n",
    "# x-axis: matchtime\n",
    "# y-axis: players\n",
    "matchtime = ['0 - 15', '15 - 20', '20 - 25',\n",
    "             '25 - 30', '30 - 35', '35 - 40',\n",
    "             '40+']\n",
    "players = ['Controleed Freak', 'Ender Dragon 3', 'cyrs7', 'Belox', 'TF Blade', 'Adrian Riven']\n",
    "\n",
    "parser = RiotParser('Controleed Freak')\n",
    "winrateArray = np.array([get_winRate_list(df1, parser),\n",
    "                        get_winRate_list(df2, parser),\n",
    "                        get_winRate_list(df3, parser),\n",
    "                        get_winRate_list(df4, parser),\n",
    "                        get_winRate_list(df5, parser),\n",
    "                        get_winRate_list(df6, parser)])\n",
    "\n",
    "# Heat map Specifics:\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "sb.heatmap(winrateArray, xticklabels=matchtime, yticklabels=players, annot=True, cmap = \"afmhot\", ax=ax)\n",
    "#sb.color_palette(\"dark:salmon_r\", as_cmap=True)\n",
    "\n",
    "plt.xlabel(\"Game Duration (mins)\")\n",
    "plt.ylabel(\"Players\")\n",
    "\n",
    "ax.set_title(\"% Winrate by Game Duration (300+ Games)\")\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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