2048 AI analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\cedric\\Desktop\\2048 results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Score</th>\n",
       "      <th>Max Tile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32861.720000</td>\n",
       "      <td>2001.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13584.976637</td>\n",
       "      <td>867.474585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9684.000000</td>\n",
       "      <td>512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22352.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31804.000000</td>\n",
       "      <td>2048.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37637.000000</td>\n",
       "      <td>2048.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79212.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Score     Max Tile\n",
       "count    100.000000   100.000000\n",
       "mean   32861.720000  2001.920000\n",
       "std    13584.976637   867.474585\n",
       "min     9684.000000   512.000000\n",
       "25%    22352.000000  1024.000000\n",
       "50%    31804.000000  2048.000000\n",
       "75%    37637.000000  2048.000000\n",
       "max    79212.000000  4096.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_score = data['Score']\n",
    "data_max_tile = data['Max Tile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2e40f5108>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JOGUhSUkYyJKUhIEsSUkYyJKUhIEsSUkYyJKUhIEsSUn8PyGrRQzsnbQVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=data_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage_greater_than(n):\n",
    "    return (data.loc[data['Max Tile'] >= n]['Max Tile'].count() / 100) * 100\n",
    "\n",
    "max_tile = [512, 1024, 2048, 4096]\n",
    "percentages = [percentage_greater_than(n) for n in max_tile] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pik8/iIM9DEJjd1W9bdpDs+7Tqjq1qtZ1+24b8IdV9RFgH4NR6Fj3ZnEmD7Ivq+c4rl7GYLpq6qyi6cfnqiRT+Xg5cFV3/8vAcUmmrsR4FnD7IryEeTPcf+4k4HNJvgr8G/CxqvpEkhd079ZPBz6WZOoshFcCjwX+rDt18uYks80nP2gkeR/wReBxSfYn2QS8GXhOkj3Ac7plgDcwmPP8u0w79VT3sQG4iME0ytQxdh5z79O5bAO+CdzCYC75q1X1LyOseyW5FHhNkr0MjsepEfizgK8n+QaDbPgLgG7K63XA9iS3MJja+YfFLvpIePkBSWqQI3dJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhr0/0cNueBHXvR7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = sb.barplot(y=percentages, x=max_tile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}