2022-05-11-chess-move-heatmap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "933f861f-af4c-4abb-8521-46dd891388ee",
   "metadata": {},
   "source": [
    "## Import Libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "36e436f5-e29d-4b2e-b802-6cc3f3f57dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e5067d-090d-44c6-ab62-3a8b9976382e",
   "metadata": {},
   "source": [
    "## Import dataset. Filter by piece and piece color. Drop excess columns, keeping only end square and piece code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "4d43f9c7-d187-42ed-8409-eb5b8dd12ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "piece_code = \"Q1\"\n",
    "move_color = \"white\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "712cdaa1-b090-43f0-a8d1-eaad2acab945",
   "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>end_square.x</th>\n",
       "      <th>end_square.y</th>\n",
       "      <th>piece_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    end_square.x  end_square.y piece_code\n",
       "8              4             4         Q1\n",
       "12             4             1         Q1\n",
       "28             4             6         Q1\n",
       "35             2             8         Q1\n",
       "44             4             4         Q1"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\chris\\\\Documents\\\\Datasets\\\\Chess PGN\\\\pgn_move_records.csv\", dtype={\"eco\": str})\n",
    "df = df[(df[\"piece_code\"] == piece_code) & (df[\"move_color\"] == move_color)]\n",
    "df = df[[\"end_square.x\", \"end_square.y\", \"piece_code\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54cec01-e14f-4a87-8d20-3b91c5da47fc",
   "metadata": {},
   "source": [
    "## Group by square and count occurrences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "d640c34a-bdee-434d-85b3-92502acf7c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>end_square.x</th>\n",
       "      <th>end_square.y</th>\n",
       "      <th>move_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   end_square.x  end_square.y  move_count\n",
       "0             1             1           4\n",
       "1             1             2           2\n",
       "2             1             3           4\n",
       "3             1             4          46\n",
       "4             1             5          16"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.groupby([\"end_square.x\", \"end_square.y\"]).count().reset_index()\n",
    "df.rename(columns = {\"piece_code\" : \"move_count\"}, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f705d024-5c10-4592-991d-1ce51648b640",
   "metadata": {},
   "source": [
    "## Fill our table with zeros and replace our x-values with letters so our heatmap resembles a chessboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "bdc7a627-c8d9-49e8-b841-5271c990a530",
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(1,9):\n",
    "    for y in range(1,9):\n",
    "        if (x not in list(df[\"end_square.x\"])) or (y not in list(df[\"end_square.y\"])):\n",
    "            df2 = {\"end_square.x\" : x, \"end_square.y\" : y, \"move_count\": 0}\n",
    "            df = df.append(df2, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "4a7c8acf-bc58-494b-aa92-4a86d82a8b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(1, \"a\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(2, \"b\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(3, \"c\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(4, \"d\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(5, \"e\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(6, \"f\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(7, \"g\")\n",
    "df[\"end_square.x\"] = df[\"end_square.x\"].replace(8, \"h\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a75c605-1648-4794-8370-526094b4f837",
   "metadata": {},
   "source": [
    "## Create a pivot table from our data. Fill empty/NA cells with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c6e4540e-2c39-431d-a46f-d95967131805",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>end_square.x</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_square.y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>86</td>\n",
       "      <td>134</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>112</td>\n",
       "      <td>64</td>\n",
       "      <td>156</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>119</td>\n",
       "      <td>60</td>\n",
       "      <td>48</td>\n",
       "      <td>73</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33</td>\n",
       "      <td>59</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "      <td>44</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "end_square.x   a   b   c    d    e    f   g   h\n",
       "end_square.y                                   \n",
       "1              4   6   7   36   14    7   3   3\n",
       "2              2   6  30   86  134   18  14   4\n",
       "3              4  36  46  112   64  156  38  31\n",
       "4             46  31  52  119   60   48  73  22\n",
       "5             16  45  30   68   52   35  34  74\n",
       "6             24  14  43   28   36   35  20  28\n",
       "7             33  59  31   27   29   53  44  32\n",
       "8             14  10  11   40   18   13   9  19"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df.pivot_table(index=\"end_square.y\", columns=\"end_square.x\", values=\"move_count\", fill_value=0, dropna=False)\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b00f225-2f42-4a55-b6a5-9bc3ca08987a",
   "metadata": {},
   "source": [
    "## Create heatmap using Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "acf61004-7cfc-46cd-9c04-c28b35c342e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {\"figure.figsize\" : (12,12)})\n",
    "sns.set(font_scale = 2)\n",
    "heatmap = sns.heatmap(df_pivot, fmt=\"d\", cmap=\"YlGnBu\", square=True, xticklabels=True, yticklabels=True, linewidths=2, linecolor=\"black\", cbar=False)\n",
    "heatmap.invert_yaxis()\n",
    "heatmap.set(xlabel=None, ylabel=None)\n",
    "heatmap.set_title(f\"Chess Piece - Placement Frequencies\\n{piece_code} - {move_color.capitalize()}\\n\")\n",
    "heatmap.tick_params(left=False, bottom=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "033a9b95-3efa-4185-b593-b162e9016486",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = heatmap.get_figure()\n",
    "fig.savefig(\"chess-heatmap.png\") "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}