Chess_data1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maven analytics Queen Gambit chess data set challenge analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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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>game_id</th>\n",
       "      <th>rated</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>time_increment</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_code</th>\n",
       "      <th>opening_moves</th>\n",
       "      <th>opening_fullname</th>\n",
       "      <th>opening_shortname</th>\n",
       "      <th>opening_response</th>\n",
       "      <th>opening_variation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>Out of Time</td>\n",
       "      <td>White</td>\n",
       "      <td>15+2</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>1500</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1191</td>\n",
       "      <td>d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...</td>\n",
       "      <td>D10</td>\n",
       "      <td>5</td>\n",
       "      <td>Slav Defense: Exchange Variation</td>\n",
       "      <td>Slav Defense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Exchange Variation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>Resign</td>\n",
       "      <td>Black</td>\n",
       "      <td>5+10</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1322</td>\n",
       "      <td>skinnerua</td>\n",
       "      <td>1261</td>\n",
       "      <td>d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...</td>\n",
       "      <td>B00</td>\n",
       "      <td>4</td>\n",
       "      <td>Nimzowitsch Defense: Kennedy Variation</td>\n",
       "      <td>Nimzowitsch Defense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kennedy Variation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>61</td>\n",
       "      <td>Mate</td>\n",
       "      <td>White</td>\n",
       "      <td>5+10</td>\n",
       "      <td>ischia</td>\n",
       "      <td>1496</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1500</td>\n",
       "      <td>e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...</td>\n",
       "      <td>C20</td>\n",
       "      <td>3</td>\n",
       "      <td>King's Pawn Game: Leonardis Variation</td>\n",
       "      <td>King's Pawn Game</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leonardis Variation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>61</td>\n",
       "      <td>Mate</td>\n",
       "      <td>White</td>\n",
       "      <td>20+0</td>\n",
       "      <td>daniamurashov</td>\n",
       "      <td>1439</td>\n",
       "      <td>adivanov2009</td>\n",
       "      <td>1454</td>\n",
       "      <td>d4 d5 Nf3 Bf5 Nc3 Nf6 Bf4 Ng4 e3 Nc6 Be2 Qd7 O...</td>\n",
       "      <td>D02</td>\n",
       "      <td>3</td>\n",
       "      <td>Queen's Pawn Game: Zukertort Variation</td>\n",
       "      <td>Queen's Pawn Game</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zukertort Variation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>95</td>\n",
       "      <td>Mate</td>\n",
       "      <td>White</td>\n",
       "      <td>30+3</td>\n",
       "      <td>nik221107</td>\n",
       "      <td>1523</td>\n",
       "      <td>adivanov2009</td>\n",
       "      <td>1469</td>\n",
       "      <td>e4 e5 Nf3 d6 d4 Nc6 d5 Nb4 a3 Na6 Nc3 Be7 b4 N...</td>\n",
       "      <td>C41</td>\n",
       "      <td>5</td>\n",
       "      <td>Philidor Defense</td>\n",
       "      <td>Philidor Defense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   game_id  rated  turns victory_status winner time_increment       white_id  \\\n",
       "0        1  False     13    Out of Time  White           15+2       bourgris   \n",
       "1        2   True     16         Resign  Black           5+10           a-00   \n",
       "2        3   True     61           Mate  White           5+10         ischia   \n",
       "3        4   True     61           Mate  White           20+0  daniamurashov   \n",
       "4        5   True     95           Mate  White           30+3      nik221107   \n",
       "\n",
       "   white_rating      black_id  black_rating  \\\n",
       "0          1500          a-00          1191   \n",
       "1          1322     skinnerua          1261   \n",
       "2          1496          a-00          1500   \n",
       "3          1439  adivanov2009          1454   \n",
       "4          1523  adivanov2009          1469   \n",
       "\n",
       "                                               moves opening_code  \\\n",
       "0  d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...          D10   \n",
       "1  d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...          B00   \n",
       "2  e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...          C20   \n",
       "3  d4 d5 Nf3 Bf5 Nc3 Nf6 Bf4 Ng4 e3 Nc6 Be2 Qd7 O...          D02   \n",
       "4  e4 e5 Nf3 d6 d4 Nc6 d5 Nb4 a3 Na6 Nc3 Be7 b4 N...          C41   \n",
       "\n",
       "   opening_moves                        opening_fullname    opening_shortname  \\\n",
       "0              5        Slav Defense: Exchange Variation         Slav Defense   \n",
       "1              4  Nimzowitsch Defense: Kennedy Variation  Nimzowitsch Defense   \n",
       "2              3   King's Pawn Game: Leonardis Variation     King's Pawn Game   \n",
       "3              3  Queen's Pawn Game: Zukertort Variation    Queen's Pawn Game   \n",
       "4              5                        Philidor Defense     Philidor Defense   \n",
       "\n",
       "  opening_response    opening_variation  \n",
       "0              NaN   Exchange Variation  \n",
       "1              NaN    Kennedy Variation  \n",
       "2              NaN  Leonardis Variation  \n",
       "3              NaN  Zukertort Variation  \n",
       "4              NaN                  NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess_data = pd.read_csv(\"../data/chess_games.csv\")\n",
    "chess_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "game_id                  0\n",
       "rated                    0\n",
       "turns                    0\n",
       "victory_status           0\n",
       "winner                   0\n",
       "time_increment           0\n",
       "white_id                 0\n",
       "white_rating             0\n",
       "black_id                 0\n",
       "black_rating             0\n",
       "moves                    0\n",
       "opening_code             0\n",
       "opening_moves            0\n",
       "opening_fullname         0\n",
       "opening_shortname        0\n",
       "opening_response     18851\n",
       "opening_variation     5660\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_no_of_games = len(chess_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_wins = len(chess_data.winner[chess_data.winner.values == \"White\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentage_of_white_wins = (white_wins/total_no_of_games) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of white wins:  49.86040482600458\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of white wins: \",percentage_of_white_wins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of black wins:  45.403330342008175\n"
     ]
    }
   ],
   "source": [
    "black_wins = len(chess_data.winner[chess_data.winner.values == \"Black\"])\n",
    "percentage_of_black_wins = (black_wins/total_no_of_games) * 100\n",
    "print(\"Percentage of black wins: \",percentage_of_black_wins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "950"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws =len(chess_data.winner[chess_data.winner.values == \"Draw\"])\n",
    "draws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Draws:  4.736264831987238\n"
     ]
    }
   ],
   "source": [
    "percentage_of_draws = (draws/total_no_of_games) * 100\n",
    "print(\"Percentage of Draws: \",percentage_of_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "win_df = pd.DataFrame(chess_data.winner.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Draw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>winner</th>\n",
       "      <td>10001</td>\n",
       "      <td>9107</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        White  Black  Draw\n",
       "winner  10001   9107   950"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Percentage of winners and draws'}>"
      ]
     },
     "execution_count": 44,
     "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": [
    "win_df.transpose().plot(kind=\"barh\", color ={\"White\":\"grey\",\"Black\":\"black\",\"Draw\":\"red\"},title=\"Percentage of winners and draws\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total no of openning moves\n",
    "len(chess_data[chess_data.winner.values == \"White\"].opening_shortname.value_counts()) # white was used because it had more than black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.02879712028797"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 5 winning opening moves for white\n",
    "plt.figure(figsize=(10,10))\n",
    "# ax=plt.figure()\n",
    "# ax.set_title(\"Top 5 winning Opening moves for white\",size=30)\n",
    "plt.rc('axes', titlesize=30)\n",
    "chess_data[chess_data.winner.values == \"White\"].opening_shortname.value_counts(ascending=True)[-5:].plot(kind=\"barh\", title=\"Top 5 winning Opening moves for white\",fontsize=30)\n",
    "sicilian_wins =chess_data[chess_data.winner.values == \"White\"].opening_shortname.value_counts(ascending=True)[\"Sicilian Defense\"]\n",
    "percentage_of_scilian_white = (sicilian_wins/white_wins) *100\n",
    "percentage_of_scilian_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.263753156912266"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.rc('axes', titlesize=30)\n",
    "chess_data[chess_data.winner.values == \"Black\"].opening_shortname.value_counts(ascending=True)[-5:].plot(kind=\"barh\", title=\"Top 5 winning Opening moves for Black\",fontsize=30)\n",
    "sicilian_wins_black =chess_data[chess_data.winner.values == \"Black\"].opening_shortname.value_counts(ascending=True)[\"Sicilian Defense\"]\n",
    "percentage_of_scilian_black = (sicilian_wins_black/black_wins) *100\n",
    "percentage_of_scilian_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.684210526315791"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.rc('axes', titlesize=30)\n",
    "chess_data[chess_data.winner.values == \"Draw\"].opening_shortname.value_counts(ascending=True)[-5:].plot(kind=\"barh\", title=\"Top 5 winning Opening moves for Draw\", fontsize=30)\n",
    "sicilian_wins_Draw =chess_data[chess_data.winner.values == \"Draw\"].opening_shortname.value_counts(ascending=True)[\"Sicilian Defense\"]\n",
    "percentage_of_scilian_Draw = (sicilian_wins_Draw/draws) *100\n",
    "percentage_of_scilian_Draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of games that used either of the 3 most common Openning moves : 26.308704756207003\n"
     ]
    }
   ],
   "source": [
    "# #No of wins with sicilian defense starting in all three outcomes white wins, black wins, draw\n",
    "sicilian_wins_white =chess_data[chess_data.winner.values == \"White\"].opening_shortname.value_counts(ascending=True)[\"Sicilian Defense\"]\n",
    "sicilian_wins_black =chess_data[chess_data.winner.values == \"Black\"].opening_shortname.value_counts(ascending=True)[\"Sicilian Defense\"]\n",
    "sicilian_wins_draw =chess_data[chess_data.winner.values == \"Draw\"].opening_shortname.value_counts(ascending=True)[\"Sicilian Defense\"]\n",
    "\n",
    "#No of wins with French defense starting in all three outcomes white wins, black wins, draw\n",
    "French_wins_white =chess_data[chess_data.winner.values == \"White\"].opening_shortname.value_counts(ascending=True)[\"French Defense\"]\n",
    "French_wins_black =chess_data[chess_data.winner.values == \"Black\"].opening_shortname.value_counts(ascending=True)[\"French Defense\"]\n",
    "French_wins_draw =chess_data[chess_data.winner.values == \"Draw\"].opening_shortname.value_counts(ascending=True)[\"French Defense\"]\n",
    "\n",
    "#No of wins with French defense starting in all three outcomes white wins, black wins, draw\n",
    "Queen_Pawn_wins_white =chess_data[chess_data.winner.values == \"White\"].opening_shortname.value_counts(ascending=True)[\"Queen's Pawn Game\"]\n",
    "Queen_Pawn_wins_black =chess_data[chess_data.winner.values == \"Black\"].opening_shortname.value_counts(ascending=True)[\"Queen's Pawn Game\"]\n",
    "Queen_Pawn_wins_draw =chess_data[chess_data.winner.values == \"Draw\"].opening_shortname.value_counts(ascending=True)[\"Queen's Pawn Game\"]\n",
    "\n",
    "#Total No of wins by the three most used winning openning moves as shown above\n",
    "total_no_of_wins_3_most= (sicilian_wins_white + sicilian_wins_black + French_wins_white + French_wins_black +Queen_Pawn_wins_white + Queen_Pawn_wins_black + sicilian_wins_draw + French_wins_draw + Queen_Pawn_wins_draw)\n",
    "percentage_of_wins_3_most = (total_no_of_wins_3_most/total_no_of_games) * 100\n",
    "print(\"Percentage of games that used either of the 3 most common Openning moves :\",percentage_of_wins_3_most )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mieses Opening              1\n",
       "East Indian Defense         1\n",
       "Barnes Defense              1\n",
       "Englund Gambit Complex      1\n",
       "Carr Defense                1\n",
       "                         ... \n",
       "Italian Game               47\n",
       "Ruy Lopez                  49\n",
       "Queen's Pawn Game          61\n",
       "French Defense             70\n",
       "Sicilian Defense          130\n",
       "Name: opening_shortname, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess_data[chess_data.winner.values == \"Draw\"].opening_shortname.value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "wins = len(chess_data[chess_data.winner.values == \"White\"]) + len(chess_data[chess_data.winner.values == \"Black\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#amount of chess games where when white won it had a higher rating\n",
    "white_higher_rate_wins=len(chess_data[chess_data.winner.values == \"White\"][chess_data.white_rating > chess_data.black_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#amount of chess games where when white won it had a lower rating\n",
    "white_lower_rate_wins=len(chess_data[chess_data.winner.values == \"White\"][chess_data.black_rating > chess_data.white_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#amount of chess games where when black won it had a higher rating\n",
    "black_higher_rate_wins=len(chess_data[chess_data.winner.values == \"Black\"][chess_data.black_rating > chess_data.white_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#amount of chess games where when black won it had a lower rating\n",
    "black_lower_rate_wins=len(chess_data[chess_data.winner.values == \"Black\"][chess_data.white_rating > chess_data.black_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.643081431861"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_of_higher_rating_wins = ((white_higher_rate_wins + black_higher_rate_wins)/wins) *100\n",
    "percentage_of_higher_rating_wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.2834716528347"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_of_higher_rating_wins_white= (white_higher_rate_wins/white_wins) *100\n",
    "percentage_of_higher_rating_wins_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.939826507082465"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_of_higher_rating_wins_black= (black_higher_rate_wins/black_wins) *100\n",
    "percentage_of_higher_rating_wins_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols={\"Black Wins\"}\n",
    "rows ={\"Higher Rating\",\"Lower Rating\"}\n",
    "data = {black_lower_rate_wins,black_higher_rate_wins}\n",
    "ratings_df=pd.DataFrame(data,columns=cols,index=rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df[\"White Wins\"]={white_lower_rate_wins,white_higher_rate_wins}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Higher rating VS Lower rating of wins'}>"
      ]
     },
     "execution_count": 130,
     "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": [
    "c={\"White Wins\":\"grey\",\"Black Wins\":\"black\"}\n",
    "ratings_df.plot(kind=\"barh\",color=c,title=\"Higher rating VS Lower rating of wins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "taranga               34\n",
       "ssf7                  29\n",
       "hassan1365416         28\n",
       "a_p_t_e_m_u_u         25\n",
       "vladimir-kramnik-1    22\n",
       "                      ..\n",
       "stlan                  1\n",
       "bawer234               1\n",
       "zvonkonikolic          1\n",
       "mohamad-1362           1\n",
       "amv13                  1\n",
       "Name: white_id, Length: 5437, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The player with the most wins using white\n",
    "pl_highest_wins_white =chess_data[chess_data.winner == \"White\"].white_id.value_counts()[0]\n",
    "chess_data[chess_data.winner == \"White\"].white_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl_highest_wins_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "taranga               38\n",
       "ducksandcats          29\n",
       "vladimir-kramnik-1    28\n",
       "chesscarl             27\n",
       "docboss               25\n",
       "                      ..\n",
       "onuzq                  1\n",
       "tireddoc               1\n",
       "ilchenko               1\n",
       "ddd12345               1\n",
       "coach01                1\n",
       "Name: black_id, Length: 4943, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The player with the most wins using black\n",
    "pl_highest_wins_black =chess_data[chess_data.winner == \"Black\"].black_id.value_counts()[0]\n",
    "chess_data[chess_data.winner == \"Black\"].black_id.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No of games played by taranga(player with most wins) with white\n",
    "len(chess_data[chess_data.white_id == \"taranga\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##No of games played by taranga(player with most wins) with black\n",
    "len(chess_data[chess_data.black_id == \"taranga\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#no of times taranga(Player with most wins) was higher rated player in white\n",
    "taranga_high_white = len(chess_data[chess_data.winner == \"White\"][chess_data.white_id == \"taranga\"][chess_data.white_rating > chess_data.black_rating])\n",
    "taranga_low_white = len(chess_data[chess_data.winner == \"White\"][chess_data.white_id == \"taranga\"][chess_data.black_rating > chess_data.white_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#no of times taranga(Player with most wins) was higher rated player in black\n",
    "taranga_high_black = len(chess_data[chess_data.winner == \"Black\"][chess_data.black_id == \"taranga\"][chess_data.black_rating > chess_data.white_rating])\n",
    "taranga_low_black = len(chess_data[chess_data.winner == \"Black\"][chess_data.black_id == \"taranga\"][chess_data.white_rating > chess_data.black_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Percentage_of_games_taranga_higher_rated = (taranga_high_white + taranga_high_black)/(pl_highest_wins_white+pl_highest_wins_black) * 100\n",
    "Percentage_of_games_taranga_higher_rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>White Wins</th>\n",
       "      <th>Black Wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lower Rating</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Higher Rating</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               White Wins  Black Wins\n",
       "Lower Rating           19          17\n",
       "Higher Rating          15          21"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colu={\"White Wins\"}\n",
    "row = {\"Higher Rating\",\"Lower Rating\"}\n",
    "data1 = {taranga_low_white,taranga_high_white}\n",
    "taranga_df=pd.DataFrame(data1,columns=colu,index=row)\n",
    "taranga_df[\"Black Wins\"]={taranga_low_black,taranga_high_black}\n",
    "taranga_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Taranga Black VS White wins with ratings'}>"
      ]
     },
     "execution_count": 169,
     "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": [
    "c={\"White Wins\":\"grey\",\"Black Wins\":\"black\"}\n",
    "taranga_df.transpose().plot.bar(stacked=True,rot=0,title=\"Taranga Black VS White wins with ratings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Victory Outcome'}, ylabel=' '>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of Victory outcomes \n",
    "chess_data.victory_status.value_counts().plot(kind=\"pie\",title=\"Victory Outcome\", ylabel=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Resign         11147\n",
       "Mate            6325\n",
       "Out of Time     1680\n",
       "Draw             906\n",
       "Name: victory_status, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess_data.victory_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#amount of games that ended in resignation from a lower rated player in white\n",
    "resign_high_white=len(chess_data[chess_data.victory_status == \"Resign\"][chess_data.winner == \"White\"][chess_data.white_rating > chess_data.black_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "##amount of games that ended in resignation from a lower rated player in black\n",
    "resign_high_black=len(chess_data[chess_data.victory_status == \"Resign\"][chess_data.winner == \"Black\"][chess_data.black_rating > chess_data.white_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of games that ended in resign:  55.57383587595972\n",
      "Percentage of resign games won by higher rated players:  64.32223916748902\n"
     ]
    }
   ],
   "source": [
    "#percentage of games that ended in resign \n",
    "\n",
    "no_of_resign = chess_data.victory_status.value_counts()[0]\n",
    "percentage_resign = (no_of_resign/total_no_of_games) *100\n",
    "print(\"Percentage of games that ended in resign: \",percentage_resign)\n",
    "#percentage of resign games that were won by higher rated players\n",
    "percentage_high_resign = ((resign_high_white + resign_high_black)/no_of_resign) * 100\n",
    "print(\"Percentage of resign games won by higher rated players: \",percentage_high_resign)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Percentage of Resign games won by Higher and Lower rated players'}, ylabel=' '>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co={\"Higher rated players\":64,\"Lower rated players\":36}\n",
    "index=(1)\n",
    "dat=[[64,36]]\n",
    "dat1=[36]\n",
    "#df_=pd.Series(dat,columns=[\"Higher rated players\",\"Lower rated players\"])\n",
    "df_=pd.Series(co)\n",
    "df_.plot(kind=\"pie\",ylabel=\" \", title=\"Percentage of Resign games won by Higher and Lower rated players\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "507"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##amount of games that ended in Out of Time from a lower rated player in white\n",
    "len(chess_data[chess_data.victory_status == \"Out of Time\"][chess_data.winner == \"White\"][chess_data.white_rating > chess_data.black_rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\agbaj\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "482"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##amount of games that ended in Out of Time from a lower rated player in black\n",
    "len(chess_data[chess_data.victory_status == \"Out of Time\"][chess_data.winner == \"Black\"][chess_data.black_rating > chess_data.white_rating])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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