Bonus - Scorecards.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdf = pd.read_csv('matches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "matchID = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df.match_id == 1) & (df.inning == 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Mandeep Singh', 'CH Gayle', 'KM Jadhav', 'TM Head',\n",
       "       'Sachin Baby', 'STR Binny', 'SR Watson', 'S Aravind', 'TS Mills',\n",
       "       'YS Chahal'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.player_dismissed.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "players = list(df.batsman.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CH Gayle',\n",
       " 'Mandeep Singh',\n",
       " 'TM Head',\n",
       " 'KM Jadhav',\n",
       " 'SR Watson',\n",
       " 'Sachin Baby',\n",
       " 'STR Binny',\n",
       " 'S Aravind',\n",
       " 'YS Chahal',\n",
       " 'TS Mills',\n",
       " 'A Choudhary']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs_ls = []\n",
    "balls_ls = []\n",
    "\n",
    "for player in players:\n",
    "    temp_df = df[df.batsman == player]\n",
    "    \n",
    "    if len(temp_df) > 0: \n",
    "        runs_ls.append(sum(temp_df.batsman_runs))\n",
    "        balls_ls.append(len(temp_df))\n",
    "    else:\n",
    "        runs_ls.append(0)\n",
    "        balls_ls.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "players_dismissed = list(df.player_dismissed.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan,\n",
       " 'Mandeep Singh',\n",
       " 'CH Gayle',\n",
       " 'KM Jadhav',\n",
       " 'TM Head',\n",
       " 'Sachin Baby',\n",
       " 'STR Binny',\n",
       " 'SR Watson',\n",
       " 'S Aravind',\n",
       " 'TS Mills',\n",
       " 'YS Chahal']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_dismissed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CH Gayle',\n",
       " 'Mandeep Singh',\n",
       " 'TM Head',\n",
       " 'KM Jadhav',\n",
       " 'SR Watson',\n",
       " 'Sachin Baby',\n",
       " 'STR Binny',\n",
       " 'S Aravind',\n",
       " 'YS Chahal',\n",
       " 'TS Mills',\n",
       " 'A Choudhary']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'bowled', 'caught', 'run out'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dismissal_kind.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_no_ls = []\n",
    "fielder_ls = []\n",
    "bowler_ls = []\n",
    "\n",
    "for player in players:\n",
    "    if player in players_dismissed:\n",
    "        out_no_ls.append('out')\n",
    "        temp_df = df[df.player_dismissed == player]\n",
    "        out_type = temp_df['dismissal_kind'].item()\n",
    "        \n",
    "        if out_type == 'caught':\n",
    "            fielder_ls.append(temp_df['fielder'].item())\n",
    "            bowler_ls.append(temp_df['bowler'].item())\n",
    "        elif out_type == 'bowled':\n",
    "            fielder_ls.append('')\n",
    "            bowler_ls.append(temp_df['bowler'].item())\n",
    "        else:\n",
    "            fielder_ls.append( 'runout' + '(' + str(temp_df['fielder'].item()) + ')')\n",
    "            bowler_ls.append('')\n",
    "        \n",
    "    else:\n",
    "        out_no_ls.append('no')\n",
    "        fielder_ls.append('')\n",
    "        bowler_ls.append('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['out', 'out', 'out', 'out', 'out', 'out', 'out', 'out', 'out', 'out', 'no'],\n",
       " ['DA Warner',\n",
       "  '',\n",
       "  'Yuvraj Singh',\n",
       "  'runout(BCJ Cutting)',\n",
       "  'MC Henriques',\n",
       "  'MC Henriques',\n",
       "  'Yuvraj Singh',\n",
       "  '',\n",
       "  'runout(CJ Jordan (sub))',\n",
       "  'DA Warner',\n",
       "  ''],\n",
       " ['DJ Hooda',\n",
       "  'Rashid Khan',\n",
       "  'Rashid Khan',\n",
       "  '',\n",
       "  'A Nehra',\n",
       "  'Bipul Sharma',\n",
       "  'B Kumar',\n",
       "  'A Nehra',\n",
       "  '',\n",
       "  'B Kumar',\n",
       "  ''])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_no_ls, fielder_ls, bowler_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs_balls_list = []\n",
    "\n",
    "for i in range(len(players)):\n",
    "    if out_no_ls[i] == 'no':\n",
    "        runs_balls_list.append( str(runs_ls[i]) + '*' + ' (' + str(balls_ls[i]) + ' )' )\n",
    "    else:\n",
    "        runs_balls_list.append( str(runs_ls[i]) + ' (' + str(balls_ls[i]) + ' )' )     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CH Gayle   DA Warner DJ Hooda 32 (23 )\n",
      "Mandeep Singh    Rashid Khan 24 (16 )\n",
      "TM Head   Yuvraj Singh Rashid Khan 30 (22 )\n",
      "KM Jadhav   runout(BCJ Cutting)  31 (18 )\n",
      "SR Watson   MC Henriques A Nehra 22 (17 )\n",
      "Sachin Baby   MC Henriques Bipul Sharma 1 (3 )\n",
      "STR Binny   Yuvraj Singh B Kumar 11 (10 )\n",
      "S Aravind    A Nehra 0 (2 )\n",
      "YS Chahal   runout(CJ Jordan (sub))  3 (7 )\n",
      "TS Mills   DA Warner B Kumar 6 (3 )\n",
      "A Choudhary     6* (2 )\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(players)):\n",
    "    print(players[i], ' ', fielder_ls[i], bowler_ls[i], runs_balls_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scorecard_dict = {'player':players, 'fielder':fielder_ls, 'bowler':bowler_ls, 'runs':runs_balls_list}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>player</th>\n",
       "      <th>fielder</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>32 (23 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td></td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>24 (16 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TM Head</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>30 (22 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>runout(BCJ Cutting)</td>\n",
       "      <td></td>\n",
       "      <td>31 (18 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SR Watson</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>22 (17 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Bipul Sharma</td>\n",
       "      <td>1 (3 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>STR Binny</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>11 (10 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S Aravind</td>\n",
       "      <td></td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0 (2 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>runout(CJ Jordan (sub))</td>\n",
       "      <td></td>\n",
       "      <td>3 (7 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TS Mills</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>6 (3 )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6* (2 )</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           player                  fielder        bowler      runs\n",
       "0        CH Gayle                DA Warner      DJ Hooda  32 (23 )\n",
       "1   Mandeep Singh                            Rashid Khan  24 (16 )\n",
       "2         TM Head             Yuvraj Singh   Rashid Khan  30 (22 )\n",
       "3       KM Jadhav      runout(BCJ Cutting)                31 (18 )\n",
       "4       SR Watson             MC Henriques       A Nehra  22 (17 )\n",
       "5     Sachin Baby             MC Henriques  Bipul Sharma    1 (3 )\n",
       "6       STR Binny             Yuvraj Singh       B Kumar  11 (10 )\n",
       "7       S Aravind                                A Nehra    0 (2 )\n",
       "8       YS Chahal  runout(CJ Jordan (sub))                  3 (7 )\n",
       "9        TS Mills                DA Warner       B Kumar    6 (3 )\n",
       "10    A Choudhary                                          6* (2 )"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scorecard_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scorecard_df = pd.DataFrame(scorecard_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scorecard_df.to_csv('scorecard.csv')"
   ]
  }
 ],
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