first_innings_maxRuns.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "deliveries = pd.read_csv('Ipl Data/deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "deliveries=deliveries.replace(to_replace =\"Rising Pune Supergiant\", value =\"Rising Pune Supergiants\") \n",
    "deliveries=deliveries.replace(to_replace =\"Delhi Daredevils\", value =\"Delhi Capitals\") \n",
    "deliveries=deliveries.replace(to_replace =\"Deccan Chargers\", value =\"Sunrisers Hyderabad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "    <tr>\n",
       "      <th>1</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>2</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>3</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>4</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</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>5</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>193287</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>HV Patel</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AR Patel</td>\n",
       "      <td>caught</td>\n",
       "      <td>ASRoy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193288</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193289</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193290</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>NM Coulter-Nile</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193291</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>NM Coulter-Nile</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>MP Stoinis</td>\n",
       "      <td>caught</td>\n",
       "      <td>Q de Kock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100109 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "193287   1237181       1       Delhi Capitals               Mumbai Indians   \n",
       "193288   1237181       1       Delhi Capitals               Mumbai Indians   \n",
       "193289   1237181       1       Delhi Capitals               Mumbai Indians   \n",
       "193290   1237181       1       Delhi Capitals               Mumbai Indians   \n",
       "193291   1237181       1       Delhi Capitals               Mumbai Indians   \n",
       "\n",
       "        over  ball    batsman non_striker           bowler  is_super_over  \\\n",
       "0          1     1  DA Warner    S Dhawan         TS Mills              0   \n",
       "1          1     2  DA Warner    S Dhawan         TS Mills              0   \n",
       "2          1     3  DA Warner    S Dhawan         TS Mills              0   \n",
       "3          1     4  DA Warner    S Dhawan         TS Mills              0   \n",
       "4          1     5  DA Warner    S Dhawan         TS Mills              0   \n",
       "...      ...   ...        ...         ...              ...            ...   \n",
       "193287    20     2   HV Patel     SS Iyer  NM Coulter-Nile              0   \n",
       "193288    20     3    SS Iyer   SS Tiwary  NM Coulter-Nile              0   \n",
       "193289    20     4    SS Iyer   SS Tiwary  NM Coulter-Nile              0   \n",
       "193290    20     5    SS Iyer   SS Tiwary  NM Coulter-Nile              0   \n",
       "193291    20     6    SS Iyer   SS Tiwary  NM Coulter-Nile              0   \n",
       "\n",
       "        ...  bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0       ...         0            0            0             0             0   \n",
       "1       ...         0            0            0             0             0   \n",
       "2       ...         0            0            0             0             4   \n",
       "3       ...         0            0            0             0             0   \n",
       "4       ...         0            0            0             0             0   \n",
       "...     ...       ...          ...          ...           ...           ...   \n",
       "193287  ...         0            0            0             0             0   \n",
       "193288  ...         0            0            0             0             0   \n",
       "193289  ...         0            0            0             0             0   \n",
       "193290  ...         0            0            0             0             6   \n",
       "193291  ...         0            0            0             0             0   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind    fielder  \n",
       "0                0           0               NaN            NaN        NaN  \n",
       "1                0           0               NaN            NaN        NaN  \n",
       "2                0           4               NaN            NaN        NaN  \n",
       "3                0           0               NaN            NaN        NaN  \n",
       "4                2           2               NaN            NaN        NaN  \n",
       "...            ...         ...               ...            ...        ...  \n",
       "193287           0           0          AR Patel         caught      ASRoy  \n",
       "193288           0           0               NaN            NaN        NaN  \n",
       "193289           0           0               NaN            NaN        NaN  \n",
       "193290           0           6               NaN            NaN        NaN  \n",
       "193291           0           0        MP Stoinis         caught  Q de Kock  \n",
       "\n",
       "[100109 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries[deliveries['inning']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_runs = []\n",
    "matches = []\n",
    "batting_team = []\n",
    "for match_no in deliveries['match_id'].unique():\n",
    "    df = deliveries[(deliveries['match_id']==match_no) & (deliveries['inning']==1)]\n",
    "    runs = df['total_runs'].sum()\n",
    "    total_runs.append(runs)\n",
    "    matches.append(match_no)\n",
    "    batting_team.append(df['batting_team'].unique()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_no</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>1216495</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>1237177</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>1237178</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>1237180</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>1237181</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>816 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_no                 batting_team  total_runs\n",
       "0           1          Sunrisers Hyderabad         207\n",
       "1           2               Mumbai Indians         184\n",
       "2           3                Gujarat Lions         183\n",
       "3           4      Rising Pune Supergiants         163\n",
       "4           5  Royal Challengers Bangalore         157\n",
       "..        ...                          ...         ...\n",
       "811   1216495               Mumbai Indians         149\n",
       "812   1237177               Mumbai Indians         200\n",
       "813   1237178  Royal Challengers Bangalore         131\n",
       "814   1237180               Delhi Capitals         189\n",
       "815   1237181               Delhi Capitals         155\n",
       "\n",
       "[816 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame()\n",
    "df1['match_no'] = matches\n",
    "df1['batting_team'] = batting_team\n",
    "df1['total_runs'] = total_runs\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([233, 230, 208, 195, 263, 250, 231, 252, 246, 231, 178, 192],\n",
       " ['Sunrisers Hyderabad',\n",
       "  'Mumbai Indians',\n",
       "  'Gujarat Lions',\n",
       "  'Rising Pune Supergiants',\n",
       "  'Royal Challengers Bangalore',\n",
       "  'Kolkata Knight Riders',\n",
       "  'Delhi Capitals',\n",
       "  'Kings XI Punjab',\n",
       "  'Chennai Super Kings',\n",
       "  'Rajasthan Royals',\n",
       "  'Kochi Tuskers Kerala',\n",
       "  'Pune Warriors'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_runs = []\n",
    "final_team = []\n",
    "for team in df1['batting_team'].unique():\n",
    "    max_run = df1[df1['batting_team']==team]['total_runs'].max()\n",
    "    final_runs.append(max_run)\n",
    "    final_team.append(team)\n",
    "final_runs, final_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kavan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.barplot(final_runs, final_team)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}