10. Venue and Toss Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5c09dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option(\"display.precision\", 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb258a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = pd.read_csv(\"C:/Users/utkar/OneDrive/Desktop/Analytics/Data/IPL Matches 2008-2020.csv\")\n",
    "deliveres = pd.read_csv(\"C:/Users/utkar/OneDrive/Desktop/Analytics/Data/IPL Ball-by-Ball 2008-2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "337e2def",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = matches.copy()\n",
    "del_df = deliveres.copy()\n",
    "\n",
    "# Take data from 2016 onwards only \n",
    "match_df = match_df[match_df.id >= 980901]\n",
    "del_df = del_df[del_df.id >= 980901]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c5bd53",
   "metadata": {},
   "source": [
    "### Toss Advantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51ece20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# Get Team wise Win Percentage after winning the toss\n",
    "# Which teams are able to use Toss advantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da1053f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df['TossWinnerIsWinner'] = match_df.apply(lambda x: x['toss_winner'] == x['winner'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19961e04",
   "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>toss_winner</th>\n",
       "      <th>WinPercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>61.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>61.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>60.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>59.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>43.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>41.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>37.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   toss_winner  WinPercentage\n",
       "0                Gujarat Lions          66.67\n",
       "1      Rising Pune Supergiants          61.54\n",
       "2          Chennai Super Kings          61.29\n",
       "3               Mumbai Indians          60.98\n",
       "4        Kolkata Knight Riders          60.00\n",
       "5          Sunrisers Hyderabad          59.46\n",
       "6             Delhi Daredevils          50.00\n",
       "7  Royal Challengers Bangalore          43.75\n",
       "8             Rajasthan Royals          41.67\n",
       "9              Kings XI Punjab          37.04"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Win Percentage after winning the toss for each team\n",
    "pd.DataFrame(100 * match_df.groupby('toss_winner').TossWinnerIsWinner.sum() / match_df.groupby('toss_winner').TossWinnerIsWinner.count()).rename(columns = {'TossWinnerIsWinner' : 'WinPercentage'}).sort_values(by = 'WinPercentage', ascending = False).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb467f6",
   "metadata": {},
   "source": [
    "### Venue wise match results while batting 1st or 2nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f67b4148",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# venue-wise impact of batting first or second on match result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04e9a26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTeamBattingFirst(t1, t2, toss_winner, toss_decision): \n",
    "    if (toss_decision == 'bat'): \n",
    "        return toss_winner\n",
    "    else: \n",
    "        if (toss_winner == t1): \n",
    "            return t2\n",
    "        else: \n",
    "            return t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "002569c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTeamBattingSecond(t1, t2, toss_winner, toss_decision): \n",
    "    if (toss_decision == 'field'): \n",
    "        return toss_winner\n",
    "    else: \n",
    "        if (toss_winner == t1): \n",
    "            return t2\n",
    "        else: \n",
    "            return t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d117197",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df['TeamBattingFirst'] = match_df.apply(lambda x: getTeamBattingFirst(x['team1'] , x['team2'] , x['toss_winner'] , x['toss_decision']) , axis=1)\n",
    "match_df['TeamBattingSecond'] = match_df.apply(lambda x: getTeamBattingSecond(x['team1'] , x['team2'] , x['toss_winner'] , x['toss_decision']) , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bd3e68e",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df['TeamBattingFirstWins'] = match_df.apply( lambda x : 1 if x['TeamBattingFirst'] == x['winner'] else 0 , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0378a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df['TeamBattingSecondWins'] = match_df.apply( lambda x : 1 if x['TeamBattingSecond'] == x['winner'] else 0 , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b469d619",
   "metadata": {},
   "outputs": [],
   "source": [
    "WinPercentage = pd.DataFrame(100 * match_df.groupby('venue')['TeamBattingFirstWins'].sum() / match_df.groupby('venue')['TeamBattingFirstWins'].count()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a2f966e",
   "metadata": {},
   "outputs": [],
   "source": [
    "WinPercentage.columns = ['venue', 'WinBattingFirst']\n",
    "WinPercentage['WinBattingSecond'] = (100 - WinPercentage['WinBattingFirst'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea171b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue</th>\n",
       "      <th>WinBattingFirst</th>\n",
       "      <th>WinBattingSecond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>61.54</td>\n",
       "      <td>38.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>53.57</td>\n",
       "      <td>46.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>44.44</td>\n",
       "      <td>55.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>42.86</td>\n",
       "      <td>57.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>41.94</td>\n",
       "      <td>58.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>41.67</td>\n",
       "      <td>58.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>40.91</td>\n",
       "      <td>59.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>40.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>39.29</td>\n",
       "      <td>60.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>37.50</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>35.71</td>\n",
       "      <td>64.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>30.00</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>29.41</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>14.29</td>\n",
       "      <td>85.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Green Park</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                venue  WinBattingFirst  \\\n",
       "1                 Dubai International Cricket Stadium            61.54   \n",
       "3                                    Feroz Shah Kotla            53.57   \n",
       "10          Rajiv Gandhi International Stadium, Uppal            50.00   \n",
       "7                     MA Chidambaram Stadium, Chepauk            44.44   \n",
       "9   Punjab Cricket Association IS Bindra Stadium, ...            42.86   \n",
       "6                               M Chinnaswamy Stadium            41.94   \n",
       "14                            Sharjah Cricket Stadium            41.67   \n",
       "15                               Sheikh Zayed Stadium            40.91   \n",
       "2                                        Eden Gardens            40.00   \n",
       "16                                   Wankhede Stadium            39.29   \n",
       "0   Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...            37.50   \n",
       "12                             Sawai Mansingh Stadium            35.71   \n",
       "11             Saurashtra Cricket Association Stadium            30.00   \n",
       "8             Maharashtra Cricket Association Stadium            29.41   \n",
       "5                              Holkar Cricket Stadium            14.29   \n",
       "13   Shaheed Veer Narayan Singh International Stadium             0.00   \n",
       "4                                          Green Park             0.00   \n",
       "\n",
       "    WinBattingSecond  \n",
       "1              38.46  \n",
       "3              46.43  \n",
       "10             50.00  \n",
       "7              55.56  \n",
       "9              57.14  \n",
       "6              58.06  \n",
       "14             58.33  \n",
       "15             59.09  \n",
       "2              60.00  \n",
       "16             60.71  \n",
       "0              62.50  \n",
       "12             64.29  \n",
       "11             70.00  \n",
       "8              70.59  \n",
       "5              85.71  \n",
       "13            100.00  \n",
       "4             100.00  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WinPercentage.sort_values(by = 'WinBattingFirst', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ca5fc4",
   "metadata": {},
   "source": [
    "### Average RPI for each Venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a7929afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# Get Venue Wise average first innings and second innings score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e7b1ca9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "comb = pd.merge(del_df, match_df, how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29bb6d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAverageRPI(df, current_innings): \n",
    "    df = df[df.inning == current_innings]\n",
    "    df.reset_index(inplace = True, drop = True)\n",
    "    \n",
    "    if (current_innings == 1): \n",
    "        col_name = '1st_Innings_Average'\n",
    "    else:\n",
    "        col_name = '2nd_Innings_Average'\n",
    "        \n",
    "    df_RunsPerMatch = pd.DataFrame(df.groupby(['id' , 'venue']).total_runs.sum()).rename(columns = {'total_runs' : col_name}).reset_index()\n",
    "    \n",
    "    df_venueAverage = df_RunsPerMatch.groupby('venue')[col_name].mean().reset_index()\n",
    "    \n",
    "    return df_venueAverage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e7f7eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1stinnings = getAverageRPI(comb,1)\n",
    "df_2ndinnings = getAverageRPI(comb,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4fc15513",
   "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>venue</th>\n",
       "      <th>1st_Innings_Average</th>\n",
       "      <th>2nd_Innings_Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>177.92</td>\n",
       "      <td>163.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>172.48</td>\n",
       "      <td>156.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>171.57</td>\n",
       "      <td>155.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>171.54</td>\n",
       "      <td>149.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>170.18</td>\n",
       "      <td>154.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>169.68</td>\n",
       "      <td>163.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>169.57</td>\n",
       "      <td>164.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>169.41</td>\n",
       "      <td>156.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>168.90</td>\n",
       "      <td>162.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>166.86</td>\n",
       "      <td>164.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>162.50</td>\n",
       "      <td>152.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>162.36</td>\n",
       "      <td>152.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Green Park</td>\n",
       "      <td>161.25</td>\n",
       "      <td>163.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>158.87</td>\n",
       "      <td>146.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>155.75</td>\n",
       "      <td>130.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>150.56</td>\n",
       "      <td>134.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>148.00</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                venue  1st_Innings_Average  \\\n",
       "14                            Sharjah Cricket Stadium               177.92   \n",
       "6                               M Chinnaswamy Stadium               172.48   \n",
       "2                                        Eden Gardens               171.57   \n",
       "1                 Dubai International Cricket Stadium               171.54   \n",
       "3                                    Feroz Shah Kotla               170.18   \n",
       "16                                   Wankhede Stadium               169.68   \n",
       "9   Punjab Cricket Association IS Bindra Stadium, ...               169.57   \n",
       "8             Maharashtra Cricket Association Stadium               169.41   \n",
       "11             Saurashtra Cricket Association Stadium               168.90   \n",
       "5                              Holkar Cricket Stadium               166.86   \n",
       "15                               Sheikh Zayed Stadium               162.50   \n",
       "12                             Sawai Mansingh Stadium               162.36   \n",
       "4                                          Green Park               161.25   \n",
       "10          Rajiv Gandhi International Stadium, Uppal               158.87   \n",
       "0   Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...               155.75   \n",
       "7                     MA Chidambaram Stadium, Chepauk               150.56   \n",
       "13   Shaheed Veer Narayan Singh International Stadium               148.00   \n",
       "\n",
       "    2nd_Innings_Average  \n",
       "14               163.33  \n",
       "6                156.87  \n",
       "2                155.20  \n",
       "1                149.15  \n",
       "3                154.39  \n",
       "16               163.46  \n",
       "9                164.76  \n",
       "8                156.76  \n",
       "11               162.70  \n",
       "5                164.29  \n",
       "15               152.00  \n",
       "12               152.14  \n",
       "4                163.25  \n",
       "10               146.27  \n",
       "0                130.38  \n",
       "7                134.22  \n",
       "13               150.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df_1stinnings, df_2ndinnings, on = 'venue').sort_values(by = '1st_Innings_Average' , ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5d77e8",
   "metadata": {},
   "source": [
    "### Bowler / Batsmen Friendly Venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fe9db64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# Most Bowler or Batsmen friendly venues ? \n",
    "# Venues with least/most run per wicket per match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ed0f0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_run_per_match = pd.DataFrame(comb.groupby(['venue']).total_runs.sum() / comb.groupby('venue').id.nunique()).reset_index()\n",
    "avg_run_per_match.columns = ['venue', 'AvgRuns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d1b8bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_wkt_per_match = pd.DataFrame(comb.groupby(['venue']).is_wicket.sum() / comb.groupby('venue').id.nunique()).reset_index()\n",
    "avg_wkt_per_match.columns = ['venue', 'AvgWkts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c476c36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.merge(avg_run_per_match , avg_wkt_per_match, on = 'venue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "52ad803f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,8))\n",
    "plt.scatter(final_df.AvgRuns, final_df.AvgWkts, color = 'red')\n",
    "\n",
    "for i in range(len(final_df)): \n",
    "        plt.text(final_df['AvgRuns'][i], final_df['AvgWkts'][i], final_df['venue'][i])\n",
    "  \n",
    "plt.axvline(final_df['AvgRuns'].median() , ls ='--')\n",
    "plt.axhline(final_df['AvgWkts'].median() , ls ='--')\n",
    "plt.title('Batting and Bowler Friendly Venues')\n",
    "plt.ylabel('Average Wickets Per Match')\n",
    "plt.xlabel('Average Runs Per Match')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d87cf67a",
   "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>venue</th>\n",
       "      <th>AvgRuns</th>\n",
       "      <th>AvgWkts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>286.12</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>320.69</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>326.77</td>\n",
       "      <td>11.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>324.57</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Green Park</td>\n",
       "      <td>324.50</td>\n",
       "      <td>12.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>331.14</td>\n",
       "      <td>9.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>329.35</td>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>284.78</td>\n",
       "      <td>11.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>326.18</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>334.33</td>\n",
       "      <td>10.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>305.13</td>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>331.60</td>\n",
       "      <td>10.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>314.50</td>\n",
       "      <td>11.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>298.00</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>341.25</td>\n",
       "      <td>10.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>314.50</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>333.14</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                venue  AvgRuns  AvgWkts\n",
       "0   Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...   286.12    13.00\n",
       "1                 Dubai International Cricket Stadium   320.69    11.77\n",
       "2                                        Eden Gardens   326.77    11.57\n",
       "3                                    Feroz Shah Kotla   324.57    12.43\n",
       "4                                          Green Park   324.50    12.25\n",
       "5                              Holkar Cricket Stadium   331.14     9.86\n",
       "6                               M Chinnaswamy Stadium   329.35    12.03\n",
       "7                     MA Chidambaram Stadium, Chepauk   284.78    11.11\n",
       "8             Maharashtra Cricket Association Stadium   326.18    10.88\n",
       "9   Punjab Cricket Association IS Bindra Stadium, ...   334.33    10.43\n",
       "10          Rajiv Gandhi International Stadium, Uppal   305.13    12.03\n",
       "11             Saurashtra Cricket Association Stadium   331.60    10.70\n",
       "12                             Sawai Mansingh Stadium   314.50    11.14\n",
       "13   Shaheed Veer Narayan Singh International Stadium   298.00    11.50\n",
       "14                            Sharjah Cricket Stadium   341.25    10.92\n",
       "15                               Sheikh Zayed Stadium   314.50    10.50\n",
       "16                                   Wankhede Stadium   333.14    12.04"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# debug\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e641c1ad",
   "metadata": {},
   "source": [
    "### Bowling Styles Comparison for each venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "700f0ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# For a given venue, plot Bowler Strike Rate vs Economy\n",
    "# for each kind of Bowling Style\n",
    "# For Example - At Eden Gardens, how does \"Left arm wrist spin\" compare to \"Legbreak\" etc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b61bf2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populated with data from cricinfo for bowlers who have bowled in atleasst 10 matches\n",
    "bowler_data = pd.read_csv(\"C:/Users/utkar/OneDrive/Desktop/Analytics/Data/Bowlers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e82067d",
   "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>bowler</th>\n",
       "      <th>numMatches</th>\n",
       "      <th>BowlingStyle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Mishra</td>\n",
       "      <td>52</td>\n",
       "      <td>Legbreak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Nehra</td>\n",
       "      <td>14</td>\n",
       "      <td>Left arm medium fast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Nortje</td>\n",
       "      <td>16</td>\n",
       "      <td>Right arm fast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Zampa</td>\n",
       "      <td>14</td>\n",
       "      <td>Legbreak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>12</td>\n",
       "      <td>Right arm medium fast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bowler  numMatches           BowlingStyle\n",
       "0  A Mishra          52               Legbreak\n",
       "1   A Nehra          14   Left arm medium fast\n",
       "2  A Nortje          16         Right arm fast\n",
       "3   A Zampa          14               Legbreak\n",
       "4  AB Dinda          12  Right arm medium fast"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cea64230",
   "metadata": {},
   "outputs": [],
   "source": [
    "comb1 = comb.merge(bowler_data, on='bowler', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1de48cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "venue_list = comb1.venue.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1ecf8e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(comb1.groupby(['venue','BowlingStyle']).is_wicket.sum()).rename(columns = {'is_wicket' : 'numWickets'}).reset_index()\n",
    "new_df = new_df.merge(pd.DataFrame(comb1.groupby(['venue','BowlingStyle']).ball.count()).rename(columns = {'ball' : 'numBalls'}) , on = ['venue', 'BowlingStyle'])\n",
    "new_df = new_df.merge(pd.DataFrame(comb1.groupby(['venue','BowlingStyle']).total_runs.sum()).rename(columns = {'total_runs' : 'numRuns'}) , on = ['venue', 'BowlingStyle'])\n",
    "\n",
    "new_df['BallPerWicket'] = new_df['numBalls'] / new_df['numWickets']\n",
    "new_df['RunsPerOver'] = 6 * new_df['numRuns'] / new_df['numBalls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f3e90ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "venue_drop = widgets.Dropdown(options = venue_list , description='Venue' , value='Eden Gardens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "44990ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_change_venue(change):\n",
    "    if change['type'] == 'change' and change['name'] == 'value':\n",
    "        print(\"Venue changed to %s\" % change['new'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3f00200e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfdadd48e0fa493db79bfc8c195f58fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Venue', index=1, options=('Wankhede Stadium', 'Eden Gardens', 'Punjab Cricket Associatio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "venue_drop.observe(on_change_venue)\n",
    "display(venue_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ca506940",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_venue = venue_drop.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69602901",
   "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>venue</th>\n",
       "      <th>BowlingStyle</th>\n",
       "      <th>numWickets</th>\n",
       "      <th>numBalls</th>\n",
       "      <th>numRuns</th>\n",
       "      <th>BallPerWicket</th>\n",
       "      <th>RunsPerOver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Left arm fast</td>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>142</td>\n",
       "      <td>31.67</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Slow left arm orthodox</td>\n",
       "      <td>18</td>\n",
       "      <td>484</td>\n",
       "      <td>662</td>\n",
       "      <td>26.89</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Left arm medium</td>\n",
       "      <td>6</td>\n",
       "      <td>161</td>\n",
       "      <td>282</td>\n",
       "      <td>26.83</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Right arm medium</td>\n",
       "      <td>29</td>\n",
       "      <td>669</td>\n",
       "      <td>902</td>\n",
       "      <td>23.07</td>\n",
       "      <td>8.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Right arm medium fast</td>\n",
       "      <td>23</td>\n",
       "      <td>517</td>\n",
       "      <td>762</td>\n",
       "      <td>22.48</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Left arm fast medium</td>\n",
       "      <td>10</td>\n",
       "      <td>212</td>\n",
       "      <td>282</td>\n",
       "      <td>21.20</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Left arm wrist spin</td>\n",
       "      <td>23</td>\n",
       "      <td>460</td>\n",
       "      <td>637</td>\n",
       "      <td>20.00</td>\n",
       "      <td>8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Right arm fast</td>\n",
       "      <td>60</td>\n",
       "      <td>1181</td>\n",
       "      <td>1725</td>\n",
       "      <td>19.68</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Right arm fast medium</td>\n",
       "      <td>37</td>\n",
       "      <td>688</td>\n",
       "      <td>962</td>\n",
       "      <td>18.59</td>\n",
       "      <td>8.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Left arm medium fast</td>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>134</td>\n",
       "      <td>17.80</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Right arm offbreak</td>\n",
       "      <td>49</td>\n",
       "      <td>836</td>\n",
       "      <td>1081</td>\n",
       "      <td>17.06</td>\n",
       "      <td>7.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Legbreak</td>\n",
       "      <td>53</td>\n",
       "      <td>899</td>\n",
       "      <td>1274</td>\n",
       "      <td>16.96</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           venue            BowlingStyle  numWickets  numBalls  numRuns  \\\n",
       "21  Eden Gardens           Left arm fast           3        95      142   \n",
       "32  Eden Gardens  Slow left arm orthodox          18       484      662   \n",
       "23  Eden Gardens         Left arm medium           6       161      282   \n",
       "29  Eden Gardens        Right arm medium          29       669      902   \n",
       "30  Eden Gardens   Right arm medium fast          23       517      762   \n",
       "22  Eden Gardens    Left arm fast medium          10       212      282   \n",
       "25  Eden Gardens     Left arm wrist spin          23       460      637   \n",
       "27  Eden Gardens          Right arm fast          60      1181     1725   \n",
       "28  Eden Gardens   Right arm fast medium          37       688      962   \n",
       "24  Eden Gardens    Left arm medium fast           5        89      134   \n",
       "31  Eden Gardens      Right arm offbreak          49       836     1081   \n",
       "26  Eden Gardens                Legbreak          53       899     1274   \n",
       "\n",
       "    BallPerWicket  RunsPerOver  \n",
       "21          31.67         8.97  \n",
       "32          26.89         8.21  \n",
       "23          26.83        10.51  \n",
       "29          23.07         8.09  \n",
       "30          22.48         8.84  \n",
       "22          21.20         7.98  \n",
       "25          20.00         8.31  \n",
       "27          19.68         8.76  \n",
       "28          18.59         8.39  \n",
       "24          17.80         9.03  \n",
       "31          17.06         7.76  \n",
       "26          16.96         8.50  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#debug \n",
    "new_df[new_df.venue == selected_venue].sort_values(by = 'BallPerWicket', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "51db0d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_df = new_df[new_df.venue == selected_venue].reset_index()\n",
    "plt.figure(figsize = (18,8))\n",
    "plt.scatter(plt_df.BallPerWicket, plt_df.RunsPerOver, color = 'red')\n",
    "\n",
    "for i in range(len(plt_df)): \n",
    "        plt.text(plt_df['BallPerWicket'][i], plt_df['RunsPerOver'][i], plt_df['BowlingStyle'][i])\n",
    "  \n",
    "plt.axvline(plt_df.BallPerWicket.median() , ls ='--')\n",
    "plt.axhline(plt_df.RunsPerOver.median() , ls ='--')\n",
    "plt.title('Bowler Eco vs Strike Rate Comparison at ' + selected_venue)\n",
    "plt.ylabel('Economy')\n",
    "plt.xlabel('Strike Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8365de0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}