4. IPL Team Comparison .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\utkhare\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (15) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/utkhare/Desktop/Documents/Onwards2015.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# Compare the RR in each phase of the innings between batting and bowling \n",
    "# For Example - How many runs per over do they give away in powerplay, \n",
    "# compared to how many runs they score. We can plot the difference and compare with all teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_powerplay = df[df.over<=5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_middle = df[(df.over > 5) & (df.over <= 15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_death = df[df.over > 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_RR_diff(orig_df): \n",
    "    df_team_score = orig_df.groupby('batting_team')['batsman_runs'].sum().reset_index()\n",
    "    df_team_score.rename(columns={\"batsman_runs\": \"team_runs\"}, inplace=True)\n",
    "    df_team_score.rename(columns={\"batting_team\": \"team\"}, inplace=True)\n",
    "    \n",
    "    df_team_score_against = orig_df.groupby('bowling_team')['batsman_runs'].sum().reset_index()\n",
    "    df_team_score_against.rename(columns={\"batsman_runs\": \"team_runs_against\"}, inplace=True)\n",
    "    df_team_score_against.rename(columns={\"bowling_team\": \"team\"}, inplace=True)\n",
    "    \n",
    "    df_team_innings = orig_df.groupby('batting_team')['id'].nunique().reset_index()\n",
    "    df_team_innings.rename(columns={\"id\": \"team_num_innings\"}, inplace=True)\n",
    "    df_team_innings.rename(columns={\"batting_team\": \"team\"}, inplace=True)\n",
    "    \n",
    "    df_team_innings_against = orig_df.groupby('bowling_team')['id'].nunique().reset_index()\n",
    "    df_team_innings_against.rename(columns={\"id\": \"team_num_innings_against\"}, inplace=True)\n",
    "    df_team_innings_against.rename(columns={\"bowling_team\": \"team\"}, inplace=True)\n",
    "    \n",
    "    df_final = df_team_score.merge(df_team_innings, on = 'team', how = 'left')\n",
    "    df_final = df_final.merge(df_team_score_against, on = 'team', how = 'left')\n",
    "    df_final = df_final.merge(df_team_innings_against, on = 'team', how = 'left')\n",
    "    \n",
    "    df_final['Difference'] = ( df_final['team_runs'] / df_final['team_num_innings'] ) - ( df_final['team_runs_against'] / df_final['team_num_innings_against'] )\n",
    "    \n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_df(orig_df , title): \n",
    "    \n",
    "    plt.figure(figsize=(12, 8))\n",
    "\n",
    "    X = ['CSK','DC','GL','KXIP','KKR','MI', 'RR','RPS','RCB', 'SRH']\n",
    "    X_axis = np.arange(len(X))\n",
    "\n",
    "    plt.bar(X_axis, orig_df['Difference'], 0.3)\n",
    "\n",
    "    plt.xticks(X_axis, X)\n",
    "    plt.axhline(0 , ls = '--', color = 'grey')\n",
    "    plt.xlabel(\"Team\")\n",
    "    plt.ylabel(\"Diff b/w Runs Scored Per Innings & Runs given away Per Innings\")\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf_updated = {}\n",
    "i = 0 \n",
    "\n",
    "for mydf in (df_powerplay, df_middle, df_death): \n",
    "    mydf_updated[i] = get_RR_diff(mydf)\n",
    "    i = i + 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Negative Difference means that the team scores less runs than it gives away \n",
    "# Similarly positive difference means that the team is scoring more runs than it is giving away\n",
    "plot_df(mydf_updated[0] , 'Team Comparison in PowerPlay')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Negative Difference means that the team scores less runs than it gives away \n",
    "# Similarly positive difference means that the team is scoring more runs than it is giving away\n",
    "plot_df(mydf_updated[1] , 'Team Comparison in Mid Overs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Negative Difference means that the team scores less runs than it gives away \n",
    "# Similarly positive difference means that the team is scoring more runs than it is giving away\n",
    "plot_df(mydf_updated[2] , 'Team Comparison in End Overs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>team</th>\n",
       "      <th>team_runs</th>\n",
       "      <th>team_num_innings</th>\n",
       "      <th>team_runs_against</th>\n",
       "      <th>team_num_innings_against</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>2704</td>\n",
       "      <td>64</td>\n",
       "      <td>2844</td>\n",
       "      <td>64</td>\n",
       "      <td>-2.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>3923</td>\n",
       "      <td>89</td>\n",
       "      <td>4080</td>\n",
       "      <td>89</td>\n",
       "      <td>-1.764045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>1480</td>\n",
       "      <td>30</td>\n",
       "      <td>1423</td>\n",
       "      <td>30</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>3855</td>\n",
       "      <td>84</td>\n",
       "      <td>4010</td>\n",
       "      <td>84</td>\n",
       "      <td>-1.845238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>4164</td>\n",
       "      <td>88</td>\n",
       "      <td>4084</td>\n",
       "      <td>88</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>4184</td>\n",
       "      <td>93</td>\n",
       "      <td>4070</td>\n",
       "      <td>93</td>\n",
       "      <td>1.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>2572</td>\n",
       "      <td>56</td>\n",
       "      <td>2605</td>\n",
       "      <td>57</td>\n",
       "      <td>0.226817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>1355</td>\n",
       "      <td>30</td>\n",
       "      <td>1345</td>\n",
       "      <td>30</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>3935</td>\n",
       "      <td>88</td>\n",
       "      <td>4030</td>\n",
       "      <td>87</td>\n",
       "      <td>-1.605930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>4378</td>\n",
       "      <td>93</td>\n",
       "      <td>4059</td>\n",
       "      <td>93</td>\n",
       "      <td>3.430108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          team  team_runs  team_num_innings  \\\n",
       "0          Chennai Super Kings       2704                64   \n",
       "1               Delhi Capitals       3923                89   \n",
       "2                Gujarat Lions       1480                30   \n",
       "3              Kings XI Punjab       3855                84   \n",
       "4        Kolkata Knight Riders       4164                88   \n",
       "5               Mumbai Indians       4184                93   \n",
       "6             Rajasthan Royals       2572                56   \n",
       "7      Rising Pune Supergiants       1355                30   \n",
       "8  Royal Challengers Bangalore       3935                88   \n",
       "9          Sunrisers Hyderabad       4378                93   \n",
       "\n",
       "   team_runs_against  team_num_innings_against  Difference  \n",
       "0               2844                        64   -2.187500  \n",
       "1               4080                        89   -1.764045  \n",
       "2               1423                        30    1.900000  \n",
       "3               4010                        84   -1.845238  \n",
       "4               4084                        88    0.909091  \n",
       "5               4070                        93    1.225806  \n",
       "6               2605                        57    0.226817  \n",
       "7               1345                        30    0.333333  \n",
       "8               4030                        87   -1.605930  \n",
       "9               4059                        93    3.430108  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# debug - PowerPlay\n",
    "mydf_updated[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>team</th>\n",
       "      <th>team_runs</th>\n",
       "      <th>team_num_innings</th>\n",
       "      <th>team_runs_against</th>\n",
       "      <th>team_num_innings_against</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>4686</td>\n",
       "      <td>64</td>\n",
       "      <td>4681</td>\n",
       "      <td>64</td>\n",
       "      <td>0.078125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>6654</td>\n",
       "      <td>88</td>\n",
       "      <td>6639</td>\n",
       "      <td>88</td>\n",
       "      <td>0.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>2279</td>\n",
       "      <td>30</td>\n",
       "      <td>2432</td>\n",
       "      <td>30</td>\n",
       "      <td>-5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>6277</td>\n",
       "      <td>84</td>\n",
       "      <td>6432</td>\n",
       "      <td>84</td>\n",
       "      <td>-1.845238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>6607</td>\n",
       "      <td>86</td>\n",
       "      <td>6648</td>\n",
       "      <td>88</td>\n",
       "      <td>1.280127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>7138</td>\n",
       "      <td>93</td>\n",
       "      <td>7060</td>\n",
       "      <td>93</td>\n",
       "      <td>0.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>4174</td>\n",
       "      <td>55</td>\n",
       "      <td>4278</td>\n",
       "      <td>55</td>\n",
       "      <td>-1.890909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>2038</td>\n",
       "      <td>30</td>\n",
       "      <td>1989</td>\n",
       "      <td>29</td>\n",
       "      <td>-0.652874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>6402</td>\n",
       "      <td>85</td>\n",
       "      <td>6325</td>\n",
       "      <td>86</td>\n",
       "      <td>1.771135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>6950</td>\n",
       "      <td>93</td>\n",
       "      <td>6721</td>\n",
       "      <td>91</td>\n",
       "      <td>0.874040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          team  team_runs  team_num_innings  \\\n",
       "0          Chennai Super Kings       4686                64   \n",
       "1               Delhi Capitals       6654                88   \n",
       "2                Gujarat Lions       2279                30   \n",
       "3              Kings XI Punjab       6277                84   \n",
       "4        Kolkata Knight Riders       6607                86   \n",
       "5               Mumbai Indians       7138                93   \n",
       "6             Rajasthan Royals       4174                55   \n",
       "7      Rising Pune Supergiants       2038                30   \n",
       "8  Royal Challengers Bangalore       6402                85   \n",
       "9          Sunrisers Hyderabad       6950                93   \n",
       "\n",
       "   team_runs_against  team_num_innings_against  Difference  \n",
       "0               4681                        64    0.078125  \n",
       "1               6639                        88    0.170455  \n",
       "2               2432                        30   -5.100000  \n",
       "3               6432                        84   -1.845238  \n",
       "4               6648                        88    1.280127  \n",
       "5               7060                        93    0.838710  \n",
       "6               4278                        55   -1.890909  \n",
       "7               1989                        29   -0.652874  \n",
       "8               6325                        86    1.771135  \n",
       "9               6721                        91    0.874040  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#debug - Mid Overs\n",
    "mydf_updated[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>team</th>\n",
       "      <th>team_runs</th>\n",
       "      <th>team_num_innings</th>\n",
       "      <th>team_runs_against</th>\n",
       "      <th>team_num_innings_against</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>2313</td>\n",
       "      <td>64</td>\n",
       "      <td>2143</td>\n",
       "      <td>63</td>\n",
       "      <td>2.124752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>2934</td>\n",
       "      <td>84</td>\n",
       "      <td>2724</td>\n",
       "      <td>82</td>\n",
       "      <td>1.709059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>868</td>\n",
       "      <td>28</td>\n",
       "      <td>1036</td>\n",
       "      <td>27</td>\n",
       "      <td>-7.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>2599</td>\n",
       "      <td>76</td>\n",
       "      <td>2834</td>\n",
       "      <td>77</td>\n",
       "      <td>-2.607826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>2785</td>\n",
       "      <td>80</td>\n",
       "      <td>2950</td>\n",
       "      <td>80</td>\n",
       "      <td>-2.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3453</td>\n",
       "      <td>88</td>\n",
       "      <td>3040</td>\n",
       "      <td>91</td>\n",
       "      <td>5.832043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>1868</td>\n",
       "      <td>54</td>\n",
       "      <td>2131</td>\n",
       "      <td>53</td>\n",
       "      <td>-5.614955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>939</td>\n",
       "      <td>26</td>\n",
       "      <td>996</td>\n",
       "      <td>28</td>\n",
       "      <td>0.543956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>3003</td>\n",
       "      <td>78</td>\n",
       "      <td>2841</td>\n",
       "      <td>76</td>\n",
       "      <td>1.118421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>2940</td>\n",
       "      <td>87</td>\n",
       "      <td>3007</td>\n",
       "      <td>88</td>\n",
       "      <td>-0.377351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          team  team_runs  team_num_innings  \\\n",
       "0          Chennai Super Kings       2313                64   \n",
       "1               Delhi Capitals       2934                84   \n",
       "2                Gujarat Lions        868                28   \n",
       "3              Kings XI Punjab       2599                76   \n",
       "4        Kolkata Knight Riders       2785                80   \n",
       "5               Mumbai Indians       3453                88   \n",
       "6             Rajasthan Royals       1868                54   \n",
       "7      Rising Pune Supergiants        939                26   \n",
       "8  Royal Challengers Bangalore       3003                78   \n",
       "9          Sunrisers Hyderabad       2940                87   \n",
       "\n",
       "   team_runs_against  team_num_innings_against  Difference  \n",
       "0               2143                        63    2.124752  \n",
       "1               2724                        82    1.709059  \n",
       "2               1036                        27   -7.370370  \n",
       "3               2834                        77   -2.607826  \n",
       "4               2950                        80   -2.062500  \n",
       "5               3040                        91    5.832043  \n",
       "6               2131                        53   -5.614955  \n",
       "7                996                        28    0.543956  \n",
       "8               2841                        76    1.118421  \n",
       "9               3007                        88   -0.377351  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#debug - End Overs\n",
    "mydf_updated[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf_updated[0] = mydf_updated[0].drop([\"team_runs\", \"team_num_innings\", \"team_runs_against\", \"team_num_innings_against\"], axis = 1)\n",
    "mydf_updated[1] = mydf_updated[1].drop([\"team_runs\", \"team_num_innings\", \"team_runs_against\", \"team_num_innings_against\"], axis = 1)\n",
    "mydf_updated[2] = mydf_updated[2].drop([\"team_runs\", \"team_num_innings\", \"team_runs_against\", \"team_num_innings_against\"], axis = 1)\n",
    "\n",
    "mydf_updated[0].rename(columns={\"Difference\": \"PowerPlay Diff\"}, inplace=True)\n",
    "mydf_updated[1].rename(columns={\"Difference\": \"MidOvers Diff\"}, inplace=True)\n",
    "mydf_updated[2].rename(columns={\"Difference\": \"EndOvers Diff\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_performance = mydf_updated[0].merge(mydf_updated[1], on = 'team', how = 'left')\n",
    "team_performance = team_performance.merge(mydf_updated[2], on = 'team', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>team</th>\n",
       "      <th>PowerPlay Diff</th>\n",
       "      <th>MidOvers Diff</th>\n",
       "      <th>EndOvers Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>-2.187500</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>2.124752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>-1.764045</td>\n",
       "      <td>0.170455</td>\n",
       "      <td>1.709059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>-5.100000</td>\n",
       "      <td>-7.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>-1.845238</td>\n",
       "      <td>-1.845238</td>\n",
       "      <td>-2.607826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.280127</td>\n",
       "      <td>-2.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>1.225806</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>5.832043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>0.226817</td>\n",
       "      <td>-1.890909</td>\n",
       "      <td>-5.614955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.652874</td>\n",
       "      <td>0.543956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>-1.605930</td>\n",
       "      <td>1.771135</td>\n",
       "      <td>1.118421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>3.430108</td>\n",
       "      <td>0.874040</td>\n",
       "      <td>-0.377351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          team  PowerPlay Diff  MidOvers Diff  EndOvers Diff\n",
       "0          Chennai Super Kings       -2.187500       0.078125       2.124752\n",
       "1               Delhi Capitals       -1.764045       0.170455       1.709059\n",
       "2                Gujarat Lions        1.900000      -5.100000      -7.370370\n",
       "3              Kings XI Punjab       -1.845238      -1.845238      -2.607826\n",
       "4        Kolkata Knight Riders        0.909091       1.280127      -2.062500\n",
       "5               Mumbai Indians        1.225806       0.838710       5.832043\n",
       "6             Rajasthan Royals        0.226817      -1.890909      -5.614955\n",
       "7      Rising Pune Supergiants        0.333333      -0.652874       0.543956\n",
       "8  Royal Challengers Bangalore       -1.605930       1.771135       1.118421\n",
       "9          Sunrisers Hyderabad        3.430108       0.874040      -0.377351"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# debug - Combined DataFrame\n",
    "team_performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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