cricket-world-cup2019-[partnership-score-card--graphframes].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a53f9647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n#JAVA INSTALLATION DETAILS:-\\nC:\\\\Windows\\\\system32>java -version\\njava version \"1.8.0_301\"\\nJava(TM) SE Runtime Environment (build 1.8.0_301-b09)\\nJava HotSpot(TM) 64-Bit Server VM (build 25.301-b09, mixed mode)\\n\\nSpark Version: spark-3.1.2-bin-hadoop2.7\\nhttps://mirrors.estointernet.in/apache/spark/spark-3.1.2/spark-3.1.2-bin-hadoop2.7.tgz\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Important Libaries Required:\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import random\n",
    "from copy import deepcopy\n",
    "import time\n",
    "import math\n",
    "import numpy as np\n",
    "#import matplotlib.pyplot as plt\n",
    "import findspark\n",
    "findspark.init()\n",
    "import pyspark\n",
    "from pyspark import SparkContext\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import desc\n",
    "from pyspark.sql import functions as F\n",
    "#from pyspark.context import SparkContext\n",
    "#from pyspark.sql.session import SparkSession\n",
    "sc= SparkContext(appName=\"my_app\", master= \"local[1]\") \n",
    "\n",
    "#sc is spark context object used to tell spark how to access the cluster. \n",
    "#we pass configuration parameters in sc i.e. app name, and master(cluster URL) which are application parameters.\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "#matplotlib.style.use('ggplot')\n",
    "from pyspark.ml import Pipeline\n",
    "#from pyspark.ml.feature import StringIndexer\n",
    "from pyspark.sql import functions as F\n",
    "#from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import *\n",
    "from graphframes import *\n",
    "from graphframes import GraphFrame\n",
    "\n",
    "import sys\n",
    "#print(sys.path)\n",
    "#pip install pyspark==3.0.0\n",
    "#spark-submit --version\n",
    "#Spark version 3.1.2\n",
    "#pip install graphframes\n",
    "\n",
    "\n",
    "\n",
    "''' Refer below link to install PySpark -'''\n",
    "#https://changhsinlee.com/install-pyspark-windows-jupyter/\n",
    "\n",
    "'''Refer below steps to install latest version of graphframe module after PySpark installion:- '''\n",
    "##1. Check the spark version installed by using below command in command prompt-\n",
    "#spark-submit --version\n",
    "#1. Check the version of graphframe from below link which is suitable for installed Spark version and install it-\n",
    "#    https://spark-packages.org/package/graphframes/graphframes\n",
    "\n",
    "\n",
    "#2. Copy the jar file to the location where python is installed in Spark(python folder in the installed spark) \n",
    "\n",
    "#3. Copy the jar file to spark location as a zip file. refer below sample command, which can be run in window power shell-\n",
    "#    cp 'C:\\\\Users\\\\XXXXX\\\\AppData\\\\Local\\\\Programs\\\\Spark\\\\spark-3.1.2-bin-hadoop2.7\\\\jars\\\\graphframes*.jar' \n",
    "#    'C:\\\\Users\\\\XXXXX\\\\AppData\\\\Local\\\\Programs\\\\Spark\\\\spark-3.1.2-bin-hadoop2.7\\\\python\\\\graphframes.zip'\n",
    "\n",
    "#4. Unzip it and cut items present in the same subfolder folder and paste them in outer folder name graphframe folder.\n",
    "\n",
    "#5. Add the new path in the environmental variables. Refer sample below-\n",
    "#    C:\\Users\\XXXXX\\AppData\\Local\\Programs\\Spark\\spark-3.1.2-bin-hadoop2.7\\python\\graphframes\\\n",
    "\n",
    "#6. Return to jupyter notebook and refresh it.Then, run -\n",
    "#from graphframe import *\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "#JAVA INSTALLATION DETAILS:-\n",
    "C:\\Windows\\system32>java -version\n",
    "java version \"1.8.0_301\"\n",
    "Java(TM) SE Runtime Environment (build 1.8.0_301-b09)\n",
    "Java HotSpot(TM) 64-Bit Server VM (build 25.301-b09, mixed mode)\n",
    "\n",
    "Spark Version: spark-3.1.2-bin-hadoop2.7\n",
    "https://mirrors.estointernet.in/apache/spark/spark-3.1.2/spark-3.1.2-bin-hadoop2.7.tgz\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "##graphframe jar file path:\n",
    "#\"C:\\\\Users\\\\...\\\\Programs\\\\Spark\\\\spark-3.1.2-bin-hadoop2.7\\\\jars\\\\graphframes*.jar\"\n",
    "#\"C:\\\\Users\\\\...\\\\Programs\\\\Spark\\\\spark-3.1.2-bin-hadoop2.7\\\\graphframes.zip\"\n",
    "\n",
    "#graphframes-0.8.1-spark3.0-s_2.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1ad3940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matches Data Top Few Observations:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_No</th>\n",
       "      <th>Match_date</th>\n",
       "      <th>Batting_team</th>\n",
       "      <th>Ball_no</th>\n",
       "      <th>Bowler</th>\n",
       "      <th>Batsmen</th>\n",
       "      <th>Runs</th>\n",
       "      <th>commentary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st match</td>\n",
       "      <td>May 30 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st match</td>\n",
       "      <td>May 30 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>49.6</td>\n",
       "      <td>Rabada</td>\n",
       "      <td>Plunkett</td>\n",
       "      <td>1</td>\n",
       "      <td>Rabada to Plunkett, 1 run, full again, can onl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st match</td>\n",
       "      <td>May 30 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>49.5</td>\n",
       "      <td>Rabada</td>\n",
       "      <td>Plunkett</td>\n",
       "      <td>2</td>\n",
       "      <td>Rabada to Plunkett, 2 runs, fired in towards t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st match</td>\n",
       "      <td>May 30 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>49.4</td>\n",
       "      <td>Rabada</td>\n",
       "      <td>Archer</td>\n",
       "      <td>1</td>\n",
       "      <td>Rabada to Archer, 1 run, pitched up and whippe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st match</td>\n",
       "      <td>May 30 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>49.3</td>\n",
       "      <td>Rabada</td>\n",
       "      <td>Archer</td>\n",
       "      <td>2</td>\n",
       "      <td>Rabada to Archer, 2 runs, fullish, 86mph and f...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28746</th>\n",
       "      <td>Final</td>\n",
       "      <td>Jul 14 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Boult</td>\n",
       "      <td>Bairstow</td>\n",
       "      <td>0</td>\n",
       "      <td>Boult to Bairstow, no run, looks to flick, bal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28747</th>\n",
       "      <td>Final</td>\n",
       "      <td>Jul 14 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Boult</td>\n",
       "      <td>Bairstow</td>\n",
       "      <td>0</td>\n",
       "      <td>Boult to Bairstow, no run, back of a length, h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28748</th>\n",
       "      <td>Final</td>\n",
       "      <td>Jul 14 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Boult</td>\n",
       "      <td>Bairstow</td>\n",
       "      <td>0</td>\n",
       "      <td>Boult to Bairstow, no run, fullish, fourth-stu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28749</th>\n",
       "      <td>Final</td>\n",
       "      <td>Jul 14 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Boult</td>\n",
       "      <td>Roy</td>\n",
       "      <td>1</td>\n",
       "      <td>Boult to Roy, 1 run, full again, more off-stum...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28750</th>\n",
       "      <td>Final</td>\n",
       "      <td>Jul 14 2019</td>\n",
       "      <td>ENG</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Boult</td>\n",
       "      <td>Roy</td>\n",
       "      <td>0</td>\n",
       "      <td>Boult to Roy, no run, gone first ball? No? Mar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28751 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Match_No    Match_date Batting_team  Ball_no  Bowler    Batsmen Runs  \\\n",
       "0      1st match   May 30 2019          ENG      NaN     NaN        NaN  NaN   \n",
       "1      1st match   May 30 2019          ENG     49.6  Rabada   Plunkett    1   \n",
       "2      1st match   May 30 2019          ENG     49.5  Rabada   Plunkett    2   \n",
       "3      1st match   May 30 2019          ENG     49.4  Rabada     Archer    1   \n",
       "4      1st match   May 30 2019          ENG     49.3  Rabada     Archer    2   \n",
       "...          ...           ...          ...      ...     ...        ...  ...   \n",
       "28746      Final   Jul 14 2019          ENG      0.5   Boult   Bairstow    0   \n",
       "28747      Final   Jul 14 2019          ENG      0.4   Boult   Bairstow    0   \n",
       "28748      Final   Jul 14 2019          ENG      0.3   Boult   Bairstow    0   \n",
       "28749      Final   Jul 14 2019          ENG      0.2   Boult        Roy    1   \n",
       "28750      Final   Jul 14 2019          ENG      0.1   Boult        Roy    0   \n",
       "\n",
       "                                              commentary  \n",
       "0                                                    NaN  \n",
       "1      Rabada to Plunkett, 1 run, full again, can onl...  \n",
       "2      Rabada to Plunkett, 2 runs, fired in towards t...  \n",
       "3      Rabada to Archer, 1 run, pitched up and whippe...  \n",
       "4      Rabada to Archer, 2 runs, fullish, 86mph and f...  \n",
       "...                                                  ...  \n",
       "28746  Boult to Bairstow, no run, looks to flick, bal...  \n",
       "28747  Boult to Bairstow, no run, back of a length, h...  \n",
       "28748  Boult to Bairstow, no run, fullish, fourth-stu...  \n",
       "28749  Boult to Roy, 1 run, full again, more off-stum...  \n",
       "28750  Boult to Roy, no run, gone first ball? No? Mar...  \n",
       "\n",
       "[28751 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " ''' Load the scrapped 'matches data', Data Preprocessing and extract the required information '''\n",
    "    \n",
    "\n",
    "#Matches_data.csv is a scrapped data from www.espncricinfo.com\n",
    "testdf = pd.read_csv(\"C:\\\\...\\\\deepenv\\\\Cricket World Cup 2019 Analysis\\\\playersdata\\\\Matches_data.csv\")\n",
    "print(\"\\nMatches Data Top Few Observations:\\n\")\n",
    "display(testdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b867d42d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessed Matches Data (top 5 observations)\n"
     ]
    },
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       "  </tbody>\n",
       "</table>\n",
       "<p>23828 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Match_No    Match_date Batting_team       Bowler    Batsmen  runs  \\\n",
       "352           1   May 30 2019          ENG  Imran Tahir       Root     0   \n",
       "353           1   May 30 2019          ENG  Imran Tahir       Root     0   \n",
       "354           1   May 30 2019          ENG  Imran Tahir       Root     0   \n",
       "355           1   May 30 2019          ENG  Imran Tahir       Root     0   \n",
       "356           1   May 30 2019          ENG  Imran Tahir   Bairstow     0   \n",
       "...         ...           ...          ...          ...        ...   ...   \n",
       "28395        48   Jul 14 2019          ENG        Boult     Stokes     0   \n",
       "28396        48   Jul 14 2019          ENG        Boult     Stokes     6   \n",
       "28397        48   Jul 14 2019          ENG        Boult     Stokes     6   \n",
       "28398        48   Jul 14 2019          ENG        Boult     Stokes     0   \n",
       "28399        48   Jul 14 2019          ENG        Boult     Stokes     0   \n",
       "\n",
       "      extras_type  over  ball  wicket   datetime month date  \n",
       "352           NaN     0     6       0 2019-05-30   May   30  \n",
       "353           NaN     0     5       0 2019-05-30   May   30  \n",
       "354           NaN     0     4       0 2019-05-30   May   30  \n",
       "355           NaN     0     3       0 2019-05-30   May   30  \n",
       "356           NaN     0     2       1 2019-05-30   May   30  \n",
       "...           ...   ...   ...     ...        ...   ...  ...  \n",
       "28395         NaN    49     5       1 2019-07-14   Jul   14  \n",
       "28396         NaN    49     4       0 2019-07-14   Jul   14  \n",
       "28397         NaN    49     3       0 2019-07-14   Jul   14  \n",
       "28398         NaN    49     2       0 2019-07-14   Jul   14  \n",
       "28399         NaN    49     1       0 2019-07-14   Jul   14  \n",
       "\n",
       "[23828 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" UseCase1 - Identify Strong & Weak Partnership of a player using Graph Analytics\"\"\"\n",
    "\n",
    "'''\n",
    "Steps - \n",
    "\n",
    "1. Data Preparation from Matches_data.csv data(scrapped file - Matches_data.csv)\n",
    "2. Generate Partnership Score data. Consume it as edges in graph analysis\n",
    "3. Generate batsmen score data. Consume it as vertices in graph analysis \n",
    "4. Use Graph Frame to get the near and far partners from a player in terms of partnership scores.\n",
    "5. Are there team where batting partnerships did not stand longer? Analysis.\n",
    "\n",
    "'''\n",
    "\n",
    "\"UseCase1.Step1 - Data Preprocessing Phase[Matches_data.csv]\"\n",
    "\n",
    "testdf1 = testdf.drop(['commentary'], axis=1)\n",
    "testdf1 = testdf1.dropna()\n",
    "testdf1['runs'] = testdf1['Runs'].astype(str).str[0]\n",
    "testdf1['extras_type'] = testdf1['Runs'].astype(str).str[1:]\n",
    "testdf1 = testdf1.replace(r'^\\s*$', np.nan, regex=True)\n",
    "\n",
    "#Split by delimiter(.) for Over column and introduce two columns- over and ball  --> \n",
    "testdf1[['over','ball']] = testdf1['Ball_no'].astype(str).str.split('.', expand=True)\n",
    "testdf1['wicket'] = np.where(testdf1['runs'] == \"W\", 1, 0)\n",
    "matchdata = testdf1.drop(['Ball_no', 'Runs'], axis=1)\n",
    "matchdata['over'] = matchdata['over'].astype(np.int64)\n",
    "matchdata['ball'] = matchdata['ball'].astype(np.int64)\n",
    "matchdata['wicket'] = matchdata['wicket'].astype(np.int64)\n",
    "del testdf1\n",
    "\n",
    "matchdata.runs.replace(['W'], ['0'], inplace= True)\n",
    "matchdata[\"runs\"] = matchdata[\"runs\"].astype('int64')\n",
    "matchdata['Match_No'] = matchdata['Match_No'].str[0:5]\n",
    "matchdata['datetime'] = pd.to_datetime(matchdata['Match_date'], dayfirst = True)\n",
    "matchdata['month'] = matchdata['datetime'].apply(lambda x: x.strftime('%b')) \n",
    "matchdata['date'] = matchdata['datetime'].apply(lambda x: x.strftime('%d'))\n",
    "\n",
    "\n",
    "matchnumber = {'1st m': 1, '2nd m': 2, '3rd m': 3,'4th m': 4, '5th m': 5, '6th m': 6, '7th m': 7, \n",
    "         '8th m': 8, '9th m': 9, '10th ': 10, '12th ': 12, '13th ': 13, '14th ': 14, '15th ': 15,\n",
    "         '17th ': 17, '19th ': 19, '20th ': 20, '21st ': 21, '22nd ': 22, '23rd ': 23, '24th ': 24,\n",
    "         '25th ': 25, '26th ': 26, '27th ': 27, '28th ': 28, '29th ': 29, '30th ': 30, '31st ': 31,\n",
    "         '32nd ': 32, '33rd ': 33, '34th ': 34, '35th ': 35, '36th ': 36, '37th ': 37, '38th ': 38,\n",
    "         '39th ': 39, '40th ': 40, '41st ': 41, '42nd ': 42, '43rd ': 43, '44th ': 44, '45th ': 45,\n",
    "         '1st S': 46, '2nd S': 47, 'Final': 48} \n",
    "matchdata.Match_No = [matchnumber[i] for i in matchdata.Match_No] \n",
    "\n",
    "\n",
    "matchdata1 = matchdata.sort_values(['Match_No', 'over'], ascending=[True, True])\n",
    "matchdata1 = matchdata1[(matchdata1['extras_type'] != 'w') & (matchdata1['extras_type'] != 'lb') & (matchdata1['extras_type'] != 'b') & (matchdata1['extras_type'] != 'nb')]\n",
    "\n",
    "print(\"Preprocessed Matches Data (top 5 observations)\")\n",
    "display(matchdata1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "acd0b886",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Partnership Score Between Players(top 25 observations)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matchno</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>batsmen1</th>\n",
       "      <th>batsmen2</th>\n",
       "      <th>run_partnership</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Roy</td>\n",
       "      <td>Bairstow</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Root</td>\n",
       "      <td>Roy</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>Root</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Stokes</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Stokes</td>\n",
       "      <td>Buttler</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>48</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Stokes</td>\n",
       "      <td>Woakes</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>48</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Stokes</td>\n",
       "      <td>Plunkett</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>48</td>\n",
       "      <td>ENG</td>\n",
       "      <td>Stokes</td>\n",
       "      <td>Archer</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>48</td>\n",
       "      <td>ENG</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>48</td>\n",
       "      <td>ENG</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>725 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     matchno batting_team batsmen1  batsmen2  run_partnership\n",
       "0          1          ENG      Roy  Bairstow                1\n",
       "1          1          ENG     Root       Roy              104\n",
       "2          1          ENG   Morgan      Root                4\n",
       "3          1          ENG   Stokes    Morgan              104\n",
       "4          1          ENG   Stokes   Buttler               30\n",
       "..       ...          ...      ...       ...              ...\n",
       "720       48          ENG   Stokes    Woakes                7\n",
       "721       48          ENG   Stokes  Plunkett               17\n",
       "722       48          ENG   Stokes    Archer                7\n",
       "723       48          ENG       NA        NA               12\n",
       "724       48          ENG       NA        NA                0\n",
       "\n",
       "[725 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "batsmen score data(top 25 observations)\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aftab Alam</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ali</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allen</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ambris</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amla</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>de Grandhomme</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>de Kock</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>de Silva</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>du Plessis</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>van der Dussen</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  runs\n",
       "0        Aftab Alam    14\n",
       "1               Ali    75\n",
       "2             Allen    51\n",
       "3            Ambris    36\n",
       "4              Amla   209\n",
       "..              ...   ...\n",
       "144   de Grandhomme   189\n",
       "145         de Kock   321\n",
       "146        de Silva   108\n",
       "147      du Plessis   388\n",
       "148  van der Dussen   310\n",
       "\n",
       "[149 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" UseCase1.Step2 -Generate Partnership Score data. Consider Partnership Scores as edges in graph analysis below\"\"\"\n",
    "\n",
    "## Baseline Code for finding partnership scores ##\n",
    "matchno = []\n",
    "batting_team = []\n",
    "batsmen1 = []\n",
    "batsmen2 = []\n",
    "run_partnership = []\n",
    "#len(matchdata1.Match_No.unique())\n",
    "for i in range(0,len(matchdata1.Match_No.unique())):\n",
    "    team0 = matchdata1[(matchdata1[\"Match_No\"] == matchdata1[\"Match_No\"].unique()[i])] \n",
    "    for j in range(0, len(team0.Batting_team.unique())):\n",
    "        team1 = team0[(team0[\"Batting_team\"] == team0[\"Batting_team\"].unique()[j])]\n",
    "        team2 = team1.sort_values(['over', 'ball'], ascending=[True, True])\n",
    "        team2['dummyinteger'] = range(0,team2.shape[0])\n",
    "        if team2[team2['wicket'] == 1].shape[0] == 0:\n",
    "            matchno.append(team2.Match_No.unique()[0])\n",
    "            batting_team.append(team2.Batting_team.unique()[0])\n",
    "            batsmen1.append(team2.Batsmen.unique()[0])\n",
    "            batsmen2.append(team2.Batsmen.unique()[1])\n",
    "            run_partnership.append(sum(team2.runs)) \n",
    "        else:\n",
    "            dummyinteger_index = team2.loc[team2['wicket'] == 1, 'dummyinteger']\n",
    "            if not dummyinteger_index.empty:\n",
    "                test0 = team2[(team2['dummyinteger'] >= 0) & (team2['dummyinteger'] <= dummyinteger_index.iloc[0])]\n",
    "                if not test0.empty:\n",
    "                    matchno.append(test0.Match_No.unique()[0])\n",
    "                    batting_team.append(test0.Batting_team.unique()[0].strip())\n",
    "                    if (len(test0.Batsmen.unique()) == 1):\n",
    "                        batsmen1.append(\"NA\")\n",
    "                        batsmen2.append(\"NA\")\n",
    "                        #batsmen1.append(test0.Batsmen.unique()[0].strip()\n",
    "                    else:\n",
    "                        batsmen1.append(test0.Batsmen.unique()[0].strip())\n",
    "                        batsmen2.append(test0.Batsmen.unique()[1].strip())\n",
    "                    run_partnership.append(sum(test0.runs))\n",
    "            \n",
    "                for k in range(0, len(dummyinteger_index)-1):\n",
    "                    test1 = team2[(team2['dummyinteger'] > dummyinteger_index.iloc[k]) & (team2['dummyinteger'] <= dummyinteger_index.iloc[k+1])]\n",
    "                    if not test1.empty:\n",
    "                        matchno01 = test1.Match_No.unique()[0]\n",
    "                        matchno.append(matchno01)\n",
    "                        batting_team01 = test1.Batting_team.unique()[0].strip()\n",
    "                        batting_team.append(batting_team01)\n",
    "                        if (len(test1.Batsmen.unique()) == 1):\n",
    "                            batsmen1.append(\"NA\")\n",
    "                            batsmen2.append(\"NA\")\n",
    "                            #batsmen01 = test1.Batsmen.unique()[0].strip()\n",
    "                        else:\n",
    "                            batsmen01 = test1.Batsmen.unique()[0].strip()\n",
    "                            batsmen1.append(batsmen01)\n",
    "                            batsmen02 = test1.Batsmen.unique()[1].strip()\n",
    "                            batsmen2.append(batsmen02)\n",
    "                        run_partnership01 =  sum(test1.runs)\n",
    "                        run_partnership.append(run_partnership01)\n",
    "\n",
    "                test2 = team2[(team2['dummyinteger'] > dummyinteger_index.iloc[len(dummyinteger_index)-1])]\n",
    "                if not test2.empty:\n",
    "                    matchno.append(test2.Match_No.unique()[0])\n",
    "                    batting_team.append(test2.Batting_team.unique()[0].strip())\n",
    "                    if (len(test2.Batsmen.unique()) == 1):\n",
    "                        batsmen1.append(\"NA\")\n",
    "                        batsmen2.append(\"NA\")\n",
    "                        #batsmen1.append(test2.Batsmen.unique()[0].strip())\n",
    "                    else:\n",
    "                        batsmen1.append(test2.Batsmen.unique()[0].strip())\n",
    "                        batsmen2.append(test2.Batsmen.unique()[1].strip())\n",
    "                    run_partnership.append(sum(test2.runs))\n",
    "\n",
    "\n",
    "partnership_score = pd.DataFrame(list(zip(matchno, batting_team, batsmen1, batsmen2, run_partnership)), columns =['matchno', 'batting_team', 'batsmen1', 'batsmen2', 'run_partnership'])\n",
    "#partnership_score.sort_values(['run_partnership'], ascending=[False])\n",
    "\n",
    "\n",
    "print(\"\\nPartnership Score Between Players(top 25 observations)\")\n",
    "display(partnership_score)\n",
    "\n",
    "\n",
    "#Write file to use in graph analysis -\n",
    "#partnership_score.to_excel(\"PartnershipScore.xlsx\")\n",
    "\n",
    "\n",
    "\"\"\" UseCase1.Step3- Generate Batsmen Score data from preprocessed data of matches. \n",
    "    We will treat Batsman total score as vertices in graph\"\"\"\n",
    "\n",
    "batsmenscore = pd.DataFrame(matchdata1.groupby(['Batsmen']).agg({'runs': np.sum})).reset_index()\n",
    "batsmenscore.columns = ['id', 'runs'] # since in vertices, first column should name as \"id\"\n",
    "batsmenscore['id'] = batsmenscore['id'].str.strip()\n",
    "print(\"\\nbatsmen score data(top 25 observations)\")\n",
    "display(batsmenscore)\n",
    "\n",
    "#batsmenscore.to_excel(\"batsmenscore11.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "00873492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "partnership_score dataframe(top 25 observations)\n",
      "+-------+------------+---------------+---------------+---------------+\n",
      "|matchno|batting_team|       batsmen1|       batsmen2|run_partnership|\n",
      "+-------+------------+---------------+---------------+---------------+\n",
      "|      1|         ENG|            Roy|       Bairstow|              1|\n",
      "|      1|         ENG|           Root|            Roy|            104|\n",
      "|      1|         ENG|         Morgan|           Root|              4|\n",
      "|      1|         ENG|         Stokes|         Morgan|            104|\n",
      "|      1|         ENG|         Stokes|        Buttler|             30|\n",
      "|      1|         ENG|            Ali|         Stokes|             12|\n",
      "|      1|         ENG|         Woakes|         Stokes|             20|\n",
      "|      1|         ENG|         Stokes|       Plunkett|             15|\n",
      "|      1|         ENG|       Plunkett|         Archer|             11|\n",
      "|      1|          SA|        de Kock|           Amla|             36|\n",
      "|      1|          SA|     du Plessis|        de Kock|              8|\n",
      "|      1|          SA|        de Kock| van der Dussen|             80|\n",
      "|      1|          SA| van der Dussen|         Duminy|             13|\n",
      "|      1|          SA| van der Dussen|      Pretorius|              1|\n",
      "|      1|          SA| van der Dussen|    Phehlukwayo|             23|\n",
      "|      1|          SA|           Amla|    Phehlukwayo|             12|\n",
      "|      1|          SA|         Rabada|           Amla|             13|\n",
      "|      1|          SA|          Ngidi|         Rabada|             10|\n",
      "|      1|          SA|             NA|             NA|              0|\n",
      "|      2|         PAK|    Imam-ul-Haq|   Fakhar Zaman|             16|\n",
      "|      2|         PAK|   Fakhar Zaman|     Babar Azam|             18|\n",
      "|      2|         PAK|   Haris Sohail|     Babar Azam|             10|\n",
      "|      2|         PAK| Sarfaraz Ahmed|     Babar Azam|             17|\n",
      "|      2|         PAK|Mohammad Hafeez| Sarfaraz Ahmed|             12|\n",
      "|      2|         PAK|     Imad Wasim|Mohammad Hafeez|              2|\n",
      "+-------+------------+---------------+---------------+---------------+\n",
      "only showing top 25 rows\n",
      "\n",
      "\n",
      "edges :: partnership run between players(top 25 observations)-\n",
      "+----------------+--------------------+-----------+\n",
      "|             src|                 dst|partnership|\n",
      "+----------------+--------------------+-----------+\n",
      "|    Haris Sohail|          Babar Azam|        131|\n",
      "| Mohammad Mithun|     Shakib Al Hasan|         38|\n",
      "|   Asghar Afghan|       Gulbadin Naib|         25|\n",
      "|      Wahab Riaz|          Imad Wasim|         23|\n",
      "|        Plunkett|              Archer|         11|\n",
      "|      Brathwaite|              Thomas|         41|\n",
      "|          Pandya|                Pant|         28|\n",
      "|         Malinga|              Lakmal|         16|\n",
      "|   Asghar Afghan|Hashmatullah Shahidi|         90|\n",
      "|         Neesham|             Santner|         20|\n",
      "|      Williamson|              Taylor|        185|\n",
      "|            Amla|             Markram|         50|\n",
      "|      Brathwaite|              Pooran|         60|\n",
      "|          Mendis|         Karunaratne|         32|\n",
      "|           Carey|               Zampa|          3|\n",
      "|         Cummins|               Carey|         46|\n",
      "|   Asghar Afghan|     Noor Ali Zadran|          0|\n",
      "|  Mohammed Shami|              Jadhav|          5|\n",
      "|     Rashid Khan|       Mohammad Nabi|         23|\n",
      "|           Carey|               Marsh|         11|\n",
      "|     Phehlukwayo|              Duminy|         23|\n",
      "|Mujeeb Ur Rahman|  Samiullah Shinwari|         12|\n",
      "|    Shoaib Malik|           Hasan Ali|         12|\n",
      "|     Mahmudullah|  Mohammad Saifuddin|          7|\n",
      "|          Thomas|          Brathwaite|          9|\n",
      "+----------------+--------------------+-----------+\n",
      "only showing top 25 rows\n",
      "\n",
      "\n",
      "edges schema:\n",
      "\n",
      "root\n",
      " |-- src: string (nullable = true)\n",
      " |-- dst: string (nullable = true)\n",
      " |-- partnership: long (nullable = true)\n",
      "\n",
      "\n",
      "\n",
      "batsmenscore dataframe(top 25 observations)\n",
      "+-------------+----+\n",
      "|           id|runs|\n",
      "+-------------+----+\n",
      "|   Aftab Alam|  14|\n",
      "|          Ali|  75|\n",
      "|        Allen|  51|\n",
      "|       Ambris|  36|\n",
      "|         Amla| 209|\n",
      "|       Archer|  13|\n",
      "|Asghar Afghan| 153|\n",
      "|     Asif Ali|  19|\n",
      "|   BKG Mendis|  69|\n",
      "|  BMAJ Mendis|  19|\n",
      "|   Babar Azam| 474|\n",
      "|     Bairstow| 532|\n",
      "|  Behrendorff|  12|\n",
      "|        Boult|  10|\n",
      "|   Brathwaite| 158|\n",
      "|        Bravo|  19|\n",
      "|       Bumrah|   1|\n",
      "|      Buttler| 311|\n",
      "|        Carey| 370|\n",
      "|       Chahal|   5|\n",
      "|     Cottrell|  33|\n",
      "| Coulter-Nile|  98|\n",
      "|      Cummins|  55|\n",
      "|Dawlat Zadran|  11|\n",
      "|       Dhawan| 125|\n",
      "+-------------+----+\n",
      "only showing top 25 rows\n",
      "\n",
      "\n",
      "vertices :: each batsman runs(top 25 observations)-\n",
      "+---------------+-----------+\n",
      "|             id|runs_scored|\n",
      "+---------------+-----------+\n",
      "|  Kuldeep Yadav|          1|\n",
      "|Mohammad Mithun|         47|\n",
      "|          Roach|         28|\n",
      "|       Bairstow|        532|\n",
      "|           Hope|        274|\n",
      "|    Phehlukwayo|        133|\n",
      "|Noor Ali Zadran|         63|\n",
      "|    BMAJ Mendis|         19|\n",
      "|          Munro|        125|\n",
      "|  Asghar Afghan|        153|\n",
      "|Mohammad Hafeez|        253|\n",
      "|          Udana|         43|\n",
      "|         Holder|        166|\n",
      "|          Marsh|         26|\n",
      "|          Dhoni|        272|\n",
      "|          Nurse|         20|\n",
      "|            Roy|        443|\n",
      "|  Gulbadin Naib|        194|\n",
      "|         Latham|        155|\n",
      "|       Plunkett|         62|\n",
      "+---------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n",
      "\n",
      "vertices schema:\n",
      "\n",
      "root\n",
      " |-- id: string (nullable = true)\n",
      " |-- runs_scored: long (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" UseCase1.Step4 -Use Graph Frame to get the close and far association between players in \n",
    "terms of partnership scores\"\"\"\n",
    "\n",
    "### Graph Analysis on Partnership Data -######\n",
    "\n",
    "'''\n",
    "Vertex DataFrame: A vertex DataFrame should contain a special column named 'id' which specifies unique IDs \n",
    "for each vertex in the graph.\n",
    "Edge DataFrame: An edge DataFrame should contain two special columns: 'src' (source vertex ID of edge) \n",
    "and 'dst' (destination vertex ID of edge).\n",
    "\n",
    "'''\n",
    "\n",
    "sqlContext = SQLContext(sc) \n",
    "'''\n",
    "The entry point into all relational functionality in Spark is the SQLContext class, \n",
    "or one of its decedents. To create a basic SQLContext, all we need is a SparkContext which is 'sc' passed in SQLContext().\n",
    "'''\n",
    "\n",
    "'''\n",
    "we obtained parternship score above (partnership_score) from scrapped data of matches (Matches_data.csv)\n",
    "''' \n",
    "# Load the partnership data file using SQL Context\n",
    "#df = sqlContext.read.load(\"file:/home/cloudera/practicumm2/PartnershipScore.csv\",format='com.databricks.spark.csv',header='true',inferSchema='true')\n",
    "df = sqlContext.createDataFrame(partnership_score)\n",
    "print(\"partnership_score dataframe(top 25 observations)\")\n",
    "df.show(25)\n",
    "\n",
    "#create temporary view as partnership-\n",
    "df.createOrReplaceTempView(\"partnership\")\n",
    "\n",
    "#Use Graph Frame to fetch good, average and below average partnerships \n",
    "    ##that a batsmen estabilished with other batsmens in world cup -\n",
    "# 1. create edges dataframe\n",
    "# 2. create vertices dataframe\n",
    "# 3. create graph frame with edges and vertices as input\n",
    "\n",
    "\n",
    "\n",
    "###EDGES\n",
    "## 1. create edge dataframe - Group by Batsmen1 and Batsmen2 and compute their total partnership scores\n",
    "partnership_runs = sqlContext.sql(\"SELECT batsmen1, batsmen2, SUM(run_partnership) as partnership_runs FROM partnership GROUP BY batsmen1, batsmen2\")\n",
    "#Edges\n",
    "edges = partnership_runs\n",
    "#edges.columns = ['src', 'dst', 'partnership_runs']\n",
    "edges = edges.selectExpr(\"batsmen1 as src\", \"batsmen2 as dst\", \"partnership_runs as partnership\")\n",
    "\n",
    "print(\"\\nedges :: partnership run between players(top 25 observations)-\")\n",
    "edges.show(25)\n",
    "\n",
    "print(\"\\nedges schema:\\n\")\n",
    "edges.printSchema()\n",
    "#edges.show(25)\n",
    "\n",
    "\n",
    "\n",
    "###VERTICES\n",
    "#as per the UseCase1.Step3, we will consider batsmen score data as vertices. Details are as follows\n",
    "# Load the batsmen score data using SQL Context\n",
    "#df1 = sqlContext.read.load(\"file:/home/cloudera/practicumm2/batsmenscore.csv\",format='com.databricks.spark.csv',header='true',inferSchema='true')\n",
    "\n",
    "df1 = sqlContext.createDataFrame(batsmenscore)\n",
    "#df1['id'] = df1['id'].str.strip()\n",
    "print(\"\\n\\nbatsmenscore dataframe(top 25 observations)\")\n",
    "df1.show(25)\n",
    "\n",
    "\n",
    "#df1.filter(\"id = 'Chahal'\").show()\n",
    "df1.createOrReplaceTempView(\"individual_runs\")\n",
    "# Group by ids to get sum of scores - motive is to create a single \n",
    "    ##entry of a particular batsmen and its total runs scored-\n",
    "individual_player_runs_scored = sqlContext.sql(\"SELECT id, SUM(runs) as runs_scored FROM individual_runs GROUP BY id\")\n",
    "#Vertices -\n",
    "vertices = individual_player_runs_scored\n",
    "\n",
    "print(\"\\nvertices :: each batsman runs(top 25 observations)-\")\n",
    "vertices.show()\n",
    "\n",
    "print(\"\\nvertices schema:\\n\")\n",
    "vertices.printSchema()\n",
    "#edges.show(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6433ee7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dhoni Partnership with Other:-\n",
      "\n",
      "+-----+--------------+-----------+\n",
      "|src  |dst           |partnership|\n",
      "+-----+--------------+-----------+\n",
      "|Dhoni|Kohli         |87         |\n",
      "|Dhoni|Pandya        |20         |\n",
      "|Dhoni|Mohammed Shami|1          |\n",
      "|Dhoni|Pant          |35         |\n",
      "|Dhoni|Sharma        |72         |\n",
      "+-----+--------------+-----------+\n",
      "\n",
      "\n",
      "\n",
      "Others Partnership with Dhoni:-\n",
      "\n",
      "+-------------+-----+-----------+\n",
      "|src          |dst  |partnership|\n",
      "+-------------+-----+-----------+\n",
      "|Kuldeep Yadav|Dhoni|16         |\n",
      "|Karthik      |Dhoni|21         |\n",
      "|Kohli        |Dhoni|13         |\n",
      "|Jadeja       |Dhoni|109        |\n",
      "|Pandya       |Dhoni|107        |\n",
      "|Kumar        |Dhoni|13         |\n",
      "|Jadhav       |Dhoni|93         |\n",
      "+-------------+-----+-----------+\n",
      "\n",
      "\n",
      "Dhoni Partnership Score Card-\n"
     ]
    },
    {
     "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>src</th>\n",
       "      <th>dst</th>\n",
       "      <th>partnership</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Jadeja</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Jadhav</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Karthik</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Kohli</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Kuldeep Yadav</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Kumar</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Pandya</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Pant</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dhoni</td>\n",
       "      <td>Sharma</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     src             dst  partnership\n",
       "0  Dhoni          Jadeja          109\n",
       "1  Dhoni          Jadhav           93\n",
       "2  Dhoni         Karthik           21\n",
       "3  Dhoni           Kohli          100\n",
       "4  Dhoni   Kuldeep Yadav           16\n",
       "5  Dhoni           Kumar           13\n",
       "6  Dhoni  Mohammed Shami            1\n",
       "7  Dhoni          Pandya          127\n",
       "8  Dhoni            Pant           35\n",
       "9  Dhoni          Sharma           72"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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96Een9FnWX399tt12W66//no+9rGPzXi8HkDGZqcZRYsWLaqxAZaSJEmS5sZ5553HIx/5yE4zLF68mF133ZVzzjmn0xyz4cUvfvG9JosZz3jXPckZVbVovP1t2ZMkSdKkzPZaasMy7DXapFFhsSdJkiRp5CxcuLAXrXrQrLk3DE7QIkmSJEk9ZLEnSZIkacpGee6PUTSd622xJ0mSJGlKVlllFa6++moLvjlSVVx99dVTnqHTMXuSJEmSpmTdddfl8ssv58orr+w6ynJjlVVWYd11153Seyz2JEmSJE3JyiuvzIYbbth1DC2D3TglSZIkqYcs9iRJkiSphyz2JEmSJKmHLPYkSZIkqYcs9iRJkiSphyz2JEmSJKmHLPYkSZIkqYcs9iRJkiSphyz2JEmSJKmHhlbsJflUkj8lOWdg238mOT/JL5N8Pcn9B157U5KLklyQ5KnDyiVJkiRJy4Nhtux9Bth5iW0nAJtX1ZbAr4E3ASTZDHgu8Kj2PR9JsuIQs0mSJElSrw2t2Kuqk4E/L7Htu1V1e/v0p8C67ePdgf+pqluq6hLgImDbYWWTJEmSpL7rcszePsC32scPBS4beO3ydpskSZIkaRo6KfaSvBm4HThyGu99eZLTk5x+5ZVXzn44SZIkSeqBOS/2krwY2BV4QVVVu/l3wHoDu63bbruXqjqsqhZV1aIFCxYMNaskSZIkjao5LfaS7Ay8Editqm4aeOkY4LlJ7ptkQ2Bj4NS5zCZJkiRJfbLSsA6c5IvADsDaSS4H3kYz++Z9gROSAPy0ql5RVecm+RLwK5runa+uqjuGlU2SJEmS+m5oxV5VPW+czYdPsP87gXcOK48kSZIkLU+6nI1TkiRJkjQkFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQyt1HWC+WXjQcV1HmJTFh+zSdQRJkiRJ85gte5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDQyv2knwqyZ+SnDOw7YFJTkhyYfv1Ae32JPlgkouS/DLJ1sPKJUmSJEnLg2G27H0G2HmJbQcB36uqjYHvtc8BngZs3P55OfDRIeaSJEmSpN4bWrFXVScDf15i8+7AEe3jI4A9BrZ/tho/Be6f5MHDyiZJkiRJfTfXY/bWqaor2sd/ANZpHz8UuGxgv8vbbZIkSZKkaVipqxNXVSWpqb4vyctpunqy/vrrz3ouScuHhQcd13WESVl8yC5dR5AkSSNqrlv2/jjWPbP9+qd2+++A9Qb2W7fddi9VdVhVLaqqRQsWLBhqWEmSJEkaVXNd7B0DvKh9/CLgGwPbX9jOyrkdcN1Ad09JkiRJ0hQNrRtnki8COwBrJ7kceBtwCPClJPsClwJ7tbsfDzwduAi4CXjJsHJJkiRJ0vJgaMVeVT1vKS89eZx9C3j1sLJIkiRJ0vJmrrtxSpIkSZLmgMWeJEmSJPWQxZ4kSZIk9ZDFniRJkiT1kMWeJEmSJPWQxZ4kSZIk9ZDFniRJkiT1kMWeJEmSJPWQxZ4kSZIk9ZDFniRJkiT1kMWeJEmSJPWQxZ4kSZIk9ZDFniRJkiT1kMWeJEmSJPWQxZ4kSZIk9dBKXQdQvy086LiuI0zK4kN26TqCJEmSNKts2ZMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB5aZrGXxt5J3to+Xz/JtsOPJkmSJEmarsm07H0EeDzwvPb5DcCHh5ZIkiRJkjRjK01in8dV1dZJfg5QVdckuc+Qc0mSJEmSZmAyLXu3JVkRKIAkC4A7h5pKkiRJkjQjkyn2Pgh8HfirJO8Efgi8a6ipJEmSJEkzMmE3ziQrAJcAbwSeDATYo6rOm4NskiRJkqRpmrDYq6o7k3y4qh4DnD9HmSRJkiRJMzSZbpzfS/KsJBl6GkmSJEnSrJhMsfePwJeBW5Pc0P65fsi5JEmSJEkzsMylF6pqzbkIIkmSJEmaPZNZZ48kuwFPbJ+eVFXHDi+SJEmSJGmmltmNM8khwP7Ar9o/+yd597CDSZIkSZKmbzIte08HtqqqOwGSHAH8HHjTMINJkiRJkqZvMhO0ANx/4PFaQ8ghSZIkSZpFk2nZezfw8yQn0iyq/kTgoKGmkiRJkiTNyGRm4/xikpOAbdpNB1bVH4aaSpIkSZI0I5OZoOXvgZuq6piqOga4OckeQ08mSZIkSZq2yYzZe1tVXTf2pKquBd42k5MmeV2Sc5Ock+SLSVZJsmGSnyW5KMlRSe4zk3NIkiRJ0vJsMsXeePtMan2+8SR5KPAaYFFVbQ6sCDwXeA/wX1X1cOAaYN/pnkOSJEmSlneTKfZOT/L+JBu1f/4LOGOG510JWDXJSsBqwBXATsBX2tePAPaY4TkkSZIkabk1mWLvn4BbgaPaPzcDr57uCavqd8B7gd/SFHnX0RSP11bV7e1ulwMPne45JEmSJGl5N5nZOG+kXWohyQNoirKa7gnbY+wObAhcC3wZ2HkK73858HKA9ddff7oxJEmSJKnXltqyl+StSR7RPr5vku8DFwF/TPK3Mzjn3wKXVNWVVXUb8DVge+D+bbdOgHWB34335qo6rKoWVdWiBQsWzCCGJEmSJPXXRN04nwNc0D5+UbvvXwFPAt41g3P+FtguyWpJAjwZ+BVwIrDnwPm+MYNzSJIkSdJybaJi79aB7ppPBb5YVXdU1XnMYDbOqvoZzUQsZwJntxkOAw4EXp/kIuBBwOHTPYckSZIkLe8mKtpuSbI58EdgR+ANA6+tNpOTVtXbuPdafRcD287kuJIkSZKkxkTF3v40LXALaNa/uwQgydOBn89BNkmSJEnSNC212Gu7Wz5inO3HA8cPM5QkSZIkaWYms86eJEmSJGnEWOxJkiRJUg9Z7EmSJElSDy1zCYUkqwCvAp4AFPBD4KNVdfOQs0mSJEmSpmky6+V9FrgBOLR9/nzgc8CzhxVKkiRJkjQzkyn2Nq+qzQaen5jkV8MKJEmSJEmaucmM2TszyXZjT5I8Djh9eJEkSZIkSTM1mZa9xwI/TvLb9vn6wAVJzgaqqrYcWjpJkiRJ0rRMptjbeegpJEmSJEmzaqnFXpL7VdX1NJOz3EtV/XloqSRJkiRJMzJRy94XgF2BM2iWXMjAawU8bIi5JEmSJEkzsNRir6p2bb9uOHdxJEmSJEmzYTJj9kjyUGCDwf2r6uRhhZIkSZIkzcwyi70k7wGeA/wKuKPdXIDFniRJkiTNU5Np2dsD2LSqbhlyFkmSJEnSLJnMouoXAysPO4gkSZIkafZMtPTCoTTdNW8CzkryPeCu1r2qes3w40mSJEnSxBYedFzXESZl8SG7zOn5JurGeXr79QzgmDnIIkmSJEmaJRMtvXDEktuSPABYr6p+OdRUkiRJkqQZWeaYvSQnJblfkgcCZwKfSPL+4UeTJEmSJE3XZCZoWauqrgeeCXy2qh4H/O1wY0mSJEmSZmIyxd5KSR4M7AUcO+Q8kiRJkqRZMJli7x3Ad4CLquq0JA8DLhxuLEmSJEnSTEy4qHqSFWkmZNlybFtVXQw8a9jBJEmSJEnTN2HLXlXdATxvjrJIkiRJkmbJhC17rR8l+RBwFHDj2MaqOnNoqSRJkiRJMzKZYm+r9uvBA9sK2GnW00iSJEmSZsUyi72q2nEugkiSJEmSZs9kFlVfJ8nhSb7VPt8syb7DjyZJkiRJmq7JLL3wGZqlFx7SPv818Noh5ZEkSZIkzYLJFHtrV9WXgDsBqup24I6hppIkSZIkzchkir0bkzyIZlIWkmwHXDfUVJIkSZKkGZnMbJyvB44BNkryI2ABsOdQU0mSJEmSZmQys3GemeRJwKZAgAuq6rahJ5MkSZIkTdtkWvYAtgUWtvtvnYSq+uzQUkmSJEmSZmSZxV6SzwEbAWdx98QsBVjsSZIkSdI8NZmWvUXAZlVVww4jSZIkSZodk5mN8xzgr4cdRJIkSZI0eybTsrc28KskpwK3jG2sqt2GlkqSJEmSNCOTKfbePuwQkiRJkqTZNZli7+lVdeDghiTvAX4wnEiSJEmSpJmazJi9p4yz7WmzHUSSJEmSNHuW2rKX5JXAq4CNkvxy4KU1gR8NO5gkSZIkafom6sb5BeBbwLuBgwa231BVfx5qKkmSJEnSjCy12Kuq65L8BXhMVV06h5kkSZIkSTM04Zi9qroDuCDJ+nOUR5IkSZI0CyYzG+cDgHPbdfZuHNvoOnuSJEmSNH9Nptj719k+aZL7A58ENgcK2Ae4ADgKWAgsBvaqqmtm+9ySJEmStDxYZrFXVcNYT+8DwLeras8k9wFWA/4F+F5VHZLkIJpJYQ6c6CCSJEmSpPEtc529JNslOS3JX5LcmuSOJNdP94RJ1gKeCBwOUFW3VtW1wO7AEe1uRwB7TPcckiRJkrS8m8yi6h8CngdcCKwKvBT48AzOuSFwJfDpJD9P8skkqwPrVNUV7T5/ANYZ781JXp7k9CSnX3nllTOIIUmSJEn9NZlij6q6CFixqu6oqk8DO8/gnCsBWwMfrarH0Ez6MriOH1VVNGP5xstyWFUtqqpFCxYsmEEMSZIkSeqvyRR7N7Xj6s5K8h9JXjfJ9y3N5cDlVfWz9vlXaIq/PyZ5MED79U8zOIckSZIkLdcmU7Tt3e63H00r3HrAs6Z7wqr6A3BZkk3bTU8GfgUcA7yo3fYi4BvTPYckSZIkLe+WOhtnko2B9wIbAWcDb6iqd8zSef8JOLJtMbwYeAlNQfmlJPsClwJ7zdK5JEmSJGm5M9HSC58CPgucDOwGHAo8czZOWlVnAYvGeenJs3F8SZIkSVreTVTsrVlVn2gf/2eSM+cikCRJkiRp5iYq9lZJ8hgg7fNVB59XlcWfJEmSJM1TExV7VwDvH3j+h4HnBew0rFCSJEmSpJlZarFXVTvOZRBJkiRJ0uyZyXp5kiRJkqR5ymJPkiRJknpowmIvjfXmKowkSZIkaXZMWOxVVQHHz1EWSZIkSdIsmUw3zjOTbDP0JJIkSZKkWTPR0gtjHge8IMmlwI006+xVVW051GSSJEmSpGmbTLH31KGnkCRJkiTNqmV246yqS4H1gJ3axzdN5n2SJEmSpO4ss2hL8jbgQOBN7aaVgc8PM5QkSZIkaWYm00L398BuNOP1qKrfA2sOM5QkSZIkaWYmU+zd2i7BUABJVh9uJEmSJEnSTE2m2PtSko8D90/yMuB/gU8MN5YkSZIkaSaWORtnVb03yVOA64FNgbdW1QlDTyZJkiRJmrZlFntJ/gn4vAWeJEmSJI2OyXTjXAc4LcmXkuycJMMOJUmSJEmamcmss/cWYGPgcODFwIVJ3pVkoyFnkyRJkiRN06QWR29n4/xD++d24AHAV5L8xxCzSZIkSZKmaTJj9vYHXghcBXwSOKCqbkuyAnAh8MbhRpQkSZIkTdUyiz3ggcAzq+rSwY1VdWeSXYcTS5IkSZI0E5NZeuFtAEn+ClhlYPtvq+q8IWaTJEmSJE3TMsfsJXlGkguBS4AfAIuBbw05lyRJkiRpBiYzQcu/A9sBv66qDYEnAz8daipJkiRJ0oxMpti7raquBlZIskJVnQgsGnIuSZIkSdIMTGaClmuTrAGcDByZ5E/AjcONJUmSJEmaicm07O0O/B/wOuDbwG+AZwwzlCRJkiRpZiYzG+dgK94RQ8wiSZIkSZolSy32ktwAFJB2U429BFRV3W/I2SRJkiRJ07TUYq+q1pzLIJIkSZKk2TNRy94qwCuAhwO/BD5VVbfPVTBJkiRJ0vRNNEHLETRLLJwNPB1435wkkiRJkiTN2EQTtGxWVVsAJDkcOHVuIkmSJEmSZmqilr3bxh7YfVOSJEmSRstELXuPTnJ9+zjAqu1zZ+OUJEmSpHluotk4V5zLIJIkSZKk2TNRN05JkiRJ0oiy2JMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB7qrNhLsmKSnyc5tn2+YZKfJbkoyVFJ7tNVNkmSJEkadV227O0PnDfw/D3Af1XVw4FrgH07SSVJkiRJPdBJsZdkXWAX4JPt8wA7AV9pdzkC2KOLbJIkSZLUB1217P038Ebgzvb5g4Brq+r29vnlwEPHe2OSlyc5PcnpV1555dCDSpIkSdIomvNiL8muwJ+q6ozpvL+qDquqRVW1aMGCBbOcTpIkSZL6YaUOzrk9sFuSpwOrAPcDPgDcP8lKbeveusDvOsgmSZIkSb0w5y17VfWmqlq3qhYCzwW+X1UvAE4E9mx3exHwjbnOJkmSJEl9MZ/W2TsQeH2Si2jG8B3ecR5JkiRJGllddOO8S1WdBJzUPr4Y2LbLPJIkSZLUF/OpZU+SJEmSNEss9iRJkiSphyz2JEmSJKmHLPYkSZIkqYcs9iRJkiSphyz2JEmSJKmHLPYkSZIkqYcs9iRJkiSphyz2JEmSJKmHVuo6gCRJkrQ8WnjQcV1HmJTFh+zSdQRNky17kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1ENzXuwlWS/JiUl+leTcJPu32x+Y5IQkF7ZfHzDX2SRJkiSpL1bq4Jy3A/9cVWcmWRM4I8kJwIuB71XVIUkOAg4CDuwgnzRvLTzouK4jLNPiQ3bpOoIkSZLooGWvqq6oqjPbxzcA5wEPBXYHjmh3OwLYY66zSZIkSVJfdDpmL8lC4DHAz4B1quqK9qU/AOss5T0vT3J6ktOvvPLKuQkqSZIkSSOms2IvyRrAV4HXVtX1g69VVQE13vuq6rCqWlRVixYsWDAHSSVJkiRp9HRS7CVZmabQO7KqvtZu/mOSB7evPxj4UxfZJEmSJKkPupiNM8DhwHlV9f6Bl44BXtQ+fhHwjbnOJkmSJEl90cVsnNsD/wCcneSsdtu/AIcAX0qyL3ApsFcH2SRJkiSpF+a82KuqHwJZystPnssskiRJktRXnc7GKUmSJEkaDos9SZIkSeohiz1JkiRJ6iGLPUmSJEnqIYs9SZIkSeohiz1JkiRJ6qEu1tmTJPXMwoOO6zrCpCw+ZJeuI0iSNGds2ZMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHrLYkyRJkqQestiTJEmSpB6y2JMkSZKkHnKdPUmS5plRWLfQNQslaf6zZU+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknrIYk+SJEmSeshiT5IkSZJ6yGJPkiRJknpo3hV7SXZOckGSi5Ic1HUeSZIkSRpFK3UdYFCSFYEPA08BLgdOS3JMVf2q22SSJGkULTzouK4jTMriQ3bpOoKkHppvLXvbAhdV1cVVdSvwP8DuHWeSJEmSpJEz34q9hwKXDTy/vN0mSZIkSZqCVFXXGe6SZE9g56p6afv8H4DHVdV+A/u8HHh5+3RT4II5Dzp1awNXdR2iR7yes8drObu8nrPL6zm7vJ6zx2s5u7yes8vrOXtG5VpuUFULxnthXo3ZA34HrDfwfN12212q6jDgsLkMNVNJTq+qRV3n6Auv5+zxWs4ur+fs8nrOLq/n7PFazi6v5+zyes6ePlzL+daN8zRg4yQbJrkP8FzgmI4zSZIkSdLImVcte1V1e5L9gO8AKwKfqqpzO44lSZIkSSNnXhV7AFV1PHB81zlm2Uh1Ox0BXs/Z47WcXV7P2eX1nF1ez9njtZxdXs/Z5fWcPSN/LefVBC2SJEmSpNkx38bsSZIkSZJmgcWe5rUkz0xy365zSBq+JCt2nUGSpD6xG+cQJFkF2Bd4FLDK2Paq2qezUCMqyaeBnYCTgaOAb1fV7d2mGm1JHlhVf15i24ZVdUlXmSSAJL8Fvk3zb/375Q+oGUmyArBdVf246yyjKslOVfX9JM8c7/Wq+tpcZ5KkqbDYG4IkXwbOB54PHAy8ADivqvbvNNiISrIy8DTgOcATgBOq6qXdphpdSX4EPK2qrm+fbwZ8qao27zbZaEnyiKo6P8nW471eVWfOdaZRl2Q1YFeaZXe2Bo4F/qeqfthpsBGW5OdV9Ziuc4yqJO+oqre1Nx6XVN7EnZkku3DvG+MHd5dodCV5T1UduKxtWra+NdpY7A3B2A/XJL+sqi3bYuWUqtqu62yjqr2GOwMvAZ5YVWt3HGlktT9c3wjsAmwKfBZ4QVWd1WWuUZPkE1X1siQnjvNyVdVOcx6qR5I8APgAzfem3TunKcl7gZ8AX7OlVPNJko8BqwE7Ap8E9gROrap9Ow02opKcWVVbL7Htl1W1ZVeZRlXfGm0s9oYgyalVtW2Sk4FXAX+g+Q/sYR1HGzlJxlr0dgBOAr4EfNeunDOTZA+agm9N4FlV9etuE0mNJE+i+Te/M3A6cFRVfbXbVKMryQ3A6sDtwM1AaG5G3K/TYCMmyf2BFwILGVi2qqpe01GkkTdwQ3zs6xrAt6rqb7rONkqSvJLmd82HAb8ZeGlN4EdVtXcnwUZY3xpt5t06ez1xWHtX+l+BY4A1gLd2G2lkvZBm/M4/VtUtXYcZZUkOBQbv7qxF84NhvyT+0jJFSxvDM8axPFOXZDHwc5qbOgdU1Y3dJhp9VbVm1xl64njgp8DZwJ0dZ+mL/2u/3pTkIcDVwIM7zDOqvgB8C3g3cNDA9huWHJ+vSbut/Xptks1pGm3+qsM8M2KxNwRV9cn24Q9o7rRomqrqeV1n6JHTl3h+Ricp+uMZE7xWgMXe1G05NpZUs6e9+bgx9xx7cnJ3iUbSKlX1+q5D9MyxbYvpfwJn0vy/+YlOE42gqroOuA54Xjuj8To0v9+vkWSNqvptpwFHU68abezGOYuS7F1Vn08y3g+EAv4MHFNV18xxtJGVZDvgUOCRwH2AFYEb7YIk9U/fBsXPB0leCuwPrAucBWwH/MQxpVOT5HXAX2gmDbqrl4ktJ7OjXWJplbZw0TQk2Q94O/BH7m59LsfsyZa92bV6+3Vp3WY2BF5J88NWk/Mhmpn5vgwsounWuUmniUZUki9V1V5Jzuae3TkB8AfC9LS/pDyLe4/lcUa5qfsczaD4pzIwKL7TRKNvf2Ab4KdVtWOSRwDv6jjTKLqVpgXqzdz9/2dh751pS3IG8CngC+1NcIdqzMxrgU2r6uqug4y6vo3RtdibRVX18fbrO5a2TxJ/AZyiqrooyYpVdQfw6SQ/B97Uda4RNDaL1K6dpuifb9B0oTkDf1mZqYdX1bOT7F5VRyT5AnBK16FG3M1VdXMSkty3XS5k065DjaB/pvn+vKrrID3yHJoZtk9LcjrwaZoJ2OxyNj2X0fws0sz1aoyuxd4QJNkE+CiwTlVtnmRLYLeq+veqGtk+vx25Kcl9gLOS/AdwBbBCx5lGUlVd0X69tOssPbNuVe3cdYie6NWg+Hni8vYu9dHACUmuAfw/YOouAm7qOkSfVNVFwJuT/CvNTchPAXe0axp+wC6yU3YxcFKS47hnV+P3dxdpZPVqjK5j9oYgyQ+AA4CPjy1mm+QcF62euiQb0PQ/vw/wOpoZJD/S/pDQNLSzSL6H5pfo4FTsM5LkMODQqjq76yyjrh1f9lVgS5q7/GsAb62qj3UarCfaZS3WAr5dVbd2nWeUJPk6zVjSE7nnL9Ij2a1rvmhvhr8EeDrwHeBI4AnAP1TVVh1GGzlJ3jbe9ol6m2l8fRuja7E3BElOq6ptxtbpaLed5X9cmg+SXAQ8o6ocCzUDA2MfV6KZ6fBimh8KY8WzYyA1L7Szyq3HPceenNldotGT5EXjba+qI+Y6S1+0Y/auBQ4Hvjq4vFKSr1XVhMvbSMOS5NXAO2m+P+8aozuq62XbjXM4rkqyEe03SJI9abofaoqSbE8zu9QG3PMXlZH8BzdP/NFCb1Y49nGWLGUG47vYDWn6kvwb8GKamxF3zdAHOBvnFFjUDcWzq+ri8V6w0Ju6JAuAN3Lv2Yz9tz51vRqja7E3HK8GDgMekeR3wCXA3t1GGlmH03TfPAO4o+MsI21gEfDTkxxFM4ZnsHuC68JNweDYxySPBv6mfXpKVf2im1Qja2wG401pZo48pn3+DODUThL1x17ARnbbnJkkG9MsWr0Z9/xF2huPUzR4cyfJvV735s60HQkcRXMj8hXAi4ArO000uno1RtdibwjaO1V/m2R1YIWquqHrTCPsuqr6VtchemJwEfCbgL8beO4i4NOUZH/gZdx9/T6f5LCqOrTDWCNlbExJkpOBrcf+z0zyduC4DqP1wTnA/YE/dZxj1H0aeBvwX8CONOPMnCxsery5MxwPqqrDk+xfVT8AfpDktK5DjagbaSYG7MUYXcfszSK7Is2eJFu3D/eiWUj9a9zzH5zjTaYpyfZV9aNlbdPkJPkl8PiqurF9vjrNotWO2ZuiJBcAW46N3WnXMPxlVblUwDQlWUSzPMg53PP/0N06CzWCkpxRVY9NcnZVbTG4retso6q9ubPLwM2dNYHjquqJ3SYbTUl+WlXbJfkO8EHg98BXqmqjjqONnL6N0bVlb3Z5t2r2vG+J54sGHjveZGYOBbaexDZNTrhnF+M72m2aus8Cp7YzHwLsAYzkD9d55Aia2Xd7sV5Uh25JsgJwYZL9gN/RzBar6VuHZrH6Mbe22zQ9/55kLZrxZocC96MZBqMpSLIi8OKq2rHrLLPFYm8W2RVp9vTpH9l8keTxwP8DFizRCn0/mtZTTc+ngZ8tUaAc3l2c0VVV70zybZqp1wFeUlU/7zJTD9xUVR/sOkQP7A+sBrwG+DeaG47j3v3XpHlzZxYkWYVmjN7DgYcCh/s71PRV1R1J7kyyVlX1YpF6u3EOgV2RZs4usbOvXWNrB5ofCoPrlt0AfLOqLuwi16hKsl5VXdY+3pq7C5RTgIdW1bGdhRtxSf6Ke06C8dsO44y0JO+n6b55DHaF1zyT5LHc/X/nyd7cmbp2wrXbaH72PA24tKr27zbVaEvyDeAxwAk04/cAx+xpQJI304w1G7xb9aWqeldnoUbMwOKg43aJrSpnN52GtnvCl6rqWV1nGXVJzgd2rqrFS2zfB3iz4ySmLsluNF24H0Izocj6wPlV9ahOg42wdoKBJZXTsU9Nkk2AA7j3MkBexxlofyatwz2vqTd3pmCJcaQr0fyO5LCMGejbmD2LvSHxbtXscAD37Evyk6p6fNc5Rl2SpwP/TfP9eWG77SDgBcDTquryDuONpCS/oOke979V9ZgkOwJ7V9W+HUfTcq793vwYSywDVFVndBZqxCX5J5oZTv/I3WOdy8mtpibJmYPF3ZLPJcfsDUlVnZHkMtquSEnW927VtDiAe/adleQY4Mvcs3uCSy9MQVUdn+QW4FtJ9gBeCmwLPLGqruk03Oi6raquTrJCkhWq6sQk/911qFGW5K3jba+qg+c6y4i7vao+2nWIntkf2LSqru46yIh7dJLr28cBVm2fjxXP9+su2mjq27qaFntDsLSuSIBdkabOAdyzbxXgau45o6nr7E1DVX0vyUuAk4AfAztV1c3dpho9Se5XVdcD1yZZAzgZODLJnxi4IaFpGbx+q9AsuHxeR1lGTpIHtg+/meRVNMMzBsc+/rmTYP1wGdCLCTC6VFVOsDb7erWupt04h8CuSLPLLrGaj5LcQFMkB7gvzQD5wa5I3k2dpCS/Ad7M3ZOIhKY77FrAkd75nz3thGHfqaodus4yCpJcwt3/zpdUo3qnfz5IcjjNuPzjuGcB7QRs6lTf1tW0ZW847Io0i+wSO7vaaZr3pWlpHuyesE9noUZQVa257L00STvRjH/cF3hlVV2ELfjDshqwbtchRkVVbdh1hh77bfvnPu0fab7o1bqaFnvDMdYV6RTsijQjdokdis/RXMOnAgfTtKDYrUudqapLgb9P8jTgR0lOY2AB8KrarbNwIy7J2TQtU9Csp7mA5t+9JiHJNsBlVfWH9vkLgWcBlwJvtxvn9I2tTSzNQ71aV9NunEOQZDXgZppuH3vTLFp9pD8Ups4usbMvyc/ba/nLqtoyycrAKVW1XdfZtPxKsinwEeAa4MPcs9j7QVe5RtXYOpBJNhjYfDvNzIc7uw7k5CQ5E/jbqvpzkicC/wP8E7AV8Miq2rPLfKMsyQLgjdy7l4nLWUizyJa9WTQwhucem9uvbx0bl1JV35vbZCPNLrGz77b267VJNgf+APxVh3m0nEtyCLA78Lqq+nbXeXrihCTjrQP5EuAtgMXe5Kw4cKP2OcBhVfVV4KtJzuouVi8cCRxFM2nQK2haTq7sNJFE/9bVtNibRRON4WkXDt2c5j+3zecs1OizS+zsOyzJA2h+4TuGph/6v3YbScu524HHOJPprHo98N0kg+tAvgl4PvCkTpONlhWTrFRVtwNPBl4+8Jq/Q83Mg6rq8CT7t633P2i7cEtd+zLNupqfYGBdzVHlf1RzpKruAH6R5NCus4yY3Wi6xO7P3V1i7ec/DWPduqrqk+2mk4GHta/t2l0yLe+q6i1dZ+gb14GcNV+kKUKuAv6P5sYjSR6OywbM1FgvkyuS7AL8HnjgBPtLc6VX62o6Zk/z0jK6xN4M2CV2ipKcTzNWZ/ES218CvKWqNuokmKShSfI3NGvD/RjYy9bTqUuyHfBg4LtVdWO7bRNgjao6s9NwI6y9yXgKsB5wKO3N3Ko6ptNgWm4NrKv5GppJAXuxrqbFnkbOYJfYqrJL7CQleTrN9Pbjdet6WlVd3mE8SbPIdSA1X7XL/7wCeDhwNnB4201W6tQ462reo0ga1XU17capkWOX2OmxW5dGQZJnAk+g+SH7w6r6eseRRpLrQGoeO4Lm5sMpwNOAzWiGakhdew7NUitXACR5Ec1SK4uBt3cXa2Zs2ZOWM3br0nyV5CM0d/u/2G56DvCbqnp1d6kkzaYkZ1fVFu3jlYBTq2rrjmNJvV1qxZY9aTkxTreuJwN/SmK3Ls0XO9H8QC2AJEcA53YbSdIsG5uYhaq6vfkRJM0LvVxqxWJPWk7YrUsj4CJgfeDS9vl67TapE0uZLOwu3iSblkcnub59HGDV9rk3HtW1Xi61MrLBJUm9syZwXpJTaX7B3hY4PckxAFW1W5fhtPwZu0mW5N+AK4DP0RQlL6CZoVNTVFUrdp1BWopeLrXimD1J0ryQZMLFvtuFl6U5l+QXVfXoZW2TNNr6uNSKLXuSpHmhqn6QZANg46r63ySrAitV1Q1dZ9Ny78YkL6CZsKGA5wE3dhtJ0myrqp+Os+3XXWSZLSt0HUCSJIAkLwO+Any83bQucHRngaS7PR/YC/hj++fZ7TZJmtfsxilJmhfa2c62BX5WVY9pt901TbskSZoaW/YkSfPFLVV169iTdg0u70iqc0k2SfK9JOe0z7dM8pauc0nSsljsSZLmix8k+ReaqdifAnwZ+GbHmSSATwBvol0jrqp+CTy300SSNAkWe5Kk+eIg4ErgbOAfgeMBW080H6xWVacuse32TpJI0hQ4G6ckaV6oqjuTfB44uaou6DqPNOCqJBvRditOsifNunuSNK/ZsidJmheS7AacBXy7fb7V2ILqUsdeTTNL7COS/A54LfDKThNJ0iQ4G6ckaV5IcgawE3CSs3FqPkqyOrCCaz9KGhV245QkzRe3VdV1SQa3eUdSnUtyf+CFwEJgpbHv0ap6TXepJGnZLPYkSfPFuUmeD6yYZGPgNcCPO84kQTNZ0E9pJg+6s+MskjRpduOUJM0LSVYD3gz8HRDgO8C/VdXNnQbTci/JmVW1ddc5JGmqLPYkSZImkOR1wF+AY4FbxrZX1Z87CyVJk2A3TklSp5J8kwnG5lXVbnMYRxrPrcB/0rQ8j32vFvCwzhJJ0iRY7EmSuvbe9uszgb8GPt8+fx7wx04SSff0z8DDq+qqroNI0lTYjVOSNC8kOb2qFi1rmzTXknwX2KOqbuo6iyRNhS17kqT5YvUkD6uqiwGSbAis3nEmCeBG4KwkJ3LPMXsuvSBpXrPYkyTNF68DTkpyMc1snBsA/9htJAmAo9s/kjRS7MYpSZo3ktwXeET79PyqumWi/SVJ0tJZ7EmSOpXkmRO9XlVfm6ss0niS7Ar8G01r80o0Lc9VVffrNJgkLYPFniSpU0k+PcHLVVX7zFkYaRxJLqKZLfbs8hcnSSPEYk+SJGkC7cQsT66qO7vOIklTYbEnSZoXkrx1vO1VdfBcZ5EGJdmGphvnD7jnbJzv7yyUJE2Cs3FKkuaLGwcerwLsCpzXURZp0DuBv9B8X96n4yySNGm27EmS5qV2Zs7vVNUOXWfR8i3JOVW1edc5JGmqVug6gCRJS7EasG7XISTg+CR/13UISZoqW/YkSfNCkrOBsR9KKwILgIOr6kPdpZIgyQ3A6jTj9W7DpRckjQjH7EmSOpVkvaq6jGaM3pjbgT8CO3eTSrpbVa3ZdQZJmg5b9iRJnUpyPrBzVS1eYvtLgLdU1UadBJMGJHkAsDHNJC0AVNXJ3SWSpGWzZU+S1LXXA99NsktVXQiQ5E3A84EndZpMApK8FNifZgzpWcB2wE+AnTqMJUnL5AQtkqROVdXxwCuBbyXZPMl/A88AnlhVl3caTmrsD2wDXFpVOwKPAa7tNJEkTYLFniSpc1X1PeAlwEnAw4CdquqaTkNJd7u5qm6GZkmQqjof2LTjTJK0THbjlCR1qp3psGhmOLwv8GTgT0mc8VDzxeVJ7g8cDZyQ5Brg0k4TSdIkOEGLJEnSJCV5ErAW8O2qurXrPJI0EYs9SZKkZUiyIrAOA72iquq33SWSpGWzG6ckSdIEkvwT8DaatR/vbDcXsGVnoSRpEmzZkyRJmkCSi4DHVdXVXWeRpKlwNk5JkqSJXQZc13UISZoqW/YkSZLGkeT17cNH0Sy1cBxwy9jrVfX+LnJJ0mQ5Zk+SJGl8a7Zff9v+uU/7R5JGgi17kiRJk5BkDYCq+kvXWSRpMhyzJ0mSNIEkmyf5OXAucG6SM5I8qutckrQsFnuSJEkTOwx4fVVtUFUbAP8MfKLjTJK0TBZ7kiRJE1u9qk4ce1JVJwGrdxdHkibHCVokSZImdnGSfwU+1z7fG7i4wzySNCm27EmSJE1sH2AB8LX2z4J2myTNa87GKUmSJEk9ZDdOSZKkcSQ5ZqLXq2q3ucoiSdNhsSdJkjS+xwOXAV8Efgak2ziSNDV245QkSRpHkhWBpwDPA7YEjgO+WFXndhpMkibJCVokSZLGUVV3VNW3q+pFwHbARcBJSfbrOJokTYrdOCVJkpYiyX2BXWha9xYCHwS+3mUmSZosu3FKkiSNI8lngc2B44H/qapzOo4kSVNisSdJkjSOJHcCN7ZPB39hClBVdb+5TyVJk2exJ0mSJEk95AQtkiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kqQ5lWTdJN9IcmGS3yT5QJL7tK9tleTpA/u+PckbZvHcn0lySZKzkpyZ5PFTfP+/zFaWcY69Q5JK8oyBbccm2WFY55Qk9ZvFniRpziQJ8DXg6KraGNgEWAN4Z7vLVsDTx3/3tM634jibD6iqrYCDgI9P8jhJsgIwa8VekvHWur0cePNsnUOStHyz2JMkzaWdgJur6tMAVXUH8DpgnyT3Aw4GntO2vD2nfc9mSU5KcnGS14wdKMneSU5t9/34WGGX5C9J3pfkF8BELXcnAw9PskaS77UtfWcn2b09zsIkF7RrrZ0DHA6s2p7vyPb185J8Ism5Sb6bZNX2vRsl+XaSM5KckuQR7fbPJPlYkp8B/zFOpl8A1yV5ypIvJHlrktOSnJPksLZwpr02/5Xk9DbPNkm+1rac/vtE16v985n2mGcned0y/wYlSSPDYk+SNJceBZwxuKGqrgd+CywE3gocVVVbVdVR7S6PAJ4KbAu8LcnKSR4JPAfYvm2luwN4Qbv/6sDPqurRVfXDCbI8AzgbuBn4+6raGtgReN9YIQVsDHykqh5VVS8B/q/N9oKB1z9cVY8CrgWe1W4/DPinqnos8AbgIwPnXRf4f1X1+qXkeifwlnG2f6iqtqmqzYFVgV0HXru1qhYBHwO+AbyaZjHwFyd50ATXayvgoVW1eVVtAXx6guslSRox43UhkSRpPjmuqm4BbknyJ2Ad4MnAY4HT2rpsVeBP7f53AF+d4Hj/meQtwJXAvjQLZL8ryROBO4GHtucAuLSqfjrBsS6pqrPax2cAC5OsAfw/4Mt314zcd+A9X25bNMdVVScnIckTlnhpxyRvBFYDHgicC3yzfe2Y9uvZwLlVdQVAkouB9YAnMP71+ibwsCSHAscB353gs0qSRozFniRpLv0K2HNwQ9t9c33gImDrcd5zy8DjO2h+dgU4oqreNM7+N09UTNGM2fvKwPlfDCwAHltVtyVZDKzSvnzjhJ/m3tlWpek1c23bgjaeZR0T7m7du73NuApN6+CiqrosydsHMg7muHOJTHeyjOuV5NE0LaevAPYC9plEPknSCLAbpyRpLn0PWC3JC+GuCVTeB3ymqm4CbgDWnORx9kzyV+1xHphkg2lmWgv4U1vo7QhMdJzbkqw80cHabqmXJHl2my1tQTVpVfVd4AHAlu2mscLuqrblcM9x37h0416vJGsDK1TVV2mKy/GKbUnSiLLYkyTNmaoq4O+BZye5EPg1zZi5sVkuT6SZkGVwgpbxjvMrmuLku0l+CZwAPHiasY4EFiU5G3ghcP4E+x4G/DLJkcs45guAfdtJYs4Fdp9GrnfSdMGkqq4FPkEzUcx3gNOmcqAJrtdDgZOSnAV8HhivpVSSNKLS/NyVJEmSJPWJLXuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQxZ7kiRJktRDFnuSJEmS1EMWe5IkSZLUQ/8fyiUUvSTvPyUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = GraphFrame(vertices, edges)\n",
    "\n",
    "#AS AN EXAMPLE, LET US TAKE PLAYER NAME- DHONI AND SEE HIS PARTNERSHIP WITH OTHERS.\n",
    "\n",
    "#below figure is an illustration of the g.find() function. As we see, column a and b are vertices.\n",
    "#and column e displays the partnership score between the player a and b.\n",
    "\n",
    "'in the below code, a and b are vertices, whereas [e] is edge'\n",
    "dhoni_with_others = g.find(\"(a)-[e]->(b)\").filter(\"e.partnership > 0\").filter(\"a.id= 'Dhoni'\")\n",
    "dhoni_with_others = dhoni_with_others.select(\"e.src\", \"e.dst\", \"e.partnership\")\n",
    "print(\"Dhoni Partnership with Other:-\\n\")\n",
    "dhoni_with_others.show(truncate=0)\n",
    "dhoni_with_others = dhoni_with_others.toPandas()\n",
    "dhoni_with_others = pd.DataFrame(dhoni_with_others)\n",
    "\n",
    "#below figure is an illustration of the g.find() function. As we see, column a and b are vertices.\n",
    "#and column e displays the partnership score between the player a and b.\n",
    "others_with_dhoni = g.find(\"(a)-[e]->(b)\").filter(\"e.partnership > 0\").filter(\"b.id= 'Dhoni'\")\n",
    "others_with_dhoni = others_with_dhoni.select(\"e.src\", \"e.dst\", \"e.partnership\")\n",
    "print(\"\\n\\nOthers Partnership with Dhoni:-\\n\")\n",
    "others_with_dhoni.show(truncate=0)\n",
    "others_with_dhoni = others_with_dhoni.toPandas()\n",
    "others_with_dhoni = pd.DataFrame(others_with_dhoni)\n",
    "others_with_dhoni.columns = ['dst','src', 'partnership']\n",
    "others_with_dhoni = others_with_dhoni[['src','dst', 'partnership']]\n",
    "dhoni_overall_partnership_score=dhoni_with_others.append(others_with_dhoni)\n",
    "\n",
    "\n",
    "\n",
    "dhoni_overall_partnership_scr = pd.DataFrame(dhoni_overall_partnership_score.groupby(['src', 'dst']).agg({'partnership': np.sum})).reset_index()\n",
    "print(\"\\nDhoni Partnership Score Card-\")\n",
    "display(dhoni_overall_partnership_scr)\n",
    "\n",
    "\n",
    "\n",
    "dhoni_overall_partnership_scr.plot(kind=\"bar\", x=\"dst\", y=[\"partnership\"], \n",
    "                                   xlabel = 'Other Partner Names', \n",
    "                                   ylabel='Player Partnership Score',\n",
    "                                   title='Partnership Score Card of a Player',\n",
    "                                   figsize=(15,7))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a07a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "'''Above plot depicts that batsman- Dhoni had a good partnership with Pandya followed by Jadeja followed by Kohli. However, \n",
    "he had weak partnership with Shami and Karthik.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b1a6f21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Teams with their Batting Partnerships Break Counts'}, xlabel='Counts of breaks in batting partnerships', ylabel='Team Name'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" UseCase1.Step5- Are there team where batting partnerships did not stand longer? Analysis.\"\"\"\n",
    "\n",
    "dup1 = partnership_score[partnership_score.batsmen1 < partnership_score.batsmen2]\n",
    "dup2 = partnership_score[partnership_score.batsmen1 > partnership_score.batsmen2]\n",
    "#z = partnership_score[partnership_score.batsmen1 == partnership_score.batsmen2] # we are not considering it since, its all NANs\n",
    "dup=dup1.append(dup2)\n",
    "partnership_score1 = pd.DataFrame(dup.groupby(['matchno', 'batting_team','batsmen1', 'batsmen2']).sum().reset_index())\n",
    "\n",
    "\n",
    "\"\"\" fetch team whose players had frequent break up in partnership\"\"\"\n",
    "\n",
    "#Subset batting team, batsmen1 and batsmen2 from partnership score data & get team with max part. breaks -\n",
    "paircount = partnership_score1[['batting_team', 'batsmen1', 'batsmen2']]\n",
    "paircount\n",
    "teams_with_partnership_breaks= paircount.groupby('batting_team').count().sort_values(by=['batsmen1'], ascending=False).reset_index()\n",
    "#note there are no duplicates in it as data is in the order batsman played\n",
    "\n",
    "teams_with_partnership_breaks.plot(kind=\"bar\", x=\"batting_team\", y=[\"batsmen1\"], \n",
    "                                   xlabel = 'Counts of breaks in batting partnerships', \n",
    "                                   ylabel='Team Name',\n",
    "                                   title='Teams with their Batting Partnerships Break Counts',\n",
    "                                   figsize=(15,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16999935",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Few points on scope of improvement:-\n",
    "'''\n",
    "\n",
    "1. Further utilities of GraphFrames which can be thought of -\n",
    "    - We can automate this approach of partnership score of any player? \n",
    "    - We can include more data from other world cups to make fine estimations?\n",
    "    - How many times a player has bowled out an another player?\n",
    "\n",
    "2. We can look and have analysis more in depth on the teams where batting partnerships did not stand longer. \n",
    "    -Is there any particular player order missing in them?\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adeb57d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#References:\n",
    "\n",
    "\n",
    "'How to install and invoke pyspark in windows environment -'\n",
    "#https://changhsinlee.com/install-pyspark-windows-jupyter/\n",
    "\n",
    "#https://www.apache.org/dyn/closer.lua/spark/spark-3.1.2/spark-3.1.2-bin-hadoop3.2.tgz\n",
    "\n",
    "#https://graphframes.github.io/graphframes/docs/_site/user-guide.html\n",
    "#https://spark.apache.org/docs/1.6.1/sql-programming-guide.html"
   ]
  }
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