clusterAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batting Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212, 7)\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>batsman</th>\n",
       "      <th>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>141</td>\n",
       "      <td>0.155695</td>\n",
       "      <td>0.163121</td>\n",
       "      <td>1.265884</td>\n",
       "      <td>37.483051</td>\n",
       "      <td>0.653220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>146</td>\n",
       "      <td>0.157879</td>\n",
       "      <td>0.102740</td>\n",
       "      <td>1.203612</td>\n",
       "      <td>31.541985</td>\n",
       "      <td>0.639225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>157</td>\n",
       "      <td>0.170971</td>\n",
       "      <td>0.146497</td>\n",
       "      <td>1.349955</td>\n",
       "      <td>33.940299</td>\n",
       "      <td>0.679083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>153</td>\n",
       "      <td>0.160965</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>1.284973</td>\n",
       "      <td>32.612403</td>\n",
       "      <td>0.638383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>126</td>\n",
       "      <td>0.157072</td>\n",
       "      <td>0.134921</td>\n",
       "      <td>1.185025</td>\n",
       "      <td>32.669725</td>\n",
       "      <td>0.604899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     batsman  matches  hard_hitting_ability  finisher  fast_scoring_ability  \\\n",
       "0    V Kohli      141              0.155695  0.163121              1.265884   \n",
       "1  G Gambhir      146              0.157879  0.102740              1.203612   \n",
       "2   SK Raina      157              0.170971  0.146497              1.349955   \n",
       "3  RG Sharma      153              0.160965  0.156863              1.284973   \n",
       "4   S Dhawan      126              0.157072  0.134921              1.185025   \n",
       "\n",
       "   consistency  running_between_wickets  \n",
       "0    37.483051                 0.653220  \n",
       "1    31.541985                 0.639225  \n",
       "2    33.940299                 0.679083  \n",
       "3    32.612403                 0.638383  \n",
       "4    32.669725                 0.604899  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfbat = pd.read_csv(\"batsmenData.csv\")\n",
    "dfbat = dfbat.loc[dfbat['matches'] >= 10]\n",
    "dfbat = dfbat.drop(columns = ['Unnamed: 0'])\n",
    "print(dfbat.shape)\n",
    "dfbat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xbat = dfbat.drop(columns = ['batsman']).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GMM - Not Advised\n",
    "# AICbat = {}\n",
    "# BICbat = {}\n",
    "# for k in range(2,10):\n",
    "#     gmbat = GaussianMixture(n_components=k, random_state=123).fit(Xbat)\n",
    "#     AICbat[k] = gmbat.aic(Xbat)\n",
    "#     BICbat[k] = gmbat.bic(Xbat)\n",
    "\n",
    "# plt.plot(BICbat.keys(),BICbat.values(), label = 'BIC')\n",
    "# plt.plot(AICbat.keys(),AICbat.values(), label = 'AIC')\n",
    "# plt.xlabel('Number of Components(K)')\n",
    "# plt.ylabel('AIC/BIC')\n",
    "# plt.legend()\n",
    "\n",
    "# gmbat6 = GaussianMixture(n_components=6).fit(Xbat)\n",
    "# ybat6 = gmbat6.predict(Xbat)\n",
    "# ybat6\n",
    "\n",
    "# batdfGMM = dfbat.copy()\n",
    "# batdfGMM['cluster6'] = ybat6\n",
    "# batdfGMM.head()\n",
    "\n",
    "\n",
    "# batdf['batsman'].loc[batdf['cluster6'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Min-Max of each attribute\n",
    "\n",
    "# print(max(dfbat['hard_hitting_ability']),min(dfbat['hard_hitting_ability']))\n",
    "# print(max(dfbat['finisher']),min(dfbat['finisher']))\n",
    "# print(max(dfbat['fast_scoring_ability']),min(dfbat['fast_scoring_ability']))\n",
    "# print(max(dfbat['consistency']),min(dfbat['consistency']))\n",
    "# print(max(dfbat['running_between_wickets']),min(dfbat['running_between_wickets']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Within Cluster Sum of Squares\n",
    "\n",
    "wcssbat = []\n",
    "for i in range(1,20):\n",
    "    kmbat = KMeans(n_clusters=i, random_state=0).fit(Xbat)\n",
    "    wcssbat.append(kmbat.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Distortion')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wcssbat)\n",
    "plt.xlabel('Number of Clusters(K)')\n",
    "plt.ylabel('Distortion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimal number of clusters is chosen as k = 6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6 clusters \n",
    "\n",
    "kmbat6 = KMeans(n_clusters=6, random_state=0).fit(Xbat)\n",
    "predbat = kmbat6.predict(Xbat)\n",
    "uniquebat, countsbat = np.unique(kmbat6.labels_, return_counts=True)\n",
    "#unique = ('0', '1', '2','3','4')\n",
    "plt.bar(uniquebat, countsbat)\n",
    "plt.xlabel('Clusters')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>batsman</th>\n",
       "      <th>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>141</td>\n",
       "      <td>0.155695</td>\n",
       "      <td>0.163121</td>\n",
       "      <td>1.265884</td>\n",
       "      <td>37.483051</td>\n",
       "      <td>0.653220</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>146</td>\n",
       "      <td>0.157879</td>\n",
       "      <td>0.102740</td>\n",
       "      <td>1.203612</td>\n",
       "      <td>31.541985</td>\n",
       "      <td>0.639225</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>157</td>\n",
       "      <td>0.170971</td>\n",
       "      <td>0.146497</td>\n",
       "      <td>1.349955</td>\n",
       "      <td>33.940299</td>\n",
       "      <td>0.679083</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>153</td>\n",
       "      <td>0.160965</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>1.284973</td>\n",
       "      <td>32.612403</td>\n",
       "      <td>0.638383</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>126</td>\n",
       "      <td>0.157072</td>\n",
       "      <td>0.134921</td>\n",
       "      <td>1.185025</td>\n",
       "      <td>32.669725</td>\n",
       "      <td>0.604899</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>143</td>\n",
       "      <td>0.169595</td>\n",
       "      <td>0.104895</td>\n",
       "      <td>1.276351</td>\n",
       "      <td>29.515625</td>\n",
       "      <td>0.619145</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>114</td>\n",
       "      <td>0.193315</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>1.383184</td>\n",
       "      <td>40.140000</td>\n",
       "      <td>0.619393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>143</td>\n",
       "      <td>0.151866</td>\n",
       "      <td>0.342657</td>\n",
       "      <td>1.328358</td>\n",
       "      <td>37.872340</td>\n",
       "      <td>0.712714</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>105</td>\n",
       "      <td>0.146042</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1.174865</td>\n",
       "      <td>33.593407</td>\n",
       "      <td>0.637714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>100</td>\n",
       "      <td>0.222354</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>1.441943</td>\n",
       "      <td>41.022472</td>\n",
       "      <td>0.440325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batsman  matches  hard_hitting_ability  finisher  fast_scoring_ability  \\\n",
       "0     V Kohli      141              0.155695  0.163121              1.265884   \n",
       "1   G Gambhir      146              0.157879  0.102740              1.203612   \n",
       "2    SK Raina      157              0.170971  0.146497              1.349955   \n",
       "3   RG Sharma      153              0.160965  0.156863              1.284973   \n",
       "4    S Dhawan      126              0.157072  0.134921              1.185025   \n",
       "5  RV Uthappa      143              0.169595  0.104895              1.276351   \n",
       "6   DA Warner      114              0.193315  0.122807              1.383184   \n",
       "7    MS Dhoni      143              0.151866  0.342657              1.328358   \n",
       "8   AM Rahane      105              0.146042  0.133333              1.174865   \n",
       "9    CH Gayle      100              0.222354  0.110000              1.441943   \n",
       "\n",
       "   consistency  running_between_wickets  kmeans6  \n",
       "0    37.483051                 0.653220        3  \n",
       "1    31.541985                 0.639225        3  \n",
       "2    33.940299                 0.679083        3  \n",
       "3    32.612403                 0.638383        3  \n",
       "4    32.669725                 0.604899        3  \n",
       "5    29.515625                 0.619145        3  \n",
       "6    40.140000                 0.619393        1  \n",
       "7    37.872340                 0.712714        3  \n",
       "8    33.593407                 0.637714        1  \n",
       "9    41.022472                 0.440325        1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batdf = dfbat.copy()\n",
    "batdf['kmeans6'] = kmbat6.labels_\n",
    "# batdf['GMM6'] = ybat6\n",
    "batdf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>batsman</th>\n",
       "      <th>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>141</td>\n",
       "      <td>0.155695</td>\n",
       "      <td>0.163121</td>\n",
       "      <td>1.265884</td>\n",
       "      <td>37.483051</td>\n",
       "      <td>0.653220</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>146</td>\n",
       "      <td>0.157879</td>\n",
       "      <td>0.102740</td>\n",
       "      <td>1.203612</td>\n",
       "      <td>31.541985</td>\n",
       "      <td>0.639225</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>157</td>\n",
       "      <td>0.170971</td>\n",
       "      <td>0.146497</td>\n",
       "      <td>1.349955</td>\n",
       "      <td>33.940299</td>\n",
       "      <td>0.679083</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>153</td>\n",
       "      <td>0.160965</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>1.284973</td>\n",
       "      <td>32.612403</td>\n",
       "      <td>0.638383</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>126</td>\n",
       "      <td>0.157072</td>\n",
       "      <td>0.134921</td>\n",
       "      <td>1.185025</td>\n",
       "      <td>32.669725</td>\n",
       "      <td>0.604899</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>143</td>\n",
       "      <td>0.169595</td>\n",
       "      <td>0.104895</td>\n",
       "      <td>1.276351</td>\n",
       "      <td>29.515625</td>\n",
       "      <td>0.619145</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>143</td>\n",
       "      <td>0.151866</td>\n",
       "      <td>0.342657</td>\n",
       "      <td>1.328358</td>\n",
       "      <td>37.872340</td>\n",
       "      <td>0.712714</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>134</td>\n",
       "      <td>0.151271</td>\n",
       "      <td>0.126866</td>\n",
       "      <td>1.230085</td>\n",
       "      <td>24.811966</td>\n",
       "      <td>0.666500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>133</td>\n",
       "      <td>0.187380</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>1.407514</td>\n",
       "      <td>29.816327</td>\n",
       "      <td>0.633076</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       batsman  matches  hard_hitting_ability  finisher  fast_scoring_ability  \\\n",
       "0      V Kohli      141              0.155695  0.163121              1.265884   \n",
       "1    G Gambhir      146              0.157879  0.102740              1.203612   \n",
       "2     SK Raina      157              0.170971  0.146497              1.349955   \n",
       "3    RG Sharma      153              0.160965  0.156863              1.284973   \n",
       "4     S Dhawan      126              0.157072  0.134921              1.185025   \n",
       "5   RV Uthappa      143              0.169595  0.104895              1.276351   \n",
       "7     MS Dhoni      143              0.151866  0.342657              1.328358   \n",
       "11  KD Karthik      134              0.151271  0.126866              1.230085   \n",
       "15   YK Pathan      133              0.187380  0.263158              1.407514   \n",
       "\n",
       "    consistency  running_between_wickets  kmeans6  \n",
       "0     37.483051                 0.653220        3  \n",
       "1     31.541985                 0.639225        3  \n",
       "2     33.940299                 0.679083        3  \n",
       "3     32.612403                 0.638383        3  \n",
       "4     32.669725                 0.604899        3  \n",
       "5     29.515625                 0.619145        3  \n",
       "7     37.872340                 0.712714        3  \n",
       "11    24.811966                 0.666500        3  \n",
       "15    29.816327                 0.633076        3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batdf.loc[batdf['kmeans6'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>batsman</th>\n",
       "      <th>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>118</td>\n",
       "      <td>0.185262</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>1.451291</td>\n",
       "      <td>38.307692</td>\n",
       "      <td>0.710271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           batsman  matches  hard_hitting_ability  finisher  \\\n",
       "10  AB de Villiers      118              0.185262  0.228814   \n",
       "\n",
       "    fast_scoring_ability  consistency  running_between_wickets  kmeans6  \n",
       "10              1.451291    38.307692                 0.710271        1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batdf.loc[batdf['batsman'] == 'AB de Villiers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    68\n",
       "0    51\n",
       "5    38\n",
       "2    29\n",
       "1    17\n",
       "3     9\n",
       "Name: kmeans6, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batdf['kmeans6'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# batdf['batsman'].loc[batdf['kmeans6'] == 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Standardize the attributes (mean = 0, sd = 1)**"
   ]
  },
  {
   "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>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.888738</td>\n",
       "      <td>0.308419</td>\n",
       "      <td>-0.485251</td>\n",
       "      <td>0.507600</td>\n",
       "      <td>1.733552</td>\n",
       "      <td>0.634660</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.032557</td>\n",
       "      <td>0.358345</td>\n",
       "      <td>-0.839192</td>\n",
       "      <td>0.214161</td>\n",
       "      <td>1.094504</td>\n",
       "      <td>0.493270</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.348960</td>\n",
       "      <td>0.657620</td>\n",
       "      <td>-0.582697</td>\n",
       "      <td>0.903759</td>\n",
       "      <td>1.352478</td>\n",
       "      <td>0.895946</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.233905</td>\n",
       "      <td>0.428888</td>\n",
       "      <td>-0.521934</td>\n",
       "      <td>0.597547</td>\n",
       "      <td>1.209643</td>\n",
       "      <td>0.484763</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.457279</td>\n",
       "      <td>0.339877</td>\n",
       "      <td>-0.650554</td>\n",
       "      <td>0.126575</td>\n",
       "      <td>1.215809</td>\n",
       "      <td>0.146483</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    matches  hard_hitting_ability  finisher  fast_scoring_ability  \\\n",
       "0  2.888738              0.308419 -0.485251              0.507600   \n",
       "1  3.032557              0.358345 -0.839192              0.214161   \n",
       "2  3.348960              0.657620 -0.582697              0.903759   \n",
       "3  3.233905              0.428888 -0.521934              0.597547   \n",
       "4  2.457279              0.339877 -0.650554              0.126575   \n",
       "\n",
       "   consistency  running_between_wickets  kmeans6  \n",
       "0     1.733552                 0.634660      3.0  \n",
       "1     1.094504                 0.493270      3.0  \n",
       "2     1.352478                 0.895946      3.0  \n",
       "3     1.209643                 0.484763      3.0  \n",
       "4     1.215809                 0.146483      3.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standdf1 = batdf.copy()\n",
    "standdf = standdf1.drop(columns = ['batsman'])\n",
    "standdf = StandardScaler().fit_transform(standdf[['matches', 'hard_hitting_ability', 'finisher', 'fast_scoring_ability','consistency', 'running_between_wickets']])\n",
    "standdf = pd.DataFrame(standdf, columns = ['matches', 'hard_hitting_ability', 'finisher', 'fast_scoring_ability','consistency', 'running_between_wickets'])\n",
    "standdf['kmeans6'] = batdf['kmeans6']\n",
    "standdf.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['LMP Simmons', 'ML Hayden', 'GC Smith', 'KL Rahul',\n",
       "       'ST Jayasuriya', 'Q de Kock', 'RS Bopara', 'JD Ryder',\n",
       "       'PC Valthaty', 'HM Amla', 'CJ Anderson', 'OA Shah', 'CA Pujara',\n",
       "       'RR Pant', 'JC Buttler', 'AL Menaria', 'J Botha', 'N Rana',\n",
       "       'SA Asnodkar', 'AD Russell', 'KS Williamson', 'DB Ravi Teja',\n",
       "       'MV Boucher', 'JR Hopes', 'KH Pandya', 'Azhar Mahmood',\n",
       "       'JEC Franklin', 'RA Tripathi', 'CH Morris', 'Y Nagar', 'DB Das',\n",
       "       'CA Lynn', 'B Chipli', 'Ishan Kishan', 'LA Pomersbach',\n",
       "       'DJG Sammy', 'RN ten Doeschate', 'BA Stokes', 'MJ Lumb',\n",
       "       'A Ashish Reddy', 'SM Katich', 'YV Takawale', 'SW Billings',\n",
       "       'AS Raut', 'SP Fleming', 'TM Head', 'R Dhawan', 'Bipul Sharma',\n",
       "       'SN Khan', 'Iqbal Abdulla', 'Sandeep Sharma'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus0 = batdf.loc[batdf['kmeans6'] == 0]\n",
    "print(clus0.shape)\n",
    "\n",
    "# players\n",
    "clus0['batsman'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 0]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Batting - Cluster 0')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute wise Min-Max, mean - Cluster 0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall\n",
      "Matches - Max: 157, Min: 10, Mean: 40.570754716981135\n",
      "Hard Hitting - Max: 0.2575757575757576, Min: 0.0, Mean: 0.14220425950395357\n",
      "Finisher - Max: 0.9090909090909092, Min: -0.05263157894736842, Mean: 0.24590244564168512\n",
      "Fast Scoring - Max: 1.7184466019417477, Min: 0.3555555555555556, Mean: 1.1581637317938736\n",
      "Consistency - Max: 44.384615384615394, Min: 1.3333333333333333, Mean: 21.366668653489164\n",
      "Running - Max: 0.8557692307692307, Min: 0.19607843137254904, Mean: 0.5904000014960268\n"
     ]
    }
   ],
   "source": [
    "print(\"Overall\")\n",
    "\n",
    "print(f\"Matches - Max: {max(batdf['matches'])}, Min: {min(batdf['matches'])}, Mean: {np.mean(batdf['matches'])}\")\n",
    "print(f\"Hard Hitting - Max: {max(batdf['hard_hitting_ability'])}, Min: {min(batdf['hard_hitting_ability'])}, Mean: {np.mean(batdf['hard_hitting_ability'])}\")\n",
    "print(f\"Finisher - Max: {max(batdf['finisher'])}, Min: {min(batdf['finisher'])}, Mean: {np.mean(batdf['finisher'])}\")\n",
    "print(f\"Fast Scoring - Max: {max(batdf['fast_scoring_ability'])}, Min: {min(batdf['fast_scoring_ability'])}, Mean: {np.mean(batdf['fast_scoring_ability'])}\")\n",
    "print(f\"Consistency - Max: {max(batdf['consistency'])}, Min: {min(batdf['consistency'])}, Mean: {np.mean(batdf['consistency'])}\")\n",
    "print(f\"Running - Max: {max(batdf['running_between_wickets'])}, Min: {min(batdf['running_between_wickets'])}, Mean: {np.mean(batdf['running_between_wickets'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For cluster 0\n",
      "Matches - Max: 32, Min: 10, Mean: 19.647058823529413\n",
      "Hard Hitting - Max: 0.2575757575757576, Min: 0.027777777777777776, Mean: 0.16403102997180505\n",
      "Finisher - Max: 0.9090909090909092, Min: -0.05263157894736842, Mean: 0.22350536974867008\n",
      "Fast Scoring - Max: 1.7184466019417477, Min: 0.7222222222222222, Mean: 1.2604053721060193\n",
      "Consistency - Max: 44.384615384615394, Min: 18.666666666666668, Mean: 26.77059706196587\n",
      "Running - Max: 0.8557692307692307, Min: 0.4017094017094017, Mean: 0.6097167465188716\n"
     ]
    }
   ],
   "source": [
    "print(\"For cluster 0\")\n",
    "\n",
    "print(f\"Matches - Max: {max(clus0['matches'])}, Min: {min(clus0['matches'])}, Mean: {np.mean(clus0['matches'])}\")\n",
    "print(f\"Hard Hitting - Max: {max(clus0['hard_hitting_ability'])}, Min: {min(clus0['hard_hitting_ability'])}, Mean: {np.mean(clus0['hard_hitting_ability'])}\")\n",
    "print(f\"Finisher - Max: {max(clus0['finisher'])}, Min: {min(clus0['finisher'])}, Mean: {np.mean(clus0['finisher'])}\")\n",
    "print(f\"Fast Scoring - Max: {max(clus0['fast_scoring_ability'])}, Min: {min(clus0['fast_scoring_ability'])}, Mean: {np.mean(clus0['fast_scoring_ability'])}\")\n",
    "print(f\"Consistency - Max: {max(clus0['consistency'])}, Min: {min(clus0['consistency'])}, Mean: {np.mean(clus0['consistency'])}\")\n",
    "print(f\"Running - Max: {max(clus0['running_between_wickets'])}, Min: {min(clus0['running_between_wickets'])}, Mean: {np.mean(clus0['running_between_wickets'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['DA Warner', 'AM Rahane', 'CH Gayle', 'AB de Villiers',\n",
       "       'JH Kallis', 'BB McCullum', 'M Vijay', 'PA Patel', 'Yuvraj Singh',\n",
       "       'AT Rayudu', 'SR Watson', 'MK Pandey', 'V Sehwag', 'DR Smith',\n",
       "       'KA Pollard', 'RA Jadeja', 'NV Ojha'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1 = batdf.loc[batdf['kmeans6'] == 1]\n",
    "print(clus1.shape)\n",
    "\n",
    "# players\n",
    "clus1['batsman'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 1]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Batting - Cluster 1')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute wise Min-Max, mean - Cluster 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall\n",
      "Matches - Max: 157, Min: 10, Mean: 40.570754716981135\n",
      "Hard Hitting - Max: 0.2575757575757576, Min: 0.0, Mean: 0.14220425950395357\n",
      "Finisher - Max: 0.9090909090909092, Min: -0.05263157894736842, Mean: 0.24590244564168512\n",
      "Fast Scoring - Max: 1.7184466019417477, Min: 0.3555555555555556, Mean: 1.1581637317938736\n",
      "Consistency - Max: 44.384615384615394, Min: 1.3333333333333333, Mean: 21.366668653489164\n",
      "Running - Max: 0.8557692307692307, Min: 0.19607843137254904, Mean: 0.5904000014960268\n"
     ]
    }
   ],
   "source": [
    "print(\"Overall\")\n",
    "\n",
    "print(f\"Matches - Max: {max(batdf['matches'])}, Min: {min(batdf['matches'])}, Mean: {np.mean(batdf['matches'])}\")\n",
    "print(f\"Hard Hitting - Max: {max(batdf['hard_hitting_ability'])}, Min: {min(batdf['hard_hitting_ability'])}, Mean: {np.mean(batdf['hard_hitting_ability'])}\")\n",
    "print(f\"Finisher - Max: {max(batdf['finisher'])}, Min: {min(batdf['finisher'])}, Mean: {np.mean(batdf['finisher'])}\")\n",
    "print(f\"Fast Scoring - Max: {max(batdf['fast_scoring_ability'])}, Min: {min(batdf['fast_scoring_ability'])}, Mean: {np.mean(batdf['fast_scoring_ability'])}\")\n",
    "print(f\"Consistency - Max: {max(batdf['consistency'])}, Min: {min(batdf['consistency'])}, Mean: {np.mean(batdf['consistency'])}\")\n",
    "print(f\"Running - Max: {max(batdf['running_between_wickets'])}, Min: {min(batdf['running_between_wickets'])}, Mean: {np.mean(batdf['running_between_wickets'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For cluster 1\n",
      "Matches - Max: 118, Min: 88, Mean: 104.05882352941177\n",
      "Hard Hitting - Max: 0.24004364429896344, Min: 0.12482853223593965, Mean: 0.17179147137576378\n",
      "Finisher - Max: 0.3333333333333333, Min: 0.040404040404040414, Mean: 0.13876857362116393\n",
      "Fast Scoring - Max: 1.4882705946535737, Min: 1.0593627237014405, Mean: 1.2743294160648513\n",
      "Consistency - Max: 41.02247191011236, Min: 20.986486486486488, Mean: 29.398601296891925\n",
      "Running - Max: 0.7102708226877874, Min: 0.4403250380904013, Mean: 0.579977014916837\n"
     ]
    }
   ],
   "source": [
    "print(\"For cluster 1\")\n",
    "\n",
    "print(f\"Matches - Max: {max(clus1['matches'])}, Min: {min(clus1['matches'])}, Mean: {np.mean(clus1['matches'])}\")\n",
    "print(f\"Hard Hitting - Max: {max(clus1['hard_hitting_ability'])}, Min: {min(clus1['hard_hitting_ability'])}, Mean: {np.mean(clus1['hard_hitting_ability'])}\")\n",
    "print(f\"Finisher - Max: {max(clus1['finisher'])}, Min: {min(clus1['finisher'])}, Mean: {np.mean(clus1['finisher'])}\")\n",
    "print(f\"Fast Scoring - Max: {max(clus1['fast_scoring_ability'])}, Min: {min(clus1['fast_scoring_ability'])}, Mean: {np.mean(clus1['fast_scoring_ability'])}\")\n",
    "print(f\"Consistency - Max: {max(clus1['consistency'])}, Min: {min(clus1['consistency'])}, Mean: {np.mean(clus1['consistency'])}\")\n",
    "print(f\"Running - Max: {max(clus1['running_between_wickets'])}, Min: {min(clus1['running_between_wickets'])}, Mean: {np.mean(clus1['running_between_wickets'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['SR Tendulkar', 'R Dravid', 'SE Marsh', 'MEK Hussey', 'JP Duminy',\n",
       "       'AC Gilchrist', 'DPMD Jayawardene', 'MK Tiwary', 'KC Sangakkara',\n",
       "       'SPD Smith', 'SC Ganguly', 'AJ Finch', 'S Badrinath', 'WP Saha',\n",
       "       'SV Samson', 'BJ Hodge', 'DA Miller', 'SS Tiwary', 'DJ Hussey',\n",
       "       'DJ Bravo', 'IK Pathan', 'Mandeep Singh', 'LRPL Taylor',\n",
       "       'GJ Maxwell', 'JA Morkel', 'KM Jadhav', 'STR Binny',\n",
       "       'Harbhajan Singh', 'PP Chawla'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus2 = batdf.loc[batdf['kmeans6'] == 2]\n",
    "print(clus2.shape)\n",
    "\n",
    "# players\n",
    "clus2['batsman'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 2]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Batting - Cluter 2')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute wise Min-Max, mean - Cluster 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall\n",
      "Matches - Max: 157, Min: 10, Mean: 40.570754716981135\n",
      "Hard Hitting - Max: 0.2575757575757576, Min: 0.0, Mean: 0.14220425950395357\n",
      "Finisher - Max: 0.9090909090909092, Min: -0.05263157894736842, Mean: 0.24590244564168512\n",
      "Fast Scoring - Max: 1.7184466019417477, Min: 0.3555555555555556, Mean: 1.1581637317938736\n",
      "Consistency - Max: 44.384615384615394, Min: 1.3333333333333333, Mean: 21.366668653489164\n",
      "Running - Max: 0.8557692307692307, Min: 0.19607843137254904, Mean: 0.5904000014960268\n"
     ]
    }
   ],
   "source": [
    "print(\"Overall\")\n",
    "\n",
    "print(f\"Matches - Max: {max(batdf['matches'])}, Min: {min(batdf['matches'])}, Mean: {np.mean(batdf['matches'])}\")\n",
    "print(f\"Hard Hitting - Max: {max(batdf['hard_hitting_ability'])}, Min: {min(batdf['hard_hitting_ability'])}, Mean: {np.mean(batdf['hard_hitting_ability'])}\")\n",
    "print(f\"Finisher - Max: {max(batdf['finisher'])}, Min: {min(batdf['finisher'])}, Mean: {np.mean(batdf['finisher'])}\")\n",
    "print(f\"Fast Scoring - Max: {max(batdf['fast_scoring_ability'])}, Min: {min(batdf['fast_scoring_ability'])}, Mean: {np.mean(batdf['fast_scoring_ability'])}\")\n",
    "print(f\"Consistency - Max: {max(batdf['consistency'])}, Min: {min(batdf['consistency'])}, Mean: {np.mean(batdf['consistency'])}\")\n",
    "print(f\"Running - Max: {max(batdf['running_between_wickets'])}, Min: {min(batdf['running_between_wickets'])}, Mean: {np.mean(batdf['running_between_wickets'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For cluster 2\n",
      "Matches - Max: 82, Min: 54, Mean: 67.62068965517241\n",
      "Hard Hitting - Max: 0.2282051282051282, Min: 0.1230110159118727, Mean: 0.15479097902371397\n",
      "Finisher - Max: 0.4029850746268657, Min: 0.04411764705882353, Mean: 0.2156342396929292\n",
      "Fast Scoring - Max: 1.5756410256410256, Min: 1.0173453996983408, Mean: 1.2318723744352649\n",
      "Consistency - Max: 39.86, Min: 12.261904761904766, Mean: 27.49334867137482\n",
      "Running - Max: 0.7379067722075637, Min: 0.4583333333333333, Mean: 0.6223307544164073\n"
     ]
    }
   ],
   "source": [
    "print(\"For cluster 2\")\n",
    "\n",
    "print(f\"Matches - Max: {max(clus2['matches'])}, Min: {min(clus2['matches'])}, Mean: {np.mean(clus2['matches'])}\")\n",
    "print(f\"Hard Hitting - Max: {max(clus2['hard_hitting_ability'])}, Min: {min(clus2['hard_hitting_ability'])}, Mean: {np.mean(clus2['hard_hitting_ability'])}\")\n",
    "print(f\"Finisher - Max: {max(clus2['finisher'])}, Min: {min(clus2['finisher'])}, Mean: {np.mean(clus2['finisher'])}\")\n",
    "print(f\"Fast Scoring - Max: {max(clus2['fast_scoring_ability'])}, Min: {min(clus2['fast_scoring_ability'])}, Mean: {np.mean(clus2['fast_scoring_ability'])}\")\n",
    "print(f\"Consistency - Max: {max(clus2['consistency'])}, Min: {min(clus2['consistency'])}, Mean: {np.mean(clus2['consistency'])}\")\n",
    "print(f\"Running - Max: {max(clus2['running_between_wickets'])}, Min: {min(clus2['running_between_wickets'])}, Mean: {np.mean(clus2['running_between_wickets'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['V Kohli', 'G Gambhir', 'SK Raina', 'RG Sharma', 'S Dhawan',\n",
       "       'RV Uthappa', 'MS Dhoni', 'KD Karthik', 'YK Pathan'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus3 = batdf.loc[batdf['kmeans6'] == 3]\n",
    "print(clus3.shape)\n",
    "\n",
    "# players\n",
    "clus3['batsman'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 3]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Batting - Cluster 3')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['GH Vihari', 'UBT Chand', 'S Sohal', 'AP Tare', 'Gurkeerat Singh',\n",
       "       'VVS Laxman', 'SP Goswami', 'M Kaif', 'K Goel', 'R Sathish',\n",
       "       'SK Warne', 'AA Jhunjhunwala', 'MD Mishra', 'MR Marsh', 'FY Fazal',\n",
       "       'R McLaren', 'MN Samuels', 'PA Reddy', 'AB Agarkar', 'CM Gautam',\n",
       "       'MG Johnson', 'RE van der Merwe', 'MJ Guptill', 'N Saini',\n",
       "       'DH Yagnik', 'P Dogra', 'Z Khan', 'SB Styris', 'MF Maharoof',\n",
       "       'S Anirudha', 'DL Vettori', 'RJ Harris', 'JDP Oram', 'M Kartik',\n",
       "       'Sachin Baby', 'MM Sharma', 'SL Malinga', 'B Lee', 'DS Kulkarni',\n",
       "       'MA Starc', 'UT Yadav', 'M Morkel', 'WD Parnell', 'AD Mascarenhas',\n",
       "       'RP Singh', 'R Sharma', 'MS Gony', 'WPUJC Vaas', 'S Nadeem',\n",
       "       'KK Cooper', 'M Rawat', 'Ankit Sharma', 'A Nehra', 'SK Trivedi',\n",
       "       'S Aravind', 'I Sharma', 'B Akhil', 'S Sreesanth',\n",
       "       'NM Coulter-Nile', 'AB Dinda', 'L Balaji', 'A Kumble',\n",
       "       'Mohammed Shami', 'MJ McClenaghan', 'PP Ojha', 'MM Patel',\n",
       "       'HV Patel', 'PJ Sangwan'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus4 = batdf.loc[batdf['kmeans6'] == 4]\n",
    "print(clus4.shape)\n",
    "\n",
    "# players\n",
    "clus4['batsman'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 4]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Batting - Cluster 4')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['TM Dilshan', 'F du Plessis', 'KK Nair', 'Y Venugopal Rao',\n",
       "       'HH Gibbs', 'A Symonds', 'CL White', 'MC Henriques',\n",
       "       'KP Pietersen', 'M Vohra', 'MS Bisla', 'EJG Morgan', 'MA Agarwal',\n",
       "       'SS Iyer', 'AD Mathews', 'AM Nayar', 'TL Suman', 'GJ Bailey',\n",
       "       'AR Patel', 'M Manhas', 'SA Yadav', 'JP Faulkner',\n",
       "       'Shakib Al Hasan', 'A Mishra', 'LR Shukla', 'DT Christian',\n",
       "       'NLTC Perera', 'P Kumar', 'HH Pandya', 'R Bhatia', 'KV Sharma',\n",
       "       'R Vinay Kumar', 'P Negi', 'DJ Hooda', 'R Ashwin', 'SP Narine',\n",
       "       'DW Steyn', 'B Kumar'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus5 = batdf.loc[batdf['kmeans6'] == 5]\n",
    "print(clus5.shape)\n",
    "\n",
    "# players\n",
    "clus5['batsman'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 5]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Batting - Cluster 5')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>batsman</th>\n",
       "      <th>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>141</td>\n",
       "      <td>0.155695</td>\n",
       "      <td>0.163121</td>\n",
       "      <td>1.265884</td>\n",
       "      <td>37.483051</td>\n",
       "      <td>0.653220</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>146</td>\n",
       "      <td>0.157879</td>\n",
       "      <td>0.102740</td>\n",
       "      <td>1.203612</td>\n",
       "      <td>31.541985</td>\n",
       "      <td>0.639225</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>157</td>\n",
       "      <td>0.170971</td>\n",
       "      <td>0.146497</td>\n",
       "      <td>1.349955</td>\n",
       "      <td>33.940299</td>\n",
       "      <td>0.679083</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>153</td>\n",
       "      <td>0.160965</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>1.284973</td>\n",
       "      <td>32.612403</td>\n",
       "      <td>0.638383</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>126</td>\n",
       "      <td>0.157072</td>\n",
       "      <td>0.134921</td>\n",
       "      <td>1.185025</td>\n",
       "      <td>32.669725</td>\n",
       "      <td>0.604899</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     batsman  matches  hard_hitting_ability  finisher  fast_scoring_ability  \\\n",
       "0    V Kohli      141              0.155695  0.163121              1.265884   \n",
       "1  G Gambhir      146              0.157879  0.102740              1.203612   \n",
       "2   SK Raina      157              0.170971  0.146497              1.349955   \n",
       "3  RG Sharma      153              0.160965  0.156863              1.284973   \n",
       "4   S Dhawan      126              0.157072  0.134921              1.185025   \n",
       "\n",
       "   consistency  running_between_wickets  kmeans6  \n",
       "0    37.483051                 0.653220        3  \n",
       "1    31.541985                 0.639225        3  \n",
       "2    33.940299                 0.679083        3  \n",
       "3    32.612403                 0.638383        3  \n",
       "4    32.669725                 0.604899        3  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>batsman</th>\n",
       "      <th>matches</th>\n",
       "      <th>hard_hitting_ability</th>\n",
       "      <th>finisher</th>\n",
       "      <th>fast_scoring_ability</th>\n",
       "      <th>consistency</th>\n",
       "      <th>running_between_wickets</th>\n",
       "      <th>kmeans6</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>141</td>\n",
       "      <td>0.155695</td>\n",
       "      <td>0.163121</td>\n",
       "      <td>1.265884</td>\n",
       "      <td>37.483051</td>\n",
       "      <td>0.653220</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>146</td>\n",
       "      <td>0.157879</td>\n",
       "      <td>0.102740</td>\n",
       "      <td>1.203612</td>\n",
       "      <td>31.541985</td>\n",
       "      <td>0.639225</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>157</td>\n",
       "      <td>0.170971</td>\n",
       "      <td>0.146497</td>\n",
       "      <td>1.349955</td>\n",
       "      <td>33.940299</td>\n",
       "      <td>0.679083</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>153</td>\n",
       "      <td>0.160965</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>1.284973</td>\n",
       "      <td>32.612403</td>\n",
       "      <td>0.638383</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>126</td>\n",
       "      <td>0.157072</td>\n",
       "      <td>0.134921</td>\n",
       "      <td>1.185025</td>\n",
       "      <td>32.669725</td>\n",
       "      <td>0.604899</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>143</td>\n",
       "      <td>0.169595</td>\n",
       "      <td>0.104895</td>\n",
       "      <td>1.276351</td>\n",
       "      <td>29.515625</td>\n",
       "      <td>0.619145</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>114</td>\n",
       "      <td>0.193315</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>1.383184</td>\n",
       "      <td>40.140000</td>\n",
       "      <td>0.619393</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>143</td>\n",
       "      <td>0.151866</td>\n",
       "      <td>0.342657</td>\n",
       "      <td>1.328358</td>\n",
       "      <td>37.872340</td>\n",
       "      <td>0.712714</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>105</td>\n",
       "      <td>0.146042</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1.174865</td>\n",
       "      <td>33.593407</td>\n",
       "      <td>0.637714</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>100</td>\n",
       "      <td>0.222354</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>1.441943</td>\n",
       "      <td>41.022472</td>\n",
       "      <td>0.440325</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batsman  matches  hard_hitting_ability  finisher  fast_scoring_ability  \\\n",
       "0     V Kohli      141              0.155695  0.163121              1.265884   \n",
       "1   G Gambhir      146              0.157879  0.102740              1.203612   \n",
       "2    SK Raina      157              0.170971  0.146497              1.349955   \n",
       "3   RG Sharma      153              0.160965  0.156863              1.284973   \n",
       "4    S Dhawan      126              0.157072  0.134921              1.185025   \n",
       "5  RV Uthappa      143              0.169595  0.104895              1.276351   \n",
       "6   DA Warner      114              0.193315  0.122807              1.383184   \n",
       "7    MS Dhoni      143              0.151866  0.342657              1.328358   \n",
       "8   AM Rahane      105              0.146042  0.133333              1.174865   \n",
       "9    CH Gayle      100              0.222354  0.110000              1.441943   \n",
       "\n",
       "   consistency  running_between_wickets  kmeans6  points  \n",
       "0    37.483051                 0.653220        3      10  \n",
       "1    31.541985                 0.639225        3      10  \n",
       "2    33.940299                 0.679083        3      10  \n",
       "3    32.612403                 0.638383        3      10  \n",
       "4    32.669725                 0.604899        3      10  \n",
       "5    29.515625                 0.619145        3      10  \n",
       "6    40.140000                 0.619393        1       9  \n",
       "7    37.872340                 0.712714        3      10  \n",
       "8    33.593407                 0.637714        1       9  \n",
       "9    41.022472                 0.440325        1       9  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = []\n",
    "for idx,row in batdf.iterrows():\n",
    "    if row.kmeans6 == 0:\n",
    "        points.append(7)\n",
    "    if row.kmeans6 == 1:\n",
    "        points.append(9)\n",
    "    if row.kmeans6 == 2:\n",
    "        points.append(8)\n",
    "    if row.kmeans6 == 3:\n",
    "        points.append(10)\n",
    "    if row.kmeans6 == 4:\n",
    "        points.append(5)\n",
    "    if row.kmeans6 == 5:\n",
    "        points.append(6)\n",
    "batdf['points']= points\n",
    "batdf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Final Cluster Result**<br>\n",
    "\n",
    "3 - Best Cluster pts 10<br>\n",
    "1 - Great pts 9<br>\n",
    "2 - Good pts 8<br>\n",
    "0 - Okay pts 7<br>\n",
    "5 - Average pts 6<br>\n",
    "4 - Worst pts 5<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bowling Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(191, 8)\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>player</th>\n",
       "      <th>innings</th>\n",
       "      <th>bm4</th>\n",
       "      <th>totalwickets</th>\n",
       "      <th>bm5</th>\n",
       "      <th>Economy</th>\n",
       "      <th>Wicket.Taking.Ability</th>\n",
       "      <th>Consistency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sohail Tanvir</td>\n",
       "      <td>11</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>24</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>6.226415</td>\n",
       "      <td>11.041667</td>\n",
       "      <td>11.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Zampa</td>\n",
       "      <td>11</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>19</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.493333</td>\n",
       "      <td>11.842105</td>\n",
       "      <td>14.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CRD Fernando</td>\n",
       "      <td>10</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>19</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.373494</td>\n",
       "      <td>13.105263</td>\n",
       "      <td>16.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>20</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>33</td>\n",
       "      <td>1.684211</td>\n",
       "      <td>7.238095</td>\n",
       "      <td>13.363636</td>\n",
       "      <td>16.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WPUJC Vaas</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>7.583333</td>\n",
       "      <td>13.090909</td>\n",
       "      <td>16.545455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player  innings       bm4  totalwickets       bm5   Economy  \\\n",
       "0  Sohail Tanvir       11  0.181818            24  2.444444  6.226415   \n",
       "1        A Zampa       11  0.090909            19  1.800000  7.493333   \n",
       "2   CRD Fernando       10  0.100000            19  2.000000  7.373494   \n",
       "3    MF Maharoof       20  0.050000            33  1.684211  7.238095   \n",
       "4     WPUJC Vaas       13  0.000000            22  1.692308  7.583333   \n",
       "\n",
       "   Wicket.Taking.Ability  Consistency  \n",
       "0              11.041667    11.458333  \n",
       "1              11.842105    14.789474  \n",
       "2              13.105263    16.105263  \n",
       "3              13.363636    16.121212  \n",
       "4              13.090909    16.545455  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xball = pd.read_csv(\"bowleratt.csv\")\n",
    "Xball = Xball.loc[Xball['innings'] >= 10]\n",
    "Xball = Xball.drop(columns = ['Unnamed: 0', 'X','count'])\n",
    "print(Xball.shape)\n",
    "Xball.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfball = Xball.drop(columns = ['totalwickets'])\n",
    "dfballA = Xball.drop(columns = ['player','totalwickets']).to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Within Cluster Sum of Squares\n",
    "\n",
    "wcssball = []\n",
    "for i in range(1,20):\n",
    "    kmball = KMeans(n_clusters=i, random_state=0).fit(dfballA)\n",
    "    wcssball.append(kmball.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Distortion')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wcssball)\n",
    "plt.xlabel('Number of Clusters(K)')\n",
    "plt.ylabel('Distortion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimal number of clusters is chosen as k = 6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6 clusters \n",
    "\n",
    "kmball6 = KMeans(n_clusters=6, random_state=0).fit(dfballA)\n",
    "predball = kmball6.predict(dfballA)\n",
    "uniqueball, countsball = np.unique(kmball6.labels_, return_counts=True)\n",
    "#unique = ('0', '1', '2','3','4')\n",
    "plt.bar(uniqueball, countsball)\n",
    "plt.xlabel('Clusters')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>innings</th>\n",
       "      <th>bm4</th>\n",
       "      <th>bm5</th>\n",
       "      <th>Economy</th>\n",
       "      <th>Wicket.Taking.Ability</th>\n",
       "      <th>Consistency</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sohail Tanvir</td>\n",
       "      <td>11</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>6.226415</td>\n",
       "      <td>11.041667</td>\n",
       "      <td>11.458333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Zampa</td>\n",
       "      <td>11</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.493333</td>\n",
       "      <td>11.842105</td>\n",
       "      <td>14.789474</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CRD Fernando</td>\n",
       "      <td>10</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.373494</td>\n",
       "      <td>13.105263</td>\n",
       "      <td>16.105263</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>20</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.684211</td>\n",
       "      <td>7.238095</td>\n",
       "      <td>13.363636</td>\n",
       "      <td>16.121212</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WPUJC Vaas</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>7.583333</td>\n",
       "      <td>13.090909</td>\n",
       "      <td>16.545455</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DE Bollinger</td>\n",
       "      <td>27</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>7.160000</td>\n",
       "      <td>13.953488</td>\n",
       "      <td>16.651163</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BJ Hodge</td>\n",
       "      <td>20</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>7.815126</td>\n",
       "      <td>13.222222</td>\n",
       "      <td>17.222222</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>110</td>\n",
       "      <td>0.081818</td>\n",
       "      <td>1.594059</td>\n",
       "      <td>6.757238</td>\n",
       "      <td>15.847059</td>\n",
       "      <td>17.847059</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MA Starc</td>\n",
       "      <td>26</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>1.541667</td>\n",
       "      <td>7.107843</td>\n",
       "      <td>15.692308</td>\n",
       "      <td>18.589744</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R Rampaul</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>6.884892</td>\n",
       "      <td>16.352941</td>\n",
       "      <td>18.764706</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player  innings       bm4       bm5   Economy  \\\n",
       "0  Sohail Tanvir       11  0.181818  2.444444  6.226415   \n",
       "1        A Zampa       11  0.090909  1.800000  7.493333   \n",
       "2   CRD Fernando       10  0.100000  2.000000  7.373494   \n",
       "3    MF Maharoof       20  0.050000  1.684211  7.238095   \n",
       "4     WPUJC Vaas       13  0.000000  1.692308  7.583333   \n",
       "5   DE Bollinger       27  0.074074  1.640000  7.160000   \n",
       "6       BJ Hodge       20  0.050000  0.894737  7.815126   \n",
       "7     SL Malinga      110  0.081818  1.594059  6.757238   \n",
       "8       MA Starc       26  0.076923  1.541667  7.107843   \n",
       "9      R Rampaul       12  0.000000  1.416667  6.884892   \n",
       "\n",
       "   Wicket.Taking.Ability  Consistency  kmeans6  \n",
       "0              11.041667    11.458333        3  \n",
       "1              11.842105    14.789474        3  \n",
       "2              13.105263    16.105263        3  \n",
       "3              13.363636    16.121212        3  \n",
       "4              13.090909    16.545455        3  \n",
       "5              13.953488    16.651163        3  \n",
       "6              13.222222    17.222222        3  \n",
       "7              15.847059    17.847059        1  \n",
       "8              15.692308    18.589744        3  \n",
       "9              16.352941    18.764706        3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balldf = dfball.copy()\n",
    "balldf['kmeans6'] = kmball6.labels_\n",
    "# batdf['GMM6'] = ybat6\n",
    "balldf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    72\n",
       "0    45\n",
       "5    35\n",
       "2    24\n",
       "1    11\n",
       "4     4\n",
       "Name: kmeans6, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balldf['kmeans6'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7           SL Malinga\n",
       "17            DJ Bravo\n",
       "43            R Ashwin\n",
       "45            A Mishra\n",
       "57       R Vinay Kumar\n",
       "59              Z Khan\n",
       "73           PP Chawla\n",
       "80     Harbhajan Singh\n",
       "104          IK Pathan\n",
       "114          RA Jadeja\n",
       "144            P Kumar\n",
       "Name: player, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balldf['player'].loc[balldf['kmeans6']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Standardize the attributes (mean = 0, sd = 1)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>innings</th>\n",
       "      <th>bm4</th>\n",
       "      <th>bm5</th>\n",
       "      <th>Economy</th>\n",
       "      <th>Wicket.Taking.Ability</th>\n",
       "      <th>Consistency</th>\n",
       "      <th>kmeans6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.858108</td>\n",
       "      <td>4.591346</td>\n",
       "      <td>4.060954</td>\n",
       "      <td>2.297763</td>\n",
       "      <td>-1.385779</td>\n",
       "      <td>-1.507160</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.858108</td>\n",
       "      <td>1.932273</td>\n",
       "      <td>2.289124</td>\n",
       "      <td>0.539700</td>\n",
       "      <td>-1.288552</td>\n",
       "      <td>-1.230881</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.891950</td>\n",
       "      <td>2.198181</td>\n",
       "      <td>2.839002</td>\n",
       "      <td>0.705997</td>\n",
       "      <td>-1.135118</td>\n",
       "      <td>-1.121752</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.553526</td>\n",
       "      <td>0.735691</td>\n",
       "      <td>1.970773</td>\n",
       "      <td>0.893886</td>\n",
       "      <td>-1.103734</td>\n",
       "      <td>-1.120429</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.790423</td>\n",
       "      <td>-0.726800</td>\n",
       "      <td>1.993036</td>\n",
       "      <td>0.414810</td>\n",
       "      <td>-1.136862</td>\n",
       "      <td>-1.085243</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    innings       bm4       bm5   Economy  Wicket.Taking.Ability  Consistency  \\\n",
       "0 -0.858108  4.591346  4.060954  2.297763              -1.385779    -1.507160   \n",
       "1 -0.858108  1.932273  2.289124  0.539700              -1.288552    -1.230881   \n",
       "2 -0.891950  2.198181  2.839002  0.705997              -1.135118    -1.121752   \n",
       "3 -0.553526  0.735691  1.970773  0.893886              -1.103734    -1.120429   \n",
       "4 -0.790423 -0.726800  1.993036  0.414810              -1.136862    -1.085243   \n",
       "\n",
       "   kmeans6  \n",
       "0        3  \n",
       "1        3  \n",
       "2        3  \n",
       "3        3  \n",
       "4        3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standdf1 = balldf.copy()\n",
    "standdf = standdf1.drop(columns = ['player'])\n",
    "standdf = StandardScaler().fit_transform(standdf[['innings', 'bm4', 'bm5','Economy', 'Wicket.Taking.Ability', 'Consistency']])\n",
    "standdf = pd.DataFrame(standdf, columns = ['innings', 'bm4', 'bm5','Economy', 'Wicket.Taking.Ability', 'Consistency'])\n",
    "standdf['kmeans6'] = balldf['kmeans6']\n",
    "standdf['Economy'] = -1 * standdf['Economy']\n",
    "standdf.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Imran Tahir', 'CH Morris', 'Sandeep Sharma', 'S Aravind',\n",
       "       'JD Unadkat', 'YS Chahal', 'A Kumble', 'RJ Harris',\n",
       "       'Shakib Al Hasan', 'P Negi', 'MG Johnson', 'MJ McClenaghan',\n",
       "       'P Awana', 'JP Faulkner', 'SK Warne', 'Iqbal Abdulla', 'KV Sharma',\n",
       "       'STR Binny', 'AD Russell', 'AR Patel', 'R Sharma', 'DL Vettori',\n",
       "       'HV Patel', 'JJ Bumrah', 'NLTC Perera', 'RG Sharma', 'S Sreesanth',\n",
       "       'PJ Sangwan', 'SB Jakati', 'DT Christian', 'MC Henriques',\n",
       "       'A Symonds', 'J Botha', 'DR Smith', 'PV Tambe', 'VR Aaron',\n",
       "       'MS Gony', 'S Nadeem', 'JP Duminy', 'Mohammed Shami', 'AB Agarkar',\n",
       "       'M Kartik', 'B Lee', 'AD Mathews', 'CH Gayle'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus0ball = balldf.loc[balldf['kmeans6'] == 0]\n",
    "print(clus0ball.shape)\n",
    "\n",
    "# players\n",
    "clus0ball['player'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 0]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Bowling - Cluster 0')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['SL Malinga', 'DJ Bravo', 'R Ashwin', 'A Mishra', 'R Vinay Kumar',\n",
       "       'Z Khan', 'PP Chawla', 'Harbhajan Singh', 'IK Pathan', 'RA Jadeja',\n",
       "       'P Kumar'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1ball = balldf.loc[balldf['kmeans6'] == 1]\n",
    "print(clus1ball.shape)\n",
    "\n",
    "# players\n",
    "clus1ball['player'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 1]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Bowling - Cluster 1')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['SP Narine', 'B Kumar', 'A Nehra', 'MM Patel', 'MM Sharma',\n",
       "       'DS Kulkarni', 'DW Steyn', 'RP Singh', 'PP Ojha', 'M Morkel',\n",
       "       'L Balaji', 'SR Watson', 'JA Morkel', 'R Bhatia', 'AB Dinda',\n",
       "       'UT Yadav', 'M Muralitharan', 'SK Trivedi', 'KA Pollard',\n",
       "       'Yuvraj Singh', 'YK Pathan', 'JH Kallis', 'SK Raina', 'I Sharma'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1ball = balldf.loc[balldf['kmeans6'] == 2]\n",
    "print(clus1ball.shape)\n",
    "\n",
    "# players\n",
    "clus1ball['player'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 2]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Bowling - Cluster 2')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(72, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Sohail Tanvir', 'A Zampa', 'CRD Fernando', 'MF Maharoof',\n",
       "       'WPUJC Vaas', 'DE Bollinger', 'BJ Hodge', 'MA Starc', 'R Rampaul',\n",
       "       'AB McDonald', 'NM Coulter-Nile', 'AD Mascarenhas', 'CJ Jordan',\n",
       "       'Rashid Khan', 'RE van der Merwe', 'A Singh', 'YA Abdulla',\n",
       "       'MR Marsh', 'Washington Sundar', 'A Ashish Reddy', 'DP Nannes',\n",
       "       'BW Hilfenhaus', 'CR Woakes', 'KK Cooper', 'A Chandila',\n",
       "       'KW Richardson', 'Azhar Mahmood', 'RS Bopara', 'VY Mahesh',\n",
       "       'BA Stokes', 'GB Hogg', 'Kuldeep Yadav', 'WD Parnell',\n",
       "       'SM Pollock', 'Harmeet Singh', 'J Theron', 'D Wiese',\n",
       "       'KP Pietersen', 'Mustafizur Rahman', 'ST Jayasuriya',\n",
       "       'JEC Franklin', 'DJG Sammy', 'SN Thakur', 'SW Tait',\n",
       "       'CR Brathwaite', 'AC Thomas', 'GD McGrath', 'Shahid Afridi',\n",
       "       'Bipul Sharma', 'KP Appanna', 'S Kaul', 'AG Murtaza', 'LR Shukla',\n",
       "       'PJ Cummins', 'JDP Oram', 'TL Suman', 'MN Samuels', 'Basil Thampi',\n",
       "       'JD Ryder', 'MP Stoinis', 'J Syed Mohammad', 'PC Valthaty',\n",
       "       'SJ Srivastava', 'S Badree', 'RR Raje', 'SC Ganguly', 'SB Styris',\n",
       "       'TA Boult', 'Anureet Singh', 'AS Rajpoot', 'BA Bhatt',\n",
       "       'Joginder Sharma'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1ball = balldf.loc[balldf['kmeans6'] == 3]\n",
    "print(clus1ball.shape)\n",
    "\n",
    "# players\n",
    "clus1ball['player'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 3]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Bowling - Cluster 3')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Parvez Rasool', 'TM Dilshan', 'V Kohli', 'AA Jhunjhunwala'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1ball = balldf.loc[balldf['kmeans6'] == 4]\n",
    "print(clus1ball.shape)\n",
    "\n",
    "# players\n",
    "clus1ball['player'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xbsHnq6qR97wO1uie2ucDnd+nmbWVg7ybOFTNbBm4s9PMrHLV1cg7OX52+PDhHduXmdmyqirI3blj3cyVDGtKVUFu1q2WtZLh0SfWDm4jNzOrnIPczKxyDnIzs8o5yM3MKucgNzOrnIPczKxyHn5otpwtaXz54l730ETrDwe52XLmMLblzU0r1pUmTZrE2LFjGTJkCGPHjmXSpElNF8msa7lGbl1n0qRJTJw4kXPPPZcddtiBqVOnMn78eAAOOOCAhktn1n2qurGEDQ5jx47lzDPPZOedd37yucmTJzNhwgSmT5/eYMnMmrWoG0s4yK3rDBkyhMcee4xhw4Y9+dzcuXNZeeWVmT9/foMlM2vWooLcbeTWdcaMGcPUqVOf9tzUqVMZM2ZMQyUy624Ocus6EydOZPz48UyePJm5c+cyefJkxo8fz8SJE5sumllXGlBnp6RPAfsAc4C/A2+NiPvbUC4bxPo6NCdMmMCMGTMYM2YMp512mjs6zRZhQG3kkl4J/Coi5kn6BEBEvHdJP+c2cjOzpbdc2sgj4mcRMa88vBp49kC2Z2ZmS6+dbeRvA37Sxu2ZmVk/LLGNXNIvgHUX8tLEiLi0vGciMA+4YDHbORw4HGDDDTdcpsJa3UaMGMHs2bM7sq/hw4cza9asjuzLrGkDHkcu6RDgCGDXiHikPz/jNvLBqZP3p/S9MK0XLaqNfKCjVvYE3gvs2N8Qt8ErTloDTl6zc/syGyQGutbKF4CVgJ+XpTivjogjBlwq60k65cGO7Wv48OHMOrljuzNr1ICCPCI2bVdBrPctS1OHm0jMlswzO83MKucgNzOrnNcjt8b5VmhmA+Mgt8Y5jM0Gxk0rZmaVc5CbmVXOQW5mVjkHuZlZ5RzkZmaVc5CbmVXOQW5mVjkHuZlZ5Qa8Hvky7VSaCdzRwV2uDfyng/vrtF4+vl4+NvDx1a7Tx7dRRIxc8MlGgrzTJE1b2GLsvaKXj6+Xjw18fLXrluNz04qZWeUc5GZmlRssQX5O0wVYznr5+Hr52MDHV7uuOL5B0UZuZtbLBkuN3MysZznIzcwq5yDvQVrSLXcq1+vHZ4PPQP+mB32Q932AkoY0XZaBkrQeQEREL4adpOdDHl/TZbG6SOr2rBsGyx7o3X5wy5UkldDbGXiXpGG1BqCkVwC/kHQk9F6YS9oDuEDSZk2XpWmSXtT3OVQQUI2StBVARDzRdFkWRmkz4BpJay5rJWVQ/xGUsHs18CVgWkTM7fsgK/wHeQiYBWwm6V3QOzVXSfsCJwHvjIibe+kEtYzGA5+C7g2oLnKipKOaLsTCSFoh0s3A74Bxfc8v7bZqC6u2krQ6+U/xhoiYLGlnSadL2rCmf5ASbI+QYX4dsImkQyVtKmn9Zku37EptRcCHgfkR8RtJ6wDHS/q4pK3K77CnLeTE9R7gQUnjFvH6oLWQz+IyYK0GitIfrWum3Aa8Hpbt5Dxog7xcxjwE3AV8S9I3gP2ADYFvS1qx0QIuhXJW/yvwW+D3wCTyj+IKyh9Ljf/s5bgC2B5YS9JFwLeAocBw4BjghQ0WsSPKleP2kl4v6cUR8Ri5UNNL+15vtoTdo3xWL5M0tjz1K+DNkvZpslwLkrQm8FtJ75W0C/A5YNNlLeegCvKWjs2tgFMlbQ0cC5wPfD4ijgBOBO4hw6JrLSKYNwHWAYaQl2l/BbaD+v7ZWzuhI+K/wLbAVsBvI+LjEfEO4J/AwQ0Wc7la4Hf8TGAn4GOl6ez3wJHuMwBJK0tat3z/TOC5wIWSjgM2J//HX1Beb2xQQ8vf9MvJ/9V9gQeAdwBfAe7mqXIuVTZ3dVi1Wzlb7wkcCbwIeA5wUkR8FkDSa4GTgVMi4pGmyrkkfZ205ftNyEO7FTgXOAzYBTgOuA/YU9KIiJjVWIGXUuvxAaNLmP9N0hhgXmlbfAK4BdiivD6/uRIvH301cfLkPD0iLpO0AfAh8kprI/LkdnOvfgb9tDWwpaRVgEMj4gWSbgZGAR8BVgRGSjonIhpbUrf8PvcBTgHeFxE/A24CvizpWGBL4P2SLo2Im5Z244PmC9gU+CN5NlwV+AzwRWAbYA3go8Br+jKy6fL243jeA/wBuJUM8FHAD4C9yuvPANZoupxtOr4jF3jt7cC1wPObLudyPP5tyCaUTwP3AvuV51cCVgE+AFwPDG26rA19PuuRVylDgYuBhxfyd7IK8Frge2TzxZAGy7sW8FPgpeXxCguWBzgBOHxptz2oauTAfGA2MCciHpH0QeBy4P3l6wMR8cQCNcKusUBNfEXyiuLlwBbAd4D/Aq+LiGhpkqjGEo7vwvLyWaVmvi9Z+1q6mkuX6/sMSqfuUGD/iPiFpF8A35E0PyIuKm//SGke3Bz4c1NlbtA48gY1Q8iRZ/cD60raFvhDRMyLiEeBSyTdTv69NHnVMoQM87nl8QoRMU/SsyPi7vJcX9PQUi3G1dNt5C1tUitLWikibiMvZXaUtH4Jui+QtfN3ROktriDEjyRrFxuRNe7rgIPIIXp9Qw+ruszu5/EdLemYiJgBHBARNzZW4OWkhPgeZCfd54DXlo75n5Cd8d+V9EYASS8Engc82FR5myBpA0k7RsTl5GCFLwLPjIi3ASsDbyJHbu0gaf/yY5sDe0ka3qmO/5b8WaPkz33AT8jmzo1KiO8AfFHS+qVpCODzS7uvng7y8k/xGuDb5EiUUeSIjl3ItqjjyMv3DwPPLe2PXakl5F4OvI7syHwEOE7SehFxNTmU8s2S1mqsoMuoH8d3DdmccoiktSLi4eZKu/wohxTuBvw/slY2D3i9pNVLmO/DU8F9D7BTS21usHg5cJqkvSJiNtn89kpJe5PNTfOA43mquQXgdmCfiJjdqYpayZ99ge8CP5e0OfAzshn3PEknAucBZ0fEPeXq4YMRMX1p99XTy9gqp3SfQwb1tmQ78kvINsa9yB7ir5Bn8TOA3SPi/kYKuwgtHXtIegNZ4z41In6inM35KuAJ4IsRcbeklSOHp1Wh14+vv8poilXIwLkxInaWNIwclTMW+BtwQUQ8WN7flc1/y5OkjcnK5z3kyf4twKcj4peSDgV2BC6MiCskbQqsFBE3NfVZlSumM8l2772BPcgT9D3kyWgt4G8RMXWgZeypGrmk9STtVL5/HjmU8PcRcUVEnAycBVwDrBgRZwHvBNYHzgbGd1uIw1OTA0qb6Q3kaIWdy2tXkW38awCHlTCY01BRl0mvH9+S9F1+R8T8cpWxGzkC45iImAt8nbw6eT75OVDeP6hCvNiGbEOeGxEXkDXd4yXtFhHnAZOBt0raPyJuabL/pJx0jgHujIg/RMQp5BXCl4BNI+LiiPhaREyFgf8+e6azs/yTbwPMKG1Nd5KdH8+R9JLyYZ5WOtGuKR0M/5V0Czmz82/Nlf5/SdoO2DAiLpT0TuBw8rLseuBQSXdFxJnlbD4f+HtN7eK9fnz90dKxuQOwKzk658fkRJ/flZfPkHQOsP4gbEJ5moiYJGlt4DpJB0fEeeU8+C5JT0TE+ZKGkv1grT/XkZPeArXqB8nhsTtJ2jciLouI08tV1lmljf+Btu27l07s5UNaDfgs2S5+FfAJ4FHgexFxbXnfJhHx99bL+m4j6VVkR+z5wGhy7Olm5EiOA4BnA5MiYmJTZRyIXj++/pK0O3lF+FWy43IO+bk8BtwInBARS9351Uu0wBj50rb8OuBtEXGjpIPJ/qGPRMTPGypj6wJ86wKPRsQlkt4DPAuYEhE/LO/dOHLgRdv0RI2870OMiLmlxn0t2XM9hxxWeCpwcAnuP5DrGgB07VksIn4kaQ55UvpTRNwq6R/klcZosuZ6pKSRETGzuZIum14/vv4ozSrbAO+NiO+VzvjdgYMi4vhy1TK80UI2SNKqEfFIRMyXtCU5Mer6iPiYpFnA+ZIOaqmJ399UWUuI7wGcDrwX+KGkg8jOzEOAvUv+XEYOmWx7AXriixxTujVZk1uBnPb6dbIDZDWylvO8psu5DMf1GnLs+34tz10GvKLpsvn42nL8HwV+CAwrj7cg23rXb3lP109OWw6fywhylvX25X/4VuD75NIEbyZPcEeSncAvaLisK5ADJr5Tfn+7kiNpRpXX1wTeB4xZXmWoukbecjmzI9nx8WPyrP1xcrTKE2Qv8dnA0dGlzSiLExGXljP7GZK2IP+QR5E939Xr9eNr1fL3+kLg2RHxY7KCcTz5j34qOVlkCOVGAzBoOzZXJcNxH7K57U0RMU3SAWRQ3hkRXyr9YWt2unCSVgLWjoh/kJnzADkp603k8OYDI+Ku0uwzA/jEcv09Nn3mbcPZ8GXk2hPbkmfGg4Ap5PAekUH+wqbL2YbjfC05M/VCYHTT5fHxLfNx7k6OQrmGrHxsXZ47H/gNuYTE65ouZzd8kRPCPkQ2sx3V8vwHyErbCg2VSyVvDidHxv2SXA7jJOBxYOPyvq3Ifo7tl3eZqu3s7OuolHQJ8GJg34j4o6RVyY6QY4B3R8SUBovZVuXK4/aIaH8bWxcYBMe3BfBJ4LjIG2ScQVY+zo7stNuEXD7irkE6TnxT4I3k2Ot5EfFY6Tc4lGxquSIifirpZeTY7IMjJ9E0Udb1KHNPgJMj4nPl+W8AG5O185eSC/BdutzLU9vfSsvl6eqR64kjaRJ5KfrG8tozgDcAf42c8WjWqFLBOJKsYEyIHNEgsrN3HeBjEXFDk2VsUhk+/BGyo/BUsob7mYi4p8wxeAfZpPJ3ctG7z0XEDxooZ1/+rEyO+X8DcDNwdUT8srxnL3Ll0XkRcV0nTsrVTQgqH+Je5JoTH5Y0KiIOIEfgXKinFov6pkPcmtQ32af8TT5CTkg7m1zzY8dIx5L/9HXVqNoscnjhpeSa3P8mp9ZfpFx3Z82I+DDwa7Lp4piI+EHf59spLSG+J9mxeRXZnLIysIekLZULuq0YEb+PXCOITlxZVVMjlzQ0cpGZl5Jn7q+T017/DZwfEX+S9DPgvxHxf02W1ayPcv3p/cmxxJ8iO3F3J8fMXxQRv2qweF1H0gkAkZNnjiKDchY5L+RG4IaIuKXDZRoaEfPK97sCXyZngl9VnhtOrv+/Edk5u190eDx719fIlfedXL2E+ChybearImIS8G5y4sTBkraKiFeSIW/WOOUCWKeS07K/TS7QtjlwAXl3ozdLGqH6bvS9PN0GbF3+1w8hOzZ3Ipd2vamBEF8HeEtpSoEc9/8ByuxSSb8l2/VPI/s/du90iEMFNXLlHVKCvMv0CuTNIPYgz3p/kjSS7DkeCkzsazc3a5qkNwF7R8Sh5fEe5LDYXcgROoo2z/DrBZJ+RC5q956IOL08t1JEPN5AWV5GNn09QI5W2Zo8Kf+BHO9/N3mCfl3rSabTndVdH+Tw5N3u/wS8OCJmS5pITgA6KSJuKGE+IvIGxGaNaGlDHRsR00sl5HByYsvdkTOPzwC+G2WxJHtK6UuYL+nF5H02j4hcD6nR29iVMeOfBmaSV1jPBR6MiH8ol77+PlmxbGy0VRWXdKWWfRx51+k1ydlw1wCnS3pRRMx0iFuTynDYvo6wS5RLKN9AXk2+A3i1cmXOV5HrrA96ksaUIZl9+ibs3U4O4XsHNHOTlJaO6ueSV0+XkBOP3k32w/2jTE76OTnZp9Ehs1XUyPsoF47/PFkbf5AyMSDKYlhmnaayHkj5fjNyqdIjI+LX5bk1yUrIuuQaMmdGxI8aKm7jWq5aXkR+LgF8tIyrb71L1FbAsyLiigbLui+5Pv6xEXG9pN3IWwzeSTavjAZWj1z/vNFx/1UFOUCp8XydXDelbctAmi2tUlt7J/DxMt75eeQCWG8tHZgrlkktK0bEHEkjImJWs6VuXhnJ837garKZ4lbgrMibQKxHBvxHI+L+pgKynGguIJe4/ktp3p1Hhve7Spk/2WSTT6sqmlZaRcRPyTv9bNV0WWzwUt6260JyNb6+dWHuA3aQtF9EPFFC/JVkaEGDq/N1i9Le/EayI/M4cgr+f4CjyszWWcCXotzkpdMh3jI2fSMyrIdIej/wA3J98f8A3wQu7ZYQhwqDHHIJ1IiY0ukJAWbw5FT775DTr78maYUy5vm/ZG3y7ZJOLDXPTwB9E0OqW7St3crIk2cAe5bH04DpZMXsMLKp4vZOl6slS9Yq5boMeIi8+v8nueLihcC2EXFVRPy502VcnCqDvE+TbVI2qI0AXhQRl5THVwAblbbynwIfJNf/eSXwgYi4bLBWOlo6DTdVrvoIuTrp2pIOKY//Vr6eQ6582XGl3X5vsqP6C5K2jIi3kAtefR3YkOyovreJ8i1JdW3kZt2gLBPxRfLy+6oyhXxR7x10C2C1Kp/V6WTF8TvkcL0tyLVn7idvLr0nOeTw6oj4ZgNlfCk5i/QL5Bh2AT8BriTv3PRdcmmAH3a6bP3hIDdbRmW69hVkp2bfTaS3JTtAT4iIrqy9dZKkF5DDhSeQs7DPBKaRoz4eJptUbgM2IG93t3dE3NrhMq5HdmzeFBETlGucH0veCHwKMJW80cf0bj0pV920YtakyNXu9iWbBfqGH54NfHuwhrikdSWdqbQaeX+A55Nrh99Lrm64FXAUsFJZa2YNcnbkGzsd4sXDZJPYXpL2iFwa99NkG/mepezToXubc10jNxugMiT2YrJmeUJE/KThIjWmjErZmLzh+T1kTfuD5BT3z0fEHWV0yqfIoZo3l597ZkTc16Ey9o1l3wpYjxwXfid5c5M3AmdExM+V9/8dFRF/70S5BsJBbtYGpZlljWhgjexu0RKQK5ALhT2PXOlxNPB2st35SxFxm6RVIuLRTk6/L8E8t2UG7lnknYZeQd4k4vfk1cIh5PrwP+tEudrBQW7WRt3ahtopkrYjp7TfSN40Yz3g9eS47KPLayeSd0Lq2HDMMnnreHI8+F/IYaFfjohfSdqFHF74I/LG34cB15ahkVVwG7lZGw22EC/DCo9qeepoYHgZinkcOVzvO8Ad5IiQsyPisQ6H+BbAReR49WsjV5ycBYwtVwS/Ijs1jyUz8as1hTg4yM1sYFYAzpD0nvJ4KDAHoIT5O8mJUpcDN0fEXzpZOElrkCNlPhsRZ5ArGEKOnFmHXF8ccoGzfwNDu2nGZn+5acXMlknLsrPPI4fonUDO2pxG3sdyDjCEvJ/uyIiY0UAZhwHnkvdJfUDSsMjlhIeTa6aMJTtmx5I3Ub6402VsBwe5mS21lhDvuwXjWHJJ13XItUjWBuaSIX5kNLTMq6S1yHt9vq9v1cmWMB9FdsbOAu6IiD/W2scxtOkCmFk9JK1N3lRhjvLGGZtL+ltE/EbSNuRMyH9HxCHK26ONbirEAcoKimcCr5f0j4i4nqfWPd8S2J480Txe3l9diIPbyM2sn0oTynnAWmX43rnASsClkvpq3bsBx0h6f0Q8BnTDDV9+QC58dUQZofJEOQl9Avh+NHALuXZzjdzMlqgM3/smOd76IWAiOat1A3Kyz37Km2ycXqblj4LuqOFGxEzlLfbeRK6Pcx2wCXBiRPy41uaUVm4jN7PFKiF+BfBoRIwpz21C3vrsbOClwN5kzffoiPhyeU/XBaSkdcimlZUi4u5uLOOycNOKmS2SpNHkyn8fAG6QdHHpLPw72bH5zxKEdwK/JGdHAt1RG19QRPwr8h6/d5fHXVfGZeEauZktkqRXk2OrLymPf0iuYvgm4FlkU8sQYHNyiN+UZko6uDnIzWyRJK0QEU/0DTMsz/2QnORzMDkFf1fg7w7x5jjIzaxfWhe4knQZEMBre6V5omZuIzezJ0kaJmnV8v2qra+VCUBDyvf7AisCW3e+lLYg18jNDABJQ8lmksfJ4YM7AkeV8eCt7+uJkR69xOPIzQyAMtX+UXKizHrAcQsJ8XXJceOPNlBEWwQ3rZjZk3e7j4irgOvJm0o/UYK77z0jgbeRww6ti7hpxWyQa7mzzxbAI+SqheuQ99G8IiLOk7Qx2bk5JyLuabC4thCukZsNciXE9yIn/ryZXJL2duB7wK6SPk1Oax/pEO9ObiM3G+TK7M2TyLVTXkaOESciLpZ0JzAGeF1E/KGxQtpiuWnFbBBqWU98VfJmEIeQt2M7ATgwIm4utfTfRMSDLT/nEStdyE0rZoNIufVZ35jwbchFr4YAB5bvtyshvg3wPhbo2HSIdycHudkgIWkV4HJJ7yhPzQJmR8S9wP7AbODUcjPls4DPRMTNzZTWloaD3GyQiIhHgY8Db5d0KDkKZWh57S/AtmTtfEXg3RFxad+wROtubiM3GwRa27Yl7Qx8BphBhvY3gFWB+8g1x3/TWEFtmbhGbtbjWsaJ7y7pQHJ44XHAi8gp+esCrwaOIGvkVhkPPzTrYS0hvht5m7O3R8RcYEppK/808K+IOKjRgtqAuGnFrAeV6fRzy13kVwTOBy6JiAv71hgv79uDDPNXAf/oW3Pc6uKmFbMeU0L8CGB4uSHEHHKEyqzylpXK+zYDpgCviIg7HOL1cpCb9ZCyFG1fW/fjwCckrQn8GfhcudP9o5LGAd8E1o2IWYveotXAbeRmPaK0h8+TdAVwHjACuBc4PiI+JGl94NeSrga2A06JiDuaK7G1i9vIzXqApFHkgldfjYj7JL0K+CjwNWA4WUM/CXgBOSX/8Yi41lPue4OD3KwHSPoI8C7gx8CJ5JjwN5O18j+RNfChwCcj4l9NldOWDzetmFWs5e72JwHPJGvcbwduBjYg/8dvBu4m73o/HHCQ9xjXyM0qVe7e8wbg2oj4naStyPts3gXMJddPeRXw64h4taTVIuLh5kpsy4tHrZjVa1VgM+A0SQeRU+7XAh6KiMvI1QunANtIeoFDvHe5Rm5WMUkrATsDXyEn9gwDXgscERE3ShoBjIiIW5orpS1vrpGbVSwiHo+InwK7k7Xzlcl28A9JWiciZjnEe59r5GY9QtIzgbHAscAewBYRcXuTZbLOcJCb9SBJW0bE9U2XwzrDQW7WQ1oXxCqPPeFnEHCQm5lVzp2dZmaVc5CbmVXOQW5mVjkHuZlZ5RzkZmaVc5CbmVXu/wO9FNoyi+s5LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 4]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Bowling - Cluster 4')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['KH Pandya', 'RN ten Doeschate', 'IC Pandey', 'R Dhawan',\n",
       "       'BAW Mendis', 'VRV Singh', 'AN Ahmed', 'BB Sran', 'M Ashwin',\n",
       "       'B Akhil', 'RR Powar', 'AM Nayar', 'Ankit Sharma', 'J Suchith',\n",
       "       'TG Southee', 'R Sathish', 'JO Holder', 'V Sehwag', 'KC Cariappa',\n",
       "       'R McLaren', 'JR Hopes', 'GJ Maxwell', 'AA Chavan', 'Pankaj Singh',\n",
       "       'VS Malik', 'DJ Hooda', 'CJ Anderson', 'AL Menaria',\n",
       "       'Y Venugopal Rao', 'DJ Hussey', 'J Yadav', 'S Kaushik', 'S Tyagi',\n",
       "       'HH Pandya', 'A Mithun'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1ball = balldf.loc[balldf['kmeans6'] == 5]\n",
    "print(clus1ball.shape)\n",
    "\n",
    "# players\n",
    "clus1ball['player'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdf = standdf.loc[standdf['kmeans6'] == 5]\n",
    "tempdf = tempdf.drop(columns = ['kmeans6'])\n",
    "cols = list(tempdf.columns)\n",
    "# clus3 = MinMaxScaler().fit_transform(clus3)\n",
    "plt.boxplot(tempdf)\n",
    "plt.hlines(y=0, xmin=0.5, xmax=6.5, color='r')\n",
    "plt.xticks([1,2,3,4,5,6],cols, rotation = 45)\n",
    "plt.suptitle('Bowling - Cluster 5')\n",
    "plt.figure(figsize=(10,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Final Cluster Result**<br>\n",
    "\n",
    "1 - Best Cluster 10<br>\n",
    "2 - Great 9<br>\n",
    "0 - Good 8<br>\n",
    "3 - Okay 7<br>\n",
    "4 - Average 6<br>\n",
    "5 - Worst 5<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>innings</th>\n",
       "      <th>bm4</th>\n",
       "      <th>bm5</th>\n",
       "      <th>Economy</th>\n",
       "      <th>Wicket.Taking.Ability</th>\n",
       "      <th>Consistency</th>\n",
       "      <th>kmeans6</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sohail Tanvir</td>\n",
       "      <td>11</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>6.226415</td>\n",
       "      <td>11.041667</td>\n",
       "      <td>11.458333</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Zampa</td>\n",
       "      <td>11</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.493333</td>\n",
       "      <td>11.842105</td>\n",
       "      <td>14.789474</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CRD Fernando</td>\n",
       "      <td>10</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.373494</td>\n",
       "      <td>13.105263</td>\n",
       "      <td>16.105263</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>20</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.684211</td>\n",
       "      <td>7.238095</td>\n",
       "      <td>13.363636</td>\n",
       "      <td>16.121212</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WPUJC Vaas</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>7.583333</td>\n",
       "      <td>13.090909</td>\n",
       "      <td>16.545455</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DE Bollinger</td>\n",
       "      <td>27</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>7.160000</td>\n",
       "      <td>13.953488</td>\n",
       "      <td>16.651163</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BJ Hodge</td>\n",
       "      <td>20</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>7.815126</td>\n",
       "      <td>13.222222</td>\n",
       "      <td>17.222222</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>110</td>\n",
       "      <td>0.081818</td>\n",
       "      <td>1.594059</td>\n",
       "      <td>6.757238</td>\n",
       "      <td>15.847059</td>\n",
       "      <td>17.847059</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MA Starc</td>\n",
       "      <td>26</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>1.541667</td>\n",
       "      <td>7.107843</td>\n",
       "      <td>15.692308</td>\n",
       "      <td>18.589744</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R Rampaul</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>6.884892</td>\n",
       "      <td>16.352941</td>\n",
       "      <td>18.764706</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player  innings       bm4       bm5   Economy  \\\n",
       "0  Sohail Tanvir       11  0.181818  2.444444  6.226415   \n",
       "1        A Zampa       11  0.090909  1.800000  7.493333   \n",
       "2   CRD Fernando       10  0.100000  2.000000  7.373494   \n",
       "3    MF Maharoof       20  0.050000  1.684211  7.238095   \n",
       "4     WPUJC Vaas       13  0.000000  1.692308  7.583333   \n",
       "5   DE Bollinger       27  0.074074  1.640000  7.160000   \n",
       "6       BJ Hodge       20  0.050000  0.894737  7.815126   \n",
       "7     SL Malinga      110  0.081818  1.594059  6.757238   \n",
       "8       MA Starc       26  0.076923  1.541667  7.107843   \n",
       "9      R Rampaul       12  0.000000  1.416667  6.884892   \n",
       "\n",
       "   Wicket.Taking.Ability  Consistency  kmeans6  points  \n",
       "0              11.041667    11.458333        3       7  \n",
       "1              11.842105    14.789474        3       7  \n",
       "2              13.105263    16.105263        3       7  \n",
       "3              13.363636    16.121212        3       7  \n",
       "4              13.090909    16.545455        3       7  \n",
       "5              13.953488    16.651163        3       7  \n",
       "6              13.222222    17.222222        3       7  \n",
       "7              15.847059    17.847059        1      10  \n",
       "8              15.692308    18.589744        3       7  \n",
       "9              16.352941    18.764706        3       7  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = []\n",
    "for idx,row in balldf.iterrows():\n",
    "    if row.kmeans6 == 0:\n",
    "        points.append(8)\n",
    "    if row.kmeans6 == 1:\n",
    "        points.append(10)\n",
    "    if row.kmeans6 == 2:\n",
    "        points.append(9)\n",
    "    if row.kmeans6 == 3:\n",
    "        points.append(7)\n",
    "    if row.kmeans6 == 4:\n",
    "        points.append(6)\n",
    "    if row.kmeans6 == 5:\n",
    "        points.append(5)\n",
    "balldf['points']= points\n",
    "balldf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chennai Super Kings</th>\n",
       "      <th>Delhi Daredevils</th>\n",
       "      <th>Kings XI Punjab</th>\n",
       "      <th>Kolkata Knight Riders</th>\n",
       "      <th>Mumbai Indians</th>\n",
       "      <th>Rajasthan Royals</th>\n",
       "      <th>Royal Challengers Bangalore</th>\n",
       "      <th>Sunrisers Hyderabad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>AD Nath</td>\n",
       "      <td>AD Russell</td>\n",
       "      <td>A Dananjaya</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>A Hales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D Shorey</td>\n",
       "      <td>Ankit Sharma</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>AS Yadav</td>\n",
       "      <td>Ankit Sharma</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>B Kumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D Willey</td>\n",
       "      <td>Avesh Khan</td>\n",
       "      <td>AJ Tye</td>\n",
       "      <td>J Searles</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>Anureet Singh</td>\n",
       "      <td>C de Grandhomme</td>\n",
       "      <td>B Stanlake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>C Munro</td>\n",
       "      <td>AR Patel</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>E Lewis</td>\n",
       "      <td>B Laughlin</td>\n",
       "      <td>CJ Anderson</td>\n",
       "      <td>Basil Thampi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>AS Rajpoot</td>\n",
       "      <td>Kuldeep Yadav</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>BA Stokes</td>\n",
       "      <td>CR Woakes</td>\n",
       "      <td>CJ Jordan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>DT Christian</td>\n",
       "      <td>BB Sran</td>\n",
       "      <td>MG Johnson</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>D Short</td>\n",
       "      <td>K Khejroliya</td>\n",
       "      <td>CR Brathwaite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>N Rana</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "      <td>M Ali</td>\n",
       "      <td>DJ Hooda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Imran Tahir</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>DA Miller</td>\n",
       "      <td>P Krishna</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>H Klaasen</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>K Ahmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KM Asif</td>\n",
       "      <td>HV Patel</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>PP Chawla</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>I Sodhi</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>KS Williamson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>J Dala</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>R Singh</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>J Archer</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>MK Pandey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KV Sharma</td>\n",
       "      <td>JJ Roy</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>M Markande</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L Ngidi</td>\n",
       "      <td>L Plunkett</td>\n",
       "      <td>MA Agarwal</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>MJ McClenaghan</td>\n",
       "      <td>JD Unadkat</td>\n",
       "      <td>P Negi</td>\n",
       "      <td>R Bhui</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M Vijay</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>MK Tiwary</td>\n",
       "      <td>S Gill</td>\n",
       "      <td>Mustafizur Rahman</td>\n",
       "      <td>K Gowtham</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>Rashid Khan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M Wood</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>MM Sharma</td>\n",
       "      <td>S Mavi</td>\n",
       "      <td>PJ Sangwan</td>\n",
       "      <td>M Lomror</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>S Dhawan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>P Shaw</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>SP Narine</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>P Chopra</td>\n",
       "      <td>SN Khan</td>\n",
       "      <td>S Kaul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>R Tewatia</td>\n",
       "      <td>R Ashwin</td>\n",
       "      <td>T Curran</td>\n",
       "      <td>0</td>\n",
       "      <td>RA Tripathi</td>\n",
       "      <td>TG Southee</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S Gopal</td>\n",
       "      <td>UT Yadav</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>S Lamichhane</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STR Binny</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>SP Goswami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SR Watson</td>\n",
       "      <td>S Nadeem</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>Washington Sundar</td>\n",
       "      <td>WP Saha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SW Billings</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>YK Pathan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>V Shankar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chennai Super Kings Delhi Daredevils Kings XI Punjab Kolkata Knight Riders  \\\n",
       "0            AT Rayudu         A Mishra         AD Nath            AD Russell   \n",
       "1             D Shorey     Ankit Sharma        AJ Finch               CA Lynn   \n",
       "2             D Willey       Avesh Khan          AJ Tye             J Searles   \n",
       "3             DJ Bravo          C Munro        AR Patel            KD Karthik   \n",
       "4            DL Chahar        CH Morris      AS Rajpoot         Kuldeep Yadav   \n",
       "5         F du Plessis     DT Christian         BB Sran            MG Johnson   \n",
       "6      Harbhajan Singh        G Gambhir        CH Gayle                N Rana   \n",
       "7          Imran Tahir       GJ Maxwell       DA Miller             P Krishna   \n",
       "8              KM Asif         HV Patel         KK Nair             PP Chawla   \n",
       "9            KM Jadhav           J Dala        KL Rahul               R Singh   \n",
       "10           KV Sharma           JJ Roy     M Ur Rahman         R Vinay Kumar   \n",
       "11             L Ngidi       L Plunkett      MA Agarwal            RV Uthappa   \n",
       "12             M Vijay   Mohammed Shami       MK Tiwary                S Gill   \n",
       "13              M Wood          NV Ojha       MM Sharma                S Mavi   \n",
       "14            MS Dhoni           P Shaw      MP Stoinis             SP Narine   \n",
       "15           RA Jadeja        R Tewatia        R Ashwin              T Curran   \n",
       "16            SK Raina          RR Pant    Yuvraj Singh                     0   \n",
       "17           SN Thakur     S Lamichhane               0                     0   \n",
       "18           SR Watson         S Nadeem               0                     0   \n",
       "19         SW Billings          SS Iyer               0                     0   \n",
       "20                   0         TA Boult               0                     0   \n",
       "21                   0        V Shankar               0                     0   \n",
       "22                   0                0               0                     0   \n",
       "23                   0                0               0                     0   \n",
       "24                   0                0               0                     0   \n",
       "25                   0                0               0                     0   \n",
       "26                   0                0               0                     0   \n",
       "27                   0                0               0                     0   \n",
       "28                   0                0               0                     0   \n",
       "29                   0                0               0                     0   \n",
       "30                   0                0               0                     0   \n",
       "31                   0                0               0                     0   \n",
       "32                   0                0               0                     0   \n",
       "33                   0                0               0                     0   \n",
       "34                   0                0               0                     0   \n",
       "\n",
       "       Mumbai Indians Rajasthan Royals Royal Challengers Bangalore  \\\n",
       "0         A Dananjaya        AM Rahane              AB de Villiers   \n",
       "1            AS Yadav     Ankit Sharma                 BB McCullum   \n",
       "2         BCJ Cutting    Anureet Singh             C de Grandhomme   \n",
       "3             E Lewis       B Laughlin                 CJ Anderson   \n",
       "4           HH Pandya        BA Stokes                   CR Woakes   \n",
       "5        Ishan Kishan          D Short                K Khejroliya   \n",
       "6           JJ Bumrah      DS Kulkarni                       M Ali   \n",
       "7           JP Duminy        H Klaasen                    M Ashwin   \n",
       "8          KA Pollard          I Sodhi                     M Vohra   \n",
       "9           KH Pandya         J Archer               Mandeep Singh   \n",
       "10         M Markande       JC Buttler              Mohammed Siraj   \n",
       "11     MJ McClenaghan       JD Unadkat                      P Negi   \n",
       "12  Mustafizur Rahman        K Gowtham                    PA Patel   \n",
       "13         PJ Sangwan         M Lomror                   Q de Kock   \n",
       "14          RG Sharma         P Chopra                     SN Khan   \n",
       "15                  0      RA Tripathi                  TG Southee   \n",
       "16                  0          S Gopal                    UT Yadav   \n",
       "17                  0        STR Binny                     V Kohli   \n",
       "18                  0        SV Samson           Washington Sundar   \n",
       "19                  0                0                   YS Chahal   \n",
       "20                  0                0                           0   \n",
       "21                  0                0                           0   \n",
       "22                  0                0                           0   \n",
       "23                  0                0                           0   \n",
       "24                  0                0                           0   \n",
       "25                  0                0                           0   \n",
       "26                  0                0                           0   \n",
       "27                  0                0                           0   \n",
       "28                  0                0                           0   \n",
       "29                  0                0                           0   \n",
       "30                  0                0                           0   \n",
       "31                  0                0                           0   \n",
       "32                  0                0                           0   \n",
       "33                  0                0                           0   \n",
       "34                  0                0                           0   \n",
       "\n",
       "   Sunrisers Hyderabad  \n",
       "0              A Hales  \n",
       "1              B Kumar  \n",
       "2           B Stanlake  \n",
       "3         Basil Thampi  \n",
       "4            CJ Jordan  \n",
       "5        CR Brathwaite  \n",
       "6             DJ Hooda  \n",
       "7              K Ahmed  \n",
       "8        KS Williamson  \n",
       "9            MK Pandey  \n",
       "10       Mohammad Nabi  \n",
       "11              R Bhui  \n",
       "12         Rashid Khan  \n",
       "13            S Dhawan  \n",
       "14              S Kaul  \n",
       "15      Sandeep Sharma  \n",
       "16     Shakib Al Hasan  \n",
       "17          SP Goswami  \n",
       "18             WP Saha  \n",
       "19           YK Pathan  \n",
       "20                   0  \n",
       "21                   0  \n",
       "22                   0  \n",
       "23                   0  \n",
       "24                   0  \n",
       "25                   0  \n",
       "26                   0  \n",
       "27                   0  \n",
       "28                   0  \n",
       "29                   0  \n",
       "30                   0  \n",
       "31                   0  \n",
       "32                   0  \n",
       "33                   0  \n",
       "34                   0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squad2018 = pd.read_csv('squad.csv')\n",
    "squad2018 = squad2018.drop(columns = ['Unnamed: 0'])\n",
    "squad2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "teamcol = []\n",
    "players = []\n",
    "for col in list(squad2018.columns):\n",
    "    teamcol.append([col] * len(squad2018[col]))\n",
    "    players.append(list(squad2018[col].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['AT Rayudu',\n",
       "  'D Shorey',\n",
       "  'D Willey',\n",
       "  'DJ Bravo',\n",
       "  'DL Chahar',\n",
       "  'F du Plessis',\n",
       "  'Harbhajan Singh',\n",
       "  'Imran Tahir',\n",
       "  'KM Asif',\n",
       "  'KM Jadhav',\n",
       "  'KV Sharma',\n",
       "  'L Ngidi',\n",
       "  'M Vijay',\n",
       "  'M Wood',\n",
       "  'MS Dhoni',\n",
       "  'RA Jadeja',\n",
       "  'SK Raina',\n",
       "  'SN Thakur',\n",
       "  'SR Watson',\n",
       "  'SW Billings',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['A Mishra',\n",
       "  'Ankit Sharma',\n",
       "  'Avesh Khan',\n",
       "  'C Munro',\n",
       "  'CH Morris',\n",
       "  'DT Christian',\n",
       "  'G Gambhir',\n",
       "  'GJ Maxwell',\n",
       "  'HV Patel',\n",
       "  'J Dala',\n",
       "  'JJ Roy',\n",
       "  'L Plunkett',\n",
       "  'Mohammed Shami',\n",
       "  'NV Ojha',\n",
       "  'P Shaw',\n",
       "  'R Tewatia',\n",
       "  'RR Pant',\n",
       "  'S Lamichhane',\n",
       "  'S Nadeem',\n",
       "  'SS Iyer',\n",
       "  'TA Boult',\n",
       "  'V Shankar',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['AD Nath',\n",
       "  'AJ Finch',\n",
       "  'AJ Tye',\n",
       "  'AR Patel',\n",
       "  'AS Rajpoot',\n",
       "  'BB Sran',\n",
       "  'CH Gayle',\n",
       "  'DA Miller',\n",
       "  'KK Nair',\n",
       "  'KL Rahul',\n",
       "  'M Ur Rahman',\n",
       "  'MA Agarwal',\n",
       "  'MK Tiwary',\n",
       "  'MM Sharma',\n",
       "  'MP Stoinis',\n",
       "  'R Ashwin',\n",
       "  'Yuvraj Singh',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['AD Russell',\n",
       "  'CA Lynn',\n",
       "  'J Searles',\n",
       "  'KD Karthik',\n",
       "  'Kuldeep Yadav',\n",
       "  'MG Johnson',\n",
       "  'N Rana',\n",
       "  'P Krishna',\n",
       "  'PP Chawla',\n",
       "  'R Singh',\n",
       "  'R Vinay Kumar',\n",
       "  'RV Uthappa',\n",
       "  'S Gill',\n",
       "  'S Mavi',\n",
       "  'SP Narine',\n",
       "  'T Curran',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['A Dananjaya',\n",
       "  'AS Yadav',\n",
       "  'BCJ Cutting',\n",
       "  'E Lewis',\n",
       "  'HH Pandya',\n",
       "  'Ishan Kishan',\n",
       "  'JJ Bumrah',\n",
       "  'JP Duminy',\n",
       "  'KA Pollard',\n",
       "  'KH Pandya',\n",
       "  'M Markande',\n",
       "  'MJ McClenaghan',\n",
       "  'Mustafizur Rahman',\n",
       "  'PJ Sangwan',\n",
       "  'RG Sharma',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['AM Rahane',\n",
       "  'Ankit Sharma',\n",
       "  'Anureet Singh',\n",
       "  'B Laughlin',\n",
       "  'BA Stokes',\n",
       "  'D Short',\n",
       "  'DS Kulkarni',\n",
       "  'H Klaasen',\n",
       "  'I Sodhi',\n",
       "  'J Archer',\n",
       "  'JC Buttler',\n",
       "  'JD Unadkat',\n",
       "  'K Gowtham',\n",
       "  'M Lomror',\n",
       "  'P Chopra',\n",
       "  'RA Tripathi',\n",
       "  'S Gopal',\n",
       "  'STR Binny',\n",
       "  'SV Samson',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['AB de Villiers',\n",
       "  'BB McCullum',\n",
       "  'C de Grandhomme',\n",
       "  'CJ Anderson',\n",
       "  'CR Woakes',\n",
       "  'K Khejroliya',\n",
       "  'M Ali',\n",
       "  'M Ashwin',\n",
       "  'M Vohra',\n",
       "  'Mandeep Singh',\n",
       "  'Mohammed Siraj',\n",
       "  'P Negi',\n",
       "  'PA Patel',\n",
       "  'Q de Kock',\n",
       "  'SN Khan',\n",
       "  'TG Southee',\n",
       "  'UT Yadav',\n",
       "  'V Kohli',\n",
       "  'Washington Sundar',\n",
       "  'YS Chahal',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0'],\n",
       " ['A Hales',\n",
       "  'B Kumar',\n",
       "  'B Stanlake',\n",
       "  'Basil Thampi',\n",
       "  'CJ Jordan',\n",
       "  'CR Brathwaite',\n",
       "  'DJ Hooda',\n",
       "  'K Ahmed',\n",
       "  'KS Williamson',\n",
       "  'MK Pandey',\n",
       "  'Mohammad Nabi',\n",
       "  'R Bhui',\n",
       "  'Rashid Khan',\n",
       "  'S Dhawan',\n",
       "  'S Kaul',\n",
       "  'Sandeep Sharma',\n",
       "  'Shakib Al Hasan',\n",
       "  'SP Goswami',\n",
       "  'WP Saha',\n",
       "  'YK Pathan',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0',\n",
       "  '0']]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AT Rayudu',\n",
       " 'D Shorey',\n",
       " 'D Willey',\n",
       " 'DJ Bravo',\n",
       " 'DL Chahar',\n",
       " 'F du Plessis',\n",
       " 'Harbhajan Singh',\n",
       " 'Imran Tahir',\n",
       " 'KM Asif',\n",
       " 'KM Jadhav',\n",
       " 'KV Sharma',\n",
       " 'L Ngidi',\n",
       " 'M Vijay',\n",
       " 'M Wood',\n",
       " 'MS Dhoni',\n",
       " 'RA Jadeja',\n",
       " 'SK Raina',\n",
       " 'SN Thakur',\n",
       " 'SR Watson',\n",
       " 'SW Billings',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'A Mishra',\n",
       " 'Ankit Sharma',\n",
       " 'Avesh Khan',\n",
       " 'C Munro',\n",
       " 'CH Morris',\n",
       " 'DT Christian',\n",
       " 'G Gambhir',\n",
       " 'GJ Maxwell',\n",
       " 'HV Patel',\n",
       " 'J Dala',\n",
       " 'JJ Roy',\n",
       " 'L Plunkett',\n",
       " 'Mohammed Shami',\n",
       " 'NV Ojha',\n",
       " 'P Shaw',\n",
       " 'R Tewatia',\n",
       " 'RR Pant',\n",
       " 'S Lamichhane',\n",
       " 'S Nadeem',\n",
       " 'SS Iyer',\n",
       " 'TA Boult',\n",
       " 'V Shankar',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'AD Nath',\n",
       " 'AJ Finch',\n",
       " 'AJ Tye',\n",
       " 'AR Patel',\n",
       " 'AS Rajpoot',\n",
       " 'BB Sran',\n",
       " 'CH Gayle',\n",
       " 'DA Miller',\n",
       " 'KK Nair',\n",
       " 'KL Rahul',\n",
       " 'M Ur Rahman',\n",
       " 'MA Agarwal',\n",
       " 'MK Tiwary',\n",
       " 'MM Sharma',\n",
       " 'MP Stoinis',\n",
       " 'R Ashwin',\n",
       " 'Yuvraj Singh',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'AD Russell',\n",
       " 'CA Lynn',\n",
       " 'J Searles',\n",
       " 'KD Karthik',\n",
       " 'Kuldeep Yadav',\n",
       " 'MG Johnson',\n",
       " 'N Rana',\n",
       " 'P Krishna',\n",
       " 'PP Chawla',\n",
       " 'R Singh',\n",
       " 'R Vinay Kumar',\n",
       " 'RV Uthappa',\n",
       " 'S Gill',\n",
       " 'S Mavi',\n",
       " 'SP Narine',\n",
       " 'T Curran',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'A Dananjaya',\n",
       " 'AS Yadav',\n",
       " 'BCJ Cutting',\n",
       " 'E Lewis',\n",
       " 'HH Pandya',\n",
       " 'Ishan Kishan',\n",
       " 'JJ Bumrah',\n",
       " 'JP Duminy',\n",
       " 'KA Pollard',\n",
       " 'KH Pandya',\n",
       " 'M Markande',\n",
       " 'MJ McClenaghan',\n",
       " 'Mustafizur Rahman',\n",
       " 'PJ Sangwan',\n",
       " 'RG Sharma',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'AM Rahane',\n",
       " 'Ankit Sharma',\n",
       " 'Anureet Singh',\n",
       " 'B Laughlin',\n",
       " 'BA Stokes',\n",
       " 'D Short',\n",
       " 'DS Kulkarni',\n",
       " 'H Klaasen',\n",
       " 'I Sodhi',\n",
       " 'J Archer',\n",
       " 'JC Buttler',\n",
       " 'JD Unadkat',\n",
       " 'K Gowtham',\n",
       " 'M Lomror',\n",
       " 'P Chopra',\n",
       " 'RA Tripathi',\n",
       " 'S Gopal',\n",
       " 'STR Binny',\n",
       " 'SV Samson',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'AB de Villiers',\n",
       " 'BB McCullum',\n",
       " 'C de Grandhomme',\n",
       " 'CJ Anderson',\n",
       " 'CR Woakes',\n",
       " 'K Khejroliya',\n",
       " 'M Ali',\n",
       " 'M Ashwin',\n",
       " 'M Vohra',\n",
       " 'Mandeep Singh',\n",
       " 'Mohammed Siraj',\n",
       " 'P Negi',\n",
       " 'PA Patel',\n",
       " 'Q de Kock',\n",
       " 'SN Khan',\n",
       " 'TG Southee',\n",
       " 'UT Yadav',\n",
       " 'V Kohli',\n",
       " 'Washington Sundar',\n",
       " 'YS Chahal',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'A Hales',\n",
       " 'B Kumar',\n",
       " 'B Stanlake',\n",
       " 'Basil Thampi',\n",
       " 'CJ Jordan',\n",
       " 'CR Brathwaite',\n",
       " 'DJ Hooda',\n",
       " 'K Ahmed',\n",
       " 'KS Williamson',\n",
       " 'MK Pandey',\n",
       " 'Mohammad Nabi',\n",
       " 'R Bhui',\n",
       " 'Rashid Khan',\n",
       " 'S Dhawan',\n",
       " 'S Kaul',\n",
       " 'Sandeep Sharma',\n",
       " 'Shakib Al Hasan',\n",
       " 'SP Goswami',\n",
       " 'WP Saha',\n",
       " 'YK Pathan',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_flat = [item for sublist in players for item in sublist]\n",
    "players_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Chennai Super Kings',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Delhi Daredevils',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kings XI Punjab',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Mumbai Indians',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Rajasthan Royals',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad',\n",
       " 'Sunrisers Hyderabad']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teamcol_flat = [item for sublist in teamcol for item in sublist]\n",
    "teamcol_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "pointsData = pd.DataFrame(columns = ['team', 'player', 'batting_score', 'bowling_score', 'overall'])\n",
    "pointsData['team'] = teamcol_flat\n",
    "pointsData['player'] = players_flat\n",
    "pointsData['batting_score'] = 0\n",
    "pointsData['bowling_score'] = 0\n",
    "pointsData['overall'] = 0\n",
    "# len(teamcol_flat), len(players_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pointsData = pointsData.loc[(pointsData[['player']] != str(0)).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, row in pointsData.iterrows():\n",
    "    if row['player'] in (batdf['batsman']).values:\n",
    "        pointsData.at[idx,'batting_score'] = int(batdf['points'].loc[batdf['batsman'] == row['player']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, row in pointsData.iterrows():\n",
    "    if row['player'] in (balldf['player']).values:\n",
    "        pointsData.at[idx,'bowling_score'] = int(balldf['points'].loc[balldf['player'] == row['player']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>player</th>\n",
       "      <th>batting_score</th>\n",
       "      <th>bowling_score</th>\n",
       "      <th>overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>D Shorey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>D Willey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>260</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>SP Goswami</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    team           player  batting_score  bowling_score  \\\n",
       "0    Chennai Super Kings        AT Rayudu              9              0   \n",
       "1    Chennai Super Kings         D Shorey              0              0   \n",
       "2    Chennai Super Kings         D Willey              0              0   \n",
       "3    Chennai Super Kings         DJ Bravo              8             10   \n",
       "4    Chennai Super Kings        DL Chahar              0              0   \n",
       "..                   ...              ...            ...            ...   \n",
       "260  Sunrisers Hyderabad   Sandeep Sharma              7              8   \n",
       "261  Sunrisers Hyderabad  Shakib Al Hasan              6              8   \n",
       "262  Sunrisers Hyderabad       SP Goswami              5              0   \n",
       "263  Sunrisers Hyderabad          WP Saha              8              0   \n",
       "264  Sunrisers Hyderabad        YK Pathan             10              9   \n",
       "\n",
       "     overall  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "..       ...  \n",
       "260        0  \n",
       "261        0  \n",
       "262        0  \n",
       "263        0  \n",
       "264        0  \n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointsData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>player</th>\n",
       "      <th>batting_score</th>\n",
       "      <th>bowling_score</th>\n",
       "      <th>overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>D Shorey</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>D Willey</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Imran Tahir</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>KM Asif</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>KV Sharma</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>L Ngidi</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>M Vijay</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>M Wood</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   team           player  batting_score  bowling_score  \\\n",
       "0   Chennai Super Kings        AT Rayudu              9              5   \n",
       "1   Chennai Super Kings         D Shorey              5              5   \n",
       "2   Chennai Super Kings         D Willey              5              5   \n",
       "3   Chennai Super Kings         DJ Bravo              8             10   \n",
       "4   Chennai Super Kings        DL Chahar              5              5   \n",
       "5   Chennai Super Kings     F du Plessis              6              5   \n",
       "6   Chennai Super Kings  Harbhajan Singh              8             10   \n",
       "7   Chennai Super Kings      Imran Tahir              5              8   \n",
       "8   Chennai Super Kings          KM Asif              5              5   \n",
       "9   Chennai Super Kings        KM Jadhav              8              5   \n",
       "10  Chennai Super Kings        KV Sharma              6              8   \n",
       "11  Chennai Super Kings          L Ngidi              5              5   \n",
       "12  Chennai Super Kings          M Vijay              9              5   \n",
       "13  Chennai Super Kings           M Wood              5              5   \n",
       "14  Chennai Super Kings         MS Dhoni             10              5   \n",
       "15  Chennai Super Kings        RA Jadeja              9             10   \n",
       "16  Chennai Super Kings         SK Raina             10              9   \n",
       "17  Chennai Super Kings        SN Thakur              5              7   \n",
       "18  Chennai Super Kings        SR Watson              9              9   \n",
       "19  Chennai Super Kings      SW Billings              7              5   \n",
       "\n",
       "    overall  \n",
       "0       7.0  \n",
       "1       5.0  \n",
       "2       5.0  \n",
       "3       9.0  \n",
       "4       5.0  \n",
       "5       5.5  \n",
       "6       9.0  \n",
       "7       6.5  \n",
       "8       5.0  \n",
       "9       6.5  \n",
       "10      7.0  \n",
       "11      5.0  \n",
       "12      7.0  \n",
       "13      5.0  \n",
       "14      7.5  \n",
       "15      9.5  \n",
       "16      9.5  \n",
       "17      6.0  \n",
       "18      9.0  \n",
       "19      6.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointsData.loc[pointsData['team'] == 'Chennai Super Kings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pointsData['batting_score'] = pointsData['batting_score'].replace(0,5).values\n",
    "pointsData['bowling_score'] = pointsData['bowling_score'].replace(0,5).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "pointsData['overall'] = (pointsData['batting_score'] + pointsData['bowling_score'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>player</th>\n",
       "      <th>batting_score</th>\n",
       "      <th>bowling_score</th>\n",
       "      <th>overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>D Shorey</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>D Willey</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</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>260</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>SP Goswami</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    team           player  batting_score  bowling_score  \\\n",
       "0    Chennai Super Kings        AT Rayudu              9              5   \n",
       "1    Chennai Super Kings         D Shorey              5              5   \n",
       "2    Chennai Super Kings         D Willey              5              5   \n",
       "3    Chennai Super Kings         DJ Bravo              8             10   \n",
       "4    Chennai Super Kings        DL Chahar              5              5   \n",
       "..                   ...              ...            ...            ...   \n",
       "260  Sunrisers Hyderabad   Sandeep Sharma              7              8   \n",
       "261  Sunrisers Hyderabad  Shakib Al Hasan              6              8   \n",
       "262  Sunrisers Hyderabad       SP Goswami              5              5   \n",
       "263  Sunrisers Hyderabad          WP Saha              8              5   \n",
       "264  Sunrisers Hyderabad        YK Pathan             10              9   \n",
       "\n",
       "     overall  \n",
       "0        7.0  \n",
       "1        5.0  \n",
       "2        5.0  \n",
       "3        9.0  \n",
       "4        5.0  \n",
       "..       ...  \n",
       "260      7.5  \n",
       "261      7.0  \n",
       "262      5.0  \n",
       "263      6.5  \n",
       "264      9.5  \n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointsData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pointsData.loc[pointsData['team'] == 'Sunrisers Hyderabad']\n",
    "# pointsData.loc[pointsData['overall'] >=8.0]\n",
    "# pointsData.loc[pointsData['player'] == 'David Warner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = list(pointsData['team'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointsData['overall'].loc[pointsData['team'] == teams[0]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot_score = {}\n",
    "for team in teams:\n",
    "    den = len(pointsData['player'].loc[pointsData['team'] == team])\n",
    "    tot_score[team] = pointsData['overall'].loc[pointsData['team'] == team].sum()/den"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Chennai Super Kings': 6.75,\n",
       " 'Kings XI Punjab': 6.352941176470588,\n",
       " 'Kolkata Knight Riders': 6.34375,\n",
       " 'Mumbai Indians': 6.266666666666667,\n",
       " 'Sunrisers Hyderabad': 6.2,\n",
       " 'Royal Challengers Bangalore': 6.075,\n",
       " 'Delhi Daredevils': 5.931818181818182,\n",
       " 'Rajasthan Royals': 5.7894736842105265}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(sorted(tot_score.items(), key = lambda x: x[1], reverse = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "codemirror_mode": {
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