clustering_cricket.ipynb

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  "metadata": {
    "colab": {
      "name": "clustering_cricket.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPl89jlt8KkfTp2KHbHgHZc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/venkatesh-eranti/clustering_cricket-data/blob/main/clustering_cricket.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rd_GnKWiS4BJ"
      },
      "source": [
        "# loading data and understanding the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbD9ArXkTpqO"
      },
      "source": [
        "## importing the required librariers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZS4lDEiCTvBe"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.cluster import KMeans\n",
        "from scipy.cluster.hierarchy import linkage,cut_tree,dendrogram"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAm58hH1S9Fu"
      },
      "source": [
        "loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmgzNClpRKQ4"
      },
      "source": [
        "data='https://raw.githubusercontent.com/venkatesh-eranti/clustering_cricket/main/Cricket.csv'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "BF3phZnOToJ3",
        "outputId": "9686aeb7-993a-46c1-a16e-6648f69aac62"
      },
      "source": [
        "df=pd.read_csv(data,encoding = 'unicode_escape')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>Span</th>\n",
              "      <th>Mat</th>\n",
              "      <th>Inns</th>\n",
              "      <th>NO</th>\n",
              "      <th>Runs</th>\n",
              "      <th>HS</th>\n",
              "      <th>Ave</th>\n",
              "      <th>BF</th>\n",
              "      <th>SR</th>\n",
              "      <th>100</th>\n",
              "      <th>50</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SR Tendulkar (INDIA)</td>\n",
              "      <td>1989-2012</td>\n",
              "      <td>463</td>\n",
              "      <td>452</td>\n",
              "      <td>41</td>\n",
              "      <td>18426</td>\n",
              "      <td>200*</td>\n",
              "      <td>44.83</td>\n",
              "      <td>21367</td>\n",
              "      <td>86.23</td>\n",
              "      <td>49</td>\n",
              "      <td>96</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KC Sangakkara (Asia/ICC/SL)</td>\n",
              "      <td>2000-2015</td>\n",
              "      <td>404</td>\n",
              "      <td>380</td>\n",
              "      <td>41</td>\n",
              "      <td>14234</td>\n",
              "      <td>169</td>\n",
              "      <td>41.98</td>\n",
              "      <td>18048</td>\n",
              "      <td>78.86</td>\n",
              "      <td>25</td>\n",
              "      <td>93</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT Ponting (AUS/ICC)</td>\n",
              "      <td>1995-2012</td>\n",
              "      <td>375</td>\n",
              "      <td>365</td>\n",
              "      <td>39</td>\n",
              "      <td>13704</td>\n",
              "      <td>164</td>\n",
              "      <td>42.03</td>\n",
              "      <td>17046</td>\n",
              "      <td>80.39</td>\n",
              "      <td>30</td>\n",
              "      <td>82</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ST Jayasuriya (Asia/SL)</td>\n",
              "      <td>1989-2011</td>\n",
              "      <td>445</td>\n",
              "      <td>433</td>\n",
              "      <td>18</td>\n",
              "      <td>13430</td>\n",
              "      <td>189</td>\n",
              "      <td>32.36</td>\n",
              "      <td>14725</td>\n",
              "      <td>91.20</td>\n",
              "      <td>28</td>\n",
              "      <td>68</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DPMD Jayawardene (Asia/SL)</td>\n",
              "      <td>1998-2015</td>\n",
              "      <td>448</td>\n",
              "      <td>418</td>\n",
              "      <td>39</td>\n",
              "      <td>12650</td>\n",
              "      <td>144</td>\n",
              "      <td>33.37</td>\n",
              "      <td>16020</td>\n",
              "      <td>78.96</td>\n",
              "      <td>19</td>\n",
              "      <td>77</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Player       Span  Mat  Inns  ...     SR  100  50   0\n",
              "0         SR Tendulkar (INDIA)  1989-2012  463   452  ...  86.23   49  96  20\n",
              "1  KC Sangakkara (Asia/ICC/SL)  2000-2015  404   380  ...  78.86   25  93  15\n",
              "2         RT Ponting (AUS/ICC)  1995-2012  375   365  ...  80.39   30  82  20\n",
              "3      ST Jayasuriya (Asia/SL)  1989-2011  445   433  ...  91.20   28  68  34\n",
              "4   DPMD Jayawardene (Asia/SL)  1998-2015  448   418  ...  78.96   19  77  28\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-aTWbkRKYBSj"
      },
      "source": [
        "## checking data types"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZis9ikCW2Zw",
        "outputId": "8a380259-1bb4-485a-dc46-63805c0e0ea3"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 79 entries, 0 to 78\n",
            "Data columns (total 13 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Player  79 non-null     object \n",
            " 1   Span    79 non-null     object \n",
            " 2   Mat     79 non-null     int64  \n",
            " 3   Inns    79 non-null     int64  \n",
            " 4   NO      79 non-null     int64  \n",
            " 5   Runs    79 non-null     int64  \n",
            " 6   HS      79 non-null     object \n",
            " 7   Ave     79 non-null     float64\n",
            " 8   BF      79 non-null     int64  \n",
            " 9   SR      79 non-null     float64\n",
            " 10  100     79 non-null     int64  \n",
            " 11  50      79 non-null     int64  \n",
            " 12  0       79 non-null     int64  \n",
            "dtypes: float64(2), int64(8), object(3)\n",
            "memory usage: 8.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNjDPGhqYFRE"
      },
      "source": [
        "High score need to be in numeric format. but it is showing as object"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6OS8NaDX9V9",
        "outputId": "89f62071-d4e1-4cfe-b216-f79f82b9935d"
      },
      "source": [
        "df['HS']"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     200*\n",
              "1      169\n",
              "2      164\n",
              "3      189\n",
              "4      144\n",
              "      ... \n",
              "74    133*\n",
              "75     96*\n",
              "76    120*\n",
              "77     156\n",
              "78     112\n",
              "Name: HS, Length: 79, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7IIwqjSZA_-"
      },
      "source": [
        "because of star, which represents not out is the reason"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EB1JU2GSY-x3",
        "outputId": "7126d730-bf69-4ba6-8ebe-e08847d6fbc5"
      },
      "source": [
        "df['HS'][df['HS'].apply(lambda x:isinstance(x,(int,float)))]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: HS, dtype: object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlcAJ9mXZ0zM"
      },
      "source": [
        "only objects filtering is not happening "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ff0aCKWZYwI"
      },
      "source": [
        "def obj_num(x):\n",
        "    if type(x) is int:\n",
        "        return x\n",
        "    else:\n",
        "        return x.strip('*')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVIJ4fDOah7T"
      },
      "source": [
        "df['HS']=df['HS'].apply(obj_num)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmSt22NJanBL"
      },
      "source": [
        "df['HS']=df['HS'].astype(int)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUV1js3JarvC",
        "outputId": "b8b60bbb-80c0-4352-c0ad-da1e2deed97c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 79 entries, 0 to 78\n",
            "Data columns (total 13 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Player  79 non-null     object \n",
            " 1   Span    79 non-null     object \n",
            " 2   Mat     79 non-null     int64  \n",
            " 3   Inns    79 non-null     int64  \n",
            " 4   NO      79 non-null     int64  \n",
            " 5   Runs    79 non-null     int64  \n",
            " 6   HS      79 non-null     int64  \n",
            " 7   Ave     79 non-null     float64\n",
            " 8   BF      79 non-null     int64  \n",
            " 9   SR      79 non-null     float64\n",
            " 10  100     79 non-null     int64  \n",
            " 11  50      79 non-null     int64  \n",
            " 12  0       79 non-null     int64  \n",
            "dtypes: float64(2), int64(9), object(2)\n",
            "memory usage: 8.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08rA571ndOud"
      },
      "source": [
        "# Data preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_ImY7mZbPJy"
      },
      "source": [
        "analyse the data by considering strike rate (SR) and batting average (Ave) as features by clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0zoj6rkbgo5"
      },
      "source": [
        "df1=df[['Player','Ave','SR']]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vvzu8ORRdKfO"
      },
      "source": [
        "## scaling the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAeEd8LHcDmL"
      },
      "source": [
        "scaler=StandardScaler()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZSd0UOPdiQY"
      },
      "source": [
        "scaled_df1=df1.copy()\n",
        "scaled_df1[scaled_df1.columns.drop('Player')]=scaler.fit_transform(scaled_df1[scaled_df1.columns.drop('Player')])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "s2TqOBTSduuq",
        "outputId": "2ec5e960-f041-4d46-fa95-5e8cf44b0890"
      },
      "source": [
        "scaled_df1.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        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>Ave</th>\n",
              "      <th>SR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SR Tendulkar (INDIA)</td>\n",
              "      <td>1.072294</td>\n",
              "      <td>0.703152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KC Sangakkara (Asia/ICC/SL)</td>\n",
              "      <td>0.587725</td>\n",
              "      <td>-0.044139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT Ponting (AUS/ICC)</td>\n",
              "      <td>0.596226</td>\n",
              "      <td>0.110997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ST Jayasuriya (Asia/SL)</td>\n",
              "      <td>-1.047909</td>\n",
              "      <td>1.207091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DPMD Jayawardene (Asia/SL)</td>\n",
              "      <td>-0.876185</td>\n",
              "      <td>-0.034000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Player       Ave        SR\n",
              "0         SR Tendulkar (INDIA)  1.072294  0.703152\n",
              "1  KC Sangakkara (Asia/ICC/SL)  0.587725 -0.044139\n",
              "2         RT Ponting (AUS/ICC)  0.596226  0.110997\n",
              "3      ST Jayasuriya (Asia/SL) -1.047909  1.207091\n",
              "4   DPMD Jayawardene (Asia/SL) -0.876185 -0.034000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecXAUG_td1nU"
      },
      "source": [
        "dropping player feature "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rVIlUwnd4_Z"
      },
      "source": [
        "scaled_df2=scaled_df1.copy()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ap22EM3Hd_Dx"
      },
      "source": [
        "scaled_df2.drop('Player',axis=1,inplace=True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IprXZfYKeHKA"
      },
      "source": [
        "# building model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jl5heliUeMmy"
      },
      "source": [
        "## clustering tendency (Hopkins Statistics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcmxGBtheaA6"
      },
      "source": [
        "The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered.\n",
        "\n",
        "If the value is between {0.01, ...,0.3}, the data is regularly spaced.\n",
        "\n",
        "If the value is around 0.5, it is random.\n",
        "\n",
        "If the value is between {0.7, ..., 0.99}, it has a high tendency to cluster."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaTYi0RmeDgI"
      },
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "from random import sample\n",
        "from numpy.random import uniform\n",
        "from math import isnan\n",
        " \n",
        "def hopkins(X):\n",
        "    d = X.shape[1]\n",
        "    n=X.shape[0]\n",
        "    m = int(0.1 * n) \n",
        "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
        " \n",
        "    rand_X = sample(range(0, n, 1), m)\n",
        " \n",
        "    ujd = []\n",
        "    wjd = []\n",
        "    for j in range(0, m):\n",
        "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
        "        ujd.append(u_dist[0][1])\n",
        "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
        "        wjd.append(w_dist[0][1])\n",
        " \n",
        "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
        "    if isnan(H):\n",
        "        print(ujd, wjd)\n",
        "        H = 0\n",
        " \n",
        "    return H"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MubzQkneXPi",
        "outputId": "5655bdeb-3f25-41d9-fa73-6d330f73c0c3"
      },
      "source": [
        "hopkins(scaled_df2)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8661520338478825"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KlN9QAuIeldS"
      },
      "source": [
        "## elbow method to find optimal clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "PbB6X9Hsehli",
        "outputId": "62d49d81-f45e-42f1-e963-4b5c4daddafd"
      },
      "source": [
        "elbow=[]\n",
        "for i in range(2,6):\n",
        "    kmeans=KMeans(n_clusters=i,random_state=42,max_iter=50)\n",
        "    kmeans.fit(scaled_df2)\n",
        "    elbow.append(kmeans.inertia_)\n",
        "sns.lineplot(x=range(2,6),y=elbow)\n",
        "plt.xlabel('number of clusters')\n",
        "plt.ylabel('cost function')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'cost function')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4338DVygHti"
      },
      "source": [
        "as per above plot 4 is the optimal number of clusters. <br>\n",
        "lets check sihoutte method also"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TuzZ1zffdhM"
      },
      "source": [
        "## checking silhoutte method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNata-iSfFEF",
        "outputId": "a0dc6a1f-94e2-4760-c2f9-52fc971a7916"
      },
      "source": [
        "for i in range(2,7):\n",
        "    kmeans=KMeans(n_clusters=i,random_state=42,max_iter=50)\n",
        "    kmeans.fit(scaled_df2)\n",
        "    sil_avg=silhouette_score(scaled_df2,kmeans.labels_)\n",
        "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(i, sil_avg))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For n_clusters=2, the silhouette score is 0.37632823237612395\n",
            "For n_clusters=3, the silhouette score is 0.37185804193936567\n",
            "For n_clusters=4, the silhouette score is 0.3836904829421138\n",
            "For n_clusters=5, the silhouette score is 0.36941231708738775\n",
            "For n_clusters=6, the silhouette score is 0.37992910667909674\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5jgn2NogRmC"
      },
      "source": [
        "as per silhoute method also 4 is the optimal number. <br>\n",
        "so we will go with 4 clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_9Z6ivLgZ8a"
      },
      "source": [
        "## building 4 clusters "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4Ow1pokf-ch",
        "outputId": "1b2375b0-d5fa-4d4a-da6f-caffd725a511"
      },
      "source": [
        "kmeans=KMeans(n_clusters=4,max_iter=50)\n",
        "kmeans.fit(scaled_df2)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
              "       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwfgxRzzgvGa"
      },
      "source": [
        "## assigning the labels to df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "rezEtN7lgmaz",
        "outputId": "7241791a-a847-4a6c-819f-49dc422d41c5"
      },
      "source": [
        "df1['labels']=kmeans.labels_\n",
        "df1.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Ave</th>\n",
              "      <th>SR</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SR Tendulkar (INDIA)</td>\n",
              "      <td>44.83</td>\n",
              "      <td>86.23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KC Sangakkara (Asia/ICC/SL)</td>\n",
              "      <td>41.98</td>\n",
              "      <td>78.86</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT Ponting (AUS/ICC)</td>\n",
              "      <td>42.03</td>\n",
              "      <td>80.39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ST Jayasuriya (Asia/SL)</td>\n",
              "      <td>32.36</td>\n",
              "      <td>91.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DPMD Jayawardene (Asia/SL)</td>\n",
              "      <td>33.37</td>\n",
              "      <td>78.96</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Player    Ave     SR  labels\n",
              "0         SR Tendulkar (INDIA)  44.83  86.23       3\n",
              "1  KC Sangakkara (Asia/ICC/SL)  41.98  78.86       0\n",
              "2         RT Ponting (AUS/ICC)  42.03  80.39       0\n",
              "3      ST Jayasuriya (Asia/SL)  32.36  91.20       2\n",
              "4   DPMD Jayawardene (Asia/SL)  33.37  78.96       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMQYdHzbhROR"
      },
      "source": [
        "# analysing the clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "gBEh8GdZhaSx",
        "outputId": "0756d000-832d-4679-dcd8-435a0fb63e8d"
      },
      "source": [
        "sns.scatterplot(df1['SR'],df1['Ave'],hue=df1['labels'],palette=['green','orange','brown','red'])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a8b29bc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLaP_9Rxh-6x"
      },
      "source": [
        "## observations from the above scatter plot\n",
        "* label 0 $\\rightarrow$ Low average and low strike rate\n",
        "* label 1 $\\rightarrow$ High average and High strike rate\n",
        "* label 2 $\\rightarrow$ High average and low strike rate\n",
        "- label 3 $\\rightarrow$ Low average and High strike rate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEexz5MWiu6C"
      },
      "source": [
        "checking High average and High strike rate group"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UxVCK8YiDeu",
        "outputId": "7e7e9458-c03a-4681-fb7e-d68b0ce06c80"
      },
      "source": [
        "df1['Player'][df1['labels']==1]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4     DPMD Jayawardene (Asia/SL)\n",
              "14          M Azharuddin (INDIA)\n",
              "16              PA de Silva (SL)\n",
              "22              MS Atapattu (SL)\n",
              "26                 HH Gibbs (SA)\n",
              "28           SP Fleming (ICC/NZ)\n",
              "30                SR Waugh (AUS)\n",
              "31              A Ranatunga (SL)\n",
              "33             Younis Khan (PAK)\n",
              "35            Saleem Malik (PAK)\n",
              "36                 NJ Astle (NZ)\n",
              "40                A Flower (ZIM)\n",
              "41            Shoaib Malik (PAK)\n",
              "43               GW Flower (ZIM)\n",
              "44              Ijaz Ahmed (PAK)\n",
              "45               AR Border (AUS)\n",
              "47            RB Richardson (WI)\n",
              "48         WU Tharanga (Asia/SL)\n",
              "52                 DC Boon (AUS)\n",
              "53                JN Rhodes (SA)\n",
              "54         Mohammad Hafeez (PAK)\n",
              "55             Rameez Raja (PAK)\n",
              "58                CL Hooper (WI)\n",
              "60           Tamim Iqbal (BDESH)\n",
              "67              A Jadeja (INDIA)\n",
              "69              BRM Taylor (ZIM)\n",
              "71            ADR Campbell (ZIM)\n",
              "72               MN Samuels (WI)\n",
              "73              RS Mahanama (SL)\n",
              "76          PD Collingwood (ENG)\n",
              "78       Abdul Razzaq (Asia/PAK)\n",
              "Name: Player, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBmwnsSWy3Ea"
      },
      "source": [
        "building Hierarchical clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_P3LdBdi6sX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "26315e42-fcdf-4fec-8d3e-5473fc0ddb72"
      },
      "source": [
        "hier_clus=linkage(scaled_df2,method='complete',metric='euclidean')\n",
        "plt.figure(figsize=(12,8))\n",
        "dendrogram(hier_clus)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VfurDdPy7eZ"
      },
      "source": [
        "hier_clus_cut=cut_tree(hier_clus,4)\n",
        "hier_clus_cut=hier_clus_cut.reshape(-1, )"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "r4dWRvzWz3kj",
        "outputId": "4dd38f41-8656-44c4-b42b-1e7295c9aa63"
      },
      "source": [
        "df1['hier_lab']=hier_clus_cut\n",
        "df1"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Ave</th>\n",
              "      <th>SR</th>\n",
              "      <th>labels</th>\n",
              "      <th>hier_lab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SR Tendulkar (INDIA)</td>\n",
              "      <td>44.83</td>\n",
              "      <td>86.23</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KC Sangakkara (Asia/ICC/SL)</td>\n",
              "      <td>41.98</td>\n",
              "      <td>78.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT Ponting (AUS/ICC)</td>\n",
              "      <td>42.03</td>\n",
              "      <td>80.39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ST Jayasuriya (Asia/SL)</td>\n",
              "      <td>32.36</td>\n",
              "      <td>91.20</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DPMD Jayawardene (Asia/SL)</td>\n",
              "      <td>33.37</td>\n",
              "      <td>78.96</td>\n",
              "      <td>1</td>\n",
              "      <td>2</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>74</th>\n",
              "      <td>CG Greenidge (WI)</td>\n",
              "      <td>45.03</td>\n",
              "      <td>64.92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Misbah-ul-Haq (PAK)</td>\n",
              "      <td>43.40</td>\n",
              "      <td>73.75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>PD Collingwood (ENG)</td>\n",
              "      <td>35.36</td>\n",
              "      <td>76.98</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>A Symonds (AUS)</td>\n",
              "      <td>39.75</td>\n",
              "      <td>92.44</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Abdul Razzaq (Asia/PAK)</td>\n",
              "      <td>29.70</td>\n",
              "      <td>81.25</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>79 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Player    Ave     SR  labels  hier_lab\n",
              "0          SR Tendulkar (INDIA)  44.83  86.23       3         0\n",
              "1   KC Sangakkara (Asia/ICC/SL)  41.98  78.86       0         0\n",
              "2          RT Ponting (AUS/ICC)  42.03  80.39       0         0\n",
              "3       ST Jayasuriya (Asia/SL)  32.36  91.20       2         1\n",
              "4    DPMD Jayawardene (Asia/SL)  33.37  78.96       1         2\n",
              "..                          ...    ...    ...     ...       ...\n",
              "74            CG Greenidge (WI)  45.03  64.92       0         0\n",
              "75          Misbah-ul-Haq (PAK)  43.40  73.75       0         0\n",
              "76         PD Collingwood (ENG)  35.36  76.98       1         2\n",
              "77              A Symonds (AUS)  39.75  92.44       2         0\n",
              "78      Abdul Razzaq (Asia/PAK)  29.70  81.25       1         2\n",
              "\n",
              "[79 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6uUN69uvz-Zm",
        "outputId": "83b2251b-5641-42f3-ca4f-29238a6b260e"
      },
      "source": [
        "df1[df1['hier_lab']==0]"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ave</th>\n",
              "      <th>SR</th>\n",
              "      <th>labels</th>\n",
              "      <th>hier_lab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SR Tendulkar (INDIA)</td>\n",
              "      <td>44.83</td>\n",
              "      <td>86.23</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KC Sangakkara (Asia/ICC/SL)</td>\n",
              "      <td>41.98</td>\n",
              "      <td>78.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT Ponting (AUS/ICC)</td>\n",
              "      <td>42.03</td>\n",
              "      <td>80.39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Inzamam-ul-Haq (Asia/PAK)</td>\n",
              "      <td>39.52</td>\n",
              "      <td>74.24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>JH Kallis (Afr/ICC/SA)</td>\n",
              "      <td>44.36</td>\n",
              "      <td>72.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SC Ganguly (Asia/INDIA)</td>\n",
              "      <td>41.02</td>\n",
              "      <td>73.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>R Dravid (Asia/ICC/INDIA)</td>\n",
              "      <td>39.16</td>\n",
              "      <td>71.24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>BC Lara (ICC/WI)</td>\n",
              "      <td>40.48</td>\n",
              "      <td>79.51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>TM Dilshan (SL)</td>\n",
              "      <td>39.27</td>\n",
              "      <td>86.23</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Mohammad Yousuf (Asia/PAK)</td>\n",
              "      <td>41.71</td>\n",
              "      <td>75.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>CH Gayle (ICC/WI)</td>\n",
              "      <td>37.33</td>\n",
              "      <td>85.11</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Saeed Anwar (PAK)</td>\n",
              "      <td>39.21</td>\n",
              "      <td>80.67</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>S Chanderpaul (WI)</td>\n",
              "      <td>41.60</td>\n",
              "      <td>70.74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Yuvraj Singh (Asia/INDIA)</td>\n",
              "      <td>36.55</td>\n",
              "      <td>87.67</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>DL Haynes (WI)</td>\n",
              "      <td>41.37</td>\n",
              "      <td>63.09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>MS Atapattu (SL)</td>\n",
              "      <td>37.57</td>\n",
              "      <td>67.72</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>ME Waugh (AUS)</td>\n",
              "      <td>39.35</td>\n",
              "      <td>76.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>HH Gibbs (SA)</td>\n",
              "      <td>36.13</td>\n",
              "      <td>83.26</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>MJ Clarke (AUS)</td>\n",
              "      <td>44.58</td>\n",
              "      <td>78.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Javed Miandad (PAK)</td>\n",
              "      <td>41.70</td>\n",
              "      <td>67.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>GC Smith (Afr/SA)</td>\n",
              "      <td>37.98</td>\n",
              "      <td>80.81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>G Kirsten (SA)</td>\n",
              "      <td>40.95</td>\n",
              "      <td>72.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>IVA Richards (WI)</td>\n",
              "      <td>47.00</td>\n",
              "      <td>90.20</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>LRPL Taylor (NZ)</td>\n",
              "      <td>44.12</td>\n",
              "      <td>82.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>ML Hayden (AUS/ICC)</td>\n",
              "      <td>43.80</td>\n",
              "      <td>78.96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>DM Jones (AUS)</td>\n",
              "      <td>44.61</td>\n",
              "      <td>72.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>DC Boon (AUS)</td>\n",
              "      <td>37.04</td>\n",
              "      <td>65.13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>RR Sarwan (WI)</td>\n",
              "      <td>42.67</td>\n",
              "      <td>75.74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>EJG Morgan (ENG/IRE)</td>\n",
              "      <td>38.73</td>\n",
              "      <td>88.62</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>SR Watson (AUS)</td>\n",
              "      <td>40.54</td>\n",
              "      <td>90.44</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>WJ Cronje (SA)</td>\n",
              "      <td>38.64</td>\n",
              "      <td>76.47</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>MJ Guptill (NZ)</td>\n",
              "      <td>43.30</td>\n",
              "      <td>87.73</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>MEK Hussey (AUS)</td>\n",
              "      <td>48.15</td>\n",
              "      <td>87.16</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>RG Sharma (INDIA)</td>\n",
              "      <td>42.46</td>\n",
              "      <td>84.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>IR Bell (ENG)</td>\n",
              "      <td>37.87</td>\n",
              "      <td>77.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>A Jadeja (INDIA)</td>\n",
              "      <td>37.47</td>\n",
              "      <td>69.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>DR Martyn (AUS)</td>\n",
              "      <td>40.80</td>\n",
              "      <td>77.73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>G Gambhir (INDIA)</td>\n",
              "      <td>39.68</td>\n",
              "      <td>85.25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>CG Greenidge (WI)</td>\n",
              "      <td>45.03</td>\n",
              "      <td>64.92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Misbah-ul-Haq (PAK)</td>\n",
              "      <td>43.40</td>\n",
              "      <td>73.75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>A Symonds (AUS)</td>\n",
              "      <td>39.75</td>\n",
              "      <td>92.44</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Player    Ave     SR  labels  hier_lab\n",
              "0          SR Tendulkar (INDIA)  44.83  86.23       3         0\n",
              "1   KC Sangakkara (Asia/ICC/SL)  41.98  78.86       0         0\n",
              "2          RT Ponting (AUS/ICC)  42.03  80.39       0         0\n",
              "5     Inzamam-ul-Haq (Asia/PAK)  39.52  74.24       0         0\n",
              "6        JH Kallis (Afr/ICC/SA)  44.36  72.89       0         0\n",
              "7       SC Ganguly (Asia/INDIA)  41.02  73.70       0         0\n",
              "8     R Dravid (Asia/ICC/INDIA)  39.16  71.24       0         0\n",
              "9              BC Lara (ICC/WI)  40.48  79.51       0         0\n",
              "10              TM Dilshan (SL)  39.27  86.23       2         0\n",
              "11   Mohammad Yousuf (Asia/PAK)  41.71  75.10       0         0\n",
              "17            CH Gayle (ICC/WI)  37.33  85.11       2         0\n",
              "18            Saeed Anwar (PAK)  39.21  80.67       0         0\n",
              "19           S Chanderpaul (WI)  41.60  70.74       0         0\n",
              "20    Yuvraj Singh (Asia/INDIA)  36.55  87.67       2         0\n",
              "21               DL Haynes (WI)  41.37  63.09       0         0\n",
              "22             MS Atapattu (SL)  37.57  67.72       1         0\n",
              "23               ME Waugh (AUS)  39.35  76.90       0         0\n",
              "26                HH Gibbs (SA)  36.13  83.26       1         0\n",
              "29              MJ Clarke (AUS)  44.58  78.98       0         0\n",
              "32          Javed Miandad (PAK)  41.70  67.01       0         0\n",
              "37            GC Smith (Afr/SA)  37.98  80.81       0         0\n",
              "39               G Kirsten (SA)  40.95  72.04       0         0\n",
              "42            IVA Richards (WI)  47.00  90.20       3         0\n",
              "46             LRPL Taylor (NZ)  44.12  82.03       0         0\n",
              "49          ML Hayden (AUS/ICC)  43.80  78.96       0         0\n",
              "51               DM Jones (AUS)  44.61  72.56       0         0\n",
              "52                DC Boon (AUS)  37.04  65.13       1         0\n",
              "56               RR Sarwan (WI)  42.67  75.74       0         0\n",
              "57         EJG Morgan (ENG/IRE)  38.73  88.62       2         0\n",
              "59              SR Watson (AUS)  40.54  90.44       2         0\n",
              "62               WJ Cronje (SA)  38.64  76.47       0         0\n",
              "63              MJ Guptill (NZ)  43.30  87.73       3         0\n",
              "64             MEK Hussey (AUS)  48.15  87.16       3         0\n",
              "65            RG Sharma (INDIA)  42.46  84.56       0         0\n",
              "66                IR Bell (ENG)  37.87  77.16       0         0\n",
              "67             A Jadeja (INDIA)  37.47  69.80       1         0\n",
              "68              DR Martyn (AUS)  40.80  77.73       0         0\n",
              "70            G Gambhir (INDIA)  39.68  85.25       0         0\n",
              "74            CG Greenidge (WI)  45.03  64.92       0         0\n",
              "75          Misbah-ul-Haq (PAK)  43.40  73.75       0         0\n",
              "77              A Symonds (AUS)  39.75  92.44       2         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uy6yKJix0Kwe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}