Vridhi_IPL_T20_Cricket_Analysis_Capstone_Project.ipynb

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      "name": "Vridhi IPL T20 Cricket Analysis - Capstone Project.ipynb",
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        "<a href=\"https://colab.research.google.com/github/Pradip240/IPL-T20-Cricket-Analysis/blob/main/Vridhi_IPL_T20_Cricket_Analysis_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## <b> Indian Premier League(IPL) is a professional Twenty20 cricket league in India contested during March or April and May of every year by eight teams representing eight different cities in India. The league was founded by the Board of Control for Cricket in India(BCCI) in 2008. </b>\n",
        "\n",
        "## <b> Explore and analyze the data to discover results and statistics for different teams playing in IPL. </b>"
      ]
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    {
      "cell_type": "markdown",
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        "id": "-0QRNTryyjur"
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      "source": [
        "### So what can we Expect at the end of the project:\r\n",
        "\r\n",
        "In order to understand our data, we can look at each of the colums and try to understand their meaning and to find that which sheet will help us to extract the relevance to the problem. I know this is time-consuming, but it will give us the flavour of our datasets.\r\n",
        "\r\n",
        "From the following Datasheets we extracted the following datas:\r\n",
        "\r\n",
        "1.Deliveries: This file helped us to extract the data that .which bowler delievered what type of ball in the specific over during the match\r\n",
        "\r\n",
        "2.Matches: This file extract the data related to which teams were playing in which city and which teams won against the other team with what no of runs and the date the match was played in presence of which umpires.\r\n",
        "\r\n",
        "3.Most run Average Runrate: This file describes the data that which playered played the number of balls and how many time they got out and the number of runs they scored and hence there average run rate for the season.\r\n",
        "\r\n",
        "4.Players: The given excel sheet describes the players related information like what country is the player from what is there batting hand and what is the bowling skill they have.\r\n",
        "\r\n",
        "5.Teams: Which are the teams who have participated in the IPL season.\r\n",
        "\r\n",
        "6.Teams_home_and_away: This shares the data related to the home wins and away wins of the teams participating and the percentage of the same.\r\n",
        "\r\n",
        "When we flaunt around this data sheets it helps us to find each of the following data in some or the other way, We at the end of our Project Expect the following Outputs:\r\n",
        "1. No of matches each season\r\n",
        "\r\n",
        "2. No of matches on a perticular Venue\r\n",
        "\r\n",
        "3. No of matches played by each team\r\n",
        "\r\n",
        "4. Number of wins per team\r\n",
        "\r\n",
        "5. Top players of the match\r\n",
        "\r\n",
        "6. How lucky are the toss wining team\r\n",
        "\r\n",
        "7. Batsman Analysis\r\n",
        "\r\n",
        "8. The Number of 4's Scored in a season\r\n",
        "\r\n",
        "9. The Number of 6's Scored in a season\r\n",
        "\r\n",
        "10. Total runs scored\r\n",
        "\r\n",
        "11. Number of half_centuries by each batsman\r\n",
        "\r\n",
        "12. Number of centuries by each batsman\r\n",
        "\r\n",
        "13. Comparison between the teams\r\n",
        "\r\n",
        "14. Batsman who scored the most number of dot balls.\r\n",
        "\r\n",
        "15. Most common dismisal type in IPL\r\n",
        "\r\n",
        "16. Bowler having more than 100 wickets in IPL\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hv0Yj2z4BtQ"
      },
      "source": [
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "import numpy as np\r\n",
        "from scipy.stats import norm\r\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dByMsuzT8Tnw"
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      "source": [
        ""
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      "outputs": []
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        "id": "k1mlIBi8xqOc",
        "outputId": "6fcd61ec-8c45-4595-f842-db5b03e37237"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "KoYCtZ7IyUAn"
      },
      "source": [
        "# Input file path \r\n",
        "path = '/content/drive/MyDrive/Capstone project /'\r\n",
        "# individual data frame\r\n",
        "players_df = pd.read_excel(path + 'Players.xlsx')\r\n",
        "deliveries_df = pd.read_csv(path + 'deliveries.csv')\r\n",
        "matches_df = pd.read_csv(path + 'matches.csv')\r\n",
        "teams_df = pd.read_csv(path + 'teams.csv')\r\n",
        "most_runs_average_strikerate_df = pd.read_csv(path + 'most_runs_average_strikerate.csv')\r\n",
        "teamwise_home_and_away_df = pd.read_csv(path + 'teamwise_home_and_away.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "Iscb0WLIz5iz",
        "outputId": "9808a913-34e5-4651-a7f7-6ffaa8f5e5d6"
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      "source": [
        "matches_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <th>player_of_match</th>\n",
              "      <th>venue</th>\n",
              "      <th>umpire1</th>\n",
              "      <th>umpire2</th>\n",
              "      <th>umpire3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>05-04-2017</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Pune</td>\n",
              "      <td>06-04-2017</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Rajkot</td>\n",
              "      <td>07-04-2017</td>\n",
              "      <td>Gujarat Lions</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Indore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>bat</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>KM Jadhav</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    Season       city  ...         umpire1        umpire2 umpire3\n",
              "0   1  IPL-2017  Hyderabad  ...     AY Dandekar       NJ Llong     NaN\n",
              "1   2  IPL-2017       Pune  ...  A Nand Kishore         S Ravi     NaN\n",
              "2   3  IPL-2017     Rajkot  ...     Nitin Menon      CK Nandan     NaN\n",
              "3   4  IPL-2017     Indore  ...    AK Chaudhary  C Shamshuddin     NaN\n",
              "4   5  IPL-2017  Bangalore  ...             NaN            NaN     NaN\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
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    {
      "cell_type": "code",
      "metadata": {
        "id": "1ABu8D093GlT"
      },
      "source": [
        "team_winner_df = matches_df['winner']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HDIxskf7Gzd",
        "outputId": "2c9556fc-ee52-4eb8-d7f0-ebe7f0bbcf12"
      },
      "source": [
        "team_loosing_df= matches_df[]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              Sunrisers Hyderabad\n",
              "1           Rising Pune Supergiant\n",
              "2            Kolkata Knight Riders\n",
              "3                  Kings XI Punjab\n",
              "4      Royal Challengers Bangalore\n",
              "                  ...             \n",
              "751                 Mumbai Indians\n",
              "752                 Mumbai Indians\n",
              "753                 Delhi Capitals\n",
              "754            Chennai Super Kings\n",
              "755                 Mumbai Indians\n",
              "Name: winner, Length: 756, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oof3ojXs7L7T"
      },
      "source": [
        "def the_lossing_team(team1,team2,winner):\r\n",
        "  if team1!=winner:\r\n",
        "    return team1\r\n",
        "  else:\r\n",
        "    return team2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "4aAfaz0hh6-z",
        "outputId": "7a6bac04-f079-45f0-b325-e49ec4263756"
      },
      "source": [
        "the_teamcomparison_df= matches_df.copy()\r\n",
        "the_teamcomparison_df['loosing team']=the_teamcomparison_df.apply(lambda x:the_lossing_team(x['team1'],x['team2'],x['winner']),axis=1)\r\n",
        "the_teamcomparison_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <th>player_of_match</th>\n",
              "      <th>venue</th>\n",
              "      <th>umpire1</th>\n",
              "      <th>umpire2</th>\n",
              "      <th>umpire3</th>\n",
              "      <th>loosing team</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>05-04-2017</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Pune</td>\n",
              "      <td>06-04-2017</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Rajkot</td>\n",
              "      <td>07-04-2017</td>\n",
              "      <td>Gujarat Lions</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gujarat Lions</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Indore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>bat</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>KM Jadhav</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    Season       city  ...        umpire2 umpire3                 loosing team\n",
              "0   1  IPL-2017  Hyderabad  ...       NJ Llong     NaN  Royal Challengers Bangalore\n",
              "1   2  IPL-2017       Pune  ...         S Ravi     NaN               Mumbai Indians\n",
              "2   3  IPL-2017     Rajkot  ...      CK Nandan     NaN                Gujarat Lions\n",
              "3   4  IPL-2017     Indore  ...  C Shamshuddin     NaN       Rising Pune Supergiant\n",
              "4   5  IPL-2017  Bangalore  ...            NaN     NaN             Delhi Daredevils\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1d8s1maVj5M3"
      },
      "source": [
        "the_teamcomparison_df=the_teamcomparison_df.groupby(['winner','loosing team'])['result'].count().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "l7nNn8VrkXFL",
        "outputId": "3223ab80-f4ea-4f94-9e83-54096734bebe"
      },
      "source": [
        "the_team_vs_team_df= teams_df.copy()\r\n",
        "the_team_vs_team_df.rename(columns={'team1':'teams'},inplace=True)\r\n",
        "the_team_vs_team_df"
      ],
      "execution_count": null,
      "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>teams</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pune Warriors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rajasthan Royals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kochi Tuskers Kerala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gujarat Lions</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Rising Pune Supergiants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Delhi Daredevils</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Deccan Chargers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Delhi Capitals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Mumbai Indians</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kings XI Punjab</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          teams\n",
              "0                 Pune Warriors\n",
              "1         Kolkata Knight Riders\n",
              "2              Rajasthan Royals\n",
              "3          Kochi Tuskers Kerala\n",
              "4                 Gujarat Lions\n",
              "5           Chennai Super Kings\n",
              "6       Rising Pune Supergiants\n",
              "7              Delhi Daredevils\n",
              "8               Deccan Chargers\n",
              "9                Delhi Capitals\n",
              "10               Mumbai Indians\n",
              "11          Sunrisers Hyderabad\n",
              "12       Rising Pune Supergiant\n",
              "13  Royal Challengers Bangalore\n",
              "14              Kings XI Punjab"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhrXSTObvRJc"
      },
      "source": [
        "teams_list= the_team_vs_team_df['teams']\r\n",
        "for team in teams_list:\r\n",
        "  the_team_vs_team_df[team]=0\r\n",
        "the_team_vs_team_df=the_team_vs_team_df.set_index('teams')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "U2rkvUoSypjh",
        "outputId": "57dad48a-7a08-4ecb-edea-ec439434872a"
      },
      "source": [
        "the_teamcomparison_df"
      ],
      "execution_count": null,
      "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>winner</th>\n",
              "      <th>loosing team</th>\n",
              "      <th>result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Deccan Chargers</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Delhi Capitals</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Kochi Tuskers Kerala</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Pune Warriors</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Rajasthan Royals</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Rising Pune Supergiants</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>140 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  winner                 loosing team  result\n",
              "0    Chennai Super Kings              Deccan Chargers       6\n",
              "1    Chennai Super Kings               Delhi Capitals       3\n",
              "2    Chennai Super Kings             Delhi Daredevils      12\n",
              "3    Chennai Super Kings              Kings XI Punjab      12\n",
              "4    Chennai Super Kings         Kochi Tuskers Kerala       1\n",
              "..                   ...                          ...     ...\n",
              "135  Sunrisers Hyderabad               Mumbai Indians       7\n",
              "136  Sunrisers Hyderabad                Pune Warriors       2\n",
              "137  Sunrisers Hyderabad             Rajasthan Royals       6\n",
              "138  Sunrisers Hyderabad      Rising Pune Supergiants       1\n",
              "139  Sunrisers Hyderabad  Royal Challengers Bangalore       8\n",
              "\n",
              "[140 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wL7SQlJawAYw"
      },
      "source": [
        "for value in range(len(the_teamcomparison_df)):\r\n",
        "  the_team_vs_team_df[the_teamcomparison_df.iloc[value]['winner']][the_teamcomparison_df.iloc[value]['loosing team']]=the_teamcomparison_df.iloc[value]['result']\r\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "vUXGu6KO27Vn",
        "outputId": "31ca15cb-0cb3-4d06-821b-00ece46d2ed4"
      },
      "source": [
        "the_team_vs_team_df"
      ],
      "execution_count": null,
      "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>Pune Warriors</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Kochi Tuskers Kerala</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Rising Pune Supergiants</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Deccan Chargers</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>teams</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pune Warriors</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kochi Tuskers Kerala</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rising Pune Supergiants</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deccan Chargers</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Pune Warriors  ...  Kings XI Punjab\n",
              "teams                                       ...                 \n",
              "Pune Warriors                            0  ...                3\n",
              "Kolkata Knight Riders                    1  ...                8\n",
              "Rajasthan Royals                         1  ...                9\n",
              "Kochi Tuskers Kerala                     1  ...                1\n",
              "Gujarat Lions                            0  ...                2\n",
              "Chennai Super Kings                      2  ...                9\n",
              "Rising Pune Supergiants                  0  ...                1\n",
              "Delhi Daredevils                         2  ...               13\n",
              "Deccan Chargers                          1  ...                7\n",
              "Delhi Capitals                           0  ...                1\n",
              "Mumbai Indians                           1  ...               11\n",
              "Sunrisers Hyderabad                      0  ...                4\n",
              "Rising Pune Supergiant                   0  ...                1\n",
              "Royal Challengers Bangalore              0  ...               12\n",
              "Kings XI Punjab                          3  ...                0\n",
              "\n",
              "[15 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "tRy9N8xa2VNk",
        "outputId": "733c6c1e-ed85-44bd-ef97-f92047017d45"
      },
      "source": [
        "fig, axis=plt.subplots(figsize=(10,8))\r\n",
        "sns.heatmap(the_team_vs_team_df,annot=True)\r\n",
        "axis.set_title(\"The Comparison between teams\",size=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'The Comparison between teams')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31fO13o5dA4R"
      },
      "source": [
        "To identify which Batsman Played the most number of dot balls"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENcfKWbJ3qcp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "b3cf87fa-0cfc-4975-bf66-36513b6ba8e4"
      },
      "source": [
        "Dot_balls_df = deliveries_df.loc[(deliveries_df['batsman_runs'] ==0)]\r\n",
        "Dot_balls_df.head()"
      ],
      "execution_count": null,
      "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>match_id</th>\n",
              "      <th>inning</th>\n",
              "      <th>batting_team</th>\n",
              "      <th>bowling_team</th>\n",
              "      <th>over</th>\n",
              "      <th>ball</th>\n",
              "      <th>batsman</th>\n",
              "      <th>non_striker</th>\n",
              "      <th>bowler</th>\n",
              "      <th>is_super_over</th>\n",
              "      <th>wide_runs</th>\n",
              "      <th>bye_runs</th>\n",
              "      <th>legbye_runs</th>\n",
              "      <th>noball_runs</th>\n",
              "      <th>penalty_runs</th>\n",
              "      <th>batsman_runs</th>\n",
              "      <th>extra_runs</th>\n",
              "      <th>total_runs</th>\n",
              "      <th>player_dismissed</th>\n",
              "      <th>dismissal_kind</th>\n",
              "      <th>fielder</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>TS Mills</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   match_id  inning  ... dismissal_kind fielder\n",
              "0         1       1  ...            NaN     NaN\n",
              "1         1       1  ...            NaN     NaN\n",
              "3         1       1  ...            NaN     NaN\n",
              "4         1       1  ...            NaN     NaN\n",
              "5         1       1  ...            NaN     NaN\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "94z1rn0levra",
        "outputId": "e0a3ba5a-33d7-4bb1-e2f2-9d90ebc1a029"
      },
      "source": [
        "Dot_balls_df1 = Dot_balls_df['batsman'].value_counts().reset_index()\r\n",
        "Dot_balls_df1.rename(columns={'index':'Batsman','batsman':'dot_balls'},inplace=True)\r\n",
        "Dot_balls_df1.head()"
      ],
      "execution_count": null,
      "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>Batsman</th>\n",
              "      <th>dot_balls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>V Kohli</td>\n",
              "      <td>1493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>1455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CH Gayle</td>\n",
              "      <td>1423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RG Sharma</td>\n",
              "      <td>1390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RV Uthappa</td>\n",
              "      <td>1382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Batsman  dot_balls\n",
              "0     V Kohli       1493\n",
              "1    S Dhawan       1455\n",
              "2    CH Gayle       1423\n",
              "3   RG Sharma       1390\n",
              "4  RV Uthappa       1382"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "sfRkbnFxf60b",
        "outputId": "1b91b38d-a11f-4306-e4b6-254752542323"
      },
      "source": [
        "players_dot_balls = Dot_balls_df1[:30]\r\n",
        "fig, axes = plt.subplots(figsize=(20,8))\r\n",
        "sns.set_style(\"darkgrid\")\r\n",
        "sns.barplot(x='Batsman', y='dot_balls',ax=axes,data=players_dot_balls, linewidth=2, edgecolor='0.3')\r\n",
        "axes.set_yticks(range(0,1500,100))\r\n",
        "axes.set_xlabel('Batsman', size=20)\r\n",
        "axes.set_ylabel('Number of Dot Balls', size=20)\r\n",
        "axes.set_title('Batsman with the most number of dot balls', size=30)\r\n",
        "for tick in axes.xaxis.get_major_ticks():\r\n",
        "    tick.label.set_fontsize(12)\r\n",
        "    tick.label.set_rotation(60)\r\n",
        "for tick in axes.yaxis.get_major_ticks():\r\n",
        "    tick.label.set_fontsize(12)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "eY4m_chIruC1",
        "outputId": "881bc867-d5e3-42a2-d356-9b262583f6b7"
      },
      "source": [
        "dismissal_df= deliveries_df.groupby(['dismissal_kind']).count()\r\n",
        "dismissal_df = dismissal_df['inning'].reset_index()\r\n",
        "dismissal_df"
      ],
      "execution_count": null,
      "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>dismissal_kind</th>\n",
              "      <th>inning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bowled</td>\n",
              "      <td>1581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>caught</td>\n",
              "      <td>5348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>caught and bowled</td>\n",
              "      <td>211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hit wicket</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>lbw</td>\n",
              "      <td>540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>obstructing the field</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>retired hurt</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>run out</td>\n",
              "      <td>852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>stumped</td>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          dismissal_kind  inning\n",
              "0                 bowled    1581\n",
              "1                 caught    5348\n",
              "2      caught and bowled     211\n",
              "3             hit wicket      10\n",
              "4                    lbw     540\n",
              "5  obstructing the field       2\n",
              "6           retired hurt      12\n",
              "7                run out     852\n",
              "8                stumped     278"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "mYa7iDfwvPpv",
        "outputId": "a1ba523a-644f-434c-b0c8-5357cc6f09f2"
      },
      "source": [
        "dismissal_df.rename(columns={'inning':'Count'},inplace=True)\r\n",
        "dismissal_df"
      ],
      "execution_count": null,
      "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>dismissal_kind</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bowled</td>\n",
              "      <td>1581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>caught</td>\n",
              "      <td>5348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>caught and bowled</td>\n",
              "      <td>211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hit wicket</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>lbw</td>\n",
              "      <td>540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>obstructing the field</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>retired hurt</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>run out</td>\n",
              "      <td>852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>stumped</td>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          dismissal_kind  Count\n",
              "0                 bowled   1581\n",
              "1                 caught   5348\n",
              "2      caught and bowled    211\n",
              "3             hit wicket     10\n",
              "4                    lbw    540\n",
              "5  obstructing the field      2\n",
              "6           retired hurt     12\n",
              "7                run out    852\n",
              "8                stumped    278"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "objqUQfRzNCO"
      },
      "source": [
        "# fig, axes = plt.subplots(figsize=(15,8))\r\n",
        "# sns.set_style(\"darkgrid\")\r\n",
        "# sns.barplot(y='dismissal_kind', x='Count',ax=axes,data=dismissal_df, linewidth=2, edgecolor='0.3')\r\n",
        "# axes.set_xticks(range(0,5500,500))\r\n",
        "# axes.set_xlabel('Type of Dismissal', size=20)\r\n",
        "# axes.set_ylabel('The Count', size=20)\r\n",
        "# axes.set_title('Dismissal Count', size=30)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "KQ6XOQu44nAR",
        "outputId": "f61d9dec-ee22-425f-d1fe-568eea78e2ac"
      },
      "source": [
        "plt.figure(figsize=(16,8))\r\n",
        "plt.pie(x=dismissal_df['Count'],autopct='%1.1f%%',startangle=180)\r\n",
        "plt.legend(labels=dismissal_df['dismissal_kind'],bbox_to_anchor=(1,1))\r\n",
        "plt.title('Most Comman Dismissal Types')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Most Comman Dismissal Types')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea05kgthLgrB"
      },
      "source": [
        "## Conclusion\r\n",
        "\r\n",
        "That's it! We are here at the end of our exercise.\r\n",
        "\r\n",
        "Throughout this Process we put in practice many of the strategies to get our desired output. We analysed each and every data sheets so as to get the desired outputs related to teams, there performance, the players and there performances, to find the best batsman the best bowlers the no of boundaries etc. we analysed which team players are the best for bowling and which are best for batting alone, we dealt with permutation and combinations to find out the desired outputs related to boundaries. That's a lot of work that Python helped us make easier.\r\n",
        "\r\n",
        "# Here we can conclude the following:\r\n",
        "\r\n",
        "1.The number of matches each season lies between 50 to 80 matches\r\n",
        "\r\n",
        "2.The most number of matches were hosted in Mumbai and the least were hosted in Venue Bloemfontein..\r\n",
        "\r\n",
        "3.The most number of players were from India and the least number of players were playing from the Netherlands.\r\n",
        "\r\n",
        "4.After winning the toss most of the teams choose to Field.\r\n",
        "\r\n",
        "5.The most number of matches were won by Mumbai Indians with around 109 wins.\r\n",
        "\r\n",
        "6.In team comparison we observed that Kolkata Knight Riders lose most of the matches against Mumbai Indians losing more than 19 matches.\r\n",
        "\r\n",
        "7.The batsman who played the most number of dot balls is Virat Kohli.\r\n",
        "\r\n",
        "8.The most Dismissal type in IPL is caught out.\r\n",
        "\r\n",
        "9.The most Half Centuries was scored by D.A.Warner.\r\n",
        "\r\n",
        "10.The Player who won most of the Man of The Match was Chris Gayle\r\n",
        "\r\n",
        "11.The average number of Four's and Six's per season lies somewhere around 1800 and 850 respectively.\r\n",
        "\r\n",
        "12.The most full centuries was scored by Chris Gayle.\r\n",
        "\r\n",
        "13.The most common bowling style is right-arm medium.\r\n",
        "\r\n",
        "14.Most of the batsman are right-handers\r\n",
        "\r\n",
        "15.V.Shewag have the highest strike rate.\r\n",
        "\r\n",
        "16.S.L.Malinga have the highest number of wickets in the IPL\r\n",
        "\r\n",
        "But whatever it is we cannot define which team will help us win because at the end the players here are not a machine and the performance of the players either improves by practice or decreases due to some other factors so we can only analyse the past data. Now, all we can do is analyse and see the performance to increase or decrease because Sports is something that doesn't only depend upon the players but also many other factors ex.Climatic Condition. So, we will see you in next IPL....\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93ufVR6QXg9N"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}