IPL_Data_Analysis_with_PySpark.ipynb

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      "source": [
        "### Section 01: Environment Setup"
      ],
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      "cell_type": "code",
      "source": [
        "# innstall java\n",
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "\n",
        "# install spark (change the version number if needed)\n",
        "!wget -q https://archive.apache.org/dist/spark/spark-3.0.0/spark-3.0.0-bin-hadoop3.2.tgz\n",
        "\n",
        "# unzip the spark file to the current folder\n",
        "!tar xf spark-3.0.0-bin-hadoop3.2.tgz\n",
        "\n",
        "# set your spark folder to your system path environment. \n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.0.0-bin-hadoop3.2\"\n",
        "\n",
        "\n",
        "# install findspark using pip\n",
        "!pip install -q findspark"
      ],
      "metadata": {
        "id": "S3j1SSMxv5m7"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import findspark\n",
        "findspark.init()\n",
        "findspark.add_packages('mysql:mysql-connector-java:8.0.11')"
      ],
      "metadata": {
        "id": "uJlcvq7Evd95"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pyspark.sql\n",
        "import pyspark\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder\\\n",
        "        .master(\"local\")\\\n",
        "        .appName(\"Colab\")\\\n",
        "        .config('spark.ui.port', '4050')\\\n",
        "        .getOrCreate()"
      ],
      "metadata": {
        "id": "rfjkpTWUwO7y"
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      "execution_count": 3,
      "outputs": []
    },
    {
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      "source": [
        "spark"
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        "id": "7Flmopi0wnYD",
        "outputId": "8bfdb9c5-5156-4db4-fa62-bc8d133c449b"
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      "execution_count": 4,
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            "text/plain": [
              "<pyspark.sql.session.SparkSession at 0x7ff28cf6ed90>"
            ],
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              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://d4fda983df32:4050\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.0.0</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>Colab</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
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      "source": [
        "ball_by_ball_df = spark.read.format(\"csv\") \\\n",
        "    .option(\"header\", True) \\\n",
        "    .load(\"/content/ipl_ball_by_ball.csv\")\n",
        "\n",
        "ipl_venue_df= spark.read.format(\"csv\") \\\n",
        "    .option(\"header\", True) \\\n",
        "    .load(\"/content/ipl_venue.csv\")\n",
        "\n",
        "ipl_matches_df = spark.read.format(\"csv\") \\\n",
        "    .option(\"header\", True) \\\n",
        "    .load(\"ipl_matches.csv\")"
      ],
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        "id": "L7oWqPnMN6qP"
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      "execution_count": 5,
      "outputs": []
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      "source": [
        "ball_by_ball_df.printSchema()"
      ],
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          "text": [
            "root\n",
            " |-- match_id: string (nullable = true)\n",
            " |-- inning: string (nullable = true)\n",
            " |-- overs: string (nullable = true)\n",
            " |-- ball: string (nullable = true)\n",
            " |-- batsman: string (nullable = true)\n",
            " |-- non_striker: string (nullable = true)\n",
            " |-- bowler: string (nullable = true)\n",
            " |-- batsman_runs: string (nullable = true)\n",
            " |-- extra_runs: string (nullable = true)\n",
            " |-- total_runs: string (nullable = true)\n",
            " |-- non_boundary: string (nullable = true)\n",
            " |-- is_wicket: string (nullable = true)\n",
            " |-- dismissal_kind: string (nullable = true)\n",
            " |-- player_dismissed: string (nullable = true)\n",
            " |-- fielder: string (nullable = true)\n",
            " |-- extras_type: string (nullable = true)\n",
            " |-- batting_team: string (nullable = true)\n",
            " |-- bowling_team: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "execution_count": 6
    },
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      "source": [
        "ipl_venue_df.printSchema()"
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        {
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          "text": [
            "root\n",
            " |-- venue_id: string (nullable = true)\n",
            " |-- venue: string (nullable = true)\n",
            " |-- city: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "execution_count": 7
    },
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      "source": [
        "ipl_matches_df.printSchema()"
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          "text": [
            "root\n",
            " |-- match_id: string (nullable = true)\n",
            " |-- date: string (nullable = true)\n",
            " |-- player_of_match: string (nullable = true)\n",
            " |-- venue_id: string (nullable = true)\n",
            " |-- neutral_venue: string (nullable = true)\n",
            " |-- team1: string (nullable = true)\n",
            " |-- team2: string (nullable = true)\n",
            " |-- toss_winner: string (nullable = true)\n",
            " |-- toss_decision: string (nullable = true)\n",
            " |-- winner: string (nullable = true)\n",
            " |-- result: string (nullable = true)\n",
            " |-- result_margin: string (nullable = true)\n",
            " |-- eliminator: string (nullable = true)\n",
            " |-- method: string (nullable = true)\n",
            " |-- umpire1: string (nullable = true)\n",
            " |-- umpire2: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating table from dataframe\n",
        "\n",
        "ipl_venue_df.createOrReplaceTempView(\"ipl_venue\")\n",
        "spark.sql(\"select * from ipl_venue\")\n",
        "ipl_matches_df.createOrReplaceTempView(\"ipl_matches\")\n",
        "spark.sql(\"select * from ipl_matches\")\n",
        "ball_by_ball_df.createOrReplaceTempView(\"ipl_ball_by_ball\")\n",
        "spark.sql(\"select * from ipl_ball_by_ball\")"
      ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[match_id: string, inning: string, overs: string, ball: string, batsman: string, non_striker: string, bowler: string, batsman_runs: string, extra_runs: string, total_runs: string, non_boundary: string, is_wicket: string, dismissal_kind: string, player_dismissed: string, fielder: string, extras_type: string, batting_team: string, bowling_team: string]"
            ]
          },
          "metadata": {},
          "execution_count": 9
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      "execution_count": 9
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      "cell_type": "markdown",
      "source": [
        "### Section 02: Analysis"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "##### Question A:\n",
        "Find the top 3 venues which hosted the most number of eliminator\n",
        "matches?"
      ],
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    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select v.venue, count(v.venue_id) venue_count from ipl_matches m \n",
        "join ipl_venue v on m.venue_id=v.venue_id\n",
        "where m.eliminator = 'Y'\n",
        "group by v.venue\n",
        "order by venue_count desc\n",
        "limit 3;\n",
        "\"\"\").show()"
      ],
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          "nuid": "7bdc79f4-1777-4787-bede-2338c5ba438f"
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        "outputId": "20879b88-d508-49ea-91bb-9a2ac5a5bdce",
        "colab": {
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        }
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-----------+\n",
            "|               venue|venue_count|\n",
            "+--------------------+-----------+\n",
            "|Dubai Internation...|          3|\n",
            "|Sheikh Zayed Stadium|          2|\n",
            "|Sardar Patel Stad...|          1|\n",
            "+--------------------+-----------+\n",
            "\n"
          ]
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question B: \n",
        "Return most number of catches taken by a player in IPL history?"
      ],
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    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select fielder, count(dismissal_kind) NoOfCatches from ipl_ball_by_ball\n",
        "where dismissal_kind = \"caught\"\n",
        "group by fielder\n",
        "order by NoOfCatches desc\n",
        "limit 1;\n",
        "\"\"\").show()"
      ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+-----------+\n",
            "|   fielder|NoOfCatches|\n",
            "+----------+-----------+\n",
            "|KD Karthik|        118|\n",
            "+----------+-----------+\n",
            "\n"
          ]
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question C:\n",
        "Write a query to return a report for highest wicket taker in matches\n",
        "which were affected by Duckworth-Lewis’s method (D/L method)."
      ],
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    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select m.match_id, bbb.bowler, count(bbb.bowler) NoOfWickets from ipl_ball_by_ball bbb\n",
        "join ipl_matches m on m.match_id = bbb.match_id\n",
        "where m.method = \"D/L\" and is_wicket = 1\n",
        "group by m.match_id, bbb.bowler\n",
        "order by NoOfWickets desc;\n",
        "\"\"\").show()"
      ],
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        "colab": {
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        }
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---------------+-----------+\n",
            "|match_id|         bowler|NoOfWickets|\n",
            "+--------+---------------+-----------+\n",
            "|  392183|     DL Vettori|          5|\n",
            "|  336025|        M Ntini|          4|\n",
            "|  980999|      YS Chahal|          4|\n",
            "|  980943|       AB Dinda|          4|\n",
            "| 1136566|       S Nadeem|          3|\n",
            "| 1136592|       TA Boult|          3|\n",
            "|  980997|       AB Dinda|          3|\n",
            "| 1136592|     JD Unadkat|          3|\n",
            "| 1082648|NM Coulter-Nile|          3|\n",
            "|  980997|        A Zampa|          3|\n",
            "|  980999|      SR Watson|          2|\n",
            "| 1136578|         AJ Tye|          2|\n",
            "|  733993|   MC Henriques|          2|\n",
            "|  829807|   MC Henriques|          2|\n",
            "|  980989|       R Ashwin|          2|\n",
            "|  980943|       MR Marsh|          2|\n",
            "|  829807|        D Wiese|          2|\n",
            "|  392186|     SC Ganguly|          2|\n",
            "|  501245|       L Balaji|          2|\n",
            "|  501255|      JH Kallis|          2|\n",
            "+--------+---------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "execution_count": 12
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question D: \n",
        "Write a query to return a report for highest strike rate by a batsman in non\n",
        "powerplay overs(7-20 overs)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select batsman, (sum(batsman_runs)/count(ball)*100) as strikeRate from ipl_ball_by_ball\n",
        "where (extras_type not in (\"wides\",\"noballs\")) \n",
        "and (overs >= 7 and overs <= 20)\n",
        "group by batsman\n",
        "order by strikeRate desc;\n",
        "\"\"\").show()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------+------------------+\n",
            "|     batsman|        strikeRate|\n",
            "+------------+------------------+\n",
            "|  B Stanlake|             250.0|\n",
            "|Kamran Akmal|213.88888888888889|\n",
            "|  ER Dwivedi|211.11111111111111|\n",
            "|    Umar Gul|205.26315789473685|\n",
            "| AC Blizzard|             200.0|\n",
            "|    RS Sodhi|             200.0|\n",
            "|    W Jaffer|             188.0|\n",
            "|   DJM Short|187.09677419354838|\n",
            "|   LJ Wright|             186.0|\n",
            "|  AD Russell|181.85185185185185|\n",
            "|Vishnu Vinod|             180.0|\n",
            "|    V Sehwag|179.92351816443596|\n",
            "|   KK Cooper|179.31034482758622|\n",
            "|      MM Ali|178.28947368421052|\n",
            "|  I Malhotra|             175.0|\n",
            "|TU Deshpande|             175.0|\n",
            "|    CH Gayle|172.80564263322884|\n",
            "|   K Gowtham|171.15384615384613|\n",
            "| Abdul Samad|170.76923076923077|\n",
            "| BCJ Cutting|170.50359712230215|\n",
            "+------------+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question E.\n",
        "Write a query to return a report for highest extra runs in a venue (stadium, city)."
      ],
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    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select v.venue, v.city, sum(bbb.extra_runs) extra_runs from ipl_ball_by_ball bbb\n",
        "inner join ipl_matches as m on bbb.match_id=m.match_id\n",
        "inner join ipl_venue as v on m.venue_id =v.venue_id\n",
        "group by venue, v.city\n",
        "order by extra_runs desc;\n",
        "\"\"\").show()"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+----------+\n",
            "|               venue|      city|extra_runs|\n",
            "+--------------------+----------+----------+\n",
            "|M.Chinnaswamy Sta...| Bengaluru|    1355.0|\n",
            "|    Wankhede Stadium|    Mumbai|    1232.0|\n",
            "|        Eden Gardens|   Kolkata|    1215.0|\n",
            "|    Feroz Shah Kotla|     Delhi|    1114.0|\n",
            "|MA Chidambaram St...|   Chennai|     905.0|\n",
            "|Rajiv Gandhi Inte...| Hyderabad|     900.0|\n",
            "|Sawai Mansingh St...|    Jaipur|     791.0|\n",
            "|Punjab Cricket As...|Chandigarh|     566.0|\n",
            "|Dubai Internation...|     Dubai|     492.0|\n",
            "|Sheikh Zayed Stadium| Abu Dhabi|     387.0|\n",
            "|Dr DY Patil Sport...|    Mumbai|     320.0|\n",
            "|Maharashtra Crick...|      Pune|     315.0|\n",
            "|Sharjah Cricket S...|   Sharjah|     287.0|\n",
            "|           Kingsmead|    Durban|     277.0|\n",
            "|Punjab Cricket As...|Chandigarh|     271.0|\n",
            "|   Brabourne Stadium|    Mumbai|     246.0|\n",
            "|Subrata Roy Sahar...|      Pune|     237.0|\n",
            "|     SuperSport Park| Centurion|     213.0|\n",
            "|Himachal Pradesh ...|Dharamsala|     177.0|\n",
            "|Sardar Patel Stad...| Ahmedabad|     175.0|\n",
            "+--------------------+----------+----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question F: \n",
        "Write a query to return a report for the cricketers with the most number of players of\n",
        "the match award in neutral venues"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "04dd0875-56d8-4089-a6f8-929ac6587552"
        },
        "id": "zN4HCSXdtkQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select m.player_of_match, count(m.player_of_match) Count_Of_Player_Of_Match from ipl_matches m\n",
        "join ipl_venue v on m.venue_id = v.venue_id\n",
        "where neutral_venue = 1\n",
        "group by m.player_of_match\n",
        "order by count(m.player_of_match) desc;\n",
        "\"\"\").show()"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f5620c85-b3d5-4af2-9aa0-d348c383c190"
        },
        "id": "cCQCC57JtkQ2",
        "outputId": "1db1b8df-a4fd-4547-aabd-ed6a7d1d1d25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------------+------------------------+\n",
            "| player_of_match|Count_Of_Player_Of_Match|\n",
            "+----------------+------------------------+\n",
            "|      GJ Maxwell|                       3|\n",
            "|       JH Kallis|                       3|\n",
            "|       JP Duminy|                       3|\n",
            "|       YK Pathan|                       3|\n",
            "|    Yuvraj Singh|                       2|\n",
            "|   KC Sangakkara|                       2|\n",
            "|  Sandeep Sharma|                       2|\n",
            "|    SR Tendulkar|                       2|\n",
            "|     LRPL Taylor|                       2|\n",
            "|        SK Raina|                       2|\n",
            "|  AB de Villiers|                       2|\n",
            "|       ML Hayden|                       2|\n",
            "|DPMD Jayawardene|                       2|\n",
            "|       RG Sharma|                       2|\n",
            "|        DR Smith|                       2|\n",
            "|  M Muralitharan|                       2|\n",
            "|       MK Pandey|                       2|\n",
            "|    AC Gilchrist|                       2|\n",
            "|     S Badrinath|                       1|\n",
            "|        V Sehwag|                       1|\n",
            "+----------------+------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question G: \n",
        "Write a query to get a list of top 10 players with the highest batting average"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "6d709ac0-8aee-4d3f-afa2-69bb1fa7b743"
        },
        "id": "Bp1Jv--xtkQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select Batsman_, sum(batsman_runs)/count(player_dismissed) as Average \n",
        "from\n",
        "(\n",
        " (select batsman as Batsman_,batsman_runs,player_dismissed from IPL_BALL_BY_BALL where player_dismissed != 'NA')\n",
        ")\n",
        "group by Batsman_ \n",
        "order by Average desc;\n",
        "\"\"\").show()"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "091a6df0-8db9-4554-9865-ccbaa4c8c41c"
        },
        "id": "f7A3V7qbtkQ3",
        "outputId": "8a3fdecc-336e-42ef-a7e1-5b03452748a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------+------------------+\n",
            "|       Batsman_|           Average|\n",
            "+---------------+------------------+\n",
            "|   CRD Fernando|               2.0|\n",
            "|     KP Appanna|               1.0|\n",
            "|    LH Ferguson|               1.0|\n",
            "|      AA Noffke|               1.0|\n",
            "|       CV Varun|               0.5|\n",
            "|      SB Jakati|               0.5|\n",
            "|     T Thushara|               0.5|\n",
            "|     WD Parnell|0.4444444444444444|\n",
            "|KB Arun Karthik|               0.4|\n",
            "|       AC Voges|0.3333333333333333|\n",
            "|        T Taibu|0.3333333333333333|\n",
            "|    DS Kulkarni|0.3333333333333333|\n",
            "|        T Kohli|0.3333333333333333|\n",
            "|    BMAJ Mendis|0.3333333333333333|\n",
            "|       Umar Gul|0.3333333333333333|\n",
            "|    RJ Peterson|0.3333333333333333|\n",
            "|    NL McCullum|0.3333333333333333|\n",
            "|          B Lee|               0.3|\n",
            "|       R Dhawan|0.2857142857142857|\n",
            "|       S Narwal|              0.25|\n",
            "+---------------+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question H:\n",
        "Write a query to find out who has officiated (as an umpire) the most\n",
        "number of matches in IPL"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "7968975f-8bf1-46ff-aae9-1f7ecda6007d"
        },
        "id": "sHdpIMf7tkQ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select umpire, count(*) as No_of_Matches\n",
        "from ((select umpire1 as umpire from ipl_matches) union all\n",
        "      (select umpire2 from ipl_matches)\n",
        "     ) ipl_matches\n",
        "group by umpire\n",
        "order by count(*) desc ;\n",
        "\"\"\").show()\n",
        "#Combing two similar columns by union and counting numbers on times they have officiated"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "995bf660-5405-43c6-a5b6-25a59034da36"
        },
        "id": "PWhqml1stkQ6",
        "outputId": "7d35d942-37e3-441f-c5d2-7b2d8c56c5d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-------------+\n",
            "|              umpire|No_of_Matches|\n",
            "+--------------------+-------------+\n",
            "|              S Ravi|          121|\n",
            "|     HDPK Dharmasena|           94|\n",
            "|        AK Chaudhary|           87|\n",
            "|       C Shamshuddin|           82|\n",
            "|           M Erasmus|           65|\n",
            "|           CK Nandan|           57|\n",
            "|         Nitin Menon|           57|\n",
            "|          SJA Taufel|           55|\n",
            "|           Asad Rauf|           51|\n",
            "|         VA Kulkarni|           50|\n",
            "|        BNJ Oxenford|           48|\n",
            "|         CB Gaffaney|           47|\n",
            "|           RJ Tucker|           46|\n",
            "|         BR Doctrove|           42|\n",
            "|         RE Koertzen|           41|\n",
            "|           Aleem Dar|           38|\n",
            "|           BF Bowden|           37|\n",
            "|            NJ Llong|           37|\n",
            "|KN Ananthapadmana...|           33|\n",
            "|      RK Illingworth|           31|\n",
            "+--------------------+-------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question I:  \n",
        "Find venue details of the match where V Kohli scored his highest individual runs in\n",
        "IPL."
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "437cd7ed-f7fe-4971-a191-f93a1b85bff9"
        },
        "id": "sUyieucctkQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spark.sql(\n",
        "\"\"\"\n",
        "select m.match_id, v.venue, v.city, sum(b.batsman_runs) TotalRuns from ipl_ball_by_ball as b\n",
        "inner join ipl_matches as m on b.match_id=m.match_id\n",
        "inner join ipl_venue as v on m.venue_id =v.venue_id\n",
        "where b.batsman= 'V Kohli'\n",
        "group by m.match_id, v.venue, v.city\n",
        "order by sum(b.batsman_runs) desc limit 1; \n",
        "\"\"\").show()"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "9f6b712e-5b48-49e1-b342-bc9a2f1c8673"
        },
        "id": "iSKOF3YetkQ-",
        "outputId": "54da1e9c-0c57-4210-af68-cb29bb3527e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+--------------------+---------+---------+\n",
            "|match_id|               venue|     city|TotalRuns|\n",
            "+--------+--------------------+---------+---------+\n",
            "|  980999|M.Chinnaswamy Sta...|Bengaluru|    113.0|\n",
            "+--------+--------------------+---------+---------+\n",
            "\n"
          ]
        }
      ],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Question J: \n",
        "Please analyze how winning/losing tosses can impact a match and it's result?\n",
        "(Bonus for Visualization here)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "7dd177f0-1af2-4484-a4b1-647901a5e8fc"
        },
        "id": "JEYjaMVYtkRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pyspark.sql.functions as F\n",
        "from pyspark.sql.functions import col\n",
        "\n",
        "# count data where match_winner = toss_winner\n",
        "team_wins_df = ipl_matches_df.filter(F.col('winner')==F.col('toss_winner')).groupBy('winner').agg(F.count('winner').alias('team_wins'))\n",
        "\n",
        "# count no of toss winner\n",
        "toss_wins_df = ipl_matches_df.groupBy('toss_winner').count().orderBy('toss_winner')\n",
        "#print(team_wins_df.show(2))\n",
        "#print(toss_wins_df.show(2))\n",
        "\n",
        "toss_wins_df = toss_wins_df.withColumnRenamed(\"toss_winner\", \"winner\").withColumnRenamed(\"count\", \"toss_wins\")\n",
        "\n",
        "# join the two dataframes (team_wins_df & toss_wins_df)\n",
        "team_toss_wins = team_wins_df.join(toss_wins_df.select('winner', 'toss_wins'), ['winner'])\n",
        "team_toss_wins = team_toss_wins.withColumnRenamed(\"winner\", \"teams\")\n"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "ee79e6ce-2223-4ef3-b715-3cb39a844d1e"
        },
        "id": "Kd86JnXstkRA"
      },
      "outputs": [],
      "execution_count": 19
    },
    {
      "cell_type": "code",
      "source": [
        "# converting spark dataframes to pandas dataframe\n",
        "\n",
        "team_toss_wins = team_toss_wins.toPandas()"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "383ce79b-b038-4ebc-8486-2f660b3e429e"
        },
        "id": "fxmdDaX8tkRC"
      },
      "outputs": [],
      "execution_count": 20
    },
    {
      "cell_type": "code",
      "source": [
        "# Graph with respect to match wins vs toss wins\n",
        "\n",
        "team_toss_wins = team_toss_wins.sort_values(by=['team_wins', 'toss_wins'], ascending = False)\n",
        "team_toss_wins.plot(x=\"teams\", y=[\"team_wins\", \"toss_wins\"], kind=\"bar\", title = \"Team & Toss Wins\")"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "4a015bdf-ff16-4dbf-89f5-a53760f2e0ef"
        },
        "id": "54ONkzNetkRD",
        "outputId": "3ec6b831-55b6-49d1-c055-72fd125575a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff285807210>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 21
    },
    {
      "cell_type": "code",
      "source": [
        "win_count = 0\n",
        "for value in ipl_matches_df.collect():\n",
        "    if(value['toss_winner']==value['winner']):\n",
        "        win_count += 1\n",
        "print(f'The number of times the team winning toss have won: {win_count}')\n",
        "prob = win_count/(ipl_matches_df.count())\n",
        "print('The probability of winning if won the toss: {:.2f}' .format(prob))"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "0ef26d18-b399-47ab-af33-42c6b595adb6"
        },
        "id": "N-OeQB_4tkRE",
        "outputId": "a50dce2a-6532-486e-b85a-7796ff0b548b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of times the team winning toss have won: 418\n",
            "The probability of winning if won the toss: 0.51\n"
          ]
        }
      ],
      "execution_count": 22
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the analysis we can say that :<br>\n",
        "1. The team with highest no. of toss wins has mostly won the match most of the times.<br>\n",
        "2. A team will have ***51%*** chance of winnning the match if it wins the toss"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "0fe2d18f-ad18-4aaa-923d-a29e71ed1115"
        },
        "id": "44Nxh9KatkRF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Section 03: Expose Data"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "48b4b4ca-d0c6-4844-b7a6-d1985364feb6"
        },
        "id": "S5Hk2M5WtkRG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "import pandas as pd\n",
        "\n",
        "# creating sqlite3 database\n",
        "con = sqlite3.connect('iplmatches.db')"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "1a83bb9d-bb14-47c9-b686-ed118cdcc0f9"
        },
        "id": "S5kU9bFttkRG"
      },
      "outputs": [],
      "execution_count": 23
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "\n",
        "df = pd.read_csv('/content/ipl_ball_by_ball.csv')\n",
        "\n",
        "# strip whitespace from headers\n",
        "df.columns = df.columns.str.strip()\n",
        "\n",
        "# drop data into database\n",
        "df.to_sql(\"ipl_ball_by_ball\", con)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "c3542d3f-224e-4cb0-abca-c5fd71771171"
        },
        "id": "YrAqZr6_tkRH"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "df = pd.read_csv('/content/ipl_venue.csv')\n",
        "\n",
        "# strip whitespace from headers\n",
        "df.columns = df.columns.str.strip()\n",
        "\n",
        "# drop data into database\n",
        "df.to_sql(\"ipl_venue\", con)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "ad18dbf7-7dd2-4925-98da-f501583cd929"
        },
        "id": "GGILlJg8tkRI"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "df = pd.read_csv('/content/ipl_matches.csv')\n",
        "\n",
        "# strip whitespace from headers\n",
        "df.columns = df.columns.str.strip()\n",
        "\n",
        "# drop data into database\n",
        "df.to_sql(\"ipl_matches\", con)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "bf2b7989-fcd2-48d1-a73c-8ae774234633"
        },
        "id": "TO-oKkd9tkRJ"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "class Database:\n",
        "    # constructor \n",
        "    def __init__(self):\n",
        "        self.db_name = \"iplmatches.db\"\n",
        "        \n",
        "    def get_conn(self):\n",
        "        try:\n",
        "            con = sqlite3.connect(self.db_name)\n",
        "            print(\"Database connected successfully\")\n",
        "            return con\n",
        "        except:\n",
        "            print(\"Unable to connect data base\")\n",
        "            \n",
        "    def get_status(self,con):\n",
        "        try:\n",
        "            con.cursor()\n",
        "            print(\"Database is connected\")\n",
        "        except Exception as ex:\n",
        "            print(\"Database is not connected\")\n",
        "            \n",
        "    def close_conn(self,con):\n",
        "        try:\n",
        "            con.close()\n",
        "            print(\"Database connection closed successfully\")\n",
        "        except:\n",
        "            print(\"Unable to close Database connection\")\n",
        "            \n",
        "    # Question(A)\n",
        "    def get_query1_result(self,con):\n",
        "        cur = con.execute(\"\"\"select v.venue, count(v.venue_id) venue_count from ipl_matches m \n",
        "                         join ipl_venue v on m.venue_id=v.venue_id\n",
        "                        where m.eliminator = 'Y'\n",
        "                        group by v.venue\n",
        "                        order by venue_count desc\n",
        "                            limit 3;\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "      \n",
        "    # Question (B)\n",
        "    def get_query2_result(self, con):\n",
        "        cur = con.execute(\"\"\"select fielder, count(dismissal_kind) NoOfCatches from ipl_ball_by_ball\n",
        "                           where dismissal_kind = \"caught\"\n",
        "                           group by fielder\n",
        "                           order by NoOfCatches desc\n",
        "                           limit 1;\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "    \n",
        "    # Question (C)\n",
        "    def get_query3_result(self, con):\n",
        "        cur=con.execute(\"\"\"select m.match_id, bbb.bowler, count(bbb.bowler) NoOfWickets from ipl_ball_by_ball bbb\n",
        "                           join ipl_matches m on m.match_id = bbb.match_id\n",
        "                           where m.method = \"D/L\" and is_wicket = 1\n",
        "                           group by m.match_id, bbb.bowler\n",
        "                           order by NoOfWickets desc;\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "    \n",
        "    # Question (D)\n",
        "    def get_query4_result(self, con):\n",
        "        cur=con.execute(\"\"\"select batsman, (sum(batsman_runs)/count(ball)*100) as strikeRate from ipl_ball_by_ball\n",
        "                           where (extras_type not in (\"wides\",\"noballs\")) \n",
        "                           and (overs >= 7 and overs <= 20)\n",
        "                           group by batsman\n",
        "                           order by strikeRate desc;\"\"\")\n",
        "\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "    \n",
        "    # Question (E)\n",
        "    def get_query5_result(self, con):\n",
        "        cur=con.execute(\"\"\"select v.venue, v.city, sum(bbb.extra_runs) extra_runs from ipl_ball_by_ball bbb\n",
        "                           inner join ipl_matches as m on bbb.match_id=m.match_id\n",
        "                           inner join ipl_venue as v on m.venue_id =v.venue_id\n",
        "                           group by venue, v.city\n",
        "                           order by extra_runs desc;\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "    \n",
        "    # Question (F)\n",
        "    def get_query6_result(self, con):\n",
        "        cur=con.execute(\"\"\"select m.player_of_match, count(m.player_of_match) from ipl_matches m\n",
        "                           join ipl_venue v on m.venue_id = v.venue_id\n",
        "                           where neutral_venue = 1\n",
        "                           group by m.player_of_match\n",
        "                           order by count(m.player_of_match) desc;\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "    \n",
        "    # Question (H)\n",
        "    def get_query7_result(self, con):\n",
        "        cur = con.execute(\"\"\"select umpire, count(*) as umpire_count from\n",
        "                               (select umpire1 as umpire from ipl_matches union all\n",
        "                                 select umpire2 from ipl_matches) ipl_matches\n",
        "                                    group by ipl_matches.umpire\n",
        "                                    order by count(*) desc\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n",
        "    \n",
        "    # Question (I)\n",
        "    def get_query8_result(self, con):\n",
        "        cur= con.execute(\"\"\"select m.match_id, v.venue, v.city, sum(b.batsman_runs) TotalRuns from ipl_ball_by_ball as b\n",
        "                            inner join ipl_matches as m on b.match_id=m.match_id\n",
        "                            inner join ipl_venue as v on m.venue_id =v.venue_id\n",
        "                            where b.batsman= 'V Kohli'\n",
        "                            group by m.match_id, v.venue, v.city\n",
        "                            order by sum(b.batsman_runs) desc limit 1;\"\"\")\n",
        "        desc = cur.description\n",
        "        column_names = [col[0] for col in desc]\n",
        "        data = [dict(zip(column_names, row))  \n",
        "        for row in cur.fetchall()]\n",
        "        return data\n"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "65f06eaf-f7ab-4ac4-8637-5b1e74866cc8"
        },
        "id": "ukrkKa4RtkR5"
      },
      "outputs": [],
      "execution_count": 29
    },
    {
      "cell_type": "code",
      "source": [
        "# creating class database object\n",
        "db = Database()\n",
        " \n",
        "# calling the instance method using the object db\n",
        "con = db.get_conn()"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "40a372e3-e3f0-44b7-9f4a-a18a116a4b1c"
        },
        "id": "Rul2Rm23tkR8",
        "outputId": "b9a9ed15-5989-4e5c-c03b-7fc76ab89b96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Database connected successfully\n"
          ]
        }
      ],
      "execution_count": 30
    },
    {
      "cell_type": "code",
      "source": [
        "qry1_result = db.get_query1_result(con);\n",
        "print(qry1_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "b6a0e400-a927-44da-a836-d20d0aa61fb6"
        },
        "id": "xtknA2kStkR9",
        "outputId": "e98854fc-7d8c-4ad9-caad-dbf47e062f9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'venue': 'Dubai International Cricket Stadium', 'venue_count': 3}, {'venue': 'Sheikh Zayed Stadium', 'venue_count': 2}, {'venue': 'Feroz Shah Kotla', 'venue_count': 1}]\n"
          ]
        }
      ],
      "execution_count": 31
    },
    {
      "cell_type": "code",
      "source": [
        "qry2_result = db.get_query2_result(con);\n",
        "print(qry2_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "eca61e1d-b4ed-43f7-9f7d-68da6dab2132"
        },
        "id": "3Tb9sXsWtkR9",
        "outputId": "bd7ac4f1-d300-485c-930c-002155eeef62",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'fielder': 'KD Karthik', 'NoOfCatches': 118}]\n"
          ]
        }
      ],
      "execution_count": 32
    },
    {
      "cell_type": "code",
      "source": [
        "qry3_result = db.get_query3_result(con);\n",
        "print(qry3_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "4105e732-ce97-44d3-b9bd-a72e3278d004"
        },
        "id": "TZUGEKADtkR-",
        "outputId": "8e33764f-b10d-413c-ee62-4098a8005a02",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'match_id': 392183, 'bowler': 'DL Vettori', 'NoOfWickets': 5}, {'match_id': 336025, 'bowler': 'M Ntini', 'NoOfWickets': 4}, {'match_id': 980943, 'bowler': 'AB Dinda', 'NoOfWickets': 4}, {'match_id': 980999, 'bowler': 'YS Chahal', 'NoOfWickets': 4}, {'match_id': 980997, 'bowler': 'A Zampa', 'NoOfWickets': 3}, {'match_id': 980997, 'bowler': 'AB Dinda', 'NoOfWickets': 3}, {'match_id': 1082648, 'bowler': 'NM Coulter-Nile', 'NoOfWickets': 3}, {'match_id': 1136566, 'bowler': 'S Nadeem', 'NoOfWickets': 3}, {'match_id': 1136592, 'bowler': 'JD Unadkat', 'NoOfWickets': 3}, {'match_id': 1136592, 'bowler': 'TA Boult', 'NoOfWickets': 3}, {'match_id': 336022, 'bowler': 'JR Hopes', 'NoOfWickets': 2}, {'match_id': 336022, 'bowler': 'RR Powar', 'NoOfWickets': 2}, {'match_id': 392186, 'bowler': 'SC Ganguly', 'NoOfWickets': 2}, {'match_id': 501215, 'bowler': 'NLTC Perera', 'NoOfWickets': 2}, {'match_id': 501215, 'bowler': 'R Ashwin', 'NoOfWickets': 2}, {'match_id': 501245, 'bowler': 'L Balaji', 'NoOfWickets': 2}, {'match_id': 501245, 'bowler': 'R Ashwin', 'NoOfWickets': 2}, {'match_id': 501255, 'bowler': 'CK Langeveldt', 'NoOfWickets': 2}, {'match_id': 501255, 'bowler': 'JH Kallis', 'NoOfWickets': 2}, {'match_id': 733993, 'bowler': 'A Mishra', 'NoOfWickets': 2}, {'match_id': 733993, 'bowler': 'DW Steyn', 'NoOfWickets': 2}, {'match_id': 733993, 'bowler': 'MC Henriques', 'NoOfWickets': 2}, {'match_id': 829743, 'bowler': 'M Morkel', 'NoOfWickets': 2}, {'match_id': 829807, 'bowler': 'D Wiese', 'NoOfWickets': 2}, {'match_id': 829807, 'bowler': 'MC Henriques', 'NoOfWickets': 2}, {'match_id': 980943, 'bowler': 'MR Marsh', 'NoOfWickets': 2}, {'match_id': 980989, 'bowler': 'PP Chawla', 'NoOfWickets': 2}, {'match_id': 980989, 'bowler': 'R Ashwin', 'NoOfWickets': 2}, {'match_id': 980999, 'bowler': 'S Aravind', 'NoOfWickets': 2}, {'match_id': 980999, 'bowler': 'SR Watson', 'NoOfWickets': 2}, {'match_id': 1082648, 'bowler': 'B Kumar', 'NoOfWickets': 2}, {'match_id': 1082648, 'bowler': 'UT Yadav', 'NoOfWickets': 2}, {'match_id': 1136566, 'bowler': 'B Laughlin', 'NoOfWickets': 2}, {'match_id': 1136578, 'bowler': 'AJ Tye', 'NoOfWickets': 2}, {'match_id': 1136578, 'bowler': 'BB Sran', 'NoOfWickets': 2}, {'match_id': 1136578, 'bowler': 'Mujeeb Ur Rahman', 'NoOfWickets': 2}, {'match_id': 336022, 'bowler': 'A Mishra', 'NoOfWickets': 1}, {'match_id': 336022, 'bowler': 'GD McGrath', 'NoOfWickets': 1}, {'match_id': 336022, 'bowler': 'PJ Sangwan', 'NoOfWickets': 1}, {'match_id': 336025, 'bowler': 'L Balaji', 'NoOfWickets': 1}, {'match_id': 392183, 'bowler': 'AM Salvi', 'NoOfWickets': 1}, {'match_id': 392183, 'bowler': 'VY Mahesh', 'NoOfWickets': 1}, {'match_id': 392186, 'bowler': 'AB Dinda', 'NoOfWickets': 1}, {'match_id': 392186, 'bowler': 'I Sharma', 'NoOfWickets': 1}, {'match_id': 392186, 'bowler': 'LR Shukla', 'NoOfWickets': 1}, {'match_id': 392186, 'bowler': 'MC Henriques', 'NoOfWickets': 1}, {'match_id': 392186, 'bowler': 'VS Malik', 'NoOfWickets': 1}, {'match_id': 392214, 'bowler': 'IK Pathan', 'NoOfWickets': 1}, {'match_id': 392214, 'bowler': 'JA Morkel', 'NoOfWickets': 1}, {'match_id': 392214, 'bowler': 'L Balaji', 'NoOfWickets': 1}, {'match_id': 392214, 'bowler': 'PP Chawla', 'NoOfWickets': 1}, {'match_id': 392214, 'bowler': 'S Sreesanth', 'NoOfWickets': 1}, {'match_id': 392214, 'bowler': 'SB Jakati', 'NoOfWickets': 1}, {'match_id': 501215, 'bowler': 'DE Bollinger', 'NoOfWickets': 1}, {'match_id': 501215, 'bowler': 'RP Singh', 'NoOfWickets': 1}, {'match_id': 501215, 'bowler': 'RV Gomez', 'NoOfWickets': 1}, {'match_id': 501245, 'bowler': 'Iqbal Abdulla', 'NoOfWickets': 1}, {'match_id': 501245, 'bowler': 'YK Pathan', 'NoOfWickets': 1}, {'match_id': 501255, 'bowler': 'B Lee', 'NoOfWickets': 1}, {'match_id': 501255, 'bowler': 'Iqbal Abdulla', 'NoOfWickets': 1}, {'match_id': 501255, 'bowler': 'JD Unadkat', 'NoOfWickets': 1}, {'match_id': 501255, 'bowler': 'L Balaji', 'NoOfWickets': 1}, {'match_id': 501255, 'bowler': 'S Aravind', 'NoOfWickets': 1}, {'match_id': 501255, 'bowler': 'Z Khan', 'NoOfWickets': 1}, {'match_id': 733993, 'bowler': 'B Kumar', 'NoOfWickets': 1}, {'match_id': 733993, 'bowler': 'R Shukla', 'NoOfWickets': 1}, {'match_id': 733993, 'bowler': 'S Kaul', 'NoOfWickets': 1}, {'match_id': 829743, 'bowler': 'AD Russell', 'NoOfWickets': 1}, {'match_id': 829743, 'bowler': 'B Kumar', 'NoOfWickets': 1}, {'match_id': 829743, 'bowler': 'MC Henriques', 'NoOfWickets': 1}, {'match_id': 829743, 'bowler': 'P Kumar', 'NoOfWickets': 1}, {'match_id': 829743, 'bowler': 'RS Bopara', 'NoOfWickets': 1}, {'match_id': 829743, 'bowler': 'UT Yadav', 'NoOfWickets': 1}, {'match_id': 829807, 'bowler': 'B Kumar', 'NoOfWickets': 1}, {'match_id': 829807, 'bowler': 'MA Starc', 'NoOfWickets': 1}, {'match_id': 829807, 'bowler': 'P Kumar', 'NoOfWickets': 1}, {'match_id': 980943, 'bowler': 'A Nehra', 'NoOfWickets': 1}, {'match_id': 980943, 'bowler': 'B Kumar', 'NoOfWickets': 1}, {'match_id': 980943, 'bowler': 'MC Henriques', 'NoOfWickets': 1}, {'match_id': 980943, 'bowler': 'NLTC Perera', 'NoOfWickets': 1}, {'match_id': 980943, 'bowler': 'R Ashwin', 'NoOfWickets': 1}, {'match_id': 980989, 'bowler': 'AD Russell', 'NoOfWickets': 1}, {'match_id': 980989, 'bowler': 'AS Rajpoot', 'NoOfWickets': 1}, {'match_id': 980989, 'bowler': 'SP Narine', 'NoOfWickets': 1}, {'match_id': 980989, 'bowler': 'Shakib Al Hasan', 'NoOfWickets': 1}, {'match_id': 980997, 'bowler': 'CH Morris', 'NoOfWickets': 1}, {'match_id': 980999, 'bowler': 'AR Patel', 'NoOfWickets': 1}, {'match_id': 980999, 'bowler': 'KJ Abbott', 'NoOfWickets': 1}, {'match_id': 980999, 'bowler': 'Sachin Baby', 'NoOfWickets': 1}, {'match_id': 980999, 'bowler': 'Sandeep Sharma', 'NoOfWickets': 1}, {'match_id': 1082648, 'bowler': 'CJ Jordan', 'NoOfWickets': 1}, {'match_id': 1082648, 'bowler': 'PP Chawla', 'NoOfWickets': 1}, {'match_id': 1082648, 'bowler': 'TA Boult', 'NoOfWickets': 1}, {'match_id': 1136566, 'bowler': 'JD Unadkat', 'NoOfWickets': 1}, {'match_id': 1136566, 'bowler': 'K Gowtham', 'NoOfWickets': 1}, {'match_id': 1136566, 'bowler': 'Mohammed Shami', 'NoOfWickets': 1}, {'match_id': 1136566, 'bowler': 'TA Boult', 'NoOfWickets': 1}, {'match_id': 1136578, 'bowler': 'R Ashwin', 'NoOfWickets': 1}, {'match_id': 1136578, 'bowler': 'SP Narine', 'NoOfWickets': 1}, {'match_id': 1136592, 'bowler': 'A Mishra', 'NoOfWickets': 1}, {'match_id': 1136592, 'bowler': 'DS Kulkarni', 'NoOfWickets': 1}, {'match_id': 1136592, 'bowler': 'GJ Maxwell', 'NoOfWickets': 1}, {'match_id': 1136592, 'bowler': 'JC Archer', 'NoOfWickets': 1}, {'match_id': 1136592, 'bowler': 'S Gopal', 'NoOfWickets': 1}]\n"
          ]
        }
      ],
      "execution_count": 33
    },
    {
      "cell_type": "code",
      "source": [
        "qry4_result = db.get_query4_result(con);\n",
        "print(qry4_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "45da2141-fffd-4079-917a-410a51191184"
        },
        "id": "WOonuaIAtkR_",
        "outputId": "b44b7e70-52ab-4c9a-a534-04fa9a803bd2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'batsman': 'A Ashish Reddy', 'strikeRate': 0}, {'batsman': 'A Chopra', 'strikeRate': 0}, {'batsman': 'A Flintoff', 'strikeRate': 0}, {'batsman': 'A Kumble', 'strikeRate': 0}, {'batsman': 'A Mishra', 'strikeRate': 0}, {'batsman': 'A Mithun', 'strikeRate': 0}, {'batsman': 'A Nehra', 'strikeRate': 0}, {'batsman': 'A Symonds', 'strikeRate': 0}, {'batsman': 'AA Bilakhia', 'strikeRate': 0}, {'batsman': 'AA Jhunjhunwala', 'strikeRate': 0}, {'batsman': 'AB Agarkar', 'strikeRate': 0}, {'batsman': 'AB Dinda', 'strikeRate': 0}, {'batsman': 'AB McDonald', 'strikeRate': 0}, {'batsman': 'AB de Villiers', 'strikeRate': 0}, {'batsman': 'AC Gilchrist', 'strikeRate': 0}, {'batsman': 'AC Voges', 'strikeRate': 0}, {'batsman': 'AD Mascarenhas', 'strikeRate': 0}, {'batsman': 'AD Mathews', 'strikeRate': 0}, {'batsman': 'AD Russell', 'strikeRate': 0}, {'batsman': 'AJ Finch', 'strikeRate': 0}, {'batsman': 'AJ Tye', 'strikeRate': 0}, {'batsman': 'AL Menaria', 'strikeRate': 0}, {'batsman': 'AM Nayar', 'strikeRate': 0}, {'batsman': 'AM Rahane', 'strikeRate': 0}, {'batsman': 'AN Ahmed', 'strikeRate': 0}, {'batsman': 'AP Majumdar', 'strikeRate': 0}, {'batsman': 'AR Patel', 'strikeRate': 0}, {'batsman': 'AS Rajpoot', 'strikeRate': 0}, {'batsman': 'AS Yadav', 'strikeRate': 0}, {'batsman': 'AT Carey', 'strikeRate': 0}, {'batsman': 'AT Rayudu', 'strikeRate': 0}, {'batsman': 'Abdul Samad', 'strikeRate': 0}, {'batsman': 'Abhishek Sharma', 'strikeRate': 0}, {'batsman': 'Ankit Soni', 'strikeRate': 0}, {'batsman': 'Anureet Singh', 'strikeRate': 0}, {'batsman': 'Azhar Mahmood', 'strikeRate': 0}, {'batsman': 'B Akhil', 'strikeRate': 0}, {'batsman': 'B Chipli', 'strikeRate': 0}, {'batsman': 'B Kumar', 'strikeRate': 0}, {'batsman': 'B Lee', 'strikeRate': 0}, {'batsman': 'BA Stokes', 'strikeRate': 0}, {'batsman': 'BB McCullum', 'strikeRate': 0}, {'batsman': 'BB Samantray', 'strikeRate': 0}, {'batsman': 'BB Sran', 'strikeRate': 0}, {'batsman': 'BCJ Cutting', 'strikeRate': 0}, {'batsman': 'BJ Hodge', 'strikeRate': 0}, {'batsman': 'BJ Rohrer', 'strikeRate': 0}, {'batsman': 'Basil Thampi', 'strikeRate': 0}, {'batsman': 'Bipul Sharma', 'strikeRate': 0}, {'batsman': 'C de Grandhomme', 'strikeRate': 0}, {'batsman': 'CA Lynn', 'strikeRate': 0}, {'batsman': 'CA Pujara', 'strikeRate': 0}, {'batsman': 'CH Gayle', 'strikeRate': 0}, {'batsman': 'CH Morris', 'strikeRate': 0}, {'batsman': 'CJ Anderson', 'strikeRate': 0}, {'batsman': 'CJ Ferguson', 'strikeRate': 0}, {'batsman': 'CJ Jordan', 'strikeRate': 0}, {'batsman': 'CK Kapugedera', 'strikeRate': 0}, {'batsman': 'CL White', 'strikeRate': 0}, {'batsman': 'CM Gautam', 'strikeRate': 0}, {'batsman': 'CR Brathwaite', 'strikeRate': 0}, {'batsman': 'CV Varun', 'strikeRate': 0}, {'batsman': 'D Kalyankrishna', 'strikeRate': 0}, {'batsman': 'D Padikkal', 'strikeRate': 0}, {'batsman': 'D Wiese', 'strikeRate': 0}, {'batsman': 'DA Miller', 'strikeRate': 0}, {'batsman': 'DA Warner', 'strikeRate': 0}, {'batsman': 'DB Das', 'strikeRate': 0}, {'batsman': 'DB Ravi Teja', 'strikeRate': 0}, {'batsman': 'DE Bollinger', 'strikeRate': 0}, {'batsman': 'DH Yagnik', 'strikeRate': 0}, {'batsman': 'DJ Bravo', 'strikeRate': 0}, {'batsman': 'DJ Hooda', 'strikeRate': 0}, {'batsman': 'DJ Hussey', 'strikeRate': 0}, {'batsman': 'DJ Thornely', 'strikeRate': 0}, {'batsman': 'DJG Sammy', 'strikeRate': 0}, {'batsman': 'DL Chahar', 'strikeRate': 0}, {'batsman': 'DL Vettori', 'strikeRate': 0}, {'batsman': 'DNT Zoysa', 'strikeRate': 0}, {'batsman': 'DPMD Jayawardene', 'strikeRate': 0}, {'batsman': 'DR Smith', 'strikeRate': 0}, {'batsman': 'DS Kulkarni', 'strikeRate': 0}, {'batsman': 'DT Christian', 'strikeRate': 0}, {'batsman': 'DW Steyn', 'strikeRate': 0}, {'batsman': 'E Lewis', 'strikeRate': 0}, {'batsman': 'EJG Morgan', 'strikeRate': 0}, {'batsman': 'ER Dwivedi', 'strikeRate': 0}, {'batsman': 'F du Plessis', 'strikeRate': 0}, {'batsman': 'FY Fazal', 'strikeRate': 0}, {'batsman': 'G Gambhir', 'strikeRate': 0}, {'batsman': 'GC Smith', 'strikeRate': 0}, {'batsman': 'GC Viljoen', 'strikeRate': 0}, {'batsman': 'GH Vihari', 'strikeRate': 0}, {'batsman': 'GJ Bailey', 'strikeRate': 0}, {'batsman': 'GJ Maxwell', 'strikeRate': 0}, {'batsman': 'Gurkeerat Singh', 'strikeRate': 0}, {'batsman': 'HH Gibbs', 'strikeRate': 0}, {'batsman': 'HH Pandya', 'strikeRate': 0}, {'batsman': 'HM Amla', 'strikeRate': 0}, {'batsman': 'HV Patel', 'strikeRate': 0}, {'batsman': 'Harbhajan Singh', 'strikeRate': 0}, {'batsman': 'I Sharma', 'strikeRate': 0}, {'batsman': 'IK Pathan', 'strikeRate': 0}, {'batsman': 'IR Jaggi', 'strikeRate': 0}, {'batsman': 'Imran Tahir', 'strikeRate': 0}, {'batsman': 'Iqbal Abdulla', 'strikeRate': 0}, {'batsman': 'Ishan Kishan', 'strikeRate': 0}, {'batsman': 'J Botha', 'strikeRate': 0}, {'batsman': 'J Suchith', 'strikeRate': 0}, {'batsman': 'JA Morkel', 'strikeRate': 0}, {'batsman': 'JC Archer', 'strikeRate': 0}, {'batsman': 'JC Buttler', 'strikeRate': 0}, {'batsman': 'JD Ryder', 'strikeRate': 0}, {'batsman': 'JD Unadkat', 'strikeRate': 0}, {'batsman': 'JDP Oram', 'strikeRate': 0}, {'batsman': 'JDS Neesham', 'strikeRate': 0}, {'batsman': 'JEC Franklin', 'strikeRate': 0}, {'batsman': 'JH Kallis', 'strikeRate': 0}, {'batsman': 'JJ Bumrah', 'strikeRate': 0}, {'batsman': 'JJ Roy', 'strikeRate': 0}, {'batsman': 'JM Bairstow', 'strikeRate': 0}, {'batsman': 'JO Holder', 'strikeRate': 0}, {'batsman': 'JP Duminy', 'strikeRate': 0}, {'batsman': 'JP Faulkner', 'strikeRate': 0}, {'batsman': 'JR Hopes', 'strikeRate': 0}, {'batsman': 'K Goel', 'strikeRate': 0}, {'batsman': 'K Gowtham', 'strikeRate': 0}, {'batsman': 'K Rabada', 'strikeRate': 0}, {'batsman': 'KA Pollard', 'strikeRate': 0}, {'batsman': 'KB Arun Karthik', 'strikeRate': 0}, {'batsman': 'KC Sangakkara', 'strikeRate': 0}, {'batsman': 'KD Karthik', 'strikeRate': 0}, {'batsman': 'KH Pandya', 'strikeRate': 0}, {'batsman': 'KK Nair', 'strikeRate': 0}, {'batsman': 'KL Rahul', 'strikeRate': 0}, {'batsman': 'KM Jadhav', 'strikeRate': 0}, {'batsman': 'KMA Paul', 'strikeRate': 0}, {'batsman': 'KMDN Kulasekara', 'strikeRate': 0}, {'batsman': 'KP Appanna', 'strikeRate': 0}, {'batsman': 'KP Pietersen', 'strikeRate': 0}, {'batsman': 'KS Williamson', 'strikeRate': 0}, {'batsman': 'KV Sharma', 'strikeRate': 0}, {'batsman': 'Kuldeep Yadav', 'strikeRate': 0}, {'batsman': 'L Balaji', 'strikeRate': 0}, {'batsman': 'LA Carseldine', 'strikeRate': 0}, {'batsman': 'LA Pomersbach', 'strikeRate': 0}, {'batsman': 'LH Ferguson', 'strikeRate': 0}, {'batsman': 'LMP Simmons', 'strikeRate': 0}, {'batsman': 'LPC Silva', 'strikeRate': 0}, {'batsman': 'LR Shukla', 'strikeRate': 0}, {'batsman': 'LRPL Taylor', 'strikeRate': 0}, {'batsman': 'M Kaif', 'strikeRate': 0}, {'batsman': 'M Kartik', 'strikeRate': 0}, {'batsman': 'M Manhas', 'strikeRate': 0}, {'batsman': 'M Markande', 'strikeRate': 0}, {'batsman': 'M Muralitharan', 'strikeRate': 0}, {'batsman': 'M Rawat', 'strikeRate': 0}, {'batsman': 'M Vijay', 'strikeRate': 0}, {'batsman': 'M Vohra', 'strikeRate': 0}, {'batsman': 'MA Agarwal', 'strikeRate': 0}, {'batsman': 'MA Khote', 'strikeRate': 0}, {'batsman': 'MA Starc', 'strikeRate': 0}, {'batsman': 'MC Henriques', 'strikeRate': 0}, {'batsman': 'MC Juneja', 'strikeRate': 0}, {'batsman': 'MEK Hussey', 'strikeRate': 0}, {'batsman': 'MF Maharoof', 'strikeRate': 0}, {'batsman': 'MG Johnson', 'strikeRate': 0}, {'batsman': 'MJ Guptill', 'strikeRate': 0}, {'batsman': 'MJ McClenaghan', 'strikeRate': 0}, {'batsman': 'MK Lomror', 'strikeRate': 0}, {'batsman': 'MK Pandey', 'strikeRate': 0}, {'batsman': 'MK Tiwary', 'strikeRate': 0}, {'batsman': 'ML Hayden', 'strikeRate': 0}, {'batsman': 'MM Ali', 'strikeRate': 0}, {'batsman': 'MM Patel', 'strikeRate': 0}, {'batsman': 'MM Sharma', 'strikeRate': 0}, {'batsman': 'MN Samuels', 'strikeRate': 0}, {'batsman': 'MN van Wyk', 'strikeRate': 0}, {'batsman': 'MP Stoinis', 'strikeRate': 0}, {'batsman': 'MR Marsh', 'strikeRate': 0}, {'batsman': 'MS Bisla', 'strikeRate': 0}, {'batsman': 'MS Dhoni', 'strikeRate': 0}, {'batsman': 'MS Gony', 'strikeRate': 0}, {'batsman': 'MV Boucher', 'strikeRate': 0}, {'batsman': 'Mandeep Singh', 'strikeRate': 0}, {'batsman': 'Mohammad Hafeez', 'strikeRate': 0}, {'batsman': 'Mohammad Nabi', 'strikeRate': 0}, {'batsman': 'Mohammed Shami', 'strikeRate': 0}, {'batsman': 'Mohammed Siraj', 'strikeRate': 0}, {'batsman': 'Mujeeb Ur Rahman', 'strikeRate': 0}, {'batsman': 'N Pooran', 'strikeRate': 0}, {'batsman': 'N Rana', 'strikeRate': 0}, {'batsman': 'NL McCullum', 'strikeRate': 0}, {'batsman': 'NLTC Perera', 'strikeRate': 0}, {'batsman': 'NS Naik', 'strikeRate': 0}, {'batsman': 'NV Ojha', 'strikeRate': 0}, {'batsman': 'Niraj Patel', 'strikeRate': 0}, {'batsman': 'OA Shah', 'strikeRate': 0}, {'batsman': 'P Dogra', 'strikeRate': 0}, {'batsman': 'P Dubey', 'strikeRate': 0}, {'batsman': 'P Kumar', 'strikeRate': 0}, {'batsman': 'P Negi', 'strikeRate': 0}, {'batsman': 'PA Patel', 'strikeRate': 0}, {'batsman': 'PC Valthaty', 'strikeRate': 0}, {'batsman': 'PD Collingwood', 'strikeRate': 0}, {'batsman': 'PJ Cummins', 'strikeRate': 0}, {'batsman': 'PJ Sangwan', 'strikeRate': 0}, {'batsman': 'PK Garg', 'strikeRate': 0}, {'batsman': 'PP Chawla', 'strikeRate': 0}, {'batsman': 'PP Ojha', 'strikeRate': 0}, {'batsman': 'PP Shaw', 'strikeRate': 0}, {'batsman': 'PR Shah', 'strikeRate': 0}, {'batsman': 'PV Tambe', 'strikeRate': 0}, {'batsman': 'Parvez Rasool', 'strikeRate': 0}, {'batsman': 'Q de Kock', 'strikeRate': 0}, {'batsman': 'R Ashwin', 'strikeRate': 0}, {'batsman': 'R Bhatia', 'strikeRate': 0}, {'batsman': 'R Dhawan', 'strikeRate': 0}, {'batsman': 'R Dravid', 'strikeRate': 0}, {'batsman': 'R McLaren', 'strikeRate': 0}, {'batsman': 'R Parag', 'strikeRate': 0}, {'batsman': 'R Rampaul', 'strikeRate': 0}, {'batsman': 'R Sathish', 'strikeRate': 0}, {'batsman': 'R Sharma', 'strikeRate': 0}, {'batsman': 'R Tewatia', 'strikeRate': 0}, {'batsman': 'R Vinay Kumar', 'strikeRate': 0}, {'batsman': 'RA Jadeja', 'strikeRate': 0}, {'batsman': 'RA Tripathi', 'strikeRate': 0}, {'batsman': 'RD Gaikwad', 'strikeRate': 0}, {'batsman': 'RE Levi', 'strikeRate': 0}, {'batsman': 'RE van der Merwe', 'strikeRate': 0}, {'batsman': 'RG Sharma', 'strikeRate': 0}, {'batsman': 'RJ Harris', 'strikeRate': 0}, {'batsman': 'RJ Peterson', 'strikeRate': 0}, {'batsman': 'RJ Quiney', 'strikeRate': 0}, {'batsman': 'RN ten Doeschate', 'strikeRate': 0}, {'batsman': 'RP Singh', 'strikeRate': 0}, {'batsman': 'RR Pant', 'strikeRate': 0}, {'batsman': 'RR Powar', 'strikeRate': 0}, {'batsman': 'RR Sarwan', 'strikeRate': 0}, {'batsman': 'RS Bopara', 'strikeRate': 0}, {'batsman': 'RT Ponting', 'strikeRate': 0}, {'batsman': 'RV Gomez', 'strikeRate': 0}, {'batsman': 'RV Uthappa', 'strikeRate': 0}, {'batsman': 'Rashid Khan', 'strikeRate': 0}, {'batsman': 'S Anirudha', 'strikeRate': 0}, {'batsman': 'S Aravind', 'strikeRate': 0}, {'batsman': 'S Badrinath', 'strikeRate': 0}, {'batsman': 'S Dhawan', 'strikeRate': 0}, {'batsman': 'S Dube', 'strikeRate': 0}, {'batsman': 'S Gopal', 'strikeRate': 0}, {'batsman': 'S Nadeem', 'strikeRate': 0}, {'batsman': 'S Rana', 'strikeRate': 0}, {'batsman': 'S Sohal', 'strikeRate': 0}, {'batsman': 'S Sreesanth', 'strikeRate': 0}, {'batsman': 'S Sriram', 'strikeRate': 0}, {'batsman': 'SA Abbott', 'strikeRate': 0}, {'batsman': 'SA Asnodkar', 'strikeRate': 0}, {'batsman': 'SA Yadav', 'strikeRate': 0}, {'batsman': 'SB Bangar', 'strikeRate': 0}, {'batsman': 'SB Styris', 'strikeRate': 0}, {'batsman': 'SC Ganguly', 'strikeRate': 0}, {'batsman': 'SD Chitnis', 'strikeRate': 0}, {'batsman': 'SE Marsh', 'strikeRate': 0}, {'batsman': 'SE Rutherford', 'strikeRate': 0}, {'batsman': 'SK Raina', 'strikeRate': 0}, {'batsman': 'SK Warne', 'strikeRate': 0}, {'batsman': 'SL Malinga', 'strikeRate': 0}, {'batsman': 'SM Harwood', 'strikeRate': 0}, {'batsman': 'SM Katich', 'strikeRate': 0}, {'batsman': 'SM Pollock', 'strikeRate': 0}, {'batsman': 'SN Khan', 'strikeRate': 0}, {'batsman': 'SO Hetmyer', 'strikeRate': 0}, {'batsman': 'SP Fleming', 'strikeRate': 0}, {'batsman': 'SP Goswami', 'strikeRate': 0}, {'batsman': 'SP Narine', 'strikeRate': 0}, {'batsman': 'SPD Smith', 'strikeRate': 0}, {'batsman': 'SR Tendulkar', 'strikeRate': 0}, {'batsman': 'SR Watson', 'strikeRate': 0}, {'batsman': 'SS Iyer', 'strikeRate': 0}, {'batsman': 'SS Tiwary', 'strikeRate': 0}, {'batsman': 'ST Jayasuriya', 'strikeRate': 0}, {'batsman': 'STR Binny', 'strikeRate': 0}, {'batsman': 'SV Samson', 'strikeRate': 0}, {'batsman': 'SW Billings', 'strikeRate': 0}, {'batsman': 'Sachin Baby', 'strikeRate': 0}, {'batsman': 'Salman Butt', 'strikeRate': 0}, {'batsman': 'Sandeep Sharma', 'strikeRate': 0}, {'batsman': 'Shakib Al Hasan', 'strikeRate': 0}, {'batsman': 'Shivam Mavi', 'strikeRate': 0}, {'batsman': 'Shubman Gill', 'strikeRate': 0}, {'batsman': 'Sunny Singh', 'strikeRate': 0}, {'batsman': 'TK Curran', 'strikeRate': 0}, {'batsman': 'TL Suman', 'strikeRate': 0}, {'batsman': 'TM Dilshan', 'strikeRate': 0}, {'batsman': 'TM Head', 'strikeRate': 0}, {'batsman': 'TR Birt', 'strikeRate': 0}, {'batsman': 'TS Mills', 'strikeRate': 0}, {'batsman': 'UBT Chand', 'strikeRate': 0}, {'batsman': 'UT Yadav', 'strikeRate': 0}, {'batsman': 'Umar Gul', 'strikeRate': 0}, {'batsman': 'V Kohli', 'strikeRate': 0}, {'batsman': 'V Sehwag', 'strikeRate': 0}, {'batsman': 'V Shankar', 'strikeRate': 0}, {'batsman': 'VRV Singh', 'strikeRate': 0}, {'batsman': 'VS Malik', 'strikeRate': 0}, {'batsman': 'VVS Laxman', 'strikeRate': 0}, {'batsman': 'VY Mahesh', 'strikeRate': 0}, {'batsman': 'W Jaffer', 'strikeRate': 0}, {'batsman': 'WA Mota', 'strikeRate': 0}, {'batsman': 'WD Parnell', 'strikeRate': 0}, {'batsman': 'WP Saha', 'strikeRate': 0}, {'batsman': 'WPUJC Vaas', 'strikeRate': 0}, {'batsman': 'Washington Sundar', 'strikeRate': 0}, {'batsman': 'Y Nagar', 'strikeRate': 0}, {'batsman': 'Y Venugopal Rao', 'strikeRate': 0}, {'batsman': 'YA Abdulla', 'strikeRate': 0}, {'batsman': 'YK Pathan', 'strikeRate': 0}, {'batsman': 'YS Chahal', 'strikeRate': 0}, {'batsman': 'YV Takawale', 'strikeRate': 0}, {'batsman': 'Yuvraj Singh', 'strikeRate': 0}, {'batsman': 'Z Khan', 'strikeRate': 0}]\n"
          ]
        }
      ],
      "execution_count": 34
    },
    {
      "cell_type": "code",
      "source": [
        "qry5_result = db.get_query5_result(con);\n",
        "print(qry5_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "92b61f2e-fa10-412d-9fb4-8121f687a177"
        },
        "id": "PEKbn8YNtkSA",
        "outputId": "3ed981eb-91ad-4417-b92e-3c3fa61c0b7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'venue': 'M.Chinnaswamy Stadium', 'city': 'Bengaluru', 'extra_runs': 1355}, {'venue': 'Wankhede Stadium', 'city': 'Mumbai', 'extra_runs': 1232}, {'venue': 'Eden Gardens', 'city': 'Kolkata', 'extra_runs': 1215}, {'venue': 'Feroz Shah Kotla', 'city': 'Delhi', 'extra_runs': 1114}, {'venue': 'MA Chidambaram Stadium, Chepauk', 'city': 'Chennai', 'extra_runs': 905}, {'venue': 'Rajiv Gandhi International Stadium, Uppal', 'city': 'Hyderabad', 'extra_runs': 900}, {'venue': 'Sawai Mansingh Stadium', 'city': 'Jaipur', 'extra_runs': 791}, {'venue': 'Punjab Cricket Association Stadium, Mohali', 'city': 'Chandigarh', 'extra_runs': 566}, {'venue': 'Dubai International Cricket Stadium', 'city': 'Dubai', 'extra_runs': 492}, {'venue': 'Sheikh Zayed Stadium', 'city': 'Abu Dhabi', 'extra_runs': 387}, {'venue': 'Dr DY Patil Sports Academy', 'city': 'Mumbai', 'extra_runs': 320}, {'venue': 'Maharashtra Cricket Association Stadium', 'city': 'Pune', 'extra_runs': 315}, {'venue': 'Sharjah Cricket Stadium', 'city': 'Sharjah', 'extra_runs': 287}, {'venue': 'Kingsmead', 'city': 'Durban', 'extra_runs': 277}, {'venue': 'Punjab Cricket Association IS Bindra Stadium, Mohali', 'city': 'Chandigarh', 'extra_runs': 271}, {'venue': 'Brabourne Stadium', 'city': 'Mumbai', 'extra_runs': 246}, {'venue': 'Subrata Roy Sahara Stadium', 'city': 'Pune', 'extra_runs': 237}, {'venue': 'SuperSport Park', 'city': 'Centurion', 'extra_runs': 213}, {'venue': 'Himachal Pradesh Cricket Association Stadium', 'city': 'Dharamsala', 'extra_runs': 177}, {'venue': 'Sardar Patel Stadium, Motera', 'city': 'Ahmedabad', 'extra_runs': 175}, {'venue': 'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium', 'city': 'Visakhapatnam', 'extra_runs': 161}, {'venue': 'Holkar Cricket Stadium', 'city': 'Indore', 'extra_runs': 137}, {'venue': 'Saurashtra Cricket Association Stadium', 'city': 'Rajkot', 'extra_runs': 131}, {'venue': \"St George's Park\", 'city': 'Port Elizabeth', 'extra_runs': 129}, {'venue': 'Newlands', 'city': 'Cape Town', 'extra_runs': 112}, {'venue': 'New Wanderers Stadium', 'city': 'Johannesburg', 'extra_runs': 108}, {'venue': 'JSCA International Stadium Complex', 'city': 'Ranchi', 'extra_runs': 100}, {'venue': 'Barabati Stadium', 'city': 'Cuttack', 'extra_runs': 83}, {'venue': 'Nehru Stadium', 'city': 'Kochi', 'extra_runs': 77}, {'venue': 'Shaheed Veer Narayan Singh International Stadium', 'city': 'Raipur', 'extra_runs': 77}, {'venue': 'Green Park', 'city': 'Kanpur', 'extra_runs': 62}, {'venue': 'Vidarbha Cricket Association Stadium, Jamtha', 'city': 'Nagpur', 'extra_runs': 60}, {'venue': 'Buffalo Park', 'city': 'East London', 'extra_runs': 57}, {'venue': 'OUTsurance Oval', 'city': 'Bloemfontein', 'extra_runs': 41}, {'venue': 'De Beers Diamond Oval', 'city': 'Kimberley', 'extra_runs': 39}]\n"
          ]
        }
      ],
      "execution_count": 35
    },
    {
      "cell_type": "code",
      "source": [
        "qry6_result = db.get_query6_result(con);\n",
        "print(qry6_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "21a3549d-1f09-4d41-8323-21243e9c5f1e"
        },
        "id": "e0tOBeDNtkSA",
        "outputId": "5235da49-a9f6-43f0-e08b-5e7d7b5114b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'player_of_match': 'GJ Maxwell', 'count(m.player_of_match)': 3}, {'player_of_match': 'JH Kallis', 'count(m.player_of_match)': 3}, {'player_of_match': 'JP Duminy', 'count(m.player_of_match)': 3}, {'player_of_match': 'YK Pathan', 'count(m.player_of_match)': 3}, {'player_of_match': 'AB de Villiers', 'count(m.player_of_match)': 2}, {'player_of_match': 'AC Gilchrist', 'count(m.player_of_match)': 2}, {'player_of_match': 'DPMD Jayawardene', 'count(m.player_of_match)': 2}, {'player_of_match': 'DR Smith', 'count(m.player_of_match)': 2}, {'player_of_match': 'KC Sangakkara', 'count(m.player_of_match)': 2}, {'player_of_match': 'LRPL Taylor', 'count(m.player_of_match)': 2}, {'player_of_match': 'M Muralitharan', 'count(m.player_of_match)': 2}, {'player_of_match': 'MK Pandey', 'count(m.player_of_match)': 2}, {'player_of_match': 'ML Hayden', 'count(m.player_of_match)': 2}, {'player_of_match': 'RG Sharma', 'count(m.player_of_match)': 2}, {'player_of_match': 'SK Raina', 'count(m.player_of_match)': 2}, {'player_of_match': 'SR Tendulkar', 'count(m.player_of_match)': 2}, {'player_of_match': 'Sandeep Sharma', 'count(m.player_of_match)': 2}, {'player_of_match': 'Yuvraj Singh', 'count(m.player_of_match)': 2}, {'player_of_match': 'A Kumble', 'count(m.player_of_match)': 1}, {'player_of_match': 'A Mishra', 'count(m.player_of_match)': 1}, {'player_of_match': 'A Nehra', 'count(m.player_of_match)': 1}, {'player_of_match': 'A Singh', 'count(m.player_of_match)': 1}, {'player_of_match': 'AJ Finch', 'count(m.player_of_match)': 1}, {'player_of_match': 'AM Rahane', 'count(m.player_of_match)': 1}, {'player_of_match': 'B Kumar', 'count(m.player_of_match)': 1}, {'player_of_match': 'B Lee', 'count(m.player_of_match)': 1}, {'player_of_match': 'BJ Hodge', 'count(m.player_of_match)': 1}, {'player_of_match': 'CA Lynn', 'count(m.player_of_match)': 1}, {'player_of_match': 'CH Gayle', 'count(m.player_of_match)': 1}, {'player_of_match': 'DL Vettori', 'count(m.player_of_match)': 1}, {'player_of_match': 'DP Nannes', 'count(m.player_of_match)': 1}, {'player_of_match': 'G Gambhir', 'count(m.player_of_match)': 1}, {'player_of_match': 'GC Smith', 'count(m.player_of_match)': 1}, {'player_of_match': 'HH Gibbs', 'count(m.player_of_match)': 1}, {'player_of_match': 'Harbhajan Singh', 'count(m.player_of_match)': 1}, {'player_of_match': 'JP Faulkner', 'count(m.player_of_match)': 1}, {'player_of_match': 'LR Shukla', 'count(m.player_of_match)': 1}, {'player_of_match': 'M Vijay', 'count(m.player_of_match)': 1}, {'player_of_match': 'MM Sharma', 'count(m.player_of_match)': 1}, {'player_of_match': 'MS Dhoni', 'count(m.player_of_match)': 1}, {'player_of_match': 'MV Boucher', 'count(m.player_of_match)': 1}, {'player_of_match': 'PA Patel', 'count(m.player_of_match)': 1}, {'player_of_match': 'PP Ojha', 'count(m.player_of_match)': 1}, {'player_of_match': 'PV Tambe', 'count(m.player_of_match)': 1}, {'player_of_match': 'R Bhatia', 'count(m.player_of_match)': 1}, {'player_of_match': 'R Dravid', 'count(m.player_of_match)': 1}, {'player_of_match': 'RA Jadeja', 'count(m.player_of_match)': 1}, {'player_of_match': 'RP Singh', 'count(m.player_of_match)': 1}, {'player_of_match': 'RS Bopara', 'count(m.player_of_match)': 1}, {'player_of_match': 'S Badrinath', 'count(m.player_of_match)': 1}, {'player_of_match': 'SB Jakati', 'count(m.player_of_match)': 1}, {'player_of_match': 'SK Warne', 'count(m.player_of_match)': 1}, {'player_of_match': 'TM Dilshan', 'count(m.player_of_match)': 1}, {'player_of_match': 'V Sehwag', 'count(m.player_of_match)': 1}, {'player_of_match': 'YS Chahal', 'count(m.player_of_match)': 1}]\n"
          ]
        }
      ],
      "execution_count": 36
    },
    {
      "cell_type": "code",
      "source": [
        "qry7_result = db.get_query7_result(con);\n",
        "print(qry7_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "8e76ce70-f49f-4bf0-88e0-8e1db1ffc39f"
        },
        "id": "RLn9YSw_tkSB",
        "outputId": "18c995c9-5e5b-46a0-ccff-dbbee9dbed14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'umpire': 'S Ravi', 'umpire_count': 121}, {'umpire': 'HDPK Dharmasena', 'umpire_count': 94}, {'umpire': 'AK Chaudhary', 'umpire_count': 87}, {'umpire': 'C Shamshuddin', 'umpire_count': 82}, {'umpire': 'M Erasmus', 'umpire_count': 65}, {'umpire': 'CK Nandan', 'umpire_count': 57}, {'umpire': 'Nitin Menon', 'umpire_count': 57}, {'umpire': 'SJA Taufel', 'umpire_count': 55}, {'umpire': 'Asad Rauf', 'umpire_count': 51}, {'umpire': 'VA Kulkarni', 'umpire_count': 50}, {'umpire': 'BNJ Oxenford', 'umpire_count': 48}, {'umpire': 'CB Gaffaney', 'umpire_count': 47}, {'umpire': 'RJ Tucker', 'umpire_count': 46}, {'umpire': 'BR Doctrove', 'umpire_count': 42}, {'umpire': 'RE Koertzen', 'umpire_count': 41}, {'umpire': 'Aleem Dar', 'umpire_count': 38}, {'umpire': 'BF Bowden', 'umpire_count': 37}, {'umpire': 'NJ Llong', 'umpire_count': 37}, {'umpire': 'KN Ananthapadmanabhan', 'umpire_count': 33}, {'umpire': 'RK Illingworth', 'umpire_count': 31}, {'umpire': 'SK Tarapore', 'umpire_count': 31}, {'umpire': 'RB Tiffin', 'umpire_count': 30}, {'umpire': 'S Asnani', 'umpire_count': 30}, {'umpire': 'AY Dandekar', 'umpire_count': 28}, {'umpire': 'DJ Harper', 'umpire_count': 27}, {'umpire': 'AM Saheba', 'umpire_count': 26}, {'umpire': 'VK Sharma', 'umpire_count': 26}, {'umpire': 'A Nand Kishore', 'umpire_count': 25}, {'umpire': 'PR Reiffel', 'umpire_count': 24}, {'umpire': 'K Hariharan', 'umpire_count': 21}, {'umpire': 'YC Barde', 'umpire_count': 19}, {'umpire': 'BG Jerling', 'umpire_count': 17}, {'umpire': 'JD Cloete', 'umpire_count': 17}, {'umpire': 'SS Hazare', 'umpire_count': 17}, {'umpire': 'K Srinath', 'umpire_count': 15}, {'umpire': 'S Das', 'umpire_count': 15}, {'umpire': 'IL Howell', 'umpire_count': 14}, {'umpire': 'SL Shastri', 'umpire_count': 14}, {'umpire': 'A Deshmukh', 'umpire_count': 12}, {'umpire': 'MR Benson', 'umpire_count': 12}, {'umpire': 'PG Pathak', 'umpire_count': 11}, {'umpire': 'UV Gandhe', 'umpire_count': 8}, {'umpire': 'GAV Baxter', 'umpire_count': 7}, {'umpire': 'RM Deshpande', 'umpire_count': 7}, {'umpire': 'SJ Davis', 'umpire_count': 7}, {'umpire': 'AL Hill', 'umpire_count': 6}, {'umpire': 'AV Jayaprakash', 'umpire_count': 6}, {'umpire': 'GA Pratapkumar', 'umpire_count': 6}, {'umpire': 'I Shivram', 'umpire_count': 6}, {'umpire': 'IJ Gould', 'umpire_count': 6}, {'umpire': 'K Srinivasan', 'umpire_count': 6}, {'umpire': 'SD Fry', 'umpire_count': 6}, {'umpire': 'TH Wijewardene', 'umpire_count': 5}, {'umpire': 'K Bharatan', 'umpire_count': 3}, {'umpire': 'SD Ranade', 'umpire_count': 2}, {'umpire': 'Subroto Das', 'umpire_count': 1}]\n"
          ]
        }
      ],
      "execution_count": 37
    },
    {
      "cell_type": "code",
      "source": [
        "qry8_result = db.get_query8_result(con);\n",
        "print(qry8_result)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "4f7ad31d-4ae1-4dd5-82de-44708ce40a2c"
        },
        "id": "H4o9deQFtkSC",
        "outputId": "e75ee45c-5e0d-4ecd-e9e3-d2dfb4680fd3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'match_id': 980999, 'venue': 'M.Chinnaswamy Stadium', 'city': 'Bengaluru', 'TotalRuns': 113}]\n"
          ]
        }
      ],
      "execution_count": 38
    },
    {
      "cell_type": "code",
      "source": [
        "db.close_conn(con)"
      ],
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "177ab62b-4379-4630-8b07-9f6bd09f267a"
        },
        "id": "E4ltAF0VtkSD",
        "outputId": "65917a7b-c180-4eee-f0da-5d5131f6e7d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Database connection closed successfully\n"
          ]
        }
      ],
      "execution_count": 39
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "FQSY5zH_Vem_"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "application/vnd.databricks.v1+notebook": {
      "notebookName": "DataGrokr Assignment",
      "dashboards": [],
      "notebookMetadata": {
        "pythonIndentUnit": 4
      },
      "language": "python",
      "widgets": {},
      "notebookOrigID": 3973652684757661
    },
    "colab": {
      "name": "IPL Data Analysis with PySpark",
      "provenance": [],
      "collapsed_sections": [
        "S5Hk2M5WtkRG"
      ],
      "include_colab_link": true
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}