notebookab4c38f8ac.ipynb

{
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "version": "3.6.4",
      "file_extension": ".py",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "mimetype": "text/x-python"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "sourceId": 9907474,
          "sourceType": "datasetVersion",
          "datasetId": 5999475
        }
      ],
      "isInternetEnabled": false,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    },
    "colab": {
      "name": "notebookab4c38f8ac",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Pshak-20000/Collab_NBA_ETL/blob/main/notebookab4c38f8ac.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "import kagglehub\n",
        "eduardopalmieri_nba_player_stats_season_2425_path = kagglehub.dataset_download('eduardopalmieri/nba-player-stats-season-2425')\n",
        "\n",
        "print('Data source import complete.')\n",
        "\n"
      ],
      "metadata": {
        "id": "ztKzAcSOfFgy",
        "outputId": "b45fc606-faaf-4b3a-9a28-6e7be2f77b6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Looks like you're using an outdated `kagglehub` version, please consider updating (latest version: 0.3.4)\n",
            "Downloading from https://www.kaggle.com/api/v1/datasets/download/eduardopalmieri/nba-player-stats-season-2425?dataset_version_number=6...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 92.3k/92.3k [00:00<00:00, 26.4MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting files...\n",
            "Data source import complete.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import SparkSession"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "htvZK1SpfFg2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "I4drJsnvgAFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spark = SparkSession.builder \\\n",
        "    .appName(\"NBA Player Stats Analysis\") \\\n",
        "    .getOrCreate()"
      ],
      "metadata": {
        "id": "4VH2zj_Kfm2L"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "filepath=os.path.join(eduardopalmieri_nba_player_stats_season_2425_path,'nba_player_stats_2425.csv')\n",
        "file_path = '/root/.cache/kagglehub/datasets/eduardopalmieri/nba-player-stats-season-2425/versions/6/nba_player_stats_2425.csv'\n",
        "print(eduardopalmieri_nba_player_stats_season_2425_path)\n"
      ],
      "metadata": {
        "id": "bSL_TVHghxme",
        "outputId": "56f26cec-3d63-4e0a-aae2-81477d51007b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/kagglehub/datasets/eduardopalmieri/nba-player-stats-season-2425/versions/6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dynamically generate the file path using os.path.join\n",
        "filepath = os.path.join(eduardopalmieri_nba_player_stats_season_2425_path, 'database_24_25.csv')\n",
        "print(\"Files in dataset directory:\", os.listdir(eduardopalmieri_nba_player_stats_season_2425_path))\n",
        "# Check the generated file path\n",
        "print(\"Generated File Path:\", filepath)\n",
        "\n",
        "# Check if the file exists at the generated path\n",
        "if os.path.exists(filepath):\n",
        "    print(f\"File exists at {filepath}\")\n",
        "else:\n",
        "    print(f\"File not found at {filepath}\")"
      ],
      "metadata": {
        "id": "Z17o-e8gjrMP",
        "outputId": "50d4139c-635b-4e76-ec4e-356840816f77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files in dataset directory: ['database_24_25.csv']\n",
            "Generated File Path: /root/.cache/kagglehub/datasets/eduardopalmieri/nba-player-stats-season-2425/versions/6/database_24_25.csv\n",
            "File exists at /root/.cache/kagglehub/datasets/eduardopalmieri/nba-player-stats-season-2425/versions/6/database_24_25.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=spark.read.csv(filepath,header=True,inferSchema=True)\n",
        "df.show(5)\n",
        "df.printSchema()"
      ],
      "metadata": {
        "id": "bHAmKEjviW5H",
        "outputId": "bdd45767-5e6b-45d5-ec0e-d69a0cf99284",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------+---+---+---+-----+---+---+-----+---+---+-----+---+---+-----+---+---+---+---+---+---+---+---+---+----+----------+\n",
            "|       Player| Tm|Opp|Res|   MP| FG|FGA|  FG%| 3P|3PA|  3P%| FT|FTA|  FT%|ORB|DRB|TRB|AST|STL|BLK|TOV| PF|PTS|GmSc|      Data|\n",
            "+-------------+---+---+---+-----+---+---+-----+---+---+-----+---+---+-----+---+---+---+---+---+---+---+---+---+----+----------+\n",
            "| Jayson Tatum|BOS|NYK|  W| 30.3| 14| 18|0.778|  8| 11|0.727|  1|  2|  0.5|  0|  4|  4| 10|  1|  1|  1|  1| 37|38.1|2024-10-22|\n",
            "|Anthony Davis|LAL|MIN|  W|37.58| 11| 23|0.478|  1|  3|0.333| 13| 15|0.867|  3| 13| 16|  4|  1|  3|  1|  1| 36|34.0|2024-10-22|\n",
            "|Derrick White|BOS|NYK|  W|26.63|  8| 13|0.615|  6| 10|  0.6|  2|  2|  1.0|  0|  3|  3|  4|  1|  0|  0|  1| 24|22.4|2024-10-22|\n",
            "| Jrue Holiday|BOS|NYK|  W|30.52|  7|  9|0.778|  4|  6|0.667|  0|  0|  0.0|  2|  2|  4|  4|  1|  0|  0|  2| 18|19.5|2024-10-22|\n",
            "|Miles McBride|NYK|BOS|  L|25.85|  8| 10|  0.8|  4|  5|  0.8|  2|  3|0.667|  0|  0|  0|  2|  0|  0|  1|  1| 22|17.8|2024-10-22|\n",
            "+-------------+---+---+---+-----+---+---+-----+---+---+-----+---+---+-----+---+---+---+---+---+---+---+---+---+----+----------+\n",
            "only showing top 5 rows\n",
            "\n",
            "root\n",
            " |-- Player: string (nullable = true)\n",
            " |-- Tm: string (nullable = true)\n",
            " |-- Opp: string (nullable = true)\n",
            " |-- Res: string (nullable = true)\n",
            " |-- MP: double (nullable = true)\n",
            " |-- FG: integer (nullable = true)\n",
            " |-- FGA: integer (nullable = true)\n",
            " |-- FG%: double (nullable = true)\n",
            " |-- 3P: integer (nullable = true)\n",
            " |-- 3PA: integer (nullable = true)\n",
            " |-- 3P%: double (nullable = true)\n",
            " |-- FT: integer (nullable = true)\n",
            " |-- FTA: integer (nullable = true)\n",
            " |-- FT%: double (nullable = true)\n",
            " |-- ORB: integer (nullable = true)\n",
            " |-- DRB: integer (nullable = true)\n",
            " |-- TRB: integer (nullable = true)\n",
            " |-- AST: integer (nullable = true)\n",
            " |-- STL: integer (nullable = true)\n",
            " |-- BLK: integer (nullable = true)\n",
            " |-- TOV: integer (nullable = true)\n",
            " |-- PF: integer (nullable = true)\n",
            " |-- PTS: integer (nullable = true)\n",
            " |-- GmSc: double (nullable = true)\n",
            " |-- Data: date (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import sum,col\n",
        "null_counts = df.select([sum(col(c).isNull().cast(\"int\")).alias(c) for c in df.columns])\n",
        "null_counts.show()"
      ],
      "metadata": {
        "id": "YM4dk19_lBFe",
        "outputId": "871780c9-1c15-4d7f-f606-b1505bb5c9f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+----+----+\n",
            "|Player| Tm|Opp|Res| MP| FG|FGA|FG%| 3P|3PA|3P%| FT|FTA|FT%|ORB|DRB|TRB|AST|STL|BLK|TOV| PF|PTS|GmSc|Data|\n",
            "+------+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+----+----+\n",
            "|     0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|  0|   0|   0|\n",
            "+------+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+----+----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_count = df.count() - df.distinct().count()\n",
        "\n",
        "print(f\"Number of duplicate rows: {duplicate_count}\")\n",
        "df.dropDuplicates()"
      ],
      "metadata": {
        "id": "GNaHaJ6d1UZ8",
        "outputId": "8f1cc129-aa85-44ef-9a4e-f98e6d44e578",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[Player: string, Tm: string, Opp: string, Res: string, MP: double, FG: int, FGA: int, FG%: double, 3P: int, 3PA: int, 3P%: double, FT: int, FTA: int, FT%: double, ORB: int, DRB: int, TRB: int, AST: int, STL: int, BLK: int, TOV: int, PF: int, PTS: int, GmSc: double, Data: date]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Points (PTS): A measure of the player's scoring performance.\n",
        "Assists (AST): How well the player is helping teammates score.\n",
        "Rebounds (TRB): A measure of the player’s effort in securing missed shots.\n",
        "Efficiency Metrics: FG%, 3P%, FT% indicate how efficient the player is in scoring.\n",
        "Game Performance: GmSc gives an overall score combining multiple performance aspects."
      ],
      "metadata": {
        "id": "RPgvrmXq8BmD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned.describe().show()"
      ],
      "metadata": {
        "id": "XGP9NKqy2CfS",
        "outputId": "6307be23-a12f-4e68-e4d3-4bb981fa568a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+---------------+----+----+----+------------------+------------------+-----------------+-------------------+-----------------+------------------+-------------------+-----------------+------------------+-------------------+------------------+------------------+-----------------+------------------+------------------+-------------------+------------------+------------------+-----------------+-----------------+\n",
            "|summary|         Player|  Tm| Opp| Res|                MP|                FG|              FGA|                FG%|               3P|               3PA|                3P%|               FT|               FTA|                FT%|               ORB|               DRB|              TRB|               AST|               STL|                BLK|               TOV|                PF|              PTS|             GmSc|\n",
            "+-------+---------------+----+----+----+------------------+------------------+-----------------+-------------------+-----------------+------------------+-------------------+-----------------+------------------+-------------------+------------------+------------------+-----------------+------------------+------------------+-------------------+------------------+------------------+-----------------+-----------------+\n",
            "|  count|           3844|3844|3844|3844|              3844|              3844|             3844|               3844|             3844|              3844|               3844|             3844|              3844|               3844|              3844|              3844|             3844|              3844|              3844|               3844|              3844|              3844|             3844|             3844|\n",
            "|   mean|           NULL|NULL|NULL|NULL|22.007866805411012| 3.740894901144641|8.070759625390219|0.42498933402705535|1.210978147762747| 3.388397502601457|0.26435665972944783|1.605098855359001| 2.063475546305931|0.42012382934443204|0.9880332986472424| 3.001040582726327|3.989073881373569|2.3514568158168574|0.7536420395421436|0.47008324661810613|1.2775754422476586|1.8077523413111343|10.29786680541103| 8.28132154006245|\n",
            "| stddev|           NULL|NULL|NULL|NULL|11.093446053017077|3.2409497408758376|6.084168793633041|0.26162318466205936|1.514428543276028|3.1464853664004506| 0.2925016766009049|2.267014487619453|2.7540574928884727| 0.4351327095315885|1.3313265080640813|2.6838825538304443|3.413613327056953| 2.568356745684318|0.9938267706844948|  0.842156194098929|1.4552771704244905| 1.490967599263244|8.831583237407784|7.755039976650167|\n",
            "|    min|     A.J. Green| ATL| ATL|   L|               0.0|                 0|                0|                0.0|                0|                 0|                0.0|                0|                 0|                0.0|                 0|                 0|                0|                 0|                 0|                  0|                 0|                 0|                0|             -6.2|\n",
            "|    max|Zion Williamson| WAS| WAS|   W|             47.78|                21|               34|                1.0|               10|                18|                1.0|               17|                22|                1.0|                10|                15|               20|                16|                 7|                  9|                10|                 6|               59|             54.2|\n",
            "+-------+---------------+----+----+----+------------------+------------------+-----------------+-------------------+-----------------+------------------+-------------------+-----------------+------------------+-------------------+------------------+------------------+-----------------+------------------+------------------+-------------------+------------------+------------------+-----------------+-----------------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filter Out Unwanted or Incorrect Data:"
      ],
      "metadata": {
        "id": "NDbG-bT34alr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned = df_cleaned.filter(df.PTS >= 0)\n"
      ],
      "metadata": {
        "id": "UoeZ4nG54bgu"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Collect the data to plot (sample data for smaller dataset size)\n",
        "data = df_cleaned.select(\"MP\", \"PTS\").toPandas()\n",
        "\n",
        "plt.scatter(data[\"MP\"], data[\"PTS\"])\n",
        "plt.title('Points vs Minutes Played')\n",
        "plt.xlabel('Minutes Played (MP)')\n",
        "plt.ylabel('Points (PTS)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "9CFeEx9a8Drq",
        "outputId": "e03755f9-0e11-4b61-af0b-9eacdb2cf2a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_players = df_cleaned.orderBy(col(\"PTS\").desc()).limit(5)\n",
        "top_players.select(['Player']).show(truncate=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3x6bEvF8k9M",
        "outputId": "4bcd0d0a-8d28-40c1-d3cd-2472969ce047"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------------------+\n",
            "|Player                 |\n",
            "+-----------------------+\n",
            "|Giannis Antetokounmpo  |\n",
            "|Paolo Banchero         |\n",
            "|Victor Wembanyama      |\n",
            "|Karl-Anthony Towns     |\n",
            "|Shai Gilgeous-Alexander|\n",
            "+-----------------------+\n",
            "\n"
          ]
        }
      ]
    }
  ]
}