Ch_2_Spark_Demonstration.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/malikbaqi12/Applied-data-science-using-pyspark-code-files/blob/main/Ch_2_Spark_Demonstration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XLisVKV7tys"
      },
      "source": [
        "# Data Manipulations in PySpark"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "5LZA30iZ7z7V",
        "outputId": "fc3c1eaf-1f4b-44c5-c68a-a4c2ef66d4da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pyspark\n"
      ],
      "metadata": {
        "id": "GD0UPOxP8KjV",
        "outputId": "bda4364d-fdee-435d-c9b8-f1947ffaedd7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.4.0)\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpXJfrq27ty0"
      },
      "source": [
        "# Chapter 2 Demonstrations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YYY_gLvC7ty2"
      },
      "outputs": [],
      "source": [
        "# Import Sparksession\n",
        "from pyspark.sql import SparkSession\n",
        "spark=SparkSession.builder.appName(\"Data_Wrangling\").getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "QpmkmbA97ty6",
        "outputId": "40ecdf84-d0c7-467e-c99b-093a3ef41970",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python version: 3.10.11 (main, Apr  5 2023, 14:15:10) [GCC 9.4.0]\n",
            "Spark version: 3.4.0\n"
          ]
        }
      ],
      "source": [
        "# Print PySpark and Python versions\n",
        "import sys\n",
        "print('Python version: '+sys.version)\n",
        "print('Spark version: '+spark.version)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "62S3xjTF7ty-"
      },
      "outputs": [],
      "source": [
        "# Read data\n",
        "file_location = \"/content/drive/MyDrive/movie_data_part1.csv\"\n",
        "file_type = \"csv\"\n",
        "infer_schema = \"false\"\n",
        "first_row_is_header = \"true\"\n",
        "delimiter = \"|\"\n",
        "\n",
        "df = spark.read.format(file_type)\\\n",
        ".option(\"inferSchema\", infer_schema)\\\n",
        ".option(\"header\", first_row_is_header)\\\n",
        ".option(\"sep\", delimiter)\\\n",
        ".load(file_location)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4rCix7-P7ty_",
        "outputId": "30a6ce39-63be-492e-ab74-88af009a7269",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- belongs_to_collection: string (nullable = true)\n",
            " |-- budget: string (nullable = true)\n",
            " |-- id: string (nullable = true)\n",
            " |-- original_language: string (nullable = true)\n",
            " |-- original_title: string (nullable = true)\n",
            " |-- overview: string (nullable = true)\n",
            " |-- popularity: string (nullable = true)\n",
            " |-- production_companies: string (nullable = true)\n",
            " |-- production_countries: string (nullable = true)\n",
            " |-- release_date: string (nullable = true)\n",
            " |-- revenue: string (nullable = true)\n",
            " |-- runtime: string (nullable = true)\n",
            " |-- status: string (nullable = true)\n",
            " |-- tagline: string (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- vote_average: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Print Metadata\n",
        "df.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Ods98mdQ7tzA",
        "outputId": "a209c692-9034-4a31-dafa-f5b6c1c4af5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('belongs_to_collection', 'string'),\n",
              " ('budget', 'string'),\n",
              " ('id', 'string'),\n",
              " ('original_language', 'string'),\n",
              " ('original_title', 'string'),\n",
              " ('overview', 'string'),\n",
              " ('popularity', 'string'),\n",
              " ('production_companies', 'string'),\n",
              " ('production_countries', 'string'),\n",
              " ('release_date', 'string'),\n",
              " ('revenue', 'string'),\n",
              " ('runtime', 'string'),\n",
              " ('status', 'string'),\n",
              " ('tagline', 'string'),\n",
              " ('title', 'string'),\n",
              " ('vote_average', 'string')]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Print Metadata - 2nd method\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-oE1LtOd7tzB",
        "outputId": "c9b522e6-cb11-4adf-837b-f403a9dac8dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total number of records in the movie dataset are 43998\n"
          ]
        }
      ],
      "source": [
        "#  Count data\n",
        "df.count()\n",
        "print('The total number of records in the movie dataset are '+str(df.count()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "tqP5Yd2v7tzC"
      },
      "outputs": [],
      "source": [
        "#  Drop Columns\n",
        "drop_columns_list=[\"overview\",\"belongs_to_collection\",\"production_companies\",\"production_countries\",\"status\",\"original_title\"]\n",
        "df1=df.drop(*drop_columns_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "P2_TO7x27tzD",
        "outputId": "84c9ddda-dea5-48d2-ae4a-21231993ad3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+-------+------------------+------------+-------+--------------------+\n",
            "|   id| budget|        popularity|release_date|revenue|               title|\n",
            "+-----+-------+------------------+------------+-------+--------------------+\n",
            "|43000|      0|             2.503|  1962-05-23|      0|The Elusive Corporal|\n",
            "|43001|      0|              5.51|  1962-11-12|      0|  Sundays and Cybele|\n",
            "|43002|      0|              5.62|  1962-05-24|      0|Lonely Are the Brave|\n",
            "|43003|      0|             7.159|  1975-03-12|      0|          F for Fake|\n",
            "|43004| 500000|             3.988|  1962-10-09|      0|Long Day's Journe...|\n",
            "|43006|      0|             3.194|  1962-03-09|      0|           My Geisha|\n",
            "|43007|      0|             2.689|  1962-10-31|      0|Period of Adjustment|\n",
            "|43008|      0|             6.537|  1959-03-13|      0|    The Hanging Tree|\n",
            "|43010|      0|             4.297|  1962-01-01|      0|Sherlock Holmes a...|\n",
            "|43011|      0|             4.417|  1962-01-01|      0|  Sodom and Gomorrah|\n",
            "|43012|7000000|4.7219999999999995|  1962-11-21|4000000|         Taras Bulba|\n",
            "|43013|      0|             2.543|  1962-04-17|      0|The Counterfeit T...|\n",
            "|43014|      0|             4.303|  1962-10-24|      0|     Tower of London|\n",
            "|43015|      0|             3.493|  1962-12-07|      0|Varan the Unbelie...|\n",
            "|43016|      0|             2.851|  1962-01-01|      0|Waltz of the Tore...|\n",
            "|43017|      0|             4.047|  1961-10-11|      0|         Back Street|\n",
            "|43018|      0|             2.661|  1961-06-02|      0|Gidget Goes Hawaiian|\n",
            "|43019|      0|             3.225|  2010-05-28|      0|Schuks Tshabalala...|\n",
            "|43020|      0|              5.72|  1961-06-15|      0|The Colossus of R...|\n",
            "|43021|      0|             3.292|  2008-08-22|      0|          Sex Galaxy|\n",
            "+-----+-------+------------------+------------+-------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Subsetting Columns\n",
        "select_columns=['id','budget','popularity','release_date','revenue','title']\n",
        "df=df.select(*select_columns)\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "eiJTbij37tzE",
        "outputId": "9f37e3ec-41f9-4ff8-fe2b-213e42fce0c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+-------+------------------+------------+-------+--------------------+\n",
            "|   id| budget|        popularity|release_date|revenue|               title|\n",
            "+-----+-------+------------------+------------+-------+--------------------+\n",
            "|43000|      0|             2.503|  1962-05-23|      0|The Elusive Corporal|\n",
            "|43001|      0|              5.51|  1962-11-12|      0|  Sundays and Cybele|\n",
            "|43002|      0|              5.62|  1962-05-24|      0|Lonely Are the Brave|\n",
            "|43003|      0|             7.159|  1975-03-12|      0|          F for Fake|\n",
            "|43004| 500000|             3.988|  1962-10-09|      0|Long Day's Journe...|\n",
            "|43006|      0|             3.194|  1962-03-09|      0|           My Geisha|\n",
            "|43007|      0|             2.689|  1962-10-31|      0|Period of Adjustment|\n",
            "|43008|      0|             6.537|  1959-03-13|      0|    The Hanging Tree|\n",
            "|43010|      0|             4.297|  1962-01-01|      0|Sherlock Holmes a...|\n",
            "|43011|      0|             4.417|  1962-01-01|      0|  Sodom and Gomorrah|\n",
            "|43012|7000000|4.7219999999999995|  1962-11-21|4000000|         Taras Bulba|\n",
            "|43013|      0|             2.543|  1962-04-17|      0|The Counterfeit T...|\n",
            "|43014|      0|             4.303|  1962-10-24|      0|     Tower of London|\n",
            "|43015|      0|             3.493|  1962-12-07|      0|Varan the Unbelie...|\n",
            "|43016|      0|             2.851|  1962-01-01|      0|Waltz of the Tore...|\n",
            "|43017|      0|             4.047|  1961-10-11|      0|         Back Street|\n",
            "|43018|      0|             2.661|  1961-06-02|      0|Gidget Goes Hawaiian|\n",
            "|43019|      0|             3.225|  2010-05-28|      0|Schuks Tshabalala...|\n",
            "|43020|      0|              5.72|  1961-06-15|      0|The Colossus of R...|\n",
            "|43021|      0|             3.292|  2008-08-22|      0|          Sex Galaxy|\n",
            "+-----+-------+------------------+------------+-------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Subsetting Columns\n",
        "df = spark.read.format(file_type)\\\n",
        ".option(\"inferSchema\", infer_schema)\\\n",
        ".option(\"header\", first_row_is_header)\\\n",
        ".option(\"sep\", delimiter)\\\n",
        ".load(file_location)\n",
        "df=df.select(df[2],df[1],df[6],df[9],df[10],df[14])\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "FqJ1GcWI7tzF",
        "outputId": "b0dedbb1-299a-408d-872a-08aad6cb3ac6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "215"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Identifying missing values\n",
        "from pyspark.sql.functions import *\n",
        "df.filter((df['popularity']=='')|df['popularity'].isNull()|isnan(df['popularity'])).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-svq2ozp7tzH",
        "outputId": "3e4b9c9e-09a5-4e6c-d157-0b422991b5a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+------+----------+------------+-------+-----+\n",
            "| id|budget|popularity|release_date|revenue|title|\n",
            "+---+------+----------+------------+-------+-----+\n",
            "|125|   125|       215|         221|    215|  304|\n",
            "+---+------+----------+------------+-------+-----+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Identifying missing values\n",
        "df.select([count(when((col(c)=='') | col(c).isNull() |isnan(c), c)).alias(c) for c in df.columns]).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "p0MI5qnO7tzI",
        "outputId": "f546f9d3-7ebb-4d5f-fb32-4c7743e57575",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-----+\n",
            "|               title|count|\n",
            "+--------------------+-----+\n",
            "|   The Corn Is Green|    1|\n",
            "|Meet The Browns -...|    1|\n",
            "|Morenita, El Esca...|    1|\n",
            "| Father Takes a Wife|    1|\n",
            "|The Werewolf of W...|    1|\n",
            "|My Wife Is a Gang...|    1|\n",
            "|Depeche Mode: Tou...|    1|\n",
            "|  A Woman Is a Woman|    1|\n",
            "|History Is Made a...|    1|\n",
            "|      Colombian Love|    1|\n",
            "|        Ace Attorney|    1|\n",
            "|     Not Like Others|    1|\n",
            "|40 Guns to Apache...|    1|\n",
            "|          Middle Men|    1|\n",
            "|         It's a Gift|    1|\n",
            "|    La Vie de Bohème|    1|\n",
            "|Rasputin: The Mad...|    1|\n",
            "|The Ballad of Jac...|    1|\n",
            "|         How to Deal|    1|\n",
            "|             Freaked|    1|\n",
            "+--------------------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# One way Frequencies\n",
        "df.groupBy(df['title']).count().show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "0OabScUy7tzJ",
        "outputId": "02a5627f-a1d5-40d4-d279-c65f941cdbaa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-----+\n",
            "|title               |count|\n",
            "+--------------------+-----+\n",
            "|null                |304  |\n",
            "|Les Misérables      |8    |\n",
            "|The Three Musketeers|8    |\n",
            "|Cinderella          |8    |\n",
            "|A Christmas Carol   |7    |\n",
            "|The Island          |7    |\n",
            "|Dracula             |7    |\n",
            "|Hamlet              |7    |\n",
            "|Frankenstein        |7    |\n",
            "|Cleopatra           |6    |\n",
            "+--------------------+-----+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Frequencies - descending order\n",
        "df.groupby(df['title']).count().sort(desc(\"count\")).show(10, False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "8F8iq9dZ7tzK",
        "outputId": "58740c83-781f-424f-da87-bfca368657f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-----+\n",
            "|title               |count|\n",
            "+--------------------+-----+\n",
            "|Les Misérables      |8    |\n",
            "|The Three Musketeers|8    |\n",
            "|Cinderella          |8    |\n",
            "|A Christmas Carol   |7    |\n",
            "|The Island          |7    |\n",
            "|Frankenstein        |7    |\n",
            "|Dracula             |7    |\n",
            "|Hamlet              |7    |\n",
            "|Treasure Island     |6    |\n",
            "|The Lost World      |6    |\n",
            "+--------------------+-----+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#  One way frquencies with filters\n",
        "df_temp=df.filter((df['title']!='')&(df['title'].isNotNull()) & (~isnan(df['title'])))\n",
        "df_temp.groupby(df_temp['title']).count().filter(\"`count` >4\").sort(col(\"count\").desc()).show(10,False)\n",
        "df_temp.groupby(df_temp['title']).count().filter(\"`count` >4\").sort(col(\"count\").desc()).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "hg-0Z6Bl7tzM"
      },
      "outputs": [],
      "source": [
        "# Casting\n",
        "df = df.withColumn('budget',df['budget'].cast(\"float\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "scrolled": true,
        "id": "CRcr6b5x7tzO",
        "outputId": "b940814b-671e-4879-d971-a0f3cde3bfe0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('id', 'string'),\n",
              " ('budget', 'float'),\n",
              " ('popularity', 'string'),\n",
              " ('release_date', 'string'),\n",
              " ('revenue', 'string'),\n",
              " ('title', 'string')]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#Before Casting\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "VCqMuHTn7tzP",
        "outputId": "d064aadd-7404-407b-a369-56e2d37589d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('id', 'string'),\n",
              " ('budget', 'float'),\n",
              " ('popularity', 'string'),\n",
              " ('release_date', 'string'),\n",
              " ('revenue', 'string'),\n",
              " ('title', 'string')]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "#After Casting\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "2H4LzTpi7tzQ"
      },
      "outputs": [],
      "source": [
        "# Casting multiple variables\n",
        "from pyspark.sql.types import *\n",
        "\n",
        "#Identifying and assiging lists of variables  \n",
        "int_vars=['id']\n",
        "float_vars=['budget', 'popularity', 'revenue']\n",
        "date_vars=['release_date']\n",
        "\n",
        "for column in int_vars:\n",
        "\tdf=df.withColumn(column,df[column].cast(IntegerType()))\n",
        "for column in float_vars:\n",
        "\tdf=df.withColumn(column,df[column].cast(FloatType()))\n",
        "for column in date_vars:\n",
        "\tdf=df.withColumn(column,df[column].cast(DateType()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "p0T7yvx97tzR",
        "outputId": "2002a81c-a3db-4d9b-9ba7-748f98f7aefe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('id', 'int'),\n",
              " ('budget', 'float'),\n",
              " ('popularity', 'float'),\n",
              " ('release_date', 'date'),\n",
              " ('revenue', 'float'),\n",
              " ('title', 'string')]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "#After Casting\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "BF4LyYn-7tzS",
        "outputId": "6e0fce50-0e27-45e5-fdb7-63866861c86c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+--------+----------+------------+-------+---------------------------------------+\n",
            "|id   |budget  |popularity|release_date|revenue|title                                  |\n",
            "+-----+--------+----------+------------+-------+---------------------------------------+\n",
            "|43000|0.0     |2.503     |1962-05-23  |0.0    |The Elusive Corporal                   |\n",
            "|43001|0.0     |5.51      |1962-11-12  |0.0    |Sundays and Cybele                     |\n",
            "|43002|0.0     |5.62      |1962-05-24  |0.0    |Lonely Are the Brave                   |\n",
            "|43003|0.0     |7.159     |1975-03-12  |0.0    |F for Fake                             |\n",
            "|43004|500000.0|3.988     |1962-10-09  |0.0    |Long Day's Journey Into Night          |\n",
            "|43006|0.0     |3.194     |1962-03-09  |0.0    |My Geisha                              |\n",
            "|43007|0.0     |2.689     |1962-10-31  |0.0    |Period of Adjustment                   |\n",
            "|43008|0.0     |6.537     |1959-03-13  |0.0    |The Hanging Tree                       |\n",
            "|43010|0.0     |4.297     |1962-01-01  |0.0    |Sherlock Holmes and the Deadly Necklace|\n",
            "|43011|0.0     |4.417     |1962-01-01  |0.0    |Sodom and Gomorrah                     |\n",
            "+-----+--------+----------+------------+-------+---------------------------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#After Casting output\n",
        "df.show(10,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "lPUctqZM7tzS",
        "outputId": "dfbdaf46-bdbe-4b2e-caf7-0e911c858481",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+------------------+--------------------+------------------+-------------------+--------------------+\n",
            "|summary|                id|              budget|        popularity|            revenue|               title|\n",
            "+-------+------------------+--------------------+------------------+-------------------+--------------------+\n",
            "|  count|             43784|               43873|             43783|              43783|               43694|\n",
            "|   mean|44502.304312077475|  3736901.8349631676| 5.295444259579189|  9697079.597382545|            Infinity|\n",
            "| stddev|  27189.6465886264|1.5871814952777285E7|6.1680305192082665|5.687938449628816E7|                 NaN|\n",
            "|    min|                 2|                 0.0|               0.6|                0.0|!Women Art Revolu...|\n",
            "|    max|            100988|               3.8E8|             180.0|       2.78796518E9|       시크릿 Secret|\n",
            "+-------+------------------+--------------------+------------------+-------------------+--------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Describe function\n",
        "df.describe().show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "iMpiKgBT7tzT",
        "outputId": "0b9d05f1-ec1c-4c7b-b87c-f5205d547b5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median of budget is [7000000.0]\n"
          ]
        }
      ],
      "source": [
        "# Median Calculation\n",
        "df_temp = df.filter((df['budget']!=0)&(df['budget'].isNotNull()) & (~isnan(df['budget'])))\n",
        "median=df_temp.approxQuantile('budget',[0.5],0.001)\n",
        "print ('The median of budget is '+str(median))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "sS9sbDqZ7tzU",
        "outputId": "3b18c92d-4b97-41e7-eab8-8614b4a6e737",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+\n",
            "|count|\n",
            "+-----+\n",
            "|41138|\n",
            "+-----+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Distinct Counts\n",
        "df.agg(countDistinct(col(\"title\")).alias(\"count\")).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "scrolled": true,
        "id": "QPE-tykv7tzV",
        "outputId": "f8f89515-0358-42a5-d674-ecc831c5a839",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------------------------------------+\n",
            "|title                                        |\n",
            "+---------------------------------------------+\n",
            "|The Corn Is Green                            |\n",
            "|Meet The Browns - The Play                   |\n",
            "|Morenita, El Escandalo                       |\n",
            "|Father Takes a Wife                          |\n",
            "|The Werewolf of Washington                   |\n",
            "|My Wife Is a Gangster                        |\n",
            "|Depeche Mode: Touring the Angel Live in Milan|\n",
            "|A Woman Is a Woman                           |\n",
            "|History Is Made at Night                     |\n",
            "|Colombian Love                               |\n",
            "+---------------------------------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Distinct Values\n",
        "df.select('title').distinct().show(10,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "u8P5Ym717tzW",
        "outputId": "01adb2d6-e15b-40ba-d2a8-fb1423a0fd20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------+------------+\n",
            "|release_year|count(title)|\n",
            "+------------+------------+\n",
            "|1959        |271         |\n",
            "|1990        |496         |\n",
            "|1975        |365         |\n",
            "|1977        |415         |\n",
            "|1924        |19          |\n",
            "|2003        |1199        |\n",
            "|2007        |1896        |\n",
            "|2018        |4           |\n",
            "|1974        |434         |\n",
            "|2015        |13          |\n",
            "+------------+------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Distinct Aggregations\n",
        "df_temp=df.withColumn('release_year',year('release_date'))\n",
        "df_temp.groupBy(\"release_year\").agg(countDistinct(\"title\")).show(10,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "D6ymmhz77tzW"
      },
      "outputs": [],
      "source": [
        "# Datetime extractions\n",
        "df_temp=df_temp.withColumn('release_month',month('release_date'))\n",
        "df_temp=df_temp.withColumn('release_day',dayofmonth('release_date'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "dFMSxmsl7tzX",
        "outputId": "8822a5fc-c413-4748-cc6c-8a299d249c8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+------------+---------+--------------------+------------+-------------+-----------+\n",
            "|   id|   budget|popularity|release_date|  revenue|               title|release_year|release_month|release_day|\n",
            "+-----+---------+----------+------------+---------+--------------------+------------+-------------+-----------+\n",
            "|43000|      0.0|     2.503|  1962-05-23|      0.0|The Elusive Corporal|        1962|            5|         23|\n",
            "|43001|      0.0|      5.51|  1962-11-12|      0.0|  Sundays and Cybele|        1962|           11|         12|\n",
            "|43002|      0.0|      5.62|  1962-05-24|      0.0|Lonely Are the Brave|        1962|            5|         24|\n",
            "|43003|      0.0|     7.159|  1975-03-12|      0.0|          F for Fake|        1975|            3|         12|\n",
            "|43004| 500000.0|     3.988|  1962-10-09|      0.0|Long Day's Journe...|        1962|           10|          9|\n",
            "|43006|      0.0|     3.194|  1962-03-09|      0.0|           My Geisha|        1962|            3|          9|\n",
            "|43007|      0.0|     2.689|  1962-10-31|      0.0|Period of Adjustment|        1962|           10|         31|\n",
            "|43008|      0.0|     6.537|  1959-03-13|      0.0|    The Hanging Tree|        1959|            3|         13|\n",
            "|43010|      0.0|     4.297|  1962-01-01|      0.0|Sherlock Holmes a...|        1962|            1|          1|\n",
            "|43011|      0.0|     4.417|  1962-01-01|      0.0|  Sodom and Gomorrah|        1962|            1|          1|\n",
            "|43012|7000000.0|     4.722|  1962-11-21|4000000.0|         Taras Bulba|        1962|           11|         21|\n",
            "|43013|      0.0|     2.543|  1962-04-17|      0.0|The Counterfeit T...|        1962|            4|         17|\n",
            "|43014|      0.0|     4.303|  1962-10-24|      0.0|     Tower of London|        1962|           10|         24|\n",
            "|43015|      0.0|     3.493|  1962-12-07|      0.0|Varan the Unbelie...|        1962|           12|          7|\n",
            "|43016|      0.0|     2.851|  1962-01-01|      0.0|Waltz of the Tore...|        1962|            1|          1|\n",
            "|43017|      0.0|     4.047|  1961-10-11|      0.0|         Back Street|        1961|           10|         11|\n",
            "|43018|      0.0|     2.661|  1961-06-02|      0.0|Gidget Goes Hawaiian|        1961|            6|          2|\n",
            "|43019|      0.0|     3.225|  2010-05-28|      0.0|Schuks Tshabalala...|        2010|            5|         28|\n",
            "|43020|      0.0|      5.72|  1961-06-15|      0.0|The Colossus of R...|        1961|            6|         15|\n",
            "|43021|      0.0|     3.292|  2008-08-22|      0.0|          Sex Galaxy|        2008|            8|         22|\n",
            "+-----+---------+----------+------------+---------+--------------------+------------+-------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Output of datetime extractions\n",
        "df_temp.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "QNdwJqrw7tzY",
        "outputId": "a7f0c3dd-f4d3-430b-ea60-e867fdce88a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+------------+-----------+--------------------------+\n",
            "|id   |budget   |popularity|release_date|revenue    |title                     |\n",
            "+-----+---------+----------+------------+-----------+--------------------------+\n",
            "|43957|500000.0 |2.649     |2005-06-28  |1000000.0  |Meet The Browns - The Play|\n",
            "|39997|0.0      |3.585     |1989-11-15  |0.0        |Meet the Hollowheads      |\n",
            "|16710|0.0      |11.495    |2008-03-21  |4.1939392E7|Meet the Browns           |\n",
            "|20430|0.0      |3.614     |2004-01-29  |0.0        |Meet Market               |\n",
            "|76435|0.0      |1.775     |2011-03-31  |0.0        |Meet the In-Laws          |\n",
            "|76516|5000000.0|4.05      |1990-11-08  |485772.0   |Meet the Applegates       |\n",
            "|7278 |3.0E7    |11.116    |2008-01-24  |8.4646832E7|Meet the Spartans         |\n",
            "|32574|0.0      |7.42      |1941-03-14  |0.0        |Meet John Doe             |\n",
            "|40506|0.0      |4.814     |1997-01-31  |0.0        |Meet Wally Sparks         |\n",
            "|40688|2.4E7    |6.848     |1998-03-27  |4562146.0  |Meet the Deedles          |\n",
            "+-----+---------+----------+------------+-----------+--------------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on like\n",
        "df.filter(df['title'].like('Meet%')).show(10,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "nwm7pcbs7tzY",
        "outputId": "99963c1c-a9f2-424e-f447-9914ab9ea25e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+--------+----------+------------+-------+---------------------------------------+\n",
            "|id   |budget  |popularity|release_date|revenue|title                                  |\n",
            "+-----+--------+----------+------------+-------+---------------------------------------+\n",
            "|43000|0.0     |2.503     |1962-05-23  |0.0    |The Elusive Corporal                   |\n",
            "|43001|0.0     |5.51      |1962-11-12  |0.0    |Sundays and Cybele                     |\n",
            "|43002|0.0     |5.62      |1962-05-24  |0.0    |Lonely Are the Brave                   |\n",
            "|43003|0.0     |7.159     |1975-03-12  |0.0    |F for Fake                             |\n",
            "|43004|500000.0|3.988     |1962-10-09  |0.0    |Long Day's Journey Into Night          |\n",
            "|43006|0.0     |3.194     |1962-03-09  |0.0    |My Geisha                              |\n",
            "|43007|0.0     |2.689     |1962-10-31  |0.0    |Period of Adjustment                   |\n",
            "|43008|0.0     |6.537     |1959-03-13  |0.0    |The Hanging Tree                       |\n",
            "|43010|0.0     |4.297     |1962-01-01  |0.0    |Sherlock Holmes and the Deadly Necklace|\n",
            "|43011|0.0     |4.417     |1962-01-01  |0.0    |Sodom and Gomorrah                     |\n",
            "+-----+--------+----------+------------+-------+---------------------------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on not like\n",
        "df.filter(~df['title'].like('%s')).show(10,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "-L1xRnDv7tzZ",
        "outputId": "f2759e05-1c1c-4799-d262-dedd367a3c4a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+------+----------+------------+------------+------------------------+\n",
            "|id   |budget|popularity|release_date|revenue     |title                   |\n",
            "+-----+------+----------+------------+------------+------------------------+\n",
            "|43100|0.0   |7.252     |1959-10-07  |0.0         |General Della Rovere    |\n",
            "|43152|0.0   |5.126     |2001-06-21  |0.0         |Love on a Diet          |\n",
            "|43191|0.0   |4.921     |1952-08-29  |0.0         |Beware, My Lovely       |\n",
            "|43281|0.0   |2.411     |1989-11-22  |0.0         |Love Without Pity       |\n",
            "|43343|0.0   |3.174     |1953-12-25  |0.0         |Easy to Love            |\n",
            "|43347|3.0E7 |14.863    |2010-11-22  |1.02820008E8|Love & Other Drugs      |\n",
            "|43362|0.0   |1.705     |1952-02-23  |0.0         |Love Is Better Than Ever|\n",
            "|43363|0.0   |2.02      |1952-05-29  |0.0         |Lovely to Look At       |\n",
            "|43395|0.0   |4.758     |1950-11-10  |0.0         |Two Weeks with Love     |\n",
            "|43455|0.0   |4.669     |1948-08-23  |0.0         |The Loves of Carmen     |\n",
            "+-----+------+----------+------------+------------+------------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on not regular expressions - P1\n",
        "df.filter(df['title'].rlike('[A-Z]*ove')).show(10,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "8qq4yob07tza",
        "outputId": "9db88917-153a-4d68-b135-b8ec94eac442",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+------+----------+------------+------------+--------------------+\n",
            "|   id|budget|popularity|release_date|     revenue|               title|\n",
            "+-----+------+----------+------------+------------+--------------------+\n",
            "|43100|   0.0|     7.252|  1959-10-07|         0.0|General Della Rovere|\n",
            "|43152|   0.0|     5.126|  2001-06-21|         0.0|      Love on a Diet|\n",
            "|43191|   0.0|     4.921|  1952-08-29|         0.0|   Beware, My Lovely|\n",
            "|43281|   0.0|     2.411|  1989-11-22|         0.0|   Love Without Pity|\n",
            "|43343|   0.0|     3.174|  1953-12-25|         0.0|        Easy to Love|\n",
            "|43347| 3.0E7|    14.863|  2010-11-22|1.02820008E8|  Love & Other Drugs|\n",
            "|43362|   0.0|     1.705|  1952-02-23|         0.0|Love Is Better Th...|\n",
            "|43363|   0.0|      2.02|  1952-05-29|         0.0|   Lovely to Look At|\n",
            "|43395|   0.0|     4.758|  1950-11-10|         0.0| Two Weeks with Love|\n",
            "|43455|   0.0|     4.669|  1948-08-23|         0.0| The Loves of Carmen|\n",
            "|43483|   0.0|     3.161|  1946-12-25|         0.0|Love Laughs at An...|\n",
            "|43547|   0.0|     7.635|  2009-01-30|         0.0|Between Love & Go...|\n",
            "|43556|   0.0|     2.418|  2006-10-31|         0.0|Joe Satriani - Li...|\n",
            "|43561|   0.0|     8.475|  2004-12-17|         0.0|   Christmas in Love|\n",
            "|43609|   0.0|     5.684|  2010-04-07|         0.0|    My Own Love Song|\n",
            "|43652|   0.0|     3.097|  2009-11-19|         0.0| Beloved Berlin Wall|\n",
            "|43739|   0.0|     4.514|  1939-04-07|         0.0|         Love Affair|\n",
            "|43742| 6.0E7|     3.723|  1994-10-21|         0.0|         Love Affair|\n",
            "|43764|   0.0|      0.73|  2006-01-01|         0.0|The Fish Fall in ...|\n",
            "|43787|   0.0|     5.769|  1942-11-19|         0.0|You Were Never Lo...|\n",
            "+-----+------+----------+------------+------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on contain function\n",
        "df.filter(df.title.contains('ove')).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "_I7SK98W7tza",
        "outputId": "ca3d279b-ab1e-4d51-eb73-faca97ed49f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1025"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Filtering based on regular expression - P2\n",
        "df.filter(df['title'].rlike('\\w*ove')).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "2-F0Hvil7tzb",
        "outputId": "2c412364-0561-4549-b69c-4e7af94797be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on regular expression with ColRegex to subset columns - P1\n",
        "df.select(df.colRegex(\"`(re)+?.+`\")).printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "UKaKSV4r7tzb",
        "outputId": "c8ceb658-86f1-4fde-db17-1086719851d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on regular expression with ColRegex to subset columns - P2\n",
        "df.select(df.colRegex(\"`re\\w*`\")).printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "x-NLnv9Z7tzd",
        "outputId": "a7412ed0-8a80-4678-b1c0-dd5c84d79a8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Filtering based on regular expression with ColRegex to subset columns - P3\n",
        "df.select(df.colRegex(\"`\\w*e`\")).printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "hosuOSck7tze",
        "outputId": "636e0106-2b53-4714-d200-3d41bfa3bdbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = false)\n",
            " |-- varaiance: double (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# New Columns\n",
        "mean_pop=df.agg({'popularity': 'mean'}).collect()[0]['avg(popularity)']\n",
        "count_obs= df.count()\n",
        "df=df.withColumn('mean_popularity',lit(mean_pop))\n",
        "df=df.withColumn('varaiance',pow((df['popularity']-df['mean_popularity']),2))\n",
        "df.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "nryplITr7tzg",
        "outputId": "c1750c35-c303-434d-afed-d52a029b17e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+\n",
            "|   id|   budget|popularity|release_date|  revenue|               title|  mean_popularity|           varaiance|\n",
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+\n",
            "|43000|      0.0|     2.503|  1962-05-23|      0.0|The Elusive Corporal|5.295444259579189|   7.797744825681142|\n",
            "|43001|      0.0|      5.51|  1962-11-12|      0.0|  Sundays and Cybele|5.295444259579189|0.046034263963398346|\n",
            "|43002|      0.0|      5.62|  1962-05-24|      0.0|Lonely Are the Brave|5.295444259579189| 0.10533635435520017|\n",
            "|43003|      0.0|     7.159|  1975-03-12|      0.0|          F for Fake|5.295444259579189|  3.4728396990815216|\n",
            "|43004| 500000.0|     3.988|  1962-10-09|      0.0|Long Day's Journe...|5.295444259579189|   1.709410711356759|\n",
            "|43006|      0.0|     3.194|  1962-03-09|      0.0|           My Geisha|5.295444259579189|   4.416067952069205|\n",
            "|43007|      0.0|     2.689|  1962-10-31|      0.0|Period of Adjustment|5.295444259579189|   6.793552245032679|\n",
            "|43008|      0.0|     6.537|  1959-03-13|      0.0|    The Hanging Tree|5.295444259579189|  1.5414611017708733|\n",
            "|43010|      0.0|     4.297|  1962-01-01|      0.0|Sherlock Holmes a...|5.295444259579189|   0.996891076602093|\n",
            "|43011|      0.0|     4.417|  1962-01-01|      0.0|  Sodom and Gomorrah|5.295444259579189|  0.7716646388835591|\n",
            "|43012|7000000.0|     4.722|  1962-11-21|4000000.0|         Taras Bulba|5.295444259579189| 0.32883817884329936|\n",
            "|43013|      0.0|     2.543|  1962-04-17|      0.0|The Counterfeit T...|5.295444259579189|   7.575949496588105|\n",
            "|43014|      0.0|     4.303|  1962-10-24|      0.0|     Tower of London|5.295444259579189|  0.9849456613739271|\n",
            "|43015|      0.0|     3.493|  1962-12-07|      0.0|Varan the Unbelie...|5.295444259579189|   3.248805198877505|\n",
            "|43016|      0.0|     2.851|  1962-01-01|      0.0|Waltz of the Tore...|5.295444259579189|   5.975307393171506|\n",
            "|43017|      0.0|     4.047|  1961-10-11|      0.0|         Back Street|5.295444259579189|  1.5586132407239628|\n",
            "|43018|      0.0|     2.661|  1961-06-02|      0.0|Gidget Goes Hawaiian|5.295444259579189|   6.940296486482491|\n",
            "|43019|      0.0|     3.225|  2010-05-28|      0.0|Schuks Tshabalala...|5.295444259579189|  4.2867398269303285|\n",
            "|43020|      0.0|      5.72|  1961-06-15|      0.0|The Colossus of R...|5.295444259579189|  0.1802473985736286|\n",
            "|43021|      0.0|     3.292|  2008-08-22|      0.0|          Sex Galaxy|5.295444259579189|   4.013788679607341|\n",
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# New Columns - output\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "GcaqcJ7U7tzg",
        "outputId": "985a19cf-92f1-46ac-b335-a4737c935c87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1665668.6984730042\n",
            "37.858688057663116\n"
          ]
        }
      ],
      "source": [
        "# variance calculation\n",
        "variance_sum=df.agg({'varaiance': 'sum'}).collect()[0]['sum(varaiance)']\n",
        "print(variance_sum)\n",
        "variance_population= variance_sum/(count_obs-1)\n",
        "print(variance_population)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "BMDGlkV27tzh",
        "outputId": "7205a574-0a9e-4868-df27-7e5681b4eecd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.152941415100839"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Standard deviation\n",
        "import math\n",
        "math.sqrt(variance_population)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "t6wzQq0v7tzi",
        "outputId": "dea5c7f9-ced7-4035-f0b5-87c39d9fcd22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+\n",
            "|   id|   budget|popularity|release_date|  revenue|               title|  mean_popularity|           varaiance|\n",
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+\n",
            "|43000|      0.0|     2.503|  1962-05-23|      0.0|The Elusive Corporal|5.295444259579189|   7.797744825681142|\n",
            "|43001|      0.0|      5.51|  1962-11-12|      0.0|  Sundays and Cybele|5.295444259579189|0.046034263963398346|\n",
            "|43002|      0.0|      5.62|  1962-05-24|      0.0|Lonely Are the Brave|5.295444259579189| 0.10533635435520017|\n",
            "|43003|      0.0|     7.159|  1975-03-12|      0.0|          F for Fake|5.295444259579189|  3.4728396990815216|\n",
            "|43004| 500000.0|     3.988|  1962-10-09|      0.0|Long Day's Journe...|5.295444259579189|   1.709410711356759|\n",
            "|43006|      0.0|     3.194|  1962-03-09|      0.0|           My Geisha|5.295444259579189|   4.416067952069205|\n",
            "|43007|      0.0|     2.689|  1962-10-31|      0.0|Period of Adjustment|5.295444259579189|   6.793552245032679|\n",
            "|43008|      0.0|     6.537|  1959-03-13|      0.0|    The Hanging Tree|5.295444259579189|  1.5414611017708733|\n",
            "|43010|      0.0|     4.297|  1962-01-01|      0.0|Sherlock Holmes a...|5.295444259579189|   0.996891076602093|\n",
            "|43011|      0.0|     4.417|  1962-01-01|      0.0|  Sodom and Gomorrah|5.295444259579189|  0.7716646388835591|\n",
            "|43012|7000000.0|     4.722|  1962-11-21|4000000.0|         Taras Bulba|5.295444259579189| 0.32883817884329936|\n",
            "|43013|      0.0|     2.543|  1962-04-17|      0.0|The Counterfeit T...|5.295444259579189|   7.575949496588105|\n",
            "|43014|      0.0|     4.303|  1962-10-24|      0.0|     Tower of London|5.295444259579189|  0.9849456613739271|\n",
            "|43015|      0.0|     3.493|  1962-12-07|      0.0|Varan the Unbelie...|5.295444259579189|   3.248805198877505|\n",
            "|43016|      0.0|     2.851|  1962-01-01|      0.0|Waltz of the Tore...|5.295444259579189|   5.975307393171506|\n",
            "|43017|      0.0|     4.047|  1961-10-11|      0.0|         Back Street|5.295444259579189|  1.5586132407239628|\n",
            "|43018|      0.0|     2.661|  1961-06-02|      0.0|Gidget Goes Hawaiian|5.295444259579189|   6.940296486482491|\n",
            "|43019|      0.0|     3.225|  2010-05-28|      0.0|Schuks Tshabalala...|5.295444259579189|  4.2867398269303285|\n",
            "|43020|      0.0|      5.72|  1961-06-15|      0.0|The Colossus of R...|5.295444259579189|  0.1802473985736286|\n",
            "|43021|      0.0|     3.292|  2008-08-22|      0.0|          Sex Galaxy|5.295444259579189|   4.013788679607341|\n",
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Variance calculation sub output\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "mnj2waI77tzj",
        "outputId": "662f09a0-8665-4604-eb3e-53f1e7defba6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+----------+-------+\n",
            "|id   |budget   |popularity|budget_cat|ratings|\n",
            "+-----+---------+----------+----------+-------+\n",
            "|43000|0.0      |2.503     |Small     |Low    |\n",
            "|43001|0.0      |5.51      |Small     |High   |\n",
            "|43002|0.0      |5.62      |Small     |High   |\n",
            "|43003|0.0      |7.159     |Small     |High   |\n",
            "|43004|500000.0 |3.988     |Small     |Mid    |\n",
            "|43006|0.0      |3.194     |Small     |Mid    |\n",
            "|43007|0.0      |2.689     |Small     |Low    |\n",
            "|43008|0.0      |6.537     |Small     |High   |\n",
            "|43010|0.0      |4.297     |Small     |Mid    |\n",
            "|43011|0.0      |4.417     |Small     |Mid    |\n",
            "|43012|7000000.0|4.722     |Small     |Mid    |\n",
            "|43013|0.0      |2.543     |Small     |Low    |\n",
            "|43014|0.0      |4.303     |Small     |Mid    |\n",
            "|43015|0.0      |3.493     |Small     |Mid    |\n",
            "|43016|0.0      |2.851     |Small     |Low    |\n",
            "+-----+---------+----------+----------+-------+\n",
            "only showing top 15 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#  New Columns - Method 2\n",
        "def new_cols(budget,popularity):\n",
        " if budget<10000000: budget_cat='Small'\n",
        " elif budget<100000000: budget_cat='Medium'\n",
        " else: budget_cat='Big'\n",
        " if popularity<3: ratings='Low'\n",
        " elif popularity<5: ratings='Mid'\n",
        " else: ratings='High'\n",
        " return budget_cat,ratings\n",
        "\n",
        "\n",
        "# Apply the user defined function on the dataframe\n",
        "udfB=udf(new_cols,StructType([StructField(\"budget_cat\", StringType(), True),StructField(\"ratings\", StringType(), True)]))\n",
        "temp_df=df.select('id','budget','popularity').withColumn(\"newcat\",udfB(\"budget\",\"popularity\"))\n",
        "# Unbundle the struct type columns into individual columns and drop the struct type \n",
        "df_with_newcols = temp_df.select('id','budget','popularity','newcat').withColumn('budget_cat', temp_df.newcat.getItem('budget_cat')).withColumn('ratings', temp_df.newcat.getItem('ratings')).drop('newcat')\n",
        "df_with_newcols.show(15,False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "8BYtE6957tzk",
        "outputId": "5103f365-bd1e-4d3d-9cad-9768f2ae6723",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- newcat: struct (nullable = true)\n",
            " |    |-- budget_cat: string (nullable = true)\n",
            " |    |-- ratings: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#  New Columns - Observe Metadata\n",
        "temp_df.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "aAWOyUsZ7tzl"
      },
      "outputs": [],
      "source": [
        "#  New Columns - Method 3\n",
        "df_with_newcols = df.select('id','budget','popularity').\\\n",
        "withColumn('budget_cat', when(df['budget']<10000000,'Small').when(df['budget']<100000000,'Medium').otherwise('Big')).\\\n",
        "withColumn('ratings', when(df['popularity']<3,'Low').when(df['popularity']<5,'Mid').otherwise('High'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "tHg6DXnX7tzm"
      },
      "outputs": [],
      "source": [
        "#  Dropping and renaming\n",
        "columns_to_drop=['budget_cat']\n",
        "df_with_newcols=df_with_newcols.drop(*columns_to_drop)\n",
        "df_with_newcols = df_with_newcols.withColumnRenamed('id','film_id') .withColumnRenamed('ratings','film_ratings')\n",
        "# You can define all the variable changes in the list \n",
        "new_names = [('budget','film_budget'),('popularity','film_popularity')]\n",
        "\n",
        "# Applying the alias function\n",
        "df_with_newcols_renamed = df_with_newcols.select(list(map(lambda old,new:col(old).alias(new),*zip(*new_names))))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "nRsPrIjf7tzn",
        "outputId": "beaf3c00-177d-4b5d-c293-2e549a635dba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+---------+----------+------------+\n",
            "|film_id|   budget|popularity|film_ratings|\n",
            "+-------+---------+----------+------------+\n",
            "|  43000|      0.0|     2.503|         Low|\n",
            "|  43001|      0.0|      5.51|        High|\n",
            "|  43002|      0.0|      5.62|        High|\n",
            "|  43003|      0.0|     7.159|        High|\n",
            "|  43004| 500000.0|     3.988|         Mid|\n",
            "|  43006|      0.0|     3.194|         Mid|\n",
            "|  43007|      0.0|     2.689|         Low|\n",
            "|  43008|      0.0|     6.537|        High|\n",
            "|  43010|      0.0|     4.297|         Mid|\n",
            "|  43011|      0.0|     4.417|         Mid|\n",
            "|  43012|7000000.0|     4.722|         Mid|\n",
            "|  43013|      0.0|     2.543|         Low|\n",
            "|  43014|      0.0|     4.303|         Mid|\n",
            "|  43015|      0.0|     3.493|         Mid|\n",
            "|  43016|      0.0|     2.851|         Low|\n",
            "|  43017|      0.0|     4.047|         Mid|\n",
            "|  43018|      0.0|     2.661|         Low|\n",
            "|  43019|      0.0|     3.225|         Mid|\n",
            "|  43020|      0.0|      5.72|        High|\n",
            "|  43021|      0.0|     3.292|         Mid|\n",
            "+-------+---------+----------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#  Observe data\n",
        "df_with_newcols.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfU-zftI7tzq"
      },
      "source": [
        "# Chapter 3 Demonstrations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "h9fWyMlf7tzq",
        "outputId": "02fbd240-842b-4e2f-a50d-ed04ca4d007f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+----------+-------+---------------------+\n",
            "|   id|   budget|popularity|budget_cat|ratings|BudgetRating_Category|\n",
            "+-----+---------+----------+----------+-------+---------------------+\n",
            "|43000|      0.0|     2.503|     Small|    Low|             smalllow|\n",
            "|43001|      0.0|      5.51|     Small|   High|            smallhigh|\n",
            "|43002|      0.0|      5.62|     Small|   High|            smallhigh|\n",
            "|43003|      0.0|     7.159|     Small|   High|            smallhigh|\n",
            "|43004| 500000.0|     3.988|     Small|    Mid|             smallmid|\n",
            "|43006|      0.0|     3.194|     Small|    Mid|             smallmid|\n",
            "|43007|      0.0|     2.689|     Small|    Low|             smalllow|\n",
            "|43008|      0.0|     6.537|     Small|   High|            smallhigh|\n",
            "|43010|      0.0|     4.297|     Small|    Mid|             smallmid|\n",
            "|43011|      0.0|     4.417|     Small|    Mid|             smallmid|\n",
            "|43012|7000000.0|     4.722|     Small|    Mid|             smallmid|\n",
            "|43013|      0.0|     2.543|     Small|    Low|             smalllow|\n",
            "|43014|      0.0|     4.303|     Small|    Mid|             smallmid|\n",
            "|43015|      0.0|     3.493|     Small|    Mid|             smallmid|\n",
            "|43016|      0.0|     2.851|     Small|    Low|             smalllow|\n",
            "|43017|      0.0|     4.047|     Small|    Mid|             smallmid|\n",
            "|43018|      0.0|     2.661|     Small|    Low|             smalllow|\n",
            "|43019|      0.0|     3.225|     Small|    Mid|             smallmid|\n",
            "|43020|      0.0|      5.72|     Small|   High|            smallhigh|\n",
            "|43021|      0.0|     3.292|     Small|    Mid|             smallmid|\n",
            "+-----+---------+----------+----------+-------+---------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# String functions\n",
        "df_with_newcols = df.select('id','budget','popularity').\\\n",
        "withColumn('budget_cat', when(df['budget']<10000000,'Small').when(df['budget']<100000000,'Medium').otherwise('Big')).\\\n",
        "withColumn('ratings', when(df['popularity']<3,'Low').when(df['popularity']<5,'Mid').otherwise('High'))\n",
        "\n",
        "df_with_newcols=df_with_newcols.withColumn('BudgetRating_Category',concat(df_with_newcols.budget_cat,df_with_newcols.ratings))\n",
        "df_with_newcols=df_with_newcols.withColumn('BudgetRating_Category',trim(lower(df_with_newcols.BudgetRating_Category)))\n",
        "df_with_newcols.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "AMQsoE8l7tzr",
        "outputId": "9a7dd67e-33e9-4a3e-8ca1-2333d4332fde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/sql/dataframe.py:330: FutureWarning: Deprecated in 2.0, use createOrReplaceTempView instead.\n",
            "  warnings.warn(\"Deprecated in 2.0, use createOrReplaceTempView instead.\", FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-------------+\n",
            "|ratings|ratings_count|\n",
            "+-------+-------------+\n",
            "|   High|        16856|\n",
            "|    Low|        14865|\n",
            "|    Mid|        12277|\n",
            "+-------+-------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Registering temporary table\n",
        "df_with_newcols.registerTempTable('temp_data')\n",
        "\n",
        "# Showing the results\n",
        "spark.sql('select ratings, count(ratings) as ratings_count from temp_data group by ratings').show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "WahhhH0w7tzs",
        "outputId": "a007ba0d-51e2-4cce-ac92-57d1e01caf4a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------------+--------------+-----------------+\n",
            "|decile_rank|min_popularity|max_popularity|count(popularity)|\n",
            "+-----------+--------------+--------------+-----------------+\n",
            "|          1|        10.185|         180.0|             4379|\n",
            "|          2|         7.481|        10.182|             4379|\n",
            "|          3|         5.841|         7.481|             4379|\n",
            "|          4|         4.823|         5.841|             4378|\n",
            "|          5|         4.054|         4.822|             4378|\n",
            "|          6|         3.383|         4.054|             4378|\n",
            "|          7|         2.747|         3.383|             4378|\n",
            "|          8|         2.075|         2.747|             4378|\n",
            "|          9|         1.389|         2.075|             4378|\n",
            "|         10|           0.6|         1.389|             4378|\n",
            "+-----------+--------------+--------------+-----------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Deciling \n",
        "from pyspark.sql.window import *\n",
        "df_with_newcols=df_with_newcols.filter( (df_with_newcols['popularity'].isNotNull()) & (~isnan(df_with_newcols['popularity'])) )\n",
        "df_with_newcols = df_with_newcols.select(\"id\",\"budget\",\"popularity\", ntile(10).over(Window.partitionBy().orderBy(df_with_newcols['popularity'].desc())).alias(\"decile_rank\"))\n",
        "df_with_newcols.groupby(\"decile_rank\").agg(min('popularity').alias('min_popularity'),max('popularity').alias('max_popularity'),count('popularity')).show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "cvvlmh9V7tzv"
      },
      "outputs": [],
      "source": [
        "# Data prep for windowing function\n",
        "df_second_best = df.select('id', 'popularity', 'release_date')\n",
        "df_second_best=df_second_best.withColumn('release_year',year('release_date')).drop('release_date')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "6z5k0S3Z7tzw"
      },
      "outputs": [],
      "source": [
        "# Defining partition function\n",
        "year_window =  Window.partitionBy(df_second_best['release_year']).orderBy(df_with_newcols['popularity'].desc())\n",
        "df_second_best=df_second_best.select('id', 'popularity', 'release_year',rank().over(year_window).alias(\"rank\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "-H1O7pYB7tzx",
        "outputId": "3236020c-159f-4f5e-f9c8-b219e6cdd695",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+----------+------------+----+\n",
            "|   id|popularity|release_year|rank|\n",
            "+-----+----------+------------+----+\n",
            "|11202|    14.029|        1970|   2|\n",
            "+-----+----------+------------+----+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_second_best.filter((df_second_best['release_year']==1970) & (df_second_best['rank']==2)).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "gpKEtU_77tz0",
        "outputId": "74936ea7-9944-4e9b-b92e-a514fc288fb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+-------+------------+------------------+\n",
            "|id  |revenue|release_year|revenue_difference|\n",
            "+----+-------+------------+------------------+\n",
            "|null|10.0   |null        |0.0               |\n",
            "|null|10.0   |null        |0.0               |\n",
            "|null|10.0   |null        |0.0               |\n",
            "|null|8.7    |null        |1.3000002         |\n",
            "|null|8.1    |null        |1.8999996         |\n",
            "|null|8.0    |null        |2.0               |\n",
            "|null|8.0    |null        |2.0               |\n",
            "|null|7.9    |null        |2.1               |\n",
            "|null|7.8    |null        |2.1999998         |\n",
            "|null|7.7    |null        |2.3000002         |\n",
            "+----+-------+------------+------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Revenue Difference\n",
        "\n",
        "df_revenue = df.select('id', 'revenue', 'release_date')\n",
        "df_revenue=df_revenue.withColumn('release_year',year('release_date')).drop('release_date')\n",
        "\n",
        "windowRev =Window.partitionBy(df_revenue['release_year']).orderBy(df_revenue['revenue'].desc()).rangeBetween(-sys.maxsize, sys.maxsize)\n",
        "\n",
        "revenue_difference = (max(df_revenue['revenue']).over(windowRev) - df_revenue['revenue'])\n",
        "df_revenue.select('id', 'revenue', 'release_year',revenue_difference.alias(\"revenue_difference\")).show(10,False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "oW4QMgtY7tz1",
        "outputId": "24f72406-78a6-4eb9-e1c5-704af91ae535",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+------------------------------------+\n",
            "|title         |collect_list(release_year)          |\n",
            "+--------------+------------------------------------+\n",
            "|The Lost World|[1999, 2001, 1925, 1960, 1992, 1998]|\n",
            "+--------------+------------------------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Collect list demonstration\n",
        "# Step 1:Create the year column from release date\n",
        "df=df.withColumn('release_year',year('release_date'))\n",
        "# Applying collect list function to gather all occurances\n",
        "df.filter(\"title=='The Lost World'\").groupby('title').agg(collect_list(\"release_year\")).show(1,False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "YC4HS7tW7tz3",
        "outputId": "9fb1021b-c17c-4822-9d0d-dd0fcfc5ad96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17621"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# Simple random sampling in pyspark without replacement\n",
        " \n",
        "df_sample = df.sample(False, 0.4, 42)\n",
        "df_sample.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "LCG79cdt7tz4",
        "outputId": "a30ad072-460d-4af3-ccd5-bc568c6d4a74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17556"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "# Simple random sampling in pyspark with replacement\n",
        " \n",
        "df_sample = df.sample(True, 0.4, 42)\n",
        "df_sample.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "H1WQ4s5q7tz6",
        "outputId": "ea64e524-2747-4772-92c8-c8e848676769",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "269"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "# Stratified sampling in pyspark \n",
        "df_strat = df.sampleBy(\"release_year\", fractions={1959: 0.2, 1960: 0.4, 1961: 0.4}, seed=11)\n",
        "df_strat.count()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "yR0IfOn27tz7",
        "outputId": "ba2e3c9a-e499-478b-dfe4-370a4a8410d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+------------+\n",
            "|   id|   budget|popularity|release_date|  revenue|               title|  mean_popularity|           varaiance|release_year|\n",
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+------------+\n",
            "|43000|      0.0|     2.503|  1962-05-23|      0.0|The Elusive Corporal|5.295444259579189|   7.797744825681142|        1962|\n",
            "|43001|      0.0|      5.51|  1962-11-12|      0.0|  Sundays and Cybele|5.295444259579189|0.046034263963398346|        1962|\n",
            "|43002|      0.0|      5.62|  1962-05-24|      0.0|Lonely Are the Brave|5.295444259579189| 0.10533635435520017|        1962|\n",
            "|43003|      0.0|     7.159|  1975-03-12|      0.0|          F for Fake|5.295444259579189|  3.4728396990815216|        1975|\n",
            "|43004| 500000.0|     3.988|  1962-10-09|      0.0|Long Day's Journe...|5.295444259579189|   1.709410711356759|        1962|\n",
            "|43006|      0.0|     3.194|  1962-03-09|      0.0|           My Geisha|5.295444259579189|   4.416067952069205|        1962|\n",
            "|43007|      0.0|     2.689|  1962-10-31|      0.0|Period of Adjustment|5.295444259579189|   6.793552245032679|        1962|\n",
            "|43008|      0.0|     6.537|  1959-03-13|      0.0|    The Hanging Tree|5.295444259579189|  1.5414611017708733|        1959|\n",
            "|43010|      0.0|     4.297|  1962-01-01|      0.0|Sherlock Holmes a...|5.295444259579189|   0.996891076602093|        1962|\n",
            "|43011|      0.0|     4.417|  1962-01-01|      0.0|  Sodom and Gomorrah|5.295444259579189|  0.7716646388835591|        1962|\n",
            "|43012|7000000.0|     4.722|  1962-11-21|4000000.0|         Taras Bulba|5.295444259579189| 0.32883817884329936|        1962|\n",
            "|43013|      0.0|     2.543|  1962-04-17|      0.0|The Counterfeit T...|5.295444259579189|   7.575949496588105|        1962|\n",
            "|43014|      0.0|     4.303|  1962-10-24|      0.0|     Tower of London|5.295444259579189|  0.9849456613739271|        1962|\n",
            "|43015|      0.0|     3.493|  1962-12-07|      0.0|Varan the Unbelie...|5.295444259579189|   3.248805198877505|        1962|\n",
            "|43016|      0.0|     2.851|  1962-01-01|      0.0|Waltz of the Tore...|5.295444259579189|   5.975307393171506|        1962|\n",
            "|43017|      0.0|     4.047|  1961-10-11|      0.0|         Back Street|5.295444259579189|  1.5586132407239628|        1961|\n",
            "|43018|      0.0|     2.661|  1961-06-02|      0.0|Gidget Goes Hawaiian|5.295444259579189|   6.940296486482491|        1961|\n",
            "|43019|      0.0|     3.225|  2010-05-28|      0.0|Schuks Tshabalala...|5.295444259579189|  4.2867398269303285|        2010|\n",
            "|43020|      0.0|      5.72|  1961-06-15|      0.0|The Colossus of R...|5.295444259579189|  0.1802473985736286|        1961|\n",
            "|43021|      0.0|     3.292|  2008-08-22|      0.0|          Sex Galaxy|5.295444259579189|   4.013788679607341|        2008|\n",
            "+-----+---------+----------+------------+---------+--------------------+-----------------+--------------------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "iyqoLoHM7tz8"
      },
      "outputs": [],
      "source": [
        "# Coalesce Demonstration\n",
        "# df.coalesce(1).write.format('csv').option('delimiter', '|').save('output_df1')\n",
        "\n",
        "df.coalesce(1).write.format('csv').option('delimiter', '|').mode('overwrite').save('output_df1')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "8xxUnpRw7tz-"
      },
      "outputs": [],
      "source": [
        "# PartitionBy Demonstration\n",
        "# df.write.partitionBy('release_year').format('csv').option('delimiter', '|').save('output_df_partitioned')\n",
        "\n",
        "df.write.partitionBy('release_year').format('csv').option('delimiter', '|').mode('overwrite').save('output_df_partitioned')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "dybxnD5M7tz_",
        "outputId": "db752075-da62-4876-8e20-be878964485e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "# SaveAs Demonstration\n",
        "# df.write.saveAsTable('film_ratings')\n",
        "\n",
        "spark.sql(\"DROP TABLE IF EXISTS film_ratings\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.write.saveAsTable('my_film_ratings')\n"
      ],
      "metadata": {
        "id": "0nx_cJ2Y_qLv"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "tfaOI4eP7t0A"
      },
      "outputs": [],
      "source": [
        "# Pyspark to Pandas\n",
        "df_pandas=df.toPandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "p2N0f9Nf7t0B"
      },
      "outputs": [],
      "source": [
        "# Pandas to PySpark\n",
        "df_py = spark.createDataFrame(df_pandas)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "DtEzwotH7t0C"
      },
      "outputs": [],
      "source": [
        "#  Reading another new dataset for joins \n",
        "\n",
        "# Input parameters\n",
        "file_location = \"/content/drive/MyDrive/movie_data_part2.csv\"\n",
        "file_type = \"csv\"\n",
        "infer_schema = \"False\"\n",
        "first_row_is_header = \"True\"\n",
        "delimiter = \"|\"\n",
        "\n",
        "# Bringing all the options together to read the csv file \n",
        "\n",
        "df_p1 = spark.read.format(file_type) \\\n",
        ".option(\"inferSchema\", infer_schema) \\\n",
        ".option(\"header\", first_row_is_header) \\\n",
        ".option(\"sep\", delimiter) \\\n",
        ".load(file_location)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "svviiScE7t0C",
        "outputId": "b36c47cc-6b1a-45c6-f9a8-8920cc2dad15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = false)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Schema of new dataset\n",
        "df.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "hWCv-GtF7t0D"
      },
      "outputs": [],
      "source": [
        "# Preparing data for joins\n",
        "df_p1 = df_p1.withColumn('id',df_p1['id'].cast(\"integer\"))\n",
        "df_p1=df_p1.filter((df_p1['id'].isNotNull()) & (~isnan(df_p1['id'])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "ccrmlg-k7t0E",
        "outputId": "e6f915ee-6cdd-491e-c659-a37b7ce6c21c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = false)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            " |-- cast: string (nullable = true)\n",
            " |-- adult: string (nullable = true)\n",
            " |-- directors: string (nullable = true)\n",
            " |-- vote_count: string (nullable = true)\n",
            " |-- spoken_languages: string (nullable = true)\n",
            " |-- poster_path: string (nullable = true)\n",
            " |-- homepage: string (nullable = true)\n",
            " |-- imdb_id: string (nullable = true)\n",
            " |-- genres: string (nullable = true)\n",
            " |-- video: string (nullable = true)\n",
            " |-- id: integer (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24998"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "# Inner Join\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'inner').printSchema()\n",
        "df.join(df_p1, df['id'] == df_p1['id']).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "_08u82V-7t0F",
        "outputId": "26241251-4cbb-444b-c689-5bde2023f397",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = false)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            " |-- cast: string (nullable = true)\n",
            " |-- adult: string (nullable = true)\n",
            " |-- directors: string (nullable = true)\n",
            " |-- vote_count: string (nullable = true)\n",
            " |-- spoken_languages: string (nullable = true)\n",
            " |-- poster_path: string (nullable = true)\n",
            " |-- homepage: string (nullable = true)\n",
            " |-- imdb_id: string (nullable = true)\n",
            " |-- genres: string (nullable = true)\n",
            " |-- video: string (nullable = true)\n",
            " |-- id: integer (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43998"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "# Left Join\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'left').printSchema()\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'left').count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "zxvQ6shX7t0G",
        "outputId": "1efc0005-21ac-4a6f-e390-e38978db4557",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = true)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            " |-- cast: string (nullable = true)\n",
            " |-- adult: string (nullable = true)\n",
            " |-- directors: string (nullable = true)\n",
            " |-- vote_count: string (nullable = true)\n",
            " |-- spoken_languages: string (nullable = true)\n",
            " |-- poster_path: string (nullable = true)\n",
            " |-- homepage: string (nullable = true)\n",
            " |-- imdb_id: string (nullable = true)\n",
            " |-- genres: string (nullable = true)\n",
            " |-- video: string (nullable = true)\n",
            " |-- id: integer (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24998"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "# Right Join\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'right').printSchema()\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'right').count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "lWlIoavX7t0G",
        "outputId": "e3bca6d8-a9fb-4610-d8c8-6c3769b8ea97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = true)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            " |-- cast: string (nullable = true)\n",
            " |-- adult: string (nullable = true)\n",
            " |-- directors: string (nullable = true)\n",
            " |-- vote_count: string (nullable = true)\n",
            " |-- spoken_languages: string (nullable = true)\n",
            " |-- poster_path: string (nullable = true)\n",
            " |-- homepage: string (nullable = true)\n",
            " |-- imdb_id: string (nullable = true)\n",
            " |-- genres: string (nullable = true)\n",
            " |-- video: string (nullable = true)\n",
            " |-- id: integer (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43998"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "# Outer Join\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'outer').printSchema()\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'outer').count()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "DMwPuGjJ7t0H",
        "outputId": "a81f65c6-3516-4bda-eaed-9e4c3e269722",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = false)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19000"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "# Left Anti Join\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'left_anti').printSchema()\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'left_anti').count()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "BYHDHhCM7t0I",
        "outputId": "129d572e-4dea-430b-ff02-da7ecbbde4ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- id: integer (nullable = true)\n",
            " |-- budget: float (nullable = true)\n",
            " |-- popularity: float (nullable = true)\n",
            " |-- release_date: date (nullable = true)\n",
            " |-- revenue: float (nullable = true)\n",
            " |-- title: string (nullable = true)\n",
            " |-- mean_popularity: double (nullable = false)\n",
            " |-- varaiance: double (nullable = true)\n",
            " |-- release_year: integer (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24998"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "# Left Semi Join\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'left_semi').printSchema()\n",
        "df.join(df_p1, df['id'] == df_p1['id'],'left_semi').count()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "6MAt87fd7t0J",
        "outputId": "376adb68-c2e9-4a94-faee-d98ba4d032cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24998"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "# Broadcast Join\n",
        "df.join(broadcast(df_p1), df['id'] == df_p1['id'],'left_semi').count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "ckJECxqi7t0K",
        "outputId": "888bfcd0-9b14-433d-9523-172d347feac0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43998"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "# Original data count\n",
        "df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "BDJNDTxu7t0K",
        "outputId": "3b1046e4-5521-4e44-c230-f35902b1a279",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43643"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# Drop Duplicates Demonstration\n",
        "df.dropDuplicates(['title','release_year']).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "7RyVldeM7t0M"
      },
      "outputs": [],
      "source": [
        "# Histogram Demonstration\n",
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "# Processing the data in Spark. We can use the histogram function from the RDD \n",
        "histogram_data = df.select('popularity').rdd.flatMap(lambda x: x).histogram(25)\n",
        "# Loading the Computed Histogram into a Pandas Dataframe for plotting\n",
        "hist_df=pd.DataFrame(list(zip(*histogram_data)), columns=['bin', 'frequency'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "ZFVojBuZ7t0N",
        "outputId": "55e35968-652d-4e3b-ab82-b77f1903f827",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plotting\n",
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "# sns.barplot(hist_df['bin'],hist_df['frequency'])\n",
        "sns.barplot(data=hist_df, x='bin', y='frequency')\n",
        "\n",
        "plt.xticks(rotation=25)\n",
        "plt.title('Distribution of Popularity')\n",
        "plt.show()\n",
        "# plt.savefig('myimage.svg', format='svg', dpi=1200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "YAfZP7tM7t0O",
        "outputId": "9b367843-963f-4dd2-9073-e6b5e050fa75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filtering the data to get a better understanding of data\n",
        "df_fil=df.filter('popularity<22')\n",
        "# Processing the data in Spark. We can use the histogram function from the RDD \n",
        "histogram_data = df_fil.select('popularity').rdd.flatMap(lambda x: x).histogram(25)\n",
        "# Loading the Computed Histogram into a Pandas Dataframe for plotting\n",
        "hist_df=pd.DataFrame(list(zip(*histogram_data)), columns=['bin', 'frequency'])\n",
        "\n",
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "# sns.barplot(hist_df['bin'],hist_df['frequency'])\n",
        "sns.barplot(data=hist_df, x='bin', y='frequency')\n",
        "\n",
        "plt.xticks(rotation=25)\n",
        "plt.title('Distribution of Popularity - Data is filtered')\n",
        "plt.show()\n",
        "# plt.savefig('myimage1.svg', format='svg', dpi=1200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "x6LJzrvx7t0Q"
      },
      "outputs": [],
      "source": [
        "# Data preparation for bar chart\n",
        "df_cat=df.filter(\"(release_year>1959) and (release_year<1971)\").groupby('release_year').count().toPandas()\n",
        "df_cat=df_cat.sort_values(by=['release_year'], ascending=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "zinDBjL47t0R",
        "outputId": "acca8d57-e1fa-447f-c81c-7959e69dc1b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-7a41e804ec51>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Plotting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'figure.figsize'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8.27\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_cat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'release_year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf_cat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of films released each year from 1960 to 1970 in our dataset'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: barplot() takes from 0 to 1 positional arguments but 2 were given"
          ]
        }
      ],
      "source": [
        "#Plotting\n",
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "sns.barplot(df_cat['release_year'],df_cat['count'])\n",
        "plt.xticks(rotation=25)\n",
        "plt.title('Number of films released each year from 1960 to 1970 in our dataset')\n",
        "plt.show()\n",
        "# plt.savefig('myimage2.svg', format='svg', dpi=1200)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}