project_p4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\spark-2.4.4'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "findspark.find()\n",
    "import pyspark\n",
    "findspark.find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext, SparkConf\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.session import SparkSession\n",
    "sc = SparkContext.getOrCreate()\n",
    "spark = SparkSession(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = spark.read.format(\"csv\")\\\n",
    ".options(header = True, inferSchema = True)\\\n",
    ".load(\"hdfs://localhost:9000/MovieData/input/users.csv\")\\\n",
    ".persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = spark.read.format(\"csv\")\\\n",
    ".options(header = True, inferSchema = True)\\\n",
    ".load(\"hdfs://localhost:9000/MovieData/input/movie.csv\")\\\n",
    ".persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = spark.read.format(\"csv\")\\\n",
    ".options(header = True, inferSchema = True)\\\n",
    ".load(\"hdfs://localhost:9000/MovieData/input/rating.csv\")\\\n",
    ".persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+---------+-----+\n",
      "|  age|   genres|count|\n",
      "+-----+---------+-----+\n",
      "|25-34|    Drama| 2096|\n",
      "|25-34|   Comedy| 2095|\n",
      "|25-34| Thriller| 2089|\n",
      "|25-34|   Action| 2089|\n",
      "|25-34|Adventure| 2088|\n",
      "|25-34|  Romance| 2083|\n",
      "|25-34|    Crime| 2079|\n",
      "|25-34|   Sci-Fi| 2058|\n",
      "|25-34|  Fantasy| 2015|\n",
      "|25-34|  Mystery| 1968|\n",
      "|25-34| Children| 1939|\n",
      "|25-34|      War| 1903|\n",
      "|25-34|   Horror| 1843|\n",
      "|25-34|Animation| 1809|\n",
      "|25-34|  Musical| 1750|\n",
      "|25-34|  Western| 1468|\n",
      "|25-34|     IMAX| 1378|\n",
      "|35-44|    Drama| 1193|\n",
      "|35-44|   Comedy| 1192|\n",
      "|35-44|  Romance| 1190|\n",
      "+-----+---------+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import split, explode\n",
    "\n",
    "popular_age_genres = users.alias(\"t1\")\\\n",
    ".join(ratings.alias(\"t2\"), col(\"t1.user_id\") == col(\"t2.userId\"))\\\n",
    ".join(movies.alias(\"t3\"), col(\"t2.movieId\") == col(\"t3.movieId\"))\\\n",
    ".select(explode(split(col(\"genres\"), \"\\\\|\")).alias(\"genres\"), col(\"age\"), col(\"userId\"))\\\n",
    ".dropDuplicates()\\\n",
    ".groupBy(col(\"age\"), col(\"genres\")).count()\\\n",
    ".orderBy(desc(\"count\"))\n",
    "\n",
    "popular_age_genres.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------------+-----------+\n",
      "|     age|popular_genre|total_count|\n",
      "+--------+-------------+-----------+\n",
      "|   18-24|       Comedy|       1103|\n",
      "|   25-34|        Drama|       2096|\n",
      "|   35-44|        Drama|       1193|\n",
      "|   45-49|        Drama|        550|\n",
      "|   50-55|        Drama|        496|\n",
      "|     56+|        Drama|        380|\n",
      "|Under 18|        Drama|        222|\n",
      "+--------+-------------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = popular_age_genres.groupBy(\"age\")\\\n",
    ".agg(first(col(\"genres\")).alias(\"popular_genre\"), first(col(\"count\")).alias(\"total_count\"))\\\n",
    ".orderBy(col(\"age\"))\n",
    "result.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.coalesce(1).write.csv(\"hdfs://localhost:9000/MovieData/output/popular_genre_age_Kgroup.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "    \n",
    "import matplotlib\n",
    "%matplotlib inline \n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "df = result.toPandas()\n",
    "bar_graph = sb.barplot(data = df, x = 'age', y = 'total_count', ci = None, palette = 'Blues_d', order = [\"Under 18\", \"18-24\", \"25-34\", \"35-44\", \"45-49\", \"50-55\", \"56+\"]);\n",
    "\n",
    "i = 0\n",
    "for p in bar_graph.patches:\n",
    "    bar_graph.annotate(df.popular_genre[i] , xy=((p.get_x()), (p.get_height()*1)), rotation=30)\n",
    "    i = i +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.coalesce(1).write.csv(\"hdfs://localhost:9000/MovieData/output/popular_genre_age_Pgroup.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}