EDA-jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q Findspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/usr/lib/jvm/java-8-openjdk-amd64'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.environ[\"JAVA_HOME\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/vishnu/hadoop/spark'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.environ[\"SPARK_HOME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, Java and Spark are configured properly and so we can now go ahead and start loading data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Start Apache Spark session and context\n",
    "import pyspark\n",
    "from pyspark.sql import SQLContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName('BigDataProject').getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-------+------+----------+\n",
      "|userId|movieId|rating| timestamp|\n",
      "+------+-------+------+----------+\n",
      "|     1|    296|   5.0|1147880044|\n",
      "|     1|    306|   3.5|1147868817|\n",
      "|     1|    307|   5.0|1147868828|\n",
      "|     1|    665|   5.0|1147878820|\n",
      "|     1|    899|   3.5|1147868510|\n",
      "+------+-------+------+----------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings = spark.read.option(\"header\", \"true\").csv(\"ml-25m/ratings.csv\")\n",
    "ratings.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------+--------------------+\n",
      "|movieId|               title|              genres|\n",
      "+-------+--------------------+--------------------+\n",
      "|      1|    Toy Story (1995)|Adventure|Animati...|\n",
      "|      2|      Jumanji (1995)|Adventure|Childre...|\n",
      "|      3|Grumpier Old Men ...|      Comedy|Romance|\n",
      "|      4|Waiting to Exhale...|Comedy|Drama|Romance|\n",
      "|      5|Father of the Bri...|              Comedy|\n",
      "+-------+--------------------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "movies = spark.read.option(\"header\", \"true\").csv(\"ml-25m/movies.csv\")\n",
    "movies.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The most popular Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import *\n",
    "\n",
    "most_popular = ratings\\\n",
    ".groupBy(\"movieId\")\\\n",
    ".agg(count(\"userId\"))\\\n",
    ".withColumnRenamed(\"count(userId)\", \"num_ratings\")\\\n",
    ".sort(desc(\"num_ratings\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----------+\n",
      "|movieId|num_ratings|\n",
      "+-------+-----------+\n",
      "|    356|      81491|\n",
      "|    318|      81482|\n",
      "|    296|      79672|\n",
      "|    593|      74127|\n",
      "|   2571|      72674|\n",
      "|    260|      68717|\n",
      "|    480|      64144|\n",
      "|    527|      60411|\n",
      "|    110|      59184|\n",
      "|   2959|      58773|\n",
      "+-------+-----------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#To show the top 10 most popular movies.\n",
    "most_popular.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see only the movieId and the number of ratings. The name of the movie is contained in the movies.csv file. So we have to use that to display the name of the movie here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----------+-------+--------------------+--------------------+\n",
      "|movieId|num_ratings|movieId|               title|              genres|\n",
      "+-------+-----------+-------+--------------------+--------------------+\n",
      "|    356|      81491|    356| Forrest Gump (1994)|Comedy|Drama|Roma...|\n",
      "|    318|      81482|    318|Shawshank Redempt...|         Crime|Drama|\n",
      "|    296|      79672|    296| Pulp Fiction (1994)|Comedy|Crime|Dram...|\n",
      "|    593|      74127|    593|Silence of the La...|Crime|Horror|Thri...|\n",
      "|   2571|      72674|   2571|  Matrix, The (1999)|Action|Sci-Fi|Thr...|\n",
      "|    260|      68717|    260|Star Wars: Episod...|Action|Adventure|...|\n",
      "|    480|      64144|    480|Jurassic Park (1993)|Action|Adventure|...|\n",
      "|    527|      60411|    527|Schindler's List ...|           Drama|War|\n",
      "|    110|      59184|    110|   Braveheart (1995)|    Action|Drama|War|\n",
      "|   2959|      58773|   2959|   Fight Club (1999)|Action|Crime|Dram...|\n",
      "|    589|      57379|    589|Terminator 2: Jud...|       Action|Sci-Fi|\n",
      "|   1196|      57361|   1196|Star Wars: Episod...|Action|Adventure|...|\n",
      "|      1|      57309|      1|    Toy Story (1995)|Adventure|Animati...|\n",
      "|   4993|      55736|   4993|Lord of the Rings...|   Adventure|Fantasy|\n",
      "|     50|      55366|     50|Usual Suspects, T...|Crime|Mystery|Thr...|\n",
      "+-------+-----------+-------+--------------------+--------------------+\n",
      "only showing top 15 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Showing the top 15 movies.\n",
    "most_popular_movies = most_popular.join(movies, most_popular.movieId == movies.movieId)\n",
    "most_popular_movies = most_popular_movies \\\n",
    ".sort(desc(\"num_ratings\"))\n",
    "most_popular_movies.show(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, most of the movies shown above are really popular and well known."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Rated Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we just sort based on the ratings, we can get movies which have 5 starts but have very few ratings. See the example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_rated = ratings\\\n",
    ".groupBy(\"movieId\")\\\n",
    ".agg(avg(col(\"rating\")))\\\n",
    ".withColumnRenamed(\"avg(rating)\", \"avg_rating\")\\\n",
    ".sort(desc(\"avg_rating\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+-------+--------------------+--------------------+\n",
      "|movieId|avg_rating|movieId|               title|              genres|\n",
      "+-------+----------+-------+--------------------+--------------------+\n",
      "| 190785|       5.0| 190785|   The Portal (2010)|              Horror|\n",
      "| 178721|       5.0| 178721|Distance Between ...|Action|Adventure|...|\n",
      "| 159349|       5.0| 159349|     Together (2009)|               Drama|\n",
      "| 195641|       5.0| 195641|Final Recourse (2...|      Drama|Thriller|\n",
      "| 139000|       5.0| 139000|   Rabid Love (2013)|  (no genres listed)|\n",
      "| 169816|       5.0| 169816|Day of the Panthe...|              Action|\n",
      "| 200316|       5.0| 200316|First We Take Bro...|         Crime|Drama|\n",
      "| 128137|       5.0| 128137|You're Jinxed, Fr...|  (no genres listed)|\n",
      "| 208477|       5.0| 208477|       Kaithi (2019)|     Action|Thriller|\n",
      "| 178147|       5.0| 178147|Beatles Stories (...|         Documentary|\n",
      "+-------+----------+-------+--------------------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_rated_movies = top_rated.join(movies, top_rated.movieId == movies.movieId).sort(desc(\"avg_rating\"))\n",
    "top_rated_movies.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that these movies are have very high average rating but are not really well known. This is because they could have very few 5 star ratings and still be shown here. To avoid this, we will have to consider top rated movies with also a good number of ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----------+----------+-------+--------------------+--------------------+\n",
      "|movieId|num_ratings|avg_rating|movieId|               title|              genres|\n",
      "+-------+-----------+----------+-------+--------------------+--------------------+\n",
      "| 165787|          3|       5.0| 165787|Lonesome Dove Chu...|             Western|\n",
      "| 179731|          3|       5.0| 179731|Sound of Christma...|               Drama|\n",
      "| 118268|          3|       5.0| 118268|Borrowed Time (2012)|               Drama|\n",
      "| 148298|          3|       5.0| 148298|       Awaken (2013)|Drama|Romance|Sci-Fi|\n",
      "| 208477|          2|       5.0| 208477|       Kaithi (2019)|     Action|Thriller|\n",
      "| 195641|          2|       5.0| 195641|Final Recourse (2...|      Drama|Thriller|\n",
      "| 169818|          2|       5.0| 169818|FB: Fighting Beat...|              Action|\n",
      "| 148114|          2|       5.0| 148114|The Ties That Bin...|  (no genres listed)|\n",
      "| 179589|          2|       5.0| 179589|  Windstorm 2 (2015)|Adventure|Childre...|\n",
      "| 161944|          2|       5.0| 161944|The Last Brickmak...|               Drama|\n",
      "+-------+-----------+----------+-------+--------------------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_rated = ratings\\\n",
    ".groupBy(\"movieId\")\\\n",
    ".agg(count(\"userId\"), avg(col(\"rating\")))\\\n",
    ".withColumnRenamed(\"count(userId)\", \"num_ratings\")\\\n",
    ".withColumnRenamed(\"avg(rating)\", \"avg_rating\")\n",
    "\n",
    "\n",
    "top_rated_movies = top_rated.join(movies, top_rated.movieId == movies.movieId).sort(desc(\"avg_rating\"), desc(\"num_ratings\"))\n",
    "top_rated_movies.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like you can see above, there are only 3 ratings for the first top rated movie. So we have to set a threshold like around 500 at the least."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----------+------------------+-------+--------------------+--------------------+\n",
      "|movieId|num_ratings|        avg_rating|movieId|               title|              genres|\n",
      "+-------+-----------+------------------+-------+--------------------+--------------------+\n",
      "| 171011|       1124| 4.483096085409253| 171011|Planet Earth II (...|         Documentary|\n",
      "| 159817|       1747| 4.464796794504865| 159817| Planet Earth (2006)|         Documentary|\n",
      "|    318|      81482| 4.413576004516335|    318|Shawshank Redempt...|         Crime|Drama|\n",
      "| 170705|       1356| 4.398598820058997| 170705|Band of Brothers ...|    Action|Drama|War|\n",
      "|    858|      52498| 4.324336165187245|    858|Godfather, The (1...|         Crime|Drama|\n",
      "| 179135|        659| 4.289833080424886| 179135|Blue Planet II (2...|         Documentary|\n",
      "|     50|      55366| 4.284353213163313|     50|Usual Suspects, T...|Crime|Mystery|Thr...|\n",
      "|   1221|      34188|4.2617585117585115|   1221|Godfather: Part I...|         Crime|Drama|\n",
      "| 163809|        546| 4.258241758241758| 163809|Over the Garden W...|Adventure|Animati...|\n",
      "|   2019|      13367|  4.25476920775043|   2019|Seven Samurai (Sh...|Action|Adventure|...|\n",
      "| 142115|        564|  4.24822695035461| 142115|The Blue Planet (...|         Documentary|\n",
      "|    527|      60411| 4.247579083279535|    527|Schindler's List ...|           Drama|War|\n",
      "|   1203|      16569| 4.243014062405697|   1203| 12 Angry Men (1957)|               Drama|\n",
      "|    904|      20162| 4.237947624243627|    904|  Rear Window (1954)|    Mystery|Thriller|\n",
      "|   2959|      58773| 4.228310618821568|   2959|   Fight Club (1999)|Action|Crime|Dram...|\n",
      "|   1193|      36058|4.2186616007543405|   1193|One Flew Over the...|               Drama|\n",
      "|    750|      26714| 4.215804447106386|    750|Dr. Strangelove o...|          Comedy|War|\n",
      "|   5618|      22719| 4.212267265284564|   5618|Spirited Away (Se...|Adventure|Animati...|\n",
      "| 166024|       1030| 4.210194174757282| 166024|     Whiplash (2013)|  (no genres listed)|\n",
      "|    912|      26890| 4.206563778356267|    912|   Casablanca (1942)|       Drama|Romance|\n",
      "+-------+-----------+------------------+-------+--------------------+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_rated_movies.where(\"num_ratings > 500\").show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see that these movies have quite a few ratings are are well known. We can tweak the number of ratings to see movies which have more ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Polarizing Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polarizing movies are those which people either hate or love. Some people love it while a few others hate it.  \n",
    "We can find these by looking for movies which have the highest standard deviation in the ratings. Standard Deviation is a measure of how much the data varies from the mean and so we hope this is a good measure for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----------+------------------+------------------+-------+--------------------+--------------------+\n",
      "|movieId|num_ratings|        avg_rating|        std_rating|movieId|               title|              genres|\n",
      "+-------+-----------+------------------+------------------+-------+--------------------+--------------------+\n",
      "|  74754|        670| 2.403731343283582|1.6649650528666515|  74754|    Room, The (2003)|Comedy|Drama|Romance|\n",
      "|  62912|        611|2.5106382978723403|1.4888552380190525|  62912|High School Music...|             Musical|\n",
      "|  98203|       1569|2.5242192479286167|1.4560043846864676|  98203|Twilight Saga: Br...|Adventure|Drama|F...|\n",
      "|  27899|        616|2.7767857142857144| 1.445519028350731|  27899|What the #$*! Do ...|Comedy|Documentar...|\n",
      "|  91104|       1896|2.3285864978902953| 1.442346851125679|  91104|Twilight Saga: Br...|Adventure|Drama|F...|\n",
      "|   1924|       2210| 2.613348416289593| 1.417228312413465|   1924|Plan 9 from Outer...|       Horror|Sci-Fi|\n",
      "|  78772|       2857|2.3773188659432973|1.4053033632980292|  78772|Twilight Saga: Ec...|Fantasy|Romance|T...|\n",
      "|  46062|       1165| 2.584978540772532|1.3779333361047366|  46062|High School Music...|Children|Comedy|D...|\n",
      "|  81535|        759|2.7259552042160737|1.3775904753516588|  81535|Saw VII 3D - The ...|Horror|Mystery|Th...|\n",
      "|  72407|       2842| 2.351161154116819|1.3774649810759643|  72407|Twilight Saga: Ne...|Drama|Fantasy|Hor...|\n",
      "|  78174|        700|2.3392857142857144|1.3729814903705224|  78174|Sex and the City ...|Comedy|Drama|Romance|\n",
      "|  61123|        690| 2.508695652173913|1.3661615663530424|  61123|High School Music...|Comedy|Drama|Musi...|\n",
      "| 100083|        592|2.3597972972972974|1.3601552462375284| 100083|     Movie 43 (2013)|              Comedy|\n",
      "|  63992|       6115| 2.391414554374489|1.3592448248808724|  63992|     Twilight (2008)|Drama|Fantasy|Rom...|\n",
      "|   4255|       1721|2.1406159209761766|1.3546798615900135|   4255|Freddy Got Finger...|              Comedy|\n",
      "+-------+-----------+------------------+------------------+-------+--------------------+--------------------+\n",
      "only showing top 15 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings_stddev = ratings\\\n",
    ".groupBy(\"movieId\")\\\n",
    ".agg(count(\"userId\").alias(\"num_ratings\"), \n",
    "     avg(col(\"rating\")).alias(\"avg_rating\"),\n",
    "     stddev(col(\"rating\")).alias(\"std_rating\")\n",
    "    )\\\n",
    ".where(\"num_ratings > 500\")\n",
    "\n",
    "marmite_movies = ratings_stddev.join(movies, ratings_stddev.movieId == movies.movieId)\n",
    "marmite_movies.sort(desc(\"std_rating\")).show(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that these movies are pretty popular and are actually in that category. A simple Google search for these movie ratings shows mixed results. Some really like it and a few hate it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting koalas\n",
      "  Downloading koalas-1.4.0-py3-none-any.whl (769 kB)\n",
      "\u001b[K     |████████████████████████████████| 769 kB 2.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.23.2 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (1.1.3)\n",
      "Requirement already satisfied: pyarrow>=0.10 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (1.0.1)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (3.3.2)\n",
      "Requirement already satisfied: numpy>=1.14 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (1.19.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (7.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.14 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (1.19.2)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (2020.6.20)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (2.4.7)\n",
      "Requirement already satisfied: six in /home/vishnu/.local/lib/python3.8/site-packages (from cycler>=0.10->matplotlib>=3.0.0->koalas) (1.15.0)\n",
      "Requirement already satisfied: numpy>=1.14 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (1.19.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/vishnu/.local/lib/python3.8/site-packages (from pandas>=0.23.2->koalas) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=3.0.0->koalas) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.14 in /home/vishnu/.local/lib/python3.8/site-packages (from koalas) (1.19.2)\n",
      "Requirement already satisfied: six in /home/vishnu/.local/lib/python3.8/site-packages (from cycler>=0.10->matplotlib>=3.0.0->koalas) (1.15.0)\n",
      "Installing collected packages: koalas\n",
      "Successfully installed koalas-1.4.0\n"
     ]
    }
   ],
   "source": [
    "#Installing koalas. Needed for some visualizations.\n",
    "!pip install koalas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting seaborn\n",
      "  Downloading seaborn-0.11.0-py3-none-any.whl (283 kB)\n",
      "\u001b[K     |████████████████████████████████| 283 kB 2.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: matplotlib>=2.2 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (3.3.2)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (1.19.2)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (1.1.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2020.6.20)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (1.19.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (7.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (1.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: six in /home/vishnu/.local/lib/python3.8/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/vishnu/.local/lib/python3.8/site-packages (from pandas>=0.23->seaborn) (2020.1)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (1.19.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/vishnu/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: six in /home/vishnu/.local/lib/python3.8/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/vishnu/.local/lib/python3.8/site-packages (from seaborn) (1.19.2)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.11.0\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing packages needed for visualizations\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "import random\n",
    "from pprint import pprint\n",
    "from matplotlib.lines import Line2D\n",
    "import databricks.koalas\n",
    "\n",
    "#Set-up\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Ratings Per User"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfRatingsKdf = ratings.to_koalas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-e79b2cda0784>:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['{:,}'.format(int(x)) for x in ax.get_xticks().tolist()])\n",
      "<ipython-input-37-e79b2cda0784>:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "userRatingGroup = dfRatingsKdf.groupby(\"userId\")['rating'].count()\n",
    "userRatingGroup.hist(bins=100, color='blue', edgecolor='black',\n",
    "                     linewidth=1.25, alpha=0.78, ax=ax)\n",
    "ax.set_xlabel('Nb. of Ratings per User')\n",
    "ax.set_xlim(0.0)\n",
    "ax.set_xticklabels(['{:,}'.format(int(x)) for x in ax.get_xticks().tolist()])\n",
    "ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above graph that most number of uesrs i.e. greater than 1,40,000 of them have rated very few movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Ratings Per Movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-ecf209e2b523>:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['{:,}'.format(int(x)) for x in ax.get_xticks().tolist()])\n",
      "<ipython-input-33-ecf209e2b523>:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "movieRatingGroup = dfRatingsKdf.groupby(\"movieId\")['rating'].count()\n",
    "movieRatingGroup.hist(bins=100, color='blue', edgecolor='black',\n",
    "                      linewidth=1.25, alpha=0.78, ax=ax)\n",
    "ax.set_xlabel('Nb. of Ratings per Movie')\n",
    "ax.set_xlim(0.0)\n",
    "ax.set_xticklabels(['{:,}'.format(int(x)) for x in ax.get_xticks().tolist()])\n",
    "ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same case as above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### //TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-ea03d2073857>:19: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movieRatingDistGroup = dfRatingsKdf['rating'].value_counts() \\\n",
    "                                             .sort_index() \\\n",
    "                                             .reset_index() \\\n",
    "                                             .to_pandas()\n",
    "\n",
    "# Create Matplotlib Figure\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Main Figure: Seaborn Barplot\n",
    "sns.barplot(data=movieRatingDistGroup, x='index', y='rating',\n",
    "            palette='RdYlGn', edgecolor=\"black\", ax=ax)\n",
    "\n",
    "# Set Xaxis and Yaxis\n",
    "ax.set_xlabel(\"User-Movie Ratings\")\n",
    "ax.set_ylabel('Nb. of Users')\n",
    "ax.xaxis.set_tick_params(rotation=45)\n",
    "\n",
    "# Thousand separator on Yaxis labels\n",
    "ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])\n",
    "\n",
    "# Add percentage text on top of each bar\n",
    "total = float(movieRatingDistGroup['rating'].sum())\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2.,\n",
    "            height+350,\n",
    "            '{0:.0%}'.format(height/total),\n",
    "            ha=\"center\") \n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}