APPeal - Data-understanding v.1.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rapid-indonesian",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pyspark\n",
    "\n",
    "from pyspark import SparkContext\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import avg\n",
    "from pyspark.sql.functions import udf\n",
    "\n",
    "from pyspark.sql.types import FloatType\n",
    "from pyspark.sql.types import BooleanType\n",
    "from pyspark.sql.types import IntegerType\n",
    "from pyspark.sql.types import DateType\n",
    "from pyspark.sql.functions import regexp_replace\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.mllib.stat import Statistics\n",
    "from pyspark.sql.functions import col,isnan, when, count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "married-threshold",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sc = pyspark.SparkContext()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "spectacular-girlfriend",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder \\\n",
    "    .master(\"local\") \\\n",
    "    .appName(\"AppName\") \\\n",
    "    .getOrCreate()\n",
    "\n",
    "#data = spark.read.option(\"header\",True).option('inferSchema', True).csv(\"Google-Playstore.csv\", sep =',')\n",
    "#data = spark.read.option(\"header\",True).option('inferSchema', True).json(\"cleaned.json\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "muslim-implement",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqlContext = SQLContext(spark)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bound-despite",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = spark.read.option(\"header\",True).option('inferSchema', True).csv(\"Google-Playstore.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fiscal-score",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- App Name: string (nullable = true)\n",
      " |-- App Id: string (nullable = true)\n",
      " |-- Category: string (nullable = true)\n",
      " |-- Rating: string (nullable = true)\n",
      " |-- Rating Count: string (nullable = true)\n",
      " |-- Installs: string (nullable = true)\n",
      " |-- Minimum Installs: string (nullable = true)\n",
      " |-- Maximum Installs: string (nullable = true)\n",
      " |-- Free: string (nullable = true)\n",
      " |-- Price: string (nullable = true)\n",
      " |-- Currency: string (nullable = true)\n",
      " |-- Size: string (nullable = true)\n",
      " |-- Minimum Android: string (nullable = true)\n",
      " |-- Developer Id: string (nullable = true)\n",
      " |-- Developer Website: string (nullable = true)\n",
      " |-- Developer Email: string (nullable = true)\n",
      " |-- Released: string (nullable = true)\n",
      " |-- Last Updated: string (nullable = true)\n",
      " |-- Content Rating: string (nullable = true)\n",
      " |-- Privacy Policy: string (nullable = true)\n",
      " |-- Ad Supported: string (nullable = true)\n",
      " |-- In App Purchases: string (nullable = true)\n",
      " |-- Editors Choice: string (nullable = true)\n",
      " |-- Scraped Time: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "settled-october",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('App Name', 'string'),\n",
       " ('App Id', 'string'),\n",
       " ('Category', 'string'),\n",
       " ('Rating', 'string'),\n",
       " ('Rating Count', 'string'),\n",
       " ('Installs', 'string'),\n",
       " ('Minimum Installs', 'string'),\n",
       " ('Maximum Installs', 'string'),\n",
       " ('Free', 'string'),\n",
       " ('Price', 'string'),\n",
       " ('Currency', 'string'),\n",
       " ('Size', 'string'),\n",
       " ('Minimum Android', 'string'),\n",
       " ('Developer Id', 'string'),\n",
       " ('Developer Website', 'string'),\n",
       " ('Developer Email', 'string'),\n",
       " ('Released', 'string'),\n",
       " ('Last Updated', 'string'),\n",
       " ('Content Rating', 'string'),\n",
       " ('Privacy Policy', 'string'),\n",
       " ('Ad Supported', 'string'),\n",
       " ('In App Purchases', 'string'),\n",
       " ('Editors Choice', 'string'),\n",
       " ('Scraped Time', 'string')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fifteen-andrews",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepariamo Installs e Content Rating alla trasformazione in numeriche\n",
    "data = data.withColumn(\"Installs\", regexp_replace(col(\"Installs\"), \"\\+\", \"\"))\n",
    "data = data.withColumn(\"Installs\", regexp_replace(col(\"Installs\"), \",\", \"\"))\n",
    "data = data.withColumn(\"Rating\", regexp_replace(col(\"Rating\"), \",\", \"\"))\n",
    "data = data.withColumn(\"Size\", regexp_replace(col(\"Size\"), \",\", \".\"))\n",
    "\n",
    "data = data.withColumn(\"Content Rating\", regexp_replace(col(\"Content Rating\"), \"-1\", \"-1\"))\n",
    "data = data.withColumn(\"Content Rating\", regexp_replace(col(\"Content Rating\"), \"Everyone\", \"0\"))\n",
    "data = data.withColumn(\"Content Rating\", regexp_replace(col(\"Content Rating\"), \"Everyone 10+\", \"1\"))\n",
    "data = data.withColumn(\"Content Rating\", regexp_replace(col(\"Content Rating\"), \"Teen\", \"2\"))\n",
    "data = data.withColumn(\"Content Rating\", regexp_replace(col(\"Content Rating\"), \"Mature 17+\", \"3\"))\n",
    "data = data.withColumn(\"Content Rating\", regexp_replace(col(\"Content Rating\"), \"Adults only 18+\", \"4\"))\n",
    "\n",
    "#Convertiamo in float...\n",
    "data = data.withColumn(\"Price\", data[\"Price\"].cast(FloatType()).alias(\"Price\"))\n",
    "data = data.withColumn(\"Rating\", data[\"Rating\"].cast(FloatType()).alias(\"Rating\"))\n",
    "data = data.withColumn(\"Rating Count\", data[\"Rating Count\"].cast(FloatType()).alias(\"Rating Count\"))\n",
    "data = data.withColumn(\"Installs\", data[\"Installs\"].cast(FloatType()).alias(\"Installs\"))\n",
    "data = data.withColumn(\"Minimum Android\", data[\"Minimum Android\"][0:3].cast(FloatType()).alias(\"Minimum Android\"))\n",
    "data = data.withColumn(\"Content Rating\", data[\"Content Rating\"].cast(IntegerType()).alias(\"Content Rating\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "immediate-willow",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.withColumnRenamed(\"Ad Supported\", \"adSupported\")\n",
    "data = data.withColumnRenamed(\"Minimum Android\", \"MinimumAndroid\")\n",
    "data = data.withColumnRenamed(\"In App Purchases\", \"inAppPurchases\")\n",
    "data = data.withColumnRenamed(\"App Name\", \"AppName\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "micro-genetics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------+--------------------+--------------+------------------+------------------+--------------------+--------------------+--------------------+------------------+------------------+------------------+------------------+------------------+--------------------+-----------------+--------------------+---------------+------------------+-------------------+-----------------------+--------------------+--------------------+--------------------+-------------------+\n",
      "|summary|             AppName|              App Id|      Category|            Rating|      Rating Count|            Installs|    Minimum Installs|    Maximum Installs|              Free|             Price|          Currency|              Size|    MinimumAndroid|        Developer Id|Developer Website|     Developer Email|       Released|      Last Updated|     Content Rating|         Privacy Policy|         adSupported|      inAppPurchases|      Editors Choice|       Scraped Time|\n",
      "+-------+--------------------+--------------------+--------------+------------------+------------------+--------------------+--------------------+--------------------+------------------+------------------+------------------+------------------+------------------+--------------------+-----------------+--------------------+---------------+------------------+-------------------+-----------------------+--------------------+--------------------+--------------------+-------------------+\n",
      "|  count|             2312944|             2312944|       2312944|           2290031|           2290050|             2312836|             2312837|             2312944|           2312944|           2312925|           2312809|           2312748|           2260180|             2312911|          1552118|             2312906|        2241889|           2312944|            2218421|                1891997|             2312940|             2312942|             2312944|            2312944|\n",
      "|   mean|            Infinity|                null|          null| 2.203143276241894|2864.7917774721072|   183443.0052843349|  183444.43167953554|   320199.1094302734|17480.206896551725|2.2744623215996014|       456349.9495|               0.0| 4.349831454183342|            Infinity|             null|                null|           null|              null|0.17703763172094025|                   null|                null|                null|                null|               null|\n",
      "| stddev|                 NaN|                null|          null|2.1062203858926427|212163.06141953653|1.5131442000635022E7|1.5131500873746514E7|2.3555005086574133E7| 48855.19147394333|3287.6877801998216|2040858.5488238432|               0.0|1.0077364041729575|                 NaN|             null|                null|           null|              null| 0.5680960181828986|                   null|                null|                null|                null|               null|\n",
      "|    min|                   !| Banyak Jual Beli !\"|  Quick notes\"|               0.0|               0.0|                 0.0|                   0|                   0|                 0|               0.0|                 0|                 0|               0.0|         \bTranAnhDuc|             LLC\"|\tdoquochentasy@gm...|    Apr 1, 2010|      Apr 01, 2011|                  0|           Dec 02, 2020|            Everyone|            Everyone|               False|2021-06-15 20:19:35|\n",
      "|    max|󠁧󠁢󠁥󠁮󠁧󠁿Radio...|         동요\"\"단어\"|고양이 쇼핑몰\"|               5.0|      1.38557568E8|              1.0E10|                   6|               99996|              True|         5000000.0|               ZAR|Varies with device|             788.0|🧙🏼Lucrezia - MM...|    Олегъ Лавровъ|              냥아지|web@skysent.com|yaj_info@yaj.co.kr|                  2|https://외식잡.com/r...|https://меркурий....|https://sites.goo...|https://yanosik.p...|               True|\n",
      "+-------+--------------------+--------------------+--------------+------------------+------------------+--------------------+--------------------+--------------------+------------------+------------------+------------------+------------------+------------------+--------------------+-----------------+--------------------+---------------+------------------+-------------------+-----------------------+--------------------+--------------------+--------------------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "contemporary-october",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "honest-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+------+--------+------+------------+--------+----------------+----------------+----+-----+--------+----+---------------+------------+-----------------+---------------+--------+------------+--------------+--------------+------------+----------------+--------------+------------+\n",
      "|App Name|App Id|Category|Rating|Rating Count|Installs|Minimum Installs|Maximum Installs|Free|Price|Currency|Size|Minimum Android|Developer Id|Developer Website|Developer Email|Released|Last Updated|Content Rating|Privacy Policy|Ad Supported|In App Purchases|Editors Choice|Scraped Time|\n",
      "+--------+------+--------+------+------------+--------+----------------+----------------+----+-----+--------+----+---------------+------------+-----------------+---------------+--------+------------+--------------+--------------+------------+----------------+--------------+------------+\n",
      "|       0|     0|       0| 22913|       22894|     108|             107|               0|   0|   19|     135| 196|          52764|          33|           760826|             38|   71055|           0|         94523|        420947|           4|               2|             0|           0|\n",
      "+--------+------+--------+------+------------+--------+----------------+----------------+----+-----+--------+----+---------------+------------+-----------------+---------------+--------+------------+--------------+--------------+------------+----------------+--------------+------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in data.columns]).show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legendary-clinton",
   "metadata": {},
   "source": [
    "## Basic statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "classified-drunk",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [\"Installs\", \"nReviews\", \"Rating\", \"Price\", \"MinimumAndroid\", \"Appeal\"]\n",
    "\n",
    "data.select(num_cols).describe().show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "measured-function",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.select(num_cols).summary().show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dental-serve",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = [x[0] for x in data.dtypes if x[1]==\"int\" or x[1]==\"float\" or x[1] == \"double\"]\n",
    "sampled_data = data.select(numeric_features).sample(False, 0.8).toPandas()\n",
    "axs = pd.plotting.scatter_matrix(sampled_data, figsize=(10, 10))\n",
    "n = len(sampled_data.columns)\n",
    "for i in range(n):\n",
    "    v = axs[i, 0]\n",
    "    v.yaxis.label.set_rotation(0)\n",
    "    v.yaxis.label.set_ha('right')\n",
    "    v.set_yticks(())\n",
    "    h = axs[n-1, i]\n",
    "    h.xaxis.label.set_rotation(90)\n",
    "    h.set_xticks(())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colonial-blake",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "korean-testimony",
   "metadata": {},
   "source": [
    "### Creazione di una vista per effettuare le query "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "wrapped-relay",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.createOrReplaceTempView(\"viewPlot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nasty-thesis",
   "metadata": {},
   "source": [
    "#### Numero di app in ciascuna categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "chemical-purpose",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+------+\n",
      "|         Category|   tot|\n",
      "+-----------------+------+\n",
      "|        Education|241086|\n",
      "|    Music & Audio|154905|\n",
      "|            Tools|143987|\n",
      "|         Business|143770|\n",
      "|    Entertainment|138271|\n",
      "|        Lifestyle|118331|\n",
      "|Books & Reference|116726|\n",
      "|  Personalization| 89210|\n",
      "| Health & Fitness| 83510|\n",
      "|     Productivity| 79695|\n",
      "|         Shopping| 75253|\n",
      "|     Food & Drink| 73926|\n",
      "|   Travel & Local| 67288|\n",
      "|          Finance| 65465|\n",
      "|           Arcade| 53792|\n",
      "|           Puzzle| 51168|\n",
      "|           Casual| 50813|\n",
      "|    Communication| 48167|\n",
      "|           Sports| 47483|\n",
      "|           Social| 44733|\n",
      "+-----------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categoryNumber = sqlContext.sql(\"\"\"SELECT Category, COUNT(*) as tot \n",
    "                                    FROM viewPlot \n",
    "                                    GROUP BY category \n",
    "                                    ORDER BY tot desc\"\"\")\n",
    "categoryNumber.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "recorded-watson",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categoryNumberPlot = categoryNumber.toPandas()\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.barplot(x = \"Category\", y = \"tot\", data  = categoryNumberPlot[:10], palette=\"rocket\")\n",
    "plt.xticks(rotation=30, fontsize='14')\n",
    "plt.yticks(fontsize='14')\n",
    "plt.ylabel(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.tight_layout\n",
    "plt.title(\"Number of app by category\", fontsize='20')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-recorder",
   "metadata": {},
   "source": [
    "#### Appeal per categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "experienced-devices",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+------+\n",
      "|     category|Appeal|\n",
      "+-------------+------+\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     1|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     1|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     1|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     1|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     1|\n",
      "|Music & Audio|     0|\n",
      "|Music & Audio|     1|\n",
      "+-------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appeal_category = sqlContext.sql(\"\"\"SELECT category, Appeal\n",
    "                                    FROM viewPlot \n",
    "                                    \"\"\")\n",
    "plt.figure(figsize = (20,10))\n",
    "appeal_category.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "destroyed-webmaster",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "\n",
    "category_appeal_plot = appeal_category.toPandas()\n",
    "\n",
    "sns.countplot(x='category',hue='Appeal',data=category_appeal_plot, palette=\"tab20b\", \n",
    "             order=category_appeal_plot.category.value_counts().iloc[:10].index)\n",
    "plt.xticks(rotation=30, fontsize='7')\n",
    "plt.yticks(fontsize='8')\n",
    "plt.ylabel(\"Number of app\")\n",
    "plt.title(\"Appeal by category\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precise-accountability",
   "metadata": {},
   "source": [
    "#### Rating medio per categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "geographic-trader",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------------+\n",
      "|            category|       avg(rating)|\n",
      "+--------------------+------------------+\n",
      "|       Music & Audio| 2.226824850719996|\n",
      "|           Education|2.2468225311926124|\n",
      "|              Trivia|2.5261784584102687|\n",
      "|     Auto & Vehicles| 2.049612834137347|\n",
      "|        Quick notes\"|              null|\n",
      "|       Entertainment|2.4354052028868614|\n",
      "|fr.visionreelle.B...|              null|\n",
      "|           Adventure| 2.814991004868682|\n",
      "|com.free074a81ba9...|              null|\n",
      "|              Arcade|2.4504809646643357|\n",
      "|    CHEER UP(CCC)\"\"\"|              null|\n",
      "|net.cleverbit.Mic...|              null|\n",
      "|              Sports|2.3155445043837792|\n",
      "|      Travel & Local|1.9211963940963501|\n",
      "|        Food & Drink|1.2945808316654184|\n",
      "|banglarhymes.ntss...|              null|\n",
      "|        Role Playing|3.3844982885828023|\n",
      "|               aves\"|              null|\n",
      "|             Finance|2.4030697207277085|\n",
      "|     Personalization| 2.758753873719551|\n",
      "+--------------------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categoryRating = data.groupBy(\"category\").avg(\"rating\").show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "supposed-synthetic",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+----------+---------+\n",
      "|             AppName|rating_avg| Installs|\n",
      "+--------------------+----------+---------+\n",
      "|Flying Ghost Robo...|       5.0|1000000.0|\n",
      "|Mechanical Excava...|       5.0|1000000.0|\n",
      "|Flying Robot Supe...|       5.0|1000000.0|\n",
      "|Wolf Robot Transf...|       5.0|1000000.0|\n",
      "|Dog Robot Transfo...|       5.0| 500000.0|\n",
      "|Ant Robot Car Tra...|       5.0| 500000.0|\n",
      "|Car Wash Service ...|       5.0| 500000.0|\n",
      "|Sand Safari Jeep ...|       5.0| 100000.0|\n",
      "|Flying Limo Car T...|       5.0| 100000.0|\n",
      "|FPS Commando Shoo...|       5.0| 100000.0|\n",
      "|Dragon Robot Hors...|       5.0| 100000.0|\n",
      "|Spider Crane Robo...|       5.0| 100000.0|\n",
      "|Flying Horse Robo...|       5.0| 100000.0|\n",
      "|Horse Robot Trans...|       5.0| 100000.0|\n",
      "|Electric Car Stun...|       5.0|  50000.0|\n",
      "|Heavy Trash Truck...|       5.0|  50000.0|\n",
      "|US Police CyberTr...|       5.0|  50000.0|\n",
      "|Dino Robot Car Ga...|       5.0|  50000.0|\n",
      "|Emergency Fire Tr...|       5.0|  50000.0|\n",
      "|Army Stuntman Bik...|       5.0|  50000.0|\n",
      "+--------------------+----------+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## visualiziamo anche le migliori app per categoria (partendo dalla top 5)\n",
    "\n",
    "categoryRating = sqlContext.sql(\"\"\"SELECT AppName, rating AS rating_avg, Installs\n",
    "                                    FROM viewPlot \n",
    "                                    WHERE Category = 'Weather'\n",
    "                                    ORDER BY rating_avg DESC, Installs DESC\"\"\")\n",
    "categoryRating.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "active-jewel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------------+\n",
      "|            category|        rating_avg|\n",
      "+--------------------+------------------+\n",
      "|        Role Playing|3.3844982885828023|\n",
      "|              Casino|3.2796822202241174|\n",
      "|          Simulation|3.2108871649004502|\n",
      "|             Weather| 3.120294525997836|\n",
      "|                Card| 3.085160326385806|\n",
      "|              Racing| 2.960233858728779|\n",
      "|Video Players & E...|2.9038420088971755|\n",
      "|                Word|2.9020762455428453|\n",
      "|            Strategy| 2.878900084171941|\n",
      "|              Comics|2.8649060628127647|\n",
      "|           Adventure| 2.814991004868682|\n",
      "|               Board|2.7821585888425138|\n",
      "|     Personalization| 2.758753873719551|\n",
      "|              Action|2.7059790497145757|\n",
      "|              Puzzle|2.6882790595351738|\n",
      "|               Music| 2.662008201359494|\n",
      "|              Trivia|2.5261784584102687|\n",
      "|         Photography|2.5197648766728986|\n",
      "|    News & Magazines|2.4836006676483584|\n",
      "|   Books & Reference| 2.467201757134877|\n",
      "+--------------------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categoryRating = sqlContext.sql(\"\"\"SELECT category, AVG(rating) AS rating_avg \n",
    "                                    FROM viewPlot \n",
    "                                    GROUP BY category\n",
    "                                    ORDER BY rating_avg DESC\"\"\")\n",
    "categoryRating.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "identified-transcript",
   "metadata": {},
   "outputs": [],
   "source": [
    "categoryRatingPandas = categoryRating.toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "banned-trout",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "sns.barplot(x = \"category\", y = \"rating_avg\", data  = categoryRatingPandas[:10], palette=\"rocket\")\n",
    "plt.xticks(rotation=30, fontsize='10')\n",
    "plt.yticks(fontsize='13')\n",
    "plt.ylabel(\"Category\")\n",
    "plt.xlabel(\"Rating (average)\")\n",
    "\n",
    "plt.title(\"Average app rating by category\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "interpreted-lottery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>rating_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Education</td>\n",
       "      <td>2.246823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     category  rating_avg\n",
       "30  Education    2.246823"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoryRatingPandas[categoryRatingPandas[\"category\"]== \"Education\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "still-battlefield",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>rating_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Social</td>\n",
       "      <td>2.398922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category  rating_avg\n",
       "27   Social    2.398922"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoryRatingPandas[categoryRatingPandas[\"category\"]== \"Social\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fuzzy-cargo",
   "metadata": {},
   "source": [
    "### Ad Supported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "inner-florence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+-----------+\n",
      "|         category|adSupported|\n",
      "+-----------------+-----------+\n",
      "|        Adventure|      False|\n",
      "|            Tools|       True|\n",
      "|     Productivity|      False|\n",
      "|    Communication|       True|\n",
      "|            Tools|      False|\n",
      "|           Social|      False|\n",
      "| Libraries & Demo|       True|\n",
      "|        Lifestyle|      False|\n",
      "|    Communication|      False|\n",
      "|  Personalization|       True|\n",
      "|           Racing|      False|\n",
      "|Maps & Navigation|      False|\n",
      "|   Travel & Local|       True|\n",
      "|Maps & Navigation|      False|\n",
      "|   Travel & Local|      False|\n",
      "|     Food & Drink|      False|\n",
      "|   Travel & Local|      False|\n",
      "|            Tools|      False|\n",
      "|        Lifestyle|      False|\n",
      "|Books & Reference|       True|\n",
      "+-----------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "adSupport = sqlContext.sql(\"\"\"SELECT category, adSupported\n",
    "                                FROM viewPlot \n",
    "                                \"\"\")\n",
    "adSupport.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "committed-removal",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "adSupportPandas = adSupport.toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "abstract-capture",
   "metadata": {},
   "outputs": [],
   "source": [
    "adsupp_dict = {\"False\": 0, \"True\": 1}\n",
    "adSupportPandas['adSupported'] = adSupportPandas['adSupported'].map(adsupp_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "powered-paint",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1162150\n",
       "1.0    1150750\n",
       "Name: adSupported, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adSupportPandas.adSupported.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "special-marks",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'adSupported app')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = adSupportPandas['adSupported'], palette='tab20b')\n",
    "plt.title(\"adSupported app\", fontsize='15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "embedded-assurance",
   "metadata": {},
   "outputs": [],
   "source": [
    "ct = pd.crosstab(adSupportPandas.category, adSupportPandas.adSupported, margins=None)\n",
    "norm_ct = ct.div(ct.sum(1).astype(float), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "physical-simulation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AdSupported app ratio per category')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_ct.sort_values(by=0)[:10].plot(kind = \"bar\",stacked = True, figsize = (9, 6), colormap = \"RdYlBu\")\n",
    "plt.title(\"AdSupported app ratio per category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conservative-communication",
   "metadata": {},
   "source": [
    "### Minimum Android Ver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "assisted-perfume",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+--------------+\n",
      "|         category|MinimumAndroid|\n",
      "+-----------------+--------------+\n",
      "|        Adventure|           7.1|\n",
      "|            Tools|           5.0|\n",
      "|     Productivity|           4.0|\n",
      "|    Communication|           4.0|\n",
      "|            Tools|           4.1|\n",
      "|           Social|           6.0|\n",
      "| Libraries & Demo|           4.1|\n",
      "|        Lifestyle|           5.0|\n",
      "|    Communication|           4.4|\n",
      "|  Personalization|           4.1|\n",
      "|           Racing|           4.4|\n",
      "|Maps & Navigation|           4.0|\n",
      "|   Travel & Local|           4.2|\n",
      "|Maps & Navigation|           6.0|\n",
      "|   Travel & Local|           4.2|\n",
      "|     Food & Drink|           4.4|\n",
      "|   Travel & Local|           4.4|\n",
      "|            Tools|           4.1|\n",
      "|        Lifestyle|           4.1|\n",
      "|Books & Reference|           4.2|\n",
      "+-----------------+--------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "minAndroid = sqlContext.sql(\"\"\"SELECT category, MinimumAndroid\n",
    "                                FROM viewPlot \n",
    "                                \"\"\")\n",
    "minAndroid.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "marine-transaction",
   "metadata": {},
   "outputs": [],
   "source": [
    "minAndroidPandas = minumumAndroid.toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "better-peoples",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1      604525\n",
       "4.4      402854\n",
       "5.0      397006\n",
       "4.0      334048\n",
       "4.2      115980\n",
       "6.0       89931\n",
       "2.3       87197\n",
       "5.1       59290\n",
       "4.3       41363\n",
       "7.0       34446\n",
       "2.2       23770\n",
       "3.0       17135\n",
       "2.1       16719\n",
       "8.0       13831\n",
       "1.6        8549\n",
       "2.0        3221\n",
       "7.1        3044\n",
       "3.2        2685\n",
       "1.5        2100\n",
       "3.1        2003\n",
       "1.0         309\n",
       "1.1         166\n",
       "788.0         1\n",
       "3.8           1\n",
       "8.4           1\n",
       "7.9           1\n",
       "6.9           1\n",
       "3.5           1\n",
       "1.9           1\n",
       "0.0           1\n",
       "Name: MinimumAndroid, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minAndroidPandas.MinimumAndroid.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "affiliated-destiny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f89a4d873a0>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_ct = ct.div(ct.sum(1).astype(float), axis=0)\n",
    "norm_ct.sort_values(by=0)[:10].plot(kind = \"bar\",stacked = True, figsize = (9, 6), colormap = \"RdYlBu\")\n",
    "plt.title(\"Min. Android version\")\n",
    "plt.legend(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suspected-subdivision",
   "metadata": {},
   "source": [
    "### Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "recognized-delivery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------+---------+\n",
      "|               category|    price|\n",
      "+-----------------------+---------+\n",
      "|                   navi|5000000.0|\n",
      "|              RK PAY\"\"\"|  10159.0|\n",
      "| 마음을 다스리는 글\"\")\"|   4106.0|\n",
      "|           far high.\"\"\"|   3725.0|\n",
      "|         고양이 쇼핑몰\"|   2108.0|\n",
      "|     \"\" mp3 Collection\"|    864.0|\n",
      "|           Productivity|    400.0|\n",
      "|           Productivity|   399.99|\n",
      "|      Books & Reference|   399.99|\n",
      "|                 Social|   399.99|\n",
      "|                  Tools|   399.99|\n",
      "|               Business|   399.99|\n",
      "|                 Social|   399.99|\n",
      "|          Entertainment|   399.99|\n",
      "|               Shopping|   399.99|\n",
      "|          Entertainment|   399.99|\n",
      "|                 Arcade|   399.99|\n",
      "|       Health & Fitness|   399.99|\n",
      "|                 Casual|   399.99|\n",
      "|                  Tools|   399.99|\n",
      "+-----------------------+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "avg_price_df = sqlContext.sql(\"\"\"SELECT category, price\n",
    "                                    FROM viewPlot \n",
    "                                    ORDER BY price DESC\"\"\")\n",
    "avg_price_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "brutal-addiction",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_price_pandas = avg_price_df.toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "helpful-accent",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_price_pandas = avg_price_pandas[avg_price_pandas[\"price\"]>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "colored-emergency",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_price = avg_price_pandas.groupby(\"category\").mean().sort_values(by= \"price\", ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "crucial-deviation",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean = avg_price[:-30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "sorted-alias",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean = clean.sort_values(by= \"price\", ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "banned-youth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Video Players &amp; Editors</td>\n",
       "      <td>5.008850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Board</td>\n",
       "      <td>4.939353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Casino</td>\n",
       "      <td>4.755973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Word</td>\n",
       "      <td>4.703300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Role Playing</td>\n",
       "      <td>4.698225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Weather</td>\n",
       "      <td>4.499798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Communication</td>\n",
       "      <td>4.460425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Beauty</td>\n",
       "      <td>4.311429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Music &amp; Audio</td>\n",
       "      <td>4.299316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>House &amp; Home</td>\n",
       "      <td>4.255781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>4.245502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Photography</td>\n",
       "      <td>3.982180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Simulation</td>\n",
       "      <td>3.963692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Card</td>\n",
       "      <td>3.750944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Puzzle</td>\n",
       "      <td>3.541972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Travel &amp; Local</td>\n",
       "      <td>3.510817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Casual</td>\n",
       "      <td>3.510220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>3.431335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Trivia</td>\n",
       "      <td>3.356484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Educational</td>\n",
       "      <td>3.325514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Action</td>\n",
       "      <td>3.123452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Libraries &amp; Demo</td>\n",
       "      <td>3.001667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Events</td>\n",
       "      <td>2.958505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Comics</td>\n",
       "      <td>2.747781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Parenting</td>\n",
       "      <td>2.728873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>News &amp; Magazines</td>\n",
       "      <td>2.669897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Racing</td>\n",
       "      <td>2.552928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Music</td>\n",
       "      <td>2.434954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Personalization</td>\n",
       "      <td>1.688500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   category     price\n",
       "0   Video Players & Editors  5.008850\n",
       "1                     Board  4.939353\n",
       "2                    Casino  4.755973\n",
       "3                      Word  4.703300\n",
       "4              Role Playing  4.698225\n",
       "5                   Weather  4.499798\n",
       "6             Communication  4.460425\n",
       "7                    Beauty  4.311429\n",
       "8             Music & Audio  4.299316\n",
       "9              House & Home  4.255781\n",
       "10                Adventure  4.245502\n",
       "11              Photography  3.982180\n",
       "12               Simulation  3.963692\n",
       "13                     Card  3.750944\n",
       "14                   Puzzle  3.541972\n",
       "15           Travel & Local  3.510817\n",
       "16                   Casual  3.510220\n",
       "17                 Strategy  3.431335\n",
       "18                   Trivia  3.356484\n",
       "19              Educational  3.325514\n",
       "20                   Action  3.123452\n",
       "21         Libraries & Demo  3.001667\n",
       "22                   Events  2.958505\n",
       "23                   Comics  2.747781\n",
       "24                Parenting  2.728873\n",
       "25         News & Magazines  2.669897\n",
       "26                   Racing  2.552928\n",
       "27                    Music  2.434954\n",
       "28          Personalization  1.688500"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "satellite-thunder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "sns.barplot(x = \"price\", y = \"category\", data = clean[:10], palette=\"rocket\")\n",
    "plt.xticks(fontsize='14', rotation = 30)\n",
    "plt.yticks(fontsize='14')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "plt.title(\"Average price per category\", fontsize = \"20\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-motel",
   "metadata": {},
   "source": [
    "### In app "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "affecting-departure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------+-------------------+\n",
      "|               category|          avg_price|\n",
      "+-----------------------+-------------------+\n",
      "|                   navi|          5000000.0|\n",
      "|              RK PAY\"\"\"|            10159.0|\n",
      "| 마음을 다스리는 글\"\")\"|             4106.0|\n",
      "|           far high.\"\"\"|             3725.0|\n",
      "|         고양이 쇼핑몰\"|             2108.0|\n",
      "|     \"\" mp3 Collection\"|              864.0|\n",
      "|             коментарі\"|              184.0|\n",
      "|                  aves\"|               89.0|\n",
      "|       CHEER UP(CCC)\"\"\"|               15.0|\n",
      "|           Quick notes\"|               15.0|\n",
      "|              Samurai]\"|               10.0|\n",
      "|                Medical| 0.7113878752016787|\n",
      "|           Role Playing| 0.3118415148830376|\n",
      "|                 Sports|0.22647534903029273|\n",
      "|               Strategy|0.22537505574945607|\n",
      "|      Books & Reference| 0.1932279309715364|\n",
      "|              Adventure|0.17967864487626678|\n",
      "|            Educational|0.17183981511271385|\n",
      "|                  Board|0.16982376960560902|\n",
      "|              Education|0.16324400691787733|\n",
      "+-----------------------+-------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "inAppPurch = sqlContext.sql(\"\"\"SELECT category, AVG(Price) AS avg_price\n",
    "                                    FROM viewPlot \n",
    "                                    GROUP BY category\n",
    "                                    ORDER BY avg_price DESC\"\"\")\n",
    "inAppPurch.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "modular-ecology",
   "metadata": {},
   "outputs": [],
   "source": [
    "inAppPurchPandas = inAppPurch.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "electoral-reception",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>avg_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>navi</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RK PAY\"\"\"</td>\n",
       "      <td>10159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>마음을 다스리는 글\"\")\"</td>\n",
       "      <td>4106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>far high.\"\"\"</td>\n",
       "      <td>3725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>고양이 쇼핑몰\"</td>\n",
       "      <td>2108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>com.ud.parchis.publi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>com.vsoapp.polls</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>com.bilgeyatirimci</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>com.blogspot.formyandroid.oknoty.widgets</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>com.hdibharatnews.app</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    category  avg_price\n",
       "0                                       navi  5000000.0\n",
       "1                                  RK PAY\"\"\"    10159.0\n",
       "2                             마음을 다스리는 글\"\")\"     4106.0\n",
       "3                               far high.\"\"\"     3725.0\n",
       "4                                   고양이 쇼핑몰\"     2108.0\n",
       "..                                       ...        ...\n",
       "73                      com.ud.parchis.publi        NaN\n",
       "74                          com.vsoapp.polls        NaN\n",
       "75                        com.bilgeyatirimci        NaN\n",
       "76  com.blogspot.formyandroid.oknoty.widgets        NaN\n",
       "77                     com.hdibharatnews.app        NaN\n",
       "\n",
       "[78 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inAppPurchPandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "civilian-planning",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'InAppPurchases'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'InAppPurchases'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-98-4c366b072d38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minAppPurchPandas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"InAppPurchases\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"orange\"\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      2\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;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'InAppPurchases'"
     ]
    }
   ],
   "source": [
    "pd.value_counts(inAppPurchPandas[\"InAppPurchases\"]).plot.bar(color = \"orange\")\n",
    "plt.xticks(rotation = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sapphire-pacific",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "incredible-mailman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_names = [x[0] for x in data.dtypes if x[1]==\"int\" or x[1]==\"float\" or x[1] == \"double\"]\n",
    "features = data.select(col_names).rdd.map(lambda row: row[0:])\n",
    "corr_mat = Statistics.corr(features, method=\"spearman\")\n",
    "corr_df = pd.DataFrame(corr_mat)\n",
    "corr_df.index, corr_df.columns = col_names, col_names\n",
    "plt.figure(figsize = (20,20))\n",
    "correlation = sns.heatmap(corr_df, cmap='BuPu', vmin=-1, vmax=1,annot=True, linewidths=.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "together-gallery",
   "metadata": {},
   "source": [
    "# Analisi target variable per Classificazione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "according-intelligence",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = spark.read.option(\"header\",True).option('inferSchema', True).json(\"cleaned_v3.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "expired-volunteer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAppeal(value):\n",
    "    \"\"\"\n",
    "    Defines target variable\n",
    "    \"\"\"\n",
    "    if float(value) >= 3.5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "data = data.withColumn(\"Appeal\", udf(getAppeal)(\"Rating\"))\n",
    "data = data.withColumn(\"Appeal\", data[\"Appeal\"].cast(IntegerType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "inappropriate-shooting",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.groupBy(\"Appeal\").count().show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unavailable-relay",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.createOrReplaceTempView(\"viewPlot\")\n",
    "appeal_count = sqlContext.sql(\"\"\"SELECT Appeal, COUNT(*) as count_Appeal \n",
    "                                    FROM viewPlot \n",
    "                                    GROUP BY Appeal \n",
    "                                    ORDER BY count_Appeal desc\"\"\")\n",
    "appealPlot = appeal_count.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "particular-inspector",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (6,5))\n",
    "sns.barplot(x = \"Appeal\", y = \"count_Appeal\", data  = appealPlot, palette=\"tab20b\")\n",
    "plt.xticks(rotation=0, fontsize='10')\n",
    "plt.yticks(fontsize='13')\n",
    "plt.ylabel(\"Number of app\")\n",
    "plt.title(\"Appeal distribution\")\n",
    "plt.show()"
   ]
  }
 ],
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