Analysis.ipynb

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       "Current session configs: <tt>{'conf': {'spark.pyspark.python': 'python3', 'spark.pyspark.virtualenv.enabled': 'true', 'spark.pyspark.virtualenv.type': 'native', 'spark.pyspark.virtualenv.bin.path': '/usr/bin/virtualenv'}, 'kind': 'pyspark'}</tt><br>"
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       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>7</td><td>application_1588563962398_0008</td><td>pyspark</td><td>dead</td><td><a target=\"_blank\" href=\"http://ip-172-31-20-186.us-east-2.compute.internal:20888/proxy/application_1588563962398_0008/\">Link</a></td><td></td><td></td></tr></table>"
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    "%info"
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   "source": [
    "# Analysis of Yelp Business Intelligence Data\n",
    "This project is to analyze a subset of Yelp's business, reviews and user data. This dataset comes to us from [kaggle](https://www.kaggle.com/yelp-dataset/yelp-dataset). Dataset is pulled into a public s3 bucket: s3://yelp-reviews-businesses-datasets/yelp_academic_dataset_business.json"
   ]
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  {
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   "source": [
    "# Part I: Installation and Initial Setup\n",
    "Begin by installing the necessary libraries that needed to conduct analysis. "
   ]
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     "text": [
      "Starting Spark application\n"
     ]
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       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>8</td><td>application_1588563962398_0009</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-20-186.us-east-2.compute.internal:20888/proxy/application_1588563962398_0009/\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-20-13.us-east-2.compute.internal:8042/node/containerlogs/container_1588563962398_0009_01_000001/livy\">Link</a></td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
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      "Package                    Version\n",
      "-------------------------- -------\n",
      "beautifulsoup4             4.8.1\n",
      "boto                       2.49.0\n",
      "jmespath                   0.9.4\n",
      "lxml                       4.4.2\n",
      "mysqlclient                1.4.6\n",
      "nltk                       3.4.5\n",
      "nose                       1.3.4\n",
      "numpy                      1.14.5\n",
      "pip                        20.1\n",
      "py-dateutil                2.2\n",
      "python36-sagemaker-pyspark 1.2.6\n",
      "pytz                       2019.3\n",
      "PyYAML                     3.11\n",
      "setuptools                 46.1.3\n",
      "six                        1.13.0\n",
      "soupsieve                  1.9.5\n",
      "wheel                      0.34.2\n",
      "windmill                   1.6"
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   "source": [
    "sc.list_packages()"
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     "text": [
      "Collecting IPython==7.12.0\n",
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      "Successfully installed scipy-1.4.1 seaborn-0.10.0"
     ]
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   ],
   "source": [
    "sc.install_pypi_package(\"IPython==7.12.0\")\n",
    "sc.install_pypi_package(\"pandas==1.0.3\")\n",
    "sc.install_pypi_package(\"matplotlib==3.2.1\")\n",
    "sc.install_pypi_package(\"seaborn==0.10.0\")"
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   "metadata": {},
   "source": [
    "# Importing\n",
    "Now, import the installed packages from the previous block below."
   ]
  },
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import IPython\n",
    "from IPython.display import display, Image\n",
    "from pyspark.sql import functions as F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data\n",
    "Using spark load the data from S3 into a dataframe object that we can manipulate further down in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
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   "source": [
    "df_business_raw = spark.read.json( 's3://yelp-reviews-businesses-datasets/yelp_academic_dataset_business.json' )"
   ]
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   "metadata": {},
   "source": [
    "# Overview of Data\n",
    "Display the number of rows and columns in the dataset.  \n",
    "Display the DataFrame schema below."
   ]
  },
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      "Total Colums:14\n",
      "Total Rows:209,393\n",
      "root\n",
      " |-- address: string (nullable = true)\n",
      " |-- attributes: struct (nullable = true)\n",
      " |    |-- AcceptsInsurance: string (nullable = true)\n",
      " |    |-- AgesAllowed: string (nullable = true)\n",
      " |    |-- Alcohol: string (nullable = true)\n",
      " |    |-- Ambience: string (nullable = true)\n",
      " |    |-- BYOB: string (nullable = true)\n",
      " |    |-- BYOBCorkage: string (nullable = true)\n",
      " |    |-- BestNights: string (nullable = true)\n",
      " |    |-- BikeParking: string (nullable = true)\n",
      " |    |-- BusinessAcceptsBitcoin: string (nullable = true)\n",
      " |    |-- BusinessAcceptsCreditCards: string (nullable = true)\n",
      " |    |-- BusinessParking: string (nullable = true)\n",
      " |    |-- ByAppointmentOnly: string (nullable = true)\n",
      " |    |-- Caters: string (nullable = true)\n",
      " |    |-- CoatCheck: string (nullable = true)\n",
      " |    |-- Corkage: string (nullable = true)\n",
      " |    |-- DietaryRestrictions: string (nullable = true)\n",
      " |    |-- DogsAllowed: string (nullable = true)\n",
      " |    |-- DriveThru: string (nullable = true)\n",
      " |    |-- GoodForDancing: string (nullable = true)\n",
      " |    |-- GoodForKids: string (nullable = true)\n",
      " |    |-- GoodForMeal: string (nullable = true)\n",
      " |    |-- HairSpecializesIn: string (nullable = true)\n",
      " |    |-- HappyHour: string (nullable = true)\n",
      " |    |-- HasTV: string (nullable = true)\n",
      " |    |-- Music: string (nullable = true)\n",
      " |    |-- NoiseLevel: string (nullable = true)\n",
      " |    |-- Open24Hours: string (nullable = true)\n",
      " |    |-- OutdoorSeating: string (nullable = true)\n",
      " |    |-- RestaurantsAttire: string (nullable = true)\n",
      " |    |-- RestaurantsCounterService: string (nullable = true)\n",
      " |    |-- RestaurantsDelivery: string (nullable = true)\n",
      " |    |-- RestaurantsGoodForGroups: string (nullable = true)\n",
      " |    |-- RestaurantsPriceRange2: string (nullable = true)\n",
      " |    |-- RestaurantsReservations: string (nullable = true)\n",
      " |    |-- RestaurantsTableService: string (nullable = true)\n",
      " |    |-- RestaurantsTakeOut: string (nullable = true)\n",
      " |    |-- Smoking: string (nullable = true)\n",
      " |    |-- WheelchairAccessible: string (nullable = true)\n",
      " |    |-- WiFi: string (nullable = true)\n",
      " |-- business_id: string (nullable = true)\n",
      " |-- categories: string (nullable = true)\n",
      " |-- city: string (nullable = true)\n",
      " |-- hours: struct (nullable = true)\n",
      " |    |-- Friday: string (nullable = true)\n",
      " |    |-- Monday: string (nullable = true)\n",
      " |    |-- Saturday: string (nullable = true)\n",
      " |    |-- Sunday: string (nullable = true)\n",
      " |    |-- Thursday: string (nullable = true)\n",
      " |    |-- Tuesday: string (nullable = true)\n",
      " |    |-- Wednesday: string (nullable = true)\n",
      " |-- is_open: long (nullable = true)\n",
      " |-- latitude: double (nullable = true)\n",
      " |-- longitude: double (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- postal_code: string (nullable = true)\n",
      " |-- review_count: long (nullable = true)\n",
      " |-- stars: double (nullable = true)\n",
      " |-- state: string (nullable = true)"
     ]
    }
   ],
   "source": [
    "print(f'Total Colums:{len(df_business_raw.dtypes)}')\n",
    "print(f'Total Rows:{df_business_raw.count():,}')\n",
    "df_business_raw.printSchema()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the first 5 rows with the following columns:\n",
    "\n",
    "*  business_id  \n",
    "*  name  \n",
    "*  city  \n",
    "*  state  \n",
    "*  categories  "
   ]
  },
  {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+---------------+-----+--------------------+\n",
      "|         business_id|                name|           city|state|          categories|\n",
      "+--------------------+--------------------+---------------+-----+--------------------+\n",
      "|f9NumwFMBDn751xgF...|The Range At Lake...|      Cornelius|   NC|Active Life, Gun/...|\n",
      "|Yzvjg0SayhoZgCljU...|   Carlos Santo, NMD|     Scottsdale|   AZ|Health & Medical,...|\n",
      "|XNoUzKckATkOD1hP6...|             Felinus|       Montreal|   QC|Pets, Pet Service...|\n",
      "|6OAZjbxqM5ol29BuH...|Nevada House of Hose|North Las Vegas|   NV|Hardware Stores, ...|\n",
      "|51M2Kk903DFYI6gnB...|USE MY GUY SERVIC...|           Mesa|   AZ|Home Services, Pl...|\n",
      "+--------------------+--------------------+---------------+-----+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "df_business_raw.select('business_id','name','city','state','categories').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part II: Analyzing Categories\n",
    "Let's now answer this question: **how many unique categories are represented in this dataset?**\n",
    "\n",
    "Essentially, we have the categories per business as a list - this is useful to quickly see what each business might be represented as but it is difficult to easily answer questions such as:\n",
    "\n",
    "* How many businesses are categorized as Active Life, for instance\n",
    "* What are the top 20 most popular categories available?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Association Table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93b69814bb684ac5b6aedf4502db8cdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.functions import split, explode\n",
    "df_business = df_business_raw.withColumn('category',\n",
    "                                     explode(split('categories',', ')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a509648bf55c498caf06aa866b633d38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+----------------+\n",
      "|         business_id|        category|\n",
      "+--------------------+----------------+\n",
      "|f9NumwFMBDn751xgF...|     Active Life|\n",
      "|f9NumwFMBDn751xgF...|Gun/Rifle Ranges|\n",
      "|f9NumwFMBDn751xgF...|     Guns & Ammo|\n",
      "|f9NumwFMBDn751xgF...|        Shopping|\n",
      "|Yzvjg0SayhoZgCljU...|Health & Medical|\n",
      "+--------------------+----------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "df_business.select('business_id','category').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Unique Categories\n",
    "\n",
    "What is the total number of unique categories available?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6ddac3e7acd4bb09f0b5f0bfef4c375",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+\n",
      "|           category|\n",
      "+-------------------+\n",
      "|     Dermatologists|\n",
      "|   Historical Tours|\n",
      "|Mobile Home Dealers|\n",
      "|     Paddleboarding|\n",
      "|   Hot Air Balloons|\n",
      "|       Aerial Tours|\n",
      "|      Skating Rinks|\n",
      "|  Signature Cuisine|\n",
      "|             Fondue|\n",
      "|      Data Recovery|\n",
      "|  Pet Waste Removal|\n",
      "|      Videographers|\n",
      "|    Pet Photography|\n",
      "|        Boat Repair|\n",
      "|            Beaches|\n",
      "|       Contract Law|\n",
      "|        Hobby Shops|\n",
      "|           Day Spas|\n",
      "|              Reiki|\n",
      "|           Honduran|\n",
      "+-------------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "df_business.select(\"category\").distinct().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Categories By Business\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counts of Businesses / Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb2b9126da8e4c8cb2e7fa8ad646698d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----+\n",
      "|           category|count|\n",
      "+-------------------+-----+\n",
      "|     Dermatologists|  341|\n",
      "|   Historical Tours|   54|\n",
      "|Mobile Home Dealers|   16|\n",
      "|     Paddleboarding|   36|\n",
      "|   Hot Air Balloons|   21|\n",
      "|       Aerial Tours|   28|\n",
      "|      Skating Rinks|  104|\n",
      "|  Signature Cuisine|    1|\n",
      "|             Fondue|   54|\n",
      "|      Data Recovery|  195|\n",
      "|  Pet Waste Removal|   34|\n",
      "|      Videographers|  169|\n",
      "|    Pet Photography|   49|\n",
      "|        Boat Repair|  100|\n",
      "|            Beaches|   74|\n",
      "|       Contract Law|   13|\n",
      "|        Hobby Shops|  828|\n",
      "|           Day Spas| 2880|\n",
      "|              Reiki|  312|\n",
      "|           Honduran|    8|\n",
      "+-------------------+-----+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "cat = df_business.groupby(\"category\").count()\n",
    "cat.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart of Top Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d37cfdf3ea74c22bd0dbedd86e7e18b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_df = cat.toPandas()\n",
    "cat_df.sort_values('count',inplace=True)\n",
    "cat_df=cat_df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "2257d9ab22ac4696b1f4dd8c9d0e8e9d",
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      "text/plain": [
       "VBox()"
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     },
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    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "cat_df.plot(kind='barh',y='count',x='category',\n",
    "            color='lightblue',title='Top Categories by Business',\n",
    "            fontsize=10)\n",
    "plt.xlabel('Count')\n",
    "plt.xlabel('Category')\n",
    "plt.tight_layout()\n",
    "%matplot plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part III: Do Yelp Reviews biased from the average rating?\n",
    "Is it true that people who write a review are those who are more dissatisfied or more satisfied with the service received.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Review Data\n",
    "Begin by loading the review data set from S3 and printing schema to determine what data is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
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   "outputs": [
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "df_review = spark.read.json('s3://yelp-reviews-businesses-datasets/yelp_academic_dataset_review.json' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "VBox()"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Colums:9\n",
      "Total Rows:8,021,122\n",
      "root\n",
      " |-- business_id: string (nullable = true)\n",
      " |-- cool: long (nullable = true)\n",
      " |-- date: string (nullable = true)\n",
      " |-- funny: long (nullable = true)\n",
      " |-- review_id: string (nullable = true)\n",
      " |-- stars: double (nullable = true)\n",
      " |-- text: string (nullable = true)\n",
      " |-- useful: long (nullable = true)\n",
      " |-- user_id: string (nullable = true)"
     ]
    }
   ],
   "source": [
    "print(f'Total Colums:{len(df_review.dtypes)}')\n",
    "print(f'Total Rows:{df_review.count():,}')\n",
    "df_review.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by listing the business_id and stars columns together for the user reviews data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "VBox()"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----+\n",
      "|         business_id|stars|\n",
      "+--------------------+-----+\n",
      "|-MhfebM0QIsKt87iD...|  2.0|\n",
      "|lbrU8StCq3yDfr-QM...|  1.0|\n",
      "|HQl28KMwrEKHqhFrr...|  5.0|\n",
      "|5JxlZaqCnk1MnbgRi...|  1.0|\n",
      "|IS4cv902ykd8wj1TR...|  4.0|\n",
      "+--------------------+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "df_review.select('business_id','stars').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's aggregate along the stars column to get a resultant dataframe that displays average stars per business as accumulated by users who took the time to submit a written review.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------------+\n",
      "|         business_id|        avg(stars)|\n",
      "+--------------------+------------------+\n",
      "|ATeOjZzE3VOI-RW7F...| 3.433734939759036|\n",
      "|RMjCnixEY5i12Ciqn...|3.5316455696202533|\n",
      "|vbUABNAQI2iwN7v0x...|2.4545454545454546|\n",
      "|VHsNB3pdGVcRgs6C3...| 3.411764705882353|\n",
      "|r-W1HPIZ3V6S2DaXh...| 4.852459016393443|\n",
      "+--------------------+------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "avg_starts = df_review.groupBy(\"business_id\").agg({'stars':'avg'})\n",
    "avg_starts.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's join our two dataframes (reviews and business data) by business_id.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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    }
   ],
   "source": [
    "joindf = avg_starts.join(df_business_raw,'business_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Rows:209,393"
     ]
    }
   ],
   "source": [
    "print(f'Total Rows:{joindf.count():,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-----+--------------------+-----------+-----+\n",
      "|        avg(stars)|stars|                name|       city|state|\n",
      "+------------------+-----+--------------------+-----------+-----+\n",
      "|  4.11784140969163|  4.0|Delmonico Steakhouse|  Las Vegas|   NV|\n",
      "|2.3333333333333335|  2.5|Neo-Paws Internat...|    Toronto|   ON|\n",
      "|             4.375|  4.5| McFarling Law Group|  Las Vegas|   NV|\n",
      "|3.3333333333333335|  3.5|Volvo of Mississauga|Mississauga|   ON|\n",
      "|               5.0|  5.0|Andre Agassi Coll...|  Las Vegas|   NV|\n",
      "+------------------+-----+--------------------+-----------+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "joindf.select('avg(stars)','stars','name','city','state').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute a new data frame that calculates what is called the deviation between the avg stars accumulated from written reviews and the actual star rating of a business (ie: the average of stars given by reviewers who wrote an actual review and reviewers who just provided a star rating).\n",
    "\n",
    "Calculated as follows: ```(row['avg(stars)'] - row['stars']) / row['stars']```\n",
    "\n",
    "If the deviation is negative, we can interpret that to be: reviewers who left a written response were more dissatisfied than normal. If skew is positive, we can interpret that to be: reviewers who left a written response were more satisfied than normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
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   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------------+\n",
      "|((avg(stars) - stars) / stars)|\n",
      "+------------------------------+\n",
      "|          -0.01893287435456...|\n",
      "|          0.009041591320072382|\n",
      "|          -0.01818181818181...|\n",
      "|          -0.02521008403361...|\n",
      "|          -0.02950819672131...|\n",
      "+------------------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "skewness_stars=joindf.select((joindf[\"avg(stars)\"]-joindf[\"stars\"])/joindf[\"stars\"])\n",
    "skewness_stars.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------------+-----+\n",
      "|((avg(stars) - stars) / stars)|count|\n",
      "+------------------------------+-----+\n",
      "|           0.01658163265306123|    1|\n",
      "|          0.027848101265822756|    6|\n",
      "|          -0.03391232423490494|    1|\n",
      "|          -0.05723905723905...|    7|\n",
      "|          -0.04145936981757872|    4|\n",
      "+------------------------------+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "skewness = skewness_stars.groupby('((avg(stars) - stars) / stars)').count()\n",
    "skewness.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, graph it!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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   ],
   "source": [
    "sk_df = skewness_stars.toPandas()\n",
    "sk_df.columns=['skew']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sk_df.plot(kind='hist', color='lightblue',bins=40)\n",
    "plt.tight_layout()\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the distribution is like a normal distribution and the variance is quite small. Thus, it may not be true that  people who write a review are those who are more dissatisfied or more satisfied with the service received.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part IV: Analyzing Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2158b0e0e2ba46859357429d393fcde5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_user = spark.read.json('s3://yelp-reviews-businesses-datasets/yelp_academic_dataset_user.json' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a390fcb6bd8c4adb857952e5e682aedb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Colums:22\n",
      "Total Rows:1,968,703\n",
      "root\n",
      " |-- average_stars: double (nullable = true)\n",
      " |-- compliment_cool: long (nullable = true)\n",
      " |-- compliment_cute: long (nullable = true)\n",
      " |-- compliment_funny: long (nullable = true)\n",
      " |-- compliment_hot: long (nullable = true)\n",
      " |-- compliment_list: long (nullable = true)\n",
      " |-- compliment_more: long (nullable = true)\n",
      " |-- compliment_note: long (nullable = true)\n",
      " |-- compliment_photos: long (nullable = true)\n",
      " |-- compliment_plain: long (nullable = true)\n",
      " |-- compliment_profile: long (nullable = true)\n",
      " |-- compliment_writer: long (nullable = true)\n",
      " |-- cool: long (nullable = true)\n",
      " |-- elite: string (nullable = true)\n",
      " |-- fans: long (nullable = true)\n",
      " |-- friends: string (nullable = true)\n",
      " |-- funny: long (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- review_count: long (nullable = true)\n",
      " |-- useful: long (nullable = true)\n",
      " |-- user_id: string (nullable = true)\n",
      " |-- yelping_since: string (nullable = true)"
     ]
    }
   ],
   "source": [
    "print(f'Total Colums:{len(df_user.dtypes)}')\n",
    "print(f'Total Rows:{df_user.count():,}')\n",
    "df_user.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45817cdbd7fa476b9cec038701180dcd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "split_col = F.split(df_user['yelping_since'], '-')\n",
    "df_user = df_user.withColumn('year', split_col.getItem(0))\n",
    "df_user = df_user.withColumn('month', split_col.getItem(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "616e9f73edc94a0794b9767a24e0875e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+\n",
      "|year|month|\n",
      "+----+-----+\n",
      "|2007|   07|\n",
      "|2008|   04|\n",
      "|2008|   08|\n",
      "|2008|   09|\n",
      "|2008|   08|\n",
      "+----+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "df_user.select('year','month').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58f69bc18c23484ca3d276b937752842",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+------+\n",
      "|(2020 - year)| count|\n",
      "+-------------+------+\n",
      "|          8.0|203180|\n",
      "|          7.0|221380|\n",
      "|          1.0| 75728|\n",
      "|          4.0|241414|\n",
      "|         11.0| 68314|\n",
      "+-------------+------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# How many years has each user been using yelp?\n",
    "\n",
    "no_yr=df_user.select(2020-df_user[\"year\"]).groupby('(2020 - year)').count()\n",
    "no_yr.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc5d7681fba9463b863f19c4a98b8c99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr_df = no_yr.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the number of users grouped by years of yelp used?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "937d1121be9041bfa0fa97874439b39e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr_df.columns = ['number of years','number of users']\n",
    "yr_df = yr_df.sort_values('number of users',ascending=False)\n",
    "\n",
    "yr_df.plot.bar(x='number of years', y='number of users',figsize=(7,6),title='Customer stickiness',color='lightblue')\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above shows that most users have been using yelp for 4 to 6 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46cbe254bd4040bf8e0fe1a65ffaa0c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+------+--------------------+\n",
      "|(2020 - year)| count|             percent|\n",
      "+-------------+------+--------------------+\n",
      "|          5.0|267267|   13.57579076173501|\n",
      "|          6.0|250827|  12.740723207106404|\n",
      "|          4.0|241414|  12.262591157731766|\n",
      "|          7.0|221380|  11.244966863970848|\n",
      "|          8.0|203180|  10.320500349722634|\n",
      "|          9.0|185076|   9.400910142362763|\n",
      "|          3.0|158881|     8.0703386950698|\n",
      "|          2.0|122892|   6.242282355439088|\n",
      "|         10.0|115106|   5.846793548849166|\n",
      "|          1.0| 75728|  3.8465934170872904|\n",
      "|         11.0| 68314|   3.470000299689694|\n",
      "|         12.0| 34327|  1.7436352766262864|\n",
      "|         13.0| 17155|  0.8713858819740713|\n",
      "|         14.0|  6052| 0.30741051341924097|\n",
      "|         15.0|  1022| 0.05191235041547659|\n",
      "|         16.0|    82|0.004165178800458982|\n",
      "+-------------+------+--------------------+"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.window import Window\n",
    "pc_yr = no_yr.withColumn('percent', 100*F.col('count')/F.sum('count').over(Window.partitionBy()))\n",
    "pc_yr.orderBy('percent', ascending=False).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 82% of the users have been using yelp for more than 4 years.  \n",
    "About 10% are new users who have been using yelp for 1~2 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## States with Most Reviewers"
   ]
  },
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   "source": [
    "### Join user, review and business dataset"
   ]
  },
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   "source": [
    "rew_user = df_user.join(df_review,'user_id')"
   ]
  },
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     "text": [
      "root\n",
      " |-- user_id: string (nullable = true)\n",
      " |-- average_stars: double (nullable = true)\n",
      " |-- compliment_cool: long (nullable = true)\n",
      " |-- compliment_cute: long (nullable = true)\n",
      " |-- compliment_funny: long (nullable = true)\n",
      " |-- compliment_hot: long (nullable = true)\n",
      " |-- compliment_list: long (nullable = true)\n",
      " |-- compliment_more: long (nullable = true)\n",
      " |-- compliment_note: long (nullable = true)\n",
      " |-- compliment_photos: long (nullable = true)\n",
      " |-- compliment_plain: long (nullable = true)\n",
      " |-- compliment_profile: long (nullable = true)\n",
      " |-- compliment_writer: long (nullable = true)\n",
      " |-- cool: long (nullable = true)\n",
      " |-- elite: string (nullable = true)\n",
      " |-- fans: long (nullable = true)\n",
      " |-- friends: string (nullable = true)\n",
      " |-- funny: long (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- review_count: long (nullable = true)\n",
      " |-- useful: long (nullable = true)\n",
      " |-- yelping_since: string (nullable = true)\n",
      " |-- year: string (nullable = true)\n",
      " |-- month: string (nullable = true)\n",
      " |-- business_id: string (nullable = true)\n",
      " |-- cool: long (nullable = true)\n",
      " |-- date: string (nullable = true)\n",
      " |-- funny: long (nullable = true)\n",
      " |-- review_id: string (nullable = true)\n",
      " |-- stars: double (nullable = true)\n",
      " |-- text: string (nullable = true)\n",
      " |-- useful: long (nullable = true)"
     ]
    }
   ],
   "source": [
    "rew_user.printSchema()"
   ]
  },
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   ],
   "source": [
    "bs_rew_user = rew_user.select('user_id','business_id').join(df_business_raw,'business_id')"
   ]
  },
  {
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     "text": [
      "root\n",
      " |-- business_id: string (nullable = true)\n",
      " |-- user_id: string (nullable = true)\n",
      " |-- address: string (nullable = true)\n",
      " |-- attributes: struct (nullable = true)\n",
      " |    |-- AcceptsInsurance: string (nullable = true)\n",
      " |    |-- AgesAllowed: string (nullable = true)\n",
      " |    |-- Alcohol: string (nullable = true)\n",
      " |    |-- Ambience: string (nullable = true)\n",
      " |    |-- BYOB: string (nullable = true)\n",
      " |    |-- BYOBCorkage: string (nullable = true)\n",
      " |    |-- BestNights: string (nullable = true)\n",
      " |    |-- BikeParking: string (nullable = true)\n",
      " |    |-- BusinessAcceptsBitcoin: string (nullable = true)\n",
      " |    |-- BusinessAcceptsCreditCards: string (nullable = true)\n",
      " |    |-- BusinessParking: string (nullable = true)\n",
      " |    |-- ByAppointmentOnly: string (nullable = true)\n",
      " |    |-- Caters: string (nullable = true)\n",
      " |    |-- CoatCheck: string (nullable = true)\n",
      " |    |-- Corkage: string (nullable = true)\n",
      " |    |-- DietaryRestrictions: string (nullable = true)\n",
      " |    |-- DogsAllowed: string (nullable = true)\n",
      " |    |-- DriveThru: string (nullable = true)\n",
      " |    |-- GoodForDancing: string (nullable = true)\n",
      " |    |-- GoodForKids: string (nullable = true)\n",
      " |    |-- GoodForMeal: string (nullable = true)\n",
      " |    |-- HairSpecializesIn: string (nullable = true)\n",
      " |    |-- HappyHour: string (nullable = true)\n",
      " |    |-- HasTV: string (nullable = true)\n",
      " |    |-- Music: string (nullable = true)\n",
      " |    |-- NoiseLevel: string (nullable = true)\n",
      " |    |-- Open24Hours: string (nullable = true)\n",
      " |    |-- OutdoorSeating: string (nullable = true)\n",
      " |    |-- RestaurantsAttire: string (nullable = true)\n",
      " |    |-- RestaurantsCounterService: string (nullable = true)\n",
      " |    |-- RestaurantsDelivery: string (nullable = true)\n",
      " |    |-- RestaurantsGoodForGroups: string (nullable = true)\n",
      " |    |-- RestaurantsPriceRange2: string (nullable = true)\n",
      " |    |-- RestaurantsReservations: string (nullable = true)\n",
      " |    |-- RestaurantsTableService: string (nullable = true)\n",
      " |    |-- RestaurantsTakeOut: string (nullable = true)\n",
      " |    |-- Smoking: string (nullable = true)\n",
      " |    |-- WheelchairAccessible: string (nullable = true)\n",
      " |    |-- WiFi: string (nullable = true)\n",
      " |-- categories: string (nullable = true)\n",
      " |-- city: string (nullable = true)\n",
      " |-- hours: struct (nullable = true)\n",
      " |    |-- Friday: string (nullable = true)\n",
      " |    |-- Monday: string (nullable = true)\n",
      " |    |-- Saturday: string (nullable = true)\n",
      " |    |-- Sunday: string (nullable = true)\n",
      " |    |-- Thursday: string (nullable = true)\n",
      " |    |-- Tuesday: string (nullable = true)\n",
      " |    |-- Wednesday: string (nullable = true)\n",
      " |-- is_open: long (nullable = true)\n",
      " |-- latitude: double (nullable = true)\n",
      " |-- longitude: double (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- postal_code: string (nullable = true)\n",
      " |-- review_count: long (nullable = true)\n",
      " |-- stars: double (nullable = true)\n",
      " |-- state: string (nullable = true)"
     ]
    }
   ],
   "source": [
    "bs_rew_user.printSchema()"
   ]
  },
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     "text": [
      "+-----+-------+\n",
      "|state|  count|\n",
      "+-----+-------+\n",
      "|   AZ|2504395|\n",
      "|   SC|  28581|\n",
      "|   OR|      9|\n",
      "|   VA|     27|\n",
      "|   QC| 208166|\n",
      "+-----+-------+\n",
      "only showing top 5 rows"
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   ],
   "source": [
    "state_p = bs_rew_user.select('user_id','state').groupby('state').count()\n",
    "state_p.show(5)"
   ]
  },
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    "dp = state_p.toPandas()\n",
    "dp = dp.sort_values('count',ascending=False).head(10)"
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp.plot.bar(x='state', y='count',figsize=(7,6),title='States with Most Reviewers',color='lightblue')\n",
    "%matplot plt"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySpark",
   "language": "",
   "name": "pysparkkernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 2
   },
   "mimetype": "text/x-python",
   "name": "pyspark",
   "pygments_lexer": "python2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}