Analysis.ipynb

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    "# Analysis of Yelp Business Intelligence Data\n",
    "\n",
    "We will analyze a subset of Yelp's business, reviews and user data. This dataset comes to us from Kaggle although we have taken steps to pull this data into a publis s3 bucket: s3://sta9760-project2-datasets/yelp-light/yelp_academic_dataset_business.json (s3://Your_S3_file_location/file_name)"
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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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    "%info"
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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>9</td><td>application_1588561620230_0010</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-30-88.us-east-2.compute.internal:20888/proxy/application_1588561620230_0010/\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-26-19.us-east-2.compute.internal:8042/node/containerlogs/container_1588561620230_0010_01_000001/livy\">Link</a></td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
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     "text": [
      "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": [
    "# Current available packages\n",
    "sc.list_packages()"
   ]
  },
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   "source": [
    "# Installation and Initial Setup\n",
    "\n",
    "Begin by installing the necessary libraries that you may need to conduct your analysis. At the very least, you must install pandas, matplotlib, and seaborn"
   ]
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     "text": [
      "Collecting pandas==1.0.3\n",
      "  Using cached pandas-1.0.3-cp36-cp36m-manylinux1_x86_64.whl (10.0 MB)\n",
      "Collecting python-dateutil>=2.6.1\n",
      "  Using cached python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib64/python3.6/site-packages (from pandas==1.0.3) (1.14.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/site-packages (from pandas==1.0.3) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas==1.0.3) (1.13.0)\n",
      "Installing collected packages: python-dateutil, pandas\n",
      "Successfully installed pandas-1.0.3 python-dateutil-2.8.1\n",
      "\n",
      "Collecting matplotlib==3.2.1\n",
      "  Using cached matplotlib-3.2.1-cp36-cp36m-manylinux1_x86_64.whl (12.4 MB)\n",
      "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1\n",
      "  Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
      "Requirement already satisfied: numpy>=1.11 in /usr/local/lib64/python3.6/site-packages (from matplotlib==3.2.1) (1.14.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /mnt/tmp/1588632399927-0/lib/python3.6/site-packages (from matplotlib==3.2.1) (2.8.1)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Using cached kiwisolver-1.2.0-cp36-cp36m-manylinux1_x86_64.whl (88 kB)\n",
      "Collecting cycler>=0.10\n",
      "  Using cached cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib==3.2.1) (1.13.0)\n",
      "Installing collected packages: pyparsing, kiwisolver, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.2.1 pyparsing-2.4.7\n",
      "\n",
      "Collecting seaborn==0.10.0\n",
      "  Using cached seaborn-0.10.0-py3-none-any.whl (215 kB)\n",
      "Collecting scipy>=1.0.1\n",
      "  Using cached scipy-1.4.1-cp36-cp36m-manylinux1_x86_64.whl (26.1 MB)\n",
      "Requirement already satisfied: matplotlib>=2.1.2 in /mnt/tmp/1588632399927-0/lib64/python3.6/site-packages (from seaborn==0.10.0) (3.2.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib64/python3.6/site-packages (from seaborn==0.10.0) (1.14.5)\n",
      "Requirement already satisfied: pandas>=0.22.0 in /mnt/tmp/1588632399927-0/lib64/python3.6/site-packages (from seaborn==0.10.0) (1.0.3)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /mnt/tmp/1588632399927-0/lib/python3.6/site-packages (from matplotlib>=2.1.2->seaborn==0.10.0) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /mnt/tmp/1588632399927-0/lib/python3.6/site-packages (from matplotlib>=2.1.2->seaborn==0.10.0) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /mnt/tmp/1588632399927-0/lib64/python3.6/site-packages (from matplotlib>=2.1.2->seaborn==0.10.0) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /mnt/tmp/1588632399927-0/lib/python3.6/site-packages (from matplotlib>=2.1.2->seaborn==0.10.0) (0.10.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/site-packages (from pandas>=0.22.0->seaborn==0.10.0) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib>=2.1.2->seaborn==0.10.0) (1.13.0)\n",
      "Installing collected packages: scipy, seaborn\n",
      "Successfully installed scipy-1.4.1 seaborn-0.10.0"
     ]
    }
   ],
   "source": [
    "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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      "Package                    Version\n",
      "-------------------------- -------\n",
      "beautifulsoup4             4.8.1\n",
      "boto                       2.49.0\n",
      "cycler                     0.10.0\n",
      "jmespath                   0.9.4\n",
      "kiwisolver                 1.2.0\n",
      "lxml                       4.4.2\n",
      "matplotlib                 3.2.1\n",
      "mysqlclient                1.4.6\n",
      "nltk                       3.4.5\n",
      "nose                       1.3.4\n",
      "numpy                      1.14.5\n",
      "pandas                     1.0.3\n",
      "pip                        20.1\n",
      "py-dateutil                2.2\n",
      "pyparsing                  2.4.7\n",
      "python-dateutil            2.8.1\n",
      "python36-sagemaker-pyspark 1.2.6\n",
      "pytz                       2019.3\n",
      "PyYAML                     3.11\n",
      "scipy                      1.4.1\n",
      "seaborn                    0.10.0\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": [
    "# Check available pakcages again\n",
    "sc.list_packages()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing\n",
    "\n",
    "Now, import the installed packages from the previous block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data\n",
    "\n",
    "We are finally ready to load data. Using spark load the data from S3 into a dataframe object that we can manipulate further down in our analysis.\n",
    "\n",
    "Make sure you have correct file_location and file_name."
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "df_bus = spark.read.json('s3://sta9760-project2-datasets/yelp_academic_dataset_business.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview of Data\n",
    "Display the number of rows and columns in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "text": [
      "Total Columns: 14\n",
      "Total Rows: 209,393"
     ]
    }
   ],
   "source": [
    "# get an overview of the dataframe\n",
    "print(f'Total Columns: {len(df_bus.dtypes)}')\n",
    "print(f'Total Rows: {df_bus.count():,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the DataFrame schema below."
   ]
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      "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": [
    "# schema\n",
    "df_bus.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the first 5 rows with the following columns:\n",
    "* business_id\n",
    "* name\n",
    "* city\n",
    "* state\n",
    "* categories"
   ]
  },
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      },
      "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|stars|          categories|\n",
      "+--------------------+--------------------+---------------+-----+-----+--------------------+\n",
      "|f9NumwFMBDn751xgF...|The Range At Lake...|      Cornelius|   NC|  3.5|Active Life, Gun/...|\n",
      "|Yzvjg0SayhoZgCljU...|   Carlos Santo, NMD|     Scottsdale|   AZ|  5.0|Health & Medical,...|\n",
      "|XNoUzKckATkOD1hP6...|             Felinus|       Montreal|   QC|  5.0|Pets, Pet Service...|\n",
      "|6OAZjbxqM5ol29BuH...|Nevada House of Hose|North Las Vegas|   NV|  2.5|Hardware Stores, ...|\n",
      "|51M2Kk903DFYI6gnB...|USE MY GUY SERVIC...|           Mesa|   AZ|  4.5|Home Services, Pl...|\n",
      "+--------------------+--------------------+---------------+-----+-----+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "df_bus.createOrReplaceTempView(\"bus\")\n",
    "df_table = spark.sql(\n",
    "'''\n",
    "SELECT business_id, name, city, state, stars, categories \n",
    "FROM bus \n",
    "'''\n",
    ")\n",
    "\n",
    "df_table.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Categories\n",
    "\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?\n",
    "\n",
    "\n",
    "Association Table\n",
    "We need to \"break out\" these categories from the business ids? One common approach to take is to build an association table mapping a single business id multiple times to each distinct category.\n",
    "\n",
    "For instance, given the following:\n",
    "\n",
    "| business_id | categories         \n",
    "| :-: |:-------------: \n",
    "|abcd123| a,b,c\n",
    "\n",
    "\n",
    "We would like to derive something like:\n",
    "\n",
    "| business_id | categories         \n",
    "| :-: |:-------------: \n",
    "|abcd123| a\n",
    "|abcd123| b\n",
    "|abcd123| c\n",
    "\n",
    "What this does is allow us to then perform a myriad of rollups and other analysis on this association table which can aid us in answering the questions asked above.\n",
    "\n",
    "Implement the code necessary to derive the table described from your original yelp dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the first 5 rows of your association table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7de082b4b7eb4295add0c4f9130eb8a3",
       "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_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|        category|\n",
      "+--------------------+----------------+\n",
      "|f9NumwFMBDn751xgF...|     Active Life|\n",
      "|f9NumwFMBDn751xgF...|Gun/Rifle Ranges|\n",
      "|f9NumwFMBDn751xgF...|     Guns & Ammo|\n",
      "|f9NumwFMBDn751xgF...|        Shopping|\n",
      "|Yzvjg0SayhoZgCljU...|Health & Medical|\n",
      "+--------------------+----------------+"
     ]
    }
   ],
   "source": [
    "# create df that only contains business id and category \n",
    "# split category and map business_id multiple times to each of its category\n",
    "\n",
    "df_cat = spark.sql(\n",
    "'''\n",
    "SELECT business_id, EXPLODE(SPLIT(categories,', ')) category \n",
    "FROM bus\n",
    "'''\n",
    ")\n",
    "\n",
    "df_cat.createOrReplaceTempView(\"cat\")\n",
    "\n",
    "df_cat = spark.sql(\n",
    "'''\n",
    "SELECT * \n",
    "FROM cat\n",
    "LIMIT 5\n",
    "''')\n",
    "\n",
    "df_cat.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Unique Categories \n",
    "Finally, we are ready to answer the question: **what is the total number of unique categories available?**\n",
    "\n",
    "Below, implement the code necessary to calculate this figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06cf655de4ee45f7acadfe8868a2873f",
       "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": [
      "1336"
     ]
    }
   ],
   "source": [
    "count = spark.sql(\n",
    "'''\n",
    "SELECT DISTINCT(category)\n",
    "FROM cat\n",
    "''')\n",
    "\n",
    "count.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Categories By Business\n",
    "\n",
    "Now let's find the top categories in this dataset by rolling up categories.\n",
    "\n",
    "So now, let's unroll our distinct count a bit and display the per count value of businesses per category.\n",
    "\n",
    "The expected output should be:\n",
    "\n",
    "| category | count      \n",
    "| :-: |:-------------: \n",
    "| a | 15\n",
    "| b | 2\n",
    "| c | 45\n",
    "\n",
    "Or something to that effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b806450135494a81b1b311649d31d294",
       "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",
      "|            category|total|\n",
      "+--------------------+-----+\n",
      "|         Restaurants|63944|\n",
      "|            Shopping|34644|\n",
      "|                Food|32991|\n",
      "|       Home Services|22487|\n",
      "|       Beauty & Spas|20520|\n",
      "|    Health & Medical|19227|\n",
      "|      Local Services|15783|\n",
      "|          Automotive|14720|\n",
      "|           Nightlife|14211|\n",
      "|                Bars|12400|\n",
      "|Event Planning & ...|11263|\n",
      "|         Active Life|10225|\n",
      "|        Coffee & Tea| 8415|\n",
      "|             Fashion| 8374|\n",
      "|           Fast Food| 8106|\n",
      "|          Sandwiches| 8064|\n",
      "|American (Traditi...| 7596|\n",
      "|       Home & Garden| 7331|\n",
      "|         Hair Salons| 7303|\n",
      "|               Pizza| 7302|\n",
      "+--------------------+-----+"
     ]
    }
   ],
   "source": [
    "# count category and show the top 20\n",
    "\n",
    "count_20 = spark.sql(\n",
    "'''\n",
    "SELECT category, COUNT(*) AS total\n",
    "FROM cat\n",
    "GROUP BY category\n",
    "ORDER BY total DESC\n",
    "LIMIT 20''')\n",
    "\n",
    "count_20.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Bar Chart of Top Categories\n",
    "With this data available, let us now build a barchart of the top 20 categories.\n",
    "\n",
    "**HINT**: don't forget about the matplotlib magic!\n",
    "\n",
    "    %matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "153b57a50bad4f6bb7df8ab7753f9389",
       "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_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"
    }
   ],
   "source": [
    "# Conver to Pandas\n",
    "count_20pd = count_20.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7216c98e4eb04e12bed8e7929ab3471f",
       "version_major": 2,
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      },
      "text/plain": [
       "VBox()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "count_20pd.sort_values(by='total',ascending=True).plot(\n",
    "x='category',\n",
    "y='total',\n",
    "kind='barh',\n",
    "figsize=(20,10),\n",
    "legend=None,\n",
    "color='lightsteelblue')\n",
    "plt.title('Top Categories by Bussiness', fontsize=20)\n",
    "plt.xlabel(\"Total Count\")\n",
    "plt.ylabel(\"Category\")\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do Yelp Reviews Skew Negative?# \n",
    "\n",
    "Oftentimes, it is said that the only people who write a written review are those who are extremely dissatisfied or extremely satisfied with the service received.\n",
    "\n",
    "How true is this really? Let's try and answer this question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading User Data\n",
    "\n",
    "Begin by loading the user data set from S3 and printing schema to determine what data is available.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b62ca236aab4a619e28d8767f700ab5",
       "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_rv = spark.read.json('s3://sta9760-project2-datasets/yelp_academic_dataset_review.json') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4616b0c1b03d443689e3074a620c5e6c",
       "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": [
      "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": [
    "# schema\n",
    "df_rv.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by listing the business_id and stars columns together for the user reviews data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26f0deda00224a459e6c968aa0d3065e",
       "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",
      "|         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_rv.createOrReplaceTempView(\"review\")\n",
    "df_table2 = spark.sql(\n",
    "'''\n",
    "SELECT business_id, stars\n",
    "FROM review\n",
    "'''\n",
    ")\n",
    "\n",
    "df_table2.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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a183c1ca3b3f4e1fbe86d299cada6536",
       "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",
      "|         business_id|      average_star|\n",
      "+--------------------+------------------+\n",
      "|VHsNB3pdGVcRgs6C3...| 3.411764705882353|\n",
      "|RMjCnixEY5i12Ciqn...|3.5316455696202533|\n",
      "|vQGZjrDiDBuznOkUe...| 4.454545454545454|\n",
      "|vyu1WGlnHGv2Z2MVE...|               5.0|\n",
      "|r-W1HPIZ3V6S2DaXh...| 4.852459016393443|\n",
      "+--------------------+------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# calculate the average review stars for each unique business id\n",
    "avg = spark.sql(\n",
    "'''\n",
    "SELECT DISTINCT business_id, AVG(stars) as average_star\n",
    "FROM review\n",
    "GROUP BY business_id\n",
    "'''\n",
    ")\n",
    "avg.createOrReplaceTempView(\"avg\")\n",
    "avg.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the fun part - let's join our two dataframes (reviews and business data) by business_id."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see a few of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea430fb739f74c718d6bedbfd36f9dd1",
       "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",
      "|average_star|stars|                name|      city|state|\n",
      "+------------+-----+--------------------+----------+-----+\n",
      "|         5.0|  5.0|          Mane.edits| Charlotte|   NC|\n",
      "|         5.0|  5.0|      Thomas S Holly|   Phoenix|   AZ|\n",
      "|         5.0|  5.0|       The Amburgeys|   Gilbert|   AZ|\n",
      "|         5.0|  5.0|Jane Bloch - Cold...|Pittsburgh|   PA|\n",
      "|         5.0|  5.0| Arizona Microgreens|   Phoenix|   AZ|\n",
      "+------------+-----+--------------------+----------+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# joining table using business_id as key\n",
    "# orbey by reversely\n",
    "join = spark.sql(\n",
    "'''\n",
    "SELECT avg.average_star, bus.stars, bus.name, bus.city, bus.state\n",
    "FROM avg\n",
    "INNER JOIN bus ON avg.business_id = bus.business_id\n",
    "ORDER BY avg.average_star DESC\n",
    "''')\n",
    "join.createOrReplaceTempView(\"joined\")\n",
    "join.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Compute a new dataframe that calculates what we will call the skew (for lack of a better word) 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",
    "The formula you can use is something like:\n",
    "\n",
    "    (row['avg(stars)'] - row['stars']) / row['stars']\n",
    "\n",
    "If the **skew** 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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dee78d3c734f4deea79538e1d5f584b0",
       "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",
      "|                skew|\n",
      "+--------------------+\n",
      "|-0.45454545454545453|\n",
      "| -0.3968253968253968|\n",
      "|  -0.380952380952381|\n",
      "| -0.3766233766233767|\n",
      "|-0.37142857142857144|\n",
      "| -0.3703703703703704|\n",
      "| -0.3571428571428571|\n",
      "|               -0.35|\n",
      "| -0.3333333333333333|\n",
      "| -0.3230769230769231|\n",
      "| -0.3214285714285714|\n",
      "|               -0.32|\n",
      "|-0.31428571428571433|\n",
      "|-0.31428571428571433|\n",
      "|-0.31428571428571433|\n",
      "|-0.31428571428571433|\n",
      "|-0.31428571428571433|\n",
      "|-0.31428571428571433|\n",
      "| -0.3137254901960785|\n",
      "|             -0.3125|\n",
      "+--------------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "# calculate skew\n",
    "skew = spark.sql(\n",
    "'''\n",
    "SELECT (average_star - stars) / stars AS skew\n",
    "FROM joined\n",
    "ORDER BY skew ASC\n",
    "''')\n",
    "skew.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "993880fb408a4ec4b37d7ab64d3cabcf",
       "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": [
    "# convert to pandas\n",
    "skew_pd = skew.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, graph it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1a02c1f4e3347269c3c2eb88e3be100",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_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"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot \n",
    "plt.figure(figsize=(20,10))\n",
    "sns.distplot(skew_pd['skew'], \n",
    "            hist=True, \n",
    "            kde=True, \n",
    "            bins=50, \n",
    "            color = 'lightsteelblue',\n",
    "            kde_kws={'linewidth':1})\n",
    "plt.title('Top Categories by Bussiness', fontsize=20)\n",
    "plt.xlabel('k')\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Should the Elite be Trusted? (Or, some other analysis of your choice)\n",
    "\n",
    "\n",
    "For the final portion - you have a choice:\n",
    "\n",
    "* Try and analyze some interesting dimension to this data. The **ONLY** requirement is that you must use the Users dataset and join on either the **business** or **reviews** dataset\n",
    "* Or, you may try and answer the question posed: how accurate or close are the ratings of an \"elite\" user (check Users table schema) vs the actual business rating.\n",
    "\n",
    "Feel free to use any and all methodologies at your disposal - only requirement is you must render one visualization in your analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data\n",
    "\n",
    "Loading user's data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa413c3a968843e1bb2687ea69e016c6",
       "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://sta9760-project2-datasets/yelp_academic_dataset_user.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing schema to determine what data is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcb3a4003edc4e558dd635e556b628d0",
       "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 Columns: 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": [
    "# get an overview of dataset and show schema\n",
    "print(f'Total Columns: {len(df_user.dtypes)}')\n",
    "print(f'Total Rows: {df_user.count():,}')\n",
    "df_user.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dbf8a6359614a76830ddef1d5699a7b",
       "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": [
      "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": [
    "# show review schema to look for key \n",
    "df_rv.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94fd31ec1cd8436e84861473efa217fd",
       "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": [
      "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": [
    "# # show business schema to look for key \n",
    "df_bus.printSchema()"
   ]
  },
  {
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   ],
   "source": [
    "# create review table\n",
    "df_rv.createOrReplaceTempView(\"review\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# create business table\n",
    "df_user.createOrReplaceTempView(\"user\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing to see the user data is run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-------------+------+--------------------+-------------------+\n",
      "|             user_id|average_stars|useful|               elite|      yelping_since|\n",
      "+--------------------+-------------+------+--------------------+-------------------+\n",
      "|ntlvfPzc8eglqvk92...|         3.57|   628|                    |2007-07-06 03:27:11|\n",
      "|FOBRPlBHa3WPHFB5q...|         3.84|   790|2008,2009,2010,20...|2008-04-28 01:29:25|\n",
      "|zZUnPeh2hEp0WydbA...|         3.44|   151|                2010|2008-08-28 23:40:05|\n",
      "|QaELAmRcDc5TfJEyl...|         3.08|   233|                2009|2008-09-20 00:08:14|\n",
      "|xvu8G900tezTzbbfq...|         4.37|  1265|2009,2010,2011,20...|2008-08-09 00:30:27|\n",
      "|z5_82komKV3mI4ASG...|         2.88|   642|                2007|2007-02-27 07:09:40|\n",
      "|ttumcu6hWshk_EJVW...|          4.0|    29|                    |2010-05-12 16:30:08|\n",
      "|f4_MRNHvN-yRn7EA8...|         3.63|  4127|2011,2012,2013,20...|2011-01-17 00:18:23|\n",
      "|UYACF30806j2mfbB5...|         3.75|    68|                    |2007-07-24 23:55:21|\n",
      "|QG13XBbgHWydzThRB...|          4.1|   587|           2008,2009|2007-06-04 00:14:10|\n",
      "+--------------------+-------------+------+--------------------+-------------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "test = spark.sql(\n",
    "'''\n",
    "SELECT user_id, average_stars, useful, elite, yelping_since\n",
    "FROM user\n",
    "''')\n",
    "test.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Elite's and Non-Elite's Users Average Starts Rating\n",
    "\n",
    "We want to know how off for users to give stars to stores compared to their overall average starts rating.\n",
    "\n",
    "We first will split the user groups into Elite and Non-Elite groups."
   ]
  },
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     "text": [
      "+--------------------+-------------+\n",
      "|             user_id|average_stars|\n",
      "+--------------------+-------------+\n",
      "|ntlvfPzc8eglqvk92...|         3.57|\n",
      "|ttumcu6hWshk_EJVW...|          4.0|\n",
      "|UYACF30806j2mfbB5...|         3.75|\n",
      "|f6YuZP6iennHFVlnF...|          3.8|\n",
      "|HwPGLzF_uXB3MF8bc...|          4.5|\n",
      "|y4UuVowA9i3zj2hHy...|         4.17|\n",
      "|6bbHSJ0PrgSxh7e5n...|         2.21|\n",
      "|pVU2DdtBFppBAX5G5...|         3.79|\n",
      "|q6QdLxYENKbVSo29E...|         2.61|\n",
      "|cWw606W2PmcjTZrwL...|         2.64|\n",
      "+--------------------+-------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "# split non-elite group from dataset\n",
    "elite_not = spark.sql(\n",
    "'''\n",
    "SELECT  user_id, average_stars\n",
    "FROM user\n",
    "WHERE elite LIKE ''\n",
    "''')\n",
    "elite_not.show(10)\n",
    "elite_not.createOrReplaceTempView(\"elite_not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "+--------------------+-------------+\n",
      "|             user_id|average_stars|\n",
      "+--------------------+-------------+\n",
      "|FOBRPlBHa3WPHFB5q...|         3.84|\n",
      "|zZUnPeh2hEp0WydbA...|         3.44|\n",
      "|QaELAmRcDc5TfJEyl...|         3.08|\n",
      "|xvu8G900tezTzbbfq...|         4.37|\n",
      "|z5_82komKV3mI4ASG...|         2.88|\n",
      "|f4_MRNHvN-yRn7EA8...|         3.63|\n",
      "|QG13XBbgHWydzThRB...|          4.1|\n",
      "|I_6wY8_RsewziNnKh...|         3.63|\n",
      "|q-v8elVPvKz0KvK69...|         3.37|\n",
      "|1WBxJ2r3A2QYfRSEz...|         3.82|\n",
      "+--------------------+-------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "# split elite group from dataset\n",
    "elite_only = spark.sql(\n",
    "'''\n",
    "SELECT user_id, average_stars \n",
    "FROM user\n",
    "WHERE elite NOT LIKE  ''\n",
    "''')\n",
    "elite_only.show(10)\n",
    "elite_only.createOrReplaceTempView(\"elite_only\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining Tables of Business and Review Datasets\n",
    "\n",
    "We will use non-elite's average stars to copare inidividual review stars base on unique business name.\n",
    "\n",
    "    [non-elite users' average stars] - [each single reviews stars for unique business name] = difference\n",
    "    [elite users' average stars] - [each single reviews stars for unique business name] = E_difference\n",
    "\n",
    "For example, if you average stars is 2.5, but you give a business store for 5.0, you are off by +2.5.\n",
    "\n",
    "By doing this we have see how off the overall percetange is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+\n",
      "|                name|          difference|\n",
      "+--------------------+--------------------+\n",
      "|    The Farish House|-0.41000000000000014|\n",
      "|       China Poblano|  0.3200000000000003|\n",
      "|Chaya Japanese Cu...| -0.8700000000000001|\n",
      "|   Lifetime Eye Care|                -1.0|\n",
      "|    Top of the World|                2.87|\n",
      "+--------------------+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# joining non-elite table using user_id connect to review table\n",
    "# joining review table using business_id connect to the business table\n",
    "# return a dataset with the unique business name and its star differences base on non-elite useravg_nonE = spark.sql\n",
    "avg_nonE = spark.sql(\n",
    "'''\n",
    "SELECT DISTINCT bus.name,(elite_not.average_stars - review.stars) AS difference\n",
    "FROM review\n",
    "INNER JOIN elite_not ON elite_not.user_id = review.user_id\n",
    "INNER JOIN bus ON bus.business_id = review.business_id\n",
    "''')\n",
    "avg_nonE.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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    "scrolled": true
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   "outputs": [
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     "text": [
      "+--------------------+-------------------+\n",
      "|                name|       E_difference|\n",
      "+--------------------+-------------------+\n",
      "|    Eden's Grill Inn|              -0.79|\n",
      "| Gordon Ramsay BurGR|              -0.79|\n",
      "|              Satara|0.20999999999999996|\n",
      "|            Javier's| 0.9199999999999999|\n",
      "|Citizens Kitchen ...|               2.45|\n",
      "+--------------------+-------------------+\n",
      "only showing top 5 rows"
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   ],
   "source": [
    "# joining elite table using user_id connect to review table\n",
    "# joining review table using business_id connect to the business table\n",
    "# return a dataset with the unique business name and its star differences base on elite useravg_nonE = spark.sql\n",
    "avg_E = spark.sql(\n",
    "'''\n",
    "SELECT DISTINCT bus.name, (elite_only.average_stars - review.stars) AS E_difference\n",
    "FROM review\n",
    "INNER JOIN elite_only ON elite_only.user_id = review.user_id\n",
    "INNER JOIN bus ON bus.business_id = review.business_id\n",
    "''')\n",
    "avg_E.show(5)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Convert to Pandas"
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  {
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   "execution_count": 39,
   "metadata": {},
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   ],
   "source": [
    "avg_nonE_pd = avg_nonE.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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   "source": [
    "avg_E_pd = avg_E.toPandas()"
   ]
  },
  {
   "cell_type": "code",
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# non-elite user plot\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.distplot(avg_nonE_pd['difference'], \n",
    "            hist=True, \n",
    "            kde=True, \n",
    "            bins=50, \n",
    "            color = 'lightsteelblue',\n",
    "            kde_kws={'linewidth':1})\n",
    "plt.title('Distribution of Non-Elite Stars Difference', fontsize=20)\n",
    "plt.xlabel('Difference')\n",
    "%matplot notebook\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c71e98fcb3b4bd0bd0346016be5b4fd",
       "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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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# elite user plot\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.distplot(avg_E_pd['E_difference'], \n",
    "            hist=True, \n",
    "            kde=True, \n",
    "            bins=50, \n",
    "            color = 'lightsteelblue',\n",
    "            kde_kws={'linewidth':1})\n",
    "plt.title('Distribution of Elite Stars Difference', fontsize=20)\n",
    "plt.xlabel('Difference')\n",
    "%matplot notebook\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "255f6d5164ce4d68a974c80772946e51",
       "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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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# overlapping to make the comparison\n",
    "sns.distplot(avg_nonE_pd['difference'], color='skyblue', label=\"Non-Elite\")\n",
    "sns.distplot(avg_E_pd['E_difference'], color='lightsteelblue', label=\"Elite\")\n",
    "plt.title('Elite Stars Difference VS Non-Elite Stars Difference', fontsize=20)\n",
    "plt.xlabel('Stars Rating Difference')\n",
    "plt.legend(loc='upper right')\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion \n",
    "\n",
    "By percentage, non-elite users tend to give higher stars compare to the elite user. Elite users tend to be more cautious while giving stars to a business store in both positive and negative ways. \n",
    "\n",
    "If the difference is not huge, we should see data points are close to the 0, that means elite users won't have high fluctuation in giving stars. However, non-elite users seem to be having higher fluctuation, which we can see that both positive and negative ways the difference is huge. Overall, base on current distribution, we can trust the elite users, but we need further analysis to confirm this conclusion is correct."
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}