Pyspark10GB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> <center>\n",
    "<font color='blue'>\n",
    "    Analysis of Yelp Business Intelligence Data\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installation and Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
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       "VBox()"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Spark application\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>2</td><td>application_1588749333705_0003</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-86-114.ec2.internal:20888/proxy/application_1588749333705_0003/\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-90-3.ec2.internal:8042/node/containerlogs/container_1588749333705_0003_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": [
      "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",
      "Collecting python-dateutil>=2.1\n",
      "  Using cached python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)\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, python-dateutil, kiwisolver, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.2.1 pyparsing-2.4.7 python-dateutil-2.8.1\n",
      "\n",
      "Collecting pandas==1.0.3\n",
      "  Using cached pandas-1.0.3-cp36-cp36m-manylinux1_x86_64.whl (10.0 MB)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /mnt/tmp/1589406452357-0/lib/python3.6/site-packages (from pandas==1.0.3) (2.8.1)\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: pandas\n",
      "Successfully installed pandas-1.0.3\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/1589406452357-0/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/1589406452357-0/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/1589406452357-0/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/1589406452357-0/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/1589406452357-0/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/1589406452357-0/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\n",
      "\n",
      "Collecting numpy==1.18.4\n",
      "  Using cached numpy-1.18.4-cp36-cp36m-manylinux1_x86_64.whl (20.2 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.14.5\n",
      "    Not uninstalling numpy at /usr/local/lib64/python3.6/site-packages, outside environment /tmp/1589406452357-0\n",
      "    Can't uninstall 'numpy'. No files were found to uninstall.\n",
      "Successfully installed numpy-1.18.4\n",
      "\n",
      "Processing /mnt/var/lib/livy/.cache/pip/wheels/84/30/e3/c51c5cd0229631e662d29d7b578a3e5949a4c8db033ffb70aa/pyspark-2.4.5-py2.py3-none-any.whl\n",
      "Collecting py4j==0.10.7\n",
      "  Using cached py4j-0.10.7-py2.py3-none-any.whl (197 kB)\n",
      "Installing collected packages: py4j, pyspark\n",
      "Successfully installed py4j-0.10.7 pyspark-2.4.5\n",
      "\n",
      "Collecting ipython==7.14.0\n",
      "  Using cached ipython-7.14.0-py3-none-any.whl (782 kB)\n",
      "Collecting traitlets>=4.2\n",
      "  Using cached traitlets-4.3.3-py2.py3-none-any.whl (75 kB)\n",
      "Collecting pexpect; sys_platform != \"win32\"\n",
      "  Using cached pexpect-4.8.0-py2.py3-none-any.whl (59 kB)\n",
      "Collecting decorator\n",
      "  Using cached decorator-4.4.2-py2.py3-none-any.whl (9.2 kB)\n",
      "Collecting pygments\n",
      "  Using cached Pygments-2.6.1-py3-none-any.whl (914 kB)\n",
      "Collecting pickleshare\n",
      "  Using cached pickleshare-0.7.5-py2.py3-none-any.whl (6.9 kB)\n",
      "Processing /mnt/var/lib/livy/.cache/pip/wheels/b4/cb/f1/d142b3bb45d488612cf3943d8a1db090eb95e6687045ba61d1/backcall-0.1.0-py3-none-any.whl\n",
      "Collecting prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0\n",
      "  Using cached prompt_toolkit-3.0.5-py3-none-any.whl (351 kB)\n",
      "Requirement already satisfied: setuptools>=18.5 in /mnt/tmp/1589406452357-0/lib/python3.6/site-packages (from ipython==7.14.0) (46.3.0)\n",
      "Collecting jedi>=0.10\n",
      "  Using cached jedi-0.17.0-py2.py3-none-any.whl (1.1 MB)\n",
      "Collecting ipython-genutils\n",
      "  Using cached ipython_genutils-0.2.0-py2.py3-none-any.whl (26 kB)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/site-packages (from traitlets>=4.2->ipython==7.14.0) (1.13.0)\n",
      "Collecting ptyprocess>=0.5\n",
      "  Using cached ptyprocess-0.6.0-py2.py3-none-any.whl (39 kB)\n",
      "Collecting wcwidth\n",
      "  Using cached wcwidth-0.1.9-py2.py3-none-any.whl (19 kB)\n",
      "Collecting parso>=0.7.0\n",
      "  Using cached parso-0.7.0-py2.py3-none-any.whl (100 kB)\n",
      "Installing collected packages: ipython-genutils, decorator, traitlets, ptyprocess, pexpect, pygments, pickleshare, backcall, wcwidth, prompt-toolkit, parso, jedi, ipython\n",
      "Successfully installed backcall-0.1.0 decorator-4.4.2 ipython-7.14.0 ipython-genutils-0.2.0 jedi-0.17.0 parso-0.7.0 pexpect-4.8.0 pickleshare-0.7.5 prompt-toolkit-3.0.5 ptyprocess-0.6.0 pygments-2.6.1 traitlets-4.3.3 wcwidth-0.1.9\n",
      "\n",
      "Processing /mnt/var/lib/livy/.cache/pip/wheels/19/20/58/cc9bc70361c517120ee83bbc4ecc35bafc5e1bfa3e9c8e93bc/subprocess.run-0.0.8-py3-none-any.whl\n",
      "Installing collected packages: subprocess.run\n",
      "Successfully installed subprocess.run-0.0.8\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "ERROR: keras-applications 1.0.6 requires h5py, which is not installed.\n",
      "ERROR: python36-sagemaker-pyspark 1.2.6 requires pyspark==2.3.2, which is not installed.\n",
      "\n",
      "ERROR: python36-sagemaker-pyspark 1.2.6 has requirement pyspark==2.3.2, but you'll have pyspark 2.4.5 which is incompatible."
     ]
    }
   ],
   "source": [
    "sc.install_pypi_package(\"matplotlib==3.2.1\")\n",
    "sc.install_pypi_package(\"pandas==1.0.3\")\n",
    "sc.install_pypi_package(\"seaborn==0.10.0\")\n",
    "sc.install_pypi_package(\"numpy==1.18.4\")\n",
    "sc.install_pypi_package(\"pyspark==2.4.5\")\n",
    "sc.install_pypi_package(\"ipython==7.14.0\")\n",
    "sc.install_pypi_package(\"subprocess.run==0.0.8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "VBox()"
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   ],
   "source": [
    "from IPython import display \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from pyspark import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "VBox()"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                    Version\n",
      "-------------------------- -------\n",
      "absl-py                    0.8.1\n",
      "astor                      0.8.0\n",
      "backcall                   0.1.0\n",
      "beautifulsoup4             4.8.1\n",
      "bleach                     3.1.0\n",
      "boto                       2.49.0\n",
      "cycler                     0.10.0\n",
      "decorator                  4.4.2\n",
      "funcsigs                   1.0.2\n",
      "gast                       0.3.2\n",
      "google-pasta               0.1.7\n",
      "grpcio                     1.24.3\n",
      "html5lib                   1.0.1\n",
      "ipython                    7.14.0\n",
      "ipython-genutils           0.2.0\n",
      "jedi                       0.17.0\n",
      "jmespath                   0.9.4\n",
      "Keras-Applications         1.0.6\n",
      "Keras-Preprocessing        1.0.5\n",
      "kiwisolver                 1.2.0\n",
      "lxml                       4.4.2\n",
      "Markdown                   3.1.1\n",
      "matplotlib                 3.2.1\n",
      "mysqlclient                1.4.6\n",
      "nltk                       3.4.5\n",
      "nose                       1.3.4\n",
      "numpy                      1.18.4\n",
      "pandas                     1.0.3\n",
      "parso                      0.7.0\n",
      "pexpect                    4.8.0\n",
      "pickleshare                0.7.5\n",
      "pip                        20.1\n",
      "prompt-toolkit             3.0.5\n",
      "protobuf                   3.10.0\n",
      "ptyprocess                 0.6.0\n",
      "py-dateutil                2.2\n",
      "py4j                       0.10.7\n",
      "Pygments                   2.6.1\n",
      "pyparsing                  2.4.7\n",
      "pyspark                    2.4.5\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                 46.3.0\n",
      "six                        1.13.0\n",
      "soupsieve                  1.9.5\n",
      "subprocess.run             0.0.8\n",
      "tensorboard                1.14.0\n",
      "tensorflow                 1.14.0\n",
      "tensorflow-estimator       1.14.0\n",
      "termcolor                  1.1.0\n",
      "traitlets                  4.3.3\n",
      "wcwidth                    0.1.9\n",
      "webencodings               0.5.1\n",
      "Werkzeug                   0.16.0\n",
      "wheel                      0.34.2\n",
      "windmill                   1.6\n",
      "wrapt                      1.11.2"
     ]
    }
   ],
   "source": [
    "sc.list_packages()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "VBox()"
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   ],
   "source": [
    "business = spark.read.json(\"s3://my-little-pony/yelp/yelp_academic_dataset_business.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overview of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "VBox()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns:14 | Rows:209393"
     ]
    }
   ],
   "source": [
    "iv = {'columns': len(business.columns), 'rows': business.count()}\n",
    "print(f\"Columns:{iv['columns']} | Rows:{iv['rows']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
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   "outputs": [
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    },
    {
     "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": [
    "business.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52efb0f4d96e457cbd75ecc40fedc2ae",
       "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",
      "|                name|state|stars|          categories|         business_id|\n",
      "+--------------------+-----+-----+--------------------+--------------------+\n",
      "|The Range At Lake...|   NC|  3.5|Active Life, Gun/...|f9NumwFMBDn751xgF...|\n",
      "|   Carlos Santo, NMD|   AZ|  5.0|Health & Medical,...|Yzvjg0SayhoZgCljU...|\n",
      "|             Felinus|   QC|  5.0|Pets, Pet Service...|XNoUzKckATkOD1hP6...|\n",
      "|Nevada House of Hose|   NV|  2.5|Hardware Stores, ...|6OAZjbxqM5ol29BuH...|\n",
      "|USE MY GUY SERVIC...|   AZ|  4.5|Home Services, Pl...|51M2Kk903DFYI6gnB...|\n",
      "+--------------------+-----+-----+--------------------+--------------------+"
     ]
    }
   ],
   "source": [
    "business.createOrReplaceTempView(\"biz\")\n",
    "biz = spark.sql(\"\"\" \n",
    "SELECT \\\n",
    "name,  \n",
    "state, \n",
    "stars, \n",
    "categories,\n",
    "business_id\n",
    "FROM biz limit 5\"\"\")\n",
    "biz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> <center> <font color='red'>\n",
    " Analyzing Categories\n",
    "    </center> </font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question** how many unique categories are represented in this dataset?\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",
    "How many businesses are categorized as Active Life, for instance\n",
    "What are the top 20 most popular categories available?\n",
    "\n",
    "#### Association Table\n",
    "\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. What this does is allow us to then perform a myriad of rollups and other analysis on this association table which can aid in answering the questions asked above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64d5475e716741f591cec768740a0346",
       "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|              col|          categories|\n",
      "+--------------------+-----------------+--------------------+\n",
      "|f9NumwFMBDn751xgF...|      Active Life|Active Life, Gun/...|\n",
      "|f9NumwFMBDn751xgF...| Gun/Rifle Ranges|Active Life, Gun/...|\n",
      "|f9NumwFMBDn751xgF...|      Guns & Ammo|Active Life, Gun/...|\n",
      "|f9NumwFMBDn751xgF...|         Shopping|Active Life, Gun/...|\n",
      "+--------------------+-----------------+--------------------+\n",
      "only showing top 4 rows\n",
      "\n",
      "+--------------------+-----+\n",
      "|            Category|count|\n",
      "+--------------------+-----+\n",
      "|         Restaurants|44488|\n",
      "|            Shopping|26128|\n",
      "|                Food|24452|\n",
      "|         Restaurants|19456|\n",
      "|       Home Services|16205|\n",
      "|       Beauty & Spas|14216|\n",
      "|    Health & Medical|13978|\n",
      "|      Local Services|11958|\n",
      "|           Nightlife|11415|\n",
      "|                Bars|10201|\n",
      "|          Automotive|10063|\n",
      "| Event Planning &...| 8911|\n",
      "|                Food| 8539|\n",
      "|            Shopping| 8516|\n",
      "|         Active Life| 7771|\n",
      "|             Fashion| 6628|\n",
      "|       Beauty & Spas| 6304|\n",
      "|       Home Services| 6282|\n",
      "|        Coffee & Tea| 6257|\n",
      "|          Sandwiches| 6132|\n",
      "+--------------------+-----+\n",
      "only showing top 20 rows\n",
      "\n",
      "There is a total of  {2528}  Unique Categories"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT \n",
    "business_id,\n",
    "explode(split(categories, ',')),\n",
    "categories\n",
    "FROM biz\"\"\"\n",
    "\n",
    "bizcat = spark.sql(query)\n",
    "bizcat.show(4)\n",
    "\n",
    "tmp = bizcat.drop(\"categories\")\\\n",
    "            .withColumnRenamed('business_id', 'ID')\\\n",
    "            .withColumnRenamed('col', 'Category')\n",
    "\n",
    "categories = tmp.groupBy(\"Category\").count()\n",
    "categories = categories.sort(\"count\", ascending = False)\n",
    "categories.show(20)\n",
    "\n",
    "pdf=categories.toPandas()\n",
    "\n",
    "pdf.head(10).plot(x='Category', \n",
    "                 y='count', \n",
    "                 kind='barh',\n",
    "                 stacked = True,\n",
    "                 colormap='OrRd', \n",
    "                 fontsize = 5.5, \n",
    "                figsize = (5, 6), \n",
    "                  align=\"center\")\n",
    "plt.title(\"Top Business Categories\")\n",
    "\n",
    "print(\"There is a total of \", {categories.count()}, \" Unique Categories\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "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"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%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.***How true is this really?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f0ed7f504c844dcaf8038b1a5438850",
       "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": [
      "['business_id', 'stars', 'review_id', 'user_id', 'text']"
     ]
    }
   ],
   "source": [
    "review = spark.read.load('s3://my-little-pony/yelp/reviews.json', \n",
    "                         format='json'\n",
    "                        ).select(\"business_id\", \n",
    "                                 \"stars\", \n",
    "                                 \"review_id\", \n",
    "                                 \"user_id\", \n",
    "                                 \"text\")\n",
    "business = spark.read.load('s3://my-little-pony/yelp/yelp_academic_dataset_business.json', \n",
    "                           format = 'json'\n",
    "                          ).select(\"business_id\", \n",
    "                                   \"stars\")\n",
    "review.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb320cf199ee46a189cebdc1fd13cae4",
       "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|         avgRating|\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\n",
      "\n",
      "+--------------------+------------------+\n",
      "|             user_id|         avgRating|\n",
      "+--------------------+------------------+\n",
      "|KWFiZKiZBANVxuhm4...| 4.142857142857143|\n",
      "|VmYpF5C3GL-7wFnvO...|               3.6|\n",
      "|1Dul59QEe-Q-7OQHT...|              3.75|\n",
      "|xS6kmkMXp0PRrFwkS...|3.9473684210526314|\n",
      "|j56G3m8vYtA_2Io6F...|              4.25|\n",
      "+--------------------+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+--------------------+-----------+\n",
      "|                 BID|TotalRating|\n",
      "+--------------------+-----------+\n",
      "|M_guz7Dj7hX0evS67...|        3.5|\n",
      "|oqbNgOBAxsa49c2WQ...|        3.5|\n",
      "|2BKKUjLSocdlgfsMC...|        5.0|\n",
      "|DloBjCZHlz0lzsrQJ...|        4.0|\n",
      "|lj0MiK5_fyv9df2tw...|        4.5|\n",
      "+--------------------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "review_sub = review.select(\"business_id\", \"stars\", \"user_id\") \n",
    "review_sub.createOrReplaceTempView(\"review_Bsub_avg\")\n",
    "review_Bsub_avg = review_sub.groupBy(\"business_id\"\n",
    "                                    ).mean(\"stars\"\n",
    "                                          ).withColumnRenamed(\n",
    "    \"avg(stars)\", \n",
    "    \"avgRating\")\n",
    "review_sub.createOrReplaceTempView(\"review_Usub_avg\")\n",
    "review_Usub_avg = review_sub.groupBy(\"user_id\").mean(\"stars\").withColumnRenamed(\"avg(stars)\", \"avgRating\")\n",
    "\n",
    "review_Bsub_avg.show(5)\n",
    "review_Usub_avg.show(5)\n",
    "\n",
    "business.createOrReplaceTempView(\"business_sub_avg\")\n",
    "business_sub_avg = spark.sql(\"\"\"\n",
    "SELECT business_id as BID, \n",
    "avg(stars) as TotalRating\n",
    "FROM biz \n",
    "GROUP BY business_id\"\"\")\n",
    "business_sub_avg.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c89d3c35c52545a49ab7b24580dcd1ce",
       "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",
      "|summary|      ReviewRating|       TotalRating|\n",
      "+-------+------------------+------------------+\n",
      "|  count|            209393|            209393|\n",
      "|   mean| 3.534304236620265|3.5380552358483808|\n",
      "| stddev|1.0152088475168215|1.0235430346225862|\n",
      "|    min|               1.0|               1.0|\n",
      "|    max|               5.0|               5.0|\n",
      "+-------+------------------+------------------+"
     ]
    }
   ],
   "source": [
    "tbl = review_Bsub_avg.join(business_sub_avg, \n",
    "                           business_sub_avg.BID == review_Bsub_avg.business_id,\n",
    "                           \"full\"\n",
    "                          ).drop('BID'\n",
    "                                ).withColumnRenamed('business_id', \n",
    "                                                    'ID'\n",
    "                                                   ).withColumnRenamed('avgRating', 'ReviewRating')\n",
    "\n",
    "tbl.select(\"ReviewRating\", \"TotalRating\").describe().show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cb7ca38824446b98bb448be0918986d",
       "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",
      "Are Business and Reviews Average Rating The Same or Skewed?\n",
      "p_value = 0.248\n",
      "actual = 0.003750999228113283\n",
      "ts max = 0.010952332491801275"
     ]
    }
   ],
   "source": [
    "import numpy \n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = tbl.toPandas()\n",
    "iters == 0\n",
    "g1 = numpy.array(df[\"ReviewRating\"])\n",
    "g2 = numpy.array(df[\"TotalRating\"])\n",
    "data  = (g1, g2 )\n",
    "\n",
    "class DiffMeansPermute(HypothesisTesting): \n",
    "    \n",
    "    def TestStatistic(self, data): \n",
    "        group1, group2 = data \n",
    "        test_stat = abs(group1.mean() - group2.mean())\n",
    "        \n",
    "        return test_stat\n",
    "    \n",
    "    def MakeModel(self): \n",
    "        group1, group2 = self.data \n",
    "        self.n, self.m = len(group1), len(group2)\n",
    "        self.pool = numpy.hstack((group1, group2))\n",
    "        \n",
    "    def RunModel(self): \n",
    "        numpy.random.shuffle(self.pool)\n",
    "        data = self.pool[:self.n], self.pool[self.n:]\n",
    "        return data \n",
    "\n",
    "class HypothesisTesting(object): \n",
    "    \n",
    "    def __init__(self, data): \n",
    "        self.data = data \n",
    "        self.MakeModel() \n",
    "        self.actual = self.TestStatistic(numpy.array(data))\n",
    "        \n",
    "        self.test_stat = None \n",
    "    \n",
    "\n",
    "    def PValue(self, iters): \n",
    "        global count \n",
    "        if iters > 0:\n",
    "            self.test_stat = numpy.array(\n",
    "                [self.TestStatistic(self.RunModel()\n",
    "                                   ) for _ in range(iters)\n",
    "                ]\n",
    "            )\n",
    "                       \n",
    "        return sum(self.test_stat >= self.actual) / len(self.test_stat)\n",
    "    \n",
    "    def MaxTestStat(self):\n",
    "        for _ in range(1, iters):\n",
    "            pass \n",
    "        return max([self.test_stat])            \n",
    "            \n",
    "    def TestStatistic(self, data): \n",
    "        pass\n",
    "        \n",
    "    def MakeModel(self): \n",
    "        pass \n",
    "    \n",
    "    def RunModel(self): \n",
    "        pass \n",
    "\n",
    "ht = DiffMeansPermute(data)\n",
    "p_value = ht.PValue(iters = 1000)\n",
    "\n",
    "print('\\nAre Business and Reviews Average Ratings The Same or Skewed?')\n",
    "print('p_value =', p_value)\n",
    "print('actual =', ht.actual)\n",
    "print('ts max =', max(ht.MaxTestStat()))\n",
    "\n",
    "from numpy import *\n",
    "import scipy.stats as ss\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as p\n",
    "stat = ht.test_stat\n",
    "ys, xs, patches = plt.hist(stat, color='#ffff99')\n",
    "x = [xs,xs]\n",
    "X = abs(log10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2c38b49697e4bda851bcb1d8023e673",
       "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": [
    "sns.distplot(X)\n",
    "plt.title(\"Review Rating vs. Business Rating Mean Difference\", fontsize= 13)\n",
    "plt.suptitle(\"Null Hypothesis:: Review Rating = Business Rating\", fontsize =8)\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Should the Elite be Trusted? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13a6b29d549940498b0ee693ffdade67",
       "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": [
      "['name', 'ID', 'YRSelite', 'eliteSTAT']"
     ]
    }
   ],
   "source": [
    "import pyspark.sql.functions as f\n",
    "\"\"\"\"\"\"\"\"\n",
    "usr.createOrReplaceTempView(\"csv\")\n",
    "query = \"\"\"\n",
    "SELECT *, \n",
    "explode(split(elite, ',')),\n",
    "elite\n",
    "FROM csv\n",
    "\"\"\"\n",
    "csv =spark.sql(query)\n",
    "view = spark.read.load('s3://my-little-pony/yelp/reviews.json', format='json')\n",
    "\n",
    "view.columns\n",
    "view.show(5)\n",
    "usr = spark.read.json(\"s3://my-little-pony/yelp/yelp_academic_dataset_user.json\")\n",
    "usr.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec6cf9bd56044c3a80eff22d8eecbb43",
       "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",
      "|    name|                  ID|year|\n",
      "+--------+--------------------+----+\n",
      "|  Rafael|ntlvfPzc8eglqvk92...|    |\n",
      "|Michelle|FOBRPlBHa3WPHFB5q...|2008|\n",
      "|Michelle|FOBRPlBHa3WPHFB5q...|2009|\n",
      "|Michelle|FOBRPlBHa3WPHFB5q...|2010|\n",
      "|Michelle|FOBRPlBHa3WPHFB5q...|2011|\n",
      "|Michelle|FOBRPlBHa3WPHFB5q...|2012|\n",
      "|Michelle|FOBRPlBHa3WPHFB5q...|2013|\n",
      "|  Martin|zZUnPeh2hEp0WydbA...|2010|\n",
      "|    John|QaELAmRcDc5TfJEyl...|2009|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2009|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2010|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2011|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2012|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2014|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2015|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2016|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2017|\n",
      "|    Anne|xvu8G900tezTzbbfq...|2018|\n",
      "|   Steve|z5_82komKV3mI4ASG...|2007|\n",
      "|  Stuart|ttumcu6hWshk_EJVW...|    |\n",
      "+--------+--------------------+----+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "usr.createOrReplaceTempView(\"usr\")\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "name,\n",
    "user_id as ID,\n",
    "explode(split(elite, ',')),\n",
    "elite\n",
    "FROM usr\"\"\"\n",
    "usr1 = spark.sql(query).withColumnRenamed(\"col\", \"year\").drop(\"elite\")\n",
    "usr1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f250154ae0af4886af31532afa089edd",
       "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",
      "|    name|                  ID|YRSelite|\n",
      "+--------+--------------------+--------+\n",
      "|  Krista|wjClsIuhEjZN2Olko...|      13|\n",
      "|   David|jX52sZoojxyYV2viW...|      13|\n",
      "|   Ellen|5F_ASUSCHFf3YotkK...|      13|\n",
      "|     Amy|YbZg36FoMgkKcTEPa...|      13|\n",
      "|   Susie|FCTEAWv-QiGr0lfva...|      13|\n",
      "| Cassidy|seAX967Wk8qiVXOYj...|      13|\n",
      "|   Vicky|LPTq2-fZ9_TsG5o8l...|      13|\n",
      "|    Lori|5DgFmyjW6hkBtXtTM...|      13|\n",
      "|     dan|m7mQf4FbNRZ6u1wrH...|      13|\n",
      "|   Anita|nzsv-p1O8gCfP3Xij...|      13|\n",
      "|    Matt|HYQoxBC1y1DGPq6pG...|      13|\n",
      "|Jennifer|Dd6ElS2Cng3Qag_h4...|      13|\n",
      "| Shannon|fyS8H0BNDv5du_X7t...|      13|\n",
      "|   Sarah|8h1r7mXd-H225yDgc...|      13|\n",
      "|  Cassie|2hMNc8G87MGYw4Pom...|      13|\n",
      "|     Hen|JnGtgOPpkjyWOvWM0...|      13|\n",
      "|  Jeremy|nkN_do3fJ9xekchVC...|      13|\n",
      "|    Mary|OGa2q6LvuQLYFUG_R...|      13|\n",
      "|    Lisa|LnZUtFx6qTWs8NV6l...|      13|\n",
      "|    Joan|23J4vG9_xxxdnmi8C...|      13|\n",
      "+--------+--------------------+--------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+------+--------------------+--------+---------+\n",
      "|  name|                  ID|YRSelite|eliteSTAT|\n",
      "+------+--------------------+--------+---------+\n",
      "| Sarah|8h1r7mXd-H225yDgc...|      13|        1|\n",
      "|Jeremy|nkN_do3fJ9xekchVC...|      13|        1|\n",
      "|   Hen|JnGtgOPpkjyWOvWM0...|      13|        1|\n",
      "|  Joan|23J4vG9_xxxdnmi8C...|      13|        1|\n",
      "|  Lisa|LnZUtFx6qTWs8NV6l...|      13|        1|\n",
      "+------+--------------------+--------+---------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "usr = usr1.groupBy(\"name\", \"ID\").count().withColumnRenamed('count', 'YRSelite').sort(\"YRSelite\", ascending=False)\n",
    "usr.show(5)\n",
    "\n",
    "usr = usr.withColumn('eliteSTAT', \n",
    "              f.when(usr.YRSelite > 1, 1).otherwise(0))\n",
    "usr.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38228941d4a34eb992a1d2a7c1dab174",
       "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",
      "|eliteSTAT|Years_Num|         Years_Avg|Years_Min|Years_Max|  total|Percent %|\n",
      "+---------+---------+------------------+---------+---------+-------+---------+\n",
      "|        1|    57230|3.7365367814083523|        2|       13|1968703|      3%\n",
      "|\n",
      "|        0|  1911473|               1.0|        1|        1|1968703|     97%\n",
      "|\n",
      "+---------+---------+------------------+---------+---------+-------+---------+"
     ]
    }
   ],
   "source": [
    "from pyspark.sql import functions as f\n",
    "from pyspark.sql.functions import rank, sum, col \n",
    "from pyspark.sql import Window \n",
    "\n",
    "window = Window.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)\n",
    "\n",
    "t = usr.select([\"YRSelite\", \"eliteSTAT\"])\\\n",
    "            .groupBy('eliteSTAT')\\\n",
    "            .agg(f.count('YRSelite')\\\n",
    "                 .alias('Years_Num'), \n",
    "                 f.mean('YRSelite')\\\n",
    "                 .alias('Years_Avg'),\n",
    "                 f.min('YRSelite')\\\n",
    "                 .alias('Years_Min'),\n",
    "                 f.max('YRSelite')\\\n",
    "                 .alias('Years_Max'))\\\n",
    ".withColumn('total', sum(col('Years_Num')).over(window))\\\n",
    ".withColumn('Percent %', f.format_string(\"%5.0f%%\\n\", col('Years_Num')*100/col('total')))\n",
    "tab = t.drop('Percent %').withColumn('Percent', col('Years_Num')*100/col('total'))\n",
    "t.drop(col('total'))\n",
    "t.show()\n",
    "tab = tab.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "928acbe0f8384231ad4ca654cc8b05f6",
       "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": [
    "plot_data = pd.DataFrame(tab)\n",
    "labels = plot_data.eliteSTAT\n",
    "sizes = plot_data.Percent\n",
    "colors = ('lightcoral', 'cyan')\n",
    "explode = (0, 0.1)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.pie(sizes, \n",
    "        explode=explode, \n",
    "        labels = ['elite', 'non-elite'], \n",
    "        colors = colors,\n",
    "        autopct='%1.1f%%',\n",
    "        shadow=True)\n",
    "plt.title(\"Elite vs Non-Elite Avg Years On Yelp \")\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2586e4a201d042a7ac32b38df73bcc54",
       "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
      },
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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": [
      "['name', 'ID', 'YRSelite', 'eliteSTAT', 'fans', 'cool', 'useful', 'average_stars', 'cc', 'cf', 'ch', 'cc', 'cm', 'cn', 'cpl', 'cph', 'cpr']"
     ]
    }
   ],
   "source": [
    "# sub = csv.select(\"user_id\", \n",
    "#                  \"fans\", \n",
    "#                  \"cool\", \n",
    "#                  \"useful\", \n",
    "#                  \"average_stars\",\n",
    "#                 \"compliment_cool\",\n",
    "#                 \"compliment_funny\", \n",
    "#                 \"compliment_hot\",\n",
    "#                 \"compliment_cool\", \n",
    "#                 \"compliment_more\",\n",
    "#                 \"compliment_note\",\n",
    "#                 \"compliment_plain\", \n",
    "#                 \"compliment_photos\",\n",
    "#                 \"compliment_profile\")\n",
    "usr.createOrReplaceTempView(\"us\")\n",
    "# us = usr.join(sub, usr.ID == sub.user_id, \"full\")\n",
    "# us = us.withColumnRenamed('compliment_cool', 'cc')\\\n",
    "#        .withColumnRenamed('compliment_funny', 'cf')\\\n",
    "#        .withColumnRenamed('compliment_profile', 'cpr')\\\n",
    "#        .withColumnRenamed('compliment_note', 'cn')\\\n",
    "#        .withColumnRenamed('compliment_more', 'cm')\\\n",
    "#        .withColumnRenamed('compliment_photos', 'cph')\\\n",
    "#        .withColumnRenamed('compliment_plain', 'cpl')\\\n",
    "#        .withColumnRenamed('compliment_hot', 'ch').drop(\"user_id\")\n",
    "\n",
    "us.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db8d551a02894dd98d7f60ad051c4035",
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----+--------------------+--------------------+--------------------+-------------------+\n",
      "|             user_id|stars|         business_id|           review_id|                text|               date|\n",
      "+--------------------+-----+--------------------+--------------------+--------------------+-------------------+\n",
      "|OwjRMXRC0KyPrIlcj...|  2.0|-MhfebM0QIsKt87iD...|xQY8N_XvtGbearJ5X...|As someone who ha...|2015-04-15 05:21:16|\n",
      "|nIJD_7ZXHq-FX8byP...|  1.0|lbrU8StCq3yDfr-QM...|UmFMZ8PyXZTY2Qcwz...|I am actually hor...|2013-12-07 03:16:52|\n",
      "|V34qejxNsCbcgD8C0...|  5.0|HQl28KMwrEKHqhFrr...|LG2ZaYiOgpr2DK_90...|I love Deagan's. ...|2015-12-05 03:18:11|\n",
      "|ofKDkJKXSKZXu5xJN...|  1.0|5JxlZaqCnk1MnbgRi...|i6g_oA9Yf9Y31qt0w...|Dismal, lukewarm,...|2011-05-27 05:30:52|\n",
      "|UgMW8bLE0QMJDCkQ1...|  4.0|IS4cv902ykd8wj1TR...|6TdNDKywdbjoTkize...|Oh happy day, fin...|2017-01-14 21:56:57|\n",
      "|5vD2kmE25YBrbayKh...|  5.0|nlxHRv1zXGT0c0K51...|L2O_INwlrRuoX05KS...|This is definitel...|2013-05-07 07:25:25|\n",
      "|aq_ZxGHiri48TUXJl...|  5.0|Pthe4qk5xh4n-ef-9...|ZayJ1zWyWgY9S_TRL...|Really good place...|2015-11-05 23:11:05|\n",
      "|dsd-KNYKMpx6ma_sR...|  5.0|FNCJpSn0tL9iqoY3J...|lpFIJYpsvDxyph-kP...|Awesome office an...|2017-07-18 18:31:54|\n",
      "|P6apihD4ASf1vpPxH...|  5.0|e_BiI4ej1CW1F0EyV...|JA-xnyHytKiOIHl_z...|Most delicious au...|2015-02-16 06:48:47|\n",
      "|jOERvhmK6_lo_XGUB...|  4.0|Ws8V970-mQt2X9CwC...|z4BCgTkfNtCu4XY5L...|I have been here ...|2009-10-13 04:16:41|\n",
      "|s5j_CRBWDCCMDJ6r7...|  5.0|PA61Rwk3AMwOEXHev...|TfVth7UNfgilv4J3e...|Maria is VERY goo...|2013-12-28 21:02:55|\n",
      "|HJECayULRM-6xh2GC...|  4.0|l-nL4BmhzpZjcavoo...|Tyx7AxYQfSRnBFUIX...|ORDER In (Deliver...|2015-10-17 01:38:13|\n",
      "|1YIQGP-a534nyksaw...|  5.0|Naa6E0YU0Wr7jCuCE...|wJMjt5C2y1RKgY0Xb...|We purchased new ...|2015-07-03 21:48:51|\n",
      "|qftVgPj_kRTildMDj...|  5.0|Ns4tjgLfqR1qawGlN...|QCxPzh7cuxJrLd6A_...|Everything that m...|2016-06-11 22:00:11|\n",
      "|5lb0POg2t-AkMFx66...|  5.0|ZlCSsWS07JulSBIQl...|qWHp2l2lysENZObh6...|Called for a 5:15...|2015-05-26 10:36:47|\n",
      "|TF4C-F5iqavACQgKT...|  1.0|7Ka9Pd8X9SRHs1D5E...|mjbs5CL4eMu4o6_Vt...|If I could give l...|2017-08-07 21:36:36|\n",
      "|2hRe26HSCAWbFRn5W...|  1.0|d4qwVw4PcN-_2mK2o...|bVTjZgRNq8Toxzvti...|10pm on a super b...|2015-02-02 06:28:00|\n",
      "|6sJN_HlM_uwpfLJ1p...|  4.0|oVuZtlCFg_zF090Nh...|Ne_2CSfcKIqXHmv_K...|A close friend wa...|2018-02-01 19:15:00|\n",
      "|kMkWON2lmw0s-M-fw...|  1.0|_iGvLfEsqDwPUxRUA...|Hy-gUXQh3RVhE8FLH...|Tried to have my ...|2017-06-28 00:39:18|\n",
      "|QodunSzok4nIYFNrT...|  3.0|poSV39UqEg-gpESXa...|UGErdm6bt48SXTVwJ...|My husband and I ...|2018-03-04 01:03:53|\n",
      "+--------------------+-----+--------------------+--------------------+--------------------+-------------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "view.createOrReplaceTempView(\"tmp2\")\n",
    "query = \"\"\"\n",
    "SELECT\n",
    "user_id,\n",
    "stars,\n",
    "business_id,\n",
    "review_id,\n",
    "text, \n",
    "date \n",
    "FROM tmp2\n",
    "\"\"\"\n",
    "tmp2 = spark.sql(query)\n",
    "tmp2.show()"
   ]
  }
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