FinalProject_group069.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following notebook, we explore the Yelp dataset. Specifically, we analyze the data for restuarants on the app by comparing the average \"stars\" rating of restaurants in different categories and with different attributes. We seek to find if any of these categories or attributes affect the rating of their restaurants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Ethan Bull-Vulpe\n",
    "- Cheuk Wah Chim\n",
    "- Qingyu Shen\n",
    "- Rongxin Zhu\n",
    "- Tong Shen\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our broad question is: what features lead to the high rating of a restaurant on Yelp?\n",
    "\n",
    "In our project, we seek to determine what makes a restaurant \"successful\" on Yelp. For our purposes, we say a restaurant is successful if it has a high average \"stars\" rating. Using this definition, we will work to find what features lead to a restaurant being successful. Some features we believe may influence average rating include: hours of operation, genre of food, cost, and other attributes. We will investigate some of these categories and others. Ultimately, we hope to see what factors affect the rating of a restaurant. However, we must limit our investigation to a realistic task, so we will not investigate all of these factors.\n",
    "\n",
    "Specifically, we will investigate: What categories and attributes affect the rating of restaurants on the Yelp app? Here, category refers to type of food (ie: \"American\", \"Japanese\", etc.) and attribute refers to a restaurant bell and wistle (ie: \"Wi-Fi\", \"Outdoor Seating\", etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "References (include links): \"Key Elements of a Successful Restaurant\" by Chris Milksen (https://smallbusiness.chron.com/key-elements-successful-restaurant-25545.html)\n",
    "\n",
    "- Food is a part of everyone's daily lives. It affects many industries from \n",
    "  consumer and business perspectives. We want to analyze the Yelp dataset to \n",
    "  better understand the features that make a successful business in the food \n",
    "  industry. In this way, we hope to find correlations and relationships \n",
    "  between restaurant success to the restaurant's rating.\n",
    "- The Yelp dataset is from the Yelp Dataset Challenge intended for student \n",
    "  use in order to research various things Yelp has suggested in challenges on \n",
    "  their webiste. We will mainly be focusing on restaurant businesses from\n",
    "  Yelp dataset and use that to answer our question and hypothesis.\n",
    "- According to \"Key Elements of a Successful Restaurant\", all successful \n",
    "  restaurants have the following attributes:\n",
    "    - Food quality - The food quality of a restaurant plays an important role\n",
    "      in determining the success of a restaurant as this is the foundational\n",
    "      service that restaurants provide. Furthermore, different \n",
    "      types of restaurant have different kinds of expectation on their food. \n",
    "      For example, an expensive Italian restaurant with a rating of 5-stars is \n",
    "      expected to serve food that is excellent wholistically (eg. good taste,\n",
    "      apetiziing appearance, and healthy). On the other hand, \n",
    "      at a fast food restaurant, people may not expect to be served as high \n",
    "      a quality of food, and may give more weight to factors such as taste over\n",
    "      health and appearance.\n",
    "    - Environment or atmosphere of the restaurant is another big aspect of whether \n",
    "      a restaurant can be successful. It is natural that a customer who \n",
    "      walks into a restaurant and sees a dirty floor and unclean dinner table \n",
    "      would walk out or rate with less stars, so it is important to have the environment \n",
    "      clean to be successful. Furthermore, a restaurant should always have its \n",
    "      restaurant's mood set up for its type of food. For example, an Itlian \n",
    "      restaurant would have dimmer lighting to produce a relaxing environment that makes \n",
    "      the customer feel a comfortable mood while enjoying their food.\n",
    "    - Customer Service and Staff attitude - This is also a significant factor for successful \n",
    "      restaurants. Restaurants with better staff attitude or better customer \n",
    "      service would have a better success rate than those that don't. This is \n",
    "      when considering that when customers walk into a sit-down restaurant, they are not only \n",
    "      buying food, but also buying service from the restaurant. Therefore, it is \n",
    "      important that the restaurant have patient staff that can explain the \n",
    "      menu to the customer and show that they do care about the customer.\n",
    "    - Cost - There are many costs of a restaurant, but there are \n",
    "      only a few that can be controlled. The utilities cost of a restaurant \n",
    "      can't be controlled, but the cost of labor and food can always be \n",
    "      controlled. Controlling the cost of labor and food is not suggesting \n",
    "      cutting corners on food quality, but instead controlling the amount of \n",
    "      food appropriately whether in portions or amounts of stock to keep. Also, \n",
    "      by controlling the cost of labor, it is suggesting making accommodations \n",
    "      according to the amount of customers expected to have. For example, if \n",
    "      not many customers are showing up, it might be better to send some of the \n",
    "      staff home earlier to minimize the cost of labor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that certain factors, such as hours of operation, category of food, and attributes of the restaurant will influence the \"stars\" rating to a statistically significant degree. We believe that longer weekly hours would lead to higher rating, because people like when restaurants are open more often. We predict that some categories will be higher rated than others, because it is reasonable to assume that some genres of food are more popular than others, but do not attempt to predict which ones will be the highest rated. We also predict that restaurants with better features will have higher rating than other restaurants. For example, those that have parking lots will beat those that do not. Those that offer take out will beat those that do not. However, we think that genre of food will affect the distribution of ratings much more than these attributes because most people that go out to dine are looking for a specific type of food, with special bells and whistles only being a secondary consideration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name: Yelp Open Dataset\n",
    "- Link to the dataset: https://www.yelp.com/dataset\n",
    "- Number of observations: 192,609 businesses\n",
    "\n",
    "For our project, we will use Yelp's public dataset. The dataset is very large (over 7 GB of data!) and has lots of information about a huge number of businesses including location, type of food, miscellaneous tags, list of reviews, and many other fields. However, we will only be investigating a subset of it, as explained in our Data Cleaning section.\n",
    "\n",
    "The dataset contains information about all businesses on Yelp in a select few cities that Yelp has released the data for. It includes all information available in the app, such as hours of operation, business type, even the text of the reviews and photos of the restaurant. However we will only be looking at a bit of this information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all the libraries we will need to perform our data cleaning and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import normaltest, mannwhitneyu\n",
    "import scipy.stats as stats\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 0: Seperating Las Vegas data and basic setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Yelp dataset is very large, we chose to work with a smaller subset of it for a few reasons:\n",
    " * The full dataset is so large that it would take a long time to perform simple operations.\n",
    " * By only using the data from a specific city we can draw more specific conclusions.\n",
    " \n",
    "For these reasons, we will only be looking at restaurants whose city is marked in the dataset as Las Vegas.\n",
    "\n",
    "We seperated this part of the dataset into a smaller .json file so that we don't have to parse the entire dataset every time we run this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can do any cleaning, we must read the json data into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "las_vegas = pd.read_json(\"las_vegas_res.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets take a peak at the top of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>attributes</th>\n",
       "      <th>business_id</th>\n",
       "      <th>categories</th>\n",
       "      <th>city</th>\n",
       "      <th>hours</th>\n",
       "      <th>is_open</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100018</th>\n",
       "      <td>4640 S Paradise Rd</td>\n",
       "      <td>{'RestaurantsGoodForGroups': 'True', 'Restaura...</td>\n",
       "      <td>JGM-SxW_vayu0fBch-U2FQ</td>\n",
       "      <td>Italian, Pizza, Restaurants, Food, Food Delive...</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>{'Monday': '16:0-0:0', 'Tuesday': '16:0-0:0', ...</td>\n",
       "      <td>0</td>\n",
       "      <td>36.105416</td>\n",
       "      <td>-115.150459</td>\n",
       "      <td>Pizza Bella</td>\n",
       "      <td>89169</td>\n",
       "      <td>22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100048</th>\n",
       "      <td>5115 Spring Mountain Rd, Unit 234</td>\n",
       "      <td>{'OutdoorSeating': 'False', 'RestaurantsReserv...</td>\n",
       "      <td>5O7qB4gNmr3NgdYAuYbP4Q</td>\n",
       "      <td>Indian, Pakistani, Halal, Himalayan/Nepalese, ...</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>{'Monday': '17:0-22:30', 'Tuesday': '17:0-22:3...</td>\n",
       "      <td>0</td>\n",
       "      <td>36.125693</td>\n",
       "      <td>-115.211278</td>\n",
       "      <td>Indian Curry House</td>\n",
       "      <td>89146</td>\n",
       "      <td>79</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100104</th>\n",
       "      <td>5300 Spring Mountain Rd, Ste 107</td>\n",
       "      <td>{'Alcohol': 'u'full_bar'', 'Caters': 'True', '...</td>\n",
       "      <td>vtkJXE_Vdz27rWm6mfAw6Q</td>\n",
       "      <td>Cantonese, Asian Fusion, Restaurants, Dim Sum,...</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>{'Monday': '9:0-1:0', 'Tuesday': '9:0-1:0', 'W...</td>\n",
       "      <td>0</td>\n",
       "      <td>36.126710</td>\n",
       "      <td>-115.213915</td>\n",
       "      <td>Cathay House Restaurant</td>\n",
       "      <td>89146</td>\n",
       "      <td>233</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100149</th>\n",
       "      <td>The Palazzo, 3325 Las Vegas Blvd S</td>\n",
       "      <td>{'BusinessParking': '{'garage': True, 'street'...</td>\n",
       "      <td>liGn7sPOvUuy3kcwbhRkbA</td>\n",
       "      <td>Chinese, Restaurants, Dim Sum</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>{'Monday': '11:0-1:0', 'Tuesday': '11:0-1:0', ...</td>\n",
       "      <td>0</td>\n",
       "      <td>36.124755</td>\n",
       "      <td>-115.168501</td>\n",
       "      <td>Zine Noodles Dim Sum</td>\n",
       "      <td>89109</td>\n",
       "      <td>238</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100164</th>\n",
       "      <td>4820 S Eastern Ave</td>\n",
       "      <td>{'RestaurantsReservations': 'False', 'Restaura...</td>\n",
       "      <td>v0oHkLaA7cI9os6VZu6NZQ</td>\n",
       "      <td>Restaurants, Mexican</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>{'Monday': '12:0-0:0', 'Tuesday': '12:0-0:0', ...</td>\n",
       "      <td>1</td>\n",
       "      <td>36.101732</td>\n",
       "      <td>-115.118541</td>\n",
       "      <td>Don Tortaco Mexican Grill</td>\n",
       "      <td>89119</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   address  \\\n",
       "100018                  4640 S Paradise Rd   \n",
       "100048   5115 Spring Mountain Rd, Unit 234   \n",
       "100104    5300 Spring Mountain Rd, Ste 107   \n",
       "100149  The Palazzo, 3325 Las Vegas Blvd S   \n",
       "100164                  4820 S Eastern Ave   \n",
       "\n",
       "                                               attributes  \\\n",
       "100018  {'RestaurantsGoodForGroups': 'True', 'Restaura...   \n",
       "100048  {'OutdoorSeating': 'False', 'RestaurantsReserv...   \n",
       "100104  {'Alcohol': 'u'full_bar'', 'Caters': 'True', '...   \n",
       "100149  {'BusinessParking': '{'garage': True, 'street'...   \n",
       "100164  {'RestaurantsReservations': 'False', 'Restaura...   \n",
       "\n",
       "                   business_id  \\\n",
       "100018  JGM-SxW_vayu0fBch-U2FQ   \n",
       "100048  5O7qB4gNmr3NgdYAuYbP4Q   \n",
       "100104  vtkJXE_Vdz27rWm6mfAw6Q   \n",
       "100149  liGn7sPOvUuy3kcwbhRkbA   \n",
       "100164  v0oHkLaA7cI9os6VZu6NZQ   \n",
       "\n",
       "                                               categories       city  \\\n",
       "100018  Italian, Pizza, Restaurants, Food, Food Delive...  Las Vegas   \n",
       "100048  Indian, Pakistani, Halal, Himalayan/Nepalese, ...  Las Vegas   \n",
       "100104  Cantonese, Asian Fusion, Restaurants, Dim Sum,...  Las Vegas   \n",
       "100149                      Chinese, Restaurants, Dim Sum  Las Vegas   \n",
       "100164                               Restaurants, Mexican  Las Vegas   \n",
       "\n",
       "                                                    hours  is_open   latitude  \\\n",
       "100018  {'Monday': '16:0-0:0', 'Tuesday': '16:0-0:0', ...        0  36.105416   \n",
       "100048  {'Monday': '17:0-22:30', 'Tuesday': '17:0-22:3...        0  36.125693   \n",
       "100104  {'Monday': '9:0-1:0', 'Tuesday': '9:0-1:0', 'W...        0  36.126710   \n",
       "100149  {'Monday': '11:0-1:0', 'Tuesday': '11:0-1:0', ...        0  36.124755   \n",
       "100164  {'Monday': '12:0-0:0', 'Tuesday': '12:0-0:0', ...        1  36.101732   \n",
       "\n",
       "         longitude                       name postal_code  review_count  \\\n",
       "100018 -115.150459                Pizza Bella       89169            22   \n",
       "100048 -115.211278         Indian Curry House       89146            79   \n",
       "100104 -115.213915    Cathay House Restaurant       89146           233   \n",
       "100149 -115.168501       Zine Noodles Dim Sum       89109           238   \n",
       "100164 -115.118541  Don Tortaco Mexican Grill       89119            11   \n",
       "\n",
       "        stars state  \n",
       "100018    2.0    NV  \n",
       "100048    4.0    NV  \n",
       "100104    3.0    NV  \n",
       "100149    3.0    NV  \n",
       "100164    3.0    NV  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las_vegas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, lets remove the `address`, `business_id`, `is_open`, `latitude`, `longitude`, `name`, `postal_code`, and `state` columns, as they will not be used in our analysis. We keep the `categories`, `attributes`, `hours`, and `stars` columns because we will use them in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "las_vegas.drop(columns = [\"address\", \"business_id\", \"is_open\", \"latitude\", \"longitude\",\n",
    "                          \"name\", \"postal_code\", \"state\"], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, because we want to create a model for `stars` we should try to remove outliers and unreliable data from our dataset. We shall remove all rows with `review_count` less than 50. In order to justify this, lets compare the percent of restaurants with less than 50 reviews that are 5 stars vs. those with 50 or more reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Less than 50 reviews 5 star percent: 0.055591467356173235\n",
      "More than 50 reviews 5 star percent: 0.00834326579261025\n"
     ]
    }
   ],
   "source": [
    "las_vegas_less_than_fifty = las_vegas[las_vegas[\"review_count\"] < 50]\n",
    "las_vegas_more_than_fifty = las_vegas[las_vegas[\"review_count\"] >= 50]\n",
    "percent_five_stars_less_than = (\n",
    "    len(las_vegas_less_than_fifty[las_vegas_less_than_fifty[\"stars\"] == 5]\n",
    "    ) / len(las_vegas_less_than_fifty)\n",
    ")\n",
    "print(\"Less than 50 reviews 5 star percent:\", percent_five_stars_less_than)\n",
    "percent_five_stars_more_than = (\n",
    "    len(las_vegas_more_than_fifty[las_vegas_more_than_fifty[\"stars\"] == 5]\n",
    "    ) / len(las_vegas_more_than_fifty)\n",
    ")\n",
    "print(\"More than 50 reviews 5 star percent:\", percent_five_stars_more_than)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, restaurants with less than 50 reviews are more than 5 times as likely to have 5 stars. We believe this is because their star rating does not have a large enough sample size and may not have stablized. This could result in a poor model! Now, to fix this, lets drop all rows where `review_count` is less than 50. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "las_vegas = las_vegas[las_vegas[\"review_count\"] >= 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Weekly Hours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the first part of our analysis, we will want to analyse how the number of hours a restaurant is open per week affects its `stars` rating. However, the `hours` data in the dataframe is saved in a dictionary that tells how many hours the restaurant is open per day. Lets parse the `hours` column into a new column `weekly_hours` that will make our analysis later easier.\n",
    "First we define sum_time, a function which takes in the dictionary from the `hours` column of the dataframe and outputs the total number of hours that restaurant is open in a week. If the restaurant does not have its hours specified, we will put -1 in the `weekly_hours` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_time(hours):\n",
    "    total_time = 0\n",
    "    if hours is None:\n",
    "        return -1\n",
    "    for time in hours.values():\n",
    "        start_time = time[:time.index('-')]\n",
    "        end_time = time[time.index('-') + 1:]\n",
    "        start_hour = int(start_time[:start_time.index(':')])\n",
    "        start_min = int(start_time[start_time.index(':') + 1:])\n",
    "        end_hour = int(end_time[:end_time.index(':')])\n",
    "        end_min = int(end_time[end_time.index(':') + 1:])\n",
    "        start = start_hour + start_min / 60\n",
    "        end = end_hour + end_min / 60\n",
    "        if end < start:\n",
    "            end += 24\n",
    "        total_time += end - start\n",
    "    return total_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we use that function to add a new column to our dataframe by iterating over each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "las_vegas[\"weekly_hours\"] = [sum_time(i) for i in las_vegas[\"hours\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second part of our analysis, we want to see if the distribution of `stars` differs for restaurants in different categories. Each restaurant in the dataset is labeled with one or more food categories. These categories describe the type or origin of the food. Examples inlcude \"Burgers\" or \"Japanese\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets try to find the most common food categories. We will do this by making a dictionary that maps the name of the category to the number of restaurants in that category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_dict = {}\n",
    "for categories_string in las_vegas[\"categories\"]:\n",
    "    categories = categories_string.replace(\" \", \"\").split(\",\")\n",
    "    for category in categories:\n",
    "        try:\n",
    "            cat_dict[category] += 1\n",
    "        except KeyError:\n",
    "            cat_dict[category] = 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets take a look at the 10 most populous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burgers 334\n",
      "Sandwiches 366\n",
      "Mexican 375\n",
      "Breakfast&Brunch 417\n",
      "American(New) 460\n",
      "American(Traditional) 583\n",
      "Bars 602\n",
      "Nightlife 628\n",
      "Food 948\n",
      "Restaurants 3356\n"
     ]
    }
   ],
   "source": [
    "cat_top_10 = sorted(cat_dict.items(), key = lambda pair: pair[1])[-10:]\n",
    "for category, count in cat_top_10:\n",
    "    print(category, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to compare categories of restaurant, we should remove vague categories like 'Restaurants' and 'Food'. We do this because these categories don't point towards a specific food type, so are not interesting in our investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "948"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_dict.pop(\"Restaurants\", None)\n",
    "cat_dict.pop(\"Food\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets get the top 10 categories again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FastFood 318\n",
      "Pizza 334\n",
      "Burgers 334\n",
      "Sandwiches 366\n",
      "Mexican 375\n",
      "Breakfast&Brunch 417\n",
      "American(New) 460\n",
      "American(Traditional) 583\n",
      "Bars 602\n",
      "Nightlife 628\n"
     ]
    }
   ],
   "source": [
    "cat_top_10 = sorted(cat_dict.items(), key = lambda pair: pair[1])[-10:]\n",
    "for category, count in cat_top_10:\n",
    "    print(category, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, this is much better, but lets do some more data cleaning. Some of these categories are redundant, lets further improve our dataset by combining similar categories. We must reconstruct cat_dict, because if we just combined \"American(New)\" and \"American(Traditional)\" we could count a restaurant twice if it was labeled in both of those categories.\n",
    "\n",
    "We will combine any category with \"american\" as a substring into the \"American\" category.\n",
    "We will combine any category with \"nightlife\" or \"bar\" into the \"Nightlife\" category because these restaurants are likely similar.\n",
    "We will combine any category with \"burgers\" or \"fastfood\" into the \"FastFood\" category because burgers are commonly fastfood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "948"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_dict = {}\n",
    "for categories_string in las_vegas[\"categories\"]:\n",
    "    categories = categories_string.replace(\" \", \"\").split(\",\")\n",
    "    \n",
    "    is_american = False\n",
    "    is_nightlife = False\n",
    "    is_fastfood = False\n",
    "    \n",
    "    for category in list(categories):\n",
    "        if \"american\" in category.lower():\n",
    "            is_american = True\n",
    "            categories.remove(category)\n",
    "        if \"nightlife\" in category.lower() or \"bar\" in category.lower():\n",
    "            is_nightlife = True\n",
    "            categories.remove(category)\n",
    "        if \"burgers\" in category.lower() or \"fastfood\" in category.lower():\n",
    "            is_fastfood = True\n",
    "            categories.remove(category)\n",
    "    \n",
    "    if is_american:\n",
    "        categories.append(\"American\")\n",
    "    if is_nightlife:\n",
    "        categories.append(\"Nightlife\")\n",
    "    if is_fastfood:\n",
    "        categories.append(\"FastFood\")\n",
    "            \n",
    "    for category in categories:\n",
    "        try:\n",
    "            cat_dict[category] += 1\n",
    "        except KeyError:\n",
    "            cat_dict[category] = 1\n",
    "\n",
    "cat_dict.pop(\"Restaurants\", None)\n",
    "cat_dict.pop(\"Food\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets get the top 10 one last time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seafood 284\n",
      "Japanese 285\n",
      "Italian 293\n",
      "Pizza 334\n",
      "Sandwiches 366\n",
      "Mexican 375\n",
      "Breakfast&Brunch 417\n",
      "FastFood 553\n",
      "American 909\n",
      "Nightlife 1066\n"
     ]
    }
   ],
   "source": [
    "cat_top_10 = sorted(cat_dict.items(), key = lambda pair: pair[1])[-10:]\n",
    "for category, count in cat_top_10:\n",
    "    print(category, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excelent, these are a good 10 categories. Now, lets add all of these categories as columns to the las_vegas dataframe. Setting True or False in each row depending if that row's restaurant has that category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column_category, count in cat_top_10:\n",
    "    column = []\n",
    "    for categories_string in las_vegas[\"categories\"]:\n",
    "        categories = categories_string.replace(\" \", \"\").split(\",\")\n",
    "\n",
    "        is_american = False\n",
    "        is_nightlife = False\n",
    "        is_fastfood = False\n",
    "\n",
    "        for category in list(categories):\n",
    "            if \"american\" in category.lower():\n",
    "                is_american = True\n",
    "                categories.remove(category)\n",
    "            if \"nightlife\" in category.lower() or \"bar\" in category.lower():\n",
    "                is_nightlife = True\n",
    "                categories.remove(category)\n",
    "            if \"burgers\" in category.lower() or \"fastfood\" in category.lower():\n",
    "                is_fastfood = True\n",
    "                categories.remove(category)\n",
    "\n",
    "        if is_american:\n",
    "            categories.append(\"American\")\n",
    "        if is_nightlife:\n",
    "            categories.append(\"Nightlife\")\n",
    "        if is_fastfood:\n",
    "            categories.append(\"FastFood\")\n",
    "            \n",
    "        if column_category in categories:\n",
    "            column.append(True)\n",
    "        else:\n",
    "            column.append(False)\n",
    "            \n",
    "    las_vegas[column_category] = column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this part of our data cleaning, we want to look at how different attributes contribute to the difference of `stars` distribution among restaurants. Every restaurant has something listed under their attribute column, we want to take out 5 most common attributes that are listed under the `attribute` column. We will do this by creating a dictionary, mapping the name of the attribute to the number of how many restaurants have that attribute listed under their `attribute` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoodForKids 3261\n",
      "BusinessParking 3291\n",
      "RestaurantsTakeOut 3297\n",
      "RestaurantsPriceRange2 3320\n",
      "BusinessAcceptsCreditCards 3333\n"
     ]
    }
   ],
   "source": [
    "att_dict = {}\n",
    "for key, value in las_vegas[\"attributes\"].items() or []:\n",
    "        for x in value or []:\n",
    "            try:\n",
    "                att_dict[x] += 1\n",
    "            except KeyError:\n",
    "                att_dict[x] = 1\n",
    "att_top_5 = sorted(att_dict.items(), key = lambda pair: pair[1])[-5:]\n",
    "for category, count in att_top_5:\n",
    "    print(category, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the 5 most popular attributes listed under attribute column from every restaurants are GoodForKids, BusinessParking, RestaurantsTakeOut, RestaurantsPriceRange2, and BusinessAcceptsCreditCards. Four of these categories are self explanatory, but for clarity, the fifth \"RestaurantsPriceRange2\", means the restaurant as two `$$` or fewer on Yelp, out of up to four `$$$$`. These will be the attributes we'll be mainly looking at in order to see if attributes contribute to differences in `stars` rating. Next, we will add the 5 most popular attributes as columns in our dataframe with a value of True or False. True meaning that restaurant does have that attributes, False meaning the opposite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "att_column_kid = []\n",
    "att_column_park = []\n",
    "att_column_takeout = []\n",
    "att_column_price = []\n",
    "att_column_card = []\n",
    "temp_dict = las_vegas[\"attributes\"]\n",
    "for key, value in temp_dict.items():\n",
    "    if value is not None:\n",
    "        if \"GoodForKids\" not in value:\n",
    "            att_column_kid.append(False)\n",
    "        if \"BusinessParking\" not in value:\n",
    "            att_column_park.append(False)\n",
    "        if \"RestaurantsTakeOut\" not in value:\n",
    "            att_column_takeout.append(False)\n",
    "        if \"RestaurantsPriceRange2\" not in value:\n",
    "            att_column_price.append(False)\n",
    "        if \"BusinessAcceptsCreditCards\" not in value:\n",
    "            att_column_card.append(False)\n",
    "        for i, j in value.items():\n",
    "            if i == \"GoodForKids\":\n",
    "                if j.lower() == \"true\":\n",
    "                    att_column_kid.append(True)\n",
    "                else:\n",
    "                    att_column_kid.append(False)\n",
    "            if i == \"BusinessParking\":\n",
    "                if \"true\" in j.lower():\n",
    "                    att_column_park.append(True)\n",
    "                else:\n",
    "                    att_column_park.append(False)\n",
    "            if i == \"RestaurantsTakeOut\":\n",
    "                if j.lower() == \"true\":\n",
    "                    att_column_takeout.append(True)\n",
    "                else:\n",
    "                    att_column_takeout.append(False)\n",
    "            if i == \"RestaurantsPriceRange2\":\n",
    "                if j == \"2\" or j == \"1\":\n",
    "                    att_column_price.append(True)\n",
    "                else:\n",
    "                    att_column_price.append(False)\n",
    "            if i == \"BusinessAcceptsCreditCards\":\n",
    "                if j.lower() == \"true\":\n",
    "                    att_column_card.append(True)\n",
    "                else:\n",
    "                    att_column_card.append(False)\n",
    "    else:\n",
    "        att_column_kid.append(False)\n",
    "        att_column_park.append(False)\n",
    "        att_column_takeout.append(False)\n",
    "        att_column_price.append(False)\n",
    "        att_column_card.append(False)\n",
    "las_vegas[\"GoodForKids\"] = att_column_kid\n",
    "las_vegas[\"BusinessParking\"] = att_column_park\n",
    "las_vegas[\"RestaurantsTakeOut\"] = att_column_takeout\n",
    "las_vegas[\"RestaurantsPriceRange2\"] = att_column_price\n",
    "las_vegas[\"BusinessAcceptsCreditCards\"] = att_column_card\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Weekly Hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first part of the Analysis, we are going to see if hours open has any impact on the number of stars a restaurant has.\n",
    "\n",
    "In order to do this, we must remove all restaurants that did not provide any weekly hours data. This includes those with a -1 in the `weekly_hours` column because those are the rows that had `None` for their operating hours and those with a 0 because these restuarants are either closed, or had no operating hours entered by mistake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "las_vegas_weekly_hours = (\n",
    "    las_vegas[(las_vegas[\"weekly_hours\"] != -1) & (las_vegas[\"weekly_hours\"] != 0)]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent, now that we have a dataframe with only those restaurants with weekly hours specified, lets graph the relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f17184c9198>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "las_vegas_weekly_hours.plot(kind = \"scatter\", x = \"weekly_hours\", y = \"stars\",\n",
    "    title = \"Weekly Hours versus stars rating of Restaurants.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmmm, it really looks like theres no obvious relationship here. There are somethings we can notice, such as how 5 star restaurants are less likely to have many opearting hours. Or how there are no 24 hour restaurants with 4.5 or 5 stars. This could lead to an interesting conclusion about the operating hours of 5 star restaurants or the quality of 24-hour restaurants. But we decided not to pursue this analysis further for a few reasons:\n",
    " * The sample size of 5 star restaurants is too small.\n",
    " * The sample size of 24 hour restaurants is too small.\n",
    " * The correlation between `weekly_hours` and `stars` does not look to be large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In part 2 of our analysis, we will pursue a different angle: How do different food categories affect `stars`? Here we will use the extra columns we created during our data cleaning in order to analyse how the top 10 most common food categories affect rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start, lets graph histograms of the `stars` distribution for each of the different categories we plan to investigate. We'll do that with a quick loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHwZJREFUeJzt3XuYXFWZ7/Hvj4RLSIcEiLaBIAmQwQsgQot6mJGOQScKAjpcZBC5aXAOCo44knBUQOUQHk4cLzM6REBAGVpEhjBcFMQ06BwJJFwMFxHEgAmB4JBAmksw8M4fe7UUze7une7atauL3+d56ul9X++qSuqttfbeaysiMDMz62ujqgMwM7Pm5ARhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBWCkkdUv6RNVxlE1SSHpGUo+kFZK+LmlUnY67Uz1iLEOzx2f14QRhNnxvi4g2YB/gMODYiuMZkKTRVcdgI4MThJVK0paSrpb0hKTVaXpyzfpuSWdJulXSU5IWSNqqZv2PJT2W1t0s6a016y6U9K+SrpG0VtIiSTvWrH+TpBskPSnpfkmH1qz7oKR7034rJH2+Zt3+ku6UtEbS/5e0W5G6RsSDwH8Bu9cca7yk8yWtTOV8rbeFIWknSTeluv1J0o/S8pvT7nellslhBd7HZZL2rZk/XdIP0/SU9Iv/OEmPAL8YznvbT3wTU0xr0vv9S0n+fhnh/AFa2TYCvg9sD7wReA74lz7bfJzsV/c2wHrgWzXrrgOmAa8Hbgcu6bPv4cAZwJbAg8CZAJLGAjcA/572PRz4Ts2X4PnA8RExDtiFl7809wAuAI4HtgbOBa6StOlgFZX0JuBvUhy9Lkp12gl4O/B+oLfr7avA9Sn2ycC3ASLiPWn92yKiLSJ+RLH3cTD7AG8G/jbND+m97Se+k4HlwOuAduBUwOP4jHQR4ZdfdX8B3cAncpbvDqzus93cmvm3AC8Ao3L2nUD2pTM+zV8InFez/oPAb9P0YcAv++x/LnBamn6ELAls0Web7wJf7bPsfmCffuoZwNPAM2n6UmDTtK4dWAeMqdn+cGBhmr4YmA9M7ue4Ow3w/vZ9H5cB+9bMnw78ME1PScfbYYDjFX5v8+IDvgIsGChmv0beyy0IK5WkzSWdK+lhSU8DNwMT+pzI/WPN9MPAxsBESaMkzZX0+7TvsrTNxJrtH6uZfhZoS9PbA+9MXR5rJK0BjgDekNb/HdmX3sOpm+fdNfud3Ge/7chaN/3ZI5V7GPBOYGzNsTYGVtYc61yyX+wAXwAE3CrpHkn9nrso+D4O5i/v8zDf2zznkLUyrpf0kKTZGxCXNSknCCvbycDOwDsjYgugt3tCNdtsVzP9RuDPwJ+AvwcOBPYFxpP9Eu67b3/+CNwUERNqXm0R8Q8AEXFbRBxI9mV9JXBZzX5n9tlv84i4dKDCInMZ8GvgyzXHWgdMrDnWFhHx1rTPYxHxyYjYhqw18x31f2XQYO/jM8DmNdu/gVer7fIZznv76gNHrI2IkyNiB+BDwOckzRjKsax5OEFY2caR9ZevSSefT8vZ5mOS3iJpc7Kuissj4sW07zrgv8m+/P7vBpR7NfBXko6UtHF6vUPSmyVtIukISeMj4s9kXUQvpv2+B3xK0juVGStpP0njCpY7F5gl6Q0RsZLsHMM8SVtI2kjSjpL2AZB0SM2J5tVkX+C9cTwO7FBz3MHexzuBj6Z6dgAHDxLncN7bV8WXTuzvJEm8/H6+2N/ONjI4QViZAvgGMIasRXAL8NOc7X5A1uf9GLAZcGJafjFZl9MK4N60f7GCI9aSnRD+KPBoOvbZQO/J5iOBZal75VPAx9J+i4FPkp0AXk3WbXL0BpS7FLgJ+Ke06OPAJin+1cDlwKS07h3AIkk9wFXASRHxh7TudOCi1DV1KIO/j18CdkxlnEF2cn4gQ35v+4lvGvBzoIesFfWdiOjewGNak1GELzSw+pN0O/CViLhykO26yU6mnteQwMysMLcgrO7SpaRvBu6oOhYzGzonCKsrSWeT9bufEhEPVx2PmQ2du5jMzCyXWxBmZpartEG7JF0A7A+siohd0rJzyK6RfgH4PXBMRKxJ6+YAx5FdGndiRPxssDImTpwYU6ZMKacCdfLMM88wduzYwTccAVqlLq1SD3BdmtFIqMeSJUv+FBGvG3TDsm7RJruRZw/g7ppl7wdGp+mzgbPT9FuAu8guQZxKljxeNdRC39eee+4ZzW7hwoVVh1A3rVKXVqlHhOvSjEZCPYDFUeVQGxFxM/Bkn2XXR8T6NHsL2QBlkN3R2RUR6yK7DvxBYK+yYjMzs8FVeQ7iWLLRJAG25ZXj8SxPy8zMrCKVPDhE0v8hGwK5d3jhvPFfci+vkjQLmAXQ3t5Od3d3GSHWTU9PT9PHWFSr1KVV6gGuSzNqlXpABQlC0lFkJ69npL4wyFoMtQO2TSYbHuFVImI+2RDJdHR0RGdnZ3nB1kF3dzfNHmNRrVKXVqkHuC7NqFXqAQ3uYpI0EzgFOCAinq1ZdRXZQGObSppKNq7LrY2MzczMXqnMy1wvBTrJxvVfTjb65ByyK5VuyAZ95JaI+FRE3CPpMrJBw9YDJ0Q2mqeZmVWktAQREYfnLD5/gO3PJD3S0MzMquc7qc3MLJcThJmZ5arkMlczs76WrniKo2dfU2oZy+buV+rxW41bEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1ylJQhJF0haJenummVbSbpB0gPp75ZpuSR9S9KDkn4jaY+y4jIzs2LKbEFcCMzss2w2cGNETANuTPMAHwCmpdcs4LslxmVmZgWUliAi4mbgyT6LDwQuStMXAQfVLL84MrcAEyRNKis2MzMbnCKivINLU4CrI2KXNL8mIibUrF8dEVtKuhqYGxG/SstvBE6JiMU5x5xF1sqgvb19z66urtLir4eenh7a2tqqDqMuWqUurVIPaK26rHryKR5/rtwydt12fLkFMDI+k+nTpy+JiI7BthvdiGAKUM6y3MwVEfOB+QAdHR3R2dlZYljD193dTbPHWFSr1KVV6gGtVZdvX7KAeUvL/UpadkRnqceH1vpMGn0V0+O9XUfp76q0fDmwXc12k4FHGxybmZnVaHSCuAo4Kk0fBSyoWf7xdDXTu4CnImJlg2MzM7MapbXnJF0KdAITJS0HTgPmApdJOg54BDgkbX4t8EHgQeBZ4Jiy4jIzs2JKSxARcXg/q2bkbBvACWXFYmZmG853UpuZWS4nCDMzy+UEYWZmuZwgzMwslxOEmZnlcoIwM7NcThBmZpbLCcLMzHINmiAkHSJpXJr+oqQr/EAfM7PWV6QF8aWIWCvpr4G/JXuOgx/oY2bW4ookiBfT3/2A70bEAmCT8kIyM7NmUCRBrJB0LnAocK2kTQvuZ2ZmI1iRL/pDgZ8BMyNiDbAV8E+lRmVmZpUrkiDOjYgrIuIBgPSchiPLDcvMzKpWJEG8tXZG0ihgz3LCMTOzZtFvgpA0R9JaYDdJT6fXWrLHhC7obz8zM2sN/SaIiDgrIsYB50TEFuk1LiK2jog5DYzRzMwqMOgT5SJijqRtge1rt4+Im8sMzMzMqjVogpA0F/gocC8v3xMRgBOEmVkLK/JM6g8DO0fEurKDMTOz5lHkKqaHgI3LDsTMzJpLkRbEs8Cdkm4E/tKKiIgTS4vKzMwqVyRBXJVeZmb2GlLkKqaLGhGImZk1lyJXMU0DzgLeAmzWuzwidigxLjMzq1iRk9TfJ3v+w3pgOnAx8IMygzIzs+oVSRBjIuJGQBHxcEScDry33LDMzKxqRRLE85I2Ah6Q9GlJHwZeP5xCJf2jpHsk3S3pUkmbSZoqaZGkByT9SJIfSmRmVqEiCeKzwObAiWSjuH4MOGqoBaZhO04EOiJiF2AU2Z3aZwP/HBHTgNXAcUMtw8zMhm/ABJGG9j40InoiYnlEHBMRfxcRtwyz3NHAGEmjyZLPSrJuq8vT+ouAg4ZZhpmZDYMiYuANpF8AM2KwDTekUOkk4EzgOeB64CTglojYKa3fDrgutTD67jsLmAXQ3t6+Z1dXV73CKkVPTw9tbW1Vh1EXrVKXVqkHtFZdVj35FI8/V24Zu247vtwCGBmfyfTp05dERMdg2xW5Ue4OYIGkHwPP9C6MiCuGEpikLYEDganAGuDHwAdyNs1NSBExH5gP0NHREZ2dnUMJo2G6u7tp9hiLapW6tEo9oLXq8u1LFjBvaZGvpKFbdkRnqceH1vpMinwaWwH/zSuvXApgSAkC2Bf4Q0Q8ASDpCuB/ARMkjY6I9cBk4NEhHt/MzOqgyJ3Ux9S5zEeAd0nanKyLaQawGFgIHAx0kZ0E91PrzMwqVORO6u+T090TEccOpcCIWCTpcuB2spvv7iDrMroG6JL0tbTs/KEc38zM6qNIF9PVNdObkT0fYljdPxFxGnBan8UPAXsN57hmZgOZMvua0su4cObY0stolCJdTD+pnZd0KfDz0iIyM7OmUORGub6mAW+sdyBmZtZcipyDWEt2DkLp72PAKSXHZWZmFSvSxTSuEYGYmVlzKXRXSrq5bRqvfB7EzWUFZWZm1SvSxfQJsqEwJgN3Au8Cfo2H/DYza2lFTlKfBLwDeDgipgNvB54oNSozM6tcoedBRMTzAJI2jYjfAjuXG5aZmVWtyDmI5ZImAFcCN0hajcdJMjNreUWuYvpwmjxd0kJgPHBdqVGZmVnlBu1ikvSD3umIuCkirgIuKDUqMzOrXJFzEG+tnUlPmduznHDMzKxZ9JsgJM1Jd1HvJunp9FoLrMJDcZuZtbx+E0REnJXuoj4nIrZIr3ERsXVEzGlgjGZmVoEiXUxXSxoLIOljkr4uafuS4zIzs4oVSRDfBZ6V9DbgC8DDwMWlRmVmZpUrkiDWR0QABwLfjIhvAh7Az8ysxRW5UW6tpDnAkcDfpKuYNi43LDMzq1qRFsRhwDrg2Ih4DNgWOKfUqMzMrHKDJoiUFH4CbJoW/Qn4jzKDMjOz6hW5k/qTwOXAuWnRtmTjMpmZWQsrcg7iBGAvYBFARDwg6fWlRmVmTWPK7GsaUs7JuzakGNsARc5BrIuIF3pnJI0meza1mZm1sCIJ4iZJpwJjJL0P+DHwn+WGZWZmVSuSIGaTPUFuKXA8cC3wxTKDMjOz6hV5HsRLwPfSCwBJewP/VWJcZmZWsX4TRLoh7lCyq5Z+GhF3S9ofOBUYQ/ZsajMza1EDtSDOB7YDbgW+Jelh4N3A7IgY1mWu6RGm5wG7kJ3wPha4H/gRMAVYBhwaEauHU46ZmQ3dQAmiA9gtIl6StBnZDXI7pRvnhuubZK2SgyVtAmxO1jK5MSLmSppNdu7jlDqUZWZmQzDQSeoX0vkHIuJ54Hf1SA6StgDeQ9ZCISJeiIg1ZIMBXpQ2uwg4aLhlmZnZ0CkbqDVnhfQs8GDvLLBjmhcQEbHbkAqUdgfmA/cCbwOWACcBKyJiQs12qyNiy5z9ZwGzANrb2/fs6uoaShgN09PTQ1tbW9Vh1EWr1KVV6gGNqcvSFU+Vevxe7WPg8ecaUlSppo4f1fT/vqZPn74kIjoG226gBDHgQ4Ei4uGhBCapA7gF2DsiFkn6JvA08JkiCaJWR0dHLF68eChhNEx3dzednZ1Vh1EXrVKXVqkHNKYujbuTej3zlhYZ3KG5XThzbNP/+5JUKEH0+2kMNQEUsBxYHhGL0vzlZOcbHpc0KSJWSppE9uxrMzOrSJEb5eoqncf4o6Sd06IZZN1NVwFHpWVHAQsaHZuZmb2sqvbcZ4BL0hVMDwHHkCWryyQdBzwCHFJRbGZmxsA3yt0YETMknR0Rdb3cNCLuJLuMtq8Z9SzHzMyGbqAWxCRJ+wAHSOoiu3rpLyLi9lIjMzOzSg2UIL5MdvJ4MvD1PusCeG9ZQZmZWfUGuorpcuBySV+KiK82MCYzM2sCRUZz/aqkA8jufgbojoiryw3LzMyqVuSZ1GeR3el8b3qdlJaZmVkLK3KZ637A7r3jMkm6CLgDmFNmYGZmVq2iN8pNqJkeX0YgZmbWXIq0IM4C7pC0kOxS1/fg1oOZWcsrcpL6UkndwDvIEsQpdXomhJmZNbFCQ21ExEqysZLMzOw1ouGD9ZmZ2cjgBGFmZrkGTBCSNpJ0d6OCMTOz5jFggkj3Ptwl6Y0NisfMzJpEkZPUk4B7JN0KPNO7MCIOKC0qMzOrXJEEcUbpUZiZWdMpch/ETZK2B6ZFxM8lbQ6MKj80MzOrUpHB+j4JXA6cmxZtC1xZZlBmZla9Ipe5ngDsDTwNEBEPAK8vMygzM6tekQSxLiJe6J2RNJrsiXJmZtbCiiSImySdCoyR9D7gx8B/lhuWmZlVrUiCmA08ASwFjgeuBb5YZlBmZla9IlcxvZQeErSIrGvp/ohwF5OZWYsbNEFI2g/4N+D3ZMN9T5V0fERcV3ZwZmZWnSI3ys0DpkfEgwCSdgSuAZwgzMxaWJFzEKt6k0PyELCqpHjMzKxJ9NuCkPSRNHmPpGuBy8jOQRwC3DbcgiWNAhYDKyJif0lTgS5gK+B24Mjay2vNzKyxBmpBfCi9NgMeB/YBOsmuaNqyDmWfBNxXM3828M8RMQ1YDRxXhzLMzGyI+m1BRMQxZRUqaTKwH3Am8DlJAt4L/H3a5CLgdOC7ZcVgZmYD02BXrKaun88AU6hJKMMZ7lvS5cBZwDjg88DRwC0RsVNavx1wXUTskrPvLGAWQHt7+55dXV1DDaMhenp6aGtrqzqMumiVurRKPaAxdVm64qlSj9+rfQw8/lxDiirV1PGjmv7f1/Tp05dERMdg2xW5iulK4Hyyu6dfGm5gkvYnO/G9RFJn7+KcTXMzV0TMB+YDdHR0RGdnZ95mTaO7u5tmj7GoVqlLq9QDGlOXo2dfU+rxe52863rmLS3yldTcLpw5tmX+fRX5NJ6PiG/Vscy9gQMkfZDs/MYWwDeACZJGR8R6YDLwaB3LNDOzDVTkMtdvSjpN0rsl7dH7GmqBETEnIiZHxBTgo8AvIuIIYCFwcNrsKGDBUMswM7PhK9KC2BU4kuwkcm8XU6T5ejoF6JL0NeAOsm4tMzOrSJEE8WFghzLuSYiIbqA7TT8E7FXvMszMbGiKdDHdBUwoOxAzM2suRVoQ7cBvJd0GrOtdOJzLXM3MrPkVSRCnlR6FmZk1nSLPg7ipEYGYmVlzKfI8iLW8fNPaJsDGwDMRsUWZgZmZWbWKtCDG1c5LOghfbWRm1vKKXMX0ChFxJfW/B8LMzJpMkS6mj9TMbgR00M84SWZm1jqKXMX0oZrp9cAy4MBSojEzs6ZR5BxEac+FMDOz5jXQI0e/PMB+ERFfLSEeMzNrEgO1IJ7JWTaW7FGgWwNOEGZmLWygR47O652WNI7sGdLHAF3AvP72MzOz1jDgOQhJWwGfA44ge070HhGxuhGBmZlZtQY6B3EO8BGyx3vuGhE9DYvKzMwqN9CNcicD2wBfBB6V9HR6rZX0dGPCMzOzqgx0DmKD77I2M7PW4SRgZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCxXwxOEpO0kLZR0n6R7JJ2Ulm8l6QZJD6S/WzY6NjMze1kVLYj1wMkR8WbgXcAJkt4CzAZujIhpwI1p3szMKtLwBBERKyPi9jS9FrgP2BY4kOyZE6S/BzU6NjMze5kiorrCpSnAzcAuwCMRMaFm3eqIeFU3k6RZwCyA9vb2Pbu6uhoT7BD19PTQ1tZWdRh10Sp1aZV6QGPqsnTFU6Uev1f7GHj8uYYUVaqp40c1/b+v6dOnL4mIjsG2qyxBSGoDbgLOjIgrJK0pkiBqdXR0xOLFi8sOdVi6u7vp7OysOoy6aJW6tEo9oDF1mTL7mlKP3+vkXdczb+mAD7kcES6cObbp/31JKpQgKvk0JG0M/AS4JCKuSIsflzQpIlZKmgSsqiI2s5Fk6YqnOLpBX+D22lPFVUwCzgfui4iv16y6CjgqTR8FLGh0bGZm9rIqWhB7A0cCSyXdmZadCswFLpN0HPAIcEgFsZmZWdLwBBERvwLUz+oZjYzFzMz65zupzcwslxOEmZnlcoIwM7NcThBmZpbLCcLMzHI5QZiZWS4nCDMzy+UEYWZmuZwgzMwslxOEmZnlcoIwM7NcThBmZpbLCcLMzHI5QZiZWS4nCDMzy+UEYWZmuZwgzMwslxOEmZnlcoIwM7NcThBmZpbLCcLMzHKNrjoAs1Y1ZfY1pZdx8q6lF2GvYW5BmJlZLicIMzPL5QRhZma5nCDMzCyXT1KbmdXR0hVPcXQDLlBYNne/0stouhaEpJmS7pf0oKTZVcdjZvZa1VQtCEmjgH8F3gcsB26TdFVE3FttZNYojbg09MKZY0svw6wVNFsLYi/gwYh4KCJeALqAAyuOyczsNUkRUXUMfyHpYGBmRHwizR8JvDMiPl2zzSxgVprdGbi/4YFumInAn6oOok5apS6tUg9wXZrRSKjH9hHxusE2aqouJkA5y16RwSJiPjC/MeEMn6TFEdFRdRz10Cp1aZV6gOvSjFqlHtB8XUzLge1q5icDj1YUi5nZa1qzJYjbgGmSpkraBPgocFXFMZmZvSY1VRdTRKyX9GngZ8Ao4IKIuKfisIZrxHSHFdAqdWmVeoDr0oxapR7NdZLazMyaR7N1MZmZWZNwgjAzs1xOECVplSFDJF0gaZWku6uOZbgkbSdpoaT7JN0j6aSqYxoqSZtJulXSXakuZ1Qd03BIGiXpDklXVx3LcEhaJmmppDslLa46nuHyOYgSpCFDfkfNkCHA4SNxyBBJ7wF6gIsjYpeq4xkOSZOASRFxu6RxwBLgoBH6uQgYGxE9kjYGfgWcFBG3VBzakEj6HNABbBER+1cdz1BJWgZ0RESz3yhXiFsQ5WiZIUMi4mbgyarjqIeIWBkRt6fptcB9wLbVRjU0kelJsxun14j8tSdpMrAfcF7VsdgrOUGUY1vgjzXzyxmhX0StStIU4O3AomojGbrULXMnsAq4ISJGal2+AXwBeKnqQOoggOslLUnDAo1oThDlGHTIEKuOpDbgJ8BnI+LpquMZqoh4MSJ2JxtxYC9JI64LUNL+wKqIWFJ1LHWyd0TsAXwAOCF10Y5YThDl8JAhTSr11/8EuCQirqg6nnqIiDVANzCz4lCGYm/ggNR33wW8V9IPqw1p6CLi0fR3FfAfZN3NI5YTRDk8ZEgTSid2zwfui4ivVx3PcEh6naQJaXoMsC/w22qj2nARMSciJkfEFLL/J7+IiI9VHNaQSBqbLn5A0ljg/cCIvvrPCaIEEbEe6B0y5D7gspE6ZIikS4FfAztLWi7puKpjGoa9gSPJfqXemV4frDqoIZoELJT0G7IfJDdExIi+RLQFtAO/knQXcCtwTUT8tOKYhsWXuZqZWS63IMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUHYiCUpJM2rmf+8pNPrdOwLJR1cj2MNUs4haXTZhX2WbyTpW5LuTqOD3iZpalp3atlxmYEThI1s64CPSJpYdSC10mi+RR0H/O+ImN5n+WHANsBuEbEr8GFgTVq3wQliA2MyA5wgbGRbT/b833/su6JvC0BST/rbKekmSZdJ+p2kuZKOSM9WWCppx5rD7Cvpl2m7/dP+oySdk37R/0bS8TXHXSjp34GlOfEcno5/t6Sz07IvA38N/Jukc/rsMglYGREvAUTE8ohYLWkuMCbd5HdJOs6VaXC4e2oHiJPUI+krkhYB7051vTfF/f829M2216CI8MuvEfkie07FFsAyYDzweeD0tO5C4ODabdPfTrJf4pOATYEVwBlp3UnAN2r2/ynZj6hpZONrbQbMAr6YttkUWAxMTcd9BpiaE+c2wCPA64DRwC/InkMB2RhKHTn7TE71uhOYB7y9b11q5rdKf8eQDe2wdZoP4NDebYD7efnm2AlVf35+Nf/LLQgb0SIbjfVi4MQN2O22yJ4NsQ74PXB9Wr4UmFKz3WUR8VJEPAA8BLyJbHydj6dhthcBW5MlEIBbI+IPOeW9A+iOiCciG4blEmDAUT4jYjmwMzCHbBjsGyXN6GfzE9PwDreQDRLZG8+LZAMTAjwNPA+cJ+kjwLMDlW8G2a8Zs5HuG8DtwPdrlq0ndaGmQfo2qVm3rmb6pZr5l3jl/4m+49AE2VDun4mIn9WukNRJ1oLIkzf8+6BSArsOuE7S48BBwI055e4LvDsinpXUTdbSAXg+Il5Mx1ovaS9gBtmgeJ8G3juUuOy1wy0IG/Ei4kngMrITvr2WAXum6QPJnri2oQ5JVxPtCOxA1kXzM+Af0rDhSPqrNHLnQBYB+0iamE4WHw7cNNAOkvaQtE2a3gjYDXg4rf5zb/lkXWurU3J4E/Cufo7XBoyPiGuBzwK7DxKzmVsQ1jLmkf0q7vU9YIGkW8l+dff3634g95N9kbcDn4qI5yWdR9YNdXtqmTxB9su+XxGxUtIcYCFZa+LaiFgwSNmvB74nadM0fyvwL2l6PvAbSbcDxwKfSqO63k/WzZRnHNn7sVmK4VUn9s368miuZmaWy11MZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5fofX8T2p3XfJRsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for category, count in cat_top_10:\n",
    "    cat_df = las_vegas[las_vegas[category] == True]\n",
    "    chart = cat_df.hist(column = \"stars\", bins = [-0.5, 0, 0.5, 1, 1.5, 2, 2.5,\n",
    "                                                  3, 3.5, 4, 4.5, 5, 5.5])\n",
    "    for ax in chart.flatten():\n",
    "        ax.set_xlabel(\"Number of Stars\")\n",
    "        ax.set_ylabel(\"Number of Restaurants\")\n",
    "        ax.set_title(category + \" Restaurants\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the above graphs, all of the categories  have their maximum number of restaurants sitting at around 3.5 stars to 4.5 stars. However, it looks to be skewed left. This could cause inconviences for the next step in our analysis. Since these 10 categories are the most populous among all others, we will investigate to see if the distribution of those restaurants in each category is different from those out of each category. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to see if there is significant difference between the `stars` rating of restaurants in different categories. To do this, we want to use a t-test. But before we can do that, we must see if the data for each category is normally distributed. We will use an alpha value of 0.01. If both the distribution of restaurants in a category and those out of the category have normally distributed ratings, then we will add them to the list `normal_categories`. At the end of this code block, the list will contain all categories of restaurant for which the `stars` rating is normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seafood: p for those in category: 0.00167835860485\n",
      "Seafood: p for those out of category: 2.92030501396e-40\n",
      "Japanese: p for those in category: 1.27812580568e-09\n",
      "Japanese: p for those out of category: 6.68673314685e-38\n",
      "Italian: p for those in category: 0.00054779932962\n",
      "Italian: p for those out of category: 1.66641084376e-41\n",
      "Pizza: p for those in category: 1.26653881571e-06\n",
      "Pizza: p for those out of category: 9.62744398723e-41\n",
      "Sandwiches: p for those in category: 4.73492347268e-05\n",
      "Sandwiches: p for those out of category: 1.67800432918e-42\n",
      "Mexican: p for those in category: 0.0026794475926\n",
      "Mexican: p for those out of category: 6.34841144578e-48\n",
      "Breakfast&Brunch: p for those in category: 8.01971489664e-05\n",
      "Breakfast&Brunch: p for those out of category: 8.73367592313e-44\n",
      "FastFood: p for those in category: 3.45911723293e-05\n",
      "FastFood: p for those out of category: 6.98250892332e-33\n",
      "American: p for those in category: 5.24923564013e-07\n",
      "American: p for those out of category: 2.3525534484e-47\n",
      "Nightlife: p for those in category: 1.20406082421e-14\n",
      "Nightlife: p for those out of category: 1.37479391342e-27\n",
      "Normal Categories: []\n"
     ]
    }
   ],
   "source": [
    "p = 0.01\n",
    "normal_categories = []\n",
    "for category, count in cat_top_10:\n",
    "    in_cat = las_vegas[las_vegas[category] == True][\"stars\"].values\n",
    "    out_cat = las_vegas[las_vegas[category] == False][\"stars\"].values\n",
    "    \n",
    "    st_in, p_in = normaltest(in_cat)\n",
    "    st_out, p_out = normaltest(out_cat)\n",
    "    \n",
    "    if p_in >= p and p_out >= p:\n",
    "        normal_categories.append(category)\n",
    "        \n",
    "    print(category + \":\", \"p for those in category:\", p_in)\n",
    "    print(category + \":\", \"p for those out of category:\", p_out)\n",
    "    \n",
    "print(\"Normal Categories:\", normal_categories)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uh, oh... none of the categories are added to `normal_categories`. This means that when using an alpha value of 0.01, there are no categories for which `stars` is normally distributed for both those restuarants in and out of the category. Lets get a visual for the first few categories comparing the actual distribution of restuarants in those categories with the normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 0.01\n",
    "for category, count in cat_top_10[-3:]:\n",
    "    in_cat = las_vegas[las_vegas[category] == True][\"stars\"].values\n",
    "    out_cat = las_vegas[las_vegas[category] == False][\"stars\"].values\n",
    "    \n",
    "    fig1, ax1 = plt.subplots()\n",
    "    xs = np.arange(in_cat.min(), in_cat.max(), 0.1)\n",
    "    fit = stats.norm.pdf(xs, np.mean(in_cat), np.std(in_cat))\n",
    "    ax1.plot(xs, fit, label='Normal Dist.', lw=4)\n",
    "    ax1.hist(in_cat, density=True, label='Actual Data', bins = [-0.5, 0, 0.5, 1, 1.5,\n",
    "                                                                2, 2.5,3, 3.5, 4,\n",
    "                                                                4.5,5, 5.5]);\n",
    "    ax1.set_title(category + \" Restaurants\")\n",
    "    ax1.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the data really isn't normal. In fact, its so un-normal that even using an alpha value of 0.001, every single `normal_test()` would fail. One option is to attempt to normalize the data, however we decided against this because there is another good test we can use. Because we have ordinal non normal data, a Mann Whitney U test is our best option to see if the distributions differ. Here, we run a Mann Whittney U test on each pair of in-category/out-category resturaunts. For each category that does have a significant affect on the median rating using an alpha value of 0.01, we will add the name of that category and the difference in median to significant_difference_categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seafood 0.00046379496769\n",
      "Japanese 2.45266863098e-14\n",
      "Italian 0.189030451456\n",
      "Pizza 0.028171909811\n",
      "Sandwiches 0.273150028855\n",
      "Mexican 0.00204388748005\n",
      "Breakfast&Brunch 0.225432045919\n",
      "FastFood 7.60849430223e-33\n",
      "American 4.29191455872e-07\n",
      "Nightlife 1.29226042873e-07\n",
      "\n",
      "Significant difference categories:\n",
      "Seafood 0.5\n",
      "Japanese 0.5\n",
      "Mexican -0.5\n",
      "FastFood -0.5\n",
      "American -0.5\n",
      "Nightlife 0.5\n"
     ]
    }
   ],
   "source": [
    "significant_difference_categories = []\n",
    "\n",
    "for category, count in cat_top_10:\n",
    "    in_cat = las_vegas[las_vegas[category] == True][\"stars\"].values\n",
    "    out_cat = las_vegas[las_vegas[category] == False][\"stars\"].values\n",
    "    \n",
    "    stat, p = mannwhitneyu(in_cat, out_cat)\n",
    "        \n",
    "    print(category, p)\n",
    "    \n",
    "    if p < 0.01:\n",
    "        significant_difference_categories.append(\n",
    "            (category, statistics.median(in_cat) - statistics.median(out_cat))\n",
    "        )\n",
    "\n",
    "print()\n",
    "print(\"Significant difference categories:\")\n",
    "for category, difference in significant_difference_categories:\n",
    "    print(category, difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So here we see that according to our U-test, Seafood, Japanese, and Nightlife restaurants have a median star rating 0.5 higher than correspondingly, non Seafood, non Japanese, and non Nightlife restaurants. And Mexican, FastFood, and American restuarants have a median star rating 0.5 lower than, correspondingly, non Mexican, Fastfood, and American restuarants. This 0.5 difference may seem small, but considering the scale only goes from 0 to 5, 0.5 is about 1/10th of the entire scale!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this part of our analysis, we want to look at how attributes affect `stars` rating on restaurants. We will use the 5 columns we added to our dataframe to analyze how these top 5 attributes affect `stars` rating of restaurants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first want to print out the histograms showing the stars distribution of the top 5 attributes to see how it might have effect on `stars` ratings of restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for attribute, count in att_top_5:\n",
    "    att_df = las_vegas[las_vegas[attribute] == True]\n",
    "    att_chart = att_df.hist(column = \"stars\", bins = [-0.5, 0, 0.5, 1, 1.5, 2, 2.5,\n",
    "                                                      3, 3.5, 4, 4.5, 5, 5.5])\n",
    "    for axis in att_chart.flatten():\n",
    "        axis.set_xlabel(\"Number of Stars\")\n",
    "        axis.set_ylabel(\"Number of Restaurants\")\n",
    "        axis.set_title(attribute + \" Restaurants\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the attributes graphs above, majority of the restaurants are clustered around 3.5 stars and 4.5 stars possibly indicating that attributes have effect on `stars` rating. Once again, there is a large left skew."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we want to see if there is significant difference between restaurants in different attributes. To do this, we want to use a t-test. But before we can do that, we must see if the data for each attribute is normally distributed. We will use an alpha value of 0.01. If both the distribution of restaurants in a attribute and those out of the attribute have normally distributed ratings, then we will add them to the list `normal_attributes`. After performing the below code block, the list will contain all attributes of restaurant for which the `stars` rating is normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoodForKids: p_in 2.06387344892e-36 p_out 2.14562780732e-09\n",
      "BusinessParking: p_in 2.30355005382e-39 p_out 2.78569578008e-07\n",
      "RestaurantsTakeOut: p_in 3.02491017208e-44 p_out 0.00310761264759\n",
      "RestaurantsPriceRange2: p_in 4.71382958481e-37 p_out 2.21748938162e-05\n",
      "BusinessAcceptsCreditCards: p_in 2.887549172e-45 p_out 0.00499278945418\n",
      "Normal Attributes: []\n"
     ]
    }
   ],
   "source": [
    "p = 0.01\n",
    "normal_attributes = []\n",
    "for attribute, count in att_top_5:\n",
    "    in_cat = las_vegas[las_vegas[attribute] == True][\"stars\"].values\n",
    "    out_cat = las_vegas[las_vegas[attribute] == False][\"stars\"].values\n",
    "    \n",
    "    st_in, p_in = normaltest(in_cat)\n",
    "    st_out, p_out = normaltest(out_cat)\n",
    "    \n",
    "    if p_in >= p and p_out >= p:\n",
    "        normal_attributes.append(attribute)\n",
    "        \n",
    "    print(attribute + \":\", \"p_in\", p_in, \"p_out\", p_out)\n",
    "    \n",
    "print(\"Normal Attributes:\", normal_attributes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Since `normal_attributes` is empty, that means no attributes for which `stars` is normally distributed for both restaurants that are in and out of the attribute with an alpha value of 0.01. Now let's graph the normal distribution versus our actual data to better see what is going on for a few of the attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 0.01\n",
    "for attribute, count in att_top_5[-2:]:\n",
    "    in_cat = las_vegas[las_vegas[attribute] == True][\"stars\"].values\n",
    "    out_cat = las_vegas[las_vegas[attribute] == False][\"stars\"].values\n",
    "    \n",
    "    fig2, ax2 = plt.subplots()\n",
    "    xs = np.arange(in_cat.min(), in_cat.max(), 0.1)\n",
    "    fit = stats.norm.pdf(xs, np.mean(in_cat), np.std(in_cat))\n",
    "    ax2.plot(xs, fit, label='Normal Dist.', lw=4)\n",
    "    ax2.hist(in_cat, density=True, label='Actual Data', bins = [-0.5, 0, 0.5, 1, 1.5,\n",
    "                                                                2, 2.5, 3, 3.5, 4,\n",
    "                                                                4.5, 5, 5.5]);\n",
    "    ax2.set_title(attribute + \" Restaurants\")\n",
    "    ax2.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the data is not normal at all. The data is extremely un-normal that even using an alpha value of 0.001 wouldn't pass the `normal_test()`. Alternative option is to try to normalize the data, however, we decided not to because there is yet another good test we can use. It is because we have ordinal non-normal data, a Mann Whitney U test is one of our best option to see if the distributions differ. We can simply run a Mann Whittney U test on each pair of in-attribute/out-attribute resturaunts. For each attribute that does have a significant affect on the median rating using an alpha value of 0.01, we will add the name of that attribute and the difference in median to significant_difference_attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoodForKids 0.0172658541586\n",
      "BusinessParking 0.0030522327164\n",
      "RestaurantsTakeOut 0.479905046224\n",
      "RestaurantsPriceRange2 4.3741945257e-08\n",
      "BusinessAcceptsCreditCards 0.00275497329698\n",
      "\n",
      "Significant difference attributes:\n",
      "BusinessParking 0.5\n",
      "RestaurantsPriceRange2 -0.5\n",
      "BusinessAcceptsCreditCards -0.5\n"
     ]
    }
   ],
   "source": [
    "significant_difference_attributes = []\n",
    "\n",
    "for attribute, count in att_top_5:\n",
    "    in_cat = las_vegas[las_vegas[attribute] == True][\"stars\"].values\n",
    "    out_cat = las_vegas[las_vegas[attribute] == False][\"stars\"].values\n",
    "    \n",
    "    stat, p = mannwhitneyu(in_cat, out_cat)\n",
    "        \n",
    "    print(attribute, p)\n",
    "    \n",
    "    if p < 0.01:\n",
    "        significant_difference_attributes.append(\n",
    "            (attribute, statistics.median(in_cat) - statistics.median(out_cat))\n",
    "        )\n",
    "\n",
    "print()\n",
    "print(\"Significant difference attributes:\")\n",
    "for attribute, difference in significant_difference_attributes:\n",
    "    print(attribute, difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result above, we can see in our U-test, BusinessParking restaurants have a median star rating 0.5 higher than non BusinessParking restaurants. RestaurantsPriceRange2 and BusinessAcceptsCreditCards restuarants have a median star rating 0.5 lower than non RestaurantsPriceRange2 and BusinessAcceptsCreditCards restuarants. Again, the 0.5 difference may seem small, but considering the scale only goes from 0 to 5, 0.5 is about 1/10th of the entire scale!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It might strike you as odd that Businesses that accept credit cards are rated lower than those that do not. However, there is an explanation for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 22)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_credit = las_vegas[las_vegas[\"BusinessAcceptsCreditCards\"] == False]\n",
    "no_credit.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only 53 restaurants that don't accept credit cards in our dataset. It could be that the observation that Businesses that do not accept credit cards are rated better than those that do was only possible because of the relatively small sample size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data used for this project was ethically and consensually collected by Yelp and available for public use. While personal information is included in our dataset, such as names, logitude, and latitude of restaurants, meaning it is non-anonymous, this is not ethically damaging because the data in the dataset is public information that is already available on the Yelp app.\n",
    "\n",
    "There are several players who may be interested in our findings, namely the Yelp corporation and businesses which use the app. It is possible that making the facets of a successful Yelp restaurant publicly known could entice business owners or others to fraud - perhaps claiming that their business has attributes it does not because those attributes are correltated with a successful restaurant.\n",
    "\n",
    "Additionally, there are some potential biases in the dataset. Review fraud is possible and some would argue common on websites such as Yelp, as seen in [\"Don't Fall for Fake Reviews: I-Team Uncovers Them on Yelp, Facebook, Google\"](https://www.nbclosangeles.com/investigations/Fake-Reviews-on-Yelp-Facebook-Google-447796103.html) by Joel Grover and Amy Corral and [\"How to Buy Yelp Reviews\"](https://blog.hubspot.com/service/buy-yelp-reviews) by Swetha Amaresan. However, Yelp's official position is that this is not the case. Additionally, our dataset contains all the flaws that exist in any opt-in review dataset. It is possible that pleased customers are less likely to review than displeased customers, or that businesses who let their patrons know they are on Yelp with decals or other paraphernalia will have more or different reviews. Utimately, it is impossible to know exactly how these biases will affect our dataset, so we aknowledge that our conclusions are vulnerable to these faults. This is a shortcoming that many analyses that rely on survey data suffer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through our analysis we are able to draw the preliminary conclusions about parts of our hypothesis. We could not confirm that longer weekly hours lead to higher rating, because the relationship between weekly hours and `stars` rating was very weak if existant at all. We confirmed that some categories of restaurant have differently distrubyted `stars` ratings (either better or worse). And we confirmed that restaurants with different attributes have differently distributed `stars` ratings. We predicted that good attributes would be correlated with better rating, this was confirmed in regards to 'BusinessParking' which showed a higher distributed rating, but refuted in regards to 'RestaurantsTakeOut' and 'GoodForKids' which showed no signficant difference in rating.\n",
    "\n",
    "So in summary: We have found that restaurants in the categories Seafood, Japanese, and Nightlife or that have parking show a higher distribution of ratings that those not in those categories or that do not have parking by 0.5 star increase in median. We also found that restaurants in the categories  Mexican, FastFood, and American or that have two `$$` or fewer dollar signs show a low distribution of ratings than those not in those categories or that have three `$$$` or more dolars signs by 0.5 star decrease in median. There were also several categories and attributes we found to not have an effect.\n",
    "\n",
    "However, these results are only preliminary for several reasons.\n",
    " * They look only at a single city, the results could be different for different cities or the population at large.\n",
    " * They are exposed to the biases discused in the ethics and privacy section.\n",
    " * More categories and attributes should be investigated before drawing concrete conclusions.\n",
    " * Location of the restaurants might contribute whether the restaurant is rated higher or lower.\n",
    " * The race of the population might also affect ratings of restaurants, as in how different race have their different preferred \n",
    "   type of food."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}