Hotel_Stratification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import time\n",
    "import math\n",
    "import copy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "import sys\n",
    "# np.set_printoptions(threshold=sys.maxsize)\n",
    "\n",
    "\n",
    "class Haversine:\n",
    "    '''\n",
    "    from: https://nathanrooy.github.io/posts/2016-09-07/haversine-with-python/\n",
    "\n",
    "    use the haversine class to calculate the distance between\n",
    "    two lon/lat coordnate pairs.\n",
    "    output distance available in kilometers, meters, miles, and feet.\n",
    "    example usage: Haversine([lon1,lat1],[lon2,lat2]).feet\n",
    "    \n",
    "    '''\n",
    "    def __init__(self,coord1,coord2):\n",
    "        lon1,lat1=coord1\n",
    "        lon2,lat2=coord2\n",
    "        \n",
    "        R=6371000                               # radius of Earth in meters\n",
    "        phi_1=math.radians(lat1)\n",
    "        phi_2=math.radians(lat2)\n",
    "\n",
    "        delta_phi=math.radians(lat2-lat1)\n",
    "        delta_lambda=math.radians(lon2-lon1)\n",
    "\n",
    "        a=math.sin(delta_phi/2.0)**2+\\\n",
    "           math.cos(phi_1)*math.cos(phi_2)*\\\n",
    "           math.sin(delta_lambda/2.0)**2\n",
    "        c=2*math.atan2(math.sqrt(a),math.sqrt(1-a))\n",
    "        \n",
    "        self.meters=R*c                         # output distance in meters\n",
    "        self.km=self.meters/1000.0              # output distance in kilometers\n",
    "        self.miles=self.meters*0.000621371      # output distance in miles\n",
    "        self.feet=self.miles*5280               # output distance in feet\n",
    "    \n",
    "    \n",
    "    def withinTolerance(self, tolerance):\n",
    "        if self.meters <= tolerance:\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "\n",
    "\n",
    "def assignParksToHotels(hotels_df, np_df, tolerance):\n",
    "    '''\n",
    "    hotels_df = dataframe of initial hotels data\n",
    "    np_df = dataframe of initial national park\n",
    "    \n",
    "    Assigned park ids to a hotel \n",
    "    '''\n",
    "    nearby = []\n",
    "    hotels_record = {}\n",
    "    for i, hotel_row in hotels_df.iterrows():\n",
    "        hotel_coords = [hotel_row['longitude'], hotel_row['latitude']]\n",
    "        hotel_key = hash(hotel_row['longitude']) + 11 * hotel_row['latitude']\n",
    "        if hotel_key in hotels_record:     # To reference old computation bc multiple reviews exist for same hotel\n",
    "            nearby.append(hotels_record[hotel_key])\n",
    "            continue\n",
    "        nearby_np = []\n",
    "        for j, np_row in np_df.iterrows():\n",
    "            np_coords = [np_row['coordinates/longitude'], np_row['coordinates/latitude']]\n",
    "            np_radius = math.sqrt(float(np_row['area/square_km'].replace(\",\",\"\"))) * 1000  # Radius of park in meters\n",
    "            H = Haversine(hotel_coords, np_coords)\n",
    "            if H.withinTolerance(tolerance + np_radius):\n",
    "                temp_nearby = {\n",
    "                    'id': np_row['id'],\n",
    "                    'dist': H.meters - np_radius\n",
    "                }\n",
    "                nearby_np.append(temp_nearby)\n",
    "        if len(nearby_np) == 0:\n",
    "            nearby.append(np.nan)\n",
    "            hotels_record[hotel_key] = np.nan\n",
    "        elif len(nearby_np) == 1:\n",
    "            nearby.append(nearby_np[0]['id'])\n",
    "            hotels_record[hotel_key] = nearby_np[0]['id']\n",
    "        else: # more than 1 nearby national parks\n",
    "            nearby_np.sort(key=lambda x: x['dist'])\n",
    "            nearby.append(nearby_np[0]['id'])\n",
    "            hotels_record[hotel_key] = nearby_np[0]['id']\n",
    "    \n",
    "    hotels_df['nearby'] = nearby    \n",
    "    assignments = list(hotels_record.values())    \n",
    "    total_num = len(assignments)\n",
    "    total_unassigned = assignments.count(np.nan)\n",
    "    print(f\"{(total_num - total_unassigned) / total_num}% of hotels ({total_num - total_unassigned}) are near national parks\")\n",
    "    \n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_loc = \"national_parks.csv\"\n",
    "trails_loc = \"nationalparktrails.csv\"\n",
    "hotels_loc = \"7282_1.csv\"\n",
    "\n",
    "np_df = pd.read_csv(np_loc)\n",
    "trails_df = pd.read_csv(trails_loc)\n",
    "hotels_df = pd.read_csv(hotels_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print (df[\"categories\"])\n",
    "\n",
    "#Loop through data and get counts and unique categories\n",
    "categories = {}\n",
    "\n",
    "for i, data in hotels_df.iterrows():\n",
    "    line = data[\"categories\"]\n",
    "    words = line.split(\",\")\n",
    "    \n",
    "    for word in words:\n",
    "        word = word.strip(\" \")\n",
    "        if(word in categories):\n",
    "            categories[word] += 1\n",
    "        else:\n",
    "            categories[word] = 1\n",
    "    \n",
    "    #if(i > 100):\n",
    "    #    break;\n",
    "categories = pd.DataFrame.from_dict(categories, orient='index', columns=[\"Categorie\"])\n",
    "categories = categories.sort_values(by=['Categorie'], ascending=False)\n",
    "#print(np.array(categories.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Positive = ['Resorts', 'Wedding Receptions & Parties', 'Banquet Rooms', 'Casinos', 'Wedding Sites', \n",
    "            'Banquet Facilities', 'Resorts & Vacation Cottages', 'Banquet Halls & Reception Facilities',\n",
    "            'Boutique Hotels', 'Vacation Home Rental', 'Cabin Cottage & Chalet Rental', \n",
    "            'Golf Courses - Private' 'Party & Event Planners', 'Wedding Chapels', 'Resortresort_hotel',\n",
    "            'Bowling Alleys', 'Luxury Hotels', 'Resort', 'Wine Country Hotels',  \n",
    "            'Wedding Reception Locations & Services', 'Villas',  'Cottages', 'precooked and gourmet', \n",
    "            'Hotel and Resort', 'Gastronomic specialties', 'Spa', 'Health Spas', 'Golf Courses', \n",
    "            'Ski Resorts', 'Spa Resorts', 'Hotel and Brewery', 'ski & snowboard areas', 'Cocktail Lounges', \n",
    "            'Wedding Guests', 'Wedding Guest Accommodations', 'Wedding Planners', 'Driving Ranges', \n",
    "            'Wedding Workouts', 'Condominiums and Townhouses', 'Honeymoon Resorts', 'Ski Apparel', \n",
    "            'Air Sightseeing Tours', 'Resorts Reservations', 'Wedding Reception Locations', \n",
    "            'Cabins Cottages & Chalet Rental', 'Whiskey Bars', 'resort', 'Wedding Chapels & Ceremonies',\n",
    "            'Cabins & Chalets','Spas', 'Public Golf Courses','Mediation Services', 'Attorneys',  'Private Golf Courses']\n",
    "\n",
    "\n",
    "Neutral = ['Hotels', 'Hotel, Lodging', 'Family-Friendly Hotels', 'Tourist Hotels', \n",
    "           'Business Hotels', 'Convention & Meeting Facilities & Services', 'Weekend Getaway Hotels',\n",
    "           'Bed & Breakfasts', 'Conventions Conferences & Trade Shows', 'Corporate Lodging', \n",
    "           'Travel & Transport', 'Accommodations & Lodging', 'Beach Hotels', 'Bed & Breakfast & Inns',\n",
    "           'Meeting Facilities', 'Conference Room', 'Travel', 'Travel and Tourism', 'Restaurants',  \n",
    "           'hotel', 'fun hotels in seattle', 'Accommodation Reservations', 'Apartment Hotels', 'Bar',\n",
    "           'and Hotel Bar', 'Meeting Room',  'Cabins', 'Hotel & Motel Reservations', 'Real Estate Developers',\n",
    "           'Home Builders', 'Hotels and Lodging', 'Bed and Breakfast', 'Reception Centers',\n",
    "           'Hotel Downtown St. Cloud', 'B&bs / Inns',  'Inns',  'Meat', 'Vacation Packages',\n",
    "           'Meeting & Event Planning Services', 'and Meeting Room South Eugene', 'and Meeting Room',\n",
    "           'Travel & Leisure', 'Grill Restaurants', 'Swimming Pools', 'Family-friendly Hotels',\n",
    "           'Hotel and Meeting Room', 'Conference & Convention Centers', 'Conventions', 'restaurants',\n",
    "           'travel', 'preserved', 'Foods',  'Restaurant', 'Motels', 'Wine Bar', 'Breakfast Spot', \n",
    "           'Convention Center', 'Wholesale Copiers', 'American Restaurant', \"O'fallon Hotels\", \n",
    "           'and Meeting Room Downtown Houston', 'Hotels-Apartment', 'American Restaurants', \n",
    "           'Vacation Rentals', 'Bed & Breakfasts (b&b)', 'Hotel and Bed Breakfast Prudential - St. Botolph', \n",
    "           'Hotel Colorado Springs Airport', 'Corporate Housing', 'Apartments', 'operation',\n",
    "           'and Meeting Room Park 100' 'Retreat Houses','Apartment Operators', 'dallas hotels',\n",
    "           'dallas','Convention Services & Facilities', 'texas (tx)', 'united states',\n",
    "           'Eufaula Hotels and Motels', 'Conference Centers','Halls Auditoriums & Ballrooms', \n",
    "           'Eufaula', 'Bars & Clubs', 'Bed & Breakfast Reservations', 'Vacation Homes Rentals & Sales',\n",
    "           'Historical Sites', 'Tourist Homes', 'Picnic Grounds', 'E-Commerce', 'Nature Centers', 'Railroads',\n",
    "           'Mexican', 'Condominium Management', 'Coffeehouses', 'Rail Tours', 'Hotel & Motel Consultants',\n",
    "           'Tours', 'Cottages & Cabins', 'Homes & Residential Real Estate', 'Raton Hotels and Motels', 'Raton',\n",
    "           'Arcade', 'Condominium Operators', 'Hotel Pool', 'Real Estate Rentals', 'California (ca)', 'Lodges',\n",
    "           'Taverns', 'Bars', 'Retirement Apartments & Hotels', 'Uncategorized', 'Watsonville', \n",
    "           'Watsonville Hotels', 'Travel Agencies', 'Bed & Breakfast', 'Convention and Meeting Facilities and Services',\n",
    "           'Hotel & Motel Management','hotels', 'Condominiums', 'Condominium & Townhouse Rental & Leasing', \n",
    "           'Real Estate Management', 'Hanukkah Events', 'Bar & Grills', 'Brew Pubs', 'Wine Bars', 'Pubs', \n",
    "           'Camps-Recreational', 'Pizza Place', 'Places Of Interest', 'Government Contractors', \n",
    "           'Tourist Information & Attractions', 'Lodges In', 'Cruise Lines & Agents' 'Convention Visitors & Information Centers',\n",
    "           'General Contractors', 'Italian Restaurants', 'Travel Agency', 'Campgrounds', 'Marinas', 'Night Clubs',\n",
    "           'Mediterranean Restaurants', 'Amusement Places & Arcades', 'Travel Agents', 'Family Style Restaurants',\n",
    "           'Guide Service', 'Extended Stay Hotels', 'Attractions', 'solrjson:\\\\\\\\Illinois', 'Water Parks & Slides',\n",
    "           'Apartment Finder & Rental Service', 'Student Housing & Services', 'Real Estate Agents', 'lodging house',\n",
    "           'bar drinks', 'Travel Tours & Guide Services', 'Community Organizations','Educational Services',\n",
    "           'Travel & Lodging', 'food catering', 'Trade Shows Expositions & Fairs', 'Burwell', 'Nursing & Convalescent Homes',\n",
    "           'Rental Services' 'Burwell Hotels and Motels' 'MI' 'Retirement Communities', 'cakes desserts', 'Marketing Consultants',\n",
    "           'Bicycle Tours', 'Flooring Materials & Supplies', 'Real Estate Buyer Brokers', 'professional', 'Catering',\n",
    "           'Bars & Pubs', 'Tours & Charters', 'Dixfield', 'ME', 'Truck Stops & Plazas', 'Bernau Hotels', 'Executive Suites',\n",
    "           'Rest Homes', 'Homes-Institutional & Aged', 'Caterers', 'Real Estate Rental Service', \n",
    "           'Campgrounds & Recreational Vehicle Parks', 'Ironwood', 'Colorado (co)', 'Assisted Living Facilities',\n",
    "           'Nursing & Personal Care Facilities', 'Assisted Living & Elder Care Services', 'Disabled & Elderly Home Health Care',\n",
    "           'Geriatric Care Nursing Homes', 'Retirement Homes', 'Willows Hotels', 'Intermediate Care Nursing Homes', \n",
    "           'Nursing Homes', 'solrjson:\\\\\\\\Ennis', 'MT', 'Gift Shops', 'Bed Breakfast Near North Valley', \n",
    "           'Food & Entertainment', 'Clare', 'Halls & Auditoriums', 'Party Planning Service', 'Willows', 'Sioux City']\n",
    "\n",
    "\n",
    "Negative = ['Hotels & Motels', 'Motels', 'Budget Hotels', 'Extended Stay', '“Hotels Motels”', 'Hostel', 'Hotels and Motels',\n",
    "            'Hotels And Motels', 'Motel and Hotel', 'hotels & motels', 'Motel', 'RV Parks', 'Hotel and Motel'] \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive 2406\n",
      "Neutral 28407\n",
      "Negative 5099\n"
     ]
    }
   ],
   "source": [
    "positive_count = 0\n",
    "neutral_count = 0\n",
    "negative_count = 0\n",
    "\n",
    "stratify_results = []\n",
    "\n",
    "count = 0\n",
    "for i, data in hotels_df.iterrows():\n",
    "    count += 1\n",
    "    line = data[\"categories\"]\n",
    "    words = line.split(\",\")\n",
    "    \n",
    "    score = 0\n",
    "    for word in words:\n",
    "        if word in Positive:\n",
    "            score += 1\n",
    "        if word in Negative:\n",
    "            score -= 1\n",
    "    \n",
    "    if(score > 0):\n",
    "        stratify_results.append(\"High End\")\n",
    "        positive_count += 1\n",
    "    elif(score == 0):\n",
    "        stratify_results.append(\"Average\")\n",
    "        neutral_count += 1\n",
    "    elif(score < 0):\n",
    "        stratify_results.append(\"Low End\")\n",
    "        negative_count += 1\n",
    "    else:\n",
    "        print(score)\n",
    "    \n",
    "hotels_df[\"Stratify\"] = stratify_results\n",
    "# print(df[:10])    \n",
    "    \n",
    "print(\"Positive\", positive_count)\n",
    "print(\"Neutral\", neutral_count)\n",
    "print(\"Negative\", negative_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1627689429373246% of hotels (174) are near national parks\n"
     ]
    }
   ],
   "source": [
    "TOLERANCE = 80467.2  # 50 miles\n",
    "assignParksToHotels(hotels_df, np_df, TOLERANCE)\n",
    "hotels_df['Park'] = hotels_df['nearby'].notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'reviews.ratings'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'reviews.ratings'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-d5d089e881b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mHigh_Near\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhotels_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhotels_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Stratify'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"High End\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhotels_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Park'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mHigh_Ttest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mttest_ind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mHigh_Far\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'reviews.ratings'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mHigh_Near\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'reviews.ratings'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"t-statistic: \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mHigh_Ttest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"p-value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mHigh_Ttest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'reviews.ratings'"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/13404468/t-test-in-pandas\n",
    "\n",
    "#High end\n",
    "High_Far  = hotels_df[(hotels_df['Stratify'] == \"High End\") & (hotels_df['Park'] == False)]\n",
    "High_Near = hotels_df[(hotels_df['Stratify'] == \"High End\") & (hotels_df['Park'] == True)]\n",
    "\n",
    "High_Ttest = ttest_ind(High_Far['reviews.ratings'], High_Near['reviews.ratings'])\n",
    "print(\"t-statistic: \", High_Ttest[0],\"p-value\", High_Ttest[1])\n",
    "\n",
    "#Average\n",
    "Avg_Far  = hotels_df[(hotels_df['Stratify'] == \"Average\") & (hotels_df['Park'] == False)]\n",
    "Avg_Near = hotels_df[(hotels_df['Stratify'] == \"Average\") & (hotels_df['Park'] == True)]\n",
    "\n",
    "Avg_Ttest = ttest_ind(Avg_Far['reviews.ratings'], Avg_Near['reviews.ratings'])\n",
    "print(\"t-statistic: \", Avg_Ttest[0],\"p-value\", Avg_Ttest[1])\n",
    "\n",
    "#Low end\n",
    "Low_Far  = hotels_df[(hotels_df['Stratify'] == \"Average\") & (hotels_df['Park'] == False)]\n",
    "Low_Near = hotels_df[(hotels_df['Stratify'] == \"Average\") & (hotels_df['Park'] == True)]\n",
    "\n",
    "Low_Ttest = ttest_ind(Low_Far['reviews.ratings'], Low_Near['reviews.ratings'])\n",
    "print(\"t-statistic: \", Low_Ttest[0],\"p-value\", Low_Ttest[1])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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