{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63ddbf52-7bbe-4f8f-ae8f-819ed5e4ba27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pickle\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b94ff50c-9793-4553-837b-2d98a74cc48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"/root/dataset_challenge/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3c1a39f-d208-4a01-8f38-71760ca2f65f",
   "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>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FYWN1wneV18bWNgQjJ2GNg</td>\n",
       "      <td>\"Dental by Design\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"4855 E Warner Rd, Ste B9\"</td>\n",
       "      <td>Ahwatukee</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85044</td>\n",
       "      <td>33.330690</td>\n",
       "      <td>-111.978599</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Dentists;General Dentistry;Health &amp; Medical;Or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He-G7vWjzVUysIKrfNbPUQ</td>\n",
       "      <td>\"Stephen Szabo Salon\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"3101 Washington Rd\"</td>\n",
       "      <td>McMurray</td>\n",
       "      <td>PA</td>\n",
       "      <td>15317</td>\n",
       "      <td>40.291685</td>\n",
       "      <td>-80.104900</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Hair Stylists;Hair Salons;Men's Hair Salons;Bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KQPW8lFf1y5BT2MxiSZ3QA</td>\n",
       "      <td>\"Western Motor Vehicle\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"6025 N 27th Ave, Ste 1\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85017</td>\n",
       "      <td>33.524903</td>\n",
       "      <td>-112.115310</td>\n",
       "      <td>1.5</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Departments of Motor Vehicles;Public Services ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8DShNS-LuFqpEWIp0HxijA</td>\n",
       "      <td>\"Sports Authority\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"5000 Arizona Mills Cr, Ste 435\"</td>\n",
       "      <td>Tempe</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85282</td>\n",
       "      <td>33.383147</td>\n",
       "      <td>-111.964725</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Sporting Goods;Shopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PfOCPjBrlQAnz__NXj9h_w</td>\n",
       "      <td>\"Brick House Tavern + Tap\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"581 Howe Ave\"</td>\n",
       "      <td>Cuyahoga Falls</td>\n",
       "      <td>OH</td>\n",
       "      <td>44221</td>\n",
       "      <td>41.119535</td>\n",
       "      <td>-81.475690</td>\n",
       "      <td>3.5</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>American (New);Nightlife;Bars;Sandwiches;Ameri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                        name neighborhood  \\\n",
       "0  FYWN1wneV18bWNgQjJ2GNg          \"Dental by Design\"          NaN   \n",
       "1  He-G7vWjzVUysIKrfNbPUQ       \"Stephen Szabo Salon\"          NaN   \n",
       "2  KQPW8lFf1y5BT2MxiSZ3QA     \"Western Motor Vehicle\"          NaN   \n",
       "3  8DShNS-LuFqpEWIp0HxijA          \"Sports Authority\"          NaN   \n",
       "4  PfOCPjBrlQAnz__NXj9h_w  \"Brick House Tavern + Tap\"          NaN   \n",
       "\n",
       "                            address            city state postal_code  \\\n",
       "0        \"4855 E Warner Rd, Ste B9\"       Ahwatukee    AZ       85044   \n",
       "1              \"3101 Washington Rd\"        McMurray    PA       15317   \n",
       "2          \"6025 N 27th Ave, Ste 1\"         Phoenix    AZ       85017   \n",
       "3  \"5000 Arizona Mills Cr, Ste 435\"           Tempe    AZ       85282   \n",
       "4                    \"581 Howe Ave\"  Cuyahoga Falls    OH       44221   \n",
       "\n",
       "    latitude   longitude  stars  review_count  is_open  \\\n",
       "0  33.330690 -111.978599    4.0            22        1   \n",
       "1  40.291685  -80.104900    3.0            11        1   \n",
       "2  33.524903 -112.115310    1.5            18        1   \n",
       "3  33.383147 -111.964725    3.0             9        0   \n",
       "4  41.119535  -81.475690    3.5           116        1   \n",
       "\n",
       "                                          categories  \n",
       "0  Dentists;General Dentistry;Health & Medical;Or...  \n",
       "1  Hair Stylists;Hair Salons;Men's Hair Salons;Bl...  \n",
       "2  Departments of Motor Vehicles;Public Services ...  \n",
       "3                            Sporting Goods;Shopping  \n",
       "4  American (New);Nightlife;Bars;Sandwiches;Ameri...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df = pd.read_csv(data_dir + \"yelp_business.csv\")\n",
    "business_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb13f148-ea51-4acf-af50-9e9dd565c69b",
   "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>business_id</th>\n",
       "      <th>WheelchairAccessible</th>\n",
       "      <th>BikeParking</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>RestaurantsAttire</th>\n",
       "      <th>Music_background_music</th>\n",
       "      <th>Music_live</th>\n",
       "      <th>Ambience_romantic</th>\n",
       "      <th>Ambience_intimate</th>\n",
       "      <th>Ambience_classy</th>\n",
       "      <th>Ambience_hipster</th>\n",
       "      <th>Ambience_trendy</th>\n",
       "      <th>Ambience_upscale</th>\n",
       "      <th>RestaurantsGoodForGroups</th>\n",
       "      <th>RestaurantsReservations</th>\n",
       "      <th>HappyHour</th>\n",
       "      <th>RestaurantsTableService</th>\n",
       "      <th>GoodForMeal_dessert</th>\n",
       "      <th>GoodForMeal_latenight</th>\n",
       "      <th>GoodForMeal_lunch</th>\n",
       "      <th>GoodForMeal_dinner</th>\n",
       "      <th>GoodForMeal_breakfast</th>\n",
       "      <th>GoodForMeal_brunch</th>\n",
       "      <th>DogsAllowed</th>\n",
       "      <th>DietaryRestrictions_dairy-free</th>\n",
       "      <th>DietaryRestrictions_gluten-free</th>\n",
       "      <th>DietaryRestrictions_vegan</th>\n",
       "      <th>DietaryRestrictions_kosher</th>\n",
       "      <th>DietaryRestrictions_halal</th>\n",
       "      <th>DietaryRestrictions_soy-free</th>\n",
       "      <th>DietaryRestrictions_vegetarian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FYWN1wneV18bWNgQjJ2GNg</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He-G7vWjzVUysIKrfNbPUQ</td>\n",
       "      <td>Na</td>\n",
       "      <td>True</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8DShNS-LuFqpEWIp0HxijA</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>True</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PfOCPjBrlQAnz__NXj9h_w</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>False</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>o9eMRCWt5PkpLDE0gOPtcQ</td>\n",
       "      <td>Na</td>\n",
       "      <td>False</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id WheelchairAccessible BikeParking Alcohol  \\\n",
       "0  FYWN1wneV18bWNgQjJ2GNg                   Na          Na      Na   \n",
       "1  He-G7vWjzVUysIKrfNbPUQ                   Na        True      Na   \n",
       "2  8DShNS-LuFqpEWIp0HxijA                   Na          Na    True   \n",
       "3  PfOCPjBrlQAnz__NXj9h_w                   Na          Na      Na   \n",
       "4  o9eMRCWt5PkpLDE0gOPtcQ                   Na       False      Na   \n",
       "\n",
       "  RestaurantsAttire Music_background_music Music_live Ambience_romantic  \\\n",
       "0                Na                     Na         Na                Na   \n",
       "1                Na                     Na         Na                Na   \n",
       "2                Na                     Na         Na                Na   \n",
       "3                Na                     Na         Na                Na   \n",
       "4                Na                     Na         Na                Na   \n",
       "\n",
       "  Ambience_intimate Ambience_classy Ambience_hipster Ambience_trendy  \\\n",
       "0                Na              Na               Na              Na   \n",
       "1                Na              Na               Na              Na   \n",
       "2                Na              Na               Na              Na   \n",
       "3                Na              Na               Na              Na   \n",
       "4                Na              Na               Na              Na   \n",
       "\n",
       "  Ambience_upscale RestaurantsGoodForGroups RestaurantsReservations HappyHour  \\\n",
       "0               Na                       Na                      Na        Na   \n",
       "1               Na                       Na                      Na        Na   \n",
       "2               Na                       Na                      Na        Na   \n",
       "3               Na                       Na                      Na        Na   \n",
       "4               Na                       Na                      Na        Na   \n",
       "\n",
       "  RestaurantsTableService GoodForMeal_dessert GoodForMeal_latenight  \\\n",
       "0                      Na                  Na                    Na   \n",
       "1                      Na                  Na                    Na   \n",
       "2                      Na                  Na                    Na   \n",
       "3                      Na                  Na                    Na   \n",
       "4                      Na                  Na                    Na   \n",
       "\n",
       "  GoodForMeal_lunch GoodForMeal_dinner GoodForMeal_breakfast  \\\n",
       "0                Na                 Na                    Na   \n",
       "1                Na                 Na                    Na   \n",
       "2                Na                 Na                    Na   \n",
       "3                Na                 Na                    Na   \n",
       "4                Na                 Na                    Na   \n",
       "\n",
       "  GoodForMeal_brunch DogsAllowed DietaryRestrictions_dairy-free  \\\n",
       "0                 Na          Na                             Na   \n",
       "1                 Na          Na                             Na   \n",
       "2                 Na          Na                             Na   \n",
       "3                 Na       False                             Na   \n",
       "4                 Na          Na                             Na   \n",
       "\n",
       "  DietaryRestrictions_gluten-free DietaryRestrictions_vegan  \\\n",
       "0                              Na                        Na   \n",
       "1                              Na                        Na   \n",
       "2                              Na                        Na   \n",
       "3                              Na                        Na   \n",
       "4                              Na                        Na   \n",
       "\n",
       "  DietaryRestrictions_kosher DietaryRestrictions_halal  \\\n",
       "0                         Na                        Na   \n",
       "1                         Na                        Na   \n",
       "2                         Na                        Na   \n",
       "3                         Na                        Na   \n",
       "4                         Na                        Na   \n",
       "\n",
       "  DietaryRestrictions_soy-free DietaryRestrictions_vegetarian  \n",
       "0                           Na                             Na  \n",
       "1                           Na                             Na  \n",
       "2                           Na                             Na  \n",
       "3                           Na                             Na  \n",
       "4                           Na                             Na  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_attrib_df = pd.read_csv(data_dir + \"yelp_business_attributes.csv\")[[\"business_id\", \"WheelchairAccessible\", \"BikeParking\", \"Alcohol\", \"RestaurantsAttire\", \"Music_background_music\", \"Music_live\", \"Ambience_romantic\", \"Ambience_intimate\", \"Ambience_classy\", \"Ambience_hipster\", \"Ambience_trendy\", \"Ambience_upscale\", \"RestaurantsGoodForGroups\", \"RestaurantsReservations\", \"HappyHour\", \"RestaurantsTableService\", \"GoodForMeal_dessert\", \"GoodForMeal_latenight\", \"GoodForMeal_lunch\", \"GoodForMeal_dinner\", \"GoodForMeal_breakfast\", \"GoodForMeal_brunch\", \"DogsAllowed\", \"DietaryRestrictions_dairy-free\", \"DietaryRestrictions_gluten-free\", \"DietaryRestrictions_vegan\", \"DietaryRestrictions_kosher\", \"DietaryRestrictions_halal\", \"DietaryRestrictions_soy-free\", \"DietaryRestrictions_vegetarian\"]] \n",
    "business_attrib_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d544761-df63-43f4-8188-bfa938da7f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "business_df = business_df[business_df['categories'].str.contains(\"Restaurants\", case=False, na=False)][[\"business_id\", \"city\", \"state\", \"postal_code\", \"latitude\", \"longitude\", \"stars\", \"review_count\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "274bc8b5-133b-4e86-b978-31130a83ebd2",
   "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>business_id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PfOCPjBrlQAnz__NXj9h_w</td>\n",
       "      <td>Cuyahoga Falls</td>\n",
       "      <td>OH</td>\n",
       "      <td>44221</td>\n",
       "      <td>41.119535</td>\n",
       "      <td>-81.475690</td>\n",
       "      <td>3.5</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>o9eMRCWt5PkpLDE0gOPtcQ</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>BW</td>\n",
       "      <td>70567</td>\n",
       "      <td>48.727200</td>\n",
       "      <td>9.147950</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XOSRcvtaKc_Q5H1SAzN20A</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>40.241548</td>\n",
       "      <td>-80.212815</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fNMVV_ZX7CJSDWQGdOM8Nw</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28202</td>\n",
       "      <td>35.221647</td>\n",
       "      <td>-80.839345</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>l09JfMeQ6ynYs5MCJtrcmQ</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M4P 2H6</td>\n",
       "      <td>43.711399</td>\n",
       "      <td>-79.399339</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               business_id            city state postal_code   latitude  \\\n",
       "4   PfOCPjBrlQAnz__NXj9h_w  Cuyahoga Falls    OH       44221  41.119535   \n",
       "5   o9eMRCWt5PkpLDE0gOPtcQ       Stuttgart    BW       70567  48.727200   \n",
       "10  XOSRcvtaKc_Q5H1SAzN20A         Houston    PA       15342  40.241548   \n",
       "14  fNMVV_ZX7CJSDWQGdOM8Nw       Charlotte    NC       28202  35.221647   \n",
       "15  l09JfMeQ6ynYs5MCJtrcmQ         Toronto    ON     M4P 2H6  43.711399   \n",
       "\n",
       "    longitude  business_avg_stars  business_review_count  \n",
       "4  -81.475690                 3.5                    116  \n",
       "5    9.147950                 4.0                      5  \n",
       "10 -80.212815                 4.5                      3  \n",
       "14 -80.839345                 3.5                      7  \n",
       "15 -79.399339                 3.0                     12  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.rename({'stars': 'business_avg_stars', \"review_count\": \"business_review_count\"}, axis=1, inplace=True)\n",
    "business_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8197208b-14e5-4e1f-a693-ef58cb6f5e94",
   "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>business_id</th>\n",
       "      <th>monday</th>\n",
       "      <th>tuesday</th>\n",
       "      <th>wednesday</th>\n",
       "      <th>thursday</th>\n",
       "      <th>friday</th>\n",
       "      <th>saturday</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FYWN1wneV18bWNgQjJ2GNg</td>\n",
       "      <td>7:30-17:0</td>\n",
       "      <td>7:30-17:0</td>\n",
       "      <td>7:30-17:0</td>\n",
       "      <td>7:30-17:0</td>\n",
       "      <td>7:30-17:0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He-G7vWjzVUysIKrfNbPUQ</td>\n",
       "      <td>9:0-20:0</td>\n",
       "      <td>9:0-20:0</td>\n",
       "      <td>9:0-20:0</td>\n",
       "      <td>9:0-20:0</td>\n",
       "      <td>9:0-16:0</td>\n",
       "      <td>8:0-16:0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KQPW8lFf1y5BT2MxiSZ3QA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8DShNS-LuFqpEWIp0HxijA</td>\n",
       "      <td>10:0-21:0</td>\n",
       "      <td>10:0-21:0</td>\n",
       "      <td>10:0-21:0</td>\n",
       "      <td>10:0-21:0</td>\n",
       "      <td>10:0-21:0</td>\n",
       "      <td>10:0-21:0</td>\n",
       "      <td>11:0-19:0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PfOCPjBrlQAnz__NXj9h_w</td>\n",
       "      <td>11:0-1:0</td>\n",
       "      <td>11:0-1:0</td>\n",
       "      <td>11:0-1:0</td>\n",
       "      <td>11:0-1:0</td>\n",
       "      <td>11:0-1:0</td>\n",
       "      <td>11:0-2:0</td>\n",
       "      <td>11:0-0:0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id     monday    tuesday  wednesday   thursday  \\\n",
       "0  FYWN1wneV18bWNgQjJ2GNg  7:30-17:0  7:30-17:0  7:30-17:0  7:30-17:0   \n",
       "1  He-G7vWjzVUysIKrfNbPUQ   9:0-20:0   9:0-20:0   9:0-20:0   9:0-20:0   \n",
       "2  KQPW8lFf1y5BT2MxiSZ3QA        NaN        NaN        NaN        NaN   \n",
       "3  8DShNS-LuFqpEWIp0HxijA  10:0-21:0  10:0-21:0  10:0-21:0  10:0-21:0   \n",
       "4  PfOCPjBrlQAnz__NXj9h_w   11:0-1:0   11:0-1:0   11:0-1:0   11:0-1:0   \n",
       "\n",
       "      friday   saturday     sunday  \n",
       "0  7:30-17:0        NaN        NaN  \n",
       "1   9:0-16:0   8:0-16:0        NaN  \n",
       "2        NaN        NaN        NaN  \n",
       "3  10:0-21:0  10:0-21:0  11:0-19:0  \n",
       "4   11:0-1:0   11:0-2:0   11:0-0:0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biz_hours_df = pd.read_csv(data_dir + \"yelp_business_hours.csv\")\n",
    "biz_hours_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04ae373b-69be-4d53-bbb9-7f8bd222c8a6",
   "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>business_id</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>checkins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3Mc-LxcqeguOXOVT_2ZtCg</td>\n",
       "      <td>Tue</td>\n",
       "      <td>0:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVFx6_epO22bZTZnKwlX7g</td>\n",
       "      <td>Wed</td>\n",
       "      <td>0:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vW9aLivd4-IorAfStzsHww</td>\n",
       "      <td>Tue</td>\n",
       "      <td>14:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tEzxhauTQddACyqdJ0OPEQ</td>\n",
       "      <td>Fri</td>\n",
       "      <td>19:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CEyZU32P-vtMhgqRCaXzMA</td>\n",
       "      <td>Tue</td>\n",
       "      <td>17:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id weekday   hour  checkins\n",
       "0  3Mc-LxcqeguOXOVT_2ZtCg     Tue   0:00        12\n",
       "1  SVFx6_epO22bZTZnKwlX7g     Wed   0:00         4\n",
       "2  vW9aLivd4-IorAfStzsHww     Tue  14:00         1\n",
       "3  tEzxhauTQddACyqdJ0OPEQ     Fri  19:00         1\n",
       "4  CEyZU32P-vtMhgqRCaXzMA     Tue  17:00         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkin_df = pd.read_csv(data_dir + \"yelp_checkin.csv\")\n",
    "checkin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d155fc04-55eb-4b22-b576-3d9951fee9c9",
   "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>review_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vkVSCC7xljjrAI4UGfnKEQ</td>\n",
       "      <td>bv2nCi5Qv5vroFiqKGopiw</td>\n",
       "      <td>AEx2SYEUJmTxVVB18LlCwA</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>Super simple place but amazing nonetheless. It...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n6QzIUObkYshz4dz2QRJTw</td>\n",
       "      <td>bv2nCi5Qv5vroFiqKGopiw</td>\n",
       "      <td>VR6GpWIda3SfvPC-lg9H3w</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>Small unassuming place that changes their menu...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MV3CcKScW05u5LVfF6ok0g</td>\n",
       "      <td>bv2nCi5Qv5vroFiqKGopiw</td>\n",
       "      <td>CKC0-MOWMqoeWf6s-szl8g</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>Lester's is located in a beautiful neighborhoo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IXvOzsEMYtiJI0CARmj77Q</td>\n",
       "      <td>bv2nCi5Qv5vroFiqKGopiw</td>\n",
       "      <td>ACFtxLv8pGrrxMm6EgjreA</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>Love coming here. Yes the place always needs t...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L_9BTb55X0GDtThi6GlZ6w</td>\n",
       "      <td>bv2nCi5Qv5vroFiqKGopiw</td>\n",
       "      <td>s2I_Ni76bjJNK9yG60iD-Q</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>Had their chocolate almond croissant and it wa...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                review_id                 user_id             business_id  \\\n",
       "0  vkVSCC7xljjrAI4UGfnKEQ  bv2nCi5Qv5vroFiqKGopiw  AEx2SYEUJmTxVVB18LlCwA   \n",
       "1  n6QzIUObkYshz4dz2QRJTw  bv2nCi5Qv5vroFiqKGopiw  VR6GpWIda3SfvPC-lg9H3w   \n",
       "2  MV3CcKScW05u5LVfF6ok0g  bv2nCi5Qv5vroFiqKGopiw  CKC0-MOWMqoeWf6s-szl8g   \n",
       "3  IXvOzsEMYtiJI0CARmj77Q  bv2nCi5Qv5vroFiqKGopiw  ACFtxLv8pGrrxMm6EgjreA   \n",
       "4  L_9BTb55X0GDtThi6GlZ6w  bv2nCi5Qv5vroFiqKGopiw  s2I_Ni76bjJNK9yG60iD-Q   \n",
       "\n",
       "   stars        date                                               text  \\\n",
       "0      5  2016-05-28  Super simple place but amazing nonetheless. It...   \n",
       "1      5  2016-05-28  Small unassuming place that changes their menu...   \n",
       "2      5  2016-05-28  Lester's is located in a beautiful neighborhoo...   \n",
       "3      4  2016-05-28  Love coming here. Yes the place always needs t...   \n",
       "4      4  2016-05-28  Had their chocolate almond croissant and it wa...   \n",
       "\n",
       "   useful  funny  cool  \n",
       "0       0      0     0  \n",
       "1       0      0     0  \n",
       "2       0      0     0  \n",
       "3       0      0     0  \n",
       "4       0      0     0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df = pd.read_csv(data_dir + \"yelp_review.csv\")\n",
    "review_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3de62e2-5b8f-4088-86b5-9e1ae02c1c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    3,    5,    9,    2,    4,    8,   11,    6,   15,\n",
       "          7,   10,   22,   14,   19,   25,   12,   13,   16,   26,   20,\n",
       "         18,   27,   17,   23,   24,   21,   47,   33,   35,   28,   30,\n",
       "         32,   37,   49,   31,   95,   34,   38,   40,   54,   43,   44,\n",
       "         68,   41,   59,   53,   39,   45,   56,   52,   42,   71,   74,\n",
       "         36,   29,   58,   86,   46,  187,   62,   51,   90,   99,   97,\n",
       "         91,   92,  110,   89,   83, 1456,  500,  120,  250,   76,  638,\n",
       "         50,   93,   67,  276,   57,   48,  127,   80,   72,   63,  967,\n",
       "        218,  179,  200,  222,  203,  219,  226,  217,  216,  215,  221,\n",
       "        212,  246,  197,  201,  228,  164,  193,  181,  194,  209,  243,\n",
       "        192,  233,  207,  117,  777,  185,  105,   87,  271,  184,   65,\n",
       "        103,   79,   66,  134,  132,  141,  100,  283,   60,   78,   55,\n",
       "         84,  111,   70,  416,  168,   73,   61,   77,  312,  463,   69,\n",
       "        180,  189,  412,  118,  135,  109,  204,  114,  116,  360,   82,\n",
       "        208,   96,   64,  241,   94,   81,   75,   85,  167,  106,  121,\n",
       "       1698,  286,  273,  124,   88,  305,  502,  445,  147,  107,  151,\n",
       "        415,  108,  125, 1212,  165,  600,  688,  128,  123,  144,  129,\n",
       "        113,  145,  162,   98,  397,  237,  131,  333,  156,  139,  142,\n",
       "        136, 1608,  245,  257,  782,  213,  647,  188,  112,  627,  336,\n",
       "        102,  198,  202,  140,  122,  130,  199,  190,  146,  155,  487,\n",
       "        173,  133,  248,  679,  115,  238,  176,  262,  410,  357,  211,\n",
       "        924,  255, 2133,  511,  313,  533,  150,  316, 1525, 1404,  254,\n",
       "        161,  954, 1160,  525,  119,  178,  256,  279,  643, 1341,  292,\n",
       "        170,  159,  152,  143,  259,  775,  797,  892,  266,  169,  174,\n",
       "        869,  660,  249,  503,  282,  306,  615,  319,  175,  163,   -1,\n",
       "        214,  281,  353,  358,  462,  251,  388,  287,  786,  260,  154,\n",
       "        101,  522,  693,  104, 1112,  191,  137, 3364])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df[\"useful\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7d419a9-bf1d-4d38-99e3-b80db9fa60f8",
   "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>text</th>\n",
       "      <th>date</th>\n",
       "      <th>likes</th>\n",
       "      <th>business_id</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Great breakfast large portions and friendly wa...</td>\n",
       "      <td>2015-08-12</td>\n",
       "      <td>0</td>\n",
       "      <td>jH19V2I9fIslnNhDzPmdkA</td>\n",
       "      <td>ZcLKXikTHYOnYt5VYRO5sg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nice place. Great staff.  A fixture in the tow...</td>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>0</td>\n",
       "      <td>dAa0hB2yrnHzVmsCkN4YvQ</td>\n",
       "      <td>oaYhjqBbh18ZhU0bpyzSuw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Happy hour 5-7 Monday - Friday</td>\n",
       "      <td>2016-10-12</td>\n",
       "      <td>0</td>\n",
       "      <td>dAa0hB2yrnHzVmsCkN4YvQ</td>\n",
       "      <td>ulQ8Nyj7jCUR8M83SUMoRQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parking is a premium, keep circling, you will ...</td>\n",
       "      <td>2017-01-28</td>\n",
       "      <td>0</td>\n",
       "      <td>ESzO3Av0b1_TzKOiqzbQYQ</td>\n",
       "      <td>ulQ8Nyj7jCUR8M83SUMoRQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Homemade pasta is the best in the area</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0</td>\n",
       "      <td>k7WRPbDd7rztjHcGGkEjlw</td>\n",
       "      <td>ulQ8Nyj7jCUR8M83SUMoRQ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text        date  likes  \\\n",
       "0  Great breakfast large portions and friendly wa...  2015-08-12      0   \n",
       "1  Nice place. Great staff.  A fixture in the tow...  2014-06-20      0   \n",
       "2                     Happy hour 5-7 Monday - Friday  2016-10-12      0   \n",
       "3  Parking is a premium, keep circling, you will ...  2017-01-28      0   \n",
       "4             Homemade pasta is the best in the area  2017-02-25      0   \n",
       "\n",
       "              business_id                 user_id  \n",
       "0  jH19V2I9fIslnNhDzPmdkA  ZcLKXikTHYOnYt5VYRO5sg  \n",
       "1  dAa0hB2yrnHzVmsCkN4YvQ  oaYhjqBbh18ZhU0bpyzSuw  \n",
       "2  dAa0hB2yrnHzVmsCkN4YvQ  ulQ8Nyj7jCUR8M83SUMoRQ  \n",
       "3  ESzO3Av0b1_TzKOiqzbQYQ  ulQ8Nyj7jCUR8M83SUMoRQ  \n",
       "4  k7WRPbDd7rztjHcGGkEjlw  ulQ8Nyj7jCUR8M83SUMoRQ  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tip_df = pd.read_csv(data_dir + \"yelp_tip.csv\")\n",
    "tip_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f09f5008-784e-4cdc-909d-2eff37045075",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JJ-aSuM4pCFPdkfoZ34q0Q</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-09-24</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>uUzsFQn_6cXDh6rPNGbIFA</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mBneaEEH5EMyxaVyqS-72A</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W5mJGs-dcDWRGEhAzUYtoA</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id  user_review_count yelping_since  user_average_stars\n",
       "0  JJ-aSuM4pCFPdkfoZ34q0Q                 10    2013-09-24                3.70\n",
       "1  uUzsFQn_6cXDh6rPNGbIFA                  1    2017-03-02                2.00\n",
       "2  mBneaEEH5EMyxaVyqS-72A                  6    2015-03-13                4.67\n",
       "3  W5mJGs-dcDWRGEhAzUYtoA                  3    2016-09-08                4.67\n",
       "4  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16                3.45"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_df = pd.read_csv(data_dir + \"yelp_user.csv\")[[\"user_id\", \"review_count\", \"yelping_since\", \"average_stars\"]]\n",
    "user_df.rename({'average_stars': 'user_average_stars', \"review_count\": \"user_review_count\"}, inplace=True, axis=1)\n",
    "user_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44c0cfb7-ccd8-4041-97e5-1ecd80d98783",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = user_df.merge(review_df, how=\"inner\", on=\"user_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d457d569-b786-4c37-908e-682f7da5a2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df.merge(business_df, how='inner', on='business_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4e06e52-4172-42c7-8f14-7f2607957d4c",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uUzsFQn_6cXDh6rPNGbIFA</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>2.00</td>\n",
       "      <td>wUiZNdOmQ-QmkcyMInVcHw</td>\n",
       "      <td>eLFfWcdb7VkqNyTONksHiQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>I went last night to celebrate my boyfriend's ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>NV</td>\n",
       "      <td>89014</td>\n",
       "      <td>36.066776</td>\n",
       "      <td>-115.042471</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mBneaEEH5EMyxaVyqS-72A</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "      <td>NcsYfWE4QxZgMQMMIoHqaQ</td>\n",
       "      <td>yT-ASP05C0yQ0hJBaYCYcg</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>My wife and I regularly hit up Ah-So for Happy...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Goodyear</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85395</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "      <td>3.0</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W5mJGs-dcDWRGEhAzUYtoA</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>4.67</td>\n",
       "      <td>gGghNe3sGyxZdwCVdZ_njQ</td>\n",
       "      <td>gGt00WW8sNY2Sop7jYa7Tw</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-07-28</td>\n",
       "      <td>Good service, good food.  The queso was great ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28203</td>\n",
       "      <td>35.202035</td>\n",
       "      <td>-80.844459</td>\n",
       "      <td>4.0</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>c3WHyv3zX3iZLXkznxIpxQ</td>\n",
       "      <td>SGZAsdQAp0SjtMDjXWBPYw</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>We came in for dinner based on the yelp (and s...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>OH</td>\n",
       "      <td>44113</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>UP2K5EJYYChSw48Pjm3FBw</td>\n",
       "      <td>7sN4uA7jPakOKerNEQ2Mdg</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>If I could give you a 0, I would. I called and...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Valley City</td>\n",
       "      <td>OH</td>\n",
       "      <td>44280</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id  user_review_count yelping_since  \\\n",
       "0  uUzsFQn_6cXDh6rPNGbIFA                  1    2017-03-02   \n",
       "1  mBneaEEH5EMyxaVyqS-72A                  6    2015-03-13   \n",
       "2  W5mJGs-dcDWRGEhAzUYtoA                  3    2016-09-08   \n",
       "3  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16   \n",
       "4  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16   \n",
       "\n",
       "   user_average_stars               review_id             business_id  stars  \\\n",
       "0                2.00  wUiZNdOmQ-QmkcyMInVcHw  eLFfWcdb7VkqNyTONksHiQ      2   \n",
       "1                4.67  NcsYfWE4QxZgMQMMIoHqaQ  yT-ASP05C0yQ0hJBaYCYcg      4   \n",
       "2                4.67  gGghNe3sGyxZdwCVdZ_njQ  gGt00WW8sNY2Sop7jYa7Tw      4   \n",
       "3                3.45  c3WHyv3zX3iZLXkznxIpxQ  SGZAsdQAp0SjtMDjXWBPYw      3   \n",
       "4                3.45  UP2K5EJYYChSw48Pjm3FBw  7sN4uA7jPakOKerNEQ2Mdg      1   \n",
       "\n",
       "         date                                               text  useful  \\\n",
       "0  2017-03-02  I went last night to celebrate my boyfriend's ...       0   \n",
       "1  2015-03-13  My wife and I regularly hit up Ah-So for Happy...       0   \n",
       "2  2017-07-28  Good service, good food.  The queso was great ...       0   \n",
       "3  2013-06-08  We came in for dinner based on the yelp (and s...       0   \n",
       "4  2012-07-22  If I could give you a 0, I would. I called and...       3   \n",
       "\n",
       "   funny  cool         city state postal_code   latitude   longitude  \\\n",
       "0      0     0    Henderson    NV       89014  36.066776 -115.042471   \n",
       "1      0     0     Goodyear    AZ       85395  33.462856 -112.391772   \n",
       "2      0     0    Charlotte    NC       28203  35.202035  -80.844459   \n",
       "3      1     0    Cleveland    OH       44113  41.488915  -81.709046   \n",
       "4      2     0  Valley City    OH       44280  41.237658  -81.930948   \n",
       "\n",
       "   business_avg_stars  business_review_count  \n",
       "0                 4.0                   1652  \n",
       "1                 3.0                    140  \n",
       "2                 4.0                    325  \n",
       "3                 4.0                    138  \n",
       "4                 3.0                     16  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6aed9d8-4080-4831-b129-00ef09034f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "thin_df = merged_df[['user_id', 'user_review_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c19f7d3-3deb-41a1-9a0e-6ead5597e634",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11724/1087313096.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  thin_df['review_bin'] = pd.cut(thin_df['user_review_count'], bins=bins, labels=labels, right=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "review_bin\n",
      "0-4       453124\n",
      "5-9       857731\n",
      "10-14    1143238\n",
      "15-19    1359277\n",
      "20-24    1521928\n",
      "25-29    1650450\n",
      "30-34    1757266\n",
      "35-39    1846267\n",
      "40-44    1923152\n",
      "45-49    1991007\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define bins for review_count\n",
    "bins = np.arange(0, 55, 5)  # Bins from 0 to 50 in steps of 5\n",
    "labels = [f\"{bins[i]}-{bins[i+1]-1}\" for i in range(len(bins)-1)]\n",
    "\n",
    "# Bin the data\n",
    "thin_df['review_bin'] = pd.cut(thin_df['user_review_count'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Count the number of users per bin\n",
    "bin_counts = thin_df['review_bin'].value_counts().sort_index()\n",
    "bin_counts = bin_counts.cumsum()\n",
    "\n",
    "print(bin_counts)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(bin_counts.index, bin_counts.values)\n",
    "plt.xlabel(\"Review Count Bins\")\n",
    "plt.ylabel(\"Number of Users\")\n",
    "plt.title(\"Distribution of Users by Review Count Cumulatively Binned\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a105998b-e44b-4843-ace8-085ab4203d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the drop in user count between consecutive bins\n",
    "bin_drops = bin_counts.diff().dropna()\n",
    "\n",
    "bin_diff_labels = [\"5-core to 10-core\", \"10-core to 15-core\", \"15-core to 20-core\", \"20-core to 25-core\", \"25-core to 30-core\", \"30-core to 35-core\", \"35-core to 40-core\", \"40-core to 45-core\", \"45-core to 50-core\"]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(bin_diff_labels, bin_drops.values, color='red')\n",
    "plt.xlabel(\"Review Count Bins\")\n",
    "plt.ylabel(\"Drop in Number of Users\")\n",
    "plt.title(\"Drop in Users Between Consecutive Review Count Bins\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a378f668-fada-4d92-903f-a61d78aaa86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter Users with less than 10 reviews:\n",
    "merged_df = merged_df[merged_df[\"user_review_count\"]>=5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c1c30c0-f773-4b7c-bd46-347d77d26a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "thin_df = merged_df[['business_id', 'business_review_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e3e9cf3-ff04-44e5-b59f-2bdf2d64807e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11724/3789166886.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  thin_df['review_bin'] = pd.cut(thin_df['business_review_count'], bins=bins, labels=labels, right=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "review_bin\n",
      "0-4       21993\n",
      "5-9       77649\n",
      "10-14    134453\n",
      "15-19    191018\n",
      "20-24    245853\n",
      "25-29    300543\n",
      "30-34    351658\n",
      "35-39    399590\n",
      "40-44    447180\n",
      "45-49    495720\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define bins for review_count\n",
    "bins = np.arange(0, 55, 5)  # Bins from 0 to 50 in steps of 5\n",
    "labels = [f\"{bins[i]}-{bins[i+1]-1}\" for i in range(len(bins)-1)]\n",
    "\n",
    "# Bin the data\n",
    "thin_df['review_bin'] = pd.cut(thin_df['business_review_count'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Count the number of users per bin\n",
    "bin_counts = thin_df['review_bin'].value_counts().sort_index()\n",
    "bin_counts = bin_counts.cumsum()\n",
    "\n",
    "print(bin_counts)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(bin_counts.index, bin_counts.values)\n",
    "plt.xlabel(\"Review Count Bins\")\n",
    "plt.ylabel(\"Number of Businesses\")\n",
    "plt.title(\"Distribution of Businesses by Review Count\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d6fb75f-7fc1-4169-847b-4f7705c514ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the drop in user count between consecutive bins\n",
    "bin_drops = bin_counts.diff().dropna()\n",
    "\n",
    "bin_diff_labels = [\"5-core to 10-core\", \"10-core to 15-core\", \"15-core to 20-core\", \"20-core to 25-core\", \"25-core to 30-core\", \"30-core to 35-core\", \"35-core to 40-core\", \"40-core to 45-core\", \"45-core to 50-core\"]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(bin_diff_labels, bin_drops.values, color='red')\n",
    "plt.xlabel(\"Review Count Bins\")\n",
    "plt.ylabel(\"Drop in Number of Businesses\")\n",
    "plt.title(\"Drop in Businesses Between Consecutive Review Count Bins\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "22fee8ba-c602-4a85-ad55-6bae0d60301c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter Business with less than 10\n",
    "merged_df = merged_df[merged_df[\"business_review_count\"]>=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d48590ee-40be-40b6-a5ac-6292903fb502",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df.merge(business_attrib_df, how='inner', on='business_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc20c665-dc2f-4e3f-9967-5f444fb04f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'user_review_count', 'yelping_since', 'user_average_stars',\n",
       "       'review_id', 'business_id', 'stars', 'date', 'text', 'useful', 'funny',\n",
       "       'cool', 'city', 'state', 'postal_code', 'latitude', 'longitude',\n",
       "       'business_avg_stars', 'business_review_count', 'WheelchairAccessible',\n",
       "       'BikeParking', 'Alcohol', 'RestaurantsAttire', 'Music_background_music',\n",
       "       'Music_live', 'Ambience_romantic', 'Ambience_intimate',\n",
       "       'Ambience_classy', 'Ambience_hipster', 'Ambience_trendy',\n",
       "       'Ambience_upscale', 'RestaurantsGoodForGroups',\n",
       "       'RestaurantsReservations', 'HappyHour', 'RestaurantsTableService',\n",
       "       'GoodForMeal_dessert', 'GoodForMeal_latenight', 'GoodForMeal_lunch',\n",
       "       'GoodForMeal_dinner', 'GoodForMeal_breakfast', 'GoodForMeal_brunch',\n",
       "       'DogsAllowed', 'DietaryRestrictions_dairy-free',\n",
       "       'DietaryRestrictions_gluten-free', 'DietaryRestrictions_vegan',\n",
       "       'DietaryRestrictions_kosher', 'DietaryRestrictions_halal',\n",
       "       'DietaryRestrictions_soy-free', 'DietaryRestrictions_vegetarian'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94d31cfd-0246-42e4-8e2c-c29b03650c89",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "      <th>WheelchairAccessible</th>\n",
       "      <th>BikeParking</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>RestaurantsAttire</th>\n",
       "      <th>Music_background_music</th>\n",
       "      <th>Music_live</th>\n",
       "      <th>Ambience_romantic</th>\n",
       "      <th>Ambience_intimate</th>\n",
       "      <th>Ambience_classy</th>\n",
       "      <th>Ambience_hipster</th>\n",
       "      <th>Ambience_trendy</th>\n",
       "      <th>Ambience_upscale</th>\n",
       "      <th>RestaurantsGoodForGroups</th>\n",
       "      <th>RestaurantsReservations</th>\n",
       "      <th>HappyHour</th>\n",
       "      <th>RestaurantsTableService</th>\n",
       "      <th>GoodForMeal_dessert</th>\n",
       "      <th>GoodForMeal_latenight</th>\n",
       "      <th>GoodForMeal_lunch</th>\n",
       "      <th>GoodForMeal_dinner</th>\n",
       "      <th>GoodForMeal_breakfast</th>\n",
       "      <th>GoodForMeal_brunch</th>\n",
       "      <th>DogsAllowed</th>\n",
       "      <th>DietaryRestrictions_dairy-free</th>\n",
       "      <th>DietaryRestrictions_gluten-free</th>\n",
       "      <th>DietaryRestrictions_vegan</th>\n",
       "      <th>DietaryRestrictions_kosher</th>\n",
       "      <th>DietaryRestrictions_halal</th>\n",
       "      <th>DietaryRestrictions_soy-free</th>\n",
       "      <th>DietaryRestrictions_vegetarian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mBneaEEH5EMyxaVyqS-72A</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "      <td>NcsYfWE4QxZgMQMMIoHqaQ</td>\n",
       "      <td>yT-ASP05C0yQ0hJBaYCYcg</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>My wife and I regularly hit up Ah-So for Happy...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Goodyear</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85395</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "      <td>3.0</td>\n",
       "      <td>140</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>c3WHyv3zX3iZLXkznxIpxQ</td>\n",
       "      <td>SGZAsdQAp0SjtMDjXWBPYw</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>We came in for dinner based on the yelp (and s...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>OH</td>\n",
       "      <td>44113</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "      <td>Na</td>\n",
       "      <td>True</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>UP2K5EJYYChSw48Pjm3FBw</td>\n",
       "      <td>7sN4uA7jPakOKerNEQ2Mdg</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>If I could give you a 0, I would. I called and...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Valley City</td>\n",
       "      <td>OH</td>\n",
       "      <td>44280</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>cYKLqMKGmo9VwrZcF2WmEA</td>\n",
       "      <td>9QiUKGLa4tJlQrV-LiPIbw</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>we made a reservation for saturday night at 83...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Strongsville</td>\n",
       "      <td>OH</td>\n",
       "      <td>44136</td>\n",
       "      <td>41.313731</td>\n",
       "      <td>-81.816986</td>\n",
       "      <td>3.5</td>\n",
       "      <td>125</td>\n",
       "      <td>True</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E8--zUZO1Rr1IBK4_83fg</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>BQSfk_qqH-dXq2ltJddXvg</td>\n",
       "      <td>IfdwBSEuDK3fKvVHVX9k0A</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>Had lunch here with my bf and his friend.  We ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Berea</td>\n",
       "      <td>OH</td>\n",
       "      <td>44017</td>\n",
       "      <td>41.373883</td>\n",
       "      <td>-81.891878</td>\n",
       "      <td>3.5</td>\n",
       "      <td>211</td>\n",
       "      <td>Na</td>\n",
       "      <td>False</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id  user_review_count yelping_since  \\\n",
       "0  mBneaEEH5EMyxaVyqS-72A                  6    2015-03-13   \n",
       "1  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16   \n",
       "2  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16   \n",
       "3  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16   \n",
       "4  4E8--zUZO1Rr1IBK4_83fg                 11    2012-07-16   \n",
       "\n",
       "   user_average_stars               review_id             business_id  stars  \\\n",
       "0                4.67  NcsYfWE4QxZgMQMMIoHqaQ  yT-ASP05C0yQ0hJBaYCYcg      4   \n",
       "1                3.45  c3WHyv3zX3iZLXkznxIpxQ  SGZAsdQAp0SjtMDjXWBPYw      3   \n",
       "2                3.45  UP2K5EJYYChSw48Pjm3FBw  7sN4uA7jPakOKerNEQ2Mdg      1   \n",
       "3                3.45  cYKLqMKGmo9VwrZcF2WmEA  9QiUKGLa4tJlQrV-LiPIbw      5   \n",
       "4                3.45  BQSfk_qqH-dXq2ltJddXvg  IfdwBSEuDK3fKvVHVX9k0A      3   \n",
       "\n",
       "         date                                               text  useful  \\\n",
       "0  2015-03-13  My wife and I regularly hit up Ah-So for Happy...       0   \n",
       "1  2013-06-08  We came in for dinner based on the yelp (and s...       0   \n",
       "2  2012-07-22  If I could give you a 0, I would. I called and...       3   \n",
       "3  2012-11-15  we made a reservation for saturday night at 83...       5   \n",
       "4  2012-07-22  Had lunch here with my bf and his friend.  We ...       2   \n",
       "\n",
       "   funny  cool          city state postal_code   latitude   longitude  \\\n",
       "0      0     0      Goodyear    AZ       85395  33.462856 -112.391772   \n",
       "1      1     0     Cleveland    OH       44113  41.488915  -81.709046   \n",
       "2      2     0   Valley City    OH       44280  41.237658  -81.930948   \n",
       "3      0     0  Strongsville    OH       44136  41.313731  -81.816986   \n",
       "4      0     0         Berea    OH       44017  41.373883  -81.891878   \n",
       "\n",
       "   business_avg_stars  business_review_count WheelchairAccessible BikeParking  \\\n",
       "0                 3.0                    140                   Na          Na   \n",
       "1                 4.0                    138                   Na        True   \n",
       "2                 3.0                     16                   Na          Na   \n",
       "3                 3.5                    125                 True          Na   \n",
       "4                 3.5                    211                   Na       False   \n",
       "\n",
       "  Alcohol RestaurantsAttire Music_background_music Music_live  \\\n",
       "0      Na                Na                     Na         Na   \n",
       "1      Na                Na                     Na         Na   \n",
       "2      Na                Na                     Na         Na   \n",
       "3      Na                Na                     Na         Na   \n",
       "4      Na                Na                     Na         Na   \n",
       "\n",
       "  Ambience_romantic Ambience_intimate Ambience_classy Ambience_hipster  \\\n",
       "0                Na                Na              Na               Na   \n",
       "1                Na                Na              Na               Na   \n",
       "2                Na                Na              Na               Na   \n",
       "3                Na                Na              Na               Na   \n",
       "4                Na                Na              Na               Na   \n",
       "\n",
       "  Ambience_trendy Ambience_upscale RestaurantsGoodForGroups  \\\n",
       "0              Na               Na                       Na   \n",
       "1              Na               Na                       Na   \n",
       "2              Na               Na                       Na   \n",
       "3              Na               Na                       Na   \n",
       "4              Na               Na                       Na   \n",
       "\n",
       "  RestaurantsReservations HappyHour RestaurantsTableService  \\\n",
       "0                      Na        Na                      Na   \n",
       "1                      Na        Na                      Na   \n",
       "2                      Na        Na                      Na   \n",
       "3                      Na        Na                      Na   \n",
       "4                      Na        Na                      Na   \n",
       "\n",
       "  GoodForMeal_dessert GoodForMeal_latenight GoodForMeal_lunch  \\\n",
       "0                  Na                 False             False   \n",
       "1                  Na                    Na                Na   \n",
       "2                  Na                    Na                Na   \n",
       "3                  Na                    Na                Na   \n",
       "4                  Na                    Na                Na   \n",
       "\n",
       "  GoodForMeal_dinner GoodForMeal_breakfast GoodForMeal_brunch DogsAllowed  \\\n",
       "0               True                  True              False          Na   \n",
       "1                 Na                    Na                 Na          Na   \n",
       "2                 Na                    Na                 Na          Na   \n",
       "3                 Na                    Na                 Na          Na   \n",
       "4                 Na                    Na                 Na          Na   \n",
       "\n",
       "  DietaryRestrictions_dairy-free DietaryRestrictions_gluten-free  \\\n",
       "0                             Na                              Na   \n",
       "1                             Na                              Na   \n",
       "2                             Na                              Na   \n",
       "3                             Na                              Na   \n",
       "4                             Na                              Na   \n",
       "\n",
       "  DietaryRestrictions_vegan DietaryRestrictions_kosher  \\\n",
       "0                        Na                         Na   \n",
       "1                        Na                         Na   \n",
       "2                        Na                         Na   \n",
       "3                        Na                         Na   \n",
       "4                        Na                         Na   \n",
       "\n",
       "  DietaryRestrictions_halal DietaryRestrictions_soy-free  \\\n",
       "0                        Na                           Na   \n",
       "1                        Na                           Na   \n",
       "2                        Na                           Na   \n",
       "3                        Na                           Na   \n",
       "4                        Na                           Na   \n",
       "\n",
       "  DietaryRestrictions_vegetarian  \n",
       "0                             Na  \n",
       "1                             Na  \n",
       "2                             Na  \n",
       "3                             Na  \n",
       "4                             Na  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23ff2786-d1b6-46a6-a8b8-bcfd4ab38f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "int_types = ['user_review_count', 'stars', 'useful', 'funny', 'business_review_count']\n",
    "long_types = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ab4aee8c-c8fc-47bd-9052-4a83dd0d5c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "float_types = ['user_average_stars', 'latitude', 'longitude', 'business_avg_stars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e6fc132-b074-47a7-a1bf-cf51f5a5e541",
   "metadata": {},
   "outputs": [],
   "source": [
    "string_types = ['text', 'date', 'yelping_since', 'postal_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fd43e0c9-17ae-4a8b-b241-6b8b3db5fd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_types = set(merged_df.columns).difference(set(int_types)).difference(set(float_types)).difference(set(string_types)).difference(set(long_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2fbd0f1-8c65-4499-81a6-74b53795ee17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alcohol',\n",
       " 'Ambience_classy',\n",
       " 'Ambience_hipster',\n",
       " 'Ambience_intimate',\n",
       " 'Ambience_romantic',\n",
       " 'Ambience_trendy',\n",
       " 'Ambience_upscale',\n",
       " 'BikeParking',\n",
       " 'DietaryRestrictions_dairy-free',\n",
       " 'DietaryRestrictions_gluten-free',\n",
       " 'DietaryRestrictions_halal',\n",
       " 'DietaryRestrictions_kosher',\n",
       " 'DietaryRestrictions_soy-free',\n",
       " 'DietaryRestrictions_vegan',\n",
       " 'DietaryRestrictions_vegetarian',\n",
       " 'DogsAllowed',\n",
       " 'GoodForMeal_breakfast',\n",
       " 'GoodForMeal_brunch',\n",
       " 'GoodForMeal_dessert',\n",
       " 'GoodForMeal_dinner',\n",
       " 'GoodForMeal_latenight',\n",
       " 'GoodForMeal_lunch',\n",
       " 'HappyHour',\n",
       " 'Music_background_music',\n",
       " 'Music_live',\n",
       " 'RestaurantsAttire',\n",
       " 'RestaurantsGoodForGroups',\n",
       " 'RestaurantsReservations',\n",
       " 'RestaurantsTableService',\n",
       " 'WheelchairAccessible',\n",
       " 'business_id',\n",
       " 'city',\n",
       " 'cool',\n",
       " 'review_id',\n",
       " 'state',\n",
       " 'user_id'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5bfe40cb-3c05-4d72-9fe7-9c5460eb2680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ambience_romantic': 'category',\n",
       " 'RestaurantsAttire': 'category',\n",
       " 'state': 'category',\n",
       " 'DietaryRestrictions_kosher': 'category',\n",
       " 'HappyHour': 'category',\n",
       " 'Ambience_trendy': 'category',\n",
       " 'DietaryRestrictions_dairy-free': 'category',\n",
       " 'RestaurantsGoodForGroups': 'category',\n",
       " 'GoodForMeal_dinner': 'category',\n",
       " 'Music_live': 'category',\n",
       " 'cool': 'category',\n",
       " 'DietaryRestrictions_gluten-free': 'category',\n",
       " 'GoodForMeal_dessert': 'category',\n",
       " 'Ambience_intimate': 'category',\n",
       " 'GoodForMeal_latenight': 'category',\n",
       " 'Ambience_upscale': 'category',\n",
       " 'review_id': 'category',\n",
       " 'GoodForMeal_brunch': 'category',\n",
       " 'DogsAllowed': 'category',\n",
       " 'Alcohol': 'category',\n",
       " 'WheelchairAccessible': 'category',\n",
       " 'DietaryRestrictions_vegetarian': 'category',\n",
       " 'DietaryRestrictions_soy-free': 'category',\n",
       " 'DietaryRestrictions_halal': 'category',\n",
       " 'DietaryRestrictions_vegan': 'category',\n",
       " 'city': 'category',\n",
       " 'RestaurantsTableService': 'category',\n",
       " 'GoodForMeal_lunch': 'category',\n",
       " 'Ambience_classy': 'category',\n",
       " 'user_id': 'category',\n",
       " 'business_id': 'category',\n",
       " 'Ambience_hipster': 'category',\n",
       " 'Music_background_music': 'category',\n",
       " 'BikeParking': 'category',\n",
       " 'GoodForMeal_breakfast': 'category',\n",
       " 'RestaurantsReservations': 'category',\n",
       " 'text': 'string',\n",
       " 'date': 'string',\n",
       " 'yelping_since': 'string',\n",
       " 'postal_code': 'string',\n",
       " 'user_average_stars': 'float',\n",
       " 'latitude': 'float',\n",
       " 'longitude': 'float',\n",
       " 'business_avg_stars': 'float',\n",
       " 'user_review_count': 'int',\n",
       " 'stars': 'int',\n",
       " 'useful': 'int',\n",
       " 'funny': 'int',\n",
       " 'business_review_count': 'int'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import ChainMap\n",
    "int_types_map = {k: 'int' for k in int_types}\n",
    "float_types_map = {k: 'float' for k in float_types}\n",
    "string_types_map = {k: 'string' for k in string_types}\n",
    "categorical_types_map = {k: 'category' for k in categorical_types}\n",
    "long_types_map = {k: 'uint64' for k in long_types}\n",
    "lst_mappings = [int_types_map, float_types_map, string_types_map, categorical_types_map, long_types_map]\n",
    "dtypes_map = dict(ChainMap(*lst_mappings))\n",
    "dtypes_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "73c3276c-f7e2-49dc-b8ab-6284ecb675a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def binarize_bool(cell):\n",
    "    if cell is True:\n",
    "        return 1\n",
    "    elif cell is False:\n",
    "        return 0\n",
    "    else:\n",
    "        return cell\n",
    "\n",
    "# Handle NAs for int types\n",
    "for col in int_types:\n",
    "    merged_df[col] = merged_df[col].fillna(0) # if NA, we set it to 0\n",
    "\n",
    "\n",
    "# Handle NAs for float types:\n",
    "merged_df['user_average_stars'] = merged_df['user_average_stars'].fillna(0.0)\n",
    "merged_df['business_avg_stars'] = merged_df['business_avg_stars'].fillna(0.0)\n",
    "merged_df['latitude'] = merged_df['latitude'].dropna()\n",
    "merged_df['longitude'] = merged_df['longitude'].dropna()\n",
    "\n",
    "# Handle NAs for long\n",
    "for col in long_types:\n",
    "    merged_df[col] = merged_df[col].fillna(0) # if NA, we set it to 0\n",
    "\n",
    "# Handle NAs for str\n",
    "merged_df['text'] = merged_df['text'].fillna('')\n",
    "merged_df['date'] = merged_df['date'].dropna()\n",
    "merged_df['yelping_since'] = merged_df['yelping_since'].dropna()\n",
    "merged_df['postal_code'] = merged_df['postal_code'].fillna('')\n",
    "\n",
    "cols_to_drop = []\n",
    "\n",
    "# Handle NAs for categorical types:\n",
    "for col in categorical_types:\n",
    "    if \"_id\" in col:\n",
    "        # drop all rows with missing ids\n",
    "        merged_df[col] = merged_df[col].dropna()\n",
    "    else:\n",
    "        # check if Na is the only value for the col, drop it\n",
    "        if list(merged_df[col].unique()) == ['Na']:\n",
    "            cols_to_drop.append(col)\n",
    "        else:\n",
    "            # otherwise fillna\n",
    "            merged_df[col] = merged_df[col].fillna(-1)\n",
    "    \n",
    "            # binarize boolean values (1 for True, 0 for False):\n",
    "            merged_df[col] = merged_df[col].apply(binarize_bool)\n",
    "\n",
    "# Drop cols\n",
    "merged_df.drop(cols_to_drop, inplace=True, axis=1)\n",
    "for col in cols_to_drop:\n",
    "    del dtypes_map[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a935ca09-e10d-44db-bf9d-456168c05b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 36/36 [00:37<00:00,  1.04s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "df_metadata = {\"dtypes_map\": dtypes_map}\n",
    "col_cat_enc_map = {}\n",
    "for col in tqdm(categorical_types_map.keys()):\n",
    "    if col in merged_df.columns:\n",
    "        enc = LabelEncoder()\n",
    "        enc.fit(merged_df[col])\n",
    "        merged_df[col] = enc.transform(merged_df[col])\n",
    "        col_cat_enc_map[col] = enc\n",
    "df_metadata['col_cat_enc_map'] = col_cat_enc_map\n",
    "\n",
    "with open(data_dir + \"df_metadata.pkl\", 'wb') as file:\n",
    "    pickle.dump(df_metadata, file, protocol=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0e8e5590-2142-44b5-a2ce-f83a2492cb53",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df.astype(dtypes_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "110af391-0c3c-43d3-bb87-486107bdccda",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "      <th>WheelchairAccessible</th>\n",
       "      <th>BikeParking</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>RestaurantsAttire</th>\n",
       "      <th>Ambience_intimate</th>\n",
       "      <th>Ambience_classy</th>\n",
       "      <th>Ambience_hipster</th>\n",
       "      <th>Ambience_trendy</th>\n",
       "      <th>Ambience_upscale</th>\n",
       "      <th>RestaurantsGoodForGroups</th>\n",
       "      <th>RestaurantsReservations</th>\n",
       "      <th>HappyHour</th>\n",
       "      <th>RestaurantsTableService</th>\n",
       "      <th>GoodForMeal_dessert</th>\n",
       "      <th>GoodForMeal_latenight</th>\n",
       "      <th>GoodForMeal_lunch</th>\n",
       "      <th>GoodForMeal_dinner</th>\n",
       "      <th>GoodForMeal_breakfast</th>\n",
       "      <th>GoodForMeal_brunch</th>\n",
       "      <th>DogsAllowed</th>\n",
       "      <th>DietaryRestrictions_gluten-free</th>\n",
       "      <th>DietaryRestrictions_vegan</th>\n",
       "      <th>DietaryRestrictions_kosher</th>\n",
       "      <th>DietaryRestrictions_halal</th>\n",
       "      <th>DietaryRestrictions_soy-free</th>\n",
       "      <th>DietaryRestrictions_vegetarian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>435362</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1036613</td>\n",
       "      <td>35338</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>My wife and I regularly hit up Ah-So for Happy...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>85395</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "      <td>3.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1684017</td>\n",
       "      <td>16561</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>We came in for dinner based on the yelp (and s...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>17</td>\n",
       "      <td>44113</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1321092</td>\n",
       "      <td>4949</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>If I could give you a 0, I would. I called and...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>488</td>\n",
       "      <td>17</td>\n",
       "      <td>44280</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1704099</td>\n",
       "      <td>5847</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>we made a reservation for saturday night at 83...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>457</td>\n",
       "      <td>17</td>\n",
       "      <td>44136</td>\n",
       "      <td>41.313731</td>\n",
       "      <td>-81.816986</td>\n",
       "      <td>3.5</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>522208</td>\n",
       "      <td>11183</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>Had lunch here with my bf and his friend.  We ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>44017</td>\n",
       "      <td>41.373883</td>\n",
       "      <td>-81.891878</td>\n",
       "      <td>3.5</td>\n",
       "      <td>211</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_id  user_review_count yelping_since  user_average_stars review_id  \\\n",
       "0  435362                  6    2015-03-13                4.67   1036613   \n",
       "1   45222                 11    2012-07-16                3.45   1684017   \n",
       "2   45222                 11    2012-07-16                3.45   1321092   \n",
       "3   45222                 11    2012-07-16                3.45   1704099   \n",
       "4   45222                 11    2012-07-16                3.45    522208   \n",
       "\n",
       "  business_id  stars        date  \\\n",
       "0       35338      4  2015-03-13   \n",
       "1       16561      3  2013-06-08   \n",
       "2        4949      1  2012-07-22   \n",
       "3        5847      5  2012-11-15   \n",
       "4       11183      3  2012-07-22   \n",
       "\n",
       "                                                text  useful  funny cool city  \\\n",
       "0  My wife and I regularly hit up Ah-So for Happy...       0      0    1  162   \n",
       "1  We came in for dinner based on the yelp (and s...       0      1    1   82   \n",
       "2  If I could give you a 0, I would. I called and...       3      2    1  488   \n",
       "3  we made a reservation for saturday night at 83...       5      0    1  457   \n",
       "4  Had lunch here with my bf and his friend.  We ...       2      0    1   32   \n",
       "\n",
       "  state postal_code   latitude   longitude  business_avg_stars  \\\n",
       "0     1       85395  33.462856 -112.391772                 3.0   \n",
       "1    17       44113  41.488915  -81.709046                 4.0   \n",
       "2    17       44280  41.237658  -81.930948                 3.0   \n",
       "3    17       44136  41.313731  -81.816986                 3.5   \n",
       "4    17       44017  41.373883  -81.891878                 3.5   \n",
       "\n",
       "   business_review_count WheelchairAccessible BikeParking Alcohol  \\\n",
       "0                    140                    1           1       1   \n",
       "1                    138                    1           2       1   \n",
       "2                     16                    1           1       1   \n",
       "3                    125                    2           1       1   \n",
       "4                    211                    1           0       1   \n",
       "\n",
       "  RestaurantsAttire Ambience_intimate Ambience_classy Ambience_hipster  \\\n",
       "0                 0                 1               1                1   \n",
       "1                 0                 1               1                1   \n",
       "2                 0                 1               1                1   \n",
       "3                 0                 1               1                1   \n",
       "4                 0                 1               1                1   \n",
       "\n",
       "  Ambience_trendy Ambience_upscale RestaurantsGoodForGroups  \\\n",
       "0               1                1                        1   \n",
       "1               1                1                        1   \n",
       "2               1                1                        1   \n",
       "3               1                1                        1   \n",
       "4               1                1                        1   \n",
       "\n",
       "  RestaurantsReservations HappyHour RestaurantsTableService  \\\n",
       "0                       0         1                       1   \n",
       "1                       0         1                       1   \n",
       "2                       0         1                       1   \n",
       "3                       0         1                       1   \n",
       "4                       0         1                       1   \n",
       "\n",
       "  GoodForMeal_dessert GoodForMeal_latenight GoodForMeal_lunch  \\\n",
       "0                   1                     0                 0   \n",
       "1                   1                     1                 1   \n",
       "2                   1                     1                 1   \n",
       "3                   1                     1                 1   \n",
       "4                   1                     1                 1   \n",
       "\n",
       "  GoodForMeal_dinner GoodForMeal_breakfast GoodForMeal_brunch DogsAllowed  \\\n",
       "0                  2                     2                  0           1   \n",
       "1                  1                     1                  1           1   \n",
       "2                  1                     1                  1           1   \n",
       "3                  1                     1                  1           1   \n",
       "4                  1                     1                  1           1   \n",
       "\n",
       "  DietaryRestrictions_gluten-free DietaryRestrictions_vegan  \\\n",
       "0                               1                         1   \n",
       "1                               1                         1   \n",
       "2                               1                         1   \n",
       "3                               1                         1   \n",
       "4                               1                         1   \n",
       "\n",
       "  DietaryRestrictions_kosher DietaryRestrictions_halal  \\\n",
       "0                          1                         1   \n",
       "1                          1                         1   \n",
       "2                          1                         1   \n",
       "3                          1                         1   \n",
       "4                          1                         1   \n",
       "\n",
       "  DietaryRestrictions_soy-free DietaryRestrictions_vegetarian  \n",
       "0                            1                              1  \n",
       "1                            1                              1  \n",
       "2                            1                              1  \n",
       "3                            1                              1  \n",
       "4                            1                              1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fd527ec5-f3b7-46a0-a855-7117081d21d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "_data_dir = \"/root/dataset_challenge\"\n",
    "merged_df.to_csv(f\"{_data_dir}/pre_processed_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ddafcf3-566a-401a-868a-7523ad9d830b",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/root/dataset_challenge/pre_processed_data.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[40], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      2\u001b[0m _data_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/root/dataset_challenge\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 3\u001b[0m merged_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43m_data_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/pre_processed_data.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m merged_df\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/root/dataset_challenge/pre_processed_data.csv'"
     ]
    }
   ],
   "source": [
    "#import pandas as pd\n",
    "#_data_dir = \"/root/dataset_challenge\"\n",
    "#merged_df = pd.read_csv(f\"{_data_dir}/pre_processed_data.csv\")\n",
    "#merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "73ae0af5-4d08-44d6-a796-4019dac4da2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "384f00df-d978-4643-9573-e8d8b41a3d50",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "      <th>WheelchairAccessible</th>\n",
       "      <th>BikeParking</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>RestaurantsAttire</th>\n",
       "      <th>Ambience_intimate</th>\n",
       "      <th>Ambience_classy</th>\n",
       "      <th>Ambience_hipster</th>\n",
       "      <th>Ambience_trendy</th>\n",
       "      <th>Ambience_upscale</th>\n",
       "      <th>RestaurantsGoodForGroups</th>\n",
       "      <th>RestaurantsReservations</th>\n",
       "      <th>HappyHour</th>\n",
       "      <th>RestaurantsTableService</th>\n",
       "      <th>GoodForMeal_dessert</th>\n",
       "      <th>GoodForMeal_latenight</th>\n",
       "      <th>GoodForMeal_lunch</th>\n",
       "      <th>GoodForMeal_dinner</th>\n",
       "      <th>GoodForMeal_breakfast</th>\n",
       "      <th>GoodForMeal_brunch</th>\n",
       "      <th>DogsAllowed</th>\n",
       "      <th>DietaryRestrictions_gluten-free</th>\n",
       "      <th>DietaryRestrictions_vegan</th>\n",
       "      <th>DietaryRestrictions_kosher</th>\n",
       "      <th>DietaryRestrictions_halal</th>\n",
       "      <th>DietaryRestrictions_soy-free</th>\n",
       "      <th>DietaryRestrictions_vegetarian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>435362</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1036613</td>\n",
       "      <td>35338</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>My wife and I regularly hit up Ah-So for Happy...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>85395</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "      <td>3.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1684017</td>\n",
       "      <td>16561</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>We came in for dinner based on the yelp (and s...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>17</td>\n",
       "      <td>44113</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1321092</td>\n",
       "      <td>4949</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>If I could give you a 0, I would. I called and...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>488</td>\n",
       "      <td>17</td>\n",
       "      <td>44280</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1704099</td>\n",
       "      <td>5847</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>we made a reservation for saturday night at 83...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>457</td>\n",
       "      <td>17</td>\n",
       "      <td>44136</td>\n",
       "      <td>41.313731</td>\n",
       "      <td>-81.816986</td>\n",
       "      <td>3.5</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>522208</td>\n",
       "      <td>11183</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>Had lunch here with my bf and his friend.  We ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>44017</td>\n",
       "      <td>41.373883</td>\n",
       "      <td>-81.891878</td>\n",
       "      <td>3.5</td>\n",
       "      <td>211</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689232</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>2675353</td>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>I love chipotle but not this location. Maybe I...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>323</td>\n",
       "      <td>14</td>\n",
       "      <td>89030</td>\n",
       "      <td>36.240263</td>\n",
       "      <td>-115.116185</td>\n",
       "      <td>3.0</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689233</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1994969</td>\n",
       "      <td>30791</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>This was my first time not being satisfied wit...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>14</td>\n",
       "      <td>89031</td>\n",
       "      <td>36.275981</td>\n",
       "      <td>-115.179177</td>\n",
       "      <td>1.5</td>\n",
       "      <td>139</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689234</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1754541</td>\n",
       "      <td>17300</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>I honestly wish I could give no stars. 1. The ...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "      <td>89104</td>\n",
       "      <td>36.145349</td>\n",
       "      <td>-115.156020</td>\n",
       "      <td>3.0</td>\n",
       "      <td>332</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689235</th>\n",
       "      <td>485661</td>\n",
       "      <td>33</td>\n",
       "      <td>2014-06-28</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2423384</td>\n",
       "      <td>2320</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>Went here because of the reviews and my nephew...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "      <td>89102</td>\n",
       "      <td>36.126901</td>\n",
       "      <td>-115.197803</td>\n",
       "      <td>4.0</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689236</th>\n",
       "      <td>467903</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-05-17</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2644336</td>\n",
       "      <td>25293</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>Visited last night, which was a Monday and the...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>288</td>\n",
       "      <td>21</td>\n",
       "      <td>H3B 3E9</td>\n",
       "      <td>45.504015</td>\n",
       "      <td>-73.568177</td>\n",
       "      <td>4.0</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2689237 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  user_review_count yelping_since  user_average_stars  \\\n",
       "0        435362                  6    2015-03-13                4.67   \n",
       "1         45222                 11    2012-07-16                3.45   \n",
       "2         45222                 11    2012-07-16                3.45   \n",
       "3         45222                 11    2012-07-16                3.45   \n",
       "4         45222                 11    2012-07-16                3.45   \n",
       "...         ...                ...           ...                 ...   \n",
       "2689232  393091                  9    2015-01-31                3.42   \n",
       "2689233  393091                  9    2015-01-31                3.42   \n",
       "2689234  393091                  9    2015-01-31                3.42   \n",
       "2689235  485661                 33    2014-06-28                3.97   \n",
       "2689236  467903                  5    2010-05-17                3.50   \n",
       "\n",
       "        review_id business_id  stars        date  \\\n",
       "0         1036613       35338      4  2015-03-13   \n",
       "1         1684017       16561      3  2013-06-08   \n",
       "2         1321092        4949      1  2012-07-22   \n",
       "3         1704099        5847      5  2012-11-15   \n",
       "4          522208       11183      3  2012-07-22   \n",
       "...           ...         ...    ...         ...   \n",
       "2689232   2675353         328      2  2016-06-30   \n",
       "2689233   1994969       30791      1  2017-07-23   \n",
       "2689234   1754541       17300      1  2016-07-11   \n",
       "2689235   2423384        2320      3  2015-12-28   \n",
       "2689236   2644336       25293      4  2017-04-18   \n",
       "\n",
       "                                                      text  useful  funny  \\\n",
       "0        My wife and I regularly hit up Ah-So for Happy...       0      0   \n",
       "1        We came in for dinner based on the yelp (and s...       0      1   \n",
       "2        If I could give you a 0, I would. I called and...       3      2   \n",
       "3        we made a reservation for saturday night at 83...       5      0   \n",
       "4        Had lunch here with my bf and his friend.  We ...       2      0   \n",
       "...                                                    ...     ...    ...   \n",
       "2689232  I love chipotle but not this location. Maybe I...       0      0   \n",
       "2689233  This was my first time not being satisfied wit...       1      0   \n",
       "2689234  I honestly wish I could give no stars. 1. The ...       6      0   \n",
       "2689235  Went here because of the reviews and my nephew...       0      0   \n",
       "2689236  Visited last night, which was a Monday and the...       0      0   \n",
       "\n",
       "        cool city state postal_code   latitude   longitude  \\\n",
       "0          1  162     1       85395  33.462856 -112.391772   \n",
       "1          1   82    17       44113  41.488915  -81.709046   \n",
       "2          1  488    17       44280  41.237658  -81.930948   \n",
       "3          1  457    17       44136  41.313731  -81.816986   \n",
       "4          1   32    17       44017  41.373883  -81.891878   \n",
       "...      ...  ...   ...         ...        ...         ...   \n",
       "2689232    2  323    14       89030  36.240263 -115.116185   \n",
       "2689233    1  323    14       89031  36.275981 -115.179177   \n",
       "2689234    2  216    14       89104  36.145349 -115.156020   \n",
       "2689235    1  216    14       89102  36.126901 -115.197803   \n",
       "2689236    1  288    21     H3B 3E9  45.504015  -73.568177   \n",
       "\n",
       "         business_avg_stars  business_review_count WheelchairAccessible  \\\n",
       "0                       3.0                    140                    1   \n",
       "1                       4.0                    138                    1   \n",
       "2                       3.0                     16                    1   \n",
       "3                       3.5                    125                    2   \n",
       "4                       3.5                    211                    1   \n",
       "...                     ...                    ...                  ...   \n",
       "2689232                 3.0                    103                    1   \n",
       "2689233                 1.5                    139                    2   \n",
       "2689234                 3.0                    332                    2   \n",
       "2689235                 4.0                    346                    1   \n",
       "2689236                 4.0                    292                    1   \n",
       "\n",
       "        BikeParking Alcohol RestaurantsAttire Ambience_intimate  \\\n",
       "0                 1       1                 0                 1   \n",
       "1                 2       1                 0                 1   \n",
       "2                 1       1                 0                 1   \n",
       "3                 1       1                 0                 1   \n",
       "4                 0       1                 0                 1   \n",
       "...             ...     ...               ...               ...   \n",
       "2689232           1       1                 0                 1   \n",
       "2689233           1       1                 0                 1   \n",
       "2689234           1       1                 0                 1   \n",
       "2689235           1       1                 0                 1   \n",
       "2689236           0       1                 0                 1   \n",
       "\n",
       "        Ambience_classy Ambience_hipster Ambience_trendy Ambience_upscale  \\\n",
       "0                     1                1               1                1   \n",
       "1                     1                1               1                1   \n",
       "2                     1                1               1                1   \n",
       "3                     1                1               1                1   \n",
       "4                     1                1               1                1   \n",
       "...                 ...              ...             ...              ...   \n",
       "2689232               1                1               1                1   \n",
       "2689233               1                1               1                1   \n",
       "2689234               1                1               1                1   \n",
       "2689235               1                1               1                1   \n",
       "2689236               1                1               1                1   \n",
       "\n",
       "        RestaurantsGoodForGroups RestaurantsReservations HappyHour  \\\n",
       "0                              1                       0         1   \n",
       "1                              1                       0         1   \n",
       "2                              1                       0         1   \n",
       "3                              1                       0         1   \n",
       "4                              1                       0         1   \n",
       "...                          ...                     ...       ...   \n",
       "2689232                        1                       0         1   \n",
       "2689233                        1                       0         1   \n",
       "2689234                        1                       0         1   \n",
       "2689235                        1                       0         1   \n",
       "2689236                        1                       0         1   \n",
       "\n",
       "        RestaurantsTableService GoodForMeal_dessert GoodForMeal_latenight  \\\n",
       "0                             1                   1                     0   \n",
       "1                             1                   1                     1   \n",
       "2                             1                   1                     1   \n",
       "3                             1                   1                     1   \n",
       "4                             1                   1                     1   \n",
       "...                         ...                 ...                   ...   \n",
       "2689232                       1                   1                     1   \n",
       "2689233                       1                   1                     1   \n",
       "2689234                       1                   1                     1   \n",
       "2689235                       1                   1                     0   \n",
       "2689236                       1                   1                     1   \n",
       "\n",
       "        GoodForMeal_lunch GoodForMeal_dinner GoodForMeal_breakfast  \\\n",
       "0                       0                  2                     2   \n",
       "1                       1                  1                     1   \n",
       "2                       1                  1                     1   \n",
       "3                       1                  1                     1   \n",
       "4                       1                  1                     1   \n",
       "...                   ...                ...                   ...   \n",
       "2689232                 1                  1                     1   \n",
       "2689233                 1                  1                     1   \n",
       "2689234                 1                  1                     1   \n",
       "2689235                 2                  2                     2   \n",
       "2689236                 1                  1                     1   \n",
       "\n",
       "        GoodForMeal_brunch DogsAllowed DietaryRestrictions_gluten-free  \\\n",
       "0                        0           1                               1   \n",
       "1                        1           1                               1   \n",
       "2                        1           1                               1   \n",
       "3                        1           1                               1   \n",
       "4                        1           1                               1   \n",
       "...                    ...         ...                             ...   \n",
       "2689232                  1           0                               1   \n",
       "2689233                  1           1                               1   \n",
       "2689234                  1           1                               1   \n",
       "2689235                  0           1                               1   \n",
       "2689236                  1           1                               1   \n",
       "\n",
       "        DietaryRestrictions_vegan DietaryRestrictions_kosher  \\\n",
       "0                               1                          1   \n",
       "1                               1                          1   \n",
       "2                               1                          1   \n",
       "3                               1                          1   \n",
       "4                               1                          1   \n",
       "...                           ...                        ...   \n",
       "2689232                         1                          1   \n",
       "2689233                         1                          1   \n",
       "2689234                         1                          1   \n",
       "2689235                         1                          1   \n",
       "2689236                         1                          1   \n",
       "\n",
       "        DietaryRestrictions_halal DietaryRestrictions_soy-free  \\\n",
       "0                               1                            1   \n",
       "1                               1                            1   \n",
       "2                               1                            1   \n",
       "3                               1                            1   \n",
       "4                               1                            1   \n",
       "...                           ...                          ...   \n",
       "2689232                         1                            1   \n",
       "2689233                         1                            1   \n",
       "2689234                         1                            1   \n",
       "2689235                         1                            1   \n",
       "2689236                         1                            1   \n",
       "\n",
       "        DietaryRestrictions_vegetarian  \n",
       "0                                    1  \n",
       "1                                    1  \n",
       "2                                    1  \n",
       "3                                    1  \n",
       "4                                    1  \n",
       "...                                ...  \n",
       "2689232                              1  \n",
       "2689233                              1  \n",
       "2689234                              1  \n",
       "2689235                              1  \n",
       "2689236                              1  \n",
       "\n",
       "[2689237 rows x 45 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "379bfde3-4aee-44f2-be44-e23581446f7f",
   "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>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>435362</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45222</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45222</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45222</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>41.313731</td>\n",
       "      <td>-81.816986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45222</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>41.373883</td>\n",
       "      <td>-81.891878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689232</th>\n",
       "      <td>393091</td>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>36.240263</td>\n",
       "      <td>-115.116185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689233</th>\n",
       "      <td>393091</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>36.275981</td>\n",
       "      <td>-115.179177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689234</th>\n",
       "      <td>393091</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>36.145349</td>\n",
       "      <td>-115.156020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689235</th>\n",
       "      <td>485661</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>36.126901</td>\n",
       "      <td>-115.197803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689236</th>\n",
       "      <td>467903</td>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>45.504015</td>\n",
       "      <td>-73.568177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2689237 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id        date   latitude   longitude\n",
       "0        435362  2015-03-13  33.462856 -112.391772\n",
       "1         45222  2013-06-08  41.488915  -81.709046\n",
       "2         45222  2012-07-22  41.237658  -81.930948\n",
       "3         45222  2012-11-15  41.313731  -81.816986\n",
       "4         45222  2012-07-22  41.373883  -81.891878\n",
       "...         ...         ...        ...         ...\n",
       "2689232  393091  2016-06-30  36.240263 -115.116185\n",
       "2689233  393091  2017-07-23  36.275981 -115.179177\n",
       "2689234  393091  2016-07-11  36.145349 -115.156020\n",
       "2689235  485661  2015-12-28  36.126901 -115.197803\n",
       "2689236  467903  2017-04-18  45.504015  -73.568177\n",
       "\n",
       "[2689237 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimal_df = merged_df[['user_id', 'date', 'latitude', 'longitude']]\n",
    "\n",
    "minimal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d63a93aa-5225-459c-8cc6-5d3f83cab2ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import geopy.distance\n",
    "\n",
    "def get_gen_bbox_dist(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    groups = df.groupby('user_id')\n",
    "    # For each user\n",
    "    df_lst = []\n",
    "    for group, user_df in tqdm(groups):\n",
    "        # For each interaction, we cumulatively find the minimal bounding box, starting from the 4th restaurant onwards\n",
    "        user_df.sort_values(by=\"date\", inplace=True) # Get the user's interactions in chronological order\n",
    "\n",
    "        # get lat list\n",
    "        lat_lst = user_df['latitude'].to_list()\n",
    "\n",
    "        # get lon list\n",
    "        lon_lst = user_df['longitude'].to_list()\n",
    "\n",
    "        # zip lat, lon\n",
    "        lat_long = zip(lat_lst[1:], lon_lst[1:])\n",
    "\n",
    "        # get initial bbox\n",
    "        min_lat = lat_lst[0]\n",
    "        min_lon = lon_lst[0]\n",
    "        max_lat = lat_lst[0]\n",
    "        max_lon = lon_lst[0]\n",
    "\n",
    "        bboxes = [(min_lat, min_lon, max_lat, max_lon)]\n",
    "        dists = [0]\n",
    "\n",
    "        # Iterate over lat_long\n",
    "        for (lat, lon) in lat_long:\n",
    "            assert type(lat) != str\n",
    "            assert type(lon) != str\n",
    "            center_lat = (max_lat - min_lat) / 2 + min_lat\n",
    "            center_lon = (max_lon - min_lon) / 2 + min_lon\n",
    "            \n",
    "            d = geopy.distance.geodesic((center_lat, center_lon), (lat, lon)).km\n",
    "            \n",
    "            min_lat = min(min_lat, lat)\n",
    "            min_lon = min(min_lon, lon)\n",
    "            max_lat = max(max_lat, lat)\n",
    "            max_lon = max(max_lon, lon)\n",
    "\n",
    "            bboxes.append((min_lat, min_lon, max_lat, max_lon))\n",
    "\n",
    "            dists.append(d)\n",
    "\n",
    "        user_df['dist_to_centroid'] = pd.Series(dists, index=user_df.index)\n",
    "        user_df['bboxes'] = pd.Series(bboxes, index=user_df.index)\n",
    "\n",
    "        df_lst.append(user_df)\n",
    "\n",
    "    df = pd.concat(df_lst)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2c5c4a73-cdc2-4e54-bf06-57569dc129c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "minimal_df = minimal_df[minimal_df['latitude'] != 'Na']\n",
    "minimal_df = minimal_df[minimal_df['longitude'] != 'Na']\n",
    "minimal_df = minimal_df.astype({'latitude': float, 'longitude': float})\n",
    "minimal_df = minimal_df.dropna(subset=['latitude', 'longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3996cb3b-6cb9-46b8-b2fc-b96164acf331",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11724/2121352086.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  groups = df.groupby('user_id')\n",
      "100%|██████████| 554409/554409 [26:58<00:00, 342.52it/s]  \n"
     ]
    }
   ],
   "source": [
    "minimal_df = get_gen_bbox_dist(minimal_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "34e3b57f-8e32-4b9c-906c-0db537f3242d",
   "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>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>dist_to_centroid</th>\n",
       "      <th>bboxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1806096</th>\n",
       "      <td>0</td>\n",
       "      <td>2008-11-11</td>\n",
       "      <td>36.123183</td>\n",
       "      <td>-115.169190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.123183, -115.16919, 36.123183, -115.16919)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806108</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-10-16</td>\n",
       "      <td>36.164180</td>\n",
       "      <td>-115.289630</td>\n",
       "      <td>11.755446</td>\n",
       "      <td>(36.123183, -115.289629743, 36.1641802266, -11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806117</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-10-16</td>\n",
       "      <td>36.145053</td>\n",
       "      <td>-115.232838</td>\n",
       "      <td>0.344026</td>\n",
       "      <td>(36.123183, -115.289629743, 36.1641802266, -11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806128</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-10-17</td>\n",
       "      <td>36.168099</td>\n",
       "      <td>-115.192230</td>\n",
       "      <td>4.305157</td>\n",
       "      <td>(36.123183, -115.289629743, 36.1680995, -115.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806118</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-11-05</td>\n",
       "      <td>36.158503</td>\n",
       "      <td>-115.285177</td>\n",
       "      <td>5.217482</td>\n",
       "      <td>(36.123183, -115.289629743, 36.1680995, -115.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639589</th>\n",
       "      <td>554405</td>\n",
       "      <td>2014-04-06</td>\n",
       "      <td>36.115070</td>\n",
       "      <td>-115.236655</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.1150698, -115.2366549, 36.1150698, -115.23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413562</th>\n",
       "      <td>554406</td>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>33.498914</td>\n",
       "      <td>-111.928574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(33.498914, -111.928574, 33.498914, -111.928574)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413563</th>\n",
       "      <td>554406</td>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>33.391026</td>\n",
       "      <td>-112.016741</td>\n",
       "      <td>14.504894</td>\n",
       "      <td>(33.3910264577, -112.016740994, 33.498914, -11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89188</th>\n",
       "      <td>554407</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>36.109164</td>\n",
       "      <td>-115.153493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.1091636136, -115.153493285, 36.1091636136,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838909</th>\n",
       "      <td>554408</td>\n",
       "      <td>2014-08-21</td>\n",
       "      <td>36.109658</td>\n",
       "      <td>-115.174669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.109658, -115.174669, 36.109658, -115.174669)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2689237 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id        date   latitude   longitude  dist_to_centroid  \\\n",
       "1806096       0  2008-11-11  36.123183 -115.169190          0.000000   \n",
       "1806108       0  2010-10-16  36.164180 -115.289630         11.755446   \n",
       "1806117       0  2010-10-16  36.145053 -115.232838          0.344026   \n",
       "1806128       0  2010-10-17  36.168099 -115.192230          4.305157   \n",
       "1806118       0  2010-11-05  36.158503 -115.285177          5.217482   \n",
       "...         ...         ...        ...         ...               ...   \n",
       "1639589  554405  2014-04-06  36.115070 -115.236655          0.000000   \n",
       "1413562  554406  2016-09-27  33.498914 -111.928574          0.000000   \n",
       "1413563  554406  2016-09-27  33.391026 -112.016741         14.504894   \n",
       "89188    554407  2017-11-17  36.109164 -115.153493          0.000000   \n",
       "1838909  554408  2014-08-21  36.109658 -115.174669          0.000000   \n",
       "\n",
       "                                                    bboxes  \n",
       "1806096     (36.123183, -115.16919, 36.123183, -115.16919)  \n",
       "1806108  (36.123183, -115.289629743, 36.1641802266, -11...  \n",
       "1806117  (36.123183, -115.289629743, 36.1641802266, -11...  \n",
       "1806128  (36.123183, -115.289629743, 36.1680995, -115.1...  \n",
       "1806118  (36.123183, -115.289629743, 36.1680995, -115.1...  \n",
       "...                                                    ...  \n",
       "1639589  (36.1150698, -115.2366549, 36.1150698, -115.23...  \n",
       "1413562   (33.498914, -111.928574, 33.498914, -111.928574)  \n",
       "1413563  (33.3910264577, -112.016740994, 33.498914, -11...  \n",
       "89188    (36.1091636136, -115.153493285, 36.1091636136,...  \n",
       "1838909   (36.109658, -115.174669, 36.109658, -115.174669)  \n",
       "\n",
       "[2689237 rows x 6 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "84be6997-1d44-4991-9389-9f4ee30a67b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df[merged_df['latitude'] != 'Na']\n",
    "merged_df = merged_df[merged_df['longitude'] != 'Na']\n",
    "merged_df = merged_df.astype({'latitude': float, 'longitude': float})\n",
    "merged_df = merged_df.dropna(subset=['latitude', 'longitude'])\n",
    "merged_df = merged_df.merge(minimal_df, how='inner', on=['user_id', 'date', 'latitude', 'longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "953263de-886c-4785-914b-06562dea02d3",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "      <th>WheelchairAccessible</th>\n",
       "      <th>BikeParking</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>RestaurantsAttire</th>\n",
       "      <th>Ambience_intimate</th>\n",
       "      <th>Ambience_classy</th>\n",
       "      <th>Ambience_hipster</th>\n",
       "      <th>Ambience_trendy</th>\n",
       "      <th>Ambience_upscale</th>\n",
       "      <th>RestaurantsGoodForGroups</th>\n",
       "      <th>RestaurantsReservations</th>\n",
       "      <th>HappyHour</th>\n",
       "      <th>RestaurantsTableService</th>\n",
       "      <th>GoodForMeal_dessert</th>\n",
       "      <th>GoodForMeal_latenight</th>\n",
       "      <th>GoodForMeal_lunch</th>\n",
       "      <th>GoodForMeal_dinner</th>\n",
       "      <th>GoodForMeal_breakfast</th>\n",
       "      <th>GoodForMeal_brunch</th>\n",
       "      <th>DogsAllowed</th>\n",
       "      <th>DietaryRestrictions_gluten-free</th>\n",
       "      <th>DietaryRestrictions_vegan</th>\n",
       "      <th>DietaryRestrictions_kosher</th>\n",
       "      <th>DietaryRestrictions_halal</th>\n",
       "      <th>DietaryRestrictions_soy-free</th>\n",
       "      <th>DietaryRestrictions_vegetarian</th>\n",
       "      <th>dist_to_centroid</th>\n",
       "      <th>bboxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>435362</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1036613</td>\n",
       "      <td>35338</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>My wife and I regularly hit up Ah-So for Happy...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>85395</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "      <td>3.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(33.4628556191, -112.391772403, 33.4628556191,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1684017</td>\n",
       "      <td>16561</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>We came in for dinner based on the yelp (and s...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>17</td>\n",
       "      <td>44113</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24.575284</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.4889146, -81.7090...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1321092</td>\n",
       "      <td>4949</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>If I could give you a 0, I would. I called and...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>488</td>\n",
       "      <td>17</td>\n",
       "      <td>44280</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.2376583, -81.9309...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1704099</td>\n",
       "      <td>5847</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>we made a reservation for saturday night at 83...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>457</td>\n",
       "      <td>17</td>\n",
       "      <td>44136</td>\n",
       "      <td>41.313731</td>\n",
       "      <td>-81.816986</td>\n",
       "      <td>3.5</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.956597</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.3738834, -81.8169...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>522208</td>\n",
       "      <td>11183</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>Had lunch here with my bf and his friend.  We ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>44017</td>\n",
       "      <td>41.373883</td>\n",
       "      <td>-81.891878</td>\n",
       "      <td>3.5</td>\n",
       "      <td>211</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.478901</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.3738834, -81.8918...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691758</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>2675353</td>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>I love chipotle but not this location. Maybe I...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>323</td>\n",
       "      <td>14</td>\n",
       "      <td>89030</td>\n",
       "      <td>36.240263</td>\n",
       "      <td>-115.116185</td>\n",
       "      <td>3.0</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.2402631028, -115.116185266, 36.2402631028,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691759</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1994969</td>\n",
       "      <td>30791</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>This was my first time not being satisfied wit...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>14</td>\n",
       "      <td>89031</td>\n",
       "      <td>36.275981</td>\n",
       "      <td>-115.179177</td>\n",
       "      <td>1.5</td>\n",
       "      <td>139</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.012556</td>\n",
       "      <td>(36.1453494842, -115.3320001, 36.2759814851, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691760</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1754541</td>\n",
       "      <td>17300</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>I honestly wish I could give no stars. 1. The ...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "      <td>89104</td>\n",
       "      <td>36.145349</td>\n",
       "      <td>-115.156020</td>\n",
       "      <td>3.0</td>\n",
       "      <td>332</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.179159</td>\n",
       "      <td>(36.1453494842, -115.156019926, 36.2406450132,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691761</th>\n",
       "      <td>485661</td>\n",
       "      <td>33</td>\n",
       "      <td>2014-06-28</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2423384</td>\n",
       "      <td>2320</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>Went here because of the reviews and my nephew...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "      <td>89102</td>\n",
       "      <td>36.126901</td>\n",
       "      <td>-115.197803</td>\n",
       "      <td>4.0</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.126900851, -115.197803344, 36.126900851, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691762</th>\n",
       "      <td>467903</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-05-17</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2644336</td>\n",
       "      <td>25293</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>Visited last night, which was a Monday and the...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>288</td>\n",
       "      <td>21</td>\n",
       "      <td>H3B 3E9</td>\n",
       "      <td>45.504015</td>\n",
       "      <td>-73.568177</td>\n",
       "      <td>4.0</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(45.5040151, -73.5681774, 45.5040151, -73.5681...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2691763 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  user_review_count yelping_since  user_average_stars  \\\n",
       "0        435362                  6    2015-03-13                4.67   \n",
       "1         45222                 11    2012-07-16                3.45   \n",
       "2         45222                 11    2012-07-16                3.45   \n",
       "3         45222                 11    2012-07-16                3.45   \n",
       "4         45222                 11    2012-07-16                3.45   \n",
       "...         ...                ...           ...                 ...   \n",
       "2691758  393091                  9    2015-01-31                3.42   \n",
       "2691759  393091                  9    2015-01-31                3.42   \n",
       "2691760  393091                  9    2015-01-31                3.42   \n",
       "2691761  485661                 33    2014-06-28                3.97   \n",
       "2691762  467903                  5    2010-05-17                3.50   \n",
       "\n",
       "        review_id business_id  stars        date  \\\n",
       "0         1036613       35338      4  2015-03-13   \n",
       "1         1684017       16561      3  2013-06-08   \n",
       "2         1321092        4949      1  2012-07-22   \n",
       "3         1704099        5847      5  2012-11-15   \n",
       "4          522208       11183      3  2012-07-22   \n",
       "...           ...         ...    ...         ...   \n",
       "2691758   2675353         328      2  2016-06-30   \n",
       "2691759   1994969       30791      1  2017-07-23   \n",
       "2691760   1754541       17300      1  2016-07-11   \n",
       "2691761   2423384        2320      3  2015-12-28   \n",
       "2691762   2644336       25293      4  2017-04-18   \n",
       "\n",
       "                                                      text  useful  funny  \\\n",
       "0        My wife and I regularly hit up Ah-So for Happy...       0      0   \n",
       "1        We came in for dinner based on the yelp (and s...       0      1   \n",
       "2        If I could give you a 0, I would. I called and...       3      2   \n",
       "3        we made a reservation for saturday night at 83...       5      0   \n",
       "4        Had lunch here with my bf and his friend.  We ...       2      0   \n",
       "...                                                    ...     ...    ...   \n",
       "2691758  I love chipotle but not this location. Maybe I...       0      0   \n",
       "2691759  This was my first time not being satisfied wit...       1      0   \n",
       "2691760  I honestly wish I could give no stars. 1. The ...       6      0   \n",
       "2691761  Went here because of the reviews and my nephew...       0      0   \n",
       "2691762  Visited last night, which was a Monday and the...       0      0   \n",
       "\n",
       "        cool city state postal_code   latitude   longitude  \\\n",
       "0          1  162     1       85395  33.462856 -112.391772   \n",
       "1          1   82    17       44113  41.488915  -81.709046   \n",
       "2          1  488    17       44280  41.237658  -81.930948   \n",
       "3          1  457    17       44136  41.313731  -81.816986   \n",
       "4          1   32    17       44017  41.373883  -81.891878   \n",
       "...      ...  ...   ...         ...        ...         ...   \n",
       "2691758    2  323    14       89030  36.240263 -115.116185   \n",
       "2691759    1  323    14       89031  36.275981 -115.179177   \n",
       "2691760    2  216    14       89104  36.145349 -115.156020   \n",
       "2691761    1  216    14       89102  36.126901 -115.197803   \n",
       "2691762    1  288    21     H3B 3E9  45.504015  -73.568177   \n",
       "\n",
       "         business_avg_stars  business_review_count WheelchairAccessible  \\\n",
       "0                       3.0                    140                    1   \n",
       "1                       4.0                    138                    1   \n",
       "2                       3.0                     16                    1   \n",
       "3                       3.5                    125                    2   \n",
       "4                       3.5                    211                    1   \n",
       "...                     ...                    ...                  ...   \n",
       "2691758                 3.0                    103                    1   \n",
       "2691759                 1.5                    139                    2   \n",
       "2691760                 3.0                    332                    2   \n",
       "2691761                 4.0                    346                    1   \n",
       "2691762                 4.0                    292                    1   \n",
       "\n",
       "        BikeParking Alcohol RestaurantsAttire Ambience_intimate  \\\n",
       "0                 1       1                 0                 1   \n",
       "1                 2       1                 0                 1   \n",
       "2                 1       1                 0                 1   \n",
       "3                 1       1                 0                 1   \n",
       "4                 0       1                 0                 1   \n",
       "...             ...     ...               ...               ...   \n",
       "2691758           1       1                 0                 1   \n",
       "2691759           1       1                 0                 1   \n",
       "2691760           1       1                 0                 1   \n",
       "2691761           1       1                 0                 1   \n",
       "2691762           0       1                 0                 1   \n",
       "\n",
       "        Ambience_classy Ambience_hipster Ambience_trendy Ambience_upscale  \\\n",
       "0                     1                1               1                1   \n",
       "1                     1                1               1                1   \n",
       "2                     1                1               1                1   \n",
       "3                     1                1               1                1   \n",
       "4                     1                1               1                1   \n",
       "...                 ...              ...             ...              ...   \n",
       "2691758               1                1               1                1   \n",
       "2691759               1                1               1                1   \n",
       "2691760               1                1               1                1   \n",
       "2691761               1                1               1                1   \n",
       "2691762               1                1               1                1   \n",
       "\n",
       "        RestaurantsGoodForGroups RestaurantsReservations HappyHour  \\\n",
       "0                              1                       0         1   \n",
       "1                              1                       0         1   \n",
       "2                              1                       0         1   \n",
       "3                              1                       0         1   \n",
       "4                              1                       0         1   \n",
       "...                          ...                     ...       ...   \n",
       "2691758                        1                       0         1   \n",
       "2691759                        1                       0         1   \n",
       "2691760                        1                       0         1   \n",
       "2691761                        1                       0         1   \n",
       "2691762                        1                       0         1   \n",
       "\n",
       "        RestaurantsTableService GoodForMeal_dessert GoodForMeal_latenight  \\\n",
       "0                             1                   1                     0   \n",
       "1                             1                   1                     1   \n",
       "2                             1                   1                     1   \n",
       "3                             1                   1                     1   \n",
       "4                             1                   1                     1   \n",
       "...                         ...                 ...                   ...   \n",
       "2691758                       1                   1                     1   \n",
       "2691759                       1                   1                     1   \n",
       "2691760                       1                   1                     1   \n",
       "2691761                       1                   1                     0   \n",
       "2691762                       1                   1                     1   \n",
       "\n",
       "        GoodForMeal_lunch GoodForMeal_dinner GoodForMeal_breakfast  \\\n",
       "0                       0                  2                     2   \n",
       "1                       1                  1                     1   \n",
       "2                       1                  1                     1   \n",
       "3                       1                  1                     1   \n",
       "4                       1                  1                     1   \n",
       "...                   ...                ...                   ...   \n",
       "2691758                 1                  1                     1   \n",
       "2691759                 1                  1                     1   \n",
       "2691760                 1                  1                     1   \n",
       "2691761                 2                  2                     2   \n",
       "2691762                 1                  1                     1   \n",
       "\n",
       "        GoodForMeal_brunch DogsAllowed DietaryRestrictions_gluten-free  \\\n",
       "0                        0           1                               1   \n",
       "1                        1           1                               1   \n",
       "2                        1           1                               1   \n",
       "3                        1           1                               1   \n",
       "4                        1           1                               1   \n",
       "...                    ...         ...                             ...   \n",
       "2691758                  1           0                               1   \n",
       "2691759                  1           1                               1   \n",
       "2691760                  1           1                               1   \n",
       "2691761                  0           1                               1   \n",
       "2691762                  1           1                               1   \n",
       "\n",
       "        DietaryRestrictions_vegan DietaryRestrictions_kosher  \\\n",
       "0                               1                          1   \n",
       "1                               1                          1   \n",
       "2                               1                          1   \n",
       "3                               1                          1   \n",
       "4                               1                          1   \n",
       "...                           ...                        ...   \n",
       "2691758                         1                          1   \n",
       "2691759                         1                          1   \n",
       "2691760                         1                          1   \n",
       "2691761                         1                          1   \n",
       "2691762                         1                          1   \n",
       "\n",
       "        DietaryRestrictions_halal DietaryRestrictions_soy-free  \\\n",
       "0                               1                            1   \n",
       "1                               1                            1   \n",
       "2                               1                            1   \n",
       "3                               1                            1   \n",
       "4                               1                            1   \n",
       "...                           ...                          ...   \n",
       "2691758                         1                            1   \n",
       "2691759                         1                            1   \n",
       "2691760                         1                            1   \n",
       "2691761                         1                            1   \n",
       "2691762                         1                            1   \n",
       "\n",
       "        DietaryRestrictions_vegetarian  dist_to_centroid  \\\n",
       "0                                    1          0.000000   \n",
       "1                                    1         24.575284   \n",
       "2                                    1          0.000000   \n",
       "3                                    1          7.956597   \n",
       "4                                    1         15.478901   \n",
       "...                                ...               ...   \n",
       "2691758                              1          0.000000   \n",
       "2691759                              1         10.012556   \n",
       "2691760                              1         11.179159   \n",
       "2691761                              1          0.000000   \n",
       "2691762                              1          0.000000   \n",
       "\n",
       "                                                    bboxes  \n",
       "0        (33.4628556191, -112.391772403, 33.4628556191,...  \n",
       "1        (41.2376583, -81.9309482, 41.4889146, -81.7090...  \n",
       "2        (41.2376583, -81.9309482, 41.2376583, -81.9309...  \n",
       "3        (41.2376583, -81.9309482, 41.3738834, -81.8169...  \n",
       "4        (41.2376583, -81.9309482, 41.3738834, -81.8918...  \n",
       "...                                                    ...  \n",
       "2691758  (36.2402631028, -115.116185266, 36.2402631028,...  \n",
       "2691759  (36.1453494842, -115.3320001, 36.2759814851, -...  \n",
       "2691760  (36.1453494842, -115.156019926, 36.2406450132,...  \n",
       "2691761  (36.126900851, -115.197803344, 36.126900851, -...  \n",
       "2691762  (45.5040151, -73.5681774, 45.5040151, -73.5681...  \n",
       "\n",
       "[2691763 rows x 47 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f73181fb-01d5-41dd-bd60-f129189a7878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['user_id',\n",
       " 'user_review_count',\n",
       " 'yelping_since',\n",
       " 'user_average_stars',\n",
       " 'review_id',\n",
       " 'business_id',\n",
       " 'stars',\n",
       " 'date',\n",
       " 'text',\n",
       " 'useful',\n",
       " 'funny',\n",
       " 'cool',\n",
       " 'city',\n",
       " 'state',\n",
       " 'postal_code',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'business_avg_stars',\n",
       " 'business_review_count',\n",
       " 'WheelchairAccessible',\n",
       " 'BikeParking',\n",
       " 'Alcohol',\n",
       " 'RestaurantsAttire',\n",
       " 'Ambience_intimate',\n",
       " 'Ambience_classy',\n",
       " 'Ambience_hipster',\n",
       " 'Ambience_trendy',\n",
       " 'Ambience_upscale',\n",
       " 'RestaurantsGoodForGroups',\n",
       " 'RestaurantsReservations',\n",
       " 'HappyHour',\n",
       " 'RestaurantsTableService',\n",
       " 'GoodForMeal_dessert',\n",
       " 'GoodForMeal_latenight',\n",
       " 'GoodForMeal_lunch',\n",
       " 'GoodForMeal_dinner',\n",
       " 'GoodForMeal_breakfast',\n",
       " 'GoodForMeal_brunch',\n",
       " 'DogsAllowed',\n",
       " 'DietaryRestrictions_gluten-free',\n",
       " 'DietaryRestrictions_vegan',\n",
       " 'DietaryRestrictions_kosher',\n",
       " 'DietaryRestrictions_halal',\n",
       " 'DietaryRestrictions_soy-free',\n",
       " 'DietaryRestrictions_vegetarian',\n",
       " 'dist_to_centroid',\n",
       " 'bboxes']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(merged_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bfc0efd3-3606-4086-af3b-ab3f6161e17b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_cols_to_rename\n",
      "['yelping_since']\n",
      "review_cols_to_rename\n",
      "['stars', 'date', 'useful', 'funny', 'cool', 'dist_to_centroid', 'bboxes']\n",
      "business_cols_to_rename\n",
      "['text', 'RestaurantsAttire', 'state', 'DietaryRestrictions_kosher', 'HappyHour', 'Ambience_trendy', 'latitude', 'RestaurantsGoodForGroups', 'GoodForMeal_dinner', 'DietaryRestrictions_gluten-free', 'GoodForMeal_dessert', 'Ambience_intimate', 'GoodForMeal_latenight', 'Ambience_upscale', 'GoodForMeal_brunch', 'DogsAllowed', 'Alcohol', 'WheelchairAccessible', 'DietaryRestrictions_vegetarian', 'postal_code', 'DietaryRestrictions_soy-free', 'DietaryRestrictions_halal', 'DietaryRestrictions_vegan', 'city', 'RestaurantsTableService', 'GoodForMeal_lunch', 'longitude', 'Ambience_classy', 'Ambience_hipster', 'BikeParking', 'GoodForMeal_breakfast', 'RestaurantsReservations']\n"
     ]
    }
   ],
   "source": [
    "user_cols_to_rename = [\"yelping_since\"]\n",
    "review_cols_to_rename = ['stars', 'date', 'useful', 'funny', 'cool', 'dist_to_centroid', 'bboxes']\n",
    "business_cols_to_rename = [x for x in list(set(list(merged_df.columns)).difference(set(user_cols_to_rename)).difference(set(review_cols_to_rename))) if \"user\" not in x and \"review\" not in x and \"business\" not in x]\n",
    "print(\"user_cols_to_rename\")\n",
    "print(user_cols_to_rename)\n",
    "\n",
    "print(\"review_cols_to_rename\")\n",
    "print(review_cols_to_rename)\n",
    "\n",
    "print(\"business_cols_to_rename\")\n",
    "print(business_cols_to_rename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d1e083f6-c736-43a3-b6fd-3d338b83e356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_cols_to_rename_mapping\n",
      "{'yelping_since': 'user_yelping_since'}\n",
      "review_cols_to_rename_mapping\n",
      "{'stars': 'review_stars', 'date': 'review_date', 'useful': 'review_useful', 'funny': 'review_funny', 'cool': 'review_cool', 'dist_to_centroid': 'review_dist_to_centroid', 'bboxes': 'review_bboxes'}\n",
      "business_cols_to_rename_mapping\n",
      "{'text': 'business_text', 'RestaurantsAttire': 'business_RestaurantsAttire', 'state': 'business_state', 'DietaryRestrictions_kosher': 'business_DietaryRestrictions_kosher', 'HappyHour': 'business_HappyHour', 'Ambience_trendy': 'business_Ambience_trendy', 'latitude': 'business_latitude', 'RestaurantsGoodForGroups': 'business_RestaurantsGoodForGroups', 'GoodForMeal_dinner': 'business_GoodForMeal_dinner', 'DietaryRestrictions_gluten-free': 'business_DietaryRestrictions_gluten-free', 'GoodForMeal_dessert': 'business_GoodForMeal_dessert', 'Ambience_intimate': 'business_Ambience_intimate', 'GoodForMeal_latenight': 'business_GoodForMeal_latenight', 'Ambience_upscale': 'business_Ambience_upscale', 'GoodForMeal_brunch': 'business_GoodForMeal_brunch', 'DogsAllowed': 'business_DogsAllowed', 'Alcohol': 'business_Alcohol', 'WheelchairAccessible': 'business_WheelchairAccessible', 'DietaryRestrictions_vegetarian': 'business_DietaryRestrictions_vegetarian', 'postal_code': 'business_postal_code', 'DietaryRestrictions_soy-free': 'business_DietaryRestrictions_soy-free', 'DietaryRestrictions_halal': 'business_DietaryRestrictions_halal', 'DietaryRestrictions_vegan': 'business_DietaryRestrictions_vegan', 'city': 'business_city', 'RestaurantsTableService': 'business_RestaurantsTableService', 'GoodForMeal_lunch': 'business_GoodForMeal_lunch', 'longitude': 'business_longitude', 'Ambience_classy': 'business_Ambience_classy', 'Ambience_hipster': 'business_Ambience_hipster', 'BikeParking': 'business_BikeParking', 'GoodForMeal_breakfast': 'business_GoodForMeal_breakfast', 'RestaurantsReservations': 'business_RestaurantsReservations'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "user_cols_to_rename_mapping = {x:\"user_\" + x for x in user_cols_to_rename}\n",
    "review_cols_to_rename_mapping = {x:\"review_\" + x for x in review_cols_to_rename}\n",
    "business_cols_to_rename_mapping = {x:\"business_\" + x for x in business_cols_to_rename}\n",
    "\n",
    "print(\"user_cols_to_rename_mapping\")\n",
    "print(user_cols_to_rename_mapping)\n",
    "\n",
    "print(\"review_cols_to_rename_mapping\")\n",
    "print(review_cols_to_rename_mapping)\n",
    "\n",
    "print(\"business_cols_to_rename_mapping\")\n",
    "print(business_cols_to_rename_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e5a69542-e7fd-47e9-b2b5-c9a8f11e64e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'business_text',\n",
       " 'RestaurantsAttire': 'business_RestaurantsAttire',\n",
       " 'state': 'business_state',\n",
       " 'DietaryRestrictions_kosher': 'business_DietaryRestrictions_kosher',\n",
       " 'HappyHour': 'business_HappyHour',\n",
       " 'Ambience_trendy': 'business_Ambience_trendy',\n",
       " 'latitude': 'business_latitude',\n",
       " 'RestaurantsGoodForGroups': 'business_RestaurantsGoodForGroups',\n",
       " 'GoodForMeal_dinner': 'business_GoodForMeal_dinner',\n",
       " 'DietaryRestrictions_gluten-free': 'business_DietaryRestrictions_gluten-free',\n",
       " 'GoodForMeal_dessert': 'business_GoodForMeal_dessert',\n",
       " 'Ambience_intimate': 'business_Ambience_intimate',\n",
       " 'GoodForMeal_latenight': 'business_GoodForMeal_latenight',\n",
       " 'Ambience_upscale': 'business_Ambience_upscale',\n",
       " 'GoodForMeal_brunch': 'business_GoodForMeal_brunch',\n",
       " 'DogsAllowed': 'business_DogsAllowed',\n",
       " 'Alcohol': 'business_Alcohol',\n",
       " 'WheelchairAccessible': 'business_WheelchairAccessible',\n",
       " 'DietaryRestrictions_vegetarian': 'business_DietaryRestrictions_vegetarian',\n",
       " 'postal_code': 'business_postal_code',\n",
       " 'DietaryRestrictions_soy-free': 'business_DietaryRestrictions_soy-free',\n",
       " 'DietaryRestrictions_halal': 'business_DietaryRestrictions_halal',\n",
       " 'DietaryRestrictions_vegan': 'business_DietaryRestrictions_vegan',\n",
       " 'city': 'business_city',\n",
       " 'RestaurantsTableService': 'business_RestaurantsTableService',\n",
       " 'GoodForMeal_lunch': 'business_GoodForMeal_lunch',\n",
       " 'longitude': 'business_longitude',\n",
       " 'Ambience_classy': 'business_Ambience_classy',\n",
       " 'Ambience_hipster': 'business_Ambience_hipster',\n",
       " 'BikeParking': 'business_BikeParking',\n",
       " 'GoodForMeal_breakfast': 'business_GoodForMeal_breakfast',\n",
       " 'RestaurantsReservations': 'business_RestaurantsReservations',\n",
       " 'stars': 'review_stars',\n",
       " 'date': 'review_date',\n",
       " 'useful': 'review_useful',\n",
       " 'funny': 'review_funny',\n",
       " 'cool': 'review_cool',\n",
       " 'dist_to_centroid': 'review_dist_to_centroid',\n",
       " 'bboxes': 'review_bboxes',\n",
       " 'yelping_since': 'user_yelping_since'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import ChainMap\n",
    "cols_to_rename_mapping = dict(ChainMap(*[user_cols_to_rename_mapping, review_cols_to_rename_mapping, business_cols_to_rename_mapping]))\n",
    "cols_to_rename_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f8627b71-a846-4349-a3fd-c5b33bfb1bb5",
   "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>user_id</th>\n",
       "      <th>user_review_count</th>\n",
       "      <th>user_yelping_since</th>\n",
       "      <th>user_average_stars</th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>review_stars</th>\n",
       "      <th>review_date</th>\n",
       "      <th>business_text</th>\n",
       "      <th>review_useful</th>\n",
       "      <th>review_funny</th>\n",
       "      <th>review_cool</th>\n",
       "      <th>business_city</th>\n",
       "      <th>business_state</th>\n",
       "      <th>business_postal_code</th>\n",
       "      <th>business_latitude</th>\n",
       "      <th>business_longitude</th>\n",
       "      <th>business_avg_stars</th>\n",
       "      <th>business_review_count</th>\n",
       "      <th>business_WheelchairAccessible</th>\n",
       "      <th>business_BikeParking</th>\n",
       "      <th>business_Alcohol</th>\n",
       "      <th>business_RestaurantsAttire</th>\n",
       "      <th>business_Ambience_intimate</th>\n",
       "      <th>business_Ambience_classy</th>\n",
       "      <th>business_Ambience_hipster</th>\n",
       "      <th>business_Ambience_trendy</th>\n",
       "      <th>business_Ambience_upscale</th>\n",
       "      <th>business_RestaurantsGoodForGroups</th>\n",
       "      <th>business_RestaurantsReservations</th>\n",
       "      <th>business_HappyHour</th>\n",
       "      <th>business_RestaurantsTableService</th>\n",
       "      <th>business_GoodForMeal_dessert</th>\n",
       "      <th>business_GoodForMeal_latenight</th>\n",
       "      <th>business_GoodForMeal_lunch</th>\n",
       "      <th>business_GoodForMeal_dinner</th>\n",
       "      <th>business_GoodForMeal_breakfast</th>\n",
       "      <th>business_GoodForMeal_brunch</th>\n",
       "      <th>business_DogsAllowed</th>\n",
       "      <th>business_DietaryRestrictions_gluten-free</th>\n",
       "      <th>business_DietaryRestrictions_vegan</th>\n",
       "      <th>business_DietaryRestrictions_kosher</th>\n",
       "      <th>business_DietaryRestrictions_halal</th>\n",
       "      <th>business_DietaryRestrictions_soy-free</th>\n",
       "      <th>business_DietaryRestrictions_vegetarian</th>\n",
       "      <th>review_dist_to_centroid</th>\n",
       "      <th>review_bboxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>435362</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1036613</td>\n",
       "      <td>35338</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-13</td>\n",
       "      <td>My wife and I regularly hit up Ah-So for Happy...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>85395</td>\n",
       "      <td>33.462856</td>\n",
       "      <td>-112.391772</td>\n",
       "      <td>3.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(33.4628556191, -112.391772403, 33.4628556191,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1684017</td>\n",
       "      <td>16561</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>We came in for dinner based on the yelp (and s...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>17</td>\n",
       "      <td>44113</td>\n",
       "      <td>41.488915</td>\n",
       "      <td>-81.709046</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24.575284</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.4889146, -81.7090...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1321092</td>\n",
       "      <td>4949</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>If I could give you a 0, I would. I called and...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>488</td>\n",
       "      <td>17</td>\n",
       "      <td>44280</td>\n",
       "      <td>41.237658</td>\n",
       "      <td>-81.930948</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.2376583, -81.9309...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1704099</td>\n",
       "      <td>5847</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-11-15</td>\n",
       "      <td>we made a reservation for saturday night at 83...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>457</td>\n",
       "      <td>17</td>\n",
       "      <td>44136</td>\n",
       "      <td>41.313731</td>\n",
       "      <td>-81.816986</td>\n",
       "      <td>3.5</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.956597</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.3738834, -81.8169...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45222</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3.45</td>\n",
       "      <td>522208</td>\n",
       "      <td>11183</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>Had lunch here with my bf and his friend.  We ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>44017</td>\n",
       "      <td>41.373883</td>\n",
       "      <td>-81.891878</td>\n",
       "      <td>3.5</td>\n",
       "      <td>211</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.478901</td>\n",
       "      <td>(41.2376583, -81.9309482, 41.3738834, -81.8918...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691758</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>2675353</td>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>I love chipotle but not this location. Maybe I...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>323</td>\n",
       "      <td>14</td>\n",
       "      <td>89030</td>\n",
       "      <td>36.240263</td>\n",
       "      <td>-115.116185</td>\n",
       "      <td>3.0</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.2402631028, -115.116185266, 36.2402631028,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691759</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1994969</td>\n",
       "      <td>30791</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>This was my first time not being satisfied wit...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>14</td>\n",
       "      <td>89031</td>\n",
       "      <td>36.275981</td>\n",
       "      <td>-115.179177</td>\n",
       "      <td>1.5</td>\n",
       "      <td>139</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.012556</td>\n",
       "      <td>(36.1453494842, -115.3320001, 36.2759814851, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691760</th>\n",
       "      <td>393091</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1754541</td>\n",
       "      <td>17300</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>I honestly wish I could give no stars. 1. The ...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "      <td>89104</td>\n",
       "      <td>36.145349</td>\n",
       "      <td>-115.156020</td>\n",
       "      <td>3.0</td>\n",
       "      <td>332</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.179159</td>\n",
       "      <td>(36.1453494842, -115.156019926, 36.2406450132,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691761</th>\n",
       "      <td>485661</td>\n",
       "      <td>33</td>\n",
       "      <td>2014-06-28</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2423384</td>\n",
       "      <td>2320</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>Went here because of the reviews and my nephew...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "      <td>89102</td>\n",
       "      <td>36.126901</td>\n",
       "      <td>-115.197803</td>\n",
       "      <td>4.0</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(36.126900851, -115.197803344, 36.126900851, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691762</th>\n",
       "      <td>467903</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-05-17</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2644336</td>\n",
       "      <td>25293</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>Visited last night, which was a Monday and the...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>288</td>\n",
       "      <td>21</td>\n",
       "      <td>H3B 3E9</td>\n",
       "      <td>45.504015</td>\n",
       "      <td>-73.568177</td>\n",
       "      <td>4.0</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(45.5040151, -73.5681774, 45.5040151, -73.5681...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2691763 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  user_review_count user_yelping_since  user_average_stars  \\\n",
       "0        435362                  6         2015-03-13                4.67   \n",
       "1         45222                 11         2012-07-16                3.45   \n",
       "2         45222                 11         2012-07-16                3.45   \n",
       "3         45222                 11         2012-07-16                3.45   \n",
       "4         45222                 11         2012-07-16                3.45   \n",
       "...         ...                ...                ...                 ...   \n",
       "2691758  393091                  9         2015-01-31                3.42   \n",
       "2691759  393091                  9         2015-01-31                3.42   \n",
       "2691760  393091                  9         2015-01-31                3.42   \n",
       "2691761  485661                 33         2014-06-28                3.97   \n",
       "2691762  467903                  5         2010-05-17                3.50   \n",
       "\n",
       "        review_id business_id  review_stars review_date  \\\n",
       "0         1036613       35338             4  2015-03-13   \n",
       "1         1684017       16561             3  2013-06-08   \n",
       "2         1321092        4949             1  2012-07-22   \n",
       "3         1704099        5847             5  2012-11-15   \n",
       "4          522208       11183             3  2012-07-22   \n",
       "...           ...         ...           ...         ...   \n",
       "2691758   2675353         328             2  2016-06-30   \n",
       "2691759   1994969       30791             1  2017-07-23   \n",
       "2691760   1754541       17300             1  2016-07-11   \n",
       "2691761   2423384        2320             3  2015-12-28   \n",
       "2691762   2644336       25293             4  2017-04-18   \n",
       "\n",
       "                                             business_text  review_useful  \\\n",
       "0        My wife and I regularly hit up Ah-So for Happy...              0   \n",
       "1        We came in for dinner based on the yelp (and s...              0   \n",
       "2        If I could give you a 0, I would. I called and...              3   \n",
       "3        we made a reservation for saturday night at 83...              5   \n",
       "4        Had lunch here with my bf and his friend.  We ...              2   \n",
       "...                                                    ...            ...   \n",
       "2691758  I love chipotle but not this location. Maybe I...              0   \n",
       "2691759  This was my first time not being satisfied wit...              1   \n",
       "2691760  I honestly wish I could give no stars. 1. The ...              6   \n",
       "2691761  Went here because of the reviews and my nephew...              0   \n",
       "2691762  Visited last night, which was a Monday and the...              0   \n",
       "\n",
       "         review_funny review_cool business_city business_state  \\\n",
       "0                   0           1           162              1   \n",
       "1                   1           1            82             17   \n",
       "2                   2           1           488             17   \n",
       "3                   0           1           457             17   \n",
       "4                   0           1            32             17   \n",
       "...               ...         ...           ...            ...   \n",
       "2691758             0           2           323             14   \n",
       "2691759             0           1           323             14   \n",
       "2691760             0           2           216             14   \n",
       "2691761             0           1           216             14   \n",
       "2691762             0           1           288             21   \n",
       "\n",
       "        business_postal_code  business_latitude  business_longitude  \\\n",
       "0                      85395          33.462856         -112.391772   \n",
       "1                      44113          41.488915          -81.709046   \n",
       "2                      44280          41.237658          -81.930948   \n",
       "3                      44136          41.313731          -81.816986   \n",
       "4                      44017          41.373883          -81.891878   \n",
       "...                      ...                ...                 ...   \n",
       "2691758                89030          36.240263         -115.116185   \n",
       "2691759                89031          36.275981         -115.179177   \n",
       "2691760                89104          36.145349         -115.156020   \n",
       "2691761                89102          36.126901         -115.197803   \n",
       "2691762              H3B 3E9          45.504015          -73.568177   \n",
       "\n",
       "         business_avg_stars  business_review_count  \\\n",
       "0                       3.0                    140   \n",
       "1                       4.0                    138   \n",
       "2                       3.0                     16   \n",
       "3                       3.5                    125   \n",
       "4                       3.5                    211   \n",
       "...                     ...                    ...   \n",
       "2691758                 3.0                    103   \n",
       "2691759                 1.5                    139   \n",
       "2691760                 3.0                    332   \n",
       "2691761                 4.0                    346   \n",
       "2691762                 4.0                    292   \n",
       "\n",
       "        business_WheelchairAccessible business_BikeParking business_Alcohol  \\\n",
       "0                                   1                    1                1   \n",
       "1                                   1                    2                1   \n",
       "2                                   1                    1                1   \n",
       "3                                   2                    1                1   \n",
       "4                                   1                    0                1   \n",
       "...                               ...                  ...              ...   \n",
       "2691758                             1                    1                1   \n",
       "2691759                             2                    1                1   \n",
       "2691760                             2                    1                1   \n",
       "2691761                             1                    1                1   \n",
       "2691762                             1                    0                1   \n",
       "\n",
       "        business_RestaurantsAttire business_Ambience_intimate  \\\n",
       "0                                0                          1   \n",
       "1                                0                          1   \n",
       "2                                0                          1   \n",
       "3                                0                          1   \n",
       "4                                0                          1   \n",
       "...                            ...                        ...   \n",
       "2691758                          0                          1   \n",
       "2691759                          0                          1   \n",
       "2691760                          0                          1   \n",
       "2691761                          0                          1   \n",
       "2691762                          0                          1   \n",
       "\n",
       "        business_Ambience_classy business_Ambience_hipster  \\\n",
       "0                              1                         1   \n",
       "1                              1                         1   \n",
       "2                              1                         1   \n",
       "3                              1                         1   \n",
       "4                              1                         1   \n",
       "...                          ...                       ...   \n",
       "2691758                        1                         1   \n",
       "2691759                        1                         1   \n",
       "2691760                        1                         1   \n",
       "2691761                        1                         1   \n",
       "2691762                        1                         1   \n",
       "\n",
       "        business_Ambience_trendy business_Ambience_upscale  \\\n",
       "0                              1                         1   \n",
       "1                              1                         1   \n",
       "2                              1                         1   \n",
       "3                              1                         1   \n",
       "4                              1                         1   \n",
       "...                          ...                       ...   \n",
       "2691758                        1                         1   \n",
       "2691759                        1                         1   \n",
       "2691760                        1                         1   \n",
       "2691761                        1                         1   \n",
       "2691762                        1                         1   \n",
       "\n",
       "        business_RestaurantsGoodForGroups business_RestaurantsReservations  \\\n",
       "0                                       1                                0   \n",
       "1                                       1                                0   \n",
       "2                                       1                                0   \n",
       "3                                       1                                0   \n",
       "4                                       1                                0   \n",
       "...                                   ...                              ...   \n",
       "2691758                                 1                                0   \n",
       "2691759                                 1                                0   \n",
       "2691760                                 1                                0   \n",
       "2691761                                 1                                0   \n",
       "2691762                                 1                                0   \n",
       "\n",
       "        business_HappyHour business_RestaurantsTableService  \\\n",
       "0                        1                                1   \n",
       "1                        1                                1   \n",
       "2                        1                                1   \n",
       "3                        1                                1   \n",
       "4                        1                                1   \n",
       "...                    ...                              ...   \n",
       "2691758                  1                                1   \n",
       "2691759                  1                                1   \n",
       "2691760                  1                                1   \n",
       "2691761                  1                                1   \n",
       "2691762                  1                                1   \n",
       "\n",
       "        business_GoodForMeal_dessert business_GoodForMeal_latenight  \\\n",
       "0                                  1                              0   \n",
       "1                                  1                              1   \n",
       "2                                  1                              1   \n",
       "3                                  1                              1   \n",
       "4                                  1                              1   \n",
       "...                              ...                            ...   \n",
       "2691758                            1                              1   \n",
       "2691759                            1                              1   \n",
       "2691760                            1                              1   \n",
       "2691761                            1                              0   \n",
       "2691762                            1                              1   \n",
       "\n",
       "        business_GoodForMeal_lunch business_GoodForMeal_dinner  \\\n",
       "0                                0                           2   \n",
       "1                                1                           1   \n",
       "2                                1                           1   \n",
       "3                                1                           1   \n",
       "4                                1                           1   \n",
       "...                            ...                         ...   \n",
       "2691758                          1                           1   \n",
       "2691759                          1                           1   \n",
       "2691760                          1                           1   \n",
       "2691761                          2                           2   \n",
       "2691762                          1                           1   \n",
       "\n",
       "        business_GoodForMeal_breakfast business_GoodForMeal_brunch  \\\n",
       "0                                    2                           0   \n",
       "1                                    1                           1   \n",
       "2                                    1                           1   \n",
       "3                                    1                           1   \n",
       "4                                    1                           1   \n",
       "...                                ...                         ...   \n",
       "2691758                              1                           1   \n",
       "2691759                              1                           1   \n",
       "2691760                              1                           1   \n",
       "2691761                              2                           0   \n",
       "2691762                              1                           1   \n",
       "\n",
       "        business_DogsAllowed business_DietaryRestrictions_gluten-free  \\\n",
       "0                          1                                        1   \n",
       "1                          1                                        1   \n",
       "2                          1                                        1   \n",
       "3                          1                                        1   \n",
       "4                          1                                        1   \n",
       "...                      ...                                      ...   \n",
       "2691758                    0                                        1   \n",
       "2691759                    1                                        1   \n",
       "2691760                    1                                        1   \n",
       "2691761                    1                                        1   \n",
       "2691762                    1                                        1   \n",
       "\n",
       "        business_DietaryRestrictions_vegan  \\\n",
       "0                                        1   \n",
       "1                                        1   \n",
       "2                                        1   \n",
       "3                                        1   \n",
       "4                                        1   \n",
       "...                                    ...   \n",
       "2691758                                  1   \n",
       "2691759                                  1   \n",
       "2691760                                  1   \n",
       "2691761                                  1   \n",
       "2691762                                  1   \n",
       "\n",
       "        business_DietaryRestrictions_kosher  \\\n",
       "0                                         1   \n",
       "1                                         1   \n",
       "2                                         1   \n",
       "3                                         1   \n",
       "4                                         1   \n",
       "...                                     ...   \n",
       "2691758                                   1   \n",
       "2691759                                   1   \n",
       "2691760                                   1   \n",
       "2691761                                   1   \n",
       "2691762                                   1   \n",
       "\n",
       "        business_DietaryRestrictions_halal  \\\n",
       "0                                        1   \n",
       "1                                        1   \n",
       "2                                        1   \n",
       "3                                        1   \n",
       "4                                        1   \n",
       "...                                    ...   \n",
       "2691758                                  1   \n",
       "2691759                                  1   \n",
       "2691760                                  1   \n",
       "2691761                                  1   \n",
       "2691762                                  1   \n",
       "\n",
       "        business_DietaryRestrictions_soy-free  \\\n",
       "0                                           1   \n",
       "1                                           1   \n",
       "2                                           1   \n",
       "3                                           1   \n",
       "4                                           1   \n",
       "...                                       ...   \n",
       "2691758                                     1   \n",
       "2691759                                     1   \n",
       "2691760                                     1   \n",
       "2691761                                     1   \n",
       "2691762                                     1   \n",
       "\n",
       "        business_DietaryRestrictions_vegetarian  review_dist_to_centroid  \\\n",
       "0                                             1                 0.000000   \n",
       "1                                             1                24.575284   \n",
       "2                                             1                 0.000000   \n",
       "3                                             1                 7.956597   \n",
       "4                                             1                15.478901   \n",
       "...                                         ...                      ...   \n",
       "2691758                                       1                 0.000000   \n",
       "2691759                                       1                10.012556   \n",
       "2691760                                       1                11.179159   \n",
       "2691761                                       1                 0.000000   \n",
       "2691762                                       1                 0.000000   \n",
       "\n",
       "                                             review_bboxes  \n",
       "0        (33.4628556191, -112.391772403, 33.4628556191,...  \n",
       "1        (41.2376583, -81.9309482, 41.4889146, -81.7090...  \n",
       "2        (41.2376583, -81.9309482, 41.2376583, -81.9309...  \n",
       "3        (41.2376583, -81.9309482, 41.3738834, -81.8169...  \n",
       "4        (41.2376583, -81.9309482, 41.3738834, -81.8918...  \n",
       "...                                                    ...  \n",
       "2691758  (36.2402631028, -115.116185266, 36.2402631028,...  \n",
       "2691759  (36.1453494842, -115.3320001, 36.2759814851, -...  \n",
       "2691760  (36.1453494842, -115.156019926, 36.2406450132,...  \n",
       "2691761  (36.126900851, -115.197803344, 36.126900851, -...  \n",
       "2691762  (45.5040151, -73.5681774, 45.5040151, -73.5681...  \n",
       "\n",
       "[2691763 rows x 47 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.rename(cols_to_rename_mapping, inplace=True, axis=1)\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f1d15df7-4035-49c8-9ac4-ae419066428f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrongly named cols: []\n",
      "cols: ['user_id', 'user_review_count', 'user_yelping_since', 'user_average_stars', 'review_id', 'business_id', 'review_stars', 'review_date', 'business_text', 'review_useful', 'review_funny', 'review_cool', 'business_city', 'business_state', 'business_postal_code', 'business_latitude', 'business_longitude', 'business_avg_stars', 'business_review_count', 'business_WheelchairAccessible', 'business_BikeParking', 'business_Alcohol', 'business_RestaurantsAttire', 'business_Ambience_intimate', 'business_Ambience_classy', 'business_Ambience_hipster', 'business_Ambience_trendy', 'business_Ambience_upscale', 'business_RestaurantsGoodForGroups', 'business_RestaurantsReservations', 'business_HappyHour', 'business_RestaurantsTableService', 'business_GoodForMeal_dessert', 'business_GoodForMeal_latenight', 'business_GoodForMeal_lunch', 'business_GoodForMeal_dinner', 'business_GoodForMeal_breakfast', 'business_GoodForMeal_brunch', 'business_DogsAllowed', 'business_DietaryRestrictions_gluten-free', 'business_DietaryRestrictions_vegan', 'business_DietaryRestrictions_kosher', 'business_DietaryRestrictions_halal', 'business_DietaryRestrictions_soy-free', 'business_DietaryRestrictions_vegetarian', 'review_dist_to_centroid', 'review_bboxes']\n"
     ]
    }
   ],
   "source": [
    "# Sanity check all cols properly named (prefixed with one of ['user_', 'review_', 'business_']\n",
    "cols = list(merged_df.columns)\n",
    "err_named_cols = []\n",
    "\n",
    "\n",
    "for col in cols:\n",
    "    if not (\"user_\" in col or \"review_\" in col or \"business_\" in col):\n",
    "        err_named_cols.append(col)\n",
    "\n",
    "print(\"wrongly named cols:\", err_named_cols)\n",
    "print(\"cols:\", cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "91b8c08d-4f96-4e9c-8ce5-d30abc019ea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_cols:\n",
      "['user_id', 'user_review_count', 'user_yelping_since', 'user_average_stars'] \n",
      "\n",
      "review_cols:\n",
      "['review_id', 'review_stars', 'review_date', 'review_useful', 'review_funny', 'review_cool', 'review_dist_to_centroid', 'review_bboxes'] \n",
      "\n",
      "business_cols:\n",
      "['business_id', 'business_text', 'business_city', 'business_state', 'business_postal_code', 'business_latitude', 'business_longitude', 'business_avg_stars', 'business_review_count', 'business_WheelchairAccessible', 'business_BikeParking', 'business_Alcohol', 'business_RestaurantsAttire', 'business_Ambience_intimate', 'business_Ambience_classy', 'business_Ambience_hipster', 'business_Ambience_trendy', 'business_Ambience_upscale', 'business_RestaurantsGoodForGroups', 'business_RestaurantsReservations', 'business_HappyHour', 'business_RestaurantsTableService', 'business_GoodForMeal_dessert', 'business_GoodForMeal_latenight', 'business_GoodForMeal_lunch', 'business_GoodForMeal_dinner', 'business_GoodForMeal_breakfast', 'business_GoodForMeal_brunch', 'business_DogsAllowed', 'business_DietaryRestrictions_gluten-free', 'business_DietaryRestrictions_vegan', 'business_DietaryRestrictions_kosher', 'business_DietaryRestrictions_halal', 'business_DietaryRestrictions_soy-free', 'business_DietaryRestrictions_vegetarian'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "user_cols = [col for col in merged_df.columns if col.split('_')[0] == \"user\" in col]\n",
    "review_cols = [col for col in merged_df.columns if col.split('_')[0] == \"review\" in col]\n",
    "business_cols = [col for col in merged_df.columns if col.split('_')[0] == \"business\" in col]\n",
    "\n",
    "print(\"user_cols:\")\n",
    "print(user_cols, \"\\n\")\n",
    "print(\"review_cols:\")\n",
    "print(review_cols, \"\\n\")\n",
    "print(\"business_cols:\")\n",
    "print(business_cols, \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d1ab470b-54fb-413a-ab0e-8897d869eeee",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.to_csv(f\"{_data_dir}/pre_processed_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7f4602e6-7db7-426e-80b6-cd4d7c7ec4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dtypes_map': {'RestaurantsAttire': 'category',\n",
       "  'state': 'category',\n",
       "  'DietaryRestrictions_kosher': 'category',\n",
       "  'HappyHour': 'category',\n",
       "  'Ambience_trendy': 'category',\n",
       "  'RestaurantsGoodForGroups': 'category',\n",
       "  'GoodForMeal_dinner': 'category',\n",
       "  'cool': 'category',\n",
       "  'DietaryRestrictions_gluten-free': 'category',\n",
       "  'GoodForMeal_dessert': 'category',\n",
       "  'Ambience_intimate': 'category',\n",
       "  'GoodForMeal_latenight': 'category',\n",
       "  'Ambience_upscale': 'category',\n",
       "  'review_id': 'category',\n",
       "  'GoodForMeal_brunch': 'category',\n",
       "  'DogsAllowed': 'category',\n",
       "  'Alcohol': 'category',\n",
       "  'WheelchairAccessible': 'category',\n",
       "  'DietaryRestrictions_vegetarian': 'category',\n",
       "  'DietaryRestrictions_soy-free': 'category',\n",
       "  'DietaryRestrictions_halal': 'category',\n",
       "  'DietaryRestrictions_vegan': 'category',\n",
       "  'city': 'category',\n",
       "  'RestaurantsTableService': 'category',\n",
       "  'GoodForMeal_lunch': 'category',\n",
       "  'Ambience_classy': 'category',\n",
       "  'user_id': 'category',\n",
       "  'business_id': 'category',\n",
       "  'Ambience_hipster': 'category',\n",
       "  'BikeParking': 'category',\n",
       "  'GoodForMeal_breakfast': 'category',\n",
       "  'RestaurantsReservations': 'category',\n",
       "  'text': 'string',\n",
       "  'date': 'string',\n",
       "  'yelping_since': 'string',\n",
       "  'postal_code': 'string',\n",
       "  'user_average_stars': 'float',\n",
       "  'latitude': 'float',\n",
       "  'longitude': 'float',\n",
       "  'business_avg_stars': 'float',\n",
       "  'user_review_count': 'int',\n",
       "  'stars': 'int',\n",
       "  'useful': 'int',\n",
       "  'funny': 'int',\n",
       "  'business_review_count': 'int'},\n",
       " 'col_cat_enc_map': {'RestaurantsAttire': LabelEncoder(),\n",
       "  'state': LabelEncoder(),\n",
       "  'DietaryRestrictions_kosher': LabelEncoder(),\n",
       "  'HappyHour': LabelEncoder(),\n",
       "  'Ambience_trendy': LabelEncoder(),\n",
       "  'RestaurantsGoodForGroups': LabelEncoder(),\n",
       "  'GoodForMeal_dinner': LabelEncoder(),\n",
       "  'cool': LabelEncoder(),\n",
       "  'DietaryRestrictions_gluten-free': LabelEncoder(),\n",
       "  'GoodForMeal_dessert': LabelEncoder(),\n",
       "  'Ambience_intimate': LabelEncoder(),\n",
       "  'GoodForMeal_latenight': LabelEncoder(),\n",
       "  'Ambience_upscale': LabelEncoder(),\n",
       "  'review_id': LabelEncoder(),\n",
       "  'GoodForMeal_brunch': LabelEncoder(),\n",
       "  'DogsAllowed': LabelEncoder(),\n",
       "  'Alcohol': LabelEncoder(),\n",
       "  'WheelchairAccessible': LabelEncoder(),\n",
       "  'DietaryRestrictions_vegetarian': LabelEncoder(),\n",
       "  'DietaryRestrictions_soy-free': LabelEncoder(),\n",
       "  'DietaryRestrictions_halal': LabelEncoder(),\n",
       "  'DietaryRestrictions_vegan': LabelEncoder(),\n",
       "  'city': LabelEncoder(),\n",
       "  'RestaurantsTableService': LabelEncoder(),\n",
       "  'GoodForMeal_lunch': LabelEncoder(),\n",
       "  'Ambience_classy': LabelEncoder(),\n",
       "  'user_id': LabelEncoder(),\n",
       "  'business_id': LabelEncoder(),\n",
       "  'Ambience_hipster': LabelEncoder(),\n",
       "  'BikeParking': LabelEncoder(),\n",
       "  'GoodForMeal_breakfast': LabelEncoder(),\n",
       "  'RestaurantsReservations': LabelEncoder()}}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "17c5e399-00cb-409d-b4c5-7a15628fc50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtypes_map = df_metadata['dtypes_map']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17572206-42da-47f1-9fe4-f4778158b62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update dtypes map\n",
    "new_dtypes_map = {}\n",
    "\n",
    "for col, dtype in dtypes_map.items():\n",
    "    if col in cols_to_rename_mapping.keys():\n",
    "        new_dtypes_map[cols_to_rename_mapping[col]] = dtype\n",
    "    else:\n",
    "        new_dtypes_map[col] = dtype\n",
    "df_metadata['dtypes_map'] = new_dtypes_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0d5b3fdd-c125-4db0-8f93-ceb8fbcbeb08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update encs map\n",
    "encs_map = df_metadata['col_cat_enc_map']\n",
    "new_encs_map = {}\n",
    "for col, enc in encs_map.items():\n",
    "    if col in cols_to_rename_mapping.keys():\n",
    "        new_encs_map[cols_to_rename_mapping[col]] = enc\n",
    "    else:\n",
    "        new_encs_map[col] = enc\n",
    "\n",
    "df_metadata['col_cat_enc_map'] = new_encs_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c4eae4df-52e9-4f07-ae18-7e3f82531a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'business_RestaurantsAttire': LabelEncoder(),\n",
       " 'business_state': LabelEncoder(),\n",
       " 'business_DietaryRestrictions_kosher': LabelEncoder(),\n",
       " 'business_HappyHour': LabelEncoder(),\n",
       " 'business_Ambience_trendy': LabelEncoder(),\n",
       " 'business_RestaurantsGoodForGroups': LabelEncoder(),\n",
       " 'business_GoodForMeal_dinner': LabelEncoder(),\n",
       " 'review_cool': LabelEncoder(),\n",
       " 'business_DietaryRestrictions_gluten-free': LabelEncoder(),\n",
       " 'business_GoodForMeal_dessert': LabelEncoder(),\n",
       " 'business_Ambience_intimate': LabelEncoder(),\n",
       " 'business_GoodForMeal_latenight': LabelEncoder(),\n",
       " 'business_Ambience_upscale': LabelEncoder(),\n",
       " 'review_id': LabelEncoder(),\n",
       " 'business_GoodForMeal_brunch': LabelEncoder(),\n",
       " 'business_DogsAllowed': LabelEncoder(),\n",
       " 'business_Alcohol': LabelEncoder(),\n",
       " 'business_WheelchairAccessible': LabelEncoder(),\n",
       " 'business_DietaryRestrictions_vegetarian': LabelEncoder(),\n",
       " 'business_DietaryRestrictions_soy-free': LabelEncoder(),\n",
       " 'business_DietaryRestrictions_halal': LabelEncoder(),\n",
       " 'business_DietaryRestrictions_vegan': LabelEncoder(),\n",
       " 'business_city': LabelEncoder(),\n",
       " 'business_RestaurantsTableService': LabelEncoder(),\n",
       " 'business_GoodForMeal_lunch': LabelEncoder(),\n",
       " 'business_Ambience_classy': LabelEncoder(),\n",
       " 'user_id': LabelEncoder(),\n",
       " 'business_id': LabelEncoder(),\n",
       " 'business_Ambience_hipster': LabelEncoder(),\n",
       " 'business_BikeParking': LabelEncoder(),\n",
       " 'business_GoodForMeal_breakfast': LabelEncoder(),\n",
       " 'business_RestaurantsReservations': LabelEncoder()}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_encs_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "43123e44-ff42-45ab-876f-b2b9fd7582f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(data_dir + \"df_metadata.pkl\", 'wb') as file:\n",
    "    pickle.dump(df_metadata, file, protocol=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0e9d4dd0-b774-4740-8670-364a2487316b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dtypes_map': {'business_RestaurantsAttire': 'category',\n",
       "  'business_state': 'category',\n",
       "  'business_DietaryRestrictions_kosher': 'category',\n",
       "  'business_HappyHour': 'category',\n",
       "  'business_Ambience_trendy': 'category',\n",
       "  'business_RestaurantsGoodForGroups': 'category',\n",
       "  'business_GoodForMeal_dinner': 'category',\n",
       "  'review_cool': 'category',\n",
       "  'business_DietaryRestrictions_gluten-free': 'category',\n",
       "  'business_GoodForMeal_dessert': 'category',\n",
       "  'business_Ambience_intimate': 'category',\n",
       "  'business_GoodForMeal_latenight': 'category',\n",
       "  'business_Ambience_upscale': 'category',\n",
       "  'review_id': 'category',\n",
       "  'business_GoodForMeal_brunch': 'category',\n",
       "  'business_DogsAllowed': 'category',\n",
       "  'business_Alcohol': 'category',\n",
       "  'business_WheelchairAccessible': 'category',\n",
       "  'business_DietaryRestrictions_vegetarian': 'category',\n",
       "  'business_DietaryRestrictions_soy-free': 'category',\n",
       "  'business_DietaryRestrictions_halal': 'category',\n",
       "  'business_DietaryRestrictions_vegan': 'category',\n",
       "  'business_city': 'category',\n",
       "  'business_RestaurantsTableService': 'category',\n",
       "  'business_GoodForMeal_lunch': 'category',\n",
       "  'business_Ambience_classy': 'category',\n",
       "  'user_id': 'category',\n",
       "  'business_id': 'category',\n",
       "  'business_Ambience_hipster': 'category',\n",
       "  'business_BikeParking': 'category',\n",
       "  'business_GoodForMeal_breakfast': 'category',\n",
       "  'business_RestaurantsReservations': 'category',\n",
       "  'business_text': 'string',\n",
       "  'review_date': 'string',\n",
       "  'user_yelping_since': 'string',\n",
       "  'business_postal_code': 'string',\n",
       "  'user_average_stars': 'float',\n",
       "  'business_latitude': 'float',\n",
       "  'business_longitude': 'float',\n",
       "  'business_avg_stars': 'float',\n",
       "  'user_review_count': 'int',\n",
       "  'review_stars': 'int',\n",
       "  'review_useful': 'int',\n",
       "  'review_funny': 'int',\n",
       "  'business_review_count': 'int'},\n",
       " 'col_cat_enc_map': {'business_RestaurantsAttire': LabelEncoder(),\n",
       "  'business_state': LabelEncoder(),\n",
       "  'business_DietaryRestrictions_kosher': LabelEncoder(),\n",
       "  'business_HappyHour': LabelEncoder(),\n",
       "  'business_Ambience_trendy': LabelEncoder(),\n",
       "  'business_RestaurantsGoodForGroups': LabelEncoder(),\n",
       "  'business_GoodForMeal_dinner': LabelEncoder(),\n",
       "  'review_cool': LabelEncoder(),\n",
       "  'business_DietaryRestrictions_gluten-free': LabelEncoder(),\n",
       "  'business_GoodForMeal_dessert': LabelEncoder(),\n",
       "  'business_Ambience_intimate': LabelEncoder(),\n",
       "  'business_GoodForMeal_latenight': LabelEncoder(),\n",
       "  'business_Ambience_upscale': LabelEncoder(),\n",
       "  'review_id': LabelEncoder(),\n",
       "  'business_GoodForMeal_brunch': LabelEncoder(),\n",
       "  'business_DogsAllowed': LabelEncoder(),\n",
       "  'business_Alcohol': LabelEncoder(),\n",
       "  'business_WheelchairAccessible': LabelEncoder(),\n",
       "  'business_DietaryRestrictions_vegetarian': LabelEncoder(),\n",
       "  'business_DietaryRestrictions_soy-free': LabelEncoder(),\n",
       "  'business_DietaryRestrictions_halal': LabelEncoder(),\n",
       "  'business_DietaryRestrictions_vegan': LabelEncoder(),\n",
       "  'business_city': LabelEncoder(),\n",
       "  'business_RestaurantsTableService': LabelEncoder(),\n",
       "  'business_GoodForMeal_lunch': LabelEncoder(),\n",
       "  'business_Ambience_classy': LabelEncoder(),\n",
       "  'user_id': LabelEncoder(),\n",
       "  'business_id': LabelEncoder(),\n",
       "  'business_Ambience_hipster': LabelEncoder(),\n",
       "  'business_BikeParking': LabelEncoder(),\n",
       "  'business_GoodForMeal_breakfast': LabelEncoder(),\n",
       "  'business_RestaurantsReservations': LabelEncoder()}}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d361016-5233-4e90-92ca-8f9c155b52a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}