Estimating the Size of the Online Food Delivery Market.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating the Size of the Online Food Delivery Market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"cloudkitchens.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "[The Problem](#cell1) | [Importing the Data](#cell2) | [Cleaning the Data](#cell3) | [The Solution: Creating My Target Column](#cell4) | [Discussion: Potential Next Steps](#cell5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cell1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Problem\n",
    "\n",
    "#### Cloudkitchens\n",
    "Cloudkitchens is one of a number of new ghost kitchen companies providing kitchens to various types of restaurants. By providing a space for chefs, cooks, and restaurants to prepare food, Cloudkitchens claims to handle the rest of the process for its clients, including delivering food. \n",
    "\n",
    "#### Restaurants\n",
    "In order to estimate the market size of online food delivery, it is important to know how many restaurants offer online delivery services. This would be a simple aggregation if all restaurants registered their information on online food ordering (OFO) platforms the same way. But they don’t. Differences exist and an algorithm is needed to figure out if two restaurants on different platforms are actually the same restaurant or not.\n",
    "\n",
    "It is noteworthy that the same restaurant could appear on different delivery platforms with slight restaurant name differences, and slight restaurant location differences.\n",
    "\n",
    "I downloaded restaurant data for the US from [here](https://drive.google.com/file/d/16oyMbp9mtfGF88e_MFWrztZF-Fu3vyra/view?usp=sharing). Note the use of ​ctrl-a​ as a delimiter in the csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cell2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Data\n",
    "\n",
    "Back to [Top](#cell1)\n",
    "\n",
    "Before anything else, I have to import the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_dataset = pd.read_csv('css_public_all_ofos_locations.csv', \n",
    "                   sep = chr(1), \n",
    "                   low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "      <th>standardized_name</th>\n",
       "      <th>restaurant_chain</th>\n",
       "      <th>delivery_radius</th>\n",
       "      <th>geom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9744</td>\n",
       "      <td>Loco Coco</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.765004</td>\n",
       "      <td>-73.965961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "      <td>loco_coco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4903</td>\n",
       "      <td>Musubi</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.504641</td>\n",
       "      <td>-122.643806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>musubi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4723</td>\n",
       "      <td>The Bakers' Lounge</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.924559</td>\n",
       "      <td>-76.990115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>the_bakers_lounge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  restaurant_id                name platform sub_platform   latitude  \\\n",
       "0          9744           Loco Coco   caviar          NaN  40.765004   \n",
       "1          4903              Musubi   caviar          NaN  45.504641   \n",
       "2          4723  The Bakers' Lounge   caviar          NaN  38.924559   \n",
       "\n",
       "     longitude city country active  standardized_name restaurant_chain  \\\n",
       "0   -73.965961  NaN      US   TRUE          loco_coco              NaN   \n",
       "1  -122.643806  NaN      US  FALSE             musubi              NaN   \n",
       "2   -76.990115  NaN      US  FALSE  the_bakers_lounge              NaN   \n",
       "\n",
       "  delivery_radius  geom  \n",
       "0             NaN   NaN  \n",
       "1             NaN   NaN  \n",
       "2             NaN   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 644089 entries, 0 to 644088\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   restaurant_id      644089 non-null  object \n",
      " 1   name               644086 non-null  object \n",
      " 2   platform           644087 non-null  object \n",
      " 3   sub_platform       599311 non-null  object \n",
      " 4   latitude           644086 non-null  object \n",
      " 5   longitude          644070 non-null  object \n",
      " 6   city               56196 non-null   object \n",
      " 7   country            644071 non-null  object \n",
      " 8   active             643867 non-null  object \n",
      " 9   standardized_name  643872 non-null  object \n",
      " 10  restaurant_chain   169 non-null     object \n",
      " 11  delivery_radius    354661 non-null  object \n",
      " 12  geom               0 non-null       float64\n",
      "dtypes: float64(1), object(12)\n",
      "memory usage: 63.9+ MB\n"
     ]
    }
   ],
   "source": [
    "original_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The provided data has 644089 rows and 13 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "print('The provided data has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    original_dataset.shape[0], \n",
    "    original_dataset.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cell3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Data\n",
    "\n",
    "Back to [Top](#cell1)\n",
    "\n",
    "This step is very long with many substeps. Feel free to scroll all the way to the [Solution](#cell4) or [Discussion](#cell5) at the bottom of the notebook file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a New Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to keep the original provided dataset unaltered\n",
    "# I am making a copy of the data to use for data cleaning, pre-processing, etc\n",
    "\n",
    "data = original_dataset.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to check to see if I can manually insert the missing names of the 3 rows missing values in the 'name' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    restaurant_id name      platform sub_platform latitude  \\\n",
      "34794                        None  NaN  delivery.com          NaN      NaN   \n",
      "236516  Recreation & Wellness Ctr  NaN           NaN          NaN      NaN   \n",
      "316021                  Suite 112  NaN           NaN          NaN      NaN   \n",
      "\n",
      "       longitude city country active standardized_name restaurant_chain  \\\n",
      "34794        NaN  NaN      US  FALSE               NaN              NaN   \n",
      "236516       NaN  NaN     NaN    NaN               NaN              NaN   \n",
      "316021       NaN  NaN     NaN    NaN               NaN              NaN   \n",
      "\n",
      "       delivery_radius  geom  \n",
      "34794              NaN   NaN  \n",
      "236516             NaN   NaN  \n",
      "316021             NaN   NaN  \n"
     ]
    }
   ],
   "source": [
    "# using simple boolean indexing to examine the rows with missing values for the 'name' column\n",
    "\n",
    "print(data[data['name'].isnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deleting Rows with Little to No Value\n",
    "\n",
    "Because there is almost no other data I can use, such as latitude and longitude data, to determine what these restaurants may be, I am deleting these rows of data entirely from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(subset = ['name'], inplace = True)\n",
    "data.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 644086 rows and 13 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick Data Transformations on Restaurant Names\n",
    "\n",
    "In order to prevent issues with analyses in the future, I will clean up restaurant name data using some string methods to:\n",
    "- Make every character lowercase\n",
    "- Replace all dashes (\"-\") and underscores (\"_\") with spaces\n",
    "- Removing all apostophes (\"'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['name'] = data['name'].str.lower().str.replace('-', ' ').str.replace('_', ' ').str.replace('\\'', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing Columns\n",
    "\n",
    "Some columns do not offer me much value and can be removed entirely from the data set.\n",
    "- 'city': obsolete as I have latitude and longitude coordinate data\n",
    "- 'restaurant_chain': too many missing values, although this column may be useful for ML models\n",
    "- 'delivery_radius': too many missing values\n",
    "- 'geom': no provided description of what this column should represent and filled entirely with NaN values\n",
    "- 'standardized_name': may be useful in the future but will use the 'name' column instead for the time being"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['restaurant_id', 'name', 'platform', 'sub_platform', 'latitude',\n",
       "       'longitude', 'country', 'active'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['city', 'restaurant_chain', 'delivery_radius', 'geom', 'standardized_name'], inplace = True)\n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring Missing Latitude and Longitude Values\n",
    "\n",
    "My intention is to use the latitude and longitude values either as features in machine learning algorithms or to filter out duplicates simply. These two columns are very important to my goals and must be done carefully and thoroughly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [restaurant_id, name, platform, sub_platform, latitude, longitude, country, active]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# checking what missing values may exist in the latitude column\n",
    "# using simple boolean indexing to examine the rows with missing values for the 'latitude' column\n",
    "\n",
    "print(data[data['latitude'].isnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that getting rid of the rows where the value in the 'name' column was missing also removed the rows that were missing latitude data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only a few of the rows are displayed for easier viewing.\n",
      "\n",
      "--------------------------------------------------------\n",
      "\n",
      "                                            restaurant_id name  \\\n",
      "139275                                    20 Hugus Alley\"  usd   \n",
      "187182                                           Ste 190\"  usd   \n",
      "194074                                           Ste 170\"  usd   \n",
      "201826  (b/t N P St & N Rhode Island Ave) in Logan Cir...  usd   \n",
      "204009                                           Ste 100\"  usd   \n",
      "\n",
      "                                                 platform sub_platform  \\\n",
      "139275  [[\"open: 12:00, close: 18:00\"], [\"open: 12:00,...         TRUE   \n",
      "187182                       [[], [], [], [], [], [], []]        FALSE   \n",
      "194074  [[\"open: 12:00, close: 21:00\"], [\"open: 12:00,...         TRUE   \n",
      "201826  [[\"open: 12:00, close: 21:30\"], [\"open: 12:00,...         TRUE   \n",
      "204009  [[\"open: 12:00, close: 18:30\"], [\"open: 12:00,...         TRUE   \n",
      "\n",
      "                             latitude longitude country active  \n",
      "139275                      sugarfina       NaN     NaN     45  \n",
      "187182             ohjah_noodle_house       NaN     NaN     40  \n",
      "194074             novecento_pizzeria       NaN     NaN     50  \n",
      "201826  14th_street_cafe_asian_bistro       NaN     NaN     40  \n",
      "204009  straight_from_philly_steakout       NaN     NaN     45  \n"
     ]
    }
   ],
   "source": [
    "# using the same method as above to examine the rows with missing values for the 'longitude' column\n",
    "\n",
    "print('Only a few of the rows are displayed for easier viewing.\\n')\n",
    "print('--------------------------------------------------------\\n')\n",
    "print(data[data['longitude'].isnull()][:5])    # remove the indexing to see all rows with missing 'longitude' values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that, for whatever reason, data was not input and/or collected properly for the data points above. The values within each column seem to be in the wrong place. However, I can simply:\n",
    "- Look these restaurants up on the internet\n",
    "- Fill in + replace the existing values with correct values\n",
    "\n",
    "##### Correct Information for Restaurants\n",
    "I'll be looking up the restaurants up by their names, which I am presuming are the values found in the 'latitude' column. I can confirm I am collecting information for the correct restaurants with the other information available (but misplaced) in the other columns.\n",
    "- Row 139275 (Sugarfina) [info](https://www.yelp.com/biz/sugarfina-pasadena-pasadena-2)\n",
    "- Row 187182 (Ohjah Nooodle House) [info](https://www.yelp.com/biz/ohjah-noodle-house-las-vegas-3)\n",
    "- Row 194074 (Novecento Pizzeria) [info](https://www.yelp.com/biz/novecento-pizzeria-las-vegas)\n",
    "- Row 201826 (14th St Cafe Asian Bistro) [info](https://www.yelp.com/biz/14th-st-cafe-asian-bistro-washington-2)\n",
    "- Row 204009 (Straight From Philly Steakout) [info](https://www.yelp.com/biz/straight-from-philly-steakout-henderson-3)\n",
    "- Row 229923 (Tumbao Buns - Downtown Miami) [info](https://www.yelp.com/biz/tumbao-buns-downtown-miami-miami-2)\n",
    "    - The address found on the Yelp page doesn't match with the one provided on the data set. This is most likely due to the fact that this restaurant is technically a food truck, a mobile restuarant that can have different locations. The correct address is listed [here](https://postmates.com/merchant/tumbao-buns-miami)\n",
    "- Row 250285 (Dunkin Donuts) [info](https://www.yelp.com/biz/dunkin-donuts-pompano-beach-7?osq=dunkin+donuts+locations)\n",
    "- Row 294759 (Dos Coyotes Border Cafe) [info](https://www.grubhub.com/restaurant/dos-coyotes-border-cafe-8519-bond-rd-ste-100-elk-grove/1244011)\n",
    "- Row 298938 (China One) [info](https://www.grubhub.com/restaurant/china-one-7080-n-durango-dr-ste-130-las-vegas/394082)\n",
    "- Row 302088 (Fat Boy) [info](https://www.yelp.com/biz/fat-boy-henderson-2)\n",
    "- Row 341276 (Au Bon Pain) [info](https://www.yelp.com/biz/au-bon-pain-chicago-14)\n",
    "- Row 344993 (Ohjah Noodle House 2) [info](https://www.grubhub.com/restaurant/ohjah-noodle-house-35-e-horizon-ridge-pkwy-ste-160-henderson/1121695)\n",
    "    - This may, interestingly, be the same as the other Ohjah Noodle House listed above. Investigate further.\n",
    "- Row 355632 (Cinnabon) [info](https://www.zomato.com/maricopa-county-az/scholtzskys-cinnabon-1-goodyear)\n",
    "- Row 379700 (Thailicious Authentic Thai & Vegan) [info](https://www.yelp.com/biz/thailicious-authentic-thai-and-vegan-henderson?osq=thailicious)\n",
    "- Row 391644 (Boss's Slow Smoked BBQ) [info](https://www.yelp.com/biz/bosss-slow-smoked-bbq-n-las-vegas)\n",
    "- Row 415759 (Buon Gusto Ristorante) [info](https://www.yelp.com/biz/buon-gusto-ristorante-henderson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A closer look at our actual data set shows that the rows of data above are not unique data points but instead are repeats of rows directly preceding them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               restaurant_id           name  \\\n",
      "250284  70f366b3-a6fd-4634-8e57-7364e8f23cd2  dunkin donuts   \n",
      "250285                          Fl Turnpike\"            usd   \n",
      "\n",
      "                            platform sub_platform       latitude  \\\n",
      "250284                     postmates   restaurant    28.51478909   \n",
      "250285  [[], [], [], [], [], [], []]        FALSE  dunkin_donuts   \n",
      "\n",
      "           longitude country active  \n",
      "250284  -81.49956571      US    NaN  \n",
      "250285           NaN     NaN     40  \n"
     ]
    }
   ],
   "source": [
    "print(data.iloc[250284:250286,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sugarfina\n",
      "ohjah noodle house\n",
      "novecento pizzeria\n",
      "14th street cafe asian bistro\n",
      "straight from philly steakout\n",
      "tumbao buns\n",
      "dunkin donuts\n",
      "dos coyotes border cafe\n",
      "china one\n",
      "fat boy\n",
      "au bon pain\n",
      "ohjah noodle house #2\n",
      "cinnabon\n",
      "thailicious\n",
      "bosss slow smoked bbq\n",
      "buon gusto ristorante\n"
     ]
    }
   ],
   "source": [
    "indices_missing_lat = [139275, 187182, 194074, \n",
    "                    201826, 204009, 229923, \n",
    "                    250285, 294759, 298938, \n",
    "                    302088, 341276, 344993, \n",
    "                    355632, 379700, 391644, 415759]\n",
    "\n",
    "for index in indices_missing_lat:\n",
    "    print(data.iloc[index - 1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simple solution is to remove the rows missing 'longitude' values, as they represent duplicate entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(subset = ['longitude'], inplace = True)\n",
    "data.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 644070 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A Quick Glance at Country Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 644070 entries, 0 to 644069\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   restaurant_id  644070 non-null  object\n",
      " 1   name           644070 non-null  object\n",
      " 2   platform       644070 non-null  object\n",
      " 3   sub_platform   599295 non-null  object\n",
      " 4   latitude       644070 non-null  object\n",
      " 5   longitude      644070 non-null  object\n",
      " 6   country        644070 non-null  object\n",
      " 7   active         643850 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 39.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     644070\n",
       "unique        27\n",
       "top           US\n",
       "freq      643890\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['country'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the data points have 'US' listed in the 'country' column, so there would be value in examining the rows in which this is not the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96549</th>\n",
       "      <td>,USD,[[\"\"open: 12:00</td>\n",
       "      <td>close: 20:30\"\"]</td>\n",
       "      <td>[\"\"open: 12:00</td>\n",
       "      <td>close: 20:30\"\"]</td>\n",
       "      <td>[\"\"open: 12:00</td>\n",
       "      <td>close: 20:30\"\"]</td>\n",
       "      <td>close: 20:30\"\"]</td>\n",
       "      <td>close: 20:30\"\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96958</th>\n",
       "      <td>,USD,[[\"\"open: 11:30</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>[\"\"open: 11:30</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>[\"\"open: 11:30</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97841</th>\n",
       "      <td>,USD,[[\"\"open: 13:00</td>\n",
       "      <td>close: 23:00\"\"]</td>\n",
       "      <td>[\"\"open: 13:00</td>\n",
       "      <td>close: 23:00\"\"]</td>\n",
       "      <td>[\"\"open: 13:00</td>\n",
       "      <td>close: 23:00\"\"]</td>\n",
       "      <td>close: 23:00\"\"]</td>\n",
       "      <td>close: 00:00\"\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104012</th>\n",
       "      <td>,USD,[[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104302</th>\n",
       "      <td>,USD,[[\"\"open: 17:00</td>\n",
       "      <td>close: 23:30\"\"]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>close: 23:00\"\"]</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>[]]\"</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426413</th>\n",
       "      <td>,USD,[[\"\"open: 17:00</td>\n",
       "      <td>close: 19:00\"\"]</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>close: 19:00\"\"]</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>close: 19:00\"\"]</td>\n",
       "      <td>close: 19:00\"\"]</td>\n",
       "      <td>close: 21:30\"\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428302</th>\n",
       "      <td>,USD,[[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431379</th>\n",
       "      <td>,USD,[[\"\"open: 14:00</td>\n",
       "      <td>close: 22:00\"\"]</td>\n",
       "      <td>[\"\"open: 14:00</td>\n",
       "      <td>close: 22:00\"\"]</td>\n",
       "      <td>[\"\"open: 14:00</td>\n",
       "      <td>close: 22:00\"\"]</td>\n",
       "      <td>close: 22:00\"\"]</td>\n",
       "      <td>close: 22:00\"\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433563</th>\n",
       "      <td>,USD,[[]</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>[\"\"open: 18:30</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "      <td>[\"\"open: 17:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440752</th>\n",
       "      <td>,USD,[[\"\"open: 11:00</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>[\"\"open: 11:00</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>[\"\"open: 11:00</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "      <td>close: 20:00\"\"]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               restaurant_id              name          platform  \\\n",
       "96549   ,USD,[[\"\"open: 12:00   close: 20:30\"\"]    [\"\"open: 12:00   \n",
       "96958   ,USD,[[\"\"open: 11:30   close: 20:00\"\"]    [\"\"open: 11:30   \n",
       "97841   ,USD,[[\"\"open: 13:00   close: 23:00\"\"]    [\"\"open: 13:00   \n",
       "104012              ,USD,[[]                []                []   \n",
       "104302  ,USD,[[\"\"open: 17:00   close: 23:30\"\"]                []   \n",
       "...                      ...               ...               ...   \n",
       "426413  ,USD,[[\"\"open: 17:00   close: 19:00\"\"]    [\"\"open: 17:00   \n",
       "428302              ,USD,[[]                []                []   \n",
       "431379  ,USD,[[\"\"open: 14:00   close: 22:00\"\"]    [\"\"open: 14:00   \n",
       "433563              ,USD,[[]    [\"\"open: 17:00   close: 20:00\"\"]   \n",
       "440752  ,USD,[[\"\"open: 11:00   close: 20:00\"\"]    [\"\"open: 11:00   \n",
       "\n",
       "            sub_platform          latitude         longitude  \\\n",
       "96549    close: 20:30\"\"]    [\"\"open: 12:00   close: 20:30\"\"]   \n",
       "96958    close: 20:00\"\"]    [\"\"open: 11:30   close: 20:00\"\"]   \n",
       "97841    close: 23:00\"\"]    [\"\"open: 13:00   close: 23:00\"\"]   \n",
       "104012                []                []                []   \n",
       "104302    [\"\"open: 17:00   close: 23:00\"\"]    [\"\"open: 17:00   \n",
       "...                  ...               ...               ...   \n",
       "426413   close: 19:00\"\"]    [\"\"open: 17:00   close: 19:00\"\"]   \n",
       "428302                []                []                []   \n",
       "431379   close: 22:00\"\"]    [\"\"open: 14:00   close: 22:00\"\"]   \n",
       "433563    [\"\"open: 17:00   close: 20:00\"\"]    [\"\"open: 18:30   \n",
       "440752   close: 20:00\"\"]    [\"\"open: 11:00   close: 20:00\"\"]   \n",
       "\n",
       "                 country            active  \n",
       "96549    close: 20:30\"\"]   close: 20:30\"\"]  \n",
       "96958    close: 20:00\"\"]   close: 20:00\"\"]  \n",
       "97841    close: 23:00\"\"]   close: 00:00\"\"]  \n",
       "104012             FALSE               NaN  \n",
       "104302    [\"\"open: 17:00              []]\"  \n",
       "...                  ...               ...  \n",
       "426413   close: 19:00\"\"]   close: 21:30\"\"]  \n",
       "428302             FALSE               NaN  \n",
       "431379   close: 22:00\"\"]   close: 22:00\"\"]  \n",
       "433563    [\"\"open: 17:00    [\"\"open: 17:00  \n",
       "440752   close: 20:00\"\"]   close: 20:00\"\"]  \n",
       "\n",
       "[180 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_us_data = data[data['country'] != 'US']\n",
    "\n",
    "not_us_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems possible that the rows of data that don't state 'US' in the country column are also filled with data that was entered or collected incorrectly. \n",
    "\n",
    "Upon observation, there seems to be a mix of restaurant hours, names, delivery radii, booleans, and NaNs found throughout this data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.028 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "strange_us_rows = ((644070 - 643890) / 644070) * 100\n",
    "print(round(strange_us_rows, 3), '% of the dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of time, because only 0.028% of the data is mislabeled as such, the appropriate solution at this time is to simply remove these data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are now 643890 restaurants in the dataset and 8 columns of relevant data.\n"
     ]
    }
   ],
   "source": [
    "data = data[data['country'] == 'US']\n",
    "\n",
    "print('There are now {0} restaurants in the dataset and {1} columns of relevant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coordinates: Latitude\n",
    "\n",
    "At this stage my plan to solve the problem of identifying duplicates heavily relies upon the coordinate data (latitude and longitude) values of each restaurant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant_id    object\n",
       "name             object\n",
       "platform         object\n",
       "sub_platform     object\n",
       "latitude         object\n",
       "longitude        object\n",
       "country          object\n",
       "active           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant_id          9744\n",
       "name              loco coco\n",
       "platform             caviar\n",
       "sub_platform            NaN\n",
       "latitude          40.765004\n",
       "longitude        -73.965961\n",
       "country                  US\n",
       "active                 TRUE\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['name'] = data['name'].astype('str')\n",
    "data['platform'] = data['platform'].astype('str')\n",
    "data['latitude'] = data['latitude'].astype('float')\n",
    "data['longitude'] = data['longitude'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-90.000000    1683\n",
       " 40.762646     101\n",
       " 42.825685      80\n",
       " 34.039710      67\n",
       " 35.279820      66\n",
       "              ... \n",
       " 27.036954       1\n",
       " 33.996883       1\n",
       " 40.779721       1\n",
       " 33.998372       1\n",
       " 32.162496       1\n",
       "Name: latitude, Length: 544594, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['latitude'].value_counts(dropna = True).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of curiosity, I want to run the same observations on the longitude column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.000000      1722\n",
       "-73.969185      101\n",
       "-118.298741     100\n",
       "-73.879458       80\n",
       "-118.298796      67\n",
       "               ... \n",
       "-122.433768       1\n",
       "-90.110298        1\n",
       "-117.048787       1\n",
       "-75.224099        1\n",
       "-81.751534        1\n",
       "Name: longitude, Length: 541745, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['longitude'].value_counts(dropna = False).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, there are a lot of data points with a longitude value of 0. I will explore this further in a later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      restaurant_id                name      platform sub_platform  latitude  \\\n",
      "60            59739          bequ juice  delivery.com          NaN     -90.0   \n",
      "64            63820          ginos east  delivery.com          NaN     -90.0   \n",
      "100           64603          sori sushi  delivery.com          NaN     -90.0   \n",
      "201           63305          ezras food  delivery.com          NaN     -90.0   \n",
      "212           57676   cupping room cafe  delivery.com          NaN     -90.0   \n",
      "...             ...                 ...           ...          ...       ...   \n",
      "44649            81  cozy soup n burger  delivery.com          NaN     -90.0   \n",
      "44691         61491              wichit  delivery.com          NaN     -90.0   \n",
      "44715         61250           fdgdfgdfg  delivery.com          NaN     -90.0   \n",
      "44740         58580           neo sushi  delivery.com          NaN     -90.0   \n",
      "44746         62673          moonstruck  delivery.com          NaN     -90.0   \n",
      "\n",
      "       longitude country active  \n",
      "60           0.0      US  FALSE  \n",
      "64           0.0      US  FALSE  \n",
      "100          0.0      US  FALSE  \n",
      "201          0.0      US  FALSE  \n",
      "212          0.0      US  FALSE  \n",
      "...          ...     ...    ...  \n",
      "44649        0.0      US  FALSE  \n",
      "44691        0.0      US  FALSE  \n",
      "44715        0.0      US  FALSE  \n",
      "44740        0.0      US  FALSE  \n",
      "44746        0.0      US  FALSE  \n",
      "\n",
      "[1683 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "lat_90 = data[data['latitude'] == -90.000000]\n",
    "\n",
    "print(lat_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delivery.com    1.0\n",
       "Name: platform, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_90['platform'].value_counts(dropna = False, normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FALSE    1.0\n",
       "Name: active, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_90['active'].value_counts(dropna = False, normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1.0\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_90['longitude'].value_counts(dropna = False, normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all of the data points with a latitude value of -90, three interesting things the data points all have in common are:\n",
    "\n",
    "- All are considered \"inactive\"\n",
    "- All of these data points shared the platform delivery.com\n",
    "- All have a longitude value of 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doordash        354553\n",
       "postmates       110242\n",
       "grubhub          68579\n",
       "ubereats         65741\n",
       "delivery.com     34807\n",
       "caviar            9968\n",
       "Name: platform, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['platform'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quickly checking that it's not an issue with all delivery.com data but just the ones found earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>68986</td>\n",
       "      <td>crown fried chicken</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.634279</td>\n",
       "      <td>-73.949846</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>89239</td>\n",
       "      <td>capri ristorante</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.865140</td>\n",
       "      <td>-87.789410</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>73694</td>\n",
       "      <td>fin sushi bar</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.965212</td>\n",
       "      <td>-87.673360</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>86326</td>\n",
       "      <td>bon chon   harvard square</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.371670</td>\n",
       "      <td>-71.121040</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>68391</td>\n",
       "      <td>pizza man</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.931870</td>\n",
       "      <td>-117.949910</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44772</th>\n",
       "      <td>61696</td>\n",
       "      <td>balade</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.730305</td>\n",
       "      <td>-73.983176</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44773</th>\n",
       "      <td>63702</td>\n",
       "      <td>hello pasta</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.759411</td>\n",
       "      <td>-73.970211</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44774</th>\n",
       "      <td>71933</td>\n",
       "      <td>napoli italian restaurant by papa zack</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.731403</td>\n",
       "      <td>-95.475292</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44775</th>\n",
       "      <td>54099</td>\n",
       "      <td>fatburger</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.621215</td>\n",
       "      <td>-96.342298</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44776</th>\n",
       "      <td>36134</td>\n",
       "      <td>pita grill</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.764990</td>\n",
       "      <td>-73.987687</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34807 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                    name      platform  \\\n",
       "47            68986                     crown fried chicken  delivery.com   \n",
       "48            89239                        capri ristorante  delivery.com   \n",
       "49            73694                           fin sushi bar  delivery.com   \n",
       "50            86326               bon chon   harvard square  delivery.com   \n",
       "51            68391                               pizza man  delivery.com   \n",
       "...             ...                                     ...           ...   \n",
       "44772         61696                                  balade  delivery.com   \n",
       "44773         63702                             hello pasta  delivery.com   \n",
       "44774         71933  napoli italian restaurant by papa zack  delivery.com   \n",
       "44775         54099                               fatburger  delivery.com   \n",
       "44776         36134                              pita grill  delivery.com   \n",
       "\n",
       "      sub_platform   latitude   longitude country active  \n",
       "47             NaN  40.634279  -73.949846      US  FALSE  \n",
       "48             NaN  41.865140  -87.789410      US   TRUE  \n",
       "49             NaN  41.965212  -87.673360      US   TRUE  \n",
       "50             NaN  42.371670  -71.121040      US   TRUE  \n",
       "51             NaN  33.931870 -117.949910      US   TRUE  \n",
       "...            ...        ...         ...     ...    ...  \n",
       "44772          NaN  40.730305  -73.983176      US   TRUE  \n",
       "44773          NaN  40.759411  -73.970211      US   TRUE  \n",
       "44774          NaN  29.731403  -95.475292      US   TRUE  \n",
       "44775          NaN  30.621215  -96.342298      US   TRUE  \n",
       "44776          NaN  40.764990  -73.987687      US   TRUE  \n",
       "\n",
       "[34807 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['platform'] == 'delivery.com']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My suspicion is that the data represented incorrectly with latitude values of -90 may already exist in the data set somehow. I'll check that now with some quick searches through the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>59739</td>\n",
       "      <td>bequ juice</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27394</th>\n",
       "      <td>73431</td>\n",
       "      <td>bequ juice</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.728651</td>\n",
       "      <td>-73.986022</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39550</th>\n",
       "      <td>91771</td>\n",
       "      <td>bequ juice</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.728651</td>\n",
       "      <td>-73.986022</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215698</th>\n",
       "      <td>289315</td>\n",
       "      <td>bequ juice</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.728275</td>\n",
       "      <td>-73.985367</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id        name      platform sub_platform   latitude  \\\n",
       "60             59739  bequ juice  delivery.com          NaN -90.000000   \n",
       "27394          73431  bequ juice  delivery.com          NaN  40.728651   \n",
       "39550          91771  bequ juice  delivery.com          NaN  40.728651   \n",
       "215698        289315  bequ juice       grubhub      grubhub  40.728275   \n",
       "\n",
       "        longitude country active  \n",
       "60       0.000000      US  FALSE  \n",
       "27394  -73.986022      US   TRUE  \n",
       "39550  -73.986022      US   TRUE  \n",
       "215698 -73.985367      US   TRUE  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'bequ juice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems there is enough here to support my suspicions, as it seems like information on this platform was either updated and/or re-entered. However, if there is data present with name and platform data that doesn't have location data in the form of coordinates, I would have difficulty acquiring correct latitude and longitude data anyways. The time-sensitive solution is to simply remove the rows of data with missing latitude/longitude values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can remove the entries with -90 stated for their latitude values as they are simply not updated versions of data that exist already in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.762646    101\n",
       "42.825685     80\n",
       "34.039710     67\n",
       "35.279820     66\n",
       "34.039743     61\n",
       "            ... \n",
       "38.780604      1\n",
       "37.160258      1\n",
       "33.425420      1\n",
       "47.487414      1\n",
       "25.760583      1\n",
       "Name: latitude, Length: 544593, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['latitude'] != -90.000000]\n",
    "\n",
    "data['latitude'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coordinates: Longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-73.969185     101\n",
       "-118.298741    100\n",
       "-73.879458      80\n",
       "-118.298796     67\n",
       "-120.661130     66\n",
       "              ... \n",
       "-122.433768      1\n",
       "-90.110298       1\n",
       "-117.048787      1\n",
       "-75.224099       1\n",
       "-81.751534       1\n",
       "Name: longitude, Length: 541745, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['longitude'].value_counts(dropna = False).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier, I discovered many data points had a longitude value of 0. Removing the data points with latitude values of -90 should have removed many of the rows with longitude values of 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
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       "      <th>137</th>\n",
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       "      <td>jessica hui placeholder</td>\n",
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       "      <th>160</th>\n",
       "      <td>1526</td>\n",
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       "      <th>312</th>\n",
       "      <td>2045</td>\n",
       "      <td>zipchops   venice</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>6018</td>\n",
       "      <td>red hot chili pepper</td>\n",
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       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1229</td>\n",
       "      <td>duplicate</td>\n",
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       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>6060</td>\n",
       "      <td>[deprecated] saffron indian bistro</td>\n",
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       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>9239</td>\n",
       "      <td>bonci dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2277</td>\n",
       "      <td>test only   dan schwartz</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>6595</td>\n",
       "      <td>bluestem brasserie</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5674</th>\n",
       "      <td>10338</td>\n",
       "      <td>squaretest</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
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       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>438</td>\n",
       "      <td>the gryphon</td>\n",
       "      <td>caviar</td>\n",
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       "      <td>FALSE</td>\n",
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       "    <tr>\n",
       "      <th>6129</th>\n",
       "      <td>5012</td>\n",
       "      <td>testjh</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6420</th>\n",
       "      <td>623</td>\n",
       "      <td>trash</td>\n",
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       "    <tr>\n",
       "      <th>6862</th>\n",
       "      <td>7249</td>\n",
       "      <td>manpuku</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9105</th>\n",
       "      <td>6874</td>\n",
       "      <td>libby pizza</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10351</th>\n",
       "      <td>701</td>\n",
       "      <td>blank</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10634</th>\n",
       "      <td>10367</td>\n",
       "      <td>plum cafe</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10693</th>\n",
       "      <td>1260</td>\n",
       "      <td>zento trash bucket  do not enable</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12074</th>\n",
       "      <td>61898</td>\n",
       "      <td>pie eyed pizzeria</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12875</th>\n",
       "      <td>382</td>\n",
       "      <td>available merchant id</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15747</th>\n",
       "      <td>90142</td>\n",
       "      <td>dickeys bbq pit</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16677</th>\n",
       "      <td>1919</td>\n",
       "      <td>cheetos promo preview</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16844</th>\n",
       "      <td>3308</td>\n",
       "      <td>bird &amp; bone</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17415</th>\n",
       "      <td>3366</td>\n",
       "      <td>viking soul</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19858</th>\n",
       "      <td>6653</td>\n",
       "      <td>disabled lama beans</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20259</th>\n",
       "      <td>6932</td>\n",
       "      <td>[desprecated] basil thai restaurant &amp; bar</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21283</th>\n",
       "      <td>6841</td>\n",
       "      <td>jessica test</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22880</th>\n",
       "      <td>6316</td>\n",
       "      <td>thai palace</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23763</th>\n",
       "      <td>5972</td>\n",
       "      <td>eden silk road cuisine (reuse merchant)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24301</th>\n",
       "      <td>929</td>\n",
       "      <td>michelles kitchen</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27623</th>\n",
       "      <td>2525</td>\n",
       "      <td>nyc trash</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27877</th>\n",
       "      <td>64749</td>\n",
       "      <td>abhineet bhatnagar</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30631</th>\n",
       "      <td>355</td>\n",
       "      <td>open</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39902</th>\n",
       "      <td>63760</td>\n",
       "      <td>bgg grocery</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70081</th>\n",
       "      <td>404666</td>\n",
       "      <td>taco bueno  (6001 w interstate 40)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70233</th>\n",
       "      <td>27112</td>\n",
       "      <td>magic wok</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110136</th>\n",
       "      <td>404513</td>\n",
       "      <td>bgr the burger joint  (towne ctr blvd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115763</th>\n",
       "      <td>405297</td>\n",
       "      <td>charleys philly steaks  (clear creek rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                       name      platform  \\\n",
       "137             5648                    jessica hui placeholder        caviar   \n",
       "160             1526                             the halal guys        caviar   \n",
       "312             2045                          zipchops   venice        caviar   \n",
       "325             6018                       red hot chili pepper        caviar   \n",
       "402             1229                                  duplicate        caviar   \n",
       "816             6060         [deprecated] saffron indian bistro        caviar   \n",
       "1016             653                                      blank        caviar   \n",
       "1589            9239                                  bonci dnu        caviar   \n",
       "2018            2277                   test only   dan schwartz        caviar   \n",
       "5569            6595                         bluestem brasserie        caviar   \n",
       "5674           10338                                 squaretest        caviar   \n",
       "6068             438                                the gryphon        caviar   \n",
       "6129            5012                                     testjh        caviar   \n",
       "6420             623                                      trash        caviar   \n",
       "6862            7249                                    manpuku        caviar   \n",
       "9105            6874                                libby pizza        caviar   \n",
       "10351            701                                      blank        caviar   \n",
       "10634          10367                                  plum cafe        caviar   \n",
       "10693           1260          zento trash bucket  do not enable        caviar   \n",
       "12074          61898                          pie eyed pizzeria  delivery.com   \n",
       "12875            382                      available merchant id        caviar   \n",
       "15747          90142                            dickeys bbq pit  delivery.com   \n",
       "16677           1919                      cheetos promo preview        caviar   \n",
       "16844           3308                                bird & bone        caviar   \n",
       "17415           3366                                viking soul        caviar   \n",
       "19858           6653                        disabled lama beans        caviar   \n",
       "20259           6932  [desprecated] basil thai restaurant & bar        caviar   \n",
       "21283           6841                               jessica test        caviar   \n",
       "22880           6316                                thai palace        caviar   \n",
       "23763           5972    eden silk road cuisine (reuse merchant)        caviar   \n",
       "24301            929                          michelles kitchen  delivery.com   \n",
       "27623           2525                                  nyc trash        caviar   \n",
       "27877          64749                         abhineet bhatnagar  delivery.com   \n",
       "30631            355                                       open        caviar   \n",
       "39902          63760                                bgg grocery  delivery.com   \n",
       "70081         404666         taco bueno  (6001 w interstate 40)      doordash   \n",
       "70233          27112                                  magic wok      doordash   \n",
       "110136        404513     bgr the burger joint  (towne ctr blvd)      doordash   \n",
       "115763        405297   charleys philly steaks  (clear creek rd)      doordash   \n",
       "\n",
       "       sub_platform  latitude  longitude country active  \n",
       "137             NaN       0.0        0.0      US  FALSE  \n",
       "160             NaN       0.0        0.0      US  FALSE  \n",
       "312             NaN       0.0        0.0      US  FALSE  \n",
       "325             NaN       0.0        0.0      US  FALSE  \n",
       "402             NaN       0.0        0.0      US  FALSE  \n",
       "816             NaN       0.0        0.0      US  FALSE  \n",
       "1016            NaN       0.0        0.0      US  FALSE  \n",
       "1589            NaN       0.0        0.0      US  FALSE  \n",
       "2018            NaN       0.0        0.0      US  FALSE  \n",
       "5569            NaN       0.0        0.0      US  FALSE  \n",
       "5674            NaN       0.0        0.0      US  FALSE  \n",
       "6068            NaN       0.0        0.0      US  FALSE  \n",
       "6129            NaN       0.0        0.0      US  FALSE  \n",
       "6420            NaN       0.0        0.0      US  FALSE  \n",
       "6862            NaN       0.0        0.0      US  FALSE  \n",
       "9105            NaN       0.0        0.0      US  FALSE  \n",
       "10351           NaN       0.0        0.0      US  FALSE  \n",
       "10634           NaN       0.0        0.0      US  FALSE  \n",
       "10693           NaN       0.0        0.0      US  FALSE  \n",
       "12074           NaN       0.0        0.0      US  FALSE  \n",
       "12875           NaN       0.0        0.0      US  FALSE  \n",
       "15747           NaN       0.0        0.0      US  FALSE  \n",
       "16677           NaN       0.0        0.0      US  FALSE  \n",
       "16844           NaN       0.0        0.0      US  FALSE  \n",
       "17415           NaN       0.0        0.0      US  FALSE  \n",
       "19858           NaN       0.0        0.0      US  FALSE  \n",
       "20259           NaN       0.0        0.0      US  FALSE  \n",
       "21283           NaN       0.0        0.0      US  FALSE  \n",
       "22880           NaN       0.0        0.0      US  FALSE  \n",
       "23763           NaN       0.0        0.0      US  FALSE  \n",
       "24301           NaN       0.0        0.0      US  FALSE  \n",
       "27623           NaN       0.0        0.0      US  FALSE  \n",
       "27877           NaN       0.0        0.0      US  FALSE  \n",
       "30631           NaN       0.0        0.0      US  FALSE  \n",
       "39902           NaN       0.0        0.0      US  FALSE  \n",
       "70081      doordash       0.0        0.0      US  FALSE  \n",
       "70233      doordash       0.0        0.0      US  FALSE  \n",
       "110136     doordash       0.0        0.0      US  FALSE  \n",
       "115763     doordash       0.0        0.0      US  FALSE  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['longitude'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There aren't very many rows with longitude values of 0, but interestingly the rows also have latitude values of 0. Upon closer inspection, there are a lot of restaurants with names that suggest they should not exist in the data set (e.g. \"jessica hui placeholder\", \"jessica test\"). I'll inspect the names of the restaurants more carefully later. For the time being, I will simply remove these rows as the most time-sensitive solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['longitude'] != 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [restaurant_id, name, platform, sub_platform, latitude, longitude, country, active]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['latitude'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By getting rid of rows with longitude values of 0, I also happened to get rid of rows with latitude values of 0 as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Even Further Examination on Latitude and Longitude\n",
    "\n",
    "My final step is to check the latitude and longitude values to make sure they make logical sense. All data points represent restaurants in the US, so the latitude and longitude values should reflect that.\n",
    "\n",
    "According to the information found at this [link](https://latitudelongitude.org/us/), the range of latitude and longitude values for US cities should range from:\n",
    "- Latitude: 19.50139 to 64.85694\n",
    "- Longitude: -161.75583 to -68.01197\n",
    "\n",
    "I am also using this [website](https://www.gps-coordinates.net/) to quickly check my logic when appropriate with regards to the location of restaurants.\n",
    "\n",
    "With some simple boolean indexing, I can isolate and examine rows of values that do not come close to fitting in these general coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5690</td>\n",
       "      <td>derek test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>9812</td>\n",
       "      <td>the french bakery   112th ave [disabled]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>9398</td>\n",
       "      <td>donotuse</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>9607</td>\n",
       "      <td>[dnu] toms dim sum</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>1183</td>\n",
       "      <td>test doc ricketts catering</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>10348</td>\n",
       "      <td>pho viet 11th st</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>7415</td>\n",
       "      <td>pappasitos</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10316</td>\n",
       "      <td>norah restaurant la</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5596</th>\n",
       "      <td>10335</td>\n",
       "      <td>india palace   alameda</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5604</th>\n",
       "      <td>9364</td>\n",
       "      <td>donotusepepperoncini restaurant &amp; bar</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5663</th>\n",
       "      <td>9134</td>\n",
       "      <td>[do not use] la viga</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6772</th>\n",
       "      <td>10365</td>\n",
       "      <td>holy cluck</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>10155</td>\n",
       "      <td>tangs pacific bistro</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8804</th>\n",
       "      <td>8860</td>\n",
       "      <td>[do not use]sparkys pizza   nopo</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8918</th>\n",
       "      <td>10176</td>\n",
       "      <td>smoke daddy bbq   wrigleyville   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>10337</td>\n",
       "      <td>flacos tacos   south loop</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9092</th>\n",
       "      <td>10345</td>\n",
       "      <td>galveston restaurant and cafe</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10272</th>\n",
       "      <td>10363</td>\n",
       "      <td>takorean catering   dc</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10365</th>\n",
       "      <td>10347</td>\n",
       "      <td>pinkys pizza</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10595</th>\n",
       "      <td>10255</td>\n",
       "      <td>donotuse</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10635</th>\n",
       "      <td>10353</td>\n",
       "      <td>hax hops &amp; hamburgers</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10709</th>\n",
       "      <td>10346</td>\n",
       "      <td>tasty bbq</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12390</th>\n",
       "      <td>10334</td>\n",
       "      <td>hala in</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>9280</td>\n",
       "      <td>duplicate</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12469</th>\n",
       "      <td>10336</td>\n",
       "      <td>bonafini</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13278</th>\n",
       "      <td>8861</td>\n",
       "      <td>[do not use]sparkys pizza   ne</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14300</th>\n",
       "      <td>10333</td>\n",
       "      <td>bixi beer</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14319</th>\n",
       "      <td>4077</td>\n",
       "      <td>olo</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16197</th>\n",
       "      <td>10359</td>\n",
       "      <td>i tea</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16217</th>\n",
       "      <td>10366</td>\n",
       "      <td>sons addition</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16220</th>\n",
       "      <td>10358</td>\n",
       "      <td>tsuki sushi</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16704</th>\n",
       "      <td>10324</td>\n",
       "      <td>hamiltons</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16734</th>\n",
       "      <td>9294</td>\n",
       "      <td>do not use</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17484</th>\n",
       "      <td>10356</td>\n",
       "      <td>destroyer la</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19663</th>\n",
       "      <td>9301</td>\n",
       "      <td>donotuse</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19866</th>\n",
       "      <td>9689</td>\n",
       "      <td>test by hana</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19892</th>\n",
       "      <td>9813</td>\n",
       "      <td>the french bakery   8th st. [disabled]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181</th>\n",
       "      <td>10357</td>\n",
       "      <td>norah restaurant la   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21274</th>\n",
       "      <td>10349</td>\n",
       "      <td>psychic wines</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21728</th>\n",
       "      <td>63047</td>\n",
       "      <td>ecorican cafe</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.375054</td>\n",
       "      <td>-65.722797</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23839</th>\n",
       "      <td>10362</td>\n",
       "      <td>leaning tower</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24505</th>\n",
       "      <td>10344</td>\n",
       "      <td>brotzeit lokal</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28976</th>\n",
       "      <td>10364</td>\n",
       "      <td>stonemill matcha</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29388</th>\n",
       "      <td>10360</td>\n",
       "      <td>bhuna</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29391</th>\n",
       "      <td>10354</td>\n",
       "      <td>camperdown elm</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29776</th>\n",
       "      <td>10355</td>\n",
       "      <td>wok wok southeast asian kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30276</th>\n",
       "      <td>10223</td>\n",
       "      <td>republic</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46620</th>\n",
       "      <td>45735</td>\n",
       "      <td>mod pizza</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>14.215883</td>\n",
       "      <td>121.141894</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                      name      platform  \\\n",
       "4              5690                       derek test merchant        caviar   \n",
       "166            9812  the french bakery   112th ave [disabled]        caviar   \n",
       "637            9398                                  donotuse        caviar   \n",
       "907            9607                        [dnu] toms dim sum        caviar   \n",
       "913            1183                test doc ricketts catering        caviar   \n",
       "1461          10348                          pho viet 11th st        caviar   \n",
       "2430           7415                                pappasitos        caviar   \n",
       "2819          10316                       norah restaurant la        caviar   \n",
       "5596          10335                    india palace   alameda        caviar   \n",
       "5604           9364     donotusepepperoncini restaurant & bar        caviar   \n",
       "5663           9134                      [do not use] la viga        caviar   \n",
       "6772          10365                                holy cluck        caviar   \n",
       "6995          10155                      tangs pacific bistro        caviar   \n",
       "8804           8860          [do not use]sparkys pizza   nopo        caviar   \n",
       "8918          10176      smoke daddy bbq   wrigleyville   dnu        caviar   \n",
       "8928          10337                 flacos tacos   south loop        caviar   \n",
       "9092          10345             galveston restaurant and cafe        caviar   \n",
       "10272         10363                    takorean catering   dc        caviar   \n",
       "10365         10347                              pinkys pizza        caviar   \n",
       "10595         10255                                  donotuse        caviar   \n",
       "10635         10353                     hax hops & hamburgers        caviar   \n",
       "10709         10346                                 tasty bbq        caviar   \n",
       "12390         10334                                   hala in        caviar   \n",
       "12410          9280                                 duplicate        caviar   \n",
       "12469         10336                                  bonafini        caviar   \n",
       "13278          8861            [do not use]sparkys pizza   ne        caviar   \n",
       "14300         10333                                 bixi beer        caviar   \n",
       "14319          4077                                       olo        caviar   \n",
       "16197         10359                                     i tea        caviar   \n",
       "16217         10366                             sons addition        caviar   \n",
       "16220         10358                               tsuki sushi        caviar   \n",
       "16704         10324                                 hamiltons        caviar   \n",
       "16734          9294                                do not use        caviar   \n",
       "17484         10356                              destroyer la        caviar   \n",
       "19663          9301                                  donotuse        caviar   \n",
       "19866          9689                              test by hana        caviar   \n",
       "19892          9813    the french bakery   8th st. [disabled]        caviar   \n",
       "20181         10357                 norah restaurant la   dnu        caviar   \n",
       "21274         10349                             psychic wines        caviar   \n",
       "21728         63047                             ecorican cafe  delivery.com   \n",
       "23839         10362                             leaning tower        caviar   \n",
       "24505         10344                            brotzeit lokal        caviar   \n",
       "28976         10364                          stonemill matcha        caviar   \n",
       "29388         10360                                     bhuna        caviar   \n",
       "29391         10354                            camperdown elm        caviar   \n",
       "29776         10355           wok wok southeast asian kitchen        caviar   \n",
       "30276         10223                                  republic        caviar   \n",
       "46620         45735                                 mod pizza      doordash   \n",
       "\n",
       "      sub_platform   latitude   longitude country active  \n",
       "4              NaN   1.000000    2.000000      US  FALSE  \n",
       "166            NaN   1.000000   -1.000000      US  FALSE  \n",
       "637            NaN  11.000000  -11.000000      US  FALSE  \n",
       "907            NaN  11.000000  -11.000000      US  FALSE  \n",
       "913            NaN   1.000000    2.000000      US  FALSE  \n",
       "1461           NaN   1.000000    1.000000      US  FALSE  \n",
       "2430           NaN   0.111111    0.111111      US  FALSE  \n",
       "2819           NaN   1.000000    1.000000      US  FALSE  \n",
       "5596           NaN   1.000000    1.000000      US  FALSE  \n",
       "5604           NaN  11.000000  -11.000000      US  FALSE  \n",
       "5663           NaN   1.000000    1.000000      US  FALSE  \n",
       "6772           NaN   1.000000    1.000000      US  FALSE  \n",
       "6995           NaN  11.000000  -11.000000      US  FALSE  \n",
       "8804           NaN   1.000000    1.000000      US  FALSE  \n",
       "8918           NaN   1.000000    1.000000      US  FALSE  \n",
       "8928           NaN   1.000000    1.000000      US  FALSE  \n",
       "9092           NaN   1.000000    1.000000      US  FALSE  \n",
       "10272          NaN   1.000000    1.000000      US  FALSE  \n",
       "10365          NaN   1.000000   -1.000000      US  FALSE  \n",
       "10595          NaN   1.000000    1.000000      US  FALSE  \n",
       "10635          NaN   1.000000    1.000000      US  FALSE  \n",
       "10709          NaN   1.000000   -1.000000      US  FALSE  \n",
       "12390          NaN   1.000000    1.000000      US  FALSE  \n",
       "12410          NaN  11.000000  -11.000000      US  FALSE  \n",
       "12469          NaN   1.000000    1.000000      US  FALSE  \n",
       "13278          NaN   1.000000    1.000000      US  FALSE  \n",
       "14300          NaN   1.000000    1.000000      US  FALSE  \n",
       "14319          NaN   1.000000    1.000000      US  FALSE  \n",
       "16197          NaN   1.000000   -1.000000      US  FALSE  \n",
       "16217          NaN   1.000000    1.000000      US  FALSE  \n",
       "16220          NaN   1.000000   -1.000000      US  FALSE  \n",
       "16704          NaN   1.000000    1.000000      US  FALSE  \n",
       "16734          NaN  11.000000  -11.000000      US  FALSE  \n",
       "17484          NaN   1.000000    1.000000      US  FALSE  \n",
       "19663          NaN   1.000000   -1.000000      US  FALSE  \n",
       "19866          NaN   1.000000    1.000000      US  FALSE  \n",
       "19892          NaN   1.000000   -1.000000      US  FALSE  \n",
       "20181          NaN   1.000000    1.000000      US  FALSE  \n",
       "21274          NaN   1.000000    1.000000      US  FALSE  \n",
       "21728          NaN  18.375054  -65.722797      US   TRUE  \n",
       "23839          NaN   1.000000    1.000000      US  FALSE  \n",
       "24505          NaN   1.000000    1.000000      US  FALSE  \n",
       "28976          NaN   1.000000    1.000000      US  FALSE  \n",
       "29388          NaN   1.000000   -1.000000      US  FALSE  \n",
       "29391          NaN   1.000000    1.000000      US  FALSE  \n",
       "29776          NaN   1.000000    1.000000      US  FALSE  \n",
       "30276          NaN  11.000000  -11.000000      US  FALSE  \n",
       "46620     doordash  14.215883  121.141894      US  FALSE  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['latitude'] < 19)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining strange coordinate data has in fact dug up an entirely new problem. At a glance, there are multiple rows of data with values for latitude and longitude that don't make much logical sense. When examining the names of said restuarants, one can see that many of the names of the restaurants include some form of:\n",
    "- \"DO NOT USE\"\n",
    "- \"DISABLED\"\n",
    "- \"TEST\"\n",
    "\n",
    "These need to be removed from the data set. Something to note is that all of the rows seen above that have one of the three (\"DO NOT USE\", \"DISABLED\", \"TEST\") labels on the respective restaurant name\n",
    "- Are not active restaurants\n",
    "- The platform is \"caviar\"\n",
    "\n",
    "It's possible that something related to that platform led to multiple users creating a version of their restaurant to test the functionality of the site/app/program before creating a normal profile, hence the strange location data. I'll explore this now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Taking a Closer Look at the Names of Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6878</td>\n",
       "      <td>chens chinese &amp; cuisine {do not use}</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.888853</td>\n",
       "      <td>-87.633035</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>5309</td>\n",
       "      <td>hot wok bistro too (do not use)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.495809</td>\n",
       "      <td>-122.247895</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>9211</td>\n",
       "      <td>do not use</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.877671</td>\n",
       "      <td>-87.646821</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>9370</td>\n",
       "      <td>[do not use] taylor gourmet   the loop</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.882225</td>\n",
       "      <td>-87.628988</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>3778</td>\n",
       "      <td>california pizza kitchen   lenox square (do no...</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.846469</td>\n",
       "      <td>-84.362657</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565601</th>\n",
       "      <td>184479</td>\n",
       "      <td>duplicate do not use subway (2181 n meridian s...</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>39.797165</td>\n",
       "      <td>-86.156953</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594222</th>\n",
       "      <td>87297</td>\n",
       "      <td>fuzzys tacos (coo do not use)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>29.671878</td>\n",
       "      <td>-95.460527</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614478</th>\n",
       "      <td>37588</td>\n",
       "      <td>duplicate do not use american roadside</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>35.152501</td>\n",
       "      <td>-80.841481</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627323</th>\n",
       "      <td>314371</td>\n",
       "      <td>do not use</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.777990</td>\n",
       "      <td>-79.344091</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636999</th>\n",
       "      <td>56541</td>\n",
       "      <td>krush burger, do not use, dup acct</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.773442</td>\n",
       "      <td>-121.269283</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                               name  \\\n",
       "29              6878               chens chinese & cuisine {do not use}   \n",
       "164             5309                    hot wok bistro too (do not use)   \n",
       "350             9211                                         do not use   \n",
       "351             9370             [do not use] taylor gourmet   the loop   \n",
       "370             3778  california pizza kitchen   lenox square (do no...   \n",
       "...              ...                                                ...   \n",
       "565601        184479  duplicate do not use subway (2181 n meridian s...   \n",
       "594222         87297                      fuzzys tacos (coo do not use)   \n",
       "614478         37588             duplicate do not use american roadside   \n",
       "627323        314371                                         do not use   \n",
       "636999         56541                 krush burger, do not use, dup acct   \n",
       "\n",
       "        platform sub_platform   latitude   longitude country active  \n",
       "29        caviar          NaN  41.888853  -87.633035      US  FALSE  \n",
       "164       caviar          NaN  37.495809 -122.247895      US  FALSE  \n",
       "350       caviar          NaN  41.877671  -87.646821      US  FALSE  \n",
       "351       caviar          NaN  41.882225  -87.628988      US  FALSE  \n",
       "370       caviar          NaN  33.846469  -84.362657      US  FALSE  \n",
       "...          ...          ...        ...         ...     ...    ...  \n",
       "565601  doordash     doordash  39.797165  -86.156953      US  FALSE  \n",
       "594222  doordash     doordash  29.671878  -95.460527      US  FALSE  \n",
       "614478  doordash     doordash  35.152501  -80.841481      US  FALSE  \n",
       "627323  doordash     doordash  43.777990  -79.344091      US  FALSE  \n",
       "636999  doordash     doordash  38.773442 -121.269283      US   TRUE  \n",
       "\n",
       "[146 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'].str.contains('[Dd][Oo]\\s*[Nn][Oo][Tt]\\s*[Uu][Ss][Ee]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using regular expressions (regex), we can see that there are other versions/formats that are all basically saying the same thing: to remove the row in question from the data set. I will explore more in the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
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       "      <th>29</th>\n",
       "      <td>6878</td>\n",
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       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8216</td>\n",
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       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>5074</td>\n",
       "      <td>yucatan taco stand  do not enable</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.815272</td>\n",
       "      <td>-96.770349</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>3700</td>\n",
       "      <td>[do not enable, bad duplicate] meehans (sandy ...</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.839531</td>\n",
       "      <td>-84.377089</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>725</td>\n",
       "      <td>phuong thao [do not enable]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.376854</td>\n",
       "      <td>-122.030459</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627323</th>\n",
       "      <td>314371</td>\n",
       "      <td>do not use</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.777990</td>\n",
       "      <td>-79.344091</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631284</th>\n",
       "      <td>248591</td>\n",
       "      <td>do not activate jnovaks house of ceviche &amp; man...</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.415009</td>\n",
       "      <td>-111.949098</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636574</th>\n",
       "      <td>71432</td>\n",
       "      <td>jaburritos   duplicate   do not activate</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>36.078083</td>\n",
       "      <td>-115.242157</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636999</th>\n",
       "      <td>56541</td>\n",
       "      <td>krush burger, do not use, dup acct</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.773442</td>\n",
       "      <td>-121.269283</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640228</th>\n",
       "      <td>93754</td>\n",
       "      <td>mama fus (do not activate)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>29.603282</td>\n",
       "      <td>-95.127123</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                               name  \\\n",
       "29              6878               chens chinese & cuisine {do not use}   \n",
       "30              8216                  king��s road cafe   do not enable   \n",
       "139             5074                  yucatan taco stand  do not enable   \n",
       "143             3700  [do not enable, bad duplicate] meehans (sandy ...   \n",
       "159              725                        phuong thao [do not enable]   \n",
       "...              ...                                                ...   \n",
       "627323        314371                                         do not use   \n",
       "631284        248591  do not activate jnovaks house of ceviche & man...   \n",
       "636574         71432           jaburritos   duplicate   do not activate   \n",
       "636999         56541                 krush burger, do not use, dup acct   \n",
       "640228         93754                         mama fus (do not activate)   \n",
       "\n",
       "        platform sub_platform   latitude   longitude country active  \n",
       "29        caviar          NaN  41.888853  -87.633035      US  FALSE  \n",
       "30        caviar          NaN  34.076246 -118.372312      US  FALSE  \n",
       "139       caviar          NaN  32.815272  -96.770349      US  FALSE  \n",
       "143       caviar          NaN  33.839531  -84.377089      US  FALSE  \n",
       "159       caviar          NaN  37.376854 -122.030459      US  FALSE  \n",
       "...          ...          ...        ...         ...     ...    ...  \n",
       "627323  doordash     doordash  43.777990  -79.344091      US  FALSE  \n",
       "631284  doordash     doordash  33.415009 -111.949098      US   TRUE  \n",
       "636574  doordash     doordash  36.078083 -115.242157      US  FALSE  \n",
       "636999  doordash     doordash  38.773442 -121.269283      US   TRUE  \n",
       "640228  doordash     doordash  29.603282  -95.127123      US  FALSE  \n",
       "\n",
       "[278 rows x 8 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a closer look at the string \"DO NOT\"\n",
    "\n",
    "data[data['name'].str.contains('[Dd][Oo]\\s*[Nn][Oo][Tt]\\s*')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appear to be multiple ways that rows in the data set are denoted to be excluded. While we observed \"DO NOT USE\" earlier, there appear to be other statements in restaurant names that tell us the same thing (e.g. \"DO NOT ENABLE\", \"DO NOT ACTIVATE\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>3700</td>\n",
       "      <td>[do not enable, bad duplicate] meehans (sandy ...</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.839531</td>\n",
       "      <td>-84.377089</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>601</td>\n",
       "      <td>tiffin bistro duplicate</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.934720</td>\n",
       "      <td>-75.162219</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>803</td>\n",
       "      <td>pure fare   south street duplicate</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.944246</td>\n",
       "      <td>-75.169180</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>6691</td>\n",
       "      <td>via tribunali   queen anne [duplicate]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.632169</td>\n",
       "      <td>-122.361350</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>3698</td>\n",
       "      <td>[do not enable, bad duplicate] willys mexicana...</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.783865</td>\n",
       "      <td>-84.378648</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643565</th>\n",
       "      <td>154607</td>\n",
       "      <td>papa murphys  duplicate</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.113446</td>\n",
       "      <td>-88.203063</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643595</th>\n",
       "      <td>237367</td>\n",
       "      <td>papa murphys (star shoot parkway)   duplicate</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.024013</td>\n",
       "      <td>-84.424365</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643627</th>\n",
       "      <td>253513</td>\n",
       "      <td>kreasian kitchen   duplicate</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.278121</td>\n",
       "      <td>-118.736263</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643780</th>\n",
       "      <td>254426</td>\n",
       "      <td>papa murphys (state street)   duplicate</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.306181</td>\n",
       "      <td>-85.842397</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644062</th>\n",
       "      <td>253594</td>\n",
       "      <td>h salt fish &amp; chips   duplicate</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.283007</td>\n",
       "      <td>-118.439891</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5158 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                               name  \\\n",
       "143             3700  [do not enable, bad duplicate] meehans (sandy ...   \n",
       "152              601                            tiffin bistro duplicate   \n",
       "155              803                 pure fare   south street duplicate   \n",
       "579             6691             via tribunali   queen anne [duplicate]   \n",
       "953             3698  [do not enable, bad duplicate] willys mexicana...   \n",
       "...              ...                                                ...   \n",
       "643565        154607                            papa murphys  duplicate   \n",
       "643595        237367      papa murphys (star shoot parkway)   duplicate   \n",
       "643627        253513                       kreasian kitchen   duplicate   \n",
       "643780        254426            papa murphys (state street)   duplicate   \n",
       "644062        253594                    h salt fish & chips   duplicate   \n",
       "\n",
       "        platform sub_platform   latitude   longitude country active  \n",
       "143       caviar          NaN  33.839531  -84.377089      US  FALSE  \n",
       "152       caviar          NaN  39.934720  -75.162219      US  FALSE  \n",
       "155       caviar          NaN  39.944246  -75.169180      US  FALSE  \n",
       "579       caviar          NaN  47.632169 -122.361350      US  FALSE  \n",
       "953       caviar          NaN  33.783865  -84.378648      US  FALSE  \n",
       "...          ...          ...        ...         ...     ...    ...  \n",
       "643565  doordash     doordash  40.113446  -88.203063      US  FALSE  \n",
       "643595  doordash     doordash  38.024013  -84.424365      US  FALSE  \n",
       "643627  doordash     doordash  34.278121 -118.736263      US  FALSE  \n",
       "643780  doordash     doordash  38.306181  -85.842397      US  FALSE  \n",
       "644062  doordash     doordash  34.283007 -118.439891      US  FALSE  \n",
       "\n",
       "[5158 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at names with the word \"duplicate\" in said names\n",
    "\n",
    "data[data['name'].str.contains('[Dd][Uu][Pp][Ll][Ii][Cc][Aa][Tt][Ee]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see now that some restaurants can be labeled as duplicates without \"DO NOT\" also attached to their names (and vise-versa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>2287</td>\n",
       "      <td>cafe ole copy</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.941704</td>\n",
       "      <td>-77.077104</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294</th>\n",
       "      <td>73842</td>\n",
       "      <td>choppers   infocache copy</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.911545</td>\n",
       "      <td>-87.667617</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4815</th>\n",
       "      <td>79618</td>\n",
       "      <td>duplicate   copy of masters pizza pasta &amp; gril...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.097480</td>\n",
       "      <td>-118.338670</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12710</th>\n",
       "      <td>66141</td>\n",
       "      <td>duplicate   copy of peking express before kgis...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.741802</td>\n",
       "      <td>-73.990555</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14127</th>\n",
       "      <td>64996</td>\n",
       "      <td>joannes import staging listing   for appending...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15354</th>\n",
       "      <td>73746</td>\n",
       "      <td>(temp / test duplicate) copy of my thai rboyce</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.755044</td>\n",
       "      <td>-117.099129</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16833</th>\n",
       "      <td>2138</td>\n",
       "      <td>naan n curry (2nd copy, unused)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.764284</td>\n",
       "      <td>-122.464891</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000</th>\n",
       "      <td>79654</td>\n",
       "      <td>duplicate   copy of wolfnights rid 67892</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.062306</td>\n",
       "      <td>-118.350917</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22010</th>\n",
       "      <td>71391</td>\n",
       "      <td>sample restaurant testing copyover</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24946</th>\n",
       "      <td>63955</td>\n",
       "      <td>mama mia #69568 copyover</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25896</th>\n",
       "      <td>79058</td>\n",
       "      <td>copy of menu for dozika restaurant rid: 74098</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.043040</td>\n",
       "      <td>-118.323560</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30528</th>\n",
       "      <td>87866</td>\n",
       "      <td>duplicate   copy back up of the quick fixx menu</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.943976</td>\n",
       "      <td>-75.167998</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33966</th>\n",
       "      <td>79764</td>\n",
       "      <td>duplicate   copy of las olas sushi bar to be u...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.102520</td>\n",
       "      <td>-118.291860</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38913</th>\n",
       "      <td>63512</td>\n",
       "      <td>the classic diner (copy of original update)</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42036</th>\n",
       "      <td>66266</td>\n",
       "      <td>katzs delicatessen range copyover</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.750034</td>\n",
       "      <td>-74.026457</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332502</th>\n",
       "      <td>235929</td>\n",
       "      <td>copycat co</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.900414</td>\n",
       "      <td>-76.991026</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425725</th>\n",
       "      <td>5b4ed7aa-ad1f-4714-9de8-77a015b42a3c</td>\n",
       "      <td>trademark &amp; copyright</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>37.776081</td>\n",
       "      <td>-122.408346</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "840                                     2287   \n",
       "4294                                   73842   \n",
       "4815                                   79618   \n",
       "12710                                  66141   \n",
       "14127                                  64996   \n",
       "15354                                  73746   \n",
       "16833                                   2138   \n",
       "20000                                  79654   \n",
       "22010                                  71391   \n",
       "24946                                  63955   \n",
       "25896                                  79058   \n",
       "30528                                  87866   \n",
       "33966                                  79764   \n",
       "38913                                  63512   \n",
       "42036                                  66266   \n",
       "332502                                235929   \n",
       "425725  5b4ed7aa-ad1f-4714-9de8-77a015b42a3c   \n",
       "\n",
       "                                                     name      platform  \\\n",
       "840                                         cafe ole copy        caviar   \n",
       "4294                            choppers   infocache copy  delivery.com   \n",
       "4815    duplicate   copy of masters pizza pasta & gril...  delivery.com   \n",
       "12710   duplicate   copy of peking express before kgis...  delivery.com   \n",
       "14127   joannes import staging listing   for appending...  delivery.com   \n",
       "15354      (temp / test duplicate) copy of my thai rboyce  delivery.com   \n",
       "16833                     naan n curry (2nd copy, unused)        caviar   \n",
       "20000            duplicate   copy of wolfnights rid 67892  delivery.com   \n",
       "22010                  sample restaurant testing copyover  delivery.com   \n",
       "24946                            mama mia #69568 copyover  delivery.com   \n",
       "25896       copy of menu for dozika restaurant rid: 74098  delivery.com   \n",
       "30528     duplicate   copy back up of the quick fixx menu  delivery.com   \n",
       "33966   duplicate   copy of las olas sushi bar to be u...  delivery.com   \n",
       "38913         the classic diner (copy of original update)  delivery.com   \n",
       "42036                   katzs delicatessen range copyover  delivery.com   \n",
       "332502                                         copycat co      doordash   \n",
       "425725                              trademark & copyright      ubereats   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "840             NaN  38.941704  -77.077104      US  FALSE  \n",
       "4294            NaN  41.911545  -87.667617      US  FALSE  \n",
       "4815            NaN  34.097480 -118.338670      US  FALSE  \n",
       "12710           NaN  40.741802  -73.990555      US  FALSE  \n",
       "14127           NaN  40.762646  -73.969185      US  FALSE  \n",
       "15354           NaN  32.755044 -117.099129      US  FALSE  \n",
       "16833           NaN  37.764284 -122.464891      US  FALSE  \n",
       "20000           NaN  34.062306 -118.350917      US  FALSE  \n",
       "22010           NaN  40.762646  -73.969185      US  FALSE  \n",
       "24946           NaN  40.703276  -74.009694      US  FALSE  \n",
       "25896           NaN  34.043040 -118.323560      US   TRUE  \n",
       "30528           NaN  39.943976  -75.167998      US   TRUE  \n",
       "33966           NaN  34.102520 -118.291860      US  FALSE  \n",
       "38913           NaN  40.703276  -74.009694      US  FALSE  \n",
       "42036           NaN  40.750034  -74.026457      US  FALSE  \n",
       "332502     doordash  38.900414  -76.991026      US   TRUE  \n",
       "425725     ubereats  37.776081 -122.408346      US   TRUE  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# also searching for the word \"copy\" which is synonymous for \"duplicate\"\n",
    "\n",
    "data[data['name'].str.contains('[Cc][Oo][Pp][Yy]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it is obvious that many of the restaurants with \"copy\" in their name are indeed duplicates, a few restaurants have \"copy\" in their actual names (e.g. \"Copycat Co\"). An interesting bit of text that some restaurants have included in their 'name' values is \"UPDATE\". I will explore this more below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4815</th>\n",
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       "      <td>duplicate   copy of masters pizza pasta &amp; gril...</td>\n",
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       "      <td>-118.338670</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>64847</td>\n",
       "      <td>gracies corner diner   updated menu test acct</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.777749</td>\n",
       "      <td>-73.951775</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10515</th>\n",
       "      <td>64003</td>\n",
       "      <td>bon chon chicken   fake restaurant  10/9 update</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12710</th>\n",
       "      <td>66141</td>\n",
       "      <td>duplicate   copy of peking express before kgis...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.741802</td>\n",
       "      <td>-73.990555</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15297</th>\n",
       "      <td>79429</td>\n",
       "      <td>duplicate   menu update for thai dishes   hold...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.952821</td>\n",
       "      <td>-87.677771</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22225</th>\n",
       "      <td>64414</td>\n",
       "      <td>duplicate   5th china tung before kgisl menu u...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.110374</td>\n",
       "      <td>-80.308038</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26448</th>\n",
       "      <td>79208</td>\n",
       "      <td>updated menu for als italian beef</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.869304</td>\n",
       "      <td>-87.663019</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32029</th>\n",
       "      <td>79518</td>\n",
       "      <td>red pepper pizza back up of updated menu</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.682571</td>\n",
       "      <td>-73.993097</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32632</th>\n",
       "      <td>30869</td>\n",
       "      <td>color me greens   update for 10/1.</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33966</th>\n",
       "      <td>79764</td>\n",
       "      <td>duplicate   copy of las olas sushi bar to be u...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.102520</td>\n",
       "      <td>-118.291860</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35162</th>\n",
       "      <td>60980</td>\n",
       "      <td>eddy catering (update that were inputting from...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.753786</td>\n",
       "      <td>-73.989773</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37619</th>\n",
       "      <td>91660</td>\n",
       "      <td>afternoon tea update</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38913</th>\n",
       "      <td>63512</td>\n",
       "      <td>the classic diner (copy of original update)</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                               name  \\\n",
       "4815          79618  duplicate   copy of masters pizza pasta & gril...   \n",
       "9987          64847      gracies corner diner   updated menu test acct   \n",
       "10515         64003    bon chon chicken   fake restaurant  10/9 update   \n",
       "12710         66141  duplicate   copy of peking express before kgis...   \n",
       "15297         79429  duplicate   menu update for thai dishes   hold...   \n",
       "22225         64414  duplicate   5th china tung before kgisl menu u...   \n",
       "26448         79208                  updated menu for als italian beef   \n",
       "32029         79518           red pepper pizza back up of updated menu   \n",
       "32632         30869                 color me greens   update for 10/1.   \n",
       "33966         79764  duplicate   copy of las olas sushi bar to be u...   \n",
       "35162         60980  eddy catering (update that were inputting from...   \n",
       "37619         91660                               afternoon tea update   \n",
       "38913         63512        the classic diner (copy of original update)   \n",
       "\n",
       "           platform sub_platform   latitude   longitude country active  \n",
       "4815   delivery.com          NaN  34.097480 -118.338670      US  FALSE  \n",
       "9987   delivery.com          NaN  40.777749  -73.951775      US  FALSE  \n",
       "10515  delivery.com          NaN  40.703276  -74.009694      US  FALSE  \n",
       "12710  delivery.com          NaN  40.741802  -73.990555      US  FALSE  \n",
       "15297  delivery.com          NaN  41.952821  -87.677771      US   TRUE  \n",
       "22225  delivery.com          NaN  27.110374  -80.308038      US  FALSE  \n",
       "26448  delivery.com          NaN  41.869304  -87.663019      US  FALSE  \n",
       "32029  delivery.com          NaN  40.682571  -73.993097      US   TRUE  \n",
       "32632  delivery.com          NaN  40.703276  -74.009694      US   TRUE  \n",
       "33966  delivery.com          NaN  34.102520 -118.291860      US  FALSE  \n",
       "35162  delivery.com          NaN  40.753786  -73.989773      US  FALSE  \n",
       "37619  delivery.com          NaN  42.825685  -73.879458      US   TRUE  \n",
       "38913  delivery.com          NaN  40.703276  -74.009694      US  FALSE  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'].str.contains('[Uu][Pp][Dd][Aa][Tt][Ee]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While some of these restaurant names make it very clear that they are copies or duplicates of entries, \"update\" is not as clear. Update could suggest changes to menus, locations, names, etc. I will revisit this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5690</td>\n",
       "      <td>derek test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>3879</td>\n",
       "      <td>nickys test [do not enable]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775231</td>\n",
       "      <td>-122.417528</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>64095</td>\n",
       "      <td>sample restaurant for testing complex menu</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>3542</td>\n",
       "      <td>[test2]sugarfish by sushi nozawa</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703738</td>\n",
       "      <td>-73.638472</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>1183</td>\n",
       "      <td>test doc ricketts catering</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639531</th>\n",
       "      <td>50988</td>\n",
       "      <td>dep test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.638283</td>\n",
       "      <td>-79.425290</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639594</th>\n",
       "      <td>392005</td>\n",
       "      <td>novadine v2 test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>58.372186</td>\n",
       "      <td>-134.587152</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639862</th>\n",
       "      <td>330076</td>\n",
       "      <td>test</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.495742</td>\n",
       "      <td>-86.876172</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642231</th>\n",
       "      <td>230772</td>\n",
       "      <td>testo pepesto (e main st)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.794609</td>\n",
       "      <td>-116.959992</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643046</th>\n",
       "      <td>334544</td>\n",
       "      <td>greatest b b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>29.592922</td>\n",
       "      <td>-95.527972</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>475 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                        name  \\\n",
       "4               5690                         derek test merchant   \n",
       "627             3879                 nickys test [do not enable]   \n",
       "685            64095  sample restaurant for testing complex menu   \n",
       "878             3542            [test2]sugarfish by sushi nozawa   \n",
       "913             1183                  test doc ricketts catering   \n",
       "...              ...                                         ...   \n",
       "639531         50988                              dep test store   \n",
       "639594        392005                      novadine v2 test store   \n",
       "639862        330076                                        test   \n",
       "642231        230772                   testo pepesto (e main st)   \n",
       "643046        334544                              greatest b b q   \n",
       "\n",
       "            platform sub_platform   latitude   longitude country active  \n",
       "4             caviar          NaN   1.000000    2.000000      US  FALSE  \n",
       "627           caviar          NaN  37.775231 -122.417528      US  FALSE  \n",
       "685     delivery.com          NaN  40.760541  -73.970354      US  FALSE  \n",
       "878           caviar          NaN  40.703738  -73.638472      US  FALSE  \n",
       "913           caviar          NaN   1.000000    2.000000      US  FALSE  \n",
       "...              ...          ...        ...         ...     ...    ...  \n",
       "639531      doordash     doordash  43.638283  -79.425290      US   TRUE  \n",
       "639594      doordash     doordash  58.372186 -134.587152      US   TRUE  \n",
       "639862      doordash     doordash  33.495742  -86.876172      US   TRUE  \n",
       "642231      doordash     doordash  32.794609 -116.959992      US   TRUE  \n",
       "643046      doordash     doordash  29.592922  -95.527972      US   TRUE  \n",
       "\n",
       "[475 rows x 8 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'].str.contains('[Tt][Ee][Ss][Tt]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above (e.g. \"Testo Pepesto\", \"Greatest B-B-Q\") filtering out restaurants for having some form of \"TEST\" in its name may not accurately weed out duplicates. I can adjust the regex in a later stage, but filtering by \"TEST\" must be done more carefully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing Rows by Name\n",
    "\n",
    "It's evident that there are a number of rows in the data set calling for exclusion. As seen above, many duplicates exist that can be simply filtered out using some simple regular expressions (regex). I intend to:\n",
    "- Remove rows with some version/format of the string \"Duplicate\" (Since over 5000 rows of data include this in their restaurant name)\n",
    "- Remove rows with some version/format of the string \"Do Not\" (Multiple ways of denoting the need for exclusion from the data set exist, including but not limited to \"do not use\", \"do not activate\", \"do not enable\")\n",
    "- Re-examine restaurants with some version/format of the following strings in their respective name:\n",
    "    - \"update\"\n",
    "    - \"sample\"\n",
    "    - \"copy\"\n",
    "    - \"test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 637010 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"duplicate\" in its name\n",
    "data = data[data['name'].str.contains('[Dd][Uu][Pp][Ll][Ii][Cc][Aa][Tt][Ee]') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636772 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"do not\" in its name\n",
    "data = data[data['name'].str.contains('[Dd][Oo]\\s*[Nn][Oo][Tt]\\s*') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Re-Examining Restaurants w/ Some Form of \"update\" in its Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>64847</td>\n",
       "      <td>gracies corner diner   updated menu test acct</td>\n",
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       "    <tr>\n",
       "      <th>10515</th>\n",
       "      <td>64003</td>\n",
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       "    <tr>\n",
       "      <th>26448</th>\n",
       "      <td>79208</td>\n",
       "      <td>updated menu for als italian beef</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.869304</td>\n",
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       "      <td>FALSE</td>\n",
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       "    <tr>\n",
       "      <th>32029</th>\n",
       "      <td>79518</td>\n",
       "      <td>red pepper pizza back up of updated menu</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.682571</td>\n",
       "      <td>-73.993097</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32632</th>\n",
       "      <td>30869</td>\n",
       "      <td>color me greens   update for 10/1.</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35162</th>\n",
       "      <td>60980</td>\n",
       "      <td>eddy catering (update that were inputting from...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.753786</td>\n",
       "      <td>-73.989773</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37619</th>\n",
       "      <td>91660</td>\n",
       "      <td>afternoon tea update</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38913</th>\n",
       "      <td>63512</td>\n",
       "      <td>the classic diner (copy of original update)</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                               name  \\\n",
       "9987          64847      gracies corner diner   updated menu test acct   \n",
       "10515         64003    bon chon chicken   fake restaurant  10/9 update   \n",
       "26448         79208                  updated menu for als italian beef   \n",
       "32029         79518           red pepper pizza back up of updated menu   \n",
       "32632         30869                 color me greens   update for 10/1.   \n",
       "35162         60980  eddy catering (update that were inputting from...   \n",
       "37619         91660                               afternoon tea update   \n",
       "38913         63512        the classic diner (copy of original update)   \n",
       "\n",
       "           platform sub_platform   latitude  longitude country active  \n",
       "9987   delivery.com          NaN  40.777749 -73.951775      US  FALSE  \n",
       "10515  delivery.com          NaN  40.703276 -74.009694      US  FALSE  \n",
       "26448  delivery.com          NaN  41.869304 -87.663019      US  FALSE  \n",
       "32029  delivery.com          NaN  40.682571 -73.993097      US   TRUE  \n",
       "32632  delivery.com          NaN  40.703276 -74.009694      US   TRUE  \n",
       "35162  delivery.com          NaN  40.753786 -73.989773      US  FALSE  \n",
       "37619  delivery.com          NaN  42.825685 -73.879458      US   TRUE  \n",
       "38913  delivery.com          NaN  40.703276 -74.009694      US  FALSE  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'].str.contains('[Uu][Pp][Dd][Aa][Tt][Ee]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems pretty obvious that all of these rows can be removed because the data already exists in the data set. There aren't many rows, so I'll demonstrate below how each restaurant in the dataframe above is a repeat of an existing data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109157</th>\n",
       "      <td>66226</td>\n",
       "      <td>gracies corner diner</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.777123</td>\n",
       "      <td>-73.949970</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608910</th>\n",
       "      <td>54903</td>\n",
       "      <td>gracies corner diner</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.776840</td>\n",
       "      <td>-73.949756</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                  name  platform sub_platform   latitude  \\\n",
       "109157         66226  gracies corner diner   grubhub      grubhub  40.777123   \n",
       "608910         54903  gracies corner diner  doordash     doordash  40.776840   \n",
       "\n",
       "        longitude country active  \n",
       "109157 -73.949970      US   TRUE  \n",
       "608910 -73.949756      US   TRUE  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'gracies corner diner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39492</th>\n",
       "      <td>92571</td>\n",
       "      <td>bon chon chicken</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.747378</td>\n",
       "      <td>-73.985193</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100013</th>\n",
       "      <td>297759</td>\n",
       "      <td>bon chon chicken</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.747338</td>\n",
       "      <td>-73.985088</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216793</th>\n",
       "      <td>47cc5230-84bd-432e-b2b9-3807a800cd02</td>\n",
       "      <td>bon chon chicken</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>37.371954</td>\n",
       "      <td>-121.846143</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389234</th>\n",
       "      <td>5ada474d-609a-4ef2-9dd5-4a02c2d0d00b</td>\n",
       "      <td>bon chon chicken</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>37.362701</td>\n",
       "      <td>-122.026382</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id              name      platform  \\\n",
       "39492                                  92571  bon chon chicken  delivery.com   \n",
       "100013                                297759  bon chon chicken       grubhub   \n",
       "216793  47cc5230-84bd-432e-b2b9-3807a800cd02  bon chon chicken     postmates   \n",
       "389234  5ada474d-609a-4ef2-9dd5-4a02c2d0d00b  bon chon chicken     postmates   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "39492           NaN  40.747378  -73.985193      US   TRUE  \n",
       "100013      grubhub  40.747338  -73.985088      US   TRUE  \n",
       "216793   restaurant  37.371954 -121.846143      US   TRUE  \n",
       "389234   restaurant  37.362701 -122.026382      US  FALSE  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'bon chon chicken']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>91688</td>\n",
       "      <td>afternoon tea</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     restaurant_id           name      platform sub_platform   latitude  \\\n",
       "1109         91688  afternoon tea  delivery.com          NaN  42.825685   \n",
       "\n",
       "      longitude country active  \n",
       "1109 -73.879458      US  FALSE  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'afternoon tea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39588</th>\n",
       "      <td>83868</td>\n",
       "      <td>color me greens</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.699230</td>\n",
       "      <td>-73.912720</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98149</th>\n",
       "      <td>309741</td>\n",
       "      <td>color me greens</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.699195</td>\n",
       "      <td>-73.912966</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id             name      platform sub_platform   latitude  \\\n",
       "39588         83868  color me greens  delivery.com          NaN  40.699230   \n",
       "98149        309741  color me greens       grubhub      grubhub  40.699195   \n",
       "\n",
       "       longitude country active  \n",
       "39588 -73.912720      US   TRUE  \n",
       "98149 -73.912966      US   TRUE  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'color me greens']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will filter the data set and remove these rows from my original data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636764 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"duplicate\" in its name\n",
    "data = data[data['name'].str.contains('[Uu][Pp][Dd][Aa][Tt][Ee]') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Re-Examining Restaurants w/ Some Form of \"sample\" in its Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>64095</td>\n",
       "      <td>sample restaurant for testing complex menu</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>88213</td>\n",
       "      <td>sprightliis sample bagelry</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>64529</td>\n",
       "      <td>(sample) italian</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.714577</td>\n",
       "      <td>-83.684994</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4462</th>\n",
       "      <td>68437</td>\n",
       "      <td>sample restaurant   carlos   jira testing</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4487</th>\n",
       "      <td>71056</td>\n",
       "      <td>sample smoke shop</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537881</th>\n",
       "      <td>211430</td>\n",
       "      <td>beijing express (e sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.274961</td>\n",
       "      <td>-80.116230</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578563</th>\n",
       "      <td>420740</td>\n",
       "      <td>nonnas pizza (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272976</td>\n",
       "      <td>-80.263821</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579735</th>\n",
       "      <td>262939</td>\n",
       "      <td>tijuana flats (w. sample rd.)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272877</td>\n",
       "      <td>-80.205693</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597718</th>\n",
       "      <td>262910</td>\n",
       "      <td>la granja (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.274350</td>\n",
       "      <td>-80.134681</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615093</th>\n",
       "      <td>105538</td>\n",
       "      <td>its all greek to me grill (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272743</td>\n",
       "      <td>-80.197497</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                        name  \\\n",
       "685            64095  sample restaurant for testing complex menu   \n",
       "764            88213                  sprightliis sample bagelry   \n",
       "2968           64529                            (sample) italian   \n",
       "4462           68437   sample restaurant   carlos   jira testing   \n",
       "4487           71056                           sample smoke shop   \n",
       "...              ...                                         ...   \n",
       "537881        211430               beijing express (e sample rd)   \n",
       "578563        420740                  nonnas pizza (w sample rd)   \n",
       "579735        262939               tijuana flats (w. sample rd.)   \n",
       "597718        262910                     la granja (w sample rd)   \n",
       "615093        105538     its all greek to me grill (w sample rd)   \n",
       "\n",
       "            platform sub_platform   latitude  longitude country active  \n",
       "685     delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "764     delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "2968    delivery.com          NaN  41.714577 -83.684994      US   TRUE  \n",
       "4462    delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "4487    delivery.com          NaN  42.825685 -73.879458      US  FALSE  \n",
       "...              ...          ...        ...        ...     ...    ...  \n",
       "537881      doordash     doordash  26.274961 -80.116230      US   TRUE  \n",
       "578563      doordash     doordash  26.272976 -80.263821      US   TRUE  \n",
       "579735      doordash     doordash  26.272877 -80.205693      US   TRUE  \n",
       "597718      doordash     doordash  26.274350 -80.134681      US   TRUE  \n",
       "615093      doordash     doordash  26.272743 -80.197497      US   TRUE  \n",
       "\n",
       "[84 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['name'].str.contains('sample'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At a glance, I want to differentiate between results for the platform delivery.com and doordash. There seems to be a pattern with restaurant names including the word \"sample\" that are listed on the doordash platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>64095</td>\n",
       "      <td>sample restaurant for testing complex menu</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>88213</td>\n",
       "      <td>sprightliis sample bagelry</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>64529</td>\n",
       "      <td>(sample) italian</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.714577</td>\n",
       "      <td>-83.684994</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4462</th>\n",
       "      <td>68437</td>\n",
       "      <td>sample restaurant   carlos   jira testing</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4487</th>\n",
       "      <td>71056</td>\n",
       "      <td>sample smoke shop</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4632</th>\n",
       "      <td>66521</td>\n",
       "      <td>sample restaurant   argenis genao</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4711</th>\n",
       "      <td>62133</td>\n",
       "      <td>sample foodfriendz (florida) beverages &amp; utensils</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.626630</td>\n",
       "      <td>-96.333424</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6169</th>\n",
       "      <td>71055</td>\n",
       "      <td>sample restaurant   marincorpsolutions.com</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6226</th>\n",
       "      <td>64849</td>\n",
       "      <td>fake restaurant for tara leuci   test/sample/t...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.737710</td>\n",
       "      <td>-73.987949</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9078</th>\n",
       "      <td>75107</td>\n",
       "      <td>sample restaurant   testing</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10456</th>\n",
       "      <td>68989</td>\n",
       "      <td>sample plu store</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10793</th>\n",
       "      <td>37915</td>\n",
       "      <td>sample restaurant   for nod</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11305</th>\n",
       "      <td>71089</td>\n",
       "      <td>sample restaurant   infocachecorp.com</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11371</th>\n",
       "      <td>71006</td>\n",
       "      <td>sample restaurant   idata entry us</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13554</th>\n",
       "      <td>67520</td>\n",
       "      <td>tiquillia mcperebo   sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>71151</td>\n",
       "      <td>sample restaurant   neville pereira   infocach...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13945</th>\n",
       "      <td>57348</td>\n",
       "      <td>sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14009</th>\n",
       "      <td>70314</td>\n",
       "      <td>sample restaurant   rich boyce</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14111</th>\n",
       "      <td>69545</td>\n",
       "      <td>sample restaurant   nicole williams</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15369</th>\n",
       "      <td>67970</td>\n",
       "      <td>sprightliis sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.738119</td>\n",
       "      <td>-73.988113</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15624</th>\n",
       "      <td>68988</td>\n",
       "      <td>sample plu store</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.825685</td>\n",
       "      <td>-73.879458</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16362</th>\n",
       "      <td>71154</td>\n",
       "      <td>sample restaurant   guli rajani   infocache.com</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18107</th>\n",
       "      <td>71092</td>\n",
       "      <td>sample restaurant   ebusinessware.com</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18301</th>\n",
       "      <td>68564</td>\n",
       "      <td>sample restaurant   travis ferman</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18872</th>\n",
       "      <td>71152</td>\n",
       "      <td>sample restaurant   gautam asudani   infocache...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21661</th>\n",
       "      <td>64132</td>\n",
       "      <td>sprightliis sample diner</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21950</th>\n",
       "      <td>60864</td>\n",
       "      <td>firehouse subs sample road</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.274034</td>\n",
       "      <td>-80.197916</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22010</th>\n",
       "      <td>71391</td>\n",
       "      <td>sample restaurant testing copyover</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23114</th>\n",
       "      <td>71690</td>\n",
       "      <td>sutton wine shop   sample for testing</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.759234</td>\n",
       "      <td>-73.970071</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23909</th>\n",
       "      <td>72650</td>\n",
       "      <td>marina gourmet deli   sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.738356</td>\n",
       "      <td>-73.982067</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27842</th>\n",
       "      <td>66149</td>\n",
       "      <td>richard sample restaurant 2</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.810107</td>\n",
       "      <td>-73.951075</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27882</th>\n",
       "      <td>69511</td>\n",
       "      <td>sample restaurant   reviewer 1</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29062</th>\n",
       "      <td>64531</td>\n",
       "      <td>(sample) mexican</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.659383</td>\n",
       "      <td>-83.728458</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31089</th>\n",
       "      <td>69657</td>\n",
       "      <td>sample restaurant   tamika</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31332</th>\n",
       "      <td>73523</td>\n",
       "      <td>sample restaurant    testing   anatolia issue</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31885</th>\n",
       "      <td>68471</td>\n",
       "      <td>sample restaurant   btl harlem</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.738082</td>\n",
       "      <td>-73.988011</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32133</th>\n",
       "      <td>71541</td>\n",
       "      <td>yash wine &amp; liquors sis sample listing</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.748600</td>\n",
       "      <td>-74.027525</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32452</th>\n",
       "      <td>57551</td>\n",
       "      <td>sample liquor store</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.706889</td>\n",
       "      <td>-74.004271</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32522</th>\n",
       "      <td>71513</td>\n",
       "      <td>yash wine &amp; liquors upc sample listing</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.748600</td>\n",
       "      <td>-74.027525</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32734</th>\n",
       "      <td>58326</td>\n",
       "      <td>sample freakin restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762654</td>\n",
       "      <td>-73.969233</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33897</th>\n",
       "      <td>88671</td>\n",
       "      <td>sprightlii 4 sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.738119</td>\n",
       "      <td>-73.988113</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>61061</td>\n",
       "      <td>sample restaurant for testing sanity checks</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37723</th>\n",
       "      <td>68684</td>\n",
       "      <td>sample restaurant   reviewer 2</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39034</th>\n",
       "      <td>69318</td>\n",
       "      <td>pinkberry sample listing   work in progress</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.740529</td>\n",
       "      <td>-73.982081</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39781</th>\n",
       "      <td>57382</td>\n",
       "      <td>sample restaurant ii</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.070682</td>\n",
       "      <td>-94.608825</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40289</th>\n",
       "      <td>64134</td>\n",
       "      <td>sample bagel store   complex menu tool</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41081</th>\n",
       "      <td>89253</td>\n",
       "      <td>kennys deli sample for sprightlii to review</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.751403</td>\n",
       "      <td>-73.983757</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41339</th>\n",
       "      <td>69546</td>\n",
       "      <td>sample restaurant   mike bruno</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41532</th>\n",
       "      <td>88672</td>\n",
       "      <td>sprightlii 5 sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.738119</td>\n",
       "      <td>-73.988113</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41800</th>\n",
       "      <td>68878</td>\n",
       "      <td>sample restaurant   matthew brown</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.738082</td>\n",
       "      <td>-73.988011</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42531</th>\n",
       "      <td>64798</td>\n",
       "      <td>papa johns sample template</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43301</th>\n",
       "      <td>88214</td>\n",
       "      <td>sprightliis sample pizzeria</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43366</th>\n",
       "      <td>66452</td>\n",
       "      <td>sample restaurant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760541</td>\n",
       "      <td>-73.970354</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43645</th>\n",
       "      <td>71090</td>\n",
       "      <td>sample restaurant   unitywebgroup.com</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44453</th>\n",
       "      <td>8406</td>\n",
       "      <td>sample restaurant   rep</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                               name  \\\n",
       "685           64095         sample restaurant for testing complex menu   \n",
       "764           88213                         sprightliis sample bagelry   \n",
       "2968          64529                                   (sample) italian   \n",
       "4462          68437          sample restaurant   carlos   jira testing   \n",
       "4487          71056                                  sample smoke shop   \n",
       "4632          66521                  sample restaurant   argenis genao   \n",
       "4711          62133  sample foodfriendz (florida) beverages & utensils   \n",
       "6169          71055         sample restaurant   marincorpsolutions.com   \n",
       "6226          64849  fake restaurant for tara leuci   test/sample/t...   \n",
       "9078          75107                        sample restaurant   testing   \n",
       "10456         68989                                   sample plu store   \n",
       "10793         37915                        sample restaurant   for nod   \n",
       "11305         71089              sample restaurant   infocachecorp.com   \n",
       "11371         71006                 sample restaurant   idata entry us   \n",
       "13554         67520             tiquillia mcperebo   sample restaurant   \n",
       "13656         71151  sample restaurant   neville pereira   infocach...   \n",
       "13945         57348                                  sample restaurant   \n",
       "14009         70314                     sample restaurant   rich boyce   \n",
       "14111         69545                sample restaurant   nicole williams   \n",
       "15369         67970                      sprightliis sample restaurant   \n",
       "15624         68988                                   sample plu store   \n",
       "16362         71154    sample restaurant   guli rajani   infocache.com   \n",
       "18107         71092              sample restaurant   ebusinessware.com   \n",
       "18301         68564                  sample restaurant   travis ferman   \n",
       "18872         71152  sample restaurant   gautam asudani   infocache...   \n",
       "21661         64132                           sprightliis sample diner   \n",
       "21950         60864                         firehouse subs sample road   \n",
       "22010         71391                 sample restaurant testing copyover   \n",
       "23114         71690              sutton wine shop   sample for testing   \n",
       "23909         72650            marina gourmet deli   sample restaurant   \n",
       "27842         66149                        richard sample restaurant 2   \n",
       "27882         69511                     sample restaurant   reviewer 1   \n",
       "29062         64531                                   (sample) mexican   \n",
       "31089         69657                         sample restaurant   tamika   \n",
       "31332         73523      sample restaurant    testing   anatolia issue   \n",
       "31885         68471                     sample restaurant   btl harlem   \n",
       "32133         71541             yash wine & liquors sis sample listing   \n",
       "32452         57551                                sample liquor store   \n",
       "32522         71513             yash wine & liquors upc sample listing   \n",
       "32734         58326                          sample freakin restaurant   \n",
       "33897         88671                     sprightlii 4 sample restaurant   \n",
       "35597         61061        sample restaurant for testing sanity checks   \n",
       "37723         68684                     sample restaurant   reviewer 2   \n",
       "39034         69318        pinkberry sample listing   work in progress   \n",
       "39781         57382                               sample restaurant ii   \n",
       "40289         64134             sample bagel store   complex menu tool   \n",
       "41081         89253        kennys deli sample for sprightlii to review   \n",
       "41339         69546                     sample restaurant   mike bruno   \n",
       "41532         88672                     sprightlii 5 sample restaurant   \n",
       "41800         68878                  sample restaurant   matthew brown   \n",
       "42531         64798                         papa johns sample template   \n",
       "43301         88214                        sprightliis sample pizzeria   \n",
       "43366         66452                                  sample restaurant   \n",
       "43645         71090              sample restaurant   unitywebgroup.com   \n",
       "44453          8406                            sample restaurant   rep   \n",
       "\n",
       "           platform sub_platform   latitude  longitude country active  \n",
       "685    delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "764    delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "2968   delivery.com          NaN  41.714577 -83.684994      US   TRUE  \n",
       "4462   delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "4487   delivery.com          NaN  42.825685 -73.879458      US  FALSE  \n",
       "4632   delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "4711   delivery.com          NaN  30.626630 -96.333424      US  FALSE  \n",
       "6169   delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "6226   delivery.com          NaN  40.737710 -73.987949      US  FALSE  \n",
       "9078   delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "10456  delivery.com          NaN  42.825685 -73.879458      US  FALSE  \n",
       "10793  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "11305  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "11371  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "13554  delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "13656  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "13945  delivery.com          NaN  40.760541 -73.970354      US   TRUE  \n",
       "14009  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "14111  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "15369  delivery.com          NaN  40.738119 -73.988113      US  FALSE  \n",
       "15624  delivery.com          NaN  42.825685 -73.879458      US  FALSE  \n",
       "16362  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "18107  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "18301  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "18872  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "21661  delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "21950  delivery.com          NaN  26.274034 -80.197916      US   TRUE  \n",
       "22010  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "23114  delivery.com          NaN  40.759234 -73.970071      US  FALSE  \n",
       "23909  delivery.com          NaN  40.738356 -73.982067      US  FALSE  \n",
       "27842  delivery.com          NaN  42.810107 -73.951075      US  FALSE  \n",
       "27882  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "29062  delivery.com          NaN  41.659383 -83.728458      US   TRUE  \n",
       "31089  delivery.com          NaN  40.762646 -73.969185      US   TRUE  \n",
       "31332  delivery.com          NaN  40.762646 -73.969185      US   TRUE  \n",
       "31885  delivery.com          NaN  40.738082 -73.988011      US  FALSE  \n",
       "32133  delivery.com          NaN  40.748600 -74.027525      US  FALSE  \n",
       "32452  delivery.com          NaN  40.706889 -74.004271      US   TRUE  \n",
       "32522  delivery.com          NaN  40.748600 -74.027525      US  FALSE  \n",
       "32734  delivery.com          NaN  40.762654 -73.969233      US  FALSE  \n",
       "33897  delivery.com          NaN  40.738119 -73.988113      US   TRUE  \n",
       "35597  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "37723  delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "39034  delivery.com          NaN  40.740529 -73.982081      US  FALSE  \n",
       "39781  delivery.com          NaN  39.070682 -94.608825      US   TRUE  \n",
       "40289  delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "41081  delivery.com          NaN  40.751403 -73.983757      US  FALSE  \n",
       "41339  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "41532  delivery.com          NaN  40.738119 -73.988113      US  FALSE  \n",
       "41800  delivery.com          NaN  40.738082 -73.988011      US   TRUE  \n",
       "42531  delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "43301  delivery.com          NaN  40.760541 -73.970354      US  FALSE  \n",
       "43366  delivery.com          NaN  40.760541 -73.970354      US   TRUE  \n",
       "43645  delivery.com          NaN  40.762646 -73.969185      US   TRUE  \n",
       "44453  delivery.com          NaN  40.762646 -73.969185      US   TRUE  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['name'].str.contains('sample')) & (data['platform'] == 'delivery.com')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the restaurants with the word \"sample\" in their name from delivery.com look like data points that can and should be removed. For example, \"sample freakin restaurant\" or \"sample liquor store\" may represent actual restaurants, but in all likelihood these are useless rows of data saturating the data set. Another clue into the uselessness of the rows above can also be the similarity in latitude and longitude values of these sample rows. It is logically impossible for multiple places to be found on the exact same latitude and longitude coordinates.\n",
    "\n",
    "Let's take a look at restaurants with the word \"sample\" that are not from delivery.com below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77520</th>\n",
       "      <td>191105</td>\n",
       "      <td>the sample room</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>45.009552</td>\n",
       "      <td>-93.271988</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120311</th>\n",
       "      <td>851337</td>\n",
       "      <td>subway� (w sample rd)</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>26.272694</td>\n",
       "      <td>-80.162766</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167215</th>\n",
       "      <td>548795</td>\n",
       "      <td>samples restaurant</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.165287</td>\n",
       "      <td>-105.102112</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201848</th>\n",
       "      <td>332484</td>\n",
       "      <td>miami grill   sample road (5650 w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.273810</td>\n",
       "      <td>-80.201406</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210168</th>\n",
       "      <td>249950</td>\n",
       "      <td>sofias cafe on sample home of sheesk kabab</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272481</td>\n",
       "      <td>-80.231649</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217886</th>\n",
       "      <td>366240</td>\n",
       "      <td>pinto thai bistro (e sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.275173</td>\n",
       "      <td>-80.109766</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300334</th>\n",
       "      <td>262896</td>\n",
       "      <td>firehouse subs (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.273460</td>\n",
       "      <td>-80.198948</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336442</th>\n",
       "      <td>262894</td>\n",
       "      <td>checkers (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272890</td>\n",
       "      <td>-80.259204</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358261</th>\n",
       "      <td>105883</td>\n",
       "      <td>the melting pot (sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272117</td>\n",
       "      <td>-80.264381</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389883</th>\n",
       "      <td>113995</td>\n",
       "      <td>red lantern (sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.275351</td>\n",
       "      <td>-80.134457</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422577</th>\n",
       "      <td>124266</td>\n",
       "      <td>samples restaurant (main st)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.165289</td>\n",
       "      <td>-105.102114</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452485</th>\n",
       "      <td>106019</td>\n",
       "      <td>khana khzana (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272957</td>\n",
       "      <td>-80.264204</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460064</th>\n",
       "      <td>262921</td>\n",
       "      <td>mcdonalds (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.274434</td>\n",
       "      <td>-80.132260</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460733</th>\n",
       "      <td>263000</td>\n",
       "      <td>taco bell (east sample road)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.275811</td>\n",
       "      <td>-80.099606</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463223</th>\n",
       "      <td>332592</td>\n",
       "      <td>taco loco (1058 w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.274368</td>\n",
       "      <td>-80.137032</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469270</th>\n",
       "      <td>335666</td>\n",
       "      <td>subway (57812) (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272693</td>\n",
       "      <td>-80.162765</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473871</th>\n",
       "      <td>255090</td>\n",
       "      <td>firegrills (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272823</td>\n",
       "      <td>-80.184383</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482210</th>\n",
       "      <td>220474</td>\n",
       "      <td>bawarchi indian cusine (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272072</td>\n",
       "      <td>-80.262187</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511170</th>\n",
       "      <td>262995</td>\n",
       "      <td>taco bell (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.273239</td>\n",
       "      <td>-80.278921</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511800</th>\n",
       "      <td>262925</td>\n",
       "      <td>mcdonalds (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.273454</td>\n",
       "      <td>-80.281914</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523590</th>\n",
       "      <td>262942</td>\n",
       "      <td>starbucks (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272985</td>\n",
       "      <td>-80.205697</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527767</th>\n",
       "      <td>105664</td>\n",
       "      <td>el balcon de las americas (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.271731</td>\n",
       "      <td>-80.231866</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529500</th>\n",
       "      <td>262898</td>\n",
       "      <td>golden krust caribbean bakery &amp; grill (sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272496</td>\n",
       "      <td>-80.209118</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537170</th>\n",
       "      <td>211337</td>\n",
       "      <td>sal��s italian ristorante (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272507</td>\n",
       "      <td>-80.210543</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537881</th>\n",
       "      <td>211430</td>\n",
       "      <td>beijing express (e sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.274961</td>\n",
       "      <td>-80.116230</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578563</th>\n",
       "      <td>420740</td>\n",
       "      <td>nonnas pizza (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272976</td>\n",
       "      <td>-80.263821</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579735</th>\n",
       "      <td>262939</td>\n",
       "      <td>tijuana flats (w. sample rd.)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272877</td>\n",
       "      <td>-80.205693</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597718</th>\n",
       "      <td>262910</td>\n",
       "      <td>la granja (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.274350</td>\n",
       "      <td>-80.134681</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615093</th>\n",
       "      <td>105538</td>\n",
       "      <td>its all greek to me grill (w sample rd)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>26.272743</td>\n",
       "      <td>-80.197497</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                               name  \\\n",
       "77520         191105                                    the sample room   \n",
       "120311        851337                              subway� (w sample rd)   \n",
       "167215        548795                                 samples restaurant   \n",
       "201848        332484       miami grill   sample road (5650 w sample rd)   \n",
       "210168        249950         sofias cafe on sample home of sheesk kabab   \n",
       "217886        366240                    pinto thai bistro (e sample rd)   \n",
       "300334        262896                       firehouse subs (w sample rd)   \n",
       "336442        262894                             checkers (w sample rd)   \n",
       "358261        105883                        the melting pot (sample rd)   \n",
       "389883        113995                            red lantern (sample rd)   \n",
       "422577        124266                       samples restaurant (main st)   \n",
       "452485        106019                         khana khzana (w sample rd)   \n",
       "460064        262921                            mcdonalds (w sample rd)   \n",
       "460733        263000                       taco bell (east sample road)   \n",
       "463223        332592                       taco loco (1058 w sample rd)   \n",
       "469270        335666                       subway (57812) (w sample rd)   \n",
       "473871        255090                           firegrills (w sample rd)   \n",
       "482210        220474               bawarchi indian cusine (w sample rd)   \n",
       "511170        262995                            taco bell (w sample rd)   \n",
       "511800        262925                            mcdonalds (w sample rd)   \n",
       "523590        262942                            starbucks (w sample rd)   \n",
       "527767        105664            el balcon de las americas (w sample rd)   \n",
       "529500        262898  golden krust caribbean bakery & grill (sample rd)   \n",
       "537170        211337            sal��s italian ristorante (w sample rd)   \n",
       "537881        211430                      beijing express (e sample rd)   \n",
       "578563        420740                         nonnas pizza (w sample rd)   \n",
       "579735        262939                      tijuana flats (w. sample rd.)   \n",
       "597718        262910                            la granja (w sample rd)   \n",
       "615093        105538            its all greek to me grill (w sample rd)   \n",
       "\n",
       "        platform sub_platform   latitude   longitude country active  \n",
       "77520   doordash     doordash  45.009552  -93.271988      US   TRUE  \n",
       "120311   grubhub      grubhub  26.272694  -80.162766      US   TRUE  \n",
       "167215   grubhub      grubhub  40.165287 -105.102112      US   TRUE  \n",
       "201848  doordash     doordash  26.273810  -80.201406      US   TRUE  \n",
       "210168  doordash     doordash  26.272481  -80.231649      US   TRUE  \n",
       "217886  doordash     doordash  26.275173  -80.109766      US   TRUE  \n",
       "300334  doordash     doordash  26.273460  -80.198948      US   TRUE  \n",
       "336442  doordash     doordash  26.272890  -80.259204      US   TRUE  \n",
       "358261  doordash     doordash  26.272117  -80.264381      US   TRUE  \n",
       "389883  doordash     doordash  26.275351  -80.134457      US   TRUE  \n",
       "422577  doordash     doordash  40.165289 -105.102114      US   TRUE  \n",
       "452485  doordash     doordash  26.272957  -80.264204      US   TRUE  \n",
       "460064  doordash     doordash  26.274434  -80.132260      US   TRUE  \n",
       "460733  doordash     doordash  26.275811  -80.099606      US   TRUE  \n",
       "463223  doordash     doordash  26.274368  -80.137032      US   TRUE  \n",
       "469270  doordash     doordash  26.272693  -80.162765      US   TRUE  \n",
       "473871  doordash     doordash  26.272823  -80.184383      US   TRUE  \n",
       "482210  doordash     doordash  26.272072  -80.262187      US   TRUE  \n",
       "511170  doordash     doordash  26.273239  -80.278921      US   TRUE  \n",
       "511800  doordash     doordash  26.273454  -80.281914      US   TRUE  \n",
       "523590  doordash     doordash  26.272985  -80.205697      US   TRUE  \n",
       "527767  doordash     doordash  26.271731  -80.231866      US   TRUE  \n",
       "529500  doordash     doordash  26.272496  -80.209118      US   TRUE  \n",
       "537170  doordash     doordash  26.272507  -80.210543      US   TRUE  \n",
       "537881  doordash     doordash  26.274961  -80.116230      US   TRUE  \n",
       "578563  doordash     doordash  26.272976  -80.263821      US   TRUE  \n",
       "579735  doordash     doordash  26.272877  -80.205693      US   TRUE  \n",
       "597718  doordash     doordash  26.274350  -80.134681      US   TRUE  \n",
       "615093  doordash     doordash  26.272743  -80.197497      US   TRUE  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['name'].str.contains('sample')) & (data['platform'] != 'delivery.com')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These rows of data are actually meaningful. The majority of these data points have the text \"sample\" in their names because they are located on Sample Rd in Coral Springs, Florida. This explains why the coordinates for most of these restaurants are very close together and yet different.\n",
    "\n",
    "It makes sense to remove the rows of data with the word \"sample\" only if that data point happens to be on the delivery.com platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636709 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"sample\" in its name & hosted on the platform delivery.com\n",
    "data = data[(data['name'].str.contains('sample')) & (data['platform'] == 'delivery.com') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Re-Examining Restaurants w/ Some Form of \"copy\" in its Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>2287</td>\n",
       "      <td>cafe ole copy</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.941704</td>\n",
       "      <td>-77.077104</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294</th>\n",
       "      <td>73842</td>\n",
       "      <td>choppers   infocache copy</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.911545</td>\n",
       "      <td>-87.667617</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14127</th>\n",
       "      <td>64996</td>\n",
       "      <td>joannes import staging listing   for appending...</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16833</th>\n",
       "      <td>2138</td>\n",
       "      <td>naan n curry (2nd copy, unused)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.764284</td>\n",
       "      <td>-122.464891</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24946</th>\n",
       "      <td>63955</td>\n",
       "      <td>mama mia #69568 copyover</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703276</td>\n",
       "      <td>-74.009694</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25896</th>\n",
       "      <td>79058</td>\n",
       "      <td>copy of menu for dozika restaurant rid: 74098</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.043040</td>\n",
       "      <td>-118.323560</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42036</th>\n",
       "      <td>66266</td>\n",
       "      <td>katzs delicatessen range copyover</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.750034</td>\n",
       "      <td>-74.026457</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332502</th>\n",
       "      <td>235929</td>\n",
       "      <td>copycat co</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.900414</td>\n",
       "      <td>-76.991026</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425725</th>\n",
       "      <td>5b4ed7aa-ad1f-4714-9de8-77a015b42a3c</td>\n",
       "      <td>trademark &amp; copyright</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>37.776081</td>\n",
       "      <td>-122.408346</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "840                                     2287   \n",
       "4294                                   73842   \n",
       "14127                                  64996   \n",
       "16833                                   2138   \n",
       "24946                                  63955   \n",
       "25896                                  79058   \n",
       "42036                                  66266   \n",
       "332502                                235929   \n",
       "425725  5b4ed7aa-ad1f-4714-9de8-77a015b42a3c   \n",
       "\n",
       "                                                     name      platform  \\\n",
       "840                                         cafe ole copy        caviar   \n",
       "4294                            choppers   infocache copy  delivery.com   \n",
       "14127   joannes import staging listing   for appending...  delivery.com   \n",
       "16833                     naan n curry (2nd copy, unused)        caviar   \n",
       "24946                            mama mia #69568 copyover  delivery.com   \n",
       "25896       copy of menu for dozika restaurant rid: 74098  delivery.com   \n",
       "42036                   katzs delicatessen range copyover  delivery.com   \n",
       "332502                                         copycat co      doordash   \n",
       "425725                              trademark & copyright      ubereats   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "840             NaN  38.941704  -77.077104      US  FALSE  \n",
       "4294            NaN  41.911545  -87.667617      US  FALSE  \n",
       "14127           NaN  40.762646  -73.969185      US  FALSE  \n",
       "16833           NaN  37.764284 -122.464891      US  FALSE  \n",
       "24946           NaN  40.703276  -74.009694      US  FALSE  \n",
       "25896           NaN  34.043040 -118.323560      US   TRUE  \n",
       "42036           NaN  40.750034  -74.026457      US  FALSE  \n",
       "332502     doordash  38.900414  -76.991026      US   TRUE  \n",
       "425725     ubereats  37.776081 -122.408346      US   TRUE  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'].str.contains('copy')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a function to quickly check for copies of restaurants using their names\n",
    "\n",
    "def check_reentries(name):\n",
    "    df = data[data['name'] == name]\n",
    "    if df.shape[0] > 1:\n",
    "        return True\n",
    "    if df.shape[0] == 1:\n",
    "        return False\n",
    "    else:\n",
    "        return print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# checking if duplicates exist for the doordash and ubereats listings\n",
    "print(check_reentries('copycat co'))\n",
    "print(check_reentries('trademark & copyright'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"copycat co\" and \"trademark & copyright\" appear to be unique restaurants in the data set and therefore should be left in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# checking if duplicates exist for the caviar listings\n",
    "print(check_reentries('cafe ole'))\n",
    "print(check_reentries('naan n curry'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>28407</th>\n",
       "      <td>81218</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.612860</td>\n",
       "      <td>-116.352430</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "    <tr>\n",
       "      <th>30483</th>\n",
       "      <td>4658</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>32915</th>\n",
       "      <td>81219</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-116.282140</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "    <tr>\n",
       "      <th>33210</th>\n",
       "      <td>76750</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>delivery.com</td>\n",
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       "      <td>36.113230</td>\n",
       "      <td>-95.975270</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79695</th>\n",
       "      <td>10643</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>38.941876</td>\n",
       "      <td>-77.076526</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117865</th>\n",
       "      <td>299842</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>35.119441</td>\n",
       "      <td>-89.991273</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193039</th>\n",
       "      <td>a89be976-07ed-4e81-af40-841a2d4b8722</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>39.953518</td>\n",
       "      <td>-75.144657</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511555</th>\n",
       "      <td>250540</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>29.424552</td>\n",
       "      <td>-98.487902</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539374</th>\n",
       "      <td>216075</td>\n",
       "      <td>cafe ole</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>39.953526</td>\n",
       "      <td>-75.144688</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id      name      platform  \\\n",
       "28407                                  81218  cafe ole  delivery.com   \n",
       "30483                                   4658  cafe ole        caviar   \n",
       "32915                                  81219  cafe ole  delivery.com   \n",
       "33210                                  76750  cafe ole  delivery.com   \n",
       "79695                                  10643  cafe ole      doordash   \n",
       "117865                                299842  cafe ole      doordash   \n",
       "193039  a89be976-07ed-4e81-af40-841a2d4b8722  cafe ole      ubereats   \n",
       "511555                                250540  cafe ole      doordash   \n",
       "539374                                216075  cafe ole      doordash   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "28407           NaN  43.612860 -116.352430      US   TRUE  \n",
       "30483           NaN  39.953526  -75.144688      US  FALSE  \n",
       "32915           NaN  43.607280 -116.282140      US   TRUE  \n",
       "33210           NaN  36.113230  -95.975270      US   TRUE  \n",
       "79695      doordash  38.941876  -77.076526      US   TRUE  \n",
       "117865     doordash  35.119441  -89.991273      US   TRUE  \n",
       "193039     ubereats  39.953518  -75.144657      US   TRUE  \n",
       "511555     doordash  29.424552  -98.487902      US   TRUE  \n",
       "539374     doordash  39.953526  -75.144688      US   TRUE  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'cafe ole']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>postmates</td>\n",
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       "      <td>37.977136</td>\n",
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       "      <td>FALSE</td>\n",
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       "      <th>164435</th>\n",
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       "      <td>grubhub</td>\n",
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       "    <tr>\n",
       "      <th>210461</th>\n",
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       "      <td>naan n curry</td>\n",
       "      <td>grubhub</td>\n",
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       "      <td>37.671795</td>\n",
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       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "      <th>364831</th>\n",
       "      <td>d87792b8-d7bd-4707-8985-abe042b817f7</td>\n",
       "      <td>naan n curry</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>33.957845</td>\n",
       "      <td>-84.112162</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "      <td>naan n curry</td>\n",
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       "      <td>naan n curry</td>\n",
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       "      <td>-122.419698</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id          name      platform  \\\n",
       "11644                                  78975  naan n curry  delivery.com   \n",
       "13139                                  64978  naan n curry  delivery.com   \n",
       "14864                                  67503  naan n curry  delivery.com   \n",
       "25086                                  67756  naan n curry  delivery.com   \n",
       "43152                                  62198  naan n curry  delivery.com   \n",
       "86711                                 619452  naan n curry       grubhub   \n",
       "89006                                 206126  naan n curry       grubhub   \n",
       "145415  0dda5e2f-bad8-4ee6-a90c-a4edc3638517  naan n curry     postmates   \n",
       "164435                                414987  naan n curry       grubhub   \n",
       "210461                                548983  naan n curry       grubhub   \n",
       "364831  d87792b8-d7bd-4707-8985-abe042b817f7  naan n curry      ubereats   \n",
       "379304  18c54ba5-55d5-48b2-80e2-c92fb25db4d2  naan n curry     postmates   \n",
       "383249  13c87b71-3114-426f-a865-3a7b35a3de45  naan n curry      ubereats   \n",
       "457239                                 63240  naan n curry      doordash   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "11644           NaN  37.807229 -122.416470      US   TRUE  \n",
       "13139           NaN  37.764131 -122.464746      US   TRUE  \n",
       "14864           NaN  37.760806 -122.436675      US  FALSE  \n",
       "25086           NaN  37.867378 -122.258919      US   TRUE  \n",
       "43152           NaN  37.786156 -122.410570      US   TRUE  \n",
       "86711       grubhub  37.778570 -122.392176      US   TRUE  \n",
       "89006       grubhub  37.764119 -122.464923      US   TRUE  \n",
       "145415   restaurant  37.977136 -122.035025      US  FALSE  \n",
       "164435      grubhub  33.131191 -117.260582      US   TRUE  \n",
       "210461      grubhub  37.671795 -122.472252      US   TRUE  \n",
       "364831     ubereats  33.957845  -84.112162      US   TRUE  \n",
       "379304   restaurant  33.957821  -84.112091      US   TRUE  \n",
       "383249     ubereats  37.977136 -122.035025      US   TRUE  \n",
       "457239     doordash  37.762521 -122.419698      US   TRUE  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['name'] == 'naan n curry']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be confirmed that the restaurants with \"copy\" in their names from any other platform outside of ubereats and doordash can be considered duplicate entries and can be removed from the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636702 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"copy\" in its name & hosted on the platform delivery.com or caviar\n",
    "data = data[(data['name'].str.contains('copy')) & (data['platform'] == 'delivery.com') == False]\n",
    "data = data[(data['name'].str.contains('copy')) & (data['platform'] == 'caviar') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Re-Examining Restaurants w/ Some Form of \"test\" in its Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
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       "      <th>4</th>\n",
       "      <td>5690</td>\n",
       "      <td>derek test merchant</td>\n",
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       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>3542</td>\n",
       "      <td>[test2]sugarfish by sushi nozawa</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703738</td>\n",
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       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>1183</td>\n",
       "      <td>test doc ricketts catering</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>1170</td>\n",
       "      <td>jesses test store</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775710</td>\n",
       "      <td>-122.418172</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>2947</td>\n",
       "      <td>della test kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.803639</td>\n",
       "      <td>-80.196274</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639531</th>\n",
       "      <td>50988</td>\n",
       "      <td>dep test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.638283</td>\n",
       "      <td>-79.425290</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639594</th>\n",
       "      <td>392005</td>\n",
       "      <td>novadine v2 test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>58.372186</td>\n",
       "      <td>-134.587152</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639862</th>\n",
       "      <td>330076</td>\n",
       "      <td>test</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.495742</td>\n",
       "      <td>-86.876172</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642231</th>\n",
       "      <td>230772</td>\n",
       "      <td>testo pepesto (e main st)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.794609</td>\n",
       "      <td>-116.959992</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643046</th>\n",
       "      <td>334544</td>\n",
       "      <td>greatest b b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>29.592922</td>\n",
       "      <td>-95.527972</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>447 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                              name  platform sub_platform  \\\n",
       "4               5690               derek test merchant    caviar          NaN   \n",
       "878             3542  [test2]sugarfish by sushi nozawa    caviar          NaN   \n",
       "913             1183        test doc ricketts catering    caviar          NaN   \n",
       "1009            1170                 jesses test store    caviar          NaN   \n",
       "1065            2947                della test kitchen    caviar          NaN   \n",
       "...              ...                               ...       ...          ...   \n",
       "639531         50988                    dep test store  doordash     doordash   \n",
       "639594        392005            novadine v2 test store  doordash     doordash   \n",
       "639862        330076                              test  doordash     doordash   \n",
       "642231        230772         testo pepesto (e main st)  doordash     doordash   \n",
       "643046        334544                    greatest b b q  doordash     doordash   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "4        1.000000    2.000000      US  FALSE  \n",
       "878     40.703738  -73.638472      US  FALSE  \n",
       "913      1.000000    2.000000      US  FALSE  \n",
       "1009    37.775710 -122.418172      US  FALSE  \n",
       "1065    25.803639  -80.196274      US  FALSE  \n",
       "...           ...         ...     ...    ...  \n",
       "639531  43.638283  -79.425290      US   TRUE  \n",
       "639594  58.372186 -134.587152      US   TRUE  \n",
       "639862  33.495742  -86.876172      US   TRUE  \n",
       "642231  32.794609 -116.959992      US   TRUE  \n",
       "643046  29.592922  -95.527972      US   TRUE  \n",
       "\n",
       "[447 rows x 8 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = data[data['name'].str.contains('test')]\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's again evident that whether or not the rows of data might be kept may be dependent on the platform in which that restaurant's data was found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "postmates         5\n",
       "grubhub          12\n",
       "ubereats         15\n",
       "caviar           33\n",
       "doordash        189\n",
       "delivery.com    193\n",
       "Name: platform, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['platform'].value_counts(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be examining each of the rows of data on these platforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106265</th>\n",
       "      <td>d44d89a4-4775-4537-92eb-eeb80af76638</td>\n",
       "      <td>bol   test kitchen &amp; bar</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>47.676061</td>\n",
       "      <td>-122.318264</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142359</th>\n",
       "      <td>4d5ddf5a-3228-4905-8c87-27d14b904da1</td>\n",
       "      <td>pizzeria testa</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>32.830599</td>\n",
       "      <td>-96.770478</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "    <tr>\n",
       "      <th>351915</th>\n",
       "      <td>64b4c625-6f43-4efd-bd18-991f32654ef0</td>\n",
       "      <td>project juice test kitchen</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>37.777016</td>\n",
       "      <td>-122.422349</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361862</th>\n",
       "      <td>1ebf42e8-626b-47d3-986f-654d7b8e1c50</td>\n",
       "      <td>testo pepesto italian restaurant</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>32.794609</td>\n",
       "      <td>-116.959992</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424165</th>\n",
       "      <td>e59facbe-f6a6-475e-8d19-356857327868</td>\n",
       "      <td>della test kitchen</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>25.803679</td>\n",
       "      <td>-80.196424</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "106265  d44d89a4-4775-4537-92eb-eeb80af76638   \n",
       "142359  4d5ddf5a-3228-4905-8c87-27d14b904da1   \n",
       "351915  64b4c625-6f43-4efd-bd18-991f32654ef0   \n",
       "361862  1ebf42e8-626b-47d3-986f-654d7b8e1c50   \n",
       "424165  e59facbe-f6a6-475e-8d19-356857327868   \n",
       "\n",
       "                                    name   platform sub_platform   latitude  \\\n",
       "106265          bol   test kitchen & bar  postmates   restaurant  47.676061   \n",
       "142359                    pizzeria testa  postmates   restaurant  32.830599   \n",
       "351915        project juice test kitchen  postmates   restaurant  37.777016   \n",
       "361862  testo pepesto italian restaurant  postmates   restaurant  32.794609   \n",
       "424165                della test kitchen  postmates   restaurant  25.803679   \n",
       "\n",
       "         longitude country active  \n",
       "106265 -122.318264      US   TRUE  \n",
       "142359  -96.770478      US   TRUE  \n",
       "351915 -122.422349      US   TRUE  \n",
       "361862 -116.959992      US   TRUE  \n",
       "424165  -80.196424      US   TRUE  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['platform'] == 'postmates']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The text \"test\" appears to be part of the actual names of real restaurants. This data may be kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>active</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45087</th>\n",
       "      <td>333316</td>\n",
       "      <td>dbg la test restaurant 2</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>34.039689</td>\n",
       "      <td>-118.298714</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55160</th>\n",
       "      <td>758384</td>\n",
       "      <td>[test] andreas slice of life</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>41.883061</td>\n",
       "      <td>-87.631384</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62752</th>\n",
       "      <td>308563</td>\n",
       "      <td>new whitestone diner</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.788481</td>\n",
       "      <td>-73.815340</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68488</th>\n",
       "      <td>767469</td>\n",
       "      <td>sweetest season</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>33.415089</td>\n",
       "      <td>-111.905243</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71834</th>\n",
       "      <td>245945</td>\n",
       "      <td>potestas pizza &amp; pasta</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>42.463948</td>\n",
       "      <td>-87.852301</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91917</th>\n",
       "      <td>275822</td>\n",
       "      <td>cascarinos whitestone</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.793476</td>\n",
       "      <td>-73.809410</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100848</th>\n",
       "      <td>536436</td>\n",
       "      <td>godiva   test test</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.354670</td>\n",
       "      <td>-75.987996</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112583</th>\n",
       "      <td>338254</td>\n",
       "      <td>sweetest thing</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>42.117810</td>\n",
       "      <td>-76.017966</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180157</th>\n",
       "      <td>478947</td>\n",
       "      <td>testo pepesto italian restaurant</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>32.794609</td>\n",
       "      <td>-116.959992</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230573</th>\n",
       "      <td>590593</td>\n",
       "      <td>latest food trends</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.622726</td>\n",
       "      <td>-75.481484</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250149</th>\n",
       "      <td>245958</td>\n",
       "      <td>ginos of whitestone pizzeria</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>40.775857</td>\n",
       "      <td>-73.824429</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263132</th>\n",
       "      <td>313633</td>\n",
       "      <td>potestas pizza &amp; pasta</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>42.352639</td>\n",
       "      <td>-87.873952</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                              name platform sub_platform  \\\n",
       "45087         333316          dbg la test restaurant 2  grubhub      grubhub   \n",
       "55160         758384      [test] andreas slice of life  grubhub      grubhub   \n",
       "62752         308563              new whitestone diner  grubhub      grubhub   \n",
       "68488         767469                   sweetest season  grubhub      grubhub   \n",
       "71834         245945            potestas pizza & pasta  grubhub      grubhub   \n",
       "91917         275822             cascarinos whitestone  grubhub      grubhub   \n",
       "100848        536436                godiva   test test  grubhub      grubhub   \n",
       "112583        338254                    sweetest thing  grubhub      grubhub   \n",
       "180157        478947  testo pepesto italian restaurant  grubhub      grubhub   \n",
       "230573        590593                latest food trends  grubhub      grubhub   \n",
       "250149        245958      ginos of whitestone pizzeria  grubhub      grubhub   \n",
       "263132        313633            potestas pizza & pasta  grubhub      grubhub   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "45087   34.039689 -118.298714      US   TRUE  \n",
       "55160   41.883061  -87.631384      US   TRUE  \n",
       "62752   40.788481  -73.815340      US   TRUE  \n",
       "68488   33.415089 -111.905243      US   TRUE  \n",
       "71834   42.463948  -87.852301      US   TRUE  \n",
       "91917   40.793476  -73.809410      US   TRUE  \n",
       "100848  40.354670  -75.987996      US   TRUE  \n",
       "112583  42.117810  -76.017966      US   TRUE  \n",
       "180157  32.794609 -116.959992      US   TRUE  \n",
       "230573  40.622726  -75.481484      US   TRUE  \n",
       "250149  40.775857  -73.824429      US   TRUE  \n",
       "263132  42.352639  -87.873952      US   TRUE  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['platform'] == 'grubhub']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the grubhub restaurants with \"test\" in their names, we can observe that in some instances the characters in \"test\" may have been part of a larger word (e.g. sweetest, potestas, whitestone, testo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
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       "      <th>100722</th>\n",
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       "      <td>brandons test restaurant</td>\n",
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       "      <td>ubereats</td>\n",
       "      <td>40.733690</td>\n",
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       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>27.981093</td>\n",
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       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "      <th>189306</th>\n",
       "      <td>12f96b7b-ad96-42a1-b440-93fd34db0c6e</td>\n",
       "      <td>panda express (401 w. whitestone blvd.)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>30.520203</td>\n",
       "      <td>-97.833939</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214513</th>\n",
       "      <td>c56e6def-0372-46ea-a390-a582848fdd1a</td>\n",
       "      <td>latest food trends restaurant</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>40.622722</td>\n",
       "      <td>-75.481501</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246673</th>\n",
       "      <td>52b99da5-1ea4-4912-9753-15568d0d044e</td>\n",
       "      <td>firehouse subs (1465 e whitestone blvd)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>30.527846</td>\n",
       "      <td>-97.809447</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279045</th>\n",
       "      <td>b9644439-fbf5-4280-b0a3-e5ba0e155e74</td>\n",
       "      <td>sweetest season bakery cafe</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>33.415213</td>\n",
       "      <td>-111.905139</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298572</th>\n",
       "      <td>8a94710d-fa45-4952-a988-5b4b25ebc09b</td>\n",
       "      <td>schlotzskys (east whitestone blvd)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>30.520558</td>\n",
       "      <td>-97.828776</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312009</th>\n",
       "      <td>ee30d0eb-00ad-44c9-8133-f964f99223d0</td>\n",
       "      <td>testo pepesto italian restaurant   el cajon</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>32.794597</td>\n",
       "      <td>-116.959979</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315708</th>\n",
       "      <td>2246e596-926e-45dc-abbb-75eb86d8a126</td>\n",
       "      <td>the village green   whitestone</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>40.788368</td>\n",
       "      <td>-73.813389</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354156</th>\n",
       "      <td>2040a370-6473-4dd9-8318-9588fae4e743</td>\n",
       "      <td>patsys pizzeria   whitestone</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>40.777121</td>\n",
       "      <td>-73.793601</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355770</th>\n",
       "      <td>adb5e916-c389-4274-b04a-55bcdee9325a</td>\n",
       "      <td>villaggio ristorante   whitestone</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>40.787697</td>\n",
       "      <td>-73.813080</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373981</th>\n",
       "      <td>7ceaae79-48b1-43ad-b65a-74a2b4426aaf</td>\n",
       "      <td>della bowls (formerly della test kitchen)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>25.803682</td>\n",
       "      <td>-80.196421</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405377</th>\n",
       "      <td>5d53613f-d349-4d84-957a-9ba856c625d1</td>\n",
       "      <td>freshii (1310 e whitestone)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>30.524400</td>\n",
       "      <td>-97.814500</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407381</th>\n",
       "      <td>a01cc9cc-a650-497c-99ad-e4a67ee80271</td>\n",
       "      <td>nikitas place   whitestone</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>40.780342</td>\n",
       "      <td>-73.793644</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420619</th>\n",
       "      <td>9d5c9e1e-f37e-4769-98d2-8ce4c887fba6</td>\n",
       "      <td>omani korean bbq &amp; grill   whitestone</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>40.780561</td>\n",
       "      <td>-73.802055</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "100722  8da27c6a-0d8b-4c86-a6ae-f9073268a538   \n",
       "183166  78506524-4cda-46a8-ac97-ecd9a82f7e51   \n",
       "189306  12f96b7b-ad96-42a1-b440-93fd34db0c6e   \n",
       "214513  c56e6def-0372-46ea-a390-a582848fdd1a   \n",
       "246673  52b99da5-1ea4-4912-9753-15568d0d044e   \n",
       "279045  b9644439-fbf5-4280-b0a3-e5ba0e155e74   \n",
       "298572  8a94710d-fa45-4952-a988-5b4b25ebc09b   \n",
       "312009  ee30d0eb-00ad-44c9-8133-f964f99223d0   \n",
       "315708  2246e596-926e-45dc-abbb-75eb86d8a126   \n",
       "354156  2040a370-6473-4dd9-8318-9588fae4e743   \n",
       "355770  adb5e916-c389-4274-b04a-55bcdee9325a   \n",
       "373981  7ceaae79-48b1-43ad-b65a-74a2b4426aaf   \n",
       "405377  5d53613f-d349-4d84-957a-9ba856c625d1   \n",
       "407381  a01cc9cc-a650-497c-99ad-e4a67ee80271   \n",
       "420619  9d5c9e1e-f37e-4769-98d2-8ce4c887fba6   \n",
       "\n",
       "                                                name  platform sub_platform  \\\n",
       "100722                      brandons test restaurant  ubereats     ubereats   \n",
       "183166  pdq test kitchen (4343 anchor plaza parkway)  ubereats     ubereats   \n",
       "189306       panda express (401 w. whitestone blvd.)  ubereats     ubereats   \n",
       "214513                 latest food trends restaurant  ubereats     ubereats   \n",
       "246673       firehouse subs (1465 e whitestone blvd)  ubereats     ubereats   \n",
       "279045                   sweetest season bakery cafe  ubereats     ubereats   \n",
       "298572            schlotzskys (east whitestone blvd)  ubereats     ubereats   \n",
       "312009   testo pepesto italian restaurant   el cajon  ubereats     ubereats   \n",
       "315708                the village green   whitestone  ubereats     ubereats   \n",
       "354156                  patsys pizzeria   whitestone  ubereats     ubereats   \n",
       "355770             villaggio ristorante   whitestone  ubereats     ubereats   \n",
       "373981     della bowls (formerly della test kitchen)  ubereats     ubereats   \n",
       "405377                   freshii (1310 e whitestone)  ubereats     ubereats   \n",
       "407381                    nikitas place   whitestone  ubereats     ubereats   \n",
       "420619         omani korean bbq & grill   whitestone  ubereats     ubereats   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "100722  40.733690  -73.980203      US   TRUE  \n",
       "183166  27.981093  -82.548078      US   TRUE  \n",
       "189306  30.520203  -97.833939      US   TRUE  \n",
       "214513  40.622722  -75.481501      US   TRUE  \n",
       "246673  30.527846  -97.809447      US   TRUE  \n",
       "279045  33.415213 -111.905139      US   TRUE  \n",
       "298572  30.520558  -97.828776      US   TRUE  \n",
       "312009  32.794597 -116.959979      US   TRUE  \n",
       "315708  40.788368  -73.813389      US   TRUE  \n",
       "354156  40.777121  -73.793601      US   TRUE  \n",
       "355770  40.787697  -73.813080      US   TRUE  \n",
       "373981  25.803682  -80.196421      US   TRUE  \n",
       "405377  30.524400  -97.814500      US   TRUE  \n",
       "407381  40.780342  -73.793644      US   TRUE  \n",
       "420619  40.780561  -73.802055      US   TRUE  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['platform'] == 'ubereats']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the ubereats restaurants with \"test\" in their names, we can observe again that in some instances the characters in \"test\" may have been part of a larger word (e.g. sweetest, potestas, whitestone, testo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5690</td>\n",
       "      <td>derek test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>3542</td>\n",
       "      <td>[test2]sugarfish by sushi nozawa</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.703738</td>\n",
       "      <td>-73.638472</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>1183</td>\n",
       "      <td>test doc ricketts catering</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>1170</td>\n",
       "      <td>jesses test store</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775710</td>\n",
       "      <td>-122.418172</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>2947</td>\n",
       "      <td>della test kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.803639</td>\n",
       "      <td>-80.196274</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>9601</td>\n",
       "      <td>minigrow   test merchant (dne)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.751731</td>\n",
       "      <td>-73.979705</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2765</th>\n",
       "      <td>4002</td>\n",
       "      <td>colins test ristorante</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775232</td>\n",
       "      <td>122.419717</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5528</th>\n",
       "      <td>5470</td>\n",
       "      <td>ems   test</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.779322</td>\n",
       "      <td>-96.828607</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5584</th>\n",
       "      <td>1403</td>\n",
       "      <td>test merchant chicago</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.974162</td>\n",
       "      <td>-87.907321</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5820</th>\n",
       "      <td>7038</td>\n",
       "      <td>dont use  honeygrow test 2</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.885252</td>\n",
       "      <td>-87.652084</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>8200</td>\n",
       "      <td>edwins test hrd merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.799553</td>\n",
       "      <td>-122.407773</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6805</th>\n",
       "      <td>3867</td>\n",
       "      <td>test   oya</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.894971</td>\n",
       "      <td>-77.021721</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>9095</td>\n",
       "      <td>burgerfi  test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775316</td>\n",
       "      <td>-122.417437</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10704</th>\n",
       "      <td>9842</td>\n",
       "      <td>holiday test russ and daughters at the jewish ...</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.785421</td>\n",
       "      <td>-73.957293</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12506</th>\n",
       "      <td>2397</td>\n",
       "      <td>project juice test kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.777114</td>\n",
       "      <td>-122.422392</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12549</th>\n",
       "      <td>6851</td>\n",
       "      <td>test</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775231</td>\n",
       "      <td>-122.417557</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12974</th>\n",
       "      <td>9643</td>\n",
       "      <td>honeygrow   test merchant dne</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.953810</td>\n",
       "      <td>-75.197886</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14304</th>\n",
       "      <td>7860</td>\n",
       "      <td>blaze pizza test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>1593</td>\n",
       "      <td>shake shack test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.740876</td>\n",
       "      <td>-73.987981</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15973</th>\n",
       "      <td>4001</td>\n",
       "      <td>la test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.151359</td>\n",
       "      <td>-118.254615</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16697</th>\n",
       "      <td>5656</td>\n",
       "      <td>wooster &amp; waice [dupe test]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.109301</td>\n",
       "      <td>85.741314</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17624</th>\n",
       "      <td>571</td>\n",
       "      <td>placeholder   test only   san francisco</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231.423200</td>\n",
       "      <td>-122.392876</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17736</th>\n",
       "      <td>8201</td>\n",
       "      <td>jerrys test bistro</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.785987</td>\n",
       "      <td>-122.421174</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19866</th>\n",
       "      <td>9689</td>\n",
       "      <td>test by hana</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21131</th>\n",
       "      <td>6823</td>\n",
       "      <td>verts test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775231</td>\n",
       "      <td>-122.417528</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21229</th>\n",
       "      <td>8742</td>\n",
       "      <td>bon me test kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.365026</td>\n",
       "      <td>-71.079815</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21306</th>\n",
       "      <td>1622</td>\n",
       "      <td>placeholder   test only</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784069</td>\n",
       "      <td>-122.417594</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22415</th>\n",
       "      <td>4003</td>\n",
       "      <td>simona test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.604945</td>\n",
       "      <td>-73.979885</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22465</th>\n",
       "      <td>9937</td>\n",
       "      <td>honeygrow test kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.974462</td>\n",
       "      <td>-75.134490</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24466</th>\n",
       "      <td>8668</td>\n",
       "      <td>dne minigrow   test merchant</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.881234</td>\n",
       "      <td>-87.626756</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27629</th>\n",
       "      <td>3039</td>\n",
       "      <td>test merchant   east bay</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.814322</td>\n",
       "      <td>-122.246585</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29379</th>\n",
       "      <td>7037</td>\n",
       "      <td>dont use honeygrow test</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.344448</td>\n",
       "      <td>-71.096996</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30984</th>\n",
       "      <td>8260</td>\n",
       "      <td>dont use noon test merchant (verts demo vendor)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775231</td>\n",
       "      <td>-122.417528</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                               name  \\\n",
       "4              5690                                derek test merchant   \n",
       "878            3542                   [test2]sugarfish by sushi nozawa   \n",
       "913            1183                         test doc ricketts catering   \n",
       "1009           1170                                  jesses test store   \n",
       "1065           2947                                 della test kitchen   \n",
       "2489           9601                     minigrow   test merchant (dne)   \n",
       "2765           4002                             colins test ristorante   \n",
       "5528           5470                                         ems   test   \n",
       "5584           1403                              test merchant chicago   \n",
       "5820           7038                         dont use  honeygrow test 2   \n",
       "6415           8200                           edwins test hrd merchant   \n",
       "6805           3867                                         test   oya   \n",
       "9680           9095                            burgerfi  test merchant   \n",
       "10704          9842  holiday test russ and daughters at the jewish ...   \n",
       "12506          2397                         project juice test kitchen   \n",
       "12549          6851                                               test   \n",
       "12974          9643                      honeygrow   test merchant dne   \n",
       "14304          7860                          blaze pizza test merchant   \n",
       "15915          1593                          shake shack test merchant   \n",
       "15973          4001                                   la test merchant   \n",
       "16697          5656                        wooster & waice [dupe test]   \n",
       "17624           571            placeholder   test only   san francisco   \n",
       "17736          8201                                 jerrys test bistro   \n",
       "19866          9689                                       test by hana   \n",
       "21131          6823                                verts test merchant   \n",
       "21229          8742                                bon me test kitchen   \n",
       "21306          1622                            placeholder   test only   \n",
       "22415          4003                               simona test merchant   \n",
       "22465          9937                             honeygrow test kitchen   \n",
       "24466          8668                       dne minigrow   test merchant   \n",
       "27629          3039                           test merchant   east bay   \n",
       "29379          7037                            dont use honeygrow test   \n",
       "30984          8260    dont use noon test merchant (verts demo vendor)   \n",
       "\n",
       "      platform sub_platform    latitude   longitude country active  \n",
       "4       caviar          NaN    1.000000    2.000000      US  FALSE  \n",
       "878     caviar          NaN   40.703738  -73.638472      US  FALSE  \n",
       "913     caviar          NaN    1.000000    2.000000      US  FALSE  \n",
       "1009    caviar          NaN   37.775710 -122.418172      US  FALSE  \n",
       "1065    caviar          NaN   25.803639  -80.196274      US  FALSE  \n",
       "2489    caviar          NaN   40.751731  -73.979705      US  FALSE  \n",
       "2765    caviar          NaN   37.775232  122.419717      US  FALSE  \n",
       "5528    caviar          NaN   32.779322  -96.828607      US  FALSE  \n",
       "5584    caviar          NaN   41.974162  -87.907321      US  FALSE  \n",
       "5820    caviar          NaN   41.885252  -87.652084      US  FALSE  \n",
       "6415    caviar          NaN   37.799553 -122.407773      US  FALSE  \n",
       "6805    caviar          NaN   38.894971  -77.021721      US  FALSE  \n",
       "9680    caviar          NaN   37.775316 -122.417437      US  FALSE  \n",
       "10704   caviar          NaN   40.785421  -73.957293      US  FALSE  \n",
       "12506   caviar          NaN   37.777114 -122.422392      US   TRUE  \n",
       "12549   caviar          NaN   37.775231 -122.417557      US  FALSE  \n",
       "12974   caviar          NaN   39.953810  -75.197886      US  FALSE  \n",
       "14304   caviar          NaN  555.000000  555.000000      US  FALSE  \n",
       "15915   caviar          NaN   40.740876  -73.987981      US  FALSE  \n",
       "15973   caviar          NaN   34.151359 -118.254615      US  FALSE  \n",
       "16697   caviar          NaN   40.109301   85.741314      US  FALSE  \n",
       "17624   caviar          NaN  231.423200 -122.392876      US  FALSE  \n",
       "17736   caviar          NaN   37.785987 -122.421174      US  FALSE  \n",
       "19866   caviar          NaN    1.000000    1.000000      US  FALSE  \n",
       "21131   caviar          NaN   37.775231 -122.417528      US  FALSE  \n",
       "21229   caviar          NaN   42.365026  -71.079815      US  FALSE  \n",
       "21306   caviar          NaN   37.784069 -122.417594      US  FALSE  \n",
       "22415   caviar          NaN   40.604945  -73.979885      US  FALSE  \n",
       "22465   caviar          NaN   39.974462  -75.134490      US  FALSE  \n",
       "24466   caviar          NaN   41.881234  -87.626756      US  FALSE  \n",
       "27629   caviar          NaN   37.814322 -122.246585      US  FALSE  \n",
       "29379   caviar          NaN   42.344448  -71.096996      US  FALSE  \n",
       "30984   caviar          NaN   37.775231 -122.417528      US  FALSE  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['platform'] == 'caviar']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the caviar restaurants with \"test\" in their names, we can almost every single row suggests that the data is some sort of duplicate. However, there are rare instances where the row of data, while still capable of being a duplicate, may hold valuable information for real restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
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       "      <th>longitude</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46765</th>\n",
       "      <td>421761</td>\n",
       "      <td>red robin test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>58.372194</td>\n",
       "      <td>-134.590883</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49118</th>\n",
       "      <td>385736</td>\n",
       "      <td>burger king test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>58.371417</td>\n",
       "      <td>-134.589679</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49121</th>\n",
       "      <td>387045</td>\n",
       "      <td>aloha test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>58.371404</td>\n",
       "      <td>-134.589536</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49514</th>\n",
       "      <td>175322</td>\n",
       "      <td>brians test kitchen</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>41.170561</td>\n",
       "      <td>-71.557459</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52546</th>\n",
       "      <td>50769</td>\n",
       "      <td>baskin robbins (tester)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.628429</td>\n",
       "      <td>-79.413533</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>639531</th>\n",
       "      <td>50988</td>\n",
       "      <td>dep test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.638283</td>\n",
       "      <td>-79.425290</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639594</th>\n",
       "      <td>392005</td>\n",
       "      <td>novadine v2 test store</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>58.372186</td>\n",
       "      <td>-134.587152</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639862</th>\n",
       "      <td>330076</td>\n",
       "      <td>test</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.495742</td>\n",
       "      <td>-86.876172</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "    <tr>\n",
       "      <th>642231</th>\n",
       "      <td>230772</td>\n",
       "      <td>testo pepesto (e main st)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.794609</td>\n",
       "      <td>-116.959992</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "      <th>643046</th>\n",
       "      <td>334544</td>\n",
       "      <td>greatest b b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>29.592922</td>\n",
       "      <td>-95.527972</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                       name  platform sub_platform  \\\n",
       "46765         421761       red robin test store  doordash     doordash   \n",
       "49118         385736     burger king test store  doordash     doordash   \n",
       "49121         387045           aloha test store  doordash     doordash   \n",
       "49514         175322        brians test kitchen  doordash     doordash   \n",
       "52546          50769    baskin robbins (tester)  doordash     doordash   \n",
       "...              ...                        ...       ...          ...   \n",
       "639531         50988             dep test store  doordash     doordash   \n",
       "639594        392005     novadine v2 test store  doordash     doordash   \n",
       "639862        330076                       test  doordash     doordash   \n",
       "642231        230772  testo pepesto (e main st)  doordash     doordash   \n",
       "643046        334544             greatest b b q  doordash     doordash   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "46765   58.372194 -134.590883      US  FALSE  \n",
       "49118   58.371417 -134.589679      US  FALSE  \n",
       "49121   58.371404 -134.589536      US  FALSE  \n",
       "49514   41.170561  -71.557459      US  FALSE  \n",
       "52546   43.628429  -79.413533      US  FALSE  \n",
       "...           ...         ...     ...    ...  \n",
       "639531  43.638283  -79.425290      US   TRUE  \n",
       "639594  58.372186 -134.587152      US   TRUE  \n",
       "639862  33.495742  -86.876172      US   TRUE  \n",
       "642231  32.794609 -116.959992      US   TRUE  \n",
       "643046  29.592922  -95.527972      US   TRUE  \n",
       "\n",
       "[189 rows x 8 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['platform'] == 'doordash']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the doordash restaurants with \"test\" in their names, we can almost every single row suggests that the data is some sort of duplicate. However, again there are rare instances where the row of data, while still capable of being a duplicate, may hold valuable information for real restaurants.\n",
    "\n",
    "At this stage it is becoming apparent that I need to refine the regex filter for the characters in \"test\" to exclude the use of said characters in larger words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>40.740615</td>\n",
       "      <td>-73.888321</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43867</th>\n",
       "      <td>30541</td>\n",
       "      <td>zzz test merchant    dg   new data entry tool</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.762646</td>\n",
       "      <td>-73.969185</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44144</th>\n",
       "      <td>64489</td>\n",
       "      <td>hale &amp; hearty test</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.714577</td>\n",
       "      <td>-83.684994</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44625</th>\n",
       "      <td>62179</td>\n",
       "      <td>test</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.961041</td>\n",
       "      <td>-75.194753</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44742</th>\n",
       "      <td>87883</td>\n",
       "      <td>dc taxi test merchant</td>\n",
       "      <td>delivery.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.902520</td>\n",
       "      <td>-77.030790</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                             name  \\\n",
       "1234          73943                                             test   \n",
       "1629          64841  fake restaurnt for ernestine test new menu tool   \n",
       "1719          73777                                     menu test 67   \n",
       "1732          87341                               johns pizza   test   \n",
       "1864          91972                             chowly test merchant   \n",
       "...             ...                                              ...   \n",
       "43622         71684                          testing merchant 121212   \n",
       "43867         30541    zzz test merchant    dg   new data entry tool   \n",
       "44144         64489                               hale & hearty test   \n",
       "44625         62179                                             test   \n",
       "44742         87883                            dc taxi test merchant   \n",
       "\n",
       "           platform sub_platform   latitude  longitude country active  \n",
       "1234   delivery.com          NaN  40.726933 -73.235764      US   TRUE  \n",
       "1629   delivery.com          NaN  40.737710 -73.987949      US  FALSE  \n",
       "1719   delivery.com          NaN  40.839090 -74.282754      US   TRUE  \n",
       "1732   delivery.com          NaN  40.751824 -73.986455      US   TRUE  \n",
       "1864   delivery.com          NaN  40.746854 -73.997194      US   TRUE  \n",
       "...             ...          ...        ...        ...     ...    ...  \n",
       "43622  delivery.com          NaN  40.740615 -73.888321      US   TRUE  \n",
       "43867  delivery.com          NaN  40.762646 -73.969185      US  FALSE  \n",
       "44144  delivery.com          NaN  41.714577 -83.684994      US   TRUE  \n",
       "44625  delivery.com          NaN  39.961041 -75.194753      US   TRUE  \n",
       "44742  delivery.com          NaN  38.902520 -77.030790      US   TRUE  \n",
       "\n",
       "[193 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['platform'] == 'delivery.com']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the delivery.com restaurants with \"test\" in their names, we can almost every single row suggests that the data is some sort of duplicate. However, again there are rare instances where the row of data, while still capable of being a duplicate, may hold valuable information for real restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.065 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "sketchy_test_data = (test[test['platform'] == 'caviar'].shape[0] + test[test['platform'] == 'doordash'].shape[0] + test[test['platform'] == 'delivery.com'].shape[0]) / data.shape[0] * 100\n",
    "print(round(sketchy_test_data, 3), '% of the dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the time constraints given to complete this assignment, instead of using more complex regex to distinguish between rows with the characters \"test\" in restaurant names, I will instead only keep the rows in which the platform was either postmates, grubhub, or ubereats. \n",
    "\n",
    "Upon observation, the other platforms (caviar, doordash, delivery.com) were almost entirely duplicate rows of data. Even if all of these questionable rows of data were real restaurants and not duplicates, they make up just 0.038% of the dataset and are almost inconsequential to the final results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636287 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"test\" in its name & hosted on the platforms caviar, doordash, or delivery.com\n",
    "data = data[(data['name'].str.contains('test')) & (data['platform'] == 'caviar') == False]\n",
    "data = data[(data['name'].str.contains('test')) & (data['platform'] == 'doordash') == False]\n",
    "data = data[(data['name'].str.contains('test')) & (data['platform'] == 'delivery.com') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Revisiting Strange Coordinate Data\n",
    "\n",
    "As a reminder, according to the information found at this [link](https://latitudelongitude.org/us/), the range of latitude and longitude values for US cities should range from:\n",
    "- Latitude: 19.50139 to 64.85694\n",
    "- Longitude: -161.75583 to -68.01197\n",
    "\n",
    "I am also using this website to quickly check my logic when appropriate with regards to the location of restaurants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Strange Latitude Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>9812</td>\n",
       "      <td>the french bakery   112th ave [disabled]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>9607</td>\n",
       "      <td>[dnu] toms dim sum</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>10348</td>\n",
       "      <td>pho viet 11th st</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>7415</td>\n",
       "      <td>pappasitos</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10316</td>\n",
       "      <td>norah restaurant la</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5596</th>\n",
       "      <td>10335</td>\n",
       "      <td>india palace   alameda</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6772</th>\n",
       "      <td>10365</td>\n",
       "      <td>holy cluck</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>10155</td>\n",
       "      <td>tangs pacific bistro</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8918</th>\n",
       "      <td>10176</td>\n",
       "      <td>smoke daddy bbq   wrigleyville   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>10337</td>\n",
       "      <td>flacos tacos   south loop</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9092</th>\n",
       "      <td>10345</td>\n",
       "      <td>galveston restaurant and cafe</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10272</th>\n",
       "      <td>10363</td>\n",
       "      <td>takorean catering   dc</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10365</th>\n",
       "      <td>10347</td>\n",
       "      <td>pinkys pizza</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10635</th>\n",
       "      <td>10353</td>\n",
       "      <td>hax hops &amp; hamburgers</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10709</th>\n",
       "      <td>10346</td>\n",
       "      <td>tasty bbq</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12390</th>\n",
       "      <td>10334</td>\n",
       "      <td>hala in</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12469</th>\n",
       "      <td>10336</td>\n",
       "      <td>bonafini</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14300</th>\n",
       "      <td>10333</td>\n",
       "      <td>bixi beer</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14319</th>\n",
       "      <td>4077</td>\n",
       "      <td>olo</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16197</th>\n",
       "      <td>10359</td>\n",
       "      <td>i tea</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16217</th>\n",
       "      <td>10366</td>\n",
       "      <td>sons addition</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16220</th>\n",
       "      <td>10358</td>\n",
       "      <td>tsuki sushi</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16704</th>\n",
       "      <td>10324</td>\n",
       "      <td>hamiltons</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17484</th>\n",
       "      <td>10356</td>\n",
       "      <td>destroyer la</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19892</th>\n",
       "      <td>9813</td>\n",
       "      <td>the french bakery   8th st. [disabled]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181</th>\n",
       "      <td>10357</td>\n",
       "      <td>norah restaurant la   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21274</th>\n",
       "      <td>10349</td>\n",
       "      <td>psychic wines</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23839</th>\n",
       "      <td>10362</td>\n",
       "      <td>leaning tower</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24505</th>\n",
       "      <td>10344</td>\n",
       "      <td>brotzeit lokal</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28976</th>\n",
       "      <td>10364</td>\n",
       "      <td>stonemill matcha</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29388</th>\n",
       "      <td>10360</td>\n",
       "      <td>bhuna</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29391</th>\n",
       "      <td>10354</td>\n",
       "      <td>camperdown elm</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29776</th>\n",
       "      <td>10355</td>\n",
       "      <td>wok wok southeast asian kitchen</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30276</th>\n",
       "      <td>10223</td>\n",
       "      <td>republic</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46620</th>\n",
       "      <td>45735</td>\n",
       "      <td>mod pizza</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>14.215883</td>\n",
       "      <td>121.141894</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                                      name  platform  \\\n",
       "166            9812  the french bakery   112th ave [disabled]    caviar   \n",
       "907            9607                        [dnu] toms dim sum    caviar   \n",
       "1461          10348                          pho viet 11th st    caviar   \n",
       "2430           7415                                pappasitos    caviar   \n",
       "2819          10316                       norah restaurant la    caviar   \n",
       "5596          10335                    india palace   alameda    caviar   \n",
       "6772          10365                                holy cluck    caviar   \n",
       "6995          10155                      tangs pacific bistro    caviar   \n",
       "8918          10176      smoke daddy bbq   wrigleyville   dnu    caviar   \n",
       "8928          10337                 flacos tacos   south loop    caviar   \n",
       "9092          10345             galveston restaurant and cafe    caviar   \n",
       "10272         10363                    takorean catering   dc    caviar   \n",
       "10365         10347                              pinkys pizza    caviar   \n",
       "10635         10353                     hax hops & hamburgers    caviar   \n",
       "10709         10346                                 tasty bbq    caviar   \n",
       "12390         10334                                   hala in    caviar   \n",
       "12469         10336                                  bonafini    caviar   \n",
       "14300         10333                                 bixi beer    caviar   \n",
       "14319          4077                                       olo    caviar   \n",
       "16197         10359                                     i tea    caviar   \n",
       "16217         10366                             sons addition    caviar   \n",
       "16220         10358                               tsuki sushi    caviar   \n",
       "16704         10324                                 hamiltons    caviar   \n",
       "17484         10356                              destroyer la    caviar   \n",
       "19892          9813    the french bakery   8th st. [disabled]    caviar   \n",
       "20181         10357                 norah restaurant la   dnu    caviar   \n",
       "21274         10349                             psychic wines    caviar   \n",
       "23839         10362                             leaning tower    caviar   \n",
       "24505         10344                            brotzeit lokal    caviar   \n",
       "28976         10364                          stonemill matcha    caviar   \n",
       "29388         10360                                     bhuna    caviar   \n",
       "29391         10354                            camperdown elm    caviar   \n",
       "29776         10355           wok wok southeast asian kitchen    caviar   \n",
       "30276         10223                                  republic    caviar   \n",
       "46620         45735                                 mod pizza  doordash   \n",
       "\n",
       "      sub_platform   latitude   longitude country active  \n",
       "166            NaN   1.000000   -1.000000      US  FALSE  \n",
       "907            NaN  11.000000  -11.000000      US  FALSE  \n",
       "1461           NaN   1.000000    1.000000      US  FALSE  \n",
       "2430           NaN   0.111111    0.111111      US  FALSE  \n",
       "2819           NaN   1.000000    1.000000      US  FALSE  \n",
       "5596           NaN   1.000000    1.000000      US  FALSE  \n",
       "6772           NaN   1.000000    1.000000      US  FALSE  \n",
       "6995           NaN  11.000000  -11.000000      US  FALSE  \n",
       "8918           NaN   1.000000    1.000000      US  FALSE  \n",
       "8928           NaN   1.000000    1.000000      US  FALSE  \n",
       "9092           NaN   1.000000    1.000000      US  FALSE  \n",
       "10272          NaN   1.000000    1.000000      US  FALSE  \n",
       "10365          NaN   1.000000   -1.000000      US  FALSE  \n",
       "10635          NaN   1.000000    1.000000      US  FALSE  \n",
       "10709          NaN   1.000000   -1.000000      US  FALSE  \n",
       "12390          NaN   1.000000    1.000000      US  FALSE  \n",
       "12469          NaN   1.000000    1.000000      US  FALSE  \n",
       "14300          NaN   1.000000    1.000000      US  FALSE  \n",
       "14319          NaN   1.000000    1.000000      US  FALSE  \n",
       "16197          NaN   1.000000   -1.000000      US  FALSE  \n",
       "16217          NaN   1.000000    1.000000      US  FALSE  \n",
       "16220          NaN   1.000000   -1.000000      US  FALSE  \n",
       "16704          NaN   1.000000    1.000000      US  FALSE  \n",
       "17484          NaN   1.000000    1.000000      US  FALSE  \n",
       "19892          NaN   1.000000   -1.000000      US  FALSE  \n",
       "20181          NaN   1.000000    1.000000      US  FALSE  \n",
       "21274          NaN   1.000000    1.000000      US  FALSE  \n",
       "23839          NaN   1.000000    1.000000      US  FALSE  \n",
       "24505          NaN   1.000000    1.000000      US  FALSE  \n",
       "28976          NaN   1.000000    1.000000      US  FALSE  \n",
       "29388          NaN   1.000000   -1.000000      US  FALSE  \n",
       "29391          NaN   1.000000    1.000000      US  FALSE  \n",
       "29776          NaN   1.000000    1.000000      US  FALSE  \n",
       "30276          NaN  11.000000  -11.000000      US  FALSE  \n",
       "46620     doordash  14.215883  121.141894      US  FALSE  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['latitude'] < 15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After lengthy cleaning of the data, just a handful of rows of data remain that have questionable latitude values. Almost all of the values above have an observable pattern:\n",
    "- The platform this restaurant data was taken from was caviar\n",
    "- The coordinate values are just some form of the number 1 for both latitude and longitude\n",
    "- The restaurant is listed as inactive\n",
    "\n",
    "The sole exception is the restaurant \"mod pizza\", but given its inactive status and the fact that the coordinates place the restaurant somewhere hundreds of miles below the US, I feel confident in just removing this from the dataset.\n",
    "\n",
    "Also something to note is that there are two observable rows in which \"dnu\" and \"disabled\" are mentioned in the restaurant names. More on this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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",
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       "    }\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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>8869</td>\n",
       "      <td>mi joe chinese cuisine</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.0</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>6426</td>\n",
       "      <td>(reuse cms id.)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                    name platform sub_platform  latitude  \\\n",
       "602            8869  mi joe chinese cuisine   caviar          NaN     111.0   \n",
       "12497          6426         (reuse cms id.)   caviar          NaN     123.0   \n",
       "\n",
       "       longitude country active  \n",
       "602       -111.0      US  FALSE  \n",
       "12497      456.0      US  FALSE  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['latitude'] > 70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only two rows of data contain restaurants in which the latitude is listed as greater than 70. Both of these rows hold questionable data, as the coordinates are listed as 111, -111 and 123, 456. These will be removed from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636250 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing illogical latitude values from our dataset\n",
    "data = data[data['latitude'] <= 70]\n",
    "data = data[data['latitude'] >= 15]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Strange Longitude Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [restaurant_id, name, platform, sub_platform, latitude, longitude, country, active]\n",
       "Index: []"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['longitude'] < -165]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no more outstanding longitude values in which the stated longitude is lower than the expected range of -161.75583 to -68.01197."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2677</th>\n",
       "      <td>1624</td>\n",
       "      <td>cms certification [wendy]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775505</td>\n",
       "      <td>122.418193</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6462</th>\n",
       "      <td>9300</td>\n",
       "      <td>nekter juice bar pasadena</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.139870</td>\n",
       "      <td>118.132678</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6467</th>\n",
       "      <td>769</td>\n",
       "      <td>southland flavor cafe</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.335545</td>\n",
       "      <td>37.335545</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>3409</td>\n",
       "      <td>belcampo at the yard [dnu]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.994719</td>\n",
       "      <td>-9.996014</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16037</th>\n",
       "      <td>5473</td>\n",
       "      <td>naansense   east lakeview</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.943001</td>\n",
       "      <td>41.943001</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27002</th>\n",
       "      <td>9323</td>\n",
       "      <td>nekter juice bar  santa monica</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.014169</td>\n",
       "      <td>118.496774</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27006</th>\n",
       "      <td>443</td>\n",
       "      <td>fred 62</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.104648</td>\n",
       "      <td>118.291491</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46472</th>\n",
       "      <td>51173</td>\n",
       "      <td>swiss chalet</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.730189</td>\n",
       "      <td>43.730189</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49305</th>\n",
       "      <td>16960</td>\n",
       "      <td>taco sinaloa</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.831760</td>\n",
       "      <td>118.308240</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49337</th>\n",
       "      <td>16863</td>\n",
       "      <td>island eats hale aina</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.857330</td>\n",
       "      <td>118.299230</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52507</th>\n",
       "      <td>15704</td>\n",
       "      <td>swan thai</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.748590</td>\n",
       "      <td>118.406970</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60135</th>\n",
       "      <td>16961</td>\n",
       "      <td>eddie vs prime seafood</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.818073</td>\n",
       "      <td>96.802686</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61209</th>\n",
       "      <td>51114</td>\n",
       "      <td>five guys burgers and fries</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.619486</td>\n",
       "      <td>43.619486</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63180</th>\n",
       "      <td>130021</td>\n",
       "      <td>osaka japan</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.643485</td>\n",
       "      <td>111.889372</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63286</th>\n",
       "      <td>15685</td>\n",
       "      <td>soho thai fusion bar &amp; grill</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.789260</td>\n",
       "      <td>118.317500</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66441</th>\n",
       "      <td>38059</td>\n",
       "      <td>fish &amp; camaron</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>30.191721</td>\n",
       "      <td>95.585515</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66568</th>\n",
       "      <td>15064</td>\n",
       "      <td>panda express (s figueroa)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.025876</td>\n",
       "      <td>118.277276</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69564</th>\n",
       "      <td>14335</td>\n",
       "      <td>love sushi</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.181043</td>\n",
       "      <td>96.884608</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71924</th>\n",
       "      <td>15451</td>\n",
       "      <td>einstein bros bagels</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.062088</td>\n",
       "      <td>118.350072</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73315</th>\n",
       "      <td>19011</td>\n",
       "      <td>simply fish grill</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.153560</td>\n",
       "      <td>118.757380</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73387</th>\n",
       "      <td>12253</td>\n",
       "      <td>heat 7 indian asian bistro</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.156900</td>\n",
       "      <td>118.484200</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74311</th>\n",
       "      <td>425079</td>\n",
       "      <td>rockys hot chicken shack</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>28.668361</td>\n",
       "      <td>77.951021</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77776</th>\n",
       "      <td>61958</td>\n",
       "      <td>pei wei asian diner</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>30.235956</td>\n",
       "      <td>97.733920</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80393</th>\n",
       "      <td>397598</td>\n",
       "      <td>subway�� restaurant (james street)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>53.802131</td>\n",
       "      <td>-9.514347</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81721</th>\n",
       "      <td>19044</td>\n",
       "      <td>mandarin lotus</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.144440</td>\n",
       "      <td>118.761530</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82359</th>\n",
       "      <td>16892</td>\n",
       "      <td>tapas &amp; vino</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.817440</td>\n",
       "      <td>118.388090</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84011</th>\n",
       "      <td>16727</td>\n",
       "      <td>redondo beach brewing co</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.816940</td>\n",
       "      <td>118.387090</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84227</th>\n",
       "      <td>19060</td>\n",
       "      <td>p&amp;l burgers</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.210800</td>\n",
       "      <td>118.842100</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84274</th>\n",
       "      <td>19008</td>\n",
       "      <td>petra mediterranean cuisine</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.163750</td>\n",
       "      <td>118.828020</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84486</th>\n",
       "      <td>15078</td>\n",
       "      <td>the coffee bean &amp; tea leaf</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.074048</td>\n",
       "      <td>118.323864</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86960</th>\n",
       "      <td>129958</td>\n",
       "      <td>cafe bistro (nordstrom)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.643485</td>\n",
       "      <td>111.889372</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87065</th>\n",
       "      <td>16883</td>\n",
       "      <td>ocean tava</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.823830</td>\n",
       "      <td>118.385140</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87594</th>\n",
       "      <td>21033</td>\n",
       "      <td>jongewaards bake n broil</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.824070</td>\n",
       "      <td>33.824070</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88620</th>\n",
       "      <td>12055</td>\n",
       "      <td>dana kai japanese cuisine</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.488690</td>\n",
       "      <td>117.710070</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91007</th>\n",
       "      <td>63245</td>\n",
       "      <td>hey thats amore</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.147830</td>\n",
       "      <td>118.149870</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98600</th>\n",
       "      <td>16889</td>\n",
       "      <td>patra burgers on sunset</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.076000</td>\n",
       "      <td>118.256130</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99822</th>\n",
       "      <td>131644</td>\n",
       "      <td>chen fu</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>35.135700</td>\n",
       "      <td>80.712630</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101384</th>\n",
       "      <td>153676</td>\n",
       "      <td>panda express</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.643485</td>\n",
       "      <td>111.889372</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101743</th>\n",
       "      <td>15878</td>\n",
       "      <td>tulip cafe</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.045970</td>\n",
       "      <td>118.253540</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101860</th>\n",
       "      <td>15597</td>\n",
       "      <td>the blue star</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.021740</td>\n",
       "      <td>118.232640</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102284</th>\n",
       "      <td>11142</td>\n",
       "      <td>cvs</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.996326</td>\n",
       "      <td>33.996326</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108881</th>\n",
       "      <td>153854</td>\n",
       "      <td>sbarro</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.643485</td>\n",
       "      <td>111.889372</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109287</th>\n",
       "      <td>19295</td>\n",
       "      <td>cortinas</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.825350</td>\n",
       "      <td>33.825350</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110065</th>\n",
       "      <td>65056</td>\n",
       "      <td>el charro taco shop</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.966510</td>\n",
       "      <td>118.115860</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110180</th>\n",
       "      <td>130073</td>\n",
       "      <td>hot dog on a stick (murray)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>40.643485</td>\n",
       "      <td>111.889372</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                name  platform  \\\n",
       "2677            1624           cms certification [wendy]    caviar   \n",
       "6462            9300           nekter juice bar pasadena    caviar   \n",
       "6467             769               southland flavor cafe    caviar   \n",
       "8728            3409          belcampo at the yard [dnu]    caviar   \n",
       "16037           5473           naansense   east lakeview    caviar   \n",
       "27002           9323      nekter juice bar  santa monica    caviar   \n",
       "27006            443                             fred 62    caviar   \n",
       "46472          51173                        swiss chalet  doordash   \n",
       "49305          16960                        taco sinaloa  doordash   \n",
       "49337          16863               island eats hale aina  doordash   \n",
       "52507          15704                           swan thai  doordash   \n",
       "60135          16961              eddie vs prime seafood  doordash   \n",
       "61209          51114         five guys burgers and fries  doordash   \n",
       "63180         130021                         osaka japan  doordash   \n",
       "63286          15685        soho thai fusion bar & grill  doordash   \n",
       "66441          38059                      fish & camaron  doordash   \n",
       "66568          15064          panda express (s figueroa)  doordash   \n",
       "69564          14335                          love sushi  doordash   \n",
       "71924          15451                einstein bros bagels  doordash   \n",
       "73315          19011                   simply fish grill  doordash   \n",
       "73387          12253          heat 7 indian asian bistro  doordash   \n",
       "74311         425079            rockys hot chicken shack  doordash   \n",
       "77776          61958                 pei wei asian diner  doordash   \n",
       "80393         397598  subway�� restaurant (james street)  doordash   \n",
       "81721          19044                      mandarin lotus  doordash   \n",
       "82359          16892                        tapas & vino  doordash   \n",
       "84011          16727            redondo beach brewing co  doordash   \n",
       "84227          19060                         p&l burgers  doordash   \n",
       "84274          19008         petra mediterranean cuisine  doordash   \n",
       "84486          15078          the coffee bean & tea leaf  doordash   \n",
       "86960         129958             cafe bistro (nordstrom)  doordash   \n",
       "87065          16883                          ocean tava  doordash   \n",
       "87594          21033            jongewaards bake n broil  doordash   \n",
       "88620          12055           dana kai japanese cuisine  doordash   \n",
       "91007          63245                     hey thats amore  doordash   \n",
       "98600          16889             patra burgers on sunset  doordash   \n",
       "99822         131644                             chen fu  doordash   \n",
       "101384        153676                       panda express  doordash   \n",
       "101743         15878                          tulip cafe  doordash   \n",
       "101860         15597                       the blue star  doordash   \n",
       "102284         11142                                 cvs  doordash   \n",
       "108881        153854                              sbarro  doordash   \n",
       "109287         19295                            cortinas  doordash   \n",
       "110065         65056                 el charro taco shop  doordash   \n",
       "110180        130073         hot dog on a stick (murray)  doordash   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "2677            NaN  37.775505  122.418193      US  FALSE  \n",
       "6462            NaN  34.139870  118.132678      US  FALSE  \n",
       "6467            NaN  37.335545   37.335545      US  FALSE  \n",
       "8728            NaN  19.994719   -9.996014      US  FALSE  \n",
       "16037           NaN  41.943001   41.943001      US  FALSE  \n",
       "27002           NaN  34.014169  118.496774      US  FALSE  \n",
       "27006           NaN  34.104648  118.291491      US  FALSE  \n",
       "46472      doordash  43.730189   43.730189      US  FALSE  \n",
       "49305      doordash  33.831760  118.308240      US  FALSE  \n",
       "49337      doordash  33.857330  118.299230      US  FALSE  \n",
       "52507      doordash  33.748590  118.406970      US  FALSE  \n",
       "60135      doordash  32.818073   96.802686      US  FALSE  \n",
       "61209      doordash  43.619486   43.619486      US  FALSE  \n",
       "63180      doordash  40.643485  111.889372      US  FALSE  \n",
       "63286      doordash  33.789260  118.317500      US  FALSE  \n",
       "66441      doordash  30.191721   95.585515      US  FALSE  \n",
       "66568      doordash  34.025876  118.277276      US  FALSE  \n",
       "69564      doordash  33.181043   96.884608      US  FALSE  \n",
       "71924      doordash  34.062088  118.350072      US  FALSE  \n",
       "73315      doordash  34.153560  118.757380      US  FALSE  \n",
       "73387      doordash  34.156900  118.484200      US  FALSE  \n",
       "74311      doordash  28.668361   77.951021      US  FALSE  \n",
       "77776      doordash  30.235956   97.733920      US  FALSE  \n",
       "80393      doordash  53.802131   -9.514347      US  FALSE  \n",
       "81721      doordash  34.144440  118.761530      US  FALSE  \n",
       "82359      doordash  33.817440  118.388090      US  FALSE  \n",
       "84011      doordash  33.816940  118.387090      US  FALSE  \n",
       "84227      doordash  34.210800  118.842100      US  FALSE  \n",
       "84274      doordash  34.163750  118.828020      US  FALSE  \n",
       "84486      doordash  34.074048  118.323864      US  FALSE  \n",
       "86960      doordash  40.643485  111.889372      US  FALSE  \n",
       "87065      doordash  33.823830  118.385140      US  FALSE  \n",
       "87594      doordash  33.824070   33.824070      US  FALSE  \n",
       "88620      doordash  33.488690  117.710070      US  FALSE  \n",
       "91007      doordash  34.147830  118.149870      US  FALSE  \n",
       "98600      doordash  34.076000  118.256130      US  FALSE  \n",
       "99822      doordash  35.135700   80.712630      US  FALSE  \n",
       "101384     doordash  40.643485  111.889372      US  FALSE  \n",
       "101743     doordash  34.045970  118.253540      US  FALSE  \n",
       "101860     doordash  34.021740  118.232640      US  FALSE  \n",
       "102284     doordash  33.996326   33.996326      US  FALSE  \n",
       "108881     doordash  40.643485  111.889372      US  FALSE  \n",
       "109287     doordash  33.825350   33.825350      US  FALSE  \n",
       "110065     doordash  33.966510  118.115860      US  FALSE  \n",
       "110180     doordash  40.643485  111.889372      US  FALSE  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['longitude'] > -60]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although just a few, there are some strange longitudes stated in which the longitude values are greater than -70. This could be for a variety of reasons, such as:\n",
    "- The data is indeed supposed to be removed from the dataset\n",
    "    - Some additional tags added to the names of some our datasets can be seen above (e.g. dnu, disabled)\n",
    "- The data may simply have been incorrectly input\n",
    "    - e.g. The restaurant listed at index 2677 is found in the middle of the Yellow Sea based on the given coordinates, but by making the longitude value (-) the coordinates then place the restaurant in San Francisco. That probably makes more sense than a restaurant floating in the middle of a large body of water.\n",
    "    \n",
    "Note: Some of these restaurant coordinates above are not accurately represented as some coordinates place the restaurant outside of the US or have the exact same latitude value as longitude value (highly unlikely). However, accomplishing the goal of getting rid of duplicates doesn't mean I should get rid of a restaurant solely on the observation that the data was input and/or collected incorrectly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming all longitude values to negatives\n",
    "data['longitude'] = data['longitude'].apply(lambda x: x * -1 if x > 0 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6467</th>\n",
       "      <td>769</td>\n",
       "      <td>southland flavor cafe</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.335545</td>\n",
       "      <td>-37.335545</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>3409</td>\n",
       "      <td>belcampo at the yard [dnu]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.994719</td>\n",
       "      <td>-9.996014</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16037</th>\n",
       "      <td>5473</td>\n",
       "      <td>naansense   east lakeview</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.943001</td>\n",
       "      <td>-41.943001</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46472</th>\n",
       "      <td>51173</td>\n",
       "      <td>swiss chalet</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.730189</td>\n",
       "      <td>-43.730189</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61209</th>\n",
       "      <td>51114</td>\n",
       "      <td>five guys burgers and fries</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>43.619486</td>\n",
       "      <td>-43.619486</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80393</th>\n",
       "      <td>397598</td>\n",
       "      <td>subway�� restaurant (james street)</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>53.802131</td>\n",
       "      <td>-9.514347</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87594</th>\n",
       "      <td>21033</td>\n",
       "      <td>jongewaards bake n broil</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.824070</td>\n",
       "      <td>-33.824070</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102284</th>\n",
       "      <td>11142</td>\n",
       "      <td>cvs</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.996326</td>\n",
       "      <td>-33.996326</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109287</th>\n",
       "      <td>19295</td>\n",
       "      <td>cortinas</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>33.825350</td>\n",
       "      <td>-33.825350</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                                name  platform  \\\n",
       "6467             769               southland flavor cafe    caviar   \n",
       "8728            3409          belcampo at the yard [dnu]    caviar   \n",
       "16037           5473           naansense   east lakeview    caviar   \n",
       "46472          51173                        swiss chalet  doordash   \n",
       "61209          51114         five guys burgers and fries  doordash   \n",
       "80393         397598  subway�� restaurant (james street)  doordash   \n",
       "87594          21033            jongewaards bake n broil  doordash   \n",
       "102284         11142                                 cvs  doordash   \n",
       "109287         19295                            cortinas  doordash   \n",
       "\n",
       "       sub_platform   latitude  longitude country active  \n",
       "6467            NaN  37.335545 -37.335545      US  FALSE  \n",
       "8728            NaN  19.994719  -9.996014      US  FALSE  \n",
       "16037           NaN  41.943001 -41.943001      US  FALSE  \n",
       "46472      doordash  43.730189 -43.730189      US  FALSE  \n",
       "61209      doordash  43.619486 -43.619486      US  FALSE  \n",
       "80393      doordash  53.802131  -9.514347      US  FALSE  \n",
       "87594      doordash  33.824070 -33.824070      US  FALSE  \n",
       "102284     doordash  33.996326 -33.996326      US  FALSE  \n",
       "109287     doordash  33.825350 -33.825350      US  FALSE  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['longitude'] > -60]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the exception of a few rows, the only remaining rows with illogical longitude data are those that appear to have incorrectly assigned longitude values that were the exact same as the latitude values before the values were transformed into negative values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A Final Glance at Restaurant Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned earlier, I observed a restaurant with \"dnu\" in its name, which based on previously observed data most likely is an acronym standing for \"do not use\". I also observed a restaurant with \"disabled\" in its name. I will once more run over the data set and remove anything with \"dnu\" or \"disabled\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Names w/\"Disabled\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>6663</td>\n",
       "      <td>[disabled] little toasted</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.877650</td>\n",
       "      <td>-87.638630</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>8936</td>\n",
       "      <td>prime cookie co. [disabled]</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.794389</td>\n",
       "      <td>-96.800166</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>215</td>\n",
       "      <td>(perm disabled) maiden lane</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.727128</td>\n",
       "      <td>-73.979851</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>254</td>\n",
       "      <td>penang disabled</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.351373</td>\n",
       "      <td>-71.063005</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>3829</td>\n",
       "      <td>[disabled] kelloggs nyc</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.760178</td>\n",
       "      <td>-73.984305</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30603</th>\n",
       "      <td>7203</td>\n",
       "      <td>[disabled] aybla grill   divison</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.504940</td>\n",
       "      <td>-122.634551</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30894</th>\n",
       "      <td>7236</td>\n",
       "      <td>[disabled] little taqueria</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784642</td>\n",
       "      <td>-122.464404</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30978</th>\n",
       "      <td>6171</td>\n",
       "      <td>[disabled] mau</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.762219</td>\n",
       "      <td>-122.421446</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30997</th>\n",
       "      <td>6488</td>\n",
       "      <td>(disabled) chimney coffee house</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.063084</td>\n",
       "      <td>-118.232475</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31020</th>\n",
       "      <td>2669</td>\n",
       "      <td>[disabled] roses</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.678979</td>\n",
       "      <td>-73.973429</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                              name platform sub_platform  \\\n",
       "188            6663         [disabled] little toasted   caviar          NaN   \n",
       "410            8936       prime cookie co. [disabled]   caviar          NaN   \n",
       "494             215       (perm disabled) maiden lane   caviar          NaN   \n",
       "502             254                   penang disabled   caviar          NaN   \n",
       "557            3829           [disabled] kelloggs nyc   caviar          NaN   \n",
       "...             ...                               ...      ...          ...   \n",
       "30603          7203  [disabled] aybla grill   divison   caviar          NaN   \n",
       "30894          7236        [disabled] little taqueria   caviar          NaN   \n",
       "30978          6171                    [disabled] mau   caviar          NaN   \n",
       "30997          6488   (disabled) chimney coffee house   caviar          NaN   \n",
       "31020          2669                  [disabled] roses   caviar          NaN   \n",
       "\n",
       "        latitude   longitude country active  \n",
       "188    41.877650  -87.638630      US  FALSE  \n",
       "410    32.794389  -96.800166      US  FALSE  \n",
       "494    40.727128  -73.979851      US  FALSE  \n",
       "502    42.351373  -71.063005      US  FALSE  \n",
       "557    40.760178  -73.984305      US  FALSE  \n",
       "...          ...         ...     ...    ...  \n",
       "30603  45.504940 -122.634551      US  FALSE  \n",
       "30894  37.784642 -122.464404      US  FALSE  \n",
       "30978  37.762219 -122.421446      US  FALSE  \n",
       "30997  34.063084 -118.232475      US  FALSE  \n",
       "31020  40.678979  -73.973429      US  FALSE  \n",
       "\n",
       "[115 rows x 8 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disabled_data = data[data['name'].str.contains('disable')]\n",
    "disabled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caviar    115\n",
       "Name: platform, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disabled_data['platform'].value_counts(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are over one hundred rows of data in which the restaurant is labeled \"disabled\". I suspected that most, if not all, of these rows belonged to the caviar platform due to observed tendencies in the dataset earlier. My suspicion was correct, and I will proceed to remove all of the instances in which \"disabled\" is a part of the name of the restaurant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636135 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"disable\" in its name\n",
    "data = data[data['name'].str.contains('disable') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Names w/ \"dnu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>8570</td>\n",
       "      <td>arami   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.895750</td>\n",
       "      <td>-87.673312</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>6294</td>\n",
       "      <td>[dnu] the little beet menu bank</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.757672</td>\n",
       "      <td>-73.974219</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2639</th>\n",
       "      <td>224</td>\n",
       "      <td>la sirena clandestina   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.886987</td>\n",
       "      <td>-87.651970</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2643</th>\n",
       "      <td>9629</td>\n",
       "      <td>[dnu]takumen izakaya and noodles</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.742594</td>\n",
       "      <td>-73.954678</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>9692</td>\n",
       "      <td>dnu (trash)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.896067</td>\n",
       "      <td>-96.802133</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633259</th>\n",
       "      <td>415446</td>\n",
       "      <td>podnuhs</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.409714</td>\n",
       "      <td>-93.797917</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640660</th>\n",
       "      <td>415452</td>\n",
       "      <td>podnuhs bar b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.486804</td>\n",
       "      <td>-93.707380</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642842</th>\n",
       "      <td>415430</td>\n",
       "      <td>podnuhs bar b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.545441</td>\n",
       "      <td>-93.777455</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642851</th>\n",
       "      <td>415459</td>\n",
       "      <td>podnuhs bbq</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.433664</td>\n",
       "      <td>-93.712221</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642858</th>\n",
       "      <td>415433</td>\n",
       "      <td>podnuhs bar b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.516521</td>\n",
       "      <td>-93.716983</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                              name  platform sub_platform  \\\n",
       "309             8570                       arami   dnu    caviar          NaN   \n",
       "1556            6294   [dnu] the little beet menu bank    caviar          NaN   \n",
       "2639             224       la sirena clandestina   dnu    caviar          NaN   \n",
       "2643            9629  [dnu]takumen izakaya and noodles    caviar          NaN   \n",
       "2694            9692                       dnu (trash)    caviar          NaN   \n",
       "...              ...                               ...       ...          ...   \n",
       "633259        415446                           podnuhs  doordash     doordash   \n",
       "640660        415452                   podnuhs bar b q  doordash     doordash   \n",
       "642842        415430                   podnuhs bar b q  doordash     doordash   \n",
       "642851        415459                       podnuhs bbq  doordash     doordash   \n",
       "642858        415433                   podnuhs bar b q  doordash     doordash   \n",
       "\n",
       "         latitude  longitude country active  \n",
       "309     41.895750 -87.673312      US  FALSE  \n",
       "1556    40.757672 -73.974219      US  FALSE  \n",
       "2639    41.886987 -87.651970      US  FALSE  \n",
       "2643    40.742594 -73.954678      US  FALSE  \n",
       "2694    32.896067 -96.802133      US  FALSE  \n",
       "...           ...        ...     ...    ...  \n",
       "633259  32.409714 -93.797917      US   TRUE  \n",
       "640660  32.486804 -93.707380      US   TRUE  \n",
       "642842  32.545441 -93.777455      US   TRUE  \n",
       "642851  32.433664 -93.712221      US   TRUE  \n",
       "642858  32.516521 -93.716983      US   TRUE  \n",
       "\n",
       "[87 rows x 8 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data = data[data['name'].str.contains('dnu')]\n",
    "dnu_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "postmates     1\n",
       "ubereats      2\n",
       "grubhub       2\n",
       "doordash     19\n",
       "caviar       63\n",
       "Name: platform, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data['platform'].value_counts(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will observe the values by each platform once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>397238</th>\n",
       "      <td>ac64895d-d262-4cf5-8244-a699f1980af4</td>\n",
       "      <td>spudnuts donuts</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>34.219232</td>\n",
       "      <td>-118.606498</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id             name   platform  \\\n",
       "397238  ac64895d-d262-4cf5-8244-a699f1980af4  spudnuts donuts  postmates   \n",
       "\n",
       "       sub_platform   latitude   longitude country active  \n",
       "397238   restaurant  34.219232 -118.606498      US   TRUE  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data[dnu_data['platform'] == 'postmates']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51225</th>\n",
       "      <td>697334</td>\n",
       "      <td>spudnuts donuts</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>34.176361</td>\n",
       "      <td>-118.946053</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132278</th>\n",
       "      <td>337448</td>\n",
       "      <td>art of donuts by spudnuts</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>grubhub</td>\n",
       "      <td>34.219181</td>\n",
       "      <td>-118.606526</td>\n",
       "      <td>US</td>\n",
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      ],
      "text/plain": [
       "       restaurant_id                       name platform sub_platform  \\\n",
       "51225         697334            spudnuts donuts  grubhub      grubhub   \n",
       "132278        337448  art of donuts by spudnuts  grubhub      grubhub   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "51225   34.176361 -118.946053      US   TRUE  \n",
       "132278  34.219181 -118.606526      US   TRUE  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data[dnu_data['platform'] == 'grubhub']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>169106</th>\n",
       "      <td>5c27f474-ea4b-4ee1-ad3f-fa3b183b0bde</td>\n",
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       "      <td>34.219181</td>\n",
       "      <td>-118.606526</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
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       "    <tr>\n",
       "      <th>169795</th>\n",
       "      <td>ea896f44-b029-4cbe-9c1e-84b2abc5b0a2</td>\n",
       "      <td>spudnuts donuts (thousand oaks)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>34.176361</td>\n",
       "      <td>-118.946054</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                               restaurant_id                             name  \\\n",
       "169106  5c27f474-ea4b-4ee1-ad3f-fa3b183b0bde                  spudnuts donuts   \n",
       "169795  ea896f44-b029-4cbe-9c1e-84b2abc5b0a2  spudnuts donuts (thousand oaks)   \n",
       "\n",
       "        platform sub_platform   latitude   longitude country active  \n",
       "169106  ubereats     ubereats  34.219181 -118.606526      US   TRUE  \n",
       "169795  ubereats     ubereats  34.176361 -118.946054      US   TRUE  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data[dnu_data['platform'] == 'ubereats']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>41.091404</td>\n",
       "      <td>-111.974113</td>\n",
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       "      <th>541454</th>\n",
       "      <td>242989</td>\n",
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       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.176411</td>\n",
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       "      <th>597514</th>\n",
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       "      <td>spudnuts donuts</td>\n",
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       "      <th>603273</th>\n",
       "      <td>359560</td>\n",
       "      <td>coco dnuts</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>34.200792</td>\n",
       "      <td>-118.527732</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633259</th>\n",
       "      <td>415446</td>\n",
       "      <td>podnuhs</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.409714</td>\n",
       "      <td>-93.797917</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640660</th>\n",
       "      <td>415452</td>\n",
       "      <td>podnuhs bar b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.486804</td>\n",
       "      <td>-93.707380</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642842</th>\n",
       "      <td>415430</td>\n",
       "      <td>podnuhs bar b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.545441</td>\n",
       "      <td>-93.777455</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642851</th>\n",
       "      <td>415459</td>\n",
       "      <td>podnuhs bbq</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.433664</td>\n",
       "      <td>-93.712221</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642858</th>\n",
       "      <td>415433</td>\n",
       "      <td>podnuhs bar b q</td>\n",
       "      <td>doordash</td>\n",
       "      <td>doordash</td>\n",
       "      <td>32.516521</td>\n",
       "      <td>-93.716983</td>\n",
       "      <td>US</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       restaurant_id                         name  platform sub_platform  \\\n",
       "297165        324441              podnuhs bar b q  doordash     doordash   \n",
       "312641        324448              podnuhs bar b q  doordash     doordash   \n",
       "472869        275211              spudnuts donuts  doordash     doordash   \n",
       "475255        353628            spudnut doughnuts  doordash     doordash   \n",
       "492151        343349            spudnuts & bagels  doordash     doordash   \n",
       "497584        275192              spudnuts donuts  doordash     doordash   \n",
       "497585        275203              spudnuts donuts  doordash     doordash   \n",
       "498421        261327                     spudnuts  doordash     doordash   \n",
       "504492        353629              spudnuts donuts  doordash     doordash   \n",
       "528736        335603                        dnuez  doordash     doordash   \n",
       "529482        229443           johnny os spudnuts  doordash     doordash   \n",
       "541454        242989  spudnuts donuts (kimber dr)  doordash     doordash   \n",
       "597514        242902              spudnuts donuts  doordash     doordash   \n",
       "603273        359560                   coco dnuts  doordash     doordash   \n",
       "633259        415446                      podnuhs  doordash     doordash   \n",
       "640660        415452              podnuhs bar b q  doordash     doordash   \n",
       "642842        415430              podnuhs bar b q  doordash     doordash   \n",
       "642851        415459                  podnuhs bbq  doordash     doordash   \n",
       "642858        415433              podnuhs bar b q  doordash     doordash   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "297165  30.427057  -91.055749      US   TRUE  \n",
       "312641  30.450961  -91.117869      US   TRUE  \n",
       "472869  34.412034 -119.854841      US   TRUE  \n",
       "475255  34.272667 -119.213847      US   TRUE  \n",
       "492151  34.440194 -119.741430      US   TRUE  \n",
       "497584  34.435949 -119.823791      US   TRUE  \n",
       "497585  34.419084 -119.704980      US   TRUE  \n",
       "498421  34.172656 -119.161177      US   TRUE  \n",
       "504492  34.175490 -119.221885      US   TRUE  \n",
       "528736  41.857811  -87.676162      US   TRUE  \n",
       "529482  41.091404 -111.974113      US   TRUE  \n",
       "541454  34.176411 -118.945829      US   TRUE  \n",
       "597514  34.219199 -118.606654      US   TRUE  \n",
       "603273  34.200792 -118.527732      US   TRUE  \n",
       "633259  32.409714  -93.797917      US   TRUE  \n",
       "640660  32.486804  -93.707380      US   TRUE  \n",
       "642842  32.545441  -93.777455      US   TRUE  \n",
       "642851  32.433664  -93.712221      US   TRUE  \n",
       "642858  32.516521  -93.716983      US   TRUE  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data[dnu_data['platform'] == 'doordash']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>8570</td>\n",
       "      <td>arami   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.895750</td>\n",
       "      <td>-87.673312</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>6294</td>\n",
       "      <td>[dnu] the little beet menu bank</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.757672</td>\n",
       "      <td>-73.974219</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2639</th>\n",
       "      <td>224</td>\n",
       "      <td>la sirena clandestina   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.886987</td>\n",
       "      <td>-87.651970</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2643</th>\n",
       "      <td>9629</td>\n",
       "      <td>[dnu]takumen izakaya and noodles</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.742594</td>\n",
       "      <td>-73.954678</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>9692</td>\n",
       "      <td>dnu (trash)</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.896067</td>\n",
       "      <td>-96.802133</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26963</th>\n",
       "      <td>2442</td>\n",
       "      <td>dnu california pizza kitchen   sf</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.786788</td>\n",
       "      <td>-122.402693</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27025</th>\n",
       "      <td>1607</td>\n",
       "      <td>the allis   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.883746</td>\n",
       "      <td>-87.648281</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28687</th>\n",
       "      <td>9193</td>\n",
       "      <td>urbanbelly   wicker park   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.909663</td>\n",
       "      <td>-87.677839</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28945</th>\n",
       "      <td>4948</td>\n",
       "      <td>armands victory tap   dnu</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.863503</td>\n",
       "      <td>-87.624538</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30589</th>\n",
       "      <td>5876</td>\n",
       "      <td>dnu bareburger   chelsea</td>\n",
       "      <td>caviar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.742106</td>\n",
       "      <td>-74.001160</td>\n",
       "      <td>US</td>\n",
       "      <td>FALSE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_id                               name platform sub_platform  \\\n",
       "309            8570                        arami   dnu   caviar          NaN   \n",
       "1556           6294    [dnu] the little beet menu bank   caviar          NaN   \n",
       "2639            224        la sirena clandestina   dnu   caviar          NaN   \n",
       "2643           9629   [dnu]takumen izakaya and noodles   caviar          NaN   \n",
       "2694           9692                        dnu (trash)   caviar          NaN   \n",
       "...             ...                                ...      ...          ...   \n",
       "26963          2442  dnu california pizza kitchen   sf   caviar          NaN   \n",
       "27025          1607                    the allis   dnu   caviar          NaN   \n",
       "28687          9193     urbanbelly   wicker park   dnu   caviar          NaN   \n",
       "28945          4948          armands victory tap   dnu   caviar          NaN   \n",
       "30589          5876           dnu bareburger   chelsea   caviar          NaN   \n",
       "\n",
       "        latitude   longitude country active  \n",
       "309    41.895750  -87.673312      US  FALSE  \n",
       "1556   40.757672  -73.974219      US  FALSE  \n",
       "2639   41.886987  -87.651970      US  FALSE  \n",
       "2643   40.742594  -73.954678      US  FALSE  \n",
       "2694   32.896067  -96.802133      US  FALSE  \n",
       "...          ...         ...     ...    ...  \n",
       "26963  37.786788 -122.402693      US  FALSE  \n",
       "27025  41.883746  -87.648281      US  FALSE  \n",
       "28687  41.909663  -87.677839      US  FALSE  \n",
       "28945  41.863503  -87.624538      US  FALSE  \n",
       "30589  40.742106  -74.001160      US  FALSE  \n",
       "\n",
       "[63 rows x 8 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnu_data[dnu_data['platform'] == 'caviar']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, it is only the data on the platform caviar that seems to add the characters \"dnu\" as a label/tag as opposed to those characters being a part of the name of the restaurant itself (e.g. \"spudnuts donuts\"). I will remove rows of data with \"dnu\" in the name from the dataset if the platform is caviar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset now has 636072 rows and 8 columns of restaurant data.\n"
     ]
    }
   ],
   "source": [
    "# removing restaurants with some form of \"dnu\" in its name & hosted on the platform caviar\n",
    "data = data[(data['name'].str.contains('dnu')) & (data['platform'] == 'caviar') == False]\n",
    "print('The dataset now has {0} rows and {1} columns of restaurant data.'.format(\n",
    "    data.shape[0], \n",
    "    data.shape[1])\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking Active vs. Inactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 636072 entries, 0 to 644069\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   restaurant_id  636072 non-null  object \n",
      " 1   name           636072 non-null  object \n",
      " 2   platform       636072 non-null  object \n",
      " 3   sub_platform   593950 non-null  object \n",
      " 4   latitude       636072 non-null  float64\n",
      " 5   longitude      636072 non-null  float64\n",
      " 6   country        636072 non-null  object \n",
      " 7   active         635876 non-null  object \n",
      "dtypes: float64(2), object(6)\n",
      "memory usage: 43.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96548</th>\n",
       "      <td>2783a54d-8014-46c1-b854-483964ef8605</td>\n",
       "      <td>fish dish</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>34.195293</td>\n",
       "      <td>-118.350281</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96957</th>\n",
       "      <td>c02d95df-cfd3-44d6-b511-7025df9b4cc6</td>\n",
       "      <td>waba grill</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>34.223720</td>\n",
       "      <td>-118.502206</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97840</th>\n",
       "      <td>0ece7f40-b225-4050-a075-4e677cc7cdf3</td>\n",
       "      <td>woodland</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>40.680174</td>\n",
       "      <td>-73.974945</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104011</th>\n",
       "      <td>cfa89126-1522-46cf-af7a-37ab6dde4595</td>\n",
       "      <td>leaf &amp; co.</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>30.015423</td>\n",
       "      <td>-90.239225</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104301</th>\n",
       "      <td>dd5b41fe-6bab-4bb0-a156-3e6c85766cd6</td>\n",
       "      <td>the late night spot</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>36.100607</td>\n",
       "      <td>-115.133308</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426412</th>\n",
       "      <td>5602435b-bb43-4a4d-adf1-b452eaecbaa4</td>\n",
       "      <td>grasslands meat market | bbq &amp; churrasco</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>33.805773</td>\n",
       "      <td>-117.911407</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428301</th>\n",
       "      <td>300c5357-6d7f-4931-9f74-d7f756965d4a</td>\n",
       "      <td>frank &amp; finas cocina</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>36.113486</td>\n",
       "      <td>-115.307372</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431378</th>\n",
       "      <td>3dc72f79-f55b-4cd5-9c82-d7ce453682c5</td>\n",
       "      <td>asian bistro</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>19.348821</td>\n",
       "      <td>-99.189638</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433562</th>\n",
       "      <td>a68318d7-e038-476e-9af8-38d7c2b446f4</td>\n",
       "      <td>la buca di marco</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>39.292775</td>\n",
       "      <td>-76.587802</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440642</th>\n",
       "      <td>177e25d8-9514-448c-aa6c-bd46252c5d3b</td>\n",
       "      <td>l&amp;l hawaiian bbq</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>33.657061</td>\n",
       "      <td>-117.916856</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "96548   2783a54d-8014-46c1-b854-483964ef8605   \n",
       "96957   c02d95df-cfd3-44d6-b511-7025df9b4cc6   \n",
       "97840   0ece7f40-b225-4050-a075-4e677cc7cdf3   \n",
       "104011  cfa89126-1522-46cf-af7a-37ab6dde4595   \n",
       "104301  dd5b41fe-6bab-4bb0-a156-3e6c85766cd6   \n",
       "...                                      ...   \n",
       "426412  5602435b-bb43-4a4d-adf1-b452eaecbaa4   \n",
       "428301  300c5357-6d7f-4931-9f74-d7f756965d4a   \n",
       "431378  3dc72f79-f55b-4cd5-9c82-d7ce453682c5   \n",
       "433562  a68318d7-e038-476e-9af8-38d7c2b446f4   \n",
       "440642  177e25d8-9514-448c-aa6c-bd46252c5d3b   \n",
       "\n",
       "                                            name   platform sub_platform  \\\n",
       "96548                                  fish dish  postmates   restaurant   \n",
       "96957                                 waba grill  postmates   restaurant   \n",
       "97840                                   woodland  postmates   restaurant   \n",
       "104011                                leaf & co.  postmates   restaurant   \n",
       "104301                       the late night spot  postmates   restaurant   \n",
       "...                                          ...        ...          ...   \n",
       "426412  grasslands meat market | bbq & churrasco  postmates   restaurant   \n",
       "428301                      frank & finas cocina  postmates   restaurant   \n",
       "431378                              asian bistro  postmates   restaurant   \n",
       "433562                          la buca di marco  postmates   restaurant   \n",
       "440642                          l&l hawaiian bbq  postmates   restaurant   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "96548   34.195293 -118.350281      US    NaN  \n",
       "96957   34.223720 -118.502206      US    NaN  \n",
       "97840   40.680174  -73.974945      US    NaN  \n",
       "104011  30.015423  -90.239225      US    NaN  \n",
       "104301  36.100607 -115.133308      US    NaN  \n",
       "...           ...         ...     ...    ...  \n",
       "426412  33.805773 -117.911407      US    NaN  \n",
       "428301  36.113486 -115.307372      US    NaN  \n",
       "431378  19.348821  -99.189638      US    NaN  \n",
       "433562  39.292775  -76.587802      US    NaN  \n",
       "440642  33.657061 -117.916856      US    NaN  \n",
       "\n",
       "[196 rows x 8 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_active = data[data['active'].isnull()]\n",
    "null_active"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "postmates    195\n",
       "ubereats       1\n",
       "Name: platform, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_active['platform'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost all of the NaN values in the dataset correspond to restaurants from the platform postmates. One row of data with NaN for the active value is from the platform ubereats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addressing Active/Inactive for Postmates Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110237 restaurants on postmates from the dataset\n",
      "\n",
      "\n",
      "TRUE     67.929518\n",
      "FALSE    32.070482\n",
      "Name: active, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "postmates_data = data[data['platform'] == 'postmates']\n",
    "print(postmates_data.shape[0], 'restaurants on postmates from the dataset')\n",
    "print('\\n')\n",
    "print(postmates_data['active'].value_counts(dropna = True, normalize = True) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority (67.93%) of the 110,237 restaurants on the postmates platform are listed as active in our dataset. Because a majority is listed as active, my time-sensitive solution for the rows in which the platform is postmates and the active column is NaN is to change NaN to \"TRUE\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>34.195293</td>\n",
       "      <td>-118.350281</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96957</th>\n",
       "      <td>c02d95df-cfd3-44d6-b511-7025df9b4cc6</td>\n",
       "      <td>waba grill</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>34.223720</td>\n",
       "      <td>-118.502206</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97840</th>\n",
       "      <td>0ece7f40-b225-4050-a075-4e677cc7cdf3</td>\n",
       "      <td>woodland</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>40.680174</td>\n",
       "      <td>-73.974945</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104011</th>\n",
       "      <td>cfa89126-1522-46cf-af7a-37ab6dde4595</td>\n",
       "      <td>leaf &amp; co.</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>30.015423</td>\n",
       "      <td>-90.239225</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104301</th>\n",
       "      <td>dd5b41fe-6bab-4bb0-a156-3e6c85766cd6</td>\n",
       "      <td>the late night spot</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>36.100607</td>\n",
       "      <td>-115.133308</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426412</th>\n",
       "      <td>5602435b-bb43-4a4d-adf1-b452eaecbaa4</td>\n",
       "      <td>grasslands meat market | bbq &amp; churrasco</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>33.805773</td>\n",
       "      <td>-117.911407</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428301</th>\n",
       "      <td>300c5357-6d7f-4931-9f74-d7f756965d4a</td>\n",
       "      <td>frank &amp; finas cocina</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>36.113486</td>\n",
       "      <td>-115.307372</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431378</th>\n",
       "      <td>3dc72f79-f55b-4cd5-9c82-d7ce453682c5</td>\n",
       "      <td>asian bistro</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>19.348821</td>\n",
       "      <td>-99.189638</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433562</th>\n",
       "      <td>a68318d7-e038-476e-9af8-38d7c2b446f4</td>\n",
       "      <td>la buca di marco</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>39.292775</td>\n",
       "      <td>-76.587802</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440642</th>\n",
       "      <td>177e25d8-9514-448c-aa6c-bd46252c5d3b</td>\n",
       "      <td>l&amp;l hawaiian bbq</td>\n",
       "      <td>postmates</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>33.657061</td>\n",
       "      <td>-117.916856</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "96548   2783a54d-8014-46c1-b854-483964ef8605   \n",
       "96957   c02d95df-cfd3-44d6-b511-7025df9b4cc6   \n",
       "97840   0ece7f40-b225-4050-a075-4e677cc7cdf3   \n",
       "104011  cfa89126-1522-46cf-af7a-37ab6dde4595   \n",
       "104301  dd5b41fe-6bab-4bb0-a156-3e6c85766cd6   \n",
       "...                                      ...   \n",
       "426412  5602435b-bb43-4a4d-adf1-b452eaecbaa4   \n",
       "428301  300c5357-6d7f-4931-9f74-d7f756965d4a   \n",
       "431378  3dc72f79-f55b-4cd5-9c82-d7ce453682c5   \n",
       "433562  a68318d7-e038-476e-9af8-38d7c2b446f4   \n",
       "440642  177e25d8-9514-448c-aa6c-bd46252c5d3b   \n",
       "\n",
       "                                            name   platform sub_platform  \\\n",
       "96548                                  fish dish  postmates   restaurant   \n",
       "96957                                 waba grill  postmates   restaurant   \n",
       "97840                                   woodland  postmates   restaurant   \n",
       "104011                                leaf & co.  postmates   restaurant   \n",
       "104301                       the late night spot  postmates   restaurant   \n",
       "...                                          ...        ...          ...   \n",
       "426412  grasslands meat market | bbq & churrasco  postmates   restaurant   \n",
       "428301                      frank & finas cocina  postmates   restaurant   \n",
       "431378                              asian bistro  postmates   restaurant   \n",
       "433562                          la buca di marco  postmates   restaurant   \n",
       "440642                          l&l hawaiian bbq  postmates   restaurant   \n",
       "\n",
       "         latitude   longitude country active  \n",
       "96548   34.195293 -118.350281      US    NaN  \n",
       "96957   34.223720 -118.502206      US    NaN  \n",
       "97840   40.680174  -73.974945      US    NaN  \n",
       "104011  30.015423  -90.239225      US    NaN  \n",
       "104301  36.100607 -115.133308      US    NaN  \n",
       "...           ...         ...     ...    ...  \n",
       "426412  33.805773 -117.911407      US    NaN  \n",
       "428301  36.113486 -115.307372      US    NaN  \n",
       "431378  19.348821  -99.189638      US    NaN  \n",
       "433562  39.292775  -76.587802      US    NaN  \n",
       "440642  33.657061 -117.916856      US    NaN  \n",
       "\n",
       "[195 rows x 8 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['platform'] == 'postmates') & (data['active'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addressing Active/Inactive for Ubereats Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>restaurant_id</th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>sub_platform</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>349951</th>\n",
       "      <td>70af6b2e-37d6-4ece-b5f3-ec9d03533b4c</td>\n",
       "      <td>jeremiahs italian ice (westside shoppes)</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>ubereats</td>\n",
       "      <td>28.45116</td>\n",
       "      <td>-81.562331</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant_id  \\\n",
       "349951  70af6b2e-37d6-4ece-b5f3-ec9d03533b4c   \n",
       "\n",
       "                                            name  platform sub_platform  \\\n",
       "349951  jeremiahs italian ice (westside shoppes)  ubereats     ubereats   \n",
       "\n",
       "        latitude  longitude country active  \n",
       "349951  28.45116 -81.562331      US    NaN  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_active[null_active['platform'] == 'ubereats']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because it's only one row of data, I'm simply going to look up this restaurant on the internet and label it by hand.\n",
    "\n",
    "This [restaurant](https://www.ubereats.com/orlando/food-delivery/jeremiahs-italian-ice-westside-shoppes/cK9rLjfWTs618-ydA1M7TA) is active on Ubereats, so I will change all NaN values in the dataset to be \"TRUE\" in the active column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRUE     568622\n",
       "FALSE     67450\n",
       "Name: active, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing all NaN in the active column with \"TRUE\"\n",
    "data['active'] = data['active'].replace(np.NaN, 'TRUE')\n",
    "data['active'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring Sub-Platform Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doordash      349394\n",
       "restaurant    110237\n",
       "grubhub        68579\n",
       "ubereats       65740\n",
       "NaN            42122\n",
       "Name: sub_platform, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sub_platform'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column is an interesting column because if a restaurant only offers delivery via one platform, it makes perfect sense why the sub-platform column will have NaN values.\n",
    "\n",
    "At a glance, each data point at this stage of data cleaning has data for the 'platform' column. However, it is difficult to check or confirm whether each of the data points does or doesn't have a sub-platform. \n",
    "\n",
    "For deeper analyses, it might make sense to change NaN values to something like 'None', but for the scope of this assignment I do not intend to use this column any further for analyses and will leave the column as is. In fact, it might be useful to know which rows have a sub-platform depending on the type of analyses in the future, and therefore there is value in leaving in this column for possible transformations in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cell4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Solution: Creating My Target Column\n",
    "\n",
    "Back to [Top](#cell1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### My Answer for Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "644089 total restaurants were listed in the original/provided dataset.\n",
      "636072 total restaurants exist in the cleaned dataset.\n"
     ]
    }
   ],
   "source": [
    "print('{} total restaurants were listed in the original/provided dataset.'.format(original_dataset.shape[0]))\n",
    "print('{} total restaurants exist in the cleaned dataset.'.format(data.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    554095\n",
      "True      81977\n",
      "Name: duplicate, dtype: int64\n",
      "\n",
      "\n",
      "86% of the rows from the provided dataset contain unique restaurants.\n"
     ]
    }
   ],
   "source": [
    "data['duplicate'] = data.duplicated(subset = ['latitude','longitude'], keep = 'first') \n",
    "\n",
    "# keep = False labels everything that was duplicated as True (there's a duplicate of it in the data set)\n",
    "# creating another column is possible with keep = first if you want to leave the first duplicate labeled as an original just for a simple aggregation\n",
    "\n",
    "print(data['duplicate'].value_counts())\n",
    "\n",
    "# The False indicates that it is NOT a duplicate. \n",
    "# Whatever value exists as \"FALSE\" above is what I can say are unique results \n",
    "# aggregated after data cleaning using coordinate data to filter our duplicates\n",
    "\n",
    "print('\\n')\n",
    "print('{}% of the rows from the provided dataset contain unique restaurants.'.format(round(554095 / original_dataset.shape[0] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 554,095 unique restaurants in this dataset (out of 636,072 restaurants in total)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Active vs. Inactive Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_data = data[data['active'] == \"TRUE\"].copy()\n",
    "inactive_data = data[data['active'] == \"FALSE\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active Dataset\n",
      "False    498453\n",
      "True      70169\n",
      "Name: duplicate, dtype: int64\n",
      "\n",
      "\n",
      "Inactive Dataset\n",
      "False    62824\n",
      "True      4626\n",
      "Name: duplicate, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# resetting the duplicate labels for the split active/inactive data sets\n",
    "active_data.drop(columns = ['duplicate'], inplace = True)\n",
    "inactive_data.drop(columns = ['duplicate'], inplace = True)\n",
    "\n",
    "active_data['duplicate'] = active_data.duplicated(subset = ['latitude','longitude'], keep = 'first') \n",
    "inactive_data['duplicate'] = inactive_data.duplicated(subset = ['latitude','longitude'], keep = 'first')\n",
    "\n",
    "print('Active Dataset')\n",
    "print(active_data['duplicate'].value_counts())\n",
    "print('\\n')\n",
    "print('Inactive Dataset')\n",
    "print(inactive_data['duplicate'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After splitting the dataset into active and inactive restaurants, there are a total of 561277 original restaurants from both datasets.\n"
     ]
    }
   ],
   "source": [
    "new_dupes = (498453 + 62824)\n",
    "print('After splitting the dataset into active and inactive restaurants, there are a total of {} original restaurants from both datasets.'.format(new_dupes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This difference (561,277 vs. 554,095) can be explained. There are restaurants in the dataset that have inactive duplicates, and these were taken into account to get the final number of original restaurants.\n",
    "\n",
    "___\n",
    "\n",
    "I will proceed with 554,095 being the final number of unique restaurants from the original dataset.\n",
    "\n",
    "For active restaurants, there are 498,453 unique restaurants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import ConnectionPatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pie graph of unique restaurants vs number of duplicate restaurants in the dataset\n",
    "# data['duplicate'] = data.duplicated(subset = ['latitude','longitude'], keep = 'first') \n",
    "# print(data['duplicate'].value_counts())\n",
    "fig1, ax1 = plt.subplots(figsize=(14,7))\n",
    "\n",
    "fig1_labels = 'Unique Restaurants', 'Duplicate Restaurants'\n",
    "ax1.pie([554095, 81977], \n",
    "        explode = (0, 0.1), \n",
    "        labels = fig1_labels, \n",
    "        autopct = '%1.1f%%', \n",
    "        shadow = False, \n",
    "        startangle = 180, \n",
    "        colors = ['gainsboro', 'gray'])\n",
    "ax1.axis('equal')\n",
    "ax1.set_title('The Dataset')\n",
    "\n",
    "plt.show()\n",
    "# fig1.savefig('fig1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doordash        54.929945\n",
       "postmates       17.330900\n",
       "grubhub         10.781641\n",
       "ubereats        10.335308\n",
       "delivery.com     5.134010\n",
       "caviar           1.488196\n",
       "Name: platform, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['platform'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar of pie chart on exploded unique restaurant values\n",
    "\n",
    "# make figure and assign axis objects\n",
    "fig2 = plt.figure(figsize=(15, 9))\n",
    "ax1 = fig2.add_subplot(121)\n",
    "ax2 = fig2.add_subplot(122)\n",
    "fig2.subplots_adjust(wspace=0)\n",
    "\n",
    "# pie chart parameters\n",
    "ratios = [554095, 81977]\n",
    "labels = 'Unique Restaurants', 'Duplicates'\n",
    "explode = [0.1, 0]\n",
    "# rotate so that first wedge is split by the x-axis\n",
    "angle = -180 * ratios[0]\n",
    "ax1.pie(ratios, autopct = '%1.1f%%', startangle = angle, labels = labels, explode = explode, colors = ['gainsboro', 'gray'])\n",
    "\n",
    "# bar chart parameters\n",
    "xpos = 0\n",
    "bottom = 0\n",
    "ratios = [0.54929945, 0.17330900, 0.10781641, 0.10335308, 0.05134010, 0.01488196]\n",
    "width = .2\n",
    "colors = [[1, 0, 0], [0.1, 0.5, 0.5], [0.7, 0.3, 0], [0, 1, 0.4],[0, 0.5, 1], [1, 0.5, 0]]\n",
    "\n",
    "for j in range(len(ratios)):\n",
    "    height = ratios[j]\n",
    "    ax2.bar(xpos, height, width, bottom=bottom, color=colors[j])\n",
    "    ypos = bottom + ax2.patches[j].get_height() / 2\n",
    "    bottom += height\n",
    "    ax2.text(xpos, ypos, \"%d%%\" % (ax2.patches[j].get_height() * 100), ha='center')\n",
    "\n",
    "ax2.set_title('Restaurant Delivery Platforms')\n",
    "ax2.legend(('doordash', 'postmates', 'grubhub', 'ubereats', 'delivery.com', 'caviar'), loc = 'center right')\n",
    "ax2.axis('off')\n",
    "ax2.set_xlim(- 2.5 * width, 2.5 * width)\n",
    "\n",
    "# use ConnectionPatch to draw lines between the two plots    # get the wedge data\n",
    "theta1, theta2 = ax1.patches[0].theta1, ax1.patches[0].theta2\n",
    "center, r = ax1.patches[0].center, ax1.patches[0].r\n",
    "bar_height = sum([item.get_height() for item in ax2.patches])\n",
    "\n",
    "# draw top connecting line\n",
    "con = ConnectionPatch(xyA=(- width / 2, bar_height), xyB=(-0.1, 0.94),coordsA=\"data\", coordsB=\"data\", axesA=ax2, axesB=ax1)\n",
    "con.set_color([0, 0, 0])\n",
    "con.set_linewidth(2)\n",
    "ax2.add_artist(con)\n",
    "\n",
    "# draw bottom connecting line\n",
    "con = ConnectionPatch(xyA=(- width / 2, 0), xyB=(-0.1, -1.015), coordsA=\"data\",coordsB=\"data\", axesA=ax2, axesB=ax1)\n",
    "con.set_color([0, 0, 0])\n",
    "ax2.add_artist(con)\n",
    "con.set_linewidth(2)\n",
    "\n",
    "plt.show()\n",
    "# fig2.savefig('fig2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count plot of unique restaurants vs number of duplicate restaurants in the dataset\n",
    "fig3 = sns.countplot(x = 'duplicate', data = data, palette = 'Set2')\n",
    "fig3.set_title('Unique vs. Duplicate Restaurants')\n",
    "fig3\n",
    "\n",
    "plt.show()\n",
    "fig3 = fig3.get_figure()\n",
    "# fig3.savefig('fig3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count plot of unique restaurants vs number of duplicate restaurants in the dataset for active restaurants only\n",
    "fig4 = sns.countplot(x = 'duplicate', data = active_data, palette = 'Set2')\n",
    "fig4.set_title('Unique vs. Duplicate Restaurants (Active Only)')\n",
    "fig4\n",
    "\n",
    "plt.show()\n",
    "fig4 = fig4.get_figure()\n",
    "# fig4.savefig('fig4.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### pie graph of frequencies of active vs inactive\n",
    "fig5, ax1 = plt.subplots(figsize=(14,7))\n",
    "\n",
    "fig5_labels = 'Active Restaurants', 'Inactive Restaurants'\n",
    "ax1.pie([(data[data['active'] == 'TRUE'].shape[0]), (data[data['active'] == 'FALSE'].shape[0])], \n",
    "        explode = (0, 0.1), \n",
    "        labels = fig5_labels, \n",
    "        autopct = '%1.1f%%', \n",
    "        shadow = False, \n",
    "        startangle = 180, \n",
    "        colors = ['mediumseagreen', 'lightcoral'])\n",
    "ax1.axis('equal')\n",
    "ax1.set_title('The Dataset')\n",
    "\n",
    "plt.show()\n",
    "# fig5.savefig('fig5.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count plot of active restaurants and the platforms they exist on\n",
    "fig6 = sns.countplot(x = 'platform', data = active_data, palette = 'Set1')\n",
    "fig6.set_title('Restaurant Platforms (Active Only)')\n",
    "fig6\n",
    "\n",
    "plt.show()\n",
    "fig6 = fig6.get_figure()\n",
    "# fig6.savefig('fig6.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count plot of active restaurants and the platforms they exist on\n",
    "fig7 = sns.countplot(x = 'platform', data = inactive_data, palette = 'Set1')\n",
    "fig7.set_title('Restaurant Platforms (Inactive Only)')\n",
    "fig7\n",
    "\n",
    "plt.show()\n",
    "fig7 = fig7.get_figure()\n",
    "# fig7.savefig('fig7.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cell5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion: Potential Next Steps\n",
    "\n",
    "Back to [Top](#cell1)\n",
    "\n",
    "The majority of my week was spent on data hygiene. The most important aspect of working with data is making sure the data itself is of high quality. \n",
    "\n",
    "## My Solution: 554,095 Unique Restaurants, 498,453 Unique & Active Restaurants\n",
    "I reached my solution for duplicates by creating a simple algorithm that matched duplicate entries by their latitude and longitude data. Latitude and longitude are very precise measurements of location, so it was a quick solution to identifying which rows of data were duplicates of an already existing restaurant.\n",
    "- I could not filter duplicates by platform as the same restaurant could be found on multiple platforms.\n",
    "- I could not filter duplicates by restaurant name as different restaurants could have the same name.\n",
    "\n",
    "## The Value in the Data\n",
    "With some more work, the data could be even more valuable to decision-makers:\n",
    "- Knowing how many active/inactive restaurants are available on different delivery platforms can be helpful in developing various platform/product strategies\n",
    "- Filtering data by geographic location(s) can help decision-makers decide where (state, city) to invest in properties to maximize outcomes\n",
    "- On the [Cloud Kitchens website](https://www.cloudkitchens.com/), the platform assures users that \"ghost kitchens come with all the pieces of the operations puzzle, so [restaurants] can focus on the food\". A deeper dive into which types/categories/styles of restaurants may be more/less successful based on trends can allow the platform to fulfill its promises and be more beneficial to its users, ultimately leading to:\n",
    "    - increased user retention\n",
    "    - marketing via word-of-mouth\n",
    "    - more positive cross-functional outcomes\n",
    "\n",
    "## What I Could Have Done (w/ More Time)\n",
    "After completing data cleaning, it may be possible to create machine learning algorithms to find duplicates. The problem with creating ML algorithms in this instance was time. I would have had to normalize columns of the data in much more complicated and time-consuming ways to create any sort of supervised or unsupervised model. Based on the columns I was given with the time-frame I had, I knew I didn't have enough time to engineer features in a manner that would lead to strong model results.\n",
    "\n",
    "One option I considered was using the [Dedupe Python library](https://github.com/dedupeio/dedupe). Dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data. However, I ruled this option out also. I did not feel that there was enough time to tune the model.\n",
    "\n",
    "## Visualizations\n",
    "There are plenty of different types of visualizations I could have created. However, I kept my visualizations simple and only visualized my solution(s) and related metrics. Other possible visualizations could have included:\n",
    "- Active/Inactive restaurants map of the United States\n",
    "- Active/Inactive restaurants map by state\n",
    "- Mapping restaurants by delivery platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### An Example of the Capability of Mapping Geographically Using Basemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.basemap import Basemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def draw_map(m, scale=0.2):\n",
    "    # draw a shaded-relief image\n",
    "    m.shadedrelief(scale=scale)\n",
    "    \n",
    "    # lats and longs are returned as a dictionary\n",
    "    lats = m.drawparallels(np.linspace(-90, 90, 13))\n",
    "    lons = m.drawmeridians(np.linspace(-180, 180, 13))\n",
    "\n",
    "    # keys contain the plt.Line2D instances\n",
    "    lat_lines = chain(*(tup[1][0] for tup in lats.items()))\n",
    "    lon_lines = chain(*(tup[1][0] for tup in lons.items()))\n",
    "    all_lines = chain(lat_lines, lon_lines)\n",
    "    \n",
    "    # cycle through these lines and set the desired style\n",
    "    for line in all_lines:\n",
    "        line.set(linestyle='-', alpha=0.3, color='w')\n",
    "        \n",
    "fig8 = plt.figure(figsize=(8, 6), edgecolor='w')\n",
    "m = Basemap(projection='cyl', resolution=None,\n",
    "            llcrnrlat=-90, urcrnrlat=90,\n",
    "            llcrnrlon=-180, urcrnrlon=180, )\n",
    "draw_map(m)\n",
    "\n",
    "plt.show()\n",
    "# fig8.savefig('fig8.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basemap plot all coordinates of all unique restaurants\n",
    "fig9 = plt.figure(figsize=(8, 6), edgecolor='black')\n",
    "m = Basemap(projection='lcc', resolution=None,\n",
    "            width=8E6, height=8E6, \n",
    "            lat_0=45, lon_0=-100,)\n",
    "\n",
    "longitudes = data[\"longitude\"].tolist()\n",
    "latitudes = data[\"latitude\"].tolist()\n",
    "m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, cmap=\"summer\")\n",
    "\n",
    "plt.show()\n",
    "# fig9.savefig('fig9.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to [Top](#cell1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}