The Battle of Neighborhoods (Week 2).ipynb

{"nbformat_minor": 1, "cells": [{"source": "<b>Capstone Project - The Battle of Neighborhoods</b>", "cell_type": "markdown", "metadata": {}}, {"source": "<b>1.Introduction & Business Problem:</b>", "cell_type": "markdown", "metadata": {}}, {"source": "A restaurant is a business which prepares and serves food and drink to customers in return for money, either paid before the meal, after the meal, or with an open account. The City of New York is famous for its excellent cuisine. So, it is evident that to survive in such competitive market it is very important to strategically plan.\nVarious factors need to be studied in order to decide on the Location such as:\n1. New York Population\n2. New York City Demographics\n3. Are there any Farmers Markets, Wholesale markets etc nearby so that the ingredients can be\npurchased fresh to maintain quality and cost?\n4. Are there any venues like Gyms, Entertainment zones, Parks etc nearby where floating\npopulation is high etc\n5. Who are the competitors in that location?\n6. Cuisine served / Menu of the competitors\n7. Segmentation of the Borough\nEven though well-funded XYZ Company Ltd. need to choose the correct location to start its first\nventure. If this is successful, they can replicate the same in other locations.\nThis would interest anyone who wants to start a new restaurant in New York city.\n", "cell_type": "markdown", "metadata": {}}, {"source": "<b>2. Data:</b>", "cell_type": "markdown", "metadata": {}}, {"source": "One city will be analysed in this project: New York City.\nWe will be using the below datasets for analysing New York city\n\nData 1: Neighbourhood has a total of 5 boroughs and 306 neighbourhoods. In order to segment the\nneighbourhoods and explore them, we will essentially need a dataset that contains the 5 boroughs and the neighbourhoods that exist in each borough as well as the latitude and longitude coordinates of each neighbourhood.\nData source: https://cocl.us/new_york_dataset\nDescription: This data set contains the required information. And we will use this data set to explore various neighbourhoods of New York city.\n\nDate 2: Indian restaurants in each neighbourhood of New York city.\nData source: Foursquare API\nDescription: By using this api we will get all the venues in each neighbourhood. We can filter these venues to get only Indian restaurants.\nData 3: GeoSpace data\nData source: https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm\nDescription: By using this geo space data we will get the New York Borough boundaries that will help us visualize choropleth map.\n", "cell_type": "markdown", "metadata": {}}, {"source": "<b>Analysis</b>", "cell_type": "markdown", "metadata": {}}, {"source": "We will import the required libraries for python.\n*\tpandas and numpy for handling data.\n*\trequest module for using FourSquare API.\n*\tgeopy to get co-ordinates of City of New York.\n*\tfolium to visualize the results on a map\n", "cell_type": "markdown", "metadata": {}}, {"source": "import pandas as pd\nimport numpy as np\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)\nimport requests\nfrom bs4 import BeautifulSoup\n\nimport os\n!conda install -c conda-forge folium\nimport folium # map rendering library\n!conda install -c conda-forge geocoder\n!conda install -c conda-forge geopy --yes\nimport geocoder\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n# Matplotlib and associated plotting modules\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n%matplotlib inline\n\n\nprint('Libraries imported.')", "cell_type": "code", "execution_count": 1, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/Python36\n\n  added / updated specs: \n    - folium\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    altair-3.2.0               |           py36_0         770 KB  conda-forge\n    certifi-2019.6.16          |           py36_1         149 KB  conda-forge\n    vincent-0.4.4              |             py_1          28 KB  conda-forge\n    branca-0.3.1               |             py_0          25 KB  conda-forge\n    ca-certificates-2019.6.16  |       hecc5488_0         145 KB  conda-forge\n    folium-0.10.0              |             py_0          59 KB  conda-forge\n    openssl-1.1.1c             |       h516909a_0         2.1 MB  conda-forge\n    ------------------------------------------------------------\n                                           Total:         3.3 MB\n\nThe following NEW packages will be INSTALLED:\n\n    altair:          3.2.0-py36_0      conda-forge\n    branca:          0.3.1-py_0        conda-forge\n    folium:          0.10.0-py_0       conda-forge\n    vincent:         0.4.4-py_1        conda-forge\n\nThe following packages will be UPDATED:\n\n    ca-certificates: 2019.5.15-0                   --> 2019.6.16-hecc5488_0 conda-forge\n    certifi:         2019.6.16-py36_1              --> 2019.6.16-py36_1     conda-forge\n\nThe following packages will be DOWNGRADED:\n\n    openssl:         1.1.1c-h7b6447c_1             --> 1.1.1c-h516909a_0    conda-forge\n\n\nDownloading and Extracting Packages\naltair-3.2.0         | 770 KB    | ##################################### | 100% \ncertifi-2019.6.16    | 149 KB    | ##################################### | 100% \nvincent-0.4.4        | 28 KB     | ##################################### | 100% \nbranca-0.3.1         | 25 KB     | ##################################### | 100% \nca-certificates-2019 | 145 KB    | ##################################### | 100% \nfolium-0.10.0        | 59 KB     | ##################################### | 100% \nopenssl-1.1.1c       | 2.1 MB    | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\nSolving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/Python36\n\n  added / updated specs: \n    - geocoder\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    ratelim-0.1.6              |             py_2           6 KB  conda-forge\n    geocoder-1.38.1            |             py_1          53 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:          59 KB\n\nThe following NEW packages will be INSTALLED:\n\n    geocoder: 1.38.1-py_1 conda-forge\n    ratelim:  0.1.6-py_2  conda-forge\n\n\nDownloading and Extracting Packages\nratelim-0.1.6        | 6 KB      | ##################################### | 100% \ngeocoder-1.38.1      | 53 KB     | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\nSolving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/Python36\n\n  added / updated specs: \n    - geopy\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    geopy-1.20.0               |             py_0          57 KB  conda-forge\n    geographiclib-1.49         |             py_0          32 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:          90 KB\n\nThe following NEW packages will be INSTALLED:\n\n    geographiclib: 1.49-py_0   conda-forge\n    geopy:         1.20.0-py_0 conda-forge\n\n\nDownloading and Extracting Packages\ngeopy-1.20.0         | 57 KB     | ##################################### | 100% \ngeographiclib-1.49   | 32 KB     | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\nLibraries imported.\n"}], "metadata": {}}, {"source": "Now we define a function to get the geocodes i.e latitude and longitude of a given location using geopy.", "cell_type": "markdown", "metadata": {}}, {"source": "def geo_location(address):\n    # get geo location of address\n    geolocator = Nominatim(user_agent=\"ny_explorer\")\n    location = geolocator.geocode(address)\n    latitude = location.latitude\n    longitude = location.longitude\n    return latitude,longitude", "cell_type": "code", "execution_count": 2, "outputs": [], "metadata": {}}, {"source": "We define a function to intract with FourSquare API and get top 100 venues within a radius of 1000 metres for a given latitude and longitude. Below function will return us the venue id , venue name and category.", "cell_type": "markdown", "metadata": {}}, {"source": "def get_venues(lat,lng):\n    \n    #set variables\n    radius=1000\n    LIMIT=100\n    CLIENT_ID = '0G0KFCU55P01WMUAE5ZSE1NRPQQIP5F0J5EY1ZI0IKHQDNYG' # your Foursquare ID\n    CLIENT_SECRET = 'QU5PJIGT233ERHLCAVAPFPLYPGJ1KDN1YJYTFJHRBADVWJWM' # your Foursquare Secret\n    VERSION = '20180605' # Foursquare API version\n    \n    #url to fetch data from foursquare api\n    url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n            CLIENT_ID, \n            CLIENT_SECRET, \n            VERSION, \n            lat, \n            lng, \n            radius, \n            LIMIT)\n    \n    # get all the data\n    results = requests.get(url).json()\n    venue_data=results[\"response\"]['groups'][0]['items']\n    venue_details=[]\n    for row in venue_data:\n        try:\n            venue_id=row['venue']['id']\n            venue_name=row['venue']['name']\n            venue_category=row['venue']['categories'][0]['name']\n            venue_details.append([venue_id,venue_name,venue_category])\n        except KeyError:\n            pass\n        \n    column_names=['ID','Name','Category']\n    df = pd.DataFrame(venue_details,columns=column_names)\n    return df", "cell_type": "code", "execution_count": 19, "outputs": [], "metadata": {}}, {"source": "Now we will define a function to get venue details like like count , rating , tip counts for a given venue id. This will be used for ranking.", "cell_type": "markdown", "metadata": {}}, {"source": "def get_venue_details(venue_id):\n        \n    CLIENT_ID = '0G0KFCU55P01WMUAE5ZSE1NRPQQIP5F0J5EY1ZI0IKHQDNYG' # your Foursquare ID\n    CLIENT_SECRET = 'QU5PJIGT233ERHLCAVAPFPLYPGJ1KDN1YJYTFJHRBADVWJWM'# your Foursquare Secret\n    VERSION = '20180605' # Foursquare API version\n    \n    #url to fetch data from foursquare api\n    url = 'https://api.foursquare.com/v2/venues/{}?&client_id={}&client_secret={}&v={}'.format(\n            venue_id,\n            CLIENT_ID, \n            CLIENT_SECRET, \n            VERSION)\n    \n    # get all the data\n    results = requests.get(url).json()\n    venue_data=results['response']['venue']\n    venue_details=[]\n    try:\n        venue_id=venue_data['id']\n        venue_name=venue_data['name']\n        venue_likes=venue_data['likes']['count']\n        venue_rating=venue_data['rating']\n        venue_tips=venue_data['tips']['count']\n        venue_details.append([venue_id,venue_name,venue_likes,venue_rating,venue_tips])\n    except KeyError:\n        pass\n        \n    column_names=['ID','Name','Likes','Rating','Tips']\n    df = pd.DataFrame(venue_details,columns=column_names)\n    return df", "cell_type": "code", "execution_count": 20, "outputs": [], "metadata": {}}, {"source": "Now we define a funtion to get the new york city data such as Boroughs, Neighborhoods along with their latitude and longitude.", "cell_type": "markdown", "metadata": {}}, {"source": "def get_new_york_data():\n    url='https://cocl.us/new_york_dataset'\n    resp=requests.get(url).json()\n    # all data is present in features label\n    features=resp['features']\n    \n    # define the dataframe columns\n    column_names = ['Borough', 'Neighborhood', 'Latitude', 'Longitude'] \n    # instantiate the dataframe\n    new_york_data = pd.DataFrame(columns=column_names)\n    \n    for data in features:\n        borough = data['properties']['borough'] \n        neighborhood_name = data['properties']['name']\n        \n        neighborhood_latlon = data['geometry']['coordinates']\n        neighborhood_lat = neighborhood_latlon[1]\n        neighborhood_lon = neighborhood_latlon[0]\n    \n        new_york_data = new_york_data.append({'Borough': borough,\n                                          'Neighborhood': neighborhood_name,\n                                          'Latitude': neighborhood_lat,\n                                          'Longitude': neighborhood_lon}, ignore_index=True)\n    \n    return new_york_data", "cell_type": "code", "execution_count": 21, "outputs": [], "metadata": {}}, {"source": "We will call the above funtion to get the new york city data.", "cell_type": "markdown", "metadata": {}}, {"source": "# get new york data\nnew_york_data=get_new_york_data()", "cell_type": "code", "execution_count": 22, "outputs": [], "metadata": {}}, {"source": "new_york_data.head()", "cell_type": "code", "execution_count": 23, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bronx</td>\n      <td>Wakefield</td>\n      <td>40.894705</td>\n      <td>-73.847201</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bronx</td>\n      <td>Co-op City</td>\n      <td>40.874294</td>\n      <td>-73.829939</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bronx</td>\n      <td>Eastchester</td>\n      <td>40.887556</td>\n      <td>-73.827806</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bronx</td>\n      <td>Fieldston</td>\n      <td>40.895437</td>\n      <td>-73.905643</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bronx</td>\n      <td>Riverdale</td>\n      <td>40.890834</td>\n      <td>-73.912585</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "  Borough Neighborhood   Latitude  Longitude\n0   Bronx    Wakefield  40.894705 -73.847201\n1   Bronx   Co-op City  40.874294 -73.829939\n2   Bronx  Eastchester  40.887556 -73.827806\n3   Bronx    Fieldston  40.895437 -73.905643\n4   Bronx    Riverdale  40.890834 -73.912585"}, "execution_count": 23}], "metadata": {}}, {"source": "new_york_data.shape", "cell_type": "code", "execution_count": 24, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(306, 4)"}, "execution_count": 24}], "metadata": {}}, {"source": "plt.figure(figsize=(9,5), dpi = 100)\n# title\nplt.title('Number of Neighborhood for each Borough in New York City')\n#On x-axis\nplt.xlabel('Borough', fontsize = 15)\n#On y-axis\nplt.ylabel('No.of Neighborhood', fontsize=15)\n#giving a bar plot\nnew_york_data.groupby('Borough')['Neighborhood'].count().plot(kind='bar')\n#legend\nplt.legend()\n#displays the plot\nplt.show()", "cell_type": "code", "execution_count": 25, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<Figure size 900x500 with 1 Axes>"}, "metadata": {"needs_background": "light"}}], "metadata": {}}, {"source": "Now we will collect Indian restaurants for each Neighborhood", "cell_type": "markdown", "metadata": {}}, {"source": "# prepare neighborhood list that contains indian restaurants\ncolumn_names=['Borough', 'Neighborhood', 'ID','Name']\nindian_rest_ny=pd.DataFrame(columns=column_names)\ncount=1\nfor row in new_york_data.values.tolist():\n    Borough, Neighborhood, Latitude, Longitude=row\n    venues = get_venues(Latitude,Longitude)\n    indian_restaurants=venues[venues['Category']=='Indian Restaurant']   \n    print('(',count,'/',len(new_york_data),')','Indian Restaurants in '+Neighborhood+', '+Borough+':'+str(len(indian_restaurants)))\n    for restaurant_detail in indian_restaurants.values.tolist():\n        id, name , category=restaurant_detail\n        indian_rest_ny = indian_rest_ny.append({'Borough': Borough,\n                                                'Neighborhood': Neighborhood, \n                                                'ID': id,\n                                                'Name' : name\n                                               }, ignore_index=True)\n    count+=1", "cell_type": "code", "execution_count": 26, "outputs": [{"output_type": "stream", "name": "stdout", "text": "( 1 / 306 ) Indian Restaurants in Wakefield, Bronx:0\n( 2 / 306 ) Indian Restaurants in Co-op City, Bronx:0\n( 3 / 306 ) Indian Restaurants in Eastchester, Bronx:0\n( 4 / 306 ) Indian Restaurants in Fieldston, Bronx:0\n( 5 / 306 ) Indian Restaurants in Riverdale, Bronx:0\n( 6 / 306 ) Indian Restaurants in Kingsbridge, Bronx:0\n( 7 / 306 ) Indian Restaurants in Marble Hill, Manhattan:0\n( 8 / 306 ) Indian Restaurants in Woodlawn, Bronx:1\n( 9 / 306 ) Indian Restaurants in Norwood, Bronx:0\n( 10 / 306 ) Indian Restaurants in Williamsbridge, Bronx:0\n( 11 / 306 ) Indian Restaurants in Baychester, Bronx:0\n( 12 / 306 ) Indian Restaurants in Pelham Parkway, Bronx:0\n( 13 / 306 ) Indian Restaurants in City Island, Bronx:0\n( 14 / 306 ) Indian Restaurants in Bedford Park, Bronx:0\n( 15 / 306 ) Indian Restaurants in University Heights, Bronx:0\n( 16 / 306 ) Indian Restaurants in Morris Heights, Bronx:0\n( 17 / 306 ) Indian Restaurants in Fordham, Bronx:0\n( 18 / 306 ) Indian Restaurants in East Tremont, Bronx:0\n( 19 / 306 ) Indian Restaurants in West Farms, Bronx:0\n( 20 / 306 ) Indian Restaurants in High  Bridge, Bronx:0\n( 21 / 306 ) Indian Restaurants in Melrose, Bronx:0\n( 22 / 306 ) Indian Restaurants in Mott Haven, Bronx:0\n( 23 / 306 ) Indian Restaurants in Port Morris, Bronx:0\n( 24 / 306 ) Indian Restaurants in Longwood, Bronx:0\n( 25 / 306 ) Indian Restaurants in Hunts Point, Bronx:0\n( 26 / 306 ) Indian Restaurants in Morrisania, Bronx:0\n( 27 / 306 ) Indian Restaurants in Soundview, Bronx:0\n( 28 / 306 ) Indian Restaurants in Clason Point, Bronx:0\n( 29 / 306 ) Indian Restaurants in Throgs Neck, Bronx:0\n( 30 / 306 ) Indian Restaurants in Country Club, Bronx:0\n( 31 / 306 ) Indian Restaurants in Parkchester, Bronx:1\n( 32 / 306 ) Indian Restaurants in Westchester Square, Bronx:0\n( 33 / 306 ) Indian Restaurants in Van Nest, Bronx:0\n( 34 / 306 ) Indian Restaurants in Morris Park, Bronx:0\n( 35 / 306 ) Indian Restaurants in Belmont, Bronx:0\n( 36 / 306 ) Indian Restaurants in Spuyten Duyvil, Bronx:1\n( 37 / 306 ) Indian Restaurants in North Riverdale, Bronx:0\n( 38 / 306 ) Indian Restaurants in Pelham Bay, Bronx:0\n( 39 / 306 ) Indian Restaurants in Schuylerville, Bronx:0\n( 40 / 306 ) Indian Restaurants in Edgewater Park, Bronx:0\n( 41 / 306 ) Indian Restaurants in Castle Hill, Bronx:0\n( 42 / 306 ) Indian Restaurants in Olinville, Bronx:0\n( 43 / 306 ) Indian Restaurants in Pelham Gardens, Bronx:0\n( 44 / 306 ) Indian Restaurants in Concourse, Bronx:1\n( 45 / 306 ) Indian Restaurants in Unionport, Bronx:1\n( 46 / 306 ) Indian Restaurants in Edenwald, Bronx:0\n( 47 / 306 ) Indian Restaurants in Bay Ridge, Brooklyn:2\n( 48 / 306 ) Indian Restaurants in Bensonhurst, Brooklyn:0\n( 49 / 306 ) Indian Restaurants in Sunset Park, Brooklyn:0\n( 50 / 306 ) Indian Restaurants in Greenpoint, Brooklyn:0\n( 51 / 306 ) Indian Restaurants in Gravesend, Brooklyn:0\n( 52 / 306 ) Indian Restaurants in Brighton Beach, Brooklyn:1\n( 53 / 306 ) Indian Restaurants in Sheepshead Bay, Brooklyn:0\n( 54 / 306 ) Indian Restaurants in Manhattan Terrace, Brooklyn:0\n( 55 / 306 ) Indian Restaurants in Flatbush, Brooklyn:2\n( 56 / 306 ) Indian Restaurants in Crown Heights, Brooklyn:0\n( 57 / 306 ) Indian Restaurants in East Flatbush, Brooklyn:1\n( 58 / 306 ) Indian Restaurants in Kensington, Brooklyn:2\n( 59 / 306 ) Indian Restaurants in Windsor Terrace, Brooklyn:0\n( 60 / 306 ) Indian Restaurants in Prospect Heights, Brooklyn:0\n( 61 / 306 ) Indian Restaurants in Brownsville, Brooklyn:0\n( 62 / 306 ) Indian Restaurants in Williamsburg, Brooklyn:0\n( 63 / 306 ) Indian Restaurants in Bushwick, Brooklyn:0\n( 64 / 306 ) Indian Restaurants in Bedford Stuyvesant, Brooklyn:0\n( 65 / 306 ) Indian Restaurants in Brooklyn Heights, Brooklyn:0\n( 66 / 306 ) Indian Restaurants in Cobble Hill, Brooklyn:0\n( 67 / 306 ) Indian Restaurants in Carroll Gardens, Brooklyn:0\n( 68 / 306 ) Indian Restaurants in Red Hook, Brooklyn:0\n( 69 / 306 ) Indian Restaurants in Gowanus, Brooklyn:1\n( 70 / 306 ) Indian Restaurants in Fort Greene, Brooklyn:1\n( 71 / 306 ) Indian Restaurants in Park Slope, Brooklyn:0\n( 72 / 306 ) Indian Restaurants in Cypress Hills, Brooklyn:0\n( 73 / 306 ) Indian Restaurants in East New York, Brooklyn:0\n( 74 / 306 ) Indian Restaurants in Starrett City, Brooklyn:0\n( 75 / 306 ) Indian Restaurants in Canarsie, Brooklyn:0\n( 76 / 306 ) Indian Restaurants in Flatlands, Brooklyn:0\n( 77 / 306 ) Indian Restaurants in Mill Island, Brooklyn:0\n( 78 / 306 ) Indian Restaurants in Manhattan Beach, Brooklyn:0\n( 79 / 306 ) Indian Restaurants in Coney Island, Brooklyn:0\n( 80 / 306 ) Indian Restaurants in Bath Beach, Brooklyn:0\n( 81 / 306 ) Indian Restaurants in Borough Park, Brooklyn:0\n( 82 / 306 ) Indian Restaurants in Dyker Heights, Brooklyn:0\n( 83 / 306 ) Indian Restaurants in Gerritsen Beach, Brooklyn:0\n( 84 / 306 ) Indian Restaurants in Marine Park, Brooklyn:0\n( 85 / 306 ) Indian Restaurants in Clinton Hill, Brooklyn:2\n( 86 / 306 ) Indian Restaurants in Sea Gate, Brooklyn:0\n( 87 / 306 ) Indian Restaurants in Downtown, Brooklyn:0\n( 88 / 306 ) Indian Restaurants in Boerum Hill, Brooklyn:0\n( 89 / 306 ) Indian Restaurants in Prospect Lefferts Gardens, Brooklyn:2\n( 90 / 306 ) Indian Restaurants in Ocean Hill, Brooklyn:2\n( 91 / 306 ) Indian Restaurants in City Line, Brooklyn:2\n( 92 / 306 ) Indian Restaurants in Bergen Beach, Brooklyn:0\n( 93 / 306 ) Indian Restaurants in Midwood, Brooklyn:0\n( 94 / 306 ) Indian Restaurants in Prospect Park South, Brooklyn:3\n( 95 / 306 ) Indian Restaurants in Georgetown, Brooklyn:0\n( 96 / 306 ) Indian Restaurants in East Williamsburg, Brooklyn:0\n( 97 / 306 ) Indian Restaurants in North Side, Brooklyn:1\n( 98 / 306 ) Indian Restaurants in South Side, Brooklyn:1\n( 99 / 306 ) Indian Restaurants in Ocean Parkway, Brooklyn:0\n( 100 / 306 ) Indian Restaurants in Fort Hamilton, Brooklyn:1\n( 101 / 306 ) Indian Restaurants in Chinatown, Manhattan:0\n( 102 / 306 ) Indian Restaurants in Washington Heights, Manhattan:1\n( 103 / 306 ) Indian Restaurants in Inwood, Manhattan:0\n( 104 / 306 ) Indian Restaurants in Hamilton Heights, Manhattan:2\n( 105 / 306 ) Indian Restaurants in Manhattanville, Manhattan:2\n( 106 / 306 ) Indian Restaurants in Central Harlem, Manhattan:2\n( 107 / 306 ) Indian Restaurants in East Harlem, Manhattan:1\n( 108 / 306 ) Indian Restaurants in Upper East Side, Manhattan:0\n( 109 / 306 ) Indian Restaurants in Yorkville, Manhattan:1\n( 110 / 306 ) Indian Restaurants in Lenox Hill, Manhattan:0\n( 111 / 306 ) Indian Restaurants in Roosevelt Island, Manhattan:1\n( 112 / 306 ) Indian Restaurants in Upper West Side, Manhattan:3\n( 113 / 306 ) Indian Restaurants in Lincoln Square, Manhattan:0\n( 114 / 306 ) Indian Restaurants in Clinton, Manhattan:0\n( 115 / 306 ) Indian Restaurants in Midtown, Manhattan:1\n( 116 / 306 ) Indian Restaurants in Murray Hill, Manhattan:1\n( 117 / 306 ) Indian Restaurants in Chelsea, Manhattan:1\n( 118 / 306 ) Indian Restaurants in Greenwich Village, Manhattan:0\n( 119 / 306 ) Indian Restaurants in East Village, Manhattan:0\n( 120 / 306 ) Indian Restaurants in Lower East Side, Manhattan:0\n( 121 / 306 ) Indian Restaurants in Tribeca, Manhattan:1\n( 122 / 306 ) Indian Restaurants in Little Italy, Manhattan:0\n( 123 / 306 ) Indian Restaurants in Soho, Manhattan:0\n( 124 / 306 ) Indian Restaurants in West Village, Manhattan:2\n( 125 / 306 ) Indian Restaurants in Manhattan Valley, Manhattan:4\n( 126 / 306 ) Indian Restaurants in Morningside Heights, Manhattan:2\n( 127 / 306 ) Indian Restaurants in Gramercy, Manhattan:3\n( 128 / 306 ) Indian Restaurants in Battery Park City, Manhattan:0\n( 129 / 306 ) Indian Restaurants in Financial District, Manhattan:1\n( 130 / 306 ) Indian Restaurants in Astoria, Queens:1\n( 131 / 306 ) Indian Restaurants in Woodside, Queens:6\n( 132 / 306 ) Indian Restaurants in Jackson Heights, Queens:4\n( 133 / 306 ) Indian Restaurants in Elmhurst, Queens:1\n( 134 / 306 ) Indian Restaurants in Howard Beach, Queens:0\n( 135 / 306 ) Indian Restaurants in Corona, Queens:0\n( 136 / 306 ) Indian Restaurants in Forest Hills, Queens:0\n( 137 / 306 ) Indian Restaurants in Kew Gardens, Queens:2\n( 138 / 306 ) Indian Restaurants in Richmond Hill, Queens:6\n( 139 / 306 ) Indian Restaurants in Flushing, Queens:0\n( 140 / 306 ) Indian Restaurants in Long Island City, Queens:2\n"}, {"output_type": "stream", "name": "stdout", "text": "( 141 / 306 ) Indian Restaurants in Sunnyside, Queens:1\n( 142 / 306 ) Indian Restaurants in East Elmhurst, Queens:0\n( 143 / 306 ) Indian Restaurants in Maspeth, Queens:0\n( 144 / 306 ) Indian Restaurants in Ridgewood, Queens:1\n( 145 / 306 ) Indian Restaurants in Glendale, Queens:0\n( 146 / 306 ) Indian Restaurants in Rego Park, Queens:1\n( 147 / 306 ) Indian Restaurants in Woodhaven, Queens:0\n( 148 / 306 ) Indian Restaurants in Ozone Park, Queens:1\n( 149 / 306 ) Indian Restaurants in South Ozone Park, Queens:0\n( 150 / 306 ) Indian Restaurants in College Point, Queens:0\n( 151 / 306 ) Indian Restaurants in Whitestone, Queens:0\n( 152 / 306 ) Indian Restaurants in Bayside, Queens:3\n( 153 / 306 ) Indian Restaurants in Auburndale, Queens:0\n( 154 / 306 ) Indian Restaurants in Little Neck, Queens:0\n( 155 / 306 ) Indian Restaurants in Douglaston, Queens:0\n( 156 / 306 ) Indian Restaurants in Glen Oaks, Queens:4\n( 157 / 306 ) Indian Restaurants in Bellerose, Queens:0\n( 158 / 306 ) Indian Restaurants in Kew Gardens Hills, Queens:1\n( 159 / 306 ) Indian Restaurants in Fresh Meadows, Queens:1\n( 160 / 306 ) Indian Restaurants in Briarwood, Queens:3\n( 161 / 306 ) Indian Restaurants in Jamaica Center, Queens:3\n( 162 / 306 ) Indian Restaurants in Oakland Gardens, Queens:0\n( 163 / 306 ) Indian Restaurants in Queens Village, Queens:0\n( 164 / 306 ) Indian Restaurants in Hollis, Queens:0\n( 165 / 306 ) Indian Restaurants in South Jamaica, Queens:0\n( 166 / 306 ) Indian Restaurants in St. Albans, Queens:0\n( 167 / 306 ) Indian Restaurants in Rochdale, Queens:0\n( 168 / 306 ) Indian Restaurants in Springfield Gardens, Queens:0\n( 169 / 306 ) Indian Restaurants in Cambria Heights, Queens:0\n( 170 / 306 ) Indian Restaurants in Rosedale, Queens:0\n( 171 / 306 ) Indian Restaurants in Far Rockaway, Queens:0\n( 172 / 306 ) Indian Restaurants in Broad Channel, Queens:0\n( 173 / 306 ) Indian Restaurants in Breezy Point, Queens:0\n( 174 / 306 ) Indian Restaurants in Steinway, Queens:1\n( 175 / 306 ) Indian Restaurants in Beechhurst, Queens:0\n( 176 / 306 ) Indian Restaurants in Bay Terrace, Queens:0\n( 177 / 306 ) Indian Restaurants in Edgemere, Queens:0\n( 178 / 306 ) Indian Restaurants in Arverne, Queens:0\n( 179 / 306 ) Indian Restaurants in Rockaway Beach, Queens:0\n( 180 / 306 ) Indian Restaurants in Neponsit, Queens:0\n( 181 / 306 ) Indian Restaurants in Murray Hill, Queens:0\n( 182 / 306 ) Indian Restaurants in Floral Park, Queens:8\n( 183 / 306 ) Indian Restaurants in Holliswood, Queens:1\n( 184 / 306 ) Indian Restaurants in Jamaica Estates, Queens:3\n( 185 / 306 ) Indian Restaurants in Queensboro Hill, Queens:1\n( 186 / 306 ) Indian Restaurants in Hillcrest, Queens:0\n( 187 / 306 ) Indian Restaurants in Ravenswood, Queens:1\n( 188 / 306 ) Indian Restaurants in Lindenwood, Queens:0\n( 189 / 306 ) Indian Restaurants in Laurelton, Queens:0\n( 190 / 306 ) Indian Restaurants in Lefrak City, Queens:0\n( 191 / 306 ) Indian Restaurants in Belle Harbor, Queens:0\n( 192 / 306 ) Indian Restaurants in Rockaway Park, Queens:0\n( 193 / 306 ) Indian Restaurants in Somerville, Queens:0\n( 194 / 306 ) Indian Restaurants in Brookville, Queens:0\n( 195 / 306 ) Indian Restaurants in Bellaire, Queens:1\n( 196 / 306 ) Indian Restaurants in North Corona, Queens:0\n( 197 / 306 ) Indian Restaurants in Forest Hills Gardens, Queens:1\n( 198 / 306 ) Indian Restaurants in St. George, Staten Island:0\n( 199 / 306 ) Indian Restaurants in New Brighton, Staten Island:1\n( 200 / 306 ) Indian Restaurants in Stapleton, Staten Island:0\n( 201 / 306 ) Indian Restaurants in Rosebank, Staten Island:0\n( 202 / 306 ) Indian Restaurants in West Brighton, Staten Island:0\n( 203 / 306 ) Indian Restaurants in Grymes Hill, Staten Island:0\n( 204 / 306 ) Indian Restaurants in Todt Hill, Staten Island:0\n( 205 / 306 ) Indian Restaurants in South Beach, Staten Island:0\n( 206 / 306 ) Indian Restaurants in Port Richmond, Staten Island:0\n( 207 / 306 ) Indian Restaurants in Mariner's Harbor, Staten Island:0\n( 208 / 306 ) Indian Restaurants in Port Ivory, Staten Island:0\n( 209 / 306 ) Indian Restaurants in Castleton Corners, Staten Island:0\n( 210 / 306 ) Indian Restaurants in New Springville, Staten Island:0\n( 211 / 306 ) Indian Restaurants in Travis, Staten Island:0\n( 212 / 306 ) Indian Restaurants in New Dorp, Staten Island:1\n( 213 / 306 ) Indian Restaurants in Oakwood, Staten Island:0\n( 214 / 306 ) Indian Restaurants in Great Kills, Staten Island:0\n( 215 / 306 ) Indian Restaurants in Eltingville, Staten Island:0\n( 216 / 306 ) Indian Restaurants in Annadale, Staten Island:0\n( 217 / 306 ) Indian Restaurants in Woodrow, Staten Island:0\n( 218 / 306 ) Indian Restaurants in Tottenville, Staten Island:0\n( 219 / 306 ) Indian Restaurants in Tompkinsville, Staten Island:1\n( 220 / 306 ) Indian Restaurants in Silver Lake, Staten Island:0\n( 221 / 306 ) Indian Restaurants in Sunnyside, Staten Island:0\n( 222 / 306 ) Indian Restaurants in Ditmas Park, Brooklyn:4\n( 223 / 306 ) Indian Restaurants in Wingate, Brooklyn:0\n( 224 / 306 ) Indian Restaurants in Rugby, Brooklyn:0\n( 225 / 306 ) Indian Restaurants in Park Hill, Staten Island:0\n( 226 / 306 ) Indian Restaurants in Westerleigh, Staten Island:0\n( 227 / 306 ) Indian Restaurants in Graniteville, Staten Island:0\n( 228 / 306 ) Indian Restaurants in Arlington, Staten Island:0\n( 229 / 306 ) Indian Restaurants in Arrochar, Staten Island:0\n( 230 / 306 ) Indian Restaurants in Grasmere, Staten Island:0\n( 231 / 306 ) Indian Restaurants in Old Town, Staten Island:0\n( 232 / 306 ) Indian Restaurants in Dongan Hills, Staten Island:0\n( 233 / 306 ) Indian Restaurants in Midland Beach, Staten Island:0\n( 234 / 306 ) Indian Restaurants in Grant City, Staten Island:1\n( 235 / 306 ) Indian Restaurants in New Dorp Beach, Staten Island:0\n( 236 / 306 ) Indian Restaurants in Bay Terrace, Staten Island:0\n( 237 / 306 ) Indian Restaurants in Huguenot, Staten Island:0\n( 238 / 306 ) Indian Restaurants in Pleasant Plains, Staten Island:0\n( 239 / 306 ) Indian Restaurants in Butler Manor, Staten Island:0\n( 240 / 306 ) Indian Restaurants in Charleston, Staten Island:0\n( 241 / 306 ) Indian Restaurants in Rossville, Staten Island:0\n( 242 / 306 ) Indian Restaurants in Arden Heights, Staten Island:0\n( 243 / 306 ) Indian Restaurants in Greenridge, Staten Island:0\n( 244 / 306 ) Indian Restaurants in Heartland Village, Staten Island:0\n( 245 / 306 ) Indian Restaurants in Chelsea, Staten Island:0\n( 246 / 306 ) Indian Restaurants in Bloomfield, Staten Island:0\n( 247 / 306 ) Indian Restaurants in Bulls Head, Staten Island:0\n( 248 / 306 ) Indian Restaurants in Carnegie Hill, Manhattan:2\n( 249 / 306 ) Indian Restaurants in Noho, Manhattan:0\n( 250 / 306 ) Indian Restaurants in Civic Center, Manhattan:1\n( 251 / 306 ) Indian Restaurants in Midtown South, Manhattan:1\n( 252 / 306 ) Indian Restaurants in Richmond Town, Staten Island:0\n( 253 / 306 ) Indian Restaurants in Shore Acres, Staten Island:0\n( 254 / 306 ) Indian Restaurants in Clifton, Staten Island:0\n( 255 / 306 ) Indian Restaurants in Concord, Staten Island:0\n( 256 / 306 ) Indian Restaurants in Emerson Hill, Staten Island:0\n( 257 / 306 ) Indian Restaurants in Randall Manor, Staten Island:0\n( 258 / 306 ) Indian Restaurants in Howland Hook, Staten Island:0\n( 259 / 306 ) Indian Restaurants in Elm Park, Staten Island:0\n( 260 / 306 ) Indian Restaurants in Remsen Village, Brooklyn:0\n( 261 / 306 ) Indian Restaurants in New Lots, Brooklyn:0\n( 262 / 306 ) Indian Restaurants in Paerdegat Basin, Brooklyn:0\n( 263 / 306 ) Indian Restaurants in Mill Basin, Brooklyn:0\n( 264 / 306 ) Indian Restaurants in Jamaica Hills, Queens:4\n( 265 / 306 ) Indian Restaurants in Utopia, Queens:0\n( 266 / 306 ) Indian Restaurants in Pomonok, Queens:0\n( 267 / 306 ) Indian Restaurants in Astoria Heights, Queens:1\n( 268 / 306 ) Indian Restaurants in Claremont Village, Bronx:0\n( 269 / 306 ) Indian Restaurants in Concourse Village, Bronx:1\n( 270 / 306 ) Indian Restaurants in Mount Eden, Bronx:0\n( 271 / 306 ) Indian Restaurants in Mount Hope, Bronx:0\n( 272 / 306 ) Indian Restaurants in Sutton Place, Manhattan:2\n( 273 / 306 ) Indian Restaurants in Hunters Point, Queens:0\n( 274 / 306 ) Indian Restaurants in Turtle Bay, Manhattan:3\n( 275 / 306 ) Indian Restaurants in Tudor City, Manhattan:2\n"}, {"output_type": "stream", "name": "stdout", "text": "( 276 / 306 ) Indian Restaurants in Stuyvesant Town, Manhattan:0\n( 277 / 306 ) Indian Restaurants in Flatiron, Manhattan:0\n( 278 / 306 ) Indian Restaurants in Sunnyside Gardens, Queens:1\n( 279 / 306 ) Indian Restaurants in Blissville, Queens:1\n( 280 / 306 ) Indian Restaurants in Fulton Ferry, Brooklyn:0\n( 281 / 306 ) Indian Restaurants in Vinegar Hill, Brooklyn:0\n( 282 / 306 ) Indian Restaurants in Weeksville, Brooklyn:0\n( 283 / 306 ) Indian Restaurants in Broadway Junction, Brooklyn:0\n( 284 / 306 ) Indian Restaurants in Dumbo, Brooklyn:0\n( 285 / 306 ) Indian Restaurants in Manor Heights, Staten Island:0\n( 286 / 306 ) Indian Restaurants in Willowbrook, Staten Island:0\n( 287 / 306 ) Indian Restaurants in Sandy Ground, Staten Island:0\n( 288 / 306 ) Indian Restaurants in Egbertville, Staten Island:0\n( 289 / 306 ) Indian Restaurants in Roxbury, Queens:0\n( 290 / 306 ) Indian Restaurants in Homecrest, Brooklyn:0\n( 291 / 306 ) Indian Restaurants in Middle Village, Queens:0\n( 292 / 306 ) Indian Restaurants in Prince's Bay, Staten Island:0\n( 293 / 306 ) Indian Restaurants in Lighthouse Hill, Staten Island:0\n( 294 / 306 ) Indian Restaurants in Richmond Valley, Staten Island:0\n( 295 / 306 ) Indian Restaurants in Malba, Queens:0\n( 296 / 306 ) Indian Restaurants in Highland Park, Brooklyn:0\n( 297 / 306 ) Indian Restaurants in Madison, Brooklyn:0\n( 298 / 306 ) Indian Restaurants in Bronxdale, Bronx:0\n( 299 / 306 ) Indian Restaurants in Allerton, Bronx:0\n( 300 / 306 ) Indian Restaurants in Kingsbridge Heights, Bronx:0\n( 301 / 306 ) Indian Restaurants in Erasmus, Brooklyn:1\n( 302 / 306 ) Indian Restaurants in Hudson Yards, Manhattan:0\n( 303 / 306 ) Indian Restaurants in Hammels, Queens:0\n( 304 / 306 ) Indian Restaurants in Bayswater, Queens:0\n( 305 / 306 ) Indian Restaurants in Queensbridge, Queens:2\n( 306 / 306 ) Indian Restaurants in Fox Hills, Staten Island:0\n"}], "metadata": {}}, {"source": "Now that we have got all the indian restaurants in new york city , we will analyze it", "cell_type": "markdown", "metadata": {}}, {"source": "indian_rest_ny.head()", "cell_type": "code", "execution_count": 27, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Neighborhood</th>\n      <th>ID</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bronx</td>\n      <td>Woodlawn</td>\n      <td>4c0448d9310fc9b6bf1dc761</td>\n      <td>Curry Spot</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bronx</td>\n      <td>Parkchester</td>\n      <td>4c194631838020a13e78e561</td>\n      <td>Melanies Roti Bar And Grill</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bronx</td>\n      <td>Spuyten Duyvil</td>\n      <td>4c04544df423a593ac83d116</td>\n      <td>Cumin Indian Cuisine</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bronx</td>\n      <td>Concourse</td>\n      <td>551b7f75498e86c00a0ed2e1</td>\n      <td>Hungry Bird</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bronx</td>\n      <td>Unionport</td>\n      <td>4c194631838020a13e78e561</td>\n      <td>Melanies Roti Bar And Grill</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "  Borough    Neighborhood                        ID  \\\n0   Bronx        Woodlawn  4c0448d9310fc9b6bf1dc761   \n1   Bronx     Parkchester  4c194631838020a13e78e561   \n2   Bronx  Spuyten Duyvil  4c04544df423a593ac83d116   \n3   Bronx       Concourse  551b7f75498e86c00a0ed2e1   \n4   Bronx       Unionport  4c194631838020a13e78e561   \n\n                          Name  \n0                   Curry Spot  \n1  Melanies Roti Bar And Grill  \n2         Cumin Indian Cuisine  \n3                  Hungry Bird  \n4  Melanies Roti Bar And Grill  "}, "execution_count": 27}], "metadata": {}}, {"source": "indian_rest_ny.shape", "cell_type": "code", "execution_count": 28, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(146, 4)"}, "execution_count": 28}], "metadata": {}}, {"source": "plt.figure(figsize=(9,5), dpi = 100)\n# title\nplt.title('Number of Indian Restaurants for each Borough in New York City')\n#On x-axis\nplt.xlabel('Borough', fontsize = 15)\n#On y-axis\nplt.ylabel('No.of Indian Restaurants', fontsize=15)\n#giving a bar plot\nindian_rest_ny.groupby('Borough')['ID'].count().plot(kind='bar')\n#legend\nplt.legend()\n#displays the plot\nplt.show()", "cell_type": "code", "execution_count": 29, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<Figure size 900x500 with 1 Axes>"}, "metadata": {"needs_background": "light"}}], "metadata": {}}, {"source": "plt.figure(figsize=(9,5), dpi = 100)\n# title\nplt.title('Number of Indian Restaurants for each Neighborhood in New York City')\n#On x-axis\nplt.xlabel('Neighborhood', fontsize = 15)\n#On y-axis\nplt.ylabel('No.of Indian Restaurants', fontsize=15)\n#giving a bar plot\nindian_rest_ny.groupby('Neighborhood')['ID'].count().nlargest(5).plot(kind='bar')\n#legend\nplt.legend()\n#displays the plot\nplt.show()", "cell_type": "code", "execution_count": 30, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<Figure size 900x500 with 1 Axes>"}, "metadata": {"needs_background": "light"}}], "metadata": {}}, {"source": "indian_rest_ny[indian_rest_ny['Neighborhood']=='Floral Park']", "cell_type": "code", "execution_count": 31, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Neighborhood</th>\n      <th>ID</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>100</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4e4e3e22bd4101d0d7a5c2d1</td>\n      <td>Kerala Kitchen</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>527ffc0811d2d329d5e49abd</td>\n      <td>Jackson Diner</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4b647b56f964a520c4b62ae3</td>\n      <td>Usha Foods &amp; Usha Sweets</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4b787c49f964a5209cd12ee3</td>\n      <td>Santoor Indian Restaurant</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4c0c01e0bbc676b00d6b4cd5</td>\n      <td>Mumbai Xpress</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4c76ff35a5676dcb72671721</td>\n      <td>Flavor Of India</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4df0f39dd4c04d0392c853ea</td>\n      <td>Sagar Chinese</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>Queens</td>\n      <td>Floral Park</td>\n      <td>4e6bfe1c7d8b2c711b17bbe5</td>\n      <td>Surya sweets and snacks</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "    Borough Neighborhood                        ID                       Name\n100  Queens  Floral Park  4e4e3e22bd4101d0d7a5c2d1             Kerala Kitchen\n101  Queens  Floral Park  527ffc0811d2d329d5e49abd              Jackson Diner\n102  Queens  Floral Park  4b647b56f964a520c4b62ae3   Usha Foods & Usha Sweets\n103  Queens  Floral Park  4b787c49f964a5209cd12ee3  Santoor Indian Restaurant\n104  Queens  Floral Park  4c0c01e0bbc676b00d6b4cd5              Mumbai Xpress\n105  Queens  Floral Park  4c76ff35a5676dcb72671721            Flavor Of India\n106  Queens  Floral Park  4df0f39dd4c04d0392c853ea              Sagar Chinese\n107  Queens  Floral Park  4e6bfe1c7d8b2c711b17bbe5    Surya sweets and snacks"}, "execution_count": 31}], "metadata": {}}, {"source": "Now we will get the ranking of each resturant for further analysis.", "cell_type": "markdown", "metadata": {}}, {"source": "# prepare neighborhood list that contains indian resturants\ncolumn_names=['Borough', 'Neighborhood', 'ID','Name','Likes','Rating','Tips']\nindian_rest_stats_ny=pd.DataFrame(columns=column_names)\ncount=1\n\n\nfor row in indian_rest_ny.values.tolist():\n    Borough,Neighborhood,ID,Name=row\n    try:\n        venue_details=get_venue_details(ID)\n        print(venue_details)\n        id,name,likes,rating,tips=venue_details.values.tolist()[0]\n    except IndexError:\n        print('No data available for id=',ID)\n        # we will assign 0 value for these resturants as they may have been \n        #recently opened or details does not exist in FourSquare Database\n        id,name,likes,rating,tips=[0]*5\n    print('(',count,'/',len(indian_rest_ny),')','processed')\n    indian_rest_stats_ny = indian_rest_stats_ny.append({'Borough': Borough,\n                                                'Neighborhood': Neighborhood, \n                                                'ID': id,\n                                                'Name' : name,\n                                                'Likes' : likes,\n                                                'Rating' : rating,\n                                                'Tips' : tips\n                                               }, ignore_index=True)\n    count+=1", "cell_type": "code", "execution_count": 32, "outputs": [{"output_type": "stream", "name": "stdout", "text": "                         ID        Name  Likes  Rating  Tips\n0  4c0448d9310fc9b6bf1dc761  Curry Spot      4     8.0    11\n( 1 / 146 ) processed\n                         ID                         Name  Likes  Rating  Tips\n0  4c194631838020a13e78e561  Melanies Roti Bar And Grill      3     6.3     2\n( 2 / 146 ) processed\n                         ID                  Name  Likes  Rating  Tips\n0  4c04544df423a593ac83d116  Cumin Indian Cuisine     13     6.1     9\n( 3 / 146 ) processed\n                         ID         Name  Likes  Rating  Tips\n0  551b7f75498e86c00a0ed2e1  Hungry Bird      8     7.4     3\n( 4 / 146 ) processed\n                         ID                         Name  Likes  Rating  Tips\n0  4c194631838020a13e78e561  Melanies Roti Bar And Grill      3     6.3     2\n( 5 / 146 ) processed\n                         ID       Name  Likes  Rating  Tips\n0  4b5a4dc8f964a520a2bb28e3  Taj Mahal     38     8.3    26\n( 6 / 146 ) processed\n                         ID          Name  Likes  Rating  Tips\n0  545835a1498e820edc6f636f  Bombay Grill     14     7.3     4\n( 7 / 146 ) processed\n                         ID          Name  Likes  Rating  Tips\n0  4af0d31bf964a5207ddf21e3  Pak Nasheman      9     7.6     4\n( 8 / 146 ) processed\n                         ID          Name  Likes  Rating  Tips\n0  52213c4211d295d4c57a607c  Ashoka Grill      8     7.3    14\n( 9 / 146 ) processed\n                         ID                Name  Likes  Rating  Tips\n0  564d283d498e6e851df79d87  Great Indian Curry      3     6.7     2\n( 10 / 146 ) processed\n                         ID          Name  Likes  Rating  Tips\n0  512a9ea9e4b004fb8eeb84e5  Silver Krust     12     8.3     3\n( 11 / 146 ) processed\n                         ID       Name  Likes  Rating  Tips\n0  4db0f4371e729fcc56497f20  Mashallah     19     7.7     7\n( 12 / 146 ) processed\n                         ID                          Name  Likes  Rating  Tips\n0  4b718914f964a520c04b2de3  Madina Restaurant and Sweets     17     7.1    12\n( 13 / 146 ) processed\n                         ID                       Name  Likes  Rating  Tips\n0  52f18573498ec2c34e830ffd  Kanan's Indian Restaurant     23     7.9     8\n( 14 / 146 ) processed\n                         ID         Name  Likes  Rating  Tips\n0  57596dad498e732300496b23  Dosa Royale     71     8.6    21\n( 15 / 146 ) processed\n                         ID           Name  Likes  Rating  Tips\n0  568d3902498e619efcbc3f58  Spice & Grill     19     8.1     6\n( 16 / 146 ) processed\n                         ID         Name  Likes  Rating  Tips\n0  57596dad498e732300496b23  Dosa Royale     71     8.6    21\n( 17 / 146 ) processed\n                         ID                        Name  Likes  Rating  Tips\n0  4bb93b70cf2fc9b6fe64a002  Gandhi Fine Indian Cuisine     80     8.7    45\n( 18 / 146 ) processed\n                         ID     Name  Likes  Rating  Tips\n0  4ec80587d3e3e131f2e72835  Bayleaf      6     7.0     7\n( 19 / 146 ) processed\n                         ID             Name  Likes  Rating  Tips\n0  5539753f498edbace4746b67  Tandoori Masala     12     8.3     2\n( 20 / 146 ) processed\n                         ID           Name  Likes  Rating  Tips\n0  4f6cae2ee4b0d4a5afcef5c0  Delhi Heights     22     8.2     8\n( 21 / 146 ) processed\nEmpty DataFrame\nColumns: [ID, Name, Likes, Rating, Tips]\nIndex: []\nNo data available for id= 4e4e4c8fbd4101d0d7a726b3\n( 22 / 146 ) processed\nEmpty DataFrame\nColumns: [ID, Name, Likes, Rating, Tips]\nIndex: []\nNo data available for id= 4f381772e4b08533d2f91e26\n( 23 / 146 ) processed\n                         ID                     Name  Likes  Rating  Tips\n0  519ff6c8498e1300ddcbd45c  Anarkali Indian Cuisine     14     7.9     8\n( 24 / 146 ) processed\n                         ID       Name  Likes  Rating  Tips\n0  4db0f4371e729fcc56497f20  Mashallah     19     7.7     7\n( 25 / 146 ) processed\n                         ID                          Name  Likes  Rating  Tips\n0  4b718914f964a520c04b2de3  Madina Restaurant and Sweets     17     7.1    12\n( 26 / 146 ) processed\n                         ID                Name  Likes  Rating  Tips\n0  5631511b498e3d6d7e0a4df0  Tikka Indian Grill     94     8.7    26\n( 27 / 146 ) processed\n                         ID                Name  Likes  Rating  Tips\n0  5631511b498e3d6d7e0a4df0  Tikka Indian Grill     94     8.7    26\n( 28 / 146 ) processed\n                         ID          Name  Likes  Rating  Tips\n0  545835a1498e820edc6f636f  Bombay Grill     14     7.3     4\n( 29 / 146 ) processed\n                         ID                      Name  Likes  Rating  Tips\n0  4ae7876ef964a5201eac21e3  Kismat Indian Restaurant     45     7.9    24\n( 30 / 146 ) processed\n                         ID                           Name  Likes  Rating  \\\n0  54c2bd96498eaf5142e3fe92  Clove Indian Restaurant & Bar     29     7.6   \n\n   Tips  \n0    15  \n( 31 / 146 ) processed\n                         ID           Name  Likes  Rating  Tips\n0  5914ff32b23dfa207eca38de  Mumbai Masala     13     7.4     6\n( 32 / 146 ) processed\n                         ID                 Name  Likes  Rating  Tips\n0  529d382a11d2dd5ef107e641  Chapati House - NYC     73     8.0    18\n( 33 / 146 ) processed\n                         ID                           Name  Likes  Rating  \\\n0  54c2bd96498eaf5142e3fe92  Clove Indian Restaurant & Bar     29     7.6   \n\n   Tips  \n0    15  \n( 34 / 146 ) processed\n                         ID          Name  Likes  Rating  Tips\n0  56d87f3d498ee215abee5724  Delhi Masala     13     8.6     3\n( 35 / 146 ) processed\n                         ID                           Name  Likes  Rating  \\\n0  54c2bd96498eaf5142e3fe92  Clove Indian Restaurant & Bar     29     7.6   \n\n   Tips  \n0    15  \n( 36 / 146 ) processed\n                         ID      Name  Likes  Rating  Tips\n0  519fe6f5498e30595d370c44  Bawarchi      9     7.7     4\n( 37 / 146 ) processed\n                         ID            Name  Likes  Rating  Tips\n0  5272ca4511d22488f6895caf  Drunken Munkey    206     8.5    61\n( 38 / 146 ) processed\n                         ID              Name  Likes  Rating  Tips\n0  4fe4fb50c2eee335e4fea69d  Moti Mahal Delux    183     8.4    81\n( 39 / 146 ) processed\n                         ID    Name  Likes  Rating  Tips\n0  591890f43abcaf1ddca66e85  Ashoka     19     8.5     9\n( 40 / 146 ) processed\n                         ID    Name  Likes  Rating  Tips\n0  42489a80f964a5208b201fe3  Swagat    105     8.5    38\n( 41 / 146 ) processed\n                         ID           Name  Likes  Rating  Tips\n0  4b0dec08f964a520ae5223e3  Alachi Masala     18     7.6    17\n( 42 / 146 ) processed\n                         ID                   Name  Likes  Rating  Tips\n0  49d91c12f964a520015e1fe3  The Kati Roll Company    832     8.8   258\n( 43 / 146 ) processed\n                         ID                   Name  Likes  Rating  Tips\n0  59fcd48c464d6567ed2f5e37  The Kati Roll Company     16     8.8     2\n( 44 / 146 ) processed\n                         ID               Name  Likes  Rating  Tips\n0  4b4aab62f964a520978c26e3  Dil-e Punjab Deli    104     8.7    42\n( 45 / 146 ) processed\n                         ID              Name  Likes  Rating  Tips\n0  4bbb9dbded7776b0e1ad3e51  Tamarind TriBeCa    581     9.0   145\n( 46 / 146 ) processed\n                         ID                Name  Likes  Rating  Tips\n0  5b5a2c9e66f3cd002ca0aab5  The Drunken Munkey     20     8.7     3\n( 47 / 146 ) processed\n                         ID  Name  Likes  Rating  Tips\n0  591794df2be42556988e4a8e  Rahi     96     8.8    15\n( 48 / 146 ) processed\n                         ID              Name  Likes  Rating  Tips\n0  424de080f964a520ae201fe3  Manhattan Valley     42     7.9    30\n( 49 / 146 ) processed\n                         ID        Name  Likes  Rating  Tips\n0  4d24b812836f5481518645f7  Doaba Deli     41     7.4    13\n( 50 / 146 ) processed\n"}, {"output_type": "error", "evalue": "'venue'", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)", "\u001b[0;32m<ipython-input-32-10411ff430c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mBorough\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNeighborhood\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mID\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mvenue_details\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mget_venue_details\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvenue_details\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlikes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrating\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtips\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvenue_details\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-20-81936f80231d>\u001b[0m in \u001b[0;36mget_venue_details\u001b[0;34m(venue_id)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m# get all the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mvenue_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'response'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'venue'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0mvenue_details\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'venue'"], "ename": "KeyError"}], "metadata": {}}, {"source": "indian_rest_stats_ny.head()", "cell_type": "code", "execution_count": 33, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Neighborhood</th>\n      <th>ID</th>\n      <th>Name</th>\n      <th>Likes</th>\n      <th>Rating</th>\n      <th>Tips</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bronx</td>\n      <td>Woodlawn</td>\n      <td>4c0448d9310fc9b6bf1dc761</td>\n      <td>Curry Spot</td>\n      <td>4</td>\n      <td>8.0</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bronx</td>\n      <td>Parkchester</td>\n      <td>4c194631838020a13e78e561</td>\n      <td>Melanies Roti Bar And Grill</td>\n      <td>3</td>\n      <td>6.3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bronx</td>\n      <td>Spuyten Duyvil</td>\n      <td>4c04544df423a593ac83d116</td>\n      <td>Cumin Indian Cuisine</td>\n      <td>13</td>\n      <td>6.1</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bronx</td>\n      <td>Concourse</td>\n      <td>551b7f75498e86c00a0ed2e1</td>\n      <td>Hungry Bird</td>\n      <td>8</td>\n      <td>7.4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bronx</td>\n      <td>Unionport</td>\n      <td>4c194631838020a13e78e561</td>\n      <td>Melanies Roti Bar And Grill</td>\n      <td>3</td>\n      <td>6.3</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "  Borough    Neighborhood                        ID  \\\n0   Bronx        Woodlawn  4c0448d9310fc9b6bf1dc761   \n1   Bronx     Parkchester  4c194631838020a13e78e561   \n2   Bronx  Spuyten Duyvil  4c04544df423a593ac83d116   \n3   Bronx       Concourse  551b7f75498e86c00a0ed2e1   \n4   Bronx       Unionport  4c194631838020a13e78e561   \n\n                          Name Likes  Rating Tips  \n0                   Curry Spot     4     8.0   11  \n1  Melanies Roti Bar And Grill     3     6.3    2  \n2         Cumin Indian Cuisine    13     6.1    9  \n3                  Hungry Bird     8     7.4    3  \n4  Melanies Roti Bar And Grill     3     6.3    2  "}, "execution_count": 33}], "metadata": {}}, {"source": "indian_rest_stats_ny.shape", "cell_type": "code", "execution_count": 34, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(50, 7)"}, "execution_count": 34}], "metadata": {}}, {"source": "indian_rest_ny.shape", "cell_type": "code", "execution_count": 35, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(146, 4)"}, "execution_count": 35}], "metadata": {}}, {"source": "So we got data for all resturants Now lets save this data to a csv sheet. In case we by mistake modify it. As the number of calls to get details for venue are premium call and have limit of 500 per day, we will refer to saved data sheet csv if required", "cell_type": "markdown", "metadata": {}}, {"source": "indian_rest_stats_ny.to_csv('indian_rest_stats_ny.csv', index=False)", "cell_type": "code", "execution_count": 36, "outputs": [], "metadata": {}}, {"source": "Lets verify the data from saved csv file", "cell_type": "markdown", "metadata": {}}, {"source": "indian_rest_stats_ny_csv=pd.read_csv('indian_rest_stats_ny.csv')", "cell_type": "code", "execution_count": 37, "outputs": [], "metadata": {}}, {"source": "indian_rest_stats_ny_csv.shape", "cell_type": "code", "execution_count": 38, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(50, 7)"}, "execution_count": 38}], "metadata": {}}, {"source": "indian_rest_stats_ny_csv.head()", "cell_type": "code", "execution_count": 39, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Neighborhood</th>\n      <th>ID</th>\n      <th>Name</th>\n      <th>Likes</th>\n      <th>Rating</th>\n      <th>Tips</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bronx</td>\n      <td>Woodlawn</td>\n      <td>4c0448d9310fc9b6bf1dc761</td>\n      <td>Curry Spot</td>\n      <td>4</td>\n      <td>8.0</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bronx</td>\n      <td>Parkchester</td>\n      <td>4c194631838020a13e78e561</td>\n      <td>Melanies Roti Bar And Grill</td>\n      <td>3</td>\n      <td>6.3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bronx</td>\n      <td>Spuyten Duyvil</td>\n      <td>4c04544df423a593ac83d116</td>\n      <td>Cumin Indian Cuisine</td>\n      <td>13</td>\n      <td>6.1</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bronx</td>\n      <td>Concourse</td>\n      <td>551b7f75498e86c00a0ed2e1</td>\n      <td>Hungry Bird</td>\n      <td>8</td>\n      <td>7.4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bronx</td>\n      <td>Unionport</td>\n      <td>4c194631838020a13e78e561</td>\n      <td>Melanies Roti Bar And Grill</td>\n      <td>3</td>\n      <td>6.3</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "  Borough    Neighborhood                        ID  \\\n0   Bronx        Woodlawn  4c0448d9310fc9b6bf1dc761   \n1   Bronx     Parkchester  4c194631838020a13e78e561   \n2   Bronx  Spuyten Duyvil  4c04544df423a593ac83d116   \n3   Bronx       Concourse  551b7f75498e86c00a0ed2e1   \n4   Bronx       Unionport  4c194631838020a13e78e561   \n\n                          Name  Likes  Rating  Tips  \n0                   Curry Spot      4     8.0    11  \n1  Melanies Roti Bar And Grill      3     6.3     2  \n2         Cumin Indian Cuisine     13     6.1     9  \n3                  Hungry Bird      8     7.4     3  \n4  Melanies Roti Bar And Grill      3     6.3     2  "}, "execution_count": 39}], "metadata": {}}, {"source": "indian_rest_stats_ny.info()", "cell_type": "code", "execution_count": 40, "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 50 entries, 0 to 49\nData columns (total 7 columns):\nBorough         50 non-null object\nNeighborhood    50 non-null object\nID              50 non-null object\nName            50 non-null object\nLikes           50 non-null object\nRating          50 non-null float64\nTips            50 non-null object\ndtypes: float64(1), object(6)\nmemory usage: 2.8+ KB\n"}], "metadata": {}}, {"source": "We see that values like Likes, Tips are string values. We would need to convert them into float for further analysis", "cell_type": "markdown", "metadata": {}}, {"source": "indian_rest_stats_ny['Likes']=indian_rest_stats_ny['Likes'].astype('float64')", "cell_type": "code", "execution_count": 41, "outputs": [], "metadata": {}}, {"source": "indian_rest_stats_ny['Tips']=indian_rest_stats_ny['Tips'].astype('float64')", "cell_type": "code", "execution_count": 42, "outputs": [], "metadata": {}}, {"source": "indian_rest_stats_ny.info()", "cell_type": "code", "execution_count": 43, "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 50 entries, 0 to 49\nData columns (total 7 columns):\nBorough         50 non-null object\nNeighborhood    50 non-null object\nID              50 non-null object\nName            50 non-null object\nLikes           50 non-null float64\nRating          50 non-null float64\nTips            50 non-null float64\ndtypes: float64(3), object(4)\nmemory usage: 2.8+ KB\n"}], "metadata": {}}, {"source": "# Resturant with maximum Likes\nindian_rest_stats_ny.iloc[indian_rest_stats_ny['Likes'].idxmax()]", "cell_type": "code", "execution_count": 44, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "Borough                        Manhattan\nNeighborhood                     Midtown\nID              49d91c12f964a520015e1fe3\nName               The Kati Roll Company\nLikes                                832\nRating                               8.8\nTips                                 258\nName: 42, dtype: object"}, "execution_count": 44}], "metadata": {}}, {"source": "# Resturant with maximum Rating\nindian_rest_stats_ny.iloc[indian_rest_stats_ny['Rating'].idxmax()]", "cell_type": "code", "execution_count": 45, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "Borough                        Manhattan\nNeighborhood                     Tribeca\nID              4bbb9dbded7776b0e1ad3e51\nName                    Tamarind TriBeCa\nLikes                                581\nRating                                 9\nTips                                 145\nName: 45, dtype: object"}, "execution_count": 45}], "metadata": {}}, {"source": "# Resturant with maximum Tips\nindian_rest_stats_ny.iloc[indian_rest_stats_ny['Tips'].idxmax()]", "cell_type": "code", "execution_count": 46, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "Borough                        Manhattan\nNeighborhood                     Midtown\nID              49d91c12f964a520015e1fe3\nName               The Kati Roll Company\nLikes                                832\nRating                               8.8\nTips                                 258\nName: 42, dtype: object"}, "execution_count": 46}], "metadata": {}}, {"source": "Now lets visualize neighborhood with maximum average rating of resturants", "cell_type": "markdown", "metadata": {}}, {"source": "ny_neighborhood_stats=indian_rest_stats_ny.groupby('Neighborhood',as_index=False).mean()[['Neighborhood','Rating']]\nny_neighborhood_stats.columns=['Neighborhood','Average Rating']", "cell_type": "code", "execution_count": 47, "outputs": [], "metadata": {}}, {"source": "ny_neighborhood_stats.sort_values(['Average Rating'],ascending=False).head(10)", "cell_type": "code", "execution_count": 48, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Neighborhood</th>\n      <th>Average Rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>27</th>\n      <td>Tribeca</td>\n      <td>9.00</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Midtown</td>\n      <td>8.80</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Murray Hill</td>\n      <td>8.80</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>West Village</td>\n      <td>8.75</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Chelsea</td>\n      <td>8.70</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>South Side</td>\n      <td>8.70</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>North Side</td>\n      <td>8.70</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Fort Greene</td>\n      <td>8.60</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Yorkville</td>\n      <td>8.50</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Roosevelt Island</td>\n      <td>8.40</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "        Neighborhood  Average Rating\n27           Tribeca            9.00\n17           Midtown            8.80\n18       Murray Hill            8.80\n31      West Village            8.75\n3            Chelsea            8.70\n25        South Side            8.70\n19        North Side            8.70\n10       Fort Greene            8.60\n33         Yorkville            8.50\n24  Roosevelt Island            8.40"}, "execution_count": 48}], "metadata": {}}, {"source": "Above are the top neighborhoods with top average rating of Indian restaurants", "cell_type": "markdown", "metadata": {}}, {"source": "ny_borough_stats=indian_rest_stats_ny.groupby('Borough',as_index=False).mean()[['Borough','Rating']]\nny_borough_stats.columns=['Borough','Average Rating']", "cell_type": "code", "execution_count": 50, "outputs": [], "metadata": {}}, {"source": "ny_borough_stats.sort_values(['Average Rating'],ascending=False).head()", "cell_type": "code", "execution_count": 51, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Average Rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Manhattan</td>\n      <td>8.190476</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brooklyn</td>\n      <td>7.212500</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Bronx</td>\n      <td>6.820000</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "     Borough  Average Rating\n2  Manhattan        8.190476\n1   Brooklyn        7.212500\n0      Bronx        6.820000"}, "execution_count": 51}], "metadata": {}}, {"source": "Similarly these are the average rating of Indian Resturants for each Borough\n\nLets visualize it", "cell_type": "markdown", "metadata": {}}, {"source": "plt.figure(figsize=(9,5), dpi = 100)\n# title\nplt.title('Average rating of Indian Resturants for each Borough')\n#On x-axis\nplt.xlabel('Borough', fontsize = 15)\n#On y-axis\nplt.ylabel('Average Rating', fontsize=15)\n#giving a bar plot\nindian_rest_stats_ny.groupby('Borough').mean()['Rating'].plot(kind='bar')\n#legend\nplt.legend()\n#displays the plot\nplt.show()", "cell_type": "code", "execution_count": 52, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<Figure size 900x500 with 1 Axes>"}, "metadata": {"needs_background": "light"}}], "metadata": {}}, {"source": "We will consider all the neighborhoods with average rating greater or equal 9.0 to visualize on map", "cell_type": "markdown", "metadata": {}}, {"source": "ny_neighborhood_stats=ny_neighborhood_stats[ny_neighborhood_stats['Average Rating']>=9.0]\nny_neighborhood_stats", "cell_type": "code", "execution_count": 53, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Neighborhood</th>\n      <th>Average Rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>27</th>\n      <td>Tribeca</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "   Neighborhood  Average Rating\n27      Tribeca             9.0"}, "execution_count": 53}], "metadata": {}}, {"source": "We will join this dataset to original new york data to get longitude and latitude", "cell_type": "markdown", "metadata": {}}, {"source": "ny_neighborhood_stats=pd.merge(ny_neighborhood_stats,new_york_data, on='Neighborhood')\nny_neighborhood_stats=ny_neighborhood_stats[['Borough','Neighborhood','Latitude','Longitude','Average Rating']]\nny_neighborhood_stats", "cell_type": "code", "execution_count": 54, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Average Rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Manhattan</td>\n      <td>Tribeca</td>\n      <td>40.721522</td>\n      <td>-74.010683</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "     Borough Neighborhood   Latitude  Longitude  Average Rating\n0  Manhattan      Tribeca  40.721522 -74.010683             9.0"}, "execution_count": 54}], "metadata": {}}, {"source": "Now we will show this data on a map", "cell_type": "markdown", "metadata": {}}, {"source": "# create map and display it\nny_map = folium.Map(location=geo_location('New York'), zoom_start=12)\n\n\n# instantiate a feature group for the incidents in the dataframe\nincidents = folium.map.FeatureGroup()\n\n# loop through the 100 crimes and add each to the incidents feature group\nfor lat, lng, in ny_neighborhood_stats[['Latitude','Longitude']].values:\n    incidents.add_child(\n        folium.CircleMarker(\n            [lat, lng],\n            radius=10, # define how big you want the circle markers to be\n            color='yellow',\n            fill=True,\n            fill_color='blue',\n            fill_opacity=0.6\n        )\n    )", "cell_type": "code", "execution_count": 55, "outputs": [], "metadata": {}}, {"source": "Lets add a new field to dataframe for labeling purpose", "cell_type": "markdown", "metadata": {}}, {"source": "ny_neighborhood_stats['Label']=ny_neighborhood_stats['Neighborhood']+', '+ny_neighborhood_stats['Borough']+'('+ny_neighborhood_stats['Average Rating'].map(str)+')'", "cell_type": "code", "execution_count": 56, "outputs": [], "metadata": {}}, {"source": "# add pop-up text to each marker on the map\nfor lat, lng, label in ny_neighborhood_stats[['Latitude','Longitude','Label']].values:\n    folium.Marker([lat, lng], popup=label).add_to(ny_map)        \n# add incidents to map\nny_map.add_child(incidents)", "cell_type": "code", "execution_count": 57, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe 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style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", "text/plain": "<folium.folium.Map at 0x7fb84fa40fd0>"}, "execution_count": 57}], "metadata": {}}, {"source": "<b>Results</b>\n1.We see that Queens has the highest number of Neighbourhoods.", "cell_type": "markdown", "metadata": {}}, {"source": "<b>Conclusion</b>", "cell_type": "markdown", "metadata": {}}, {"source": "* Astoria(Queens), Blissville(Queens), Civic Center(Manhattan) are some of the best neighborhoods for indian cuisine.\n* Manhattan have potential Indian Resturant Market/\n* Staten Island ranks last in average rating of Indian Resturants.\n* Manhattan is the best place to stay if you prefer Indian Cuisine.\n", "cell_type": "markdown", "metadata": {}}, {"source": "", "cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3.6", "name": "python3", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "3.6.8", "name": "python", "pygments_lexer": "ipython3", "file_extension": ".py", "codemirror_mode": {"version": 3, "name": "ipython"}}}}