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Alois-star/DP0701EN-2-2-1-Foursquare-API-py-v1.0.ipynb

Created June 27, 2020 04:42
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DP0701EN-2-2-1-Foursquare-API-py-v1.0.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Learning FourSquare API with Python</font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Introduction\n",
"\n",
"In this lab, you will learn in details how to make calls to the Foursquare API for different purposes. You will learn how to construct a URL to send a request to the API to search for a specific type of venues, to explore a particular venue, to explore a Foursquare user, to explore a geographical location, and to get trending venues around a location. Also, you will learn how to use the visualization library, Folium, to visualize the results."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Table of Contents\n",
"\n",
"1. <a href=\"#item1\">Foursquare API Search Function</a>\n",
"2. <a href=\"#item2\">Explore a Given Venue</a> \n",
"3. <a href=\"#item3\">Explore a User</a> \n",
"4. <a href=\"#item4\">Foursquare API Explore Function</a> \n",
"5. <a href=\"#item5\">Get Trending Venues</a> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Import necessary Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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},
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata (current_repodata.json): done\n",
"Solving environment: done\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /home/jupyterlab/conda/envs/python\n",
"\n",
" added / updated specs:\n",
" - geopy\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" ca-certificates-2020.6.20 | hecda079_0 145 KB conda-forge\n",
" certifi-2020.6.20 | py36h9f0ad1d_0 151 KB conda-forge\n",
" geographiclib-1.50 | py_0 34 KB conda-forge\n",
" geopy-1.22.0 | pyh9f0ad1d_0 63 KB conda-forge\n",
" ------------------------------------------------------------\n",
" Total: 393 KB\n",
"\n",
"The following NEW packages will be INSTALLED:\n",
"\n",
" geographiclib conda-forge/noarch::geographiclib-1.50-py_0\n",
" geopy conda-forge/noarch::geopy-1.22.0-pyh9f0ad1d_0\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" ca-certificates 2020.4.5.2-hecda079_0 --> 2020.6.20-hecda079_0\n",
" certifi 2020.4.5.2-py36h9f0ad1d_0 --> 2020.6.20-py36h9f0ad1d_0\n",
"\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"certifi-2020.6.20 | 151 KB | ##################################### | 100% \n",
"geopy-1.22.0 | 63 KB | ##################################### | 100% \n",
"ca-certificates-2020 | 145 KB | ##################################### | 100% \n",
"geographiclib-1.50 | 34 KB | ##################################### | 100% \n",
"Preparing transaction: done\n",
"Verifying transaction: done\n",
"Executing transaction: done\n",
"Collecting package metadata (current_repodata.json): done\n",
"Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
"Collecting package metadata (repodata.json): done\n",
"Solving environment: done\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /home/jupyterlab/conda/envs/python\n",
"\n",
" added / updated specs:\n",
" - folium=0.5.0\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" altair-4.1.0 | py_1 614 KB conda-forge\n",
" branca-0.4.1 | py_0 26 KB conda-forge\n",
" brotlipy-0.7.0 |py36h8c4c3a4_1000 346 KB conda-forge\n",
" chardet-3.0.4 |py36h9f0ad1d_1006 188 KB conda-forge\n",
" cryptography-2.9.2 | py36h45558ae_0 613 KB conda-forge\n",
" folium-0.5.0 | py_0 45 KB conda-forge\n",
" pandas-1.0.5 | py36h830a2c2_0 10.1 MB conda-forge\n",
" pysocks-1.7.1 | py36h9f0ad1d_1 27 KB conda-forge\n",
" requests-2.24.0 | pyh9f0ad1d_0 47 KB conda-forge\n",
" toolz-0.10.0 | py_0 46 KB conda-forge\n",
" vincent-0.4.4 | py_1 28 KB conda-forge\n",
" ------------------------------------------------------------\n",
" Total: 12.0 MB\n",
"\n",
"The following NEW packages will be INSTALLED:\n",
"\n",
" altair conda-forge/noarch::altair-4.1.0-py_1\n",
" attrs conda-forge/noarch::attrs-19.3.0-py_0\n",
" branca conda-forge/noarch::branca-0.4.1-py_0\n",
" brotlipy conda-forge/linux-64::brotlipy-0.7.0-py36h8c4c3a4_1000\n",
" chardet conda-forge/linux-64::chardet-3.0.4-py36h9f0ad1d_1006\n",
" cryptography conda-forge/linux-64::cryptography-2.9.2-py36h45558ae_0\n",
" entrypoints conda-forge/linux-64::entrypoints-0.3-py36h9f0ad1d_1001\n",
" folium conda-forge/noarch::folium-0.5.0-py_0\n",
" idna conda-forge/noarch::idna-2.9-py_1\n",
" importlib_metadata conda-forge/noarch::importlib_metadata-1.6.1-0\n",
" jinja2 conda-forge/noarch::jinja2-2.11.2-pyh9f0ad1d_0\n",
" jsonschema conda-forge/linux-64::jsonschema-3.2.0-py36h9f0ad1d_1\n",
" markupsafe conda-forge/linux-64::markupsafe-1.1.1-py36h8c4c3a4_1\n",
" pandas conda-forge/linux-64::pandas-1.0.5-py36h830a2c2_0\n",
" pyopenssl conda-forge/noarch::pyopenssl-19.1.0-py_1\n",
" pyrsistent conda-forge/linux-64::pyrsistent-0.16.0-py36h8c4c3a4_0\n",
" pysocks conda-forge/linux-64::pysocks-1.7.1-py36h9f0ad1d_1\n",
" pytz conda-forge/noarch::pytz-2020.1-pyh9f0ad1d_0\n",
" requests conda-forge/noarch::requests-2.24.0-pyh9f0ad1d_0\n",
" toolz conda-forge/noarch::toolz-0.10.0-py_0\n",
" urllib3 conda-forge/noarch::urllib3-1.25.9-py_0\n",
" vincent conda-forge/noarch::vincent-0.4.4-py_1\n",
"\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"pysocks-1.7.1 | 27 KB | ##################################### | 100% \n",
"toolz-0.10.0 | 46 KB | ##################################### | 100% \n",
"chardet-3.0.4 | 188 KB | ##################################### | 100% \n",
"folium-0.5.0 | 45 KB | ##################################### | 100% \n",
"branca-0.4.1 | 26 KB | ##################################### | 100% \n",
"cryptography-2.9.2 | 613 KB | ##################################### | 100% \n",
"brotlipy-0.7.0 | 346 KB | ##################################### | 100% \n",
"altair-4.1.0 | 614 KB | ##################################### | 100% \n",
"requests-2.24.0 | 47 KB | ##################################### | 100% \n",
"pandas-1.0.5 | 10.1 MB | ##################################### | 100% \n",
"vincent-0.4.4 | 28 KB | ##################################### | 100% \n",
"Preparing transaction: done\n",
"Verifying transaction: done\n",
"Executing transaction: done\n",
"Folium installed\n",
"Libraries imported.\n"
]
}
],
"source": [
"import requests # library to handle requests\n",
"import pandas as pd # library for data analsysis\n",
"import numpy as np # library to handle data in a vectorized manner\n",
"import random # library for random number generation\n",
"\n",
"!conda install -c conda-forge geopy --yes \n",
"from geopy.geocoders import Nominatim # module to convert an address into latitude and longitude values\n",
"\n",
"# libraries for displaying images\n",
"from IPython.display import Image \n",
"from IPython.core.display import HTML \n",
" \n",
"# tranforming json file into a pandas dataframe library\n",
"from pandas.io.json import json_normalize\n",
"\n",
"!conda install -c conda-forge folium=0.5.0 --yes\n",
"import folium # plotting library\n",
"\n",
"print('Folium installed')\n",
"print('Libraries imported.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Define Foursquare Credentials and Version"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"##### Make sure that you have created a Foursquare developer account and have your credentials handy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your credentails:\n",
"CLIENT_ID: your-client-ID\n",
"CLIENT_SECRET:your-client-secret\n"
]
}
],
"source": [
"CLIENT_ID = 'your-client-ID' # your Foursquare ID\n",
"CLIENT_SECRET = 'your-client-secret' # your Foursquare Secret\n",
"VERSION = '20180604'\n",
"LIMIT = 30\n",
"print('Your credentails:')\n",
"print('CLIENT_ID: ' + CLIENT_ID)\n",
"print('CLIENT_SECRET:' + CLIENT_SECRET)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Let's again assume that you are staying at the Conrad hotel. So let's start by converting the Contrad Hotel's address to its latitude and longitude coordinates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to define an instance of the geocoder, we need to define a user_agent. We will name our agent <em>foursquare_agent</em>, as shown below."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"40.7151482 -74.0156573\n"
]
}
],
"source": [
"address = '102 North End Ave, New York, NY'\n",
"\n",
"geolocator = Nominatim(user_agent=\"foursquare_agent\")\n",
"location = geolocator.geocode(address)\n",
"latitude = location.latitude\n",
"longitude = location.longitude\n",
"print(latitude, longitude)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a id=\"item1\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## 1. Search for a specific venue category\n",
"> `https://api.foursquare.com/v2/venues/`**search**`?client_id=`**CLIENT_ID**`&client_secret=`**CLIENT_SECRET**`&ll=`**LATITUDE**`,`**LONGITUDE**`&v=`**VERSION**`&query=`**QUERY**`&radius=`**RADIUS**`&limit=`**LIMIT**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Now, let's assume that it is lunch time, and you are craving Italian food. So, let's define a query to search for Italian food that is within 500 metres from the Conrad Hotel. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Italian .... OK!\n"
]
}
],
"source": [
"search_query = 'Italian'\n",
"radius = 500\n",
"print(search_query + ' .... OK!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Define the corresponding URL"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"'https://api.foursquare.com/v2/venues/search?client_id=your-client-ID&client_secret=your-client-secret&ll=40.7151482,-74.0156573&v=20180604&query=Italian&radius=500&limit=30'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'https://api.foursquare.com/v2/venues/search?client_id={}&client_secret={}&ll={},{}&v={}&query={}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, latitude, longitude, VERSION, search_query, radius, LIMIT)\n",
"url"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Send the GET Request and examine the results"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'meta': {'code': 400,\n",
" 'errorType': 'invalid_auth',\n",
" 'errorDetail': 'Missing access credentials. See https://developer.foursquare.com/docs/api/configuration/authentication for details.',\n",
" 'requestId': '5ef6cb063d724a7b3d89fc8e'},\n",
" 'response': {}}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = requests.get(url).json()\n",
"results"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Get relevant part of JSON and transform it into a *pandas* dataframe"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'venues'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-5acf500bf9ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# assign relevant part of JSON to venues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mvenues\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'venues'\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 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# tranform venues into a dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdataframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_normalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvenues\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: 'venues'"
]
}
],
"source": [
"# assign relevant part of JSON to venues\n",
"venues = results['response']['venues']\n",
"\n",
"# tranform venues into a dataframe\n",
"dataframe = json_normalize(venues)\n",
"dataframe.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Define information of interest and filter dataframe"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataframe' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-f45bc30a5e1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# keep only columns that include venue name, and anything that is associated with location\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiltered_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'categories'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'location.'\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'id'\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 3\u001b[0m \u001b[0mdataframe_filtered\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltered_columns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# function that extracts the category of the venue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'dataframe' is not defined"
]
}
],
"source": [
"# keep only columns that include venue name, and anything that is associated with location\n",
"filtered_columns = ['name', 'categories'] + [col for col in dataframe.columns if col.startswith('location.')] + ['id']\n",
"dataframe_filtered = dataframe.loc[:, filtered_columns]\n",
"\n",
"# function that extracts the category of the venue\n",
"def get_category_type(row):\n",
" try:\n",
" categories_list = row['categories']\n",
" except:\n",
" categories_list = row['venue.categories']\n",
" \n",
" if len(categories_list) == 0:\n",
" return None\n",
" else:\n",
" return categories_list[0]['name']\n",
"\n",
"# filter the category for each row\n",
"dataframe_filtered['categories'] = dataframe_filtered.apply(get_category_type, axis=1)\n",
"\n",
"# clean column names by keeping only last term\n",
"dataframe_filtered.columns = [column.split('.')[-1] for column in dataframe_filtered.columns]\n",
"\n",
"dataframe_filtered"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Let's visualize the Italian restaurants that are nearby"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataframe_filtered' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-a8d2e6052617>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataframe_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dataframe_filtered' is not defined"
]
}
],
"source": [
"dataframe_filtered.name"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataframe_filtered' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-84dd12227b0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# add the Italian restaurants as blue circle markers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlng\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataframe_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataframe_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlng\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataframe_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\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[0m\u001b[1;32m 16\u001b[0m folium.features.CircleMarker(\n\u001b[1;32m 17\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlng\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;31mNameError\u001b[0m: name 'dataframe_filtered' is not defined"
]
}
],
"source": [
"venues_map = folium.Map(location=[latitude, longitude], zoom_start=13) # generate map centred around the Conrad Hotel\n",
"\n",
"# add a red circle marker to represent the Conrad Hotel\n",
"folium.features.CircleMarker(\n",
" [latitude, longitude],\n",
" radius=10,\n",
" color='red',\n",
" popup='Conrad Hotel',\n",
" fill = True,\n",
" fill_color = 'red',\n",
" fill_opacity = 0.6\n",
").add_to(venues_map)\n",
"\n",
"# add the Italian restaurants as blue circle markers\n",
"for lat, lng, label in zip(dataframe_filtered.lat, dataframe_filtered.lng, dataframe_filtered.categories):\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" color='blue',\n",
" popup=label,\n",
" fill = True,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" ).add_to(venues_map)\n",
"\n",
"# display map\n",
"venues_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a id=\"item2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## 2. Explore a Given Venue\n",
"> `https://api.foursquare.com/v2/venues/`**VENUE_ID**`?client_id=`**CLIENT_ID**`&client_secret=`**CLIENT_SECRET**`&v=`**VERSION**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### A. Let's explore the closest Italian restaurant -- _Harry's Italian Pizza Bar_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"venue_id = '4fa862b3e4b0ebff2f749f06' # ID of Harry's Italian Pizza Bar\n",
"url = 'https://api.foursquare.com/v2/venues/{}?client_id={}&client_secret={}&v={}'.format(venue_id, CLIENT_ID, CLIENT_SECRET, VERSION)\n",
"url"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Send GET request for result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"result = requests.get(url).json()\n",
"print(result['response']['venue'].keys())\n",
"result['response']['venue']"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### B. Get the venue's overall rating"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"try:\n",
" print(result['response']['venue']['rating'])\n",
"except:\n",
" print('This venue has not been rated yet.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That is not a very good rating. Let's check the rating of the second closest Italian restaurant."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"venue_id = '4f3232e219836c91c7bfde94' # ID of Conca Cucina Italian Restaurant\n",
"url = 'https://api.foursquare.com/v2/venues/{}?client_id={}&client_secret={}&v={}'.format(venue_id, CLIENT_ID, CLIENT_SECRET, VERSION)\n",
"\n",
"result = requests.get(url).json()\n",
"try:\n",
" print(result['response']['venue']['rating'])\n",
"except:\n",
" print('This venue has not been rated yet.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since this restaurant has no ratings, let's check the third restaurant."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"venue_id = '3fd66200f964a520f4e41ee3' # ID of Ecco\n",
"url = 'https://api.foursquare.com/v2/venues/{}?client_id={}&client_secret={}&v={}'.format(venue_id, CLIENT_ID, CLIENT_SECRET, VERSION)\n",
"\n",
"result = requests.get(url).json()\n",
"try:\n",
" print(result['response']['venue']['rating'])\n",
"except:\n",
" print('This venue has not been rated yet.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since this restaurant has a slightly better rating, let's explore it further."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### C. Get the number of tips"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"result['response']['venue']['tips']['count']"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### D. Get the venue's tips\n",
"> `https://api.foursquare.com/v2/venues/`**VENUE_ID**`/tips?client_id=`**CLIENT_ID**`&client_secret=`**CLIENT_SECRET**`&v=`**VERSION**`&limit=`**LIMIT**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Create URL and send GET request. Make sure to set limit to get all tips"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"## Ecco Tips\n",
"limit = 15 # set limit to be greater than or equal to the total number of tips\n",
"url = 'https://api.foursquare.com/v2/venues/{}/tips?client_id={}&client_secret={}&v={}&limit={}'.format(venue_id, CLIENT_ID, CLIENT_SECRET, VERSION, limit)\n",
"\n",
"results = requests.get(url).json()\n",
"results"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Get tips and list of associated features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"tips = results['response']['tips']['items']\n",
"\n",
"tip = results['response']['tips']['items'][0]\n",
"tip.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Format column width and display all tips"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"pd.set_option('display.max_colwidth', -1)\n",
"\n",
"tips_df = json_normalize(tips) # json normalize tips\n",
"\n",
"# columns to keep\n",
"filtered_columns = ['text', 'agreeCount', 'disagreeCount', 'id', 'user.firstName', 'user.lastName', 'user.gender', 'user.id']\n",
"tips_filtered = tips_df.loc[:, filtered_columns]\n",
"\n",
"# display tips\n",
"tips_filtered"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now remember that because we are using a personal developer account, then we can access only 2 of the restaurant's tips, instead of all 15 tips."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a id=\"item3\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## 3. Search a Foursquare User\n",
"> `https://api.foursquare.com/v2/users/`**USER_ID**`?client_id=`**CLIENT_ID**`&client_secret=`**CLIENT_SECRET**`&v=`**VERSION**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Define URL, send GET request and display features associated with user"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"user_id = '484542633' # user ID with most agree counts and complete profile\n",
"\n",
"url = 'https://api.foursquare.com/v2/users/{}?client_id={}&client_secret={}&v={}'.format(user_id, CLIENT_ID, CLIENT_SECRET, VERSION) # define URL\n",
"\n",
"# send GET request\n",
"results = requests.get(url).json()\n",
"user_data = results['response']['user']\n",
"\n",
"# display features associated with user\n",
"user_data.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"print('First Name: ' + user_data['firstName'])\n",
"print('Last Name: ' + user_data['lastName'])\n",
"print('Home City: ' + user_data['homeCity'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### How many tips has this user submitted?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"user_data['tips']"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Wow! So it turns out that Nick is a very active Foursquare user, with more than 250 tips."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Get User's tips"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# define tips URL\n",
"url = 'https://api.foursquare.com/v2/users/{}/tips?client_id={}&client_secret={}&v={}&limit={}'.format(user_id, CLIENT_ID, CLIENT_SECRET, VERSION, limit)\n",
"\n",
"# send GET request and get user's tips\n",
"results = requests.get(url).json()\n",
"tips = results['response']['tips']['items']\n",
"\n",
"# format column width\n",
"pd.set_option('display.max_colwidth', -1)\n",
"\n",
"tips_df = json_normalize(tips)\n",
"\n",
"# filter columns\n",
"filtered_columns = ['text', 'agreeCount', 'disagreeCount', 'id']\n",
"tips_filtered = tips_df.loc[:, filtered_columns]\n",
"\n",
"# display user's tips\n",
"tips_filtered"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Let's get the venue for the tip with the greatest number of agree counts"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"tip_id = '5ab5575d73fe2516ad8f363b' # tip id\n",
"\n",
"# define URL\n",
"url = 'http://api.foursquare.com/v2/tips/{}?client_id={}&client_secret={}&v={}'.format(tip_id, CLIENT_ID, CLIENT_SECRET, VERSION)\n",
"\n",
"# send GET Request and examine results\n",
"result = requests.get(url).json()\n",
"print(result['response']['tip']['venue']['name'])\n",
"print(result['response']['tip']['venue']['location'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Get User's friends"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"user_friends = json_normalize(user_data['friends']['groups'][0]['items'])\n",
"user_friends"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Interesting. Despite being very active, it turns out that Nick does not have any friends on Foursquare. This might definitely change in the future."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Retrieve the User's Profile Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"user_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# 1. grab prefix of photo\n",
"# 2. grab suffix of photo\n",
"# 3. concatenate them using the image size \n",
"Image(url='https://igx.4sqi.net/img/user/300x300/484542633_mK2Yum7T_7Tn9fWpndidJsmw2Hof_6T5vJBKCHPLMK5OL-U5ZiJGj51iwBstcpDLYa3Zvhvis.jpg')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a id=\"item4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## 4. Explore a location\n",
"> `https://api.foursquare.com/v2/venues/`**explore**`?client_id=`**CLIENT_ID**`&client_secret=`**CLIENT_SECRET**`&ll=`**LATITUDE**`,`**LONGITUDE**`&v=`**VERSION**`&limit=`**LIMIT**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### So, you just finished your gourmet dish at Ecco, and are just curious about the popular spots around the restaurant. In order to explore the area, let's start by getting the latitude and longitude values of Ecco Restaurant."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"jupyter": {
"outputs_hidden": true
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"latitude = 40.715337\n",
"longitude = -74.008848"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Define URL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"url = 'https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&ll={},{}&v={}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, latitude, longitude, VERSION, radius, LIMIT)\n",
"url"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Send GET request and examine results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"jupyter": {
"outputs_hidden": true
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import requests"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"results = requests.get(url).json()\n",
"'There are {} around Ecco restaurant.'.format(len(results['response']['groups'][0]['items']))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Get relevant part of JSON"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"items = results['response']['groups'][0]['items']\n",
"items[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Process JSON and convert it to a clean dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"dataframe = json_normalize(items) # flatten JSON\n",
"\n",
"# filter columns\n",
"filtered_columns = ['venue.name', 'venue.categories'] + [col for col in dataframe.columns if col.startswith('venue.location.')] + ['venue.id']\n",
"dataframe_filtered = dataframe.loc[:, filtered_columns]\n",
"\n",
"# filter the category for each row\n",
"dataframe_filtered['venue.categories'] = dataframe_filtered.apply(get_category_type, axis=1)\n",
"\n",
"# clean columns\n",
"dataframe_filtered.columns = [col.split('.')[-1] for col in dataframe_filtered.columns]\n",
"\n",
"dataframe_filtered.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Let's visualize these items on the map around our location"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"venues_map = folium.Map(location=[latitude, longitude], zoom_start=15) # generate map centred around Ecco\n",
"\n",
"\n",
"# add Ecco as a red circle mark\n",
"folium.features.CircleMarker(\n",
" [latitude, longitude],\n",
" radius=10,\n",
" popup='Ecco',\n",
" fill=True,\n",
" color='red',\n",
" fill_color='red',\n",
" fill_opacity=0.6\n",
" ).add_to(venues_map)\n",
"\n",
"\n",
"# add popular spots to the map as blue circle markers\n",
"for lat, lng, label in zip(dataframe_filtered.lat, dataframe_filtered.lng, dataframe_filtered.categories):\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" fill=True,\n",
" color='blue',\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" ).add_to(venues_map)\n",
"\n",
"# display map\n",
"venues_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a id=\"item5\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## 5. Explore Trending Venues\n",
"> `https://api.foursquare.com/v2/venues/`**trending**`?client_id=`**CLIENT_ID**`&client_secret=`**CLIENT_SECRET**`&ll=`**LATITUDE**`,`**LONGITUDE**`&v=`**VERSION**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Now, instead of simply exploring the area around Ecco, you are interested in knowing the venues that are trending at the time you are done with your lunch, meaning the places with the highest foot traffic. So let's do that and get the trending venues around Ecco."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# define URL\n",
"url = 'https://api.foursquare.com/v2/venues/trending?client_id={}&client_secret={}&ll={},{}&v={}'.format(CLIENT_ID, CLIENT_SECRET, latitude, longitude, VERSION)\n",
"\n",
"# send GET request and get trending venues\n",
"results = requests.get(url).json()\n",
"results"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Check if any venues are trending at this time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"if len(results['response']['venues']) == 0:\n",
" trending_venues_df = 'No trending venues are available at the moment!'\n",
" \n",
"else:\n",
" trending_venues = results['response']['venues']\n",
" trending_venues_df = json_normalize(trending_venues)\n",
"\n",
" # filter columns\n",
" columns_filtered = ['name', 'categories'] + ['location.distance', 'location.city', 'location.postalCode', 'location.state', 'location.country', 'location.lat', 'location.lng']\n",
" trending_venues_df = trending_venues_df.loc[:, columns_filtered]\n",
"\n",
" # filter the category for each row\n",
" trending_venues_df['categories'] = trending_venues_df.apply(get_category_type, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# display trending venues\n",
"trending_venues_df"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now, depending on when you run the above code, you might get different venues since the venues with the highest foot traffic are fetched live. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Visualize trending venues"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"if len(results['response']['venues']) == 0:\n",
" venues_map = 'Cannot generate visual as no trending venues are available at the moment!'\n",
"\n",
"else:\n",
" venues_map = folium.Map(location=[latitude, longitude], zoom_start=15) # generate map centred around Ecco\n",
"\n",
"\n",
" # add Ecco as a red circle mark\n",
" folium.features.CircleMarker(\n",
" [latitude, longitude],\n",
" radius=10,\n",
" popup='Ecco',\n",
" fill=True,\n",
" color='red',\n",
" fill_color='red',\n",
" fill_opacity=0.6\n",
" ).add_to(venues_map)\n",
"\n",
"\n",
" # add the trending venues as blue circle markers\n",
" for lat, lng, label in zip(trending_venues_df['location.lat'], trending_venues_df['location.lng'], trending_venues_df['name']):\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" poup=label,\n",
" fill=True,\n",
" color='blue',\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" ).add_to(venues_map)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# display map\n",
"venues_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a id=\"item6\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was created by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"This notebook is part of a course on **Coursera** called *Applied Data Science Capstone*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DP0701EN_Coursera_Week2_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"Copyright &copy; 2018 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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