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API (Application Programming Interface)

1. API request

  • 1.1. Request with URL

    import requests
import json
from pprint import pprint

response = requests.get(url)
response.status_code # 200 is good
response.url # confirm URL
response_json = response.json()
json.dumps(response_json, indent=4, sort_keys=True)

  • 1.2. Request with URL and params

    Openweathermap API

    base_url = "http://api.openweathermap.org/data/2.5/weather"
params = {
    'q': 'london',
    'appid': api_key,
}
requests.get(base_url, params = params)

  • 1.3. Google geocoding, places, and radar APIs

    Geocoding: Get lat/lon based on address

    import requests
import json
from config import gkey

target_city = "Seattle, Washington"
params = {"address": target_city, "key": gkey}
target_url = "https://maps.googleapis.com/maps/api/geocode/json"

response = requests.get(target_url, params=params).json()
lat = response["results"][0]["geometry"]["location"]["lat"]
lng = response["results"][0]["geometry"]["location"]["lng"]

    Places: Map search based on lat/lon

    import requests
import json
from config import gkey

target_coordinates = "47.6062095, -122.3320708"
target_search = "bike"
target_radius = 8000
target_type = "store"

# set up a parameters dictionary
params = {
    "location": target_coordinates,
    "keyword": target_search,
    "radius": target_radius,
    "type": target_type,
    "key": gkey
}

# base url
base_url = "https://maps.googleapis.com/maps/api/place/nearbysearch/json"

# run a request using our params dictionary
response = requests.get(base_url, params=params).json()
# Print the name and address of the first restaurant that appears
print(response["results"][0]["name"])
print(response["results"][0]["vicinity"])

    Radar search: Count nearby map searches

    import requests
import json
from config import gkey

target_city = {"lat": 43.6187102, "lng": -116.2146068}
target_coords = f"{target_city['lat']},{target_city['lng']}"

params = {
    "location": target_coords,
    "radius": 8000,
    "keyword": "ice cream",
    "type": "food",
    "key": gkey
}

# Build the endpoint URL (Checks all ice cream shops)
base_url = "https://maps.googleapis.com/maps/api/place/radarsearch/json"

# Run a request to endpoint and convert result to json
ice_cream_data = requests.get(base_url, params=params).json()
print(len(ice_cream_data["results"]))

    Loop to get lat/lon and keyword search

    # Dependencies
import pandas as pd
import numpy as np
import requests
import json
# Import API key
from config import gkey

# Load data
cities_pd = pd.read_csv("../Resources/Cities.csv")
cities_pd.head()
cities_pd['lat'] = ""
cities_pd['lon'] = ""
cities_pd['airport name'] = ""
cities_pd['airport address'] = ""
cities_pd['airport rating'] = ""
    # Latitude and longitude
target_url = "https://maps.googleapis.com/maps/api/geocode/json"
params = {"key": gkey}

for index, row in cities_pd.iterrows():
    target_city = row['City']
    params['address'] = target_city
    print(f"Retrieving Results for Index {index}: {target_city}.")
    
    response = requests.get(target_url, params=params).json()
    results = response.get('results')
    
    if (results):
        location = response['results'][0]["geometry"]["location"]
        print(f"Geo-location of {target_city} is {location.get('lat', 'None')}, {location.get('lng', 'None')}.")
        cities_pd.loc[index, 'lat'] = location.get('lat', '') # protect from keyError
        cities_pd.loc[index, 'lon'] = location.get('lng', '')
    else:
        print("No results for " + target_city)
    print("------------")
    # Search based on lat lon
target_url = "https://maps.googleapis.com/maps/api/place/nearbysearch/json"
params = {
    "rankby": "distance",
    "type": "airport",
    "key": gkey,
    "keyword": "international+airport",
}

for index, row in cities_pd.iterrows(): # use iterrows to iterate through pandas dataframe
    target_coordinates = f"{row['lat']}, {row['lon']}"
    target_city = cities_pd.loc[index, 'City']
    params['location'] = target_coordinates
    print(f"Retrieving Results for Index {index}: {target_city}.")
    
    response = requests.get(target_url, params=params).json()
    results = response.get('results')
    
    if (results):
        airp = response['results'][0]
        print(f"International airport of {target_city} is {airp.get('name', 'None')}.")
        cities_pd.loc[index, 'airport name'] = airp.get('name', '') # protect from keyError
        cities_pd.loc[index, 'airport address'] = airp.get('vicinity', '')
        cities_pd.loc[index, 'airport rating'] = airp.get('rating', '')
    else:
        print("No results for " + target_city)
    print("------------")

2. API wrappers

API wrapper acts as a shield from internal changes of json

  • 2.1. openweathermapy

    import openweathermapy.core as owm

# Create settings dictionary with information we're interested in
settings = {"units": "metric", "appid": api_key}

# Get current weather
current_weather_paris = owm.get_current("Paris", **settings)

# Get parameters of interest
summary = ["name", "main.temp"]
data = current_weather_paris(*summary)

  • 2.2. citypy

    from citipy import citipy
city = citipy.nearest_city(22.99, 120.21) # lat, lon
city.city_name
city.country_code

  • 2.3. Census

    Parameter labels

    # Run Census Search to retrieve data on all zip codes (2013 ACS5 Census)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import requests
from census import Census
from config import api_key # Census API Key

c = Census(api_key, year=2013)
census_data = c.acs5.get(("NAME", "B19013_001E", "B01003_001E", "B01002_001E",
                        "B19301_001E", "B17001_002E"), 
                        {'for': 'zip code tabulation area:*'})

# Convert to DataFrame
census_pd = pd.DataFrame(census_data)

# Column Reordering
census_pd = census_pd.rename(columns={"B01003_001E": "Population",
                                    "B01002_001E": "Median Age",
                                    "B19013_001E": "Household Income",
                                    "B19301_001E": "Per Capita Income",
                                    "B17001_002E": "Poverty Count",
                                    "NAME": "Name", "zip code tabulation area": "Zipcode"})