Requests is a Python library for sending HTTP requests to APIs and receiving responses. In analytics workflows, it supports repeatable retrieval of current external data for stakeholder-facing analysis. section 10.1.1 section 10.4
A typical requests-based API workflow involves:
- Reviewing the API documentation and constructing a URL for the required location or resource. section 10.1.1
- Calling requests.get(url) and checking the response; status code 200 indicates a successful request. section 10.1.1
- Converting the response to JSON, inspecting nested keys, and selecting the fields needed for analysis. section 10.1.1
- Transforming the extracted records into a pandas dataframe, cleaning fields such as dates and wind speeds, and aggregating them when needed. section 10.1.1
- Assessing authentication, pagination, rate limits, processing effort, automation resources, and the value of incorporating the result into a centralized data warehouse. section 10.4
Seven-day weather forecast
A script requests forecast data for a New York City weather station, converts the response to JSON, extracts forecast periods, and builds a dataframe with forecast times, daytime indicators, temperatures, and wind speeds. Daily grouping can produce low temperature, high temperature, and maximum wind-speed columns for refreshed stakeholder analysis. section 10.1.1
import requests import json #1 url = "https://api.weather.gov/gridpoints/OKX/34,36/forecast" #2 response = requests.get(url) #3 print(response) #4
The compact listing shows the import, weather API URL, GET request, and response inspection that begin an API retrieval workflow. section 10.1.1
forecast = response.json() #1 print(forecast.keys()) #2
The listing demonstrates converting a response to JSON and inspecting its top-level keys before extracting nested forecast data. section 10.1.1
import pandas as pd #1
forecast_df = pd.DataFrame(forecast["properties"]["periods"]) #2
forecast_df[
["endTime", "isDaytime", "temperature", "windSpeed"]
].head() #3
The listing demonstrates converting nested forecast periods into a pandas dataframe and selecting analysis-ready columns. section 10.1.1
