A data frame is a tabular structure with labeled rows and columns that can hold variables of different types. It turns nested API responses or scraped HTML into a workable dataset for analysis, reporting, and stakeholder-facing workflows. section 10.1.1 section 10.2.2
A typical workflow builds a data frame from selected fields, cleans text into usable values, converts columns to appropriate types, and aggregates records for the question at hand. A forecast may become one row per day with low temperature, high temperature, and maximum wind speed. section 10.1.1 section 10.2.2
Use a data frame to:
- Select relevant fields from nested JSON or HTML rather than analyzing the entire response. section 10.1.1 section 10.2.2
- Normalize values such as dates, temperatures, and wind speeds with parsing and regular expressions. section 10.1.1 section 10.2.2
- Group records and calculate summaries that match a historical dataset or stakeholder need. section 10.1.1 section 10.2.2
Seven-day forecast
After retrieving a weather response, construct a pandas data frame from forecast periods, retain end time, temperature, and wind speed, convert end times to dates, parse numeric wind speeds, and aggregate records by day. 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
Inspect the compact pandas operation that converts forecast-period records into a data frame and previews selected columns. section 10.1.1
