Which data format is commonly used for interoperability with web APIs and can be loaded into pandas DataFrames?

• A. JSON
• B. XML
• C. CSV
• D. YAML

Answer: (A)

Interacting with web APIs relies on a format that is lightweight, easy to parse, and maps cleanly to in-memory data structures. JSON fits that role because it represents data as key-value pairs and arrays, which translate directly to Python dictionaries/lists and then to a DataFrame. API responses are commonly a JSON array of objects, where each object corresponds to a row and each key to a column; pandas can load this with read_json or by normalizing nested fields to flatten into a tabular form. This approach handles nested data when needed by flattening, making it flexible for analysis. Other formats like CSV are great for simple tables but don’t handle complex or nested structures as well; XML is verbose and harder to parse into a DataFrame; YAML is not as widely used for API payloads. Therefore JSON is the best fit for interoperability with web APIs and loading into pandas DataFrames.