A data analyst needs to join together a table data source and web API data source using Python. Which of the following is the best way to accomplish this task?

• A. Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together
• B. Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together.
• C. Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together.
• D. Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together.

Answer: (A)

Bringing both sources into a common, structured form that pandas can work with is the key. APIs usually return JSON, which maps naturally to Python objects and can be flattened into a DataFrame. The database data can be loaded into a DataFrame via a SQL query. Once you have two DataFrames with compatible key columns, you merge them on the shared key to create a single combined table. Using JSON as the intermediate format preserves the structure and data types and then converting to DataFrames makes the join straightforward with pandas. Turning data into varchar, TXT, or string formats removes the structure, making joins unreliable and requiring extra parsing and type handling.