Mastering JSON in Python: Flattening Nested JSON with pd.json_normalize

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The Problem: Nested JSON Structures

Imagine you're working on a data project involving a JSON object sourced from an API. You notice that the JSON is structured with layers – in your case, view contains fields like user_id and message, while there's another nested field called replies that also contains user_id and message. Here's a simplified overview of the required structure:

[[See Video to Reveal this Text or Code Snippet]]

You successfully extract the view fields but struggle to flatten the replies field. This often leads to frustrating errors, such as KeyErrors, while trying to access the nested data.

Step 1: Normalize the Top-Level Fields

You can start by extracting the first-level fields in the view object. Use the following code:

[[See Video to Reveal this Text or Code Snippet]]

This step creates a DataFrame with all top-level fields available in the view.

Step 2: Normalize the Nested replies Field

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Merge Both DataFrames

[[See Video to Reveal this Text or Code Snippet]]

Final Output

When you execute the merging process, you can access a structured DataFrame that represents the relationships between views and their respective replies. It will look like this:

user_id_viewmessage_viewuser_id_replymessage_reply6354message text15457message text26354message text15458message text3Conclusion

With these steps, you'll be better equipped to handle nested JSON structures and ensure your data processing is smooth and efficient. Dive into your JSON projects with confidence!