How to Normalize Nested JSON with Pandas in Python

Learn to effectively `normalize nested JSON` data using Pandas in Python with step-by-step instructions.
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How to Normalize Nested JSON with Pandas in Python

Working with nested JSON structures can be quite challenging, especially when it comes to transforming the data into a flat structure that's easy to analyze or manipulate. JSON (JavaScript Object Notation) is a popular data format for APIs and configuration files due to its readability and ease of use. However, this flexibility can also lead to complexity when the JSON contains deeper nested structures.

In this guide, we’ll tackle a specific problem: normalizing a nested JSON structure using Python’s Pandas library. We'll provide you with a comprehensive guide on how to do it and make sure you can extract the information you need efficiently.

The Problem

Our targeted scenario involves normalizing the following JSON object. Here’s a snippet of the JSON data we’re working with:

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

Output CSV Before Normalization

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

Our goal is to refine the normalization process and achieve a flat structure that includes all relevant data, resulting in the following output:

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

The Solution

Step 1: Understand the Data Structure

Before we normalize the JSON, let's analyze the structure:

At the top level, we have ActionID and Date.

SecInfo is an array of dictionaries that contains SecID and IssuerID.

Each entry in SecInfo has an associated StoreInfo that is also an array of dictionaries containing Code, Symbol, and Status.

Step 2: Code to Normalize the JSON

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

Step 3: Inspect the Output

After executing the above code, the printed DataFrame should now have all essential fields included:

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

Conclusion

Share Your Thoughts

Have you encountered challenges while normalizing JSON data? Feel free to share your experiences or ask questions in the comments below!