How to Flatten a Nested JSON Using pd.json_normalize in Python

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Introduction

Handling nested JSON data can often feel like a daunting task, especially when you're trying to extract meaningful insights or perform analysis. In this post, we will tackle a common problem faced by data practitioners: flattening a nested JSON structure using Python's pandas library.

The Problem

You might have encountered a situation where the JSON data you are working with contains multiple layers of nested structures. For example, consider the output from an Elasticsearch aggregation that includes sales data over time. The structure showcases several levels of nesting, including sales counts and additional documentation related to each entry.

Here's a simplified sample of such JSON data:

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The Solution

Step 1: Initial Flattening

Begin by applying json_normalize() to extract the first layer of data.

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Step 2: Restructure Nested Lists

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Step 3: Deep Flattening

Continue the flattening process through the nested layers of your data structure. You might need to repeat this process multiple times to access the deepest level of data.

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Step 4: Complete Flattening

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Assembly of the Returning DataFrame

At the end of these processes, you should have a well-structured DataFrame where all relevant data points are accessible, allowing for easier analysis and manipulation.

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

Conclusion

With these techniques in your toolkit, you should now feel more confident in handling nested JSON data in Python!