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Learn how to convert a nested JSON response into a well-structured `Pandas DataFrame` using `json_normalize` and `explode` techniques.
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Transforming Nested JSON to a Pandas DataFrame: A Step-by-Step Guide

Handling JSON data is a common task in data analysis and manipulation, particularly for data scientists working with APIs or complex datasets. If you’ve encountered a JSON response that includes nested dictionaries and arrays, you may find it challenging to convert this structure into a format suitable for analysis, such as a Pandas DataFrame.

In this guide, we will explore how to effectively convert a nested JSON response into a Pandas DataFrame, ensuring that each key is represented as a column.

Understanding the Problem

Imagine you receive a JSON response that looks like this:

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In this nested structure, you want to convert it into a Pandas DataFrame where each key corresponds to a column. The challenge is that the key2 can either be a simple value or a nested dictionary containing additional keys.

The Solution: Utilizing json_normalize

The tool you need to flatten this JSON structure is Pandas's json_normalize. However, there’s a catch; you’ll need to invoke it twice, leveraging methods like explode() to handle nested data correctly. Below, we break down the steps necessary to achieve this.

Step 1: Setting Up the Environment

Make sure to import the necessary libraries:

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Step 2: Using json_normalize

Start by normalizing the JSON data. The first step is to flatten the results section of your JSON:

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Step 3: Exploding the Nested Data

Once you’ve flattened the initial structure, you will need to handle the nested part in key2. Specifically, we will explode the list found under subkey20:

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Step 4: Final Normalization

Finally, you’ll need to apply json_normalize once more to ensure all the nested keys are brought to the top level, resulting in a flat structure:

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

Output Verification

By executing the above code, you will receive an output that looks something like this:

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Here, each key from the JSON is represented as a column, thereby making your data ready for further analysis.

Conclusion

Converting a nested JSON structure into a Pandas DataFrame doesn’t have to be a daunting task. By utilizing json_normalize() and explode(), you can effectively unpack the nested components into a clean, flat table. With practice, these techniques become second nature, making data manipulation smoother and more efficient.

Now that you understand this method, you can apply it to other JSON data structures, making your data analysis workflow much more versatile!