Скачать или смотреть Creating a Multilevel Index DataFrame from a Complex Nested Dictionary with Pandas

Learn how to transform a complex nested dictionary into a `multilevel index` DataFrame in Pandas by using the `json_normalize()` method and systematic data expansion techniques.
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This video is based on the question https://stackoverflow.com/q/67182374/ asked by the user 'cap' ( https://stackoverflow.com/u/10714273/ ) and on the answer https://stackoverflow.com/a/67184701/ provided by the user 'Rob Raymond' ( https://stackoverflow.com/u/9441404/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Are you struggling to convert a complex nested dictionary into a multi-level indexed pandas DataFrame? If you've found yourself in a situation where you're dealing with dictionaries that contain deep nesting—like the one below—you’re not alone.

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In this example, we have three levels of dictionaries, where each level contains arrays of dictionaries. The goal is to create a multilevel index in a DataFrame that ultimately displays as ['foo', 'bar', 'baz', 'max.value']. Let’s explore the step-by-step process to achieve this.

Step-by-Step Solution

Step 1: Import Required Libraries

To begin, you need to ensure that you have the pandas library installed and imported in your Python environment.

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Step 2: Initialize Your Data

Start by defining your complex nested dictionary in Python.

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Step 3: Normalize the JSON Structure

Use the json_normalize() function to flatten the nested structure. This allows you to access the keys at different levels more easily.

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Step 4: Expand Level 1

At this point, we need to expand the level_1.bucket_1 into a more organized format while retaining the multi-level indexing.

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Step 5: Expand Level 2

Next, we continue the process by exploding and expanding level_2:

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Step 6: Finalize DataFrame Structure

Finally, we need to convert the last level (level 2) and format it correctly:

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Output the Result

After following the steps above, your DataFrame will have a beautiful multi-level index that displays like this:

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Conclusion

Converting a complex nested dictionary into a multilevel index DataFrame in pandas doesn't have to be a daunting task. By systematically applying json_normalize() along with other pandas features, you can efficiently manage and manipulate your data. With a better understanding of multi-indexing, you'll be able to handle data more effectively and make your data analysis tasks easier.

If you have more queries or run into any issues, feel free to ask. Happy coding!