How to Flatten a Dictionary in Python Using pd.json_normalize

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The Problem: Flattening a Nested Dictionary

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When attempting to flatten this data using the code below:

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You might run into a KeyError because the data structure may not align with your expectations for keys when flattening.

The Solution: Restructuring the Data

1. Restructure the Dictionary

To effectively flatten this dataset, we first need to restructure it into a list of dictionaries that Pandas can easily interpret. The goal is to transform the original dictionary into a more uniform structure where each entry contains a single IDs dictionary for each book.

Here’s a basic outline of how to do it:

Use a simple loop: This method clearly iterates through the values of the data.

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Alternatively, you can achieve the same outcome with a more condensed one-liner:

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2. Creating the DataFrame

Define the record_path: Specify the path to the records (in this case, IDs).

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Set metadata keys for normalization:

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Final normalization step:

Now we can normalize the new structure:

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Resulting DataFrame will look like this:

StoreIDBookIDSalesIDName123445452543543533254353543267765345Thrilling Tales of Dragon Slayers111111543533254353543267765345boring Tales of Dragon Slayers112111143242323424353543boring Tales of Dragon Slayers3. Alternative DataFrame Creation

If you intend to directly construct a DataFrame without using json_normalize, you can use:

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This creates a more simplified approach to flattening your data.

4. Handling Uneven Lengths in IDs

Finally, if the number of IDs varies and you might encounter lists of different lengths, consider using zip_longest from the itertools library to mitigate missing data:

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Conclusion

By following the methods outlined in this post, you can ensure your data is prepared for analysis and processing without running into common pitfalls. Happy coding!