How to Convert a Column of Dictionaries to Multiple Columns in Pandas DataFrame

Learn how to efficiently transform a column containing lists of dictionaries into a well-structured DataFrame with multiple columns using Pandas in Python.
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Introduction

If you're working with data in Python, especially in Pandas, you might face situations where a column of a DataFrame contains complex nested data structures like lists of dictionaries. For instance, imagine you have a DataFrame that includes a column named servicePlans containing various service plans represented as dictionaries.

In this guide, we'll explore how to transform such columns into a more user-friendly format: splitting them into separate columns, thus simplifying data analysis and manipulation.

Example DataFrame

To set the stage, let's take a look at the structure of our sample DataFrame:

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In this example, the column servicePlans contains a list of dictionaries for each row. We want to convert this structure into a flat table where each dictionary's fields become separate columns.

Step-by-step Guide

Import Pandas:
Ensure you have the Pandas library imported in your environment.

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Create the Initial DataFrame:
Assuming you have the nested data structure as shown above, create the DataFrame.

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Viewing the Output:
After running the above step, you can print normalized_df to see the nicely structured DataFrame:

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

Expected Result

The final DataFrame will look like this:

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

Conclusion

Feel free to reach out if you have any questions or need further clarifications on this topic. Happy coding!