Unnesting Nested Dictionaries in Pandas: A Clear Guide to Transform Your Data

Learn how to easily unnest a Pandas DataFrame column containing nested dictionaries while keeping the key structure intact. Follow our step-by-step guide! --- This video is based on the question https://stackoverflow.com/q/74453148/ asked by the user 'KristiLuna' ( https://stackoverflow.com/u/14444816/ ) and on the answer https://stackoverflow.com/a/74453190/ provided by the user 'KristiLuna' ( https://stackoverflow.com/u/14444816/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Pandas df - unnest 1 column that has nested dictionaries, but only unnest the key not the values Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Unnesting Nested Dictionaries in Pandas: A Clear Guide to Transform Your Data When working with data in Python’s Pandas library, you may sometimes encounter a situation where columns contain nested dictionaries. This can make data manipulation quite challenging, especially if you're only interested in extracting specific keys from those dictionaries while preserving a clean structure. In this guide, we’ll discuss how to unnest a column of nested dictionaries in a Pandas DataFrame and create new columns based on these keys. The Problem: Understanding Nested Dictionaries Suppose you have a Pandas DataFrame that includes a column named cPeriod, which contains rows represented as nested dictionaries. For example: [[See Video to Reveal this Text or Code Snippet]] Your goal is to extract the keys firstDate and lastDate into their respective columns named cperiod.firstdate and cperiod.lastdate. This will help you in analyzing the data without losing the overall context of the original keys. The Initial Attempt: What Went Wrong You might try to unnest this dictionary using the json_normalize method as follows: [[See Video to Reveal this Text or Code Snippet]] However, this approach can lead to excessive unnested values if the nested structure is complex or contains more keys than you need. The Solution: A Simpler Unnesting Method To efficiently achieve your goal while keeping things organized, here is a straightforward solution. This method will allow you to create new columns without flattening the entire structure and complicating your DataFrame. Join with the Nested Data: Instead of using json_normalize, you can directly extract the nested dictionaries from the cPeriod column. Use pd.DataFrame: The pd.DataFrame constructor can be used to create a new DataFrame from the nested dictionary values. Here's a step-by-step breakdown of the correct approach: [[See Video to Reveal this Text or Code Snippet]] Explanation of the Steps pop('cPeriod'): This method removes the cPeriod column and returns the data, allowing you to work with it without cluttering your DataFrame. pd.DataFrame(cperiod_data.values.tolist()): This line creates a new DataFrame from the values of the nested dictionaries. df.join(new_columns): This method appends the newly created DataFrame to your original DataFrame. rename(columns={...}): Finally, this step renames the new columns to have the prefix cperiod. for clarity. Conclusion By following the steps outlined above, you can effectively unnest a column containing nested dictionaries without losing important structural information. This method not only simplifies your DataFrame but also improves its readability for future analysis. With these practices in place, you can enhance your data manipulation skills and prepare your dataset for insightful data exploration. Happy coding!