Скачать или смотреть Transforming Dictionary Values in a Pandas DataFrame: Keys as Columns and Values as Data

Learn how to efficiently convert dictionary keys in a Pandas DataFrame into column names, while keeping the corresponding values intact. This guide will simplify your data manipulation tasks!
---
This video is based on the question https://stackoverflow.com/q/65677566/ asked by the user 'nandi1596' ( https://stackoverflow.com/u/14555204/ ) and on the answer https://stackoverflow.com/a/65677775/ provided by the user 'Ferris' ( https://stackoverflow.com/u/6006383/ ) 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: I have dictionary as value in pandas dataframe columns. I want to make the keys columns and values as column value

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.
---
Transforming Dictionary Values in a Pandas DataFrame: Keys as Columns and Values as Data

When working with data in Python, particularly with Pandas, you may encounter situations where your DataFrame contains dictionaries as values in its columns. This can often complicate data manipulation tasks. How can you transform these dictionaries in such a way that the keys become column names, and the values remain as data in the respective columns? In this guide, we’ll walk through a clear and simple solution to this common problem.

Understanding the Problem

Imagine you have a Pandas DataFrame that looks something like this:

0120{'1536235175000': 26307.9}{'1536235176000': 0}{'1536236701000': 2630}1{'1536239919000': 1028127}{'1536239921000': 0}NaN2{'1536242709000': 2629.6}{'1536242711000': 0}NaNIn this DataFrame, each cell in the columns contains a dictionary with timestamps as keys and numerical values as values. The goal is to restructure this DataFrame so that the keys of these dictionaries become the column names, and the associated values remain in the cells.

The Solution

To achieve this transformation, we can utilize the apply method combined with some additional Pandas functions. Here’s a step-by-step breakdown of how to do this.

Step 1: Apply and Explode

The first step is to convert each row of the DataFrame into a list of the contained dictionaries and then explode these lists into individual rows. This helps in aligning everything neatly.

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

Step 2: List to DataFrame

Once you have exploded the lists, you can convert this data into a new DataFrame. The tolist() method comes in handy here, allowing you to transform the series of dictionaries into a format that can be organized into a DataFrame.

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

Step 3: Stack and Unstack

Finally, to reshape the data so that dictionary keys become the new column headers, you use the stack() and unstack() methods.

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

Example Output

After executing these steps, you’ll end up with a DataFrame that looks like this:

1536235175000153623517600015362367010001536239919000153623992100015362427090001536242711000026307.90.02630.0NaNNaNNaNNaN1NaNNaNNaN1028127.00.0NaNNaN2NaNNaNNaNNaNNaN2629.60.0Conclusion

By following the outlined steps, you can easily transform a DataFrame with dictionary values into a more usable format where keys become columns and values occupy the cells. This process not only enhances data readability but also simplifies data analysis in your workflows.

Feel free to adapt the provided code snippets to your specific DataFrame and enjoy more streamlined data handling in Pandas! Happy coding!