Efficiently Extracting Dictionary Values from a Pandas DataFrame

Описание: Learn how to quickly extract values from dictionaries in a Pandas DataFrame and restructure them into a more usable format, with a concise solution to make your data analysis simpler.
---
This video is based on the question https://stackoverflow.com/q/74435173/ asked by the user 'Edona Xhemajli' ( https://stackoverflow.com/u/19846285/ ) and on the answer https://stackoverflow.com/a/74435694/ provided by the user 'Giuseppe La Gualano' ( https://stackoverflow.com/u/20249888/ ) 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: Get the value of dict in every column in dataframe

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.
---
Efficiently Extracting Dictionary Values from a Pandas DataFrame

When working with data in Python, particularly with Pandas, we often encounter complex structures such as dictionaries nested within dataframes. This situation can arise when you have to manage financial data, inventory details, or any information that requires associative arrays.

Problem Explanation

Suppose you are given a dataframe containing IDs and a list of prices formatted as dictionaries. Here's a simplified view of the structure:

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

Your goal is to transform this complex structure into a more manageable format where each column corresponds to certain current and last prices, like so:

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

Solution Breakdown

To achieve this transformation efficiently, we can use a structured approach:

Step 1: Initialize Your DataFrame

First, create your DataFrame as given in the input data.

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

Step 2: Iterate Over Rows and Group Prices

Use the Pandas iterrows() method to go through each row in the DataFrame. For each row, group the prices into 'current' and 'last' values.

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

Step 3: Create the New DataFrame

After processing all rows, convert the list of dictionaries back into a DataFrame for easier analysis.

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

Final Output

The final output will look like this:

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

Conclusion

Transforming nested data structures in Pandas can be straightforward if you break down the problem and use effective tools like iterrows() and zip(). By following the above steps, you'll not only preserve the integrity of your data but also make it more accessible and useful for analysis.

This method significantly simplifies the extraction of values from complex data structures in your DataFrame, allowing for quicker and more efficient data processing.