Скачать или смотреть How to Apply Functions to Each Group in a Pandas DataFrame and Store Results in a New Column

Discover how to use Pandas to `group by` specific categories and apply functions to create a new column with calculated values based on criteria.
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How to Apply Functions to Each Group in a Pandas DataFrame and Store Results in a New Column

Pandas is a powerful data manipulation library in Python, widely used for working with structured data. One common scenario that data analysts encounter is the need to apply specific functions to groups of data and store the results in a new column. In this guide, we will explore how to use Pandas to achieve this, illustrated through a detailed example.

The Problem Statement

Let’s say we have a DataFrame containing information about items, including their Barcode, Description, Category, Code, Quantity, and Price. Our goal is to create a new column called Combined, which contains specific values based on conditions related to the Category of the items.

The Requirements

For items in the Category 'H', the Combined column should contain NaN.

For items in other categories (like 'M', 'S', etc.), the Combined column should contain a list of item information from entries within the same Extracted_Code that belong to Category 'H' and have a Code less than or equal to the item’s Code.

Let’s break this down and see how we can implement this solution effectively.

Step-by-Step Solution

1. Prepare the DataFrame

First, we need to create our initial DataFrame using the following code:

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

2. Performing a Self-Merge

To fulfill the requirement of checking codes and categories, we will perform a self-merge of the DataFrame on Extracted_Code:

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

3. Filtering Rows

Next, we need to filter the data to only keep relevant rows based on our conditions:

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

4. Grouping and Creating the Combined Column

Now we will group the resulting DataFrame by index and create a list of dictionaries for the Combined column:

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

5. Joining Back to the Original DataFrame

Finally, we will join the Combined data back to the original DataFrame:

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

Conclusion

By following the steps detailed above, we can successfully use the Pandas library to group our data by certain criteria, apply conditions, and store the results in a new column. The ability to manipulate and analyze data effectively is a crucial skill in data analysis, and mastering these techniques can significantly enhance your data handling capabilities.

With these tools in hand, you can tackle more complex data scenarios with confidence. Happy coding!