Скачать или смотреть How to Calculate Days By Column in a Pandas DataFrame and Stop at Condition Days = 30

Learn how to effectively calculate days in a DataFrame column using Python's Pandas library. We'll tackle the challenge of resetting the counter when the summed days exceed 30 for grouped IDs.
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This video is based on the question https://stackoverflow.com/q/64781652/ asked by the user 'mojek' ( https://stackoverflow.com/u/9831572/ ) and on the answer https://stackoverflow.com/a/64782940/ provided by the user 'Cainã Max Couto da Silva' ( https://stackoverflow.com/u/7076819/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Calculate Days By Column in a Pandas DataFrame and Stop at Condition Days <= 30

When working with financial data, such as invoices and payments, it's not uncommon to need to perform calculations based on specific conditions. A common requirement is to calculate a running total in a DataFrame while resetting the total when it exceeds a particular threshold. In this post, we’ll explore how to achieve this with Python's Pandas library.

The Problem

Consider the following DataFrame scenario:

You have a dataset of invoices, payment terms, and dates.

The goal is to compute a new column called days, which tracks a rolling total of days based on the diff column (the difference in days between invoicing and payment).

The challenge is to reset this rolling total whenever it exceeds 30 days, while also calculating the sum based on unique IDs.

Here is how the initial setup of the DataFrame looks:

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

The Solution

Step 1: Define the Calculation Function

To achieve the desired behavior of the days column, we need to create a custom function that processes each group of diff values, resetting when the cumulative sum exceeds 30:

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

Step 2: Apply the Custom Function

Next, we will apply this function to each group defined by ID within the DataFrame:

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

Step 3: Examine the Output

After running the above code, your DataFrame will now have an updated days column that respects the condition of not exceeding 30. Here is how it could look:

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

Result Interpretation

With the transformation complete, the output will properly reflect the cumulative days while ensuring that any overflow resets to comply with the condition:

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

Conclusion

Using the custom function in conjunction with Pandas' groupby and transform methods provides a powerful way to handle rolling calculations with conditions. By utilizing this approach, you're able to maintain control over your data transformations, ensuring that cumulative totals respect specified limits.

Now, you'll have a clear understanding of how to implement such calculations effectively in your Pandas DataFrame projects!