How to Use Pandas to Apply a Custom Function After Grouping DataFrame

Discover how to apply a custom function to two columns in a Pandas DataFrame grouped by a specific column. This guide breaks down the solution to the rolling beta calculation step-by-step!
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Mastering Pandas: Applying Custom Functions to Grouped DataFrames

One of the powerful features of the Python library Pandas is the ability to manipulate and analyze data efficiently. When it comes to working with grouped data, things can get a little tricky, especially if you want to apply a custom function like calculating a rolling beta coefficient. In this guide, we’ll take a closer look at a common problem and how to resolve it effectively.

The Problem at Hand

We have a DataFrame containing stock price changes for different codes over a range of dates. The objective is to calculate a rolling beta coefficient for two columns, ST_PX_CHG_PCT and BD_PX_CHG_PCT, after grouping the DataFrame by the CODE column.

Here's a snapshot of our DataFrame:

DateCODEST_PX_CHG_PCTBD_PX_CHG_PCTRotaRolling_Beta6/28/2020ABC604459NaN..................7/8/2020DEF555256NaNTo calculate the rolling beta, we tried the following grouping and lambda function:

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

However, this approach returned empty results, failing to deliver the expected output.

The Solution

The main issue here is that the lambda function needs to reflect the application of our custom function in a manner that operates correctly on each group. Below, we'll make necessary adjustments to achieve our desired outcome.

Step 1: Modify the my_beta Function

We need to adjust the my_beta function to accept a group as a parameter. This allows us to work on each grouped DataFrame separately:

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

Step 2: Apply the Function to Each Group

Now that we have modified the function, we can apply it to each code group. This is done by calling apply() on the grouped DataFrame:

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

In this line:

We group the DataFrame by the CODE column.

For each group, we apply our modified my_beta function with a rolling window of 3 days.

Final Thoughts

By making these adjustments to the my_beta function and correctly applying it to the grouped DataFrame, we can calculate the rolling beta coefficients accurately. Always remember that when dealing with grouped data, focusing on the specific group context is crucial when applying custom functions.

Now you have a tailored solution to effectively calculate rolling statistics in your DataFrames using Pandas! Happy coding!