Double Printout in Pandas GroupBy.apply Method

The GroupBy.apply method in Pandas is a powerful tool for performing operations on groups of rows within a DataFrame. However, an unexpected behavior occurs when applying a function to the first group, resulting in two printouts.

In the provided example, a DataFrame with three rows is grouped by the 'class' column. When applying the function 'checkit' to the grouped object, the first row ('A') appears twice in the output. This behavior may seem confusing at first, but it is by design.

The GroupBy.apply method calls the specified function twice on the first group to determine the shape of the returned data. This information is crucial for the method to combine the results appropriately.

Depending on the desired outcome, there are alternative methods to GroupBy.apply that return specific data shapes and avoid this double call:

• aggregate: Returns a DataFrame with a reduced number of rows, typically used for aggregations (e.g., mean, sum, count).
• transform: Applies a function element-wise to each group and returns a DataFrame with the same shape as the original DataFrame.
• filter: Creates a new DataFrame that meets a specified condition.

If the applied function does not have side effects (i.e., does not modify the original DataFrame), the double printout on the first group is typically not a concern. However, if it's crucial to prevent this behavior, choosing an appropriate alternative method from the above list is recommended.