pandas.core.groupby.GroupBy.apply

GroupBy.apply(self, func, *args, **kwargs) [source]

Apply function func group-wise and combine the results together.

The function passed to apply must take a dataframe as its first argument and return a DataFrame, Series or scalar. apply will then take care of combining the results back together into a single dataframe or series. apply is therefore a highly flexible grouping method.

While apply is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like agg or transform. Pandas offers a wide range of method that will be much faster than using apply for their specific purposes, so try to use them before reaching for apply.

Parameters:	func : callable A callable that takes a dataframe as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to func.
Returns:	applied : Series or DataFrame

See also

pipe

Apply function to the full GroupBy object instead of to each group.

aggregate

Apply aggregate function to the GroupBy object.

transform

Apply function column-by-column to the GroupBy object.

Series.apply

Apply a function to a Series.

DataFrame.apply

Apply a function to each row or column of a DataFrame.