GroupBy.apply(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.


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, kwargstuple and dict

Optional positional and keyword arguments to pass to func.

appliedSeries or DataFrame

See also


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


Apply aggregate function to the GroupBy object.


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


Apply a function to a Series.


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