pandas.core.groupby.SeriesGroupBy.apply

SeriesGroupBy.apply(func, *args, **kwargs)

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

The function passed to apply must take a series 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:

funccallable

A callable that takes a series as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments.

*argstuple

Optional positional arguments to pass to func.

**kwargsdict

Optional keyword arguments to pass to func.

Returns:

Series or DataFrame

A pandas object with the result of applying func to each group.

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.

Notes

Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed func, see the examples below.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details.

Examples

>>> s = pd.Series([0, 1, 2], index="a a b".split())
>>> g1 = s.groupby(s.index, group_keys=False)
>>> g2 = s.groupby(s.index, group_keys=True)

From s above we can see that g has two groups, a and b. Notice that g1 have g2 have two groups, a and b, and only differ in their group_keys argument. Calling apply in various ways, we can get different grouping results:

Example 1: The function passed to apply takes a Series as its argument and returns a Series. apply combines the result for each group together into a new Series.

Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed func.

>>> g1.apply(lambda x: x * 2 if x.name == "a" else x / 2)
a    0.0
a    2.0
b    1.0
dtype: float64

In the above, the groups are not part of the index. We can have them included by using g2 where group_keys=True:

>>> g2.apply(lambda x: x * 2 if x.name == "a" else x / 2)
a  a    0.0
   a    2.0
b  b    1.0
dtype: float64

Example 2: The function passed to apply takes a Series as its argument and returns a scalar. apply combines the result for each group together into a Series, including setting the index as appropriate:

>>> g1.apply(lambda x: x.max() - x.min())
a    1
b    0
dtype: int64

The group_keys argument has no effect here because the result is not like-indexed (i.e. a transform) when compared to the input.

>>> g2.apply(lambda x: x.max() - x.min())
a    1
b    0
dtype: int64