xorbits.pandas.groupby.DataFrameGroupBy.apply#
- DataFrameGroupBy.apply(func, *args, output_type=None, dtypes=None, dtype=None, name=None, index=None, skip_infer=None, **kwargs)#
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 likeagg
ortransform
. Pandas offers a wide range of method that will be much faster than usingapply
for their specific purposes, so try to use them before reaching forapply
.- 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 (tuple and dict) – Optional positional and keyword arguments to pass to
func
.kwargs (tuple and dict) – Optional positional and keyword arguments to pass to
func
.
- Return type
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(pandas): 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 gotchas.udf-mutation for more details.
Examples
>>> df = pd.DataFrame({'A': 'a a b'.split(), ... 'B': [1,2,3], ... 'C': [4,6,5]}) >>> g1 = df.groupby('A', group_keys=False) >>> g2 = df.groupby('A', group_keys=True)
Notice that
g1
andg2
have two groups,a
andb
, and only differ in theirgroup_keys
argument. Calling apply in various ways, we can get different grouping results:Example 1: below the function passed to apply takes a DataFrame as its argument and returns a DataFrame. apply combines the result for each group together into a new DataFrame:
>>> g1[['B', 'C']].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0
In the above, the groups are not part of the index. We can have them included by using
g2
wheregroup_keys=True
:>>> g2[['B', 'C']].apply(lambda x: x / x.sum()) B C A a 0 0.333333 0.4 1 0.666667 0.6 b 2 1.000000 1.0
Example 2: The function passed to apply takes a DataFrame as its argument and returns a Series. apply combines the result for each group together into a new DataFrame.
Changed in version 1.3.0(pandas): The resulting dtype will reflect the return value of the passed
func
.>>> g1[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0
>>> g2[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0
The
group_keys
argument has no effect here because the result is not like-indexed (i.e. a transform) when compared to the input.Example 3: The function passed to apply takes a DataFrame 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.C.max() - x.B.min()) A a 5 b 2 dtype: int64 Extra Parameters ---------------- output_type : {'dataframe', 'series'}, default None Specify type of returned object. See `Notes` for more details. dtypes : Series, default None Specify dtypes of returned DataFrames. See `Notes` for more details. dtype : numpy.dtype, default None Specify dtype of returned Series. See `Notes` for more details. name : str, default None Specify name of returned Series. See `Notes` for more details. index : Index, default None Specify index of returned object. See `Notes` for more details. skip_infer: bool, default False Whether infer dtypes when dtypes or output_type is not specified.
This docstring was copied from pandas.core.groupby.generic.DataFrameGroupBy.