pandas.core.groupby.SeriesGroupBy.transform¶
- SeriesGroupBy.transform(func, *args, engine=None, engine_kwargs=None, **kwargs)[source]¶
Call function producing a like-indexed Series on each group and return a Series having the same indexes as the original object filled with the transformed values
- Parameters
- ffunction
Function to apply to each group.
Can also accept a Numba JIT function with
engine='numba'
specified.If the
'numba'
engine is chosen, the function must be a user defined function withvalues
andindex
as the first and second arguments respectively in the function signature. Each group’s index will be passed to the user defined function and optionally available for use.Changed in version 1.1.0.
- *args
Positional arguments to pass to func.
- enginestr, default None
'cython'
: Runs the function through C-extensions from cython.'numba'
: Runs the function through JIT compiled code from numba.None
: Defaults to'cython'
or the global settingcompute.use_numba
New in version 1.1.0.
- engine_kwargsdict, default None
For
'cython'
engine, there are no acceptedengine_kwargs
For
'numba'
engine, the engine can acceptnopython
,nogil
andparallel
dictionary keys. The values must either beTrue
orFalse
. The defaultengine_kwargs
for the'numba'
engine is{'nopython': True, 'nogil': False, 'parallel': False}
and will be applied to the function
New in version 1.1.0.
- **kwargs
Keyword arguments to be passed into func.
- Returns
- Series
See also
Series.groupby.apply
Apply function
func
group-wise and combine the results together.Series.groupby.aggregate
Aggregate using one or more operations over the specified axis.
Series.transform
Call
func
on self producing a Series with transformed values.
Notes
Each group is endowed the attribute ‘name’ in case you need to know which group you are working on.
The current implementation imposes three requirements on f:
f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, if f returns a scalar it will be broadcast to have the same shape as the input subframe.
if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk.
f must not mutate groups. Mutation is not supported and may produce unexpected results. See Mutating with User Defined Function (UDF) methods for more details.
When using
engine='numba'
, there will be no “fall back” behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried.Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed
func
, see the examples below.Examples
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ... 'foo', 'bar'], ... 'B' : ['one', 'one', 'two', 'three', ... 'two', 'two'], ... 'C' : [1, 5, 5, 2, 5, 5], ... 'D' : [2.0, 5., 8., 1., 2., 9.]}) >>> grouped = df.groupby('A') >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) C D 0 -1.154701 -0.577350 1 0.577350 0.000000 2 0.577350 1.154701 3 -1.154701 -1.000000 4 0.577350 -0.577350 5 0.577350 1.000000
Broadcast result of the transformation
>>> grouped.transform(lambda x: x.max() - x.min()) C D 0 4 6.0 1 3 8.0 2 4 6.0 3 3 8.0 4 4 6.0 5 3 8.0
Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed
func
, for example:>>> grouped[['C', 'D']].transform(lambda x: x.astype(int).max()) C D 0 5 8 1 5 9 2 5 8 3 5 9 4 5 8 5 5 9