pandas.core.groupby.SeriesGroupBy.transform#
- SeriesGroupBy.transform(func, *args, engine=None, engine_kwargs=None, **kwargs)[source]#
Call function producing a same-indexed Series on each group.
Returns a Series having the same indexes as the original object filled with the transformed values.
- Parameters
- ffunction, str
Function to apply to each group. See the Notes section below for requirements.
Accepted inputs are:
String
Python function
Numba JIT function with
engine='numba'
specified.
Only passing a single function is supported with this engine. 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.If a string is chosen, then it needs to be the name of the groupby method you want to 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 the same axis shape as self.
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.Changed in version 2.0.0: When using
.transform
on a grouped DataFrame and the transformation function returns a DataFrame, pandas now aligns the result’s index with the input’s index. You can call.to_numpy()
on the result of the transformation function to avoid alignment.Examples
>>> ser = pd.Series( ... [390.0, 350.0, 30.0, 20.0], ... index=["Falcon", "Falcon", "Parrot", "Parrot"], ... name="Max Speed") >>> grouped = ser.groupby([1, 1, 2, 2]) >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) Falcon 0.707107 Falcon -0.707107 Parrot 0.707107 Parrot -0.707107 Name: Max Speed, dtype: float64
Broadcast result of the transformation
>>> grouped.transform(lambda x: x.max() - x.min()) Falcon 40.0 Falcon 40.0 Parrot 10.0 Parrot 10.0 Name: Max Speed, dtype: float64
>>> grouped.transform("mean") Falcon 370.0 Falcon 370.0 Parrot 25.0 Parrot 25.0 Name: Max Speed, dtype: float64
Changed in version 1.3.0.
The resulting dtype will reflect the return value of the passed
func
, for example:>>> grouped.transform(lambda x: x.astype(int).max()) Falcon 390 Falcon 390 Parrot 30 Parrot 30 Name: Max Speed, dtype: int64