Expanding.apply(func, raw=False, engine=None, engine_kwargs=None, args=None, kwargs=None)[source]#

Calculate the expanding custom aggregation function.


Must produce a single value from an ndarray input if raw=True or a single value from a Series if raw=False. Can also accept a Numba JIT function with engine='numba' specified.

rawbool, default False
  • False : passes each row or column as a Series to the function.

  • True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.

enginestr, default None
  • 'cython' : Runs rolling apply through C-extensions from cython.

  • 'numba' : Runs rolling apply through JIT compiled code from numba. Only available when raw is set to True.

  • None : Defaults to 'cython' or globally setting compute.use_numba

engine_kwargsdict, default None
  • For 'cython' engine, there are no accepted engine_kwargs

  • For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} and will be applied to both the func and the apply rolling aggregation.

argstuple, default None

Positional arguments to be passed into func.

kwargsdict, default None

Keyword arguments to be passed into func.

Series or DataFrame

Return type is the same as the original object with np.float64 dtype.

See also


Calling expanding with Series data.


Calling expanding with DataFrames.


Aggregating apply for Series.


Aggregating apply for DataFrame.


>>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
>>> ser.expanding().apply(lambda s: s.max() - 2 * s.min())
a   -1.0
b    0.0
c    1.0
d    2.0
dtype: float64