pandas.api.typing.Rolling.apply#

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

Calculate the rolling custom aggregation function.

Applies an arbitrary function to each rolling window. This is useful when the built-in rolling methods do not cover the desired computation.

Parameters:
funcfunction

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. Pandas-only attributes such as .iloc or .index are not available on ndarrays and will raise AttributeError if used inside func.

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 nogil and parallel dictionary keys. The values must either be True or False.

The default engine_kwargs for the 'numba' engine is {'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.

Returns:
Series or DataFrame

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

See also

Series.rolling

Calling rolling with Series data.

DataFrame.rolling

Calling rolling with DataFrames.

Series.apply

Aggregating apply for Series.

DataFrame.apply

Aggregating apply for DataFrame.

Notes

When raw=False, the Series passed to func is indexed by the column or Index used to compute the rolling window. If DataFrame.rolling() was called with on=col, the index of the passed Series will be the values of col rather than the original DataFrame index. When on is not specified, the index of the passed Series is the original index of the input.

Examples

>>> ser = pd.Series([1, 6, 5, 4])
>>> ser.rolling(2).apply(lambda s: s.sum() - s.min())
0    NaN
1    6.0
2    6.0
3    5.0
dtype: float64
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