dask_expr._rolling.Rolling.apply

dask_expr._rolling.Rolling.apply

Rolling.apply(func, *args, **kwargs)[source]

Calculate the rolling custom aggregation function.

This docstring was copied from pandas.core.window.rolling.Rolling.apply.

Some inconsistencies with the Dask version may exist.

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 (Not supported in Dask)

  • 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 (Not supported in Dask)

  • '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 (Not supported in Dask)

  • 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 (Not supported in Dask)

Positional arguments to be passed into func.

kwargsdict, default None (Not supported in Dask)

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

pandas.Series.rolling

Calling rolling with Series data.

pandas.DataFrame.rolling

Calling rolling with DataFrames.

pandas.Series.apply

Aggregating apply for Series.

pandas.DataFrame.apply

Aggregating apply for DataFrame.

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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