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# pandas.core.window.rolling.Rolling.apply

Rolling.apply(func, raw=False, engine=None, engine_kwargs=None, args=None, kwargs=None)

  Calculate the rolling custom aggregation function.

  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. 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.

  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