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# pandas.api.typing.Rolling.mean

Rolling.mean(numeric_only=False, engine=None, engine_kwargs=None)

  Calculate the rolling mean.

  This is equivalent to applying `numpy.mean` over each rolling window.

  Parameters:
    numeric_only bool, default False

      Include only float, int, boolean columns.

    engine str, default None

      - `'cython'` : Runs the operation through C-extensions from cython.

      - `'numba'` : Runs the operation through JIT compiled code from numba.

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

    engine_kwargs dict, 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}`.

  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.mean`

    Aggregating mean for Series.

  `DataFrame.mean`

    Aggregating mean for DataFrame.

  Notes

  See Numba engine and Numba (JIT compilation) for extended documentation and performance considerations for the Numba engine.

  Examples

  The below examples will show rolling mean calculations with window sizes of two and three, respectively.

      >>> s = pd.Series([1, 2, 3, 4])
      >>> s.rolling(2).mean()
      0    NaN
      1    1.5
      2    2.5
      3    3.5
      dtype: float64

      >>> s.rolling(3).mean()
      0    NaN
      1    NaN
      2    2.0
      3    3.0
      dtype: float64