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# pandas.api.typing.ExponentialMovingWindow.mean

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

  Calculate the ewm (exponential weighted moment) mean.

  The weighting is controlled by the `com`, `span`, `halflife`, or `alpha` parameter specified when calling `DataFrame.ewm()` or `Series.ewm()`.

  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.ewm`

    Calling ewm with Series data.

  `DataFrame.ewm`

    Calling ewm 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

      >>> ser = pd.Series([1, 2, 3, 4])
      >>> ser.ewm(alpha=0.2).mean()
      0    1.000000
      1    1.555556
      2    2.147541
      3    2.775068
      dtype: float64