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# pandas.DatetimeIndex.mean

DatetimeIndex.mean(*, skipna=True, axis=0)

  Return the mean value of the Array.

  This method computes the arithmetic mean of the datetime or timedelta values in the index, optionally skipping NaT values.

  Parameters:
    skipna bool, default True

      Whether to ignore any NaT elements.

    axis int, optional, default 0

      Axis for the function to be applied on.

  Returns:
    scalar

      Timestamp or Timedelta.

  See also

  `numpy.ndarray.mean`

    Returns the average of array elements along a given axis.

  `Series.mean`

    Return the mean value in a Series.

  Notes

  mean is only defined for Datetime and Timedelta dtypes, not for Period.

  Examples

  For `pandas.DatetimeIndex`:

      >>> idx = pd.date_range("2001-01-01 00:00", periods=3)
      >>> idx
      DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
                    dtype='datetime64[us]', freq='D')
      >>> idx.mean()
      Timestamp('2001-01-02 00:00:00')

  For `pandas.TimedeltaIndex`:

      >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit="D")
      >>> tdelta_idx
      TimedeltaIndex(['1 days', '2 days', '3 days'],
                      dtype='timedelta64[s]', freq=None)
      >>> tdelta_idx.mean()
      Timedelta('2 days 00:00:00')