pandas.DatetimeIndex.mean#

DatetimeIndex.mean(*, skipna=True, axis=0)[source]#

Return the mean value of the Array.

Parameters:

skipnabool, default True: Whether to ignore any NaT elements.
axisint, optional, default 0

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[ns]', 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[ns]', freq=None)
>>> tdelta_idx.mean()
Timedelta('2 days 00:00:00')