[ pandas.TimedeltaIndex.round ](pandas.TimedeltaIndex.round.html)
    * [ pandas.TimedeltaIndex.floor ](pandas.TimedeltaIndex.floor.html)
    * [ pandas.TimedeltaIndex.ceil ](pandas.TimedeltaIndex.ceil.html)
    * [ pandas.TimedeltaIndex.to_frame ](pandas.TimedeltaIndex.to_frame.html)
    * [ pandas.TimedeltaIndex.mean ](pandas.TimedeltaIndex.mean.html)
    * [ pandas.PeriodIndex ](pandas.PeriodIndex.html)
    * [ pandas.PeriodIndex.day ](pandas.PeriodIndex.day.html)
    * [ pandas.PeriodIndex.dayofweek ](pandas.PeriodIndex.dayofweek.html)
    * [ pandas.PeriodIndex.day_of_week ](pandas.PeriodIndex.day_of_week.html)
    * [ pandas.PeriodIndex.dayofyear ](pandas.PeriodIndex.dayofyear.html)
    * [ pandas.PeriodIndex.day_of_year ](pandas.PeriodIndex.day_of_year.html)
    * [ pandas.PeriodIndex.days_in_month ](pandas.PeriodIndex.days_in_month.html)
    * [ pandas.PeriodIndex.daysinmonth ](pandas.PeriodIndex.daysinmonth.html)
    * [ pandas.PeriodIndex.end_time ](pandas.PeriodIndex.end_time.html)
    * [ pandas.PeriodIndex.freq ](pandas.PeriodIndex.freq.html)
    * [ pandas.PeriodIndex.freqstr ](pandas.PeriodIndex.freqstr.html)
    * [ pandas.PeriodIndex.hour ](pandas.PeriodIndex.hour.html)
    * [ pandas.PeriodIndex.is_leap_year ](pandas.PeriodIndex.is_leap_year.html)
    * [ pandas.PeriodIndex.minute ](pandas.PeriodIndex.minute.html)
    * [ pandas.PeriodIndex.month ](pandas.PeriodIndex.month.html)
    * [ pandas.PeriodIndex.quarter ](pandas.PeriodIndex.quarter.html)
    * [ pandas.PeriodIndex.qyear ](pandas.PeriodIndex.qyear.html)
    * [ pandas.PeriodIndex.second ](pandas.PeriodIndex.second.html)
    * [ pandas.PeriodIndex.start_time ](pandas.PeriodIndex.start_time.html)
    * [ pandas.PeriodIndex.week ](pandas.PeriodIndex.week.html)
    * [ pandas.PeriodIndex.weekday ](pandas.PeriodIndex.weekday.html)
    * [ pandas.PeriodIndex.weekofyear ](pandas.PeriodIndex.weekofyear.html)
    * [ pandas.PeriodIndex.year ](pandas.PeriodIndex.year.html)
    * [ pandas.PeriodIndex.asfreq ](pandas.PeriodIndex.asfreq.html)
    * [ pandas.PeriodIndex.strftime ](pandas.PeriodIndex.strftime.html)
    * [ pandas.PeriodIndex.to_timestamp ](pandas.PeriodIndex.to_timestamp.html)
    * [ pandas.PeriodIndex.from_fields ](pandas.PeriodIndex.from_fields.html)
    * [ pandas.PeriodIndex.from_ordinals ](pandas.PeriodIndex.from_ordinals.html)
  * [ Date offsets ](../offset_frequency.html)
  * [ Window ](../window.html)
  * [ GroupBy ](../groupby.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * __
  * [ pandas.DatetimeIndex ](pandas.DatetimeIndex.html)
  * pandas.Datet... 

#  pandas.DatetimeIndex.mean  #

DatetimeIndex.  mean  (  _ *  _ , _ skipna  =  True  _ , _ axis  =  0  _ )  [
[source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/datetimelike.py#L95-L97) #

    

Return the mean value of the Array.

Parameters  :

    

**skipna** bool, default True

    

Whether to ignore any NaT elements.

**axis** int, optional, default 0

    
Returns  :

    

scalar

    

Timestamp or Timedelta.

See also

[ ` numpy.ndarray.mean  `
](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.mean.html#numpy.ndarray.mean
"\(in NumPy v1.26\)")

    

Returns the average of array elements along a given axis.

[ ` Series.mean  ` ](pandas.Series.mean.html#pandas.Series.mean
"pandas.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  `
](pandas.DatetimeIndex.html#pandas.DatetimeIndex "pandas.DatetimeIndex") :

    
    
    >>> idx = pd.date_range('2001-01-01 00:00', periods=3)
    >>> idx
    DatetimeIndex(['2001-01-01',