[ 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.round  #

DatetimeIndex.  round  (  _ *  args  _ , _ **  kwargs  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/extension.py#L92-L102) #

    

Perform round operation on the data to the specified  freq  .

Parameters  :

    

**freq** str or Offset

    

The frequency level to round the index to. Must be a fixed frequency like
âSâ (second) not âMEâ (month end). See [ frequency aliases
](../../user_guide/timeseries.html#timeseries-offset-aliases) for a list of
possible  freq  values.

**ambiguous** âinferâ, bool-ndarray, âNaTâ, default âraiseâ

    

Only relevant for DatetimeIndex:

  * âinferâ will attempt to infer fall dst-transition hours based on order 

  * bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) 

  * âNaTâ will return NaT where there are ambiguous times 

  * âraiseâ will raise an AmbiguousTimeError if there are ambiguous times. 

**nonexistent** âshift_forwardâ, âshift_backwardâ, âNaTâ