* [ 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)
  * [ Index objects ](../indexing.html)
  * pandas.DatetimeIndex 

#  pandas.DatetimeIndex  #

_ class  _ pandas.  DatetimeIndex  (  _ data  =  None  _ , _ freq  =
_NoDefault.no_default  _ , _ tz  =  _NoDefault.no_default  _ , _ normalize  =
_NoDefault.no_default  _ , _ closed  =  _NoDefault.no_default  _ , _ ambiguous
=  'raise'  _ , _ dayfirst  =  False  _ , _ yearfirst  =  False  _ , _ dtype
=  None  _ , _ copy  =  False  _ , _ name  =  None  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/datetimes.py#L106-L818) #

    

Immutable ndarray-like of datetime64 data.

Represented internally as int64, and which can be boxed to Timestamp objects
that are subclasses of datetime and carry metadata.

Changed in version 2.0.0:  The various numeric date/time attributes ( [ ` day
` ](pandas.DatetimeIndex.day.html#pandas.DatetimeIndex.day
"pandas.DatetimeIndex.day") , [ ` month  `
](pandas.DatetimeIndex.month.html#pandas.DatetimeIndex.month
"pandas.DatetimeIndex.month") , [ ` year  `
](pandas.DatetimeIndex.year.html#pandas.DatetimeIndex.year
"pandas.DatetimeIndex.year") etc.) now have dtype ` int32  ` . Previously they
had dtype ` int64  ` .

Parameters  :

    

**data** array-like (1-dimensional)

    

Datetime-like data to construct index with.

**freq** str or pandas offset object, optional

    

One of pandas date offset strings or corresponding objects. The string
âinferâ can be passed in order to set the frequency of the index as the
inferred frequency upon creation.

**tz** pytz.timezone or dateutil.tz.tzfile or datetime.tzinfo or str

    

Set the Timezone of the data.

**normalize** bool, default False

    

Normalize start/end dates to midnight before generating date range.

Deprecated since version 2.1.0.

**closed** {âleftâ, ârightâ},