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Showing content from https://pandas.pydata.org/docs/dev/user_guide/../reference/api/pandas.DatetimeIndex.html below:

pandas.DatetimeIndex — pandas 3.0.0.dev0+2232.ga2315af1df documentation

class pandas.DatetimeIndex(data=None, freq=<no_default>, tz=<no_default>, ambiguous='raise', dayfirst=False, yearfirst=False, dtype=None, copy=False, name=None)[source]#

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, month, year etc.) now have dtype int32. Previously they had dtype int64.

Parameters:

dataarray-like (1-dimensional)

Datetime-like data to construct index with.

freqstr or pandas offset object, optional

One of pandas date offset strings or corresponding objects. The string ‘infer’ can be passed in order to set the frequency of the index as the inferred frequency upon creation.

tzzoneinfo.ZoneInfo, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or str

Set the Timezone of the data.

ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.

• ‘infer’ will attempt to infer fall dst-transition hours based on order

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

• ‘NaT’ will return NaT where there are ambiguous times

• ‘raise’ will raise a ValueError if there are ambiguous times.

dayfirstbool, default False

If True, parse dates in data with the day first order.

yearfirstbool, default False

If True parse dates in data with the year first order.

dtypenumpy.dtype or DatetimeTZDtype or str, default None

Note that the only NumPy dtype allowed is datetime64[ns].

copybool, default False

Make a copy of input ndarray.

namelabel, default None

Name to be stored in the index.

Attributes

Methods

Notes

To learn more about the frequency strings, please see this link.

Examples

>>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
>>> idx
DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'],
dtype='datetime64[s, UTC]', freq=None)

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