pandas.DatetimeIndex#

class pandas.DatetimeIndex(data=None, freq=<no_default>, tz=<no_default>, ambiguous='raise', dayfirst=False, yearfirst=False, dtype=None, copy=None, 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 dtypes allowed are ‘datetime64[ns]’, ‘datetime64[us]’, ‘datetime64[ms]’, ‘datetime64[s]’.

copybool, default None

Whether to copy input data, only relevant for array, Series, and Index inputs (for other input, e.g. a list, a new array is created anyway). Defaults to True for array input and False for Index/Series. Set to False to avoid copying array input at your own risk (if you know the input data won’t be modified elsewhere). Set to True to force copying Series/Index up front.

namelabel, default None

Name to be stored in the index.

Attributes

`date`	Returns numpy array of python `datetime.date` objects.
`time`	Returns numpy array of `datetime.time` objects.
`timetz`	Returns numpy array of `datetime.time` objects with timezones.
`tz`	Return the timezone.
`freq`	Return the frequency object if it is set, otherwise None.
`freqstr`	Return the frequency object as a string if it's set, otherwise None.
`inferred_freq`	Return the inferred frequency of the index.

year
month
day
hour
minute
second
microsecond
nanosecond
dayofyear
day_of_year
dayofweek
day_of_week
weekday
quarter
is_month_start
is_month_end
is_quarter_start
is_quarter_end
is_year_start
is_year_end
is_leap_year

Methods

`normalize`()	Convert times to midnight.
`strftime`(date_format)	Convert to Index using specified date_format.
`snap`([freq])	Snap time stamps to nearest occurring frequency.
`tz_convert`(tz)	Convert tz-aware Datetime Array/Index from one time zone to another.
`tz_localize`(tz[, ambiguous, nonexistent])	Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
`round`(freq[, ambiguous, nonexistent])	Perform round operation on the data to the specified freq.
`floor`(freq[, ambiguous, nonexistent])	Perform floor operation on the data to the specified freq.
`ceil`(freq[, ambiguous, nonexistent])	Perform ceil operation on the data to the specified freq.
`to_period`([freq])	Cast to PeriodArray/PeriodIndex at a particular frequency.
`to_pydatetime`()	Return an ndarray of `datetime.datetime` objects.
`to_series`([index, name])	Create a Series with both index and values equal to the index keys.
`to_frame`([index, name])	Create a DataFrame with a column containing the Index.
`to_julian_date`()	Convert Timestamp to a Julian Date.
`month_name`([locale])	Return the month names with specified locale.
`day_name`([locale])	Return the day names with specified locale.
`mean`(*[, skipna, axis])	Return the mean value of the Array.
`std`([axis, dtype, out, ddof, keepdims, skipna])	Return sample standard deviation over requested axis.