pandas.DatetimeIndex

class pandas.DatetimeIndex(data=None, freq=<object object>, tz=None, normalize=False, closed=None, ambiguous='raise', dayfirst=False, yearfirst=False, dtype=None, copy=False, name=None)

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.

Parameters

dataarray-like (1-dimensional), optional

Optional 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.

tzpytz.timezone or dateutil.tz.tzfile or datetime.tzinfo or str

Set the Timezone of the data.

normalizebool, default False

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

closed{‘left’, ‘right’}, optional

Set whether to include start and end that are on the boundary. The default includes boundary points on either end.

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 an AmbiguousTimeError 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.

See also

Index

The base pandas Index type.

TimedeltaIndex

Index of timedelta64 data.

PeriodIndex

Index of Period data.

to_datetime

Convert argument to datetime.

date_range

Create a fixed-frequency DatetimeIndex.

Notes

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

Attributes

year	The year of the datetime.
month	The month as January=1, December=12.
day	The day of the datetime.
hour	The hours of the datetime.
minute	The minutes of the datetime.
second	The seconds of the datetime.
microsecond	The microseconds of the datetime.
nanosecond	The nanoseconds of the datetime.
date	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
time	Returns numpy array of datetime.time.
timetz	Returns numpy array of datetime.time also containing timezone information.
dayofyear	The ordinal day of the year.
weekofyear	(DEPRECATED) The week ordinal of the year.
week	(DEPRECATED) The week ordinal of the year.
dayofweek	The day of the week with Monday=0, Sunday=6.
weekday	The day of the week with Monday=0, Sunday=6.
quarter	The quarter of the date.
tz	Return timezone, if any.
freq	Return the frequency object if it is set, otherwise None.
freqstr	Return the frequency object as a string if its set, otherwise None.
is_month_start	Indicates whether the date is the first day of the month.
is_month_end	Indicates whether the date is the last day of the month.
is_quarter_start	Indicator for whether the date is the first day of a quarter.
is_quarter_end	Indicator for whether the date is the last day of a quarter.
is_year_start	Indicate whether the date is the first day of a year.
is_year_end	Indicate whether the date is the last day of the year.
is_leap_year	Boolean indicator if the date belongs to a leap year.
inferred_freq	Tries to return a string representing a frequency guess, generated by infer_freq.

Methods

normalize(*args, **kwargs)	Convert times to midnight.
strftime(*args, **kwargs)	Convert to Index using specified date_format.
snap([freq])	Snap time stamps to nearest occurring frequency.
tz_convert(*args, **kwargs)	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(*args, **kwargs)	Perform round operation on the data to the specified freq.
floor(*args, **kwargs)	Perform floor operation on the data to the specified freq.
ceil(*args, **kwargs)	Perform ceil operation on the data to the specified freq.
to_period(*args, **kwargs)	Cast to PeriodArray/Index at a particular frequency.
to_perioddelta(*args, **kwargs)	Calculate TimedeltaArray of difference between index values and index converted to PeriodArray at specified freq.
to_pydatetime(*args, **kwargs)	Return Datetime Array/Index as object ndarray of datetime.datetime objects.
to_series([keep_tz, index, name])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index.
to_frame([index, name])	Create a DataFrame with a column containing the Index.
month_name(*args, **kwargs)	Return the month names of the DateTimeIndex with specified locale.
day_name(*args, **kwargs)	Return the day names of the DateTimeIndex with specified locale.
mean(*args, **kwargs)	Return the mean value of the Array.

pandas.MultiIndex.get_level_values pandas.DatetimeIndex.year