class pandas.DatetimeIndex

Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.

Parameters :

data : array-like (1-dimensional), optional

Optional datetime-like data to construct index with

copy : bool

Make a copy of input ndarray

freq : string or pandas offset object, optional

One of pandas date offset strings or corresponding objects

start : starting value, datetime-like, optional

If data is None, start is used as the start point in generating regular timestamp data.

periods : int, optional, > 0

Number of periods to generate, if generating index. Takes precedence over end argument

end : end time, datetime-like, optional

If periods is none, generated index will extend to first conforming time on or just past end argument

closed : string or None, default None

Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None)

tz : pytz.timezone or

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

  • ‘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

infer_dst : boolean, default False (DEPRECATED)

Attempt to infer fall dst-transition hours based on order

name : object

Name to be stored in the index


T return the transpose, which is by definition self
base return the base object if the memory of the underlying data is shared
data return the data pointer of the underlying data
date Returns numpy array of
day The days of the datetime
dayofweek The day of the week with Monday=0, Sunday=6
dayofyear The ordinal day of the year
freq get/set the frequncy of the Index
freqstr return the frequency object as a string if its set, otherwise None
hour The hours of the datetime
is_monotonic return if the index has monotonic (only equaly or increasing) values
is_month_end Logical indicating if last day of month (defined by frequency)
is_month_start Logical indicating if first day of month (defined by frequency)
is_quarter_end Logical indicating if last day of quarter (defined by frequency)
is_quarter_start Logical indicating if first day of quarter (defined by frequency)
is_year_end Logical indicating if last day of year (defined by frequency)
is_year_start Logical indicating if first day of year (defined by frequency)
itemsize return the size of the dtype of the item of the underlying data
microsecond The microseconds of the datetime
millisecond The milliseconds of the datetime
minute The minutes of the datetime
month The month as January=1, December=12
nanosecond The nanoseconds of the datetime
nbytes return the number of bytes in the underlying data
ndim return the number of dimensions of the underlying data, by definition 1
quarter The quarter of the date
second The seconds of the datetime
shape return a tuple of the shape of the underlying data
size return the number of elements in the underlying data
strides return the strides of the underlying data
time Returns numpy array of datetime.time.
tzinfo Alias for tz attribute
values return the underlying data as an ndarray
week The week ordinal of the year
weekday The day of the week with Monday=0, Sunday=6
weekofyear The week ordinal of the year
year The year of the datetime


all([axis, out]) Returns True if all elements evaluate to True.
any([axis, out]) Returns True if any of the elements of a evaluate to True.
append(other) Append a collection of Index options together
argmax([axis]) return a ndarray of the maximum argument indexer
argmin([axis]) return a ndarray of the minimum argument indexer
argsort(*args, **kwargs) return an ndarray indexer of the underlying data
asof(label) For a sorted index, return the most recent label up to and including the passed label.
asof_locs(where, mask) where : array of timestamps
copy([names, name, dtype, deep]) Make a copy of this object.
delete(loc) Make a new DatetimeIndex with passed location(s) deleted.
diff(*args, **kwargs)
difference(other) Compute sorted set difference of two Index objects
drop(labels) Make new Index with passed list of labels deleted
drop_duplicates([take_last]) Return Index with duplicate values removed
duplicated([take_last]) Return boolean Index denoting duplicate values
equals(other) Determines if two Index objects contain the same elements.
factorize([sort, na_sentinel]) Encode the object as an enumerated type or categorical variable
format([name, formatter]) Render a string representation of the Index
get_indexer(target[, method, limit]) Compute indexer and mask for new index given the current index.
get_indexer_for(target, **kwargs) guaranteed return of an indexer even when non-unique
get_indexer_non_unique(target, **kwargs) return an indexer suitable for taking from a non unique index
get_level_values(level) Return vector of label values for requested level, equal to the length
get_loc(key) Get integer location for requested label
get_value(series, key) Fast lookup of value from 1-dimensional ndarray.
get_value_maybe_box(series, key)
get_values() return the underlying data as an ndarray
identical(other) Similar to equals, but check that other comparable attributes are
indexer_at_time(time[, asof]) Select values at particular time of day (e.g.
indexer_between_time(start_time, end_time[, ...]) Select values between particular times of day (e.g., 9:00-9:30AM)
insert(loc, item) Make new Index inserting new item at location
intersection(other) Specialized intersection for DatetimeIndex objects. May be much faster
is_(other) More flexible, faster check like is but that works through views
isin(values) Compute boolean array of whether each index value is found in the
item() return the first element of the underlying data as a python scalar
join(other[, how, level, return_indexers]) See Index.join
max([axis]) return the maximum value of the Index
min([axis]) return the minimum value of the Index
normalize() Return DatetimeIndex with times to midnight. Length is unaltered
nunique([dropna]) Return number of unique elements in the object.
order([return_indexer, ascending]) Return sorted copy of Index
putmask(mask, value) return a new Index of the values set with the mask
ravel([order]) return an ndarray of the flattened values of the underlying data
reindex(target[, method, level, limit]) Create index with target’s values (move/add/delete values as necessary)
rename(name[, inplace]) Set new names on index.
repeat(repeats[, axis]) Analogous to ndarray.repeat
searchsorted(key[, side])
set_names(names[, level, inplace]) Set new names on index.
set_value(arr, key, value) Fast lookup of value from 1-dimensional ndarray.
shift(n[, freq]) Specialized shift which produces a DatetimeIndex
slice_indexer([start, end, step]) Index.slice_indexer, customized to handle time slicing
slice_locs([start, end]) Index.slice_locs, customized to handle partial ISO-8601 string slicing
snap([freq]) Snap time stamps to nearest occurring frequency
sort(*args, **kwargs)
sym_diff(other[, result_name]) Compute the sorted symmetric difference of two Index objects.
take(indices[, axis]) Analogous to ndarray.take
to_julian_date() Convert DatetimeIndex to Float64Index of Julian Dates.
to_native_types([slicer]) slice and dice then format
to_period([freq]) Cast to PeriodIndex at a particular frequency
to_pydatetime() Return DatetimeIndex as object ndarray of datetime.datetime objects
to_series([keep_tz]) Create a Series with both index and values equal to the index keys
tolist() return a list of the underlying data
transpose() return the transpose, which is by definition self
tz_convert(tz) Convert tz-aware DatetimeIndex from one time zone to another (using pytz/dateutil)
tz_localize(*args, **kwargs) Localize tz-naive DatetimeIndex to given time zone (using pytz/dateutil),
union(other) Specialized union for DatetimeIndex objects. If combine
union_many(others) A bit of a hack to accelerate unioning a collection of indexes
unique() Index.unique with handling for DatetimeIndex/PeriodIndex metadata
value_counts([normalize, sort, ascending, ...]) Returns object containing counts of unique values.