pandas.DatetimeIndex

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 dateutil.tz.tzfile

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

Attributes

T	return the transpose, which is by definition self
asi8
asobject
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 datetime.date.
day	The days of the datetime
dayofweek	The day of the week with Monday=0, Sunday=6
dayofyear	The ordinal day of the year
days_in_month	The number of days in the month
daysinmonth	The number of days in the month
dtype
flags
freq	get/set the frequncy of the Index
freqstr	return the frequency object as a string if its set, otherwise None
has_duplicates
hour	The hours of the datetime
inferred_type
is_all_dates
is_monotonic	alias for is_monotonic_increasing (deprecated)
is_monotonic_decreasing	return if the index is monotonic decreasing (only equal or
is_monotonic_increasing	return if the index is monotonic increasing (only equal or
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
names
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
nlevels
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

hasnans
inferred_freq
is_normalized
is_unique
name
offset
resolution
tz

Methods

all([other])
any([other])
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
astype(dtype)
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 np.array 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_duplicates()
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)	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[, method])	Get integer location for requested label
get_slice_bound(label, side, kind)	Calculate slice bound that corresponds to given 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
groupby(f)
holds_integer()
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.
is_(other)	More flexible, faster check like is but that works through views
is_boolean()
is_floating()
is_integer()
is_lexsorted_for_tuple(tup)
is_mixed()
is_numeric()
is_object()
is_type_compatible(typ)
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
map(f)
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.
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, kind])	Return indexer for specified label slice.
slice_locs([start, end, step, kind])	Compute slice locations for input labels.
snap([freq])	Snap time stamps to nearest occurring frequency
sort(*args, **kwargs)
summary([name])	return a summarized representation
sym_diff(other[, result_name])	Compute the sorted symmetric difference of two Index objects.
take(indices[, axis])	Analogous to ndarray.take
to_datetime([dayfirst])
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.
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.
view([cls])