pandas.DatetimeIndex

class pandas.DatetimeIndex [source]

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 since version 0.15.0: Attempt to infer fall dst-transition hours based on order name : object 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

Notes

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

Attributes

T	return the transpose, which is by definition self
asi8
asobject	return object Index which contains boxed values
base	return the base object if the memory of the underlying data is
data	return the data pointer of the underlying data
date	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
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
dtype_str
empty
flags
freq	get/set the frequency of the Index
freqstr	Return the frequency object as a string if its set, otherwise None
has_duplicates
hasnans
hour	The hours of the datetime
inferred_freq
inferred_type
is_all_dates
is_leap_year	Logical indicating if the date belongs to a leap year
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_normalized
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_unique
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
minute	The minutes of the datetime
month	The month as January=1, December=12
name
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,
nlevels
offset
quarter	The quarter of the date
resolution
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.
tz
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
weekday_name	The name of day in a week (ex: Friday)
weekofyear	The week ordinal of the year
year	The year of the datetime

Methods

all([other])
any([other])
append(other)	Append a collection of Index options together
argmax([axis])	Returns the indices of the maximum values along an axis.
argmin([axis])	Returns the indices of the minimum values along an axis.
argsort(*args, **kwargs)	Returns the indices that would sort the index and its 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])	Create an Index with values cast to dtypes.
ceil(freq)	ceil the index to the specified freq
contains(key)	return a boolean if this key is IN the index
copy([name, deep, dtype])	Make a copy of this object.
delete(loc)	Make a new DatetimeIndex with passed location(s) deleted.
difference(other)	Return a new Index with elements from the index that are not in other.
drop(labels[, errors])	Make new Index with passed list of labels deleted
drop_duplicates([keep])	Return Index with duplicate values removed
dropna([how])	Return Index without NA/NaN values
duplicated([keep])	Return boolean np.ndarray 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
fillna([value, downcast])	Fill NA/NaN values with the specified value
floor(freq)	floor the index to the specified freq
format([name, formatter])	Render a string representation of the Index
get_duplicates()
get_indexer(target[, method, limit, tolerance])	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)	Compute indexer and mask for new index given the current index.
get_level_values(level)	Return an Index of values for requested level, equal to the length of the index.
get_loc(key[, method, tolerance])	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(values)	Group the index labels by a given array of values.
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_categorical()
is_floating()
is_integer()
is_interval()
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
isna()	Detect missing values
isnull()	Detect missing values
item()	return the first element of the underlying data as a python
join(other[, how, level, return_indexers, sort])	See Index.join
map(f)
max([axis])	Return the maximum value of the Index or maximum along an axis.
memory_usage([deep])	Memory usage of my values
min([axis])	Return the minimum value of the Index or minimum along an axis.
normalize()	Return DatetimeIndex with times to midnight.
notna()	Inverse of isna
notnull()	Inverse of isna
nunique([dropna])	Return number of unique elements in the object.
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, *args, **kwargs)	Analogous to ndarray.repeat
reshape(*args, **kwargs)	NOT IMPLEMENTED: do not call this method, as reshaping is not supported for Index objects and will raise an error.
round(freq, *args, **kwargs)	round the index to the specified freq
searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
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)
sort_values([return_indexer, ascending])	Return sorted copy of Index
sortlevel([level, ascending, sort_remaining])	For internal compatibility with with the Index API
str	alias of StringMethods
strftime(date_format)	Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library.
summary([name])	return a summarized representation
symmetric_difference(other[, result_name])	Compute the symmetric difference of two Index objects.
take(indices[, axis, allow_fill, fill_value])	return a new Index of the values selected by the indices
to_datetime([dayfirst])
to_frame([index])	Create a DataFrame with a column containing the Index.
to_julian_date()	Convert DatetimeIndex to Float64Index of Julian Dates.
to_native_types([slicer])	Format specified values of self and return them.
to_period([freq])	Cast to PeriodIndex at a particular frequency
to_perioddelta(freq)	Calcuates TimedeltaIndex of difference between index values and index converted to PeriodIndex at specified freq.
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(*args, **kwargs)	return the transpose, which is by definition self
tz_convert(tz)	Convert tz-aware DatetimeIndex from one time zone to another (using
tz_localize(tz[, ambiguous, errors])	Localize tz-naive DatetimeIndex to given time zone (using
union(other)	Specialized union for DatetimeIndex objects.
union_many(others)	A bit of a hack to accelerate unioning a collection of indexes
unique()	Return unique values in the object.
value_counts([normalize, sort, ascending, ...])	Returns object containing counts of unique values.
view([cls])
where(cond[, other])	New in version 0.19.0.