pandas.TimedeltaIndex

class pandas.TimedeltaIndex

Immutable ndarray of timedelta64 data, represented internally as int64, and which can be boxed to timedelta objects

Parameters :

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

Optional timedelta-like data to construct index with

unit: unit of the arg (D,h,m,s,ms,us,ns) denote the unit, optional

which is an integer/float number

freq: a frequency for the index, optional

copy : bool

Make a copy of input ndarray

start : starting value, timedelta-like, optional

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

periods : int, optional, > 0

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

end : end time, timedelta-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)

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
components	Return a dataframe of the components of the Timedeltas
data	return the data pointer of the underlying data
days	The number of integer days for each element
dtype
flags
freqstr	return the frequency object as a string if its set, otherwise None
hours	The number of integer hours for each element
inferred_type
is_all_dates
is_monotonic	return if the index has monotonic (only equaly or increasing) values
itemsize	return the size of the dtype of the item of the underlying data
microseconds	The number of integer microseconds for each element
milliseconds	The number of integer milliseconds for each element
minutes	The number of integer minutes for each element
names
nanoseconds	The number of integer nanoseconds for each element
nbytes	return the number of bytes in the underlying data
ndim	return the number of dimensions of the underlying data, by definition 1
nlevels
seconds	The number of integer seconds for each element
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
values	return the underlying data as an ndarray

freq
hasnans
inferred_freq
is_unique
name
resolution

Methods

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
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 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_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, **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
groupby(f)
holds_integer()
identical(other)	Similar to equals, but check that other comparable attributes are
insert(loc, item)	Make new Index inserting new item at location
intersection(other)	Specialized intersection for TimedeltaIndex objects. May be much faster
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
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])	For an ordered Index, compute the slice indexer for input labels and
slice_locs([start, end])	Index.slice_locs, customized to handle partial ISO-8601 string slicing
sort(*args, **kwargs)
summary([name])
sym_diff(other[, result_name])	Compute the sorted symmetric difference of two Index objects.
take(indices[, axis])	Analogous to ndarray.take
to_datetime([dayfirst])	For an Index containing strings or datetime.datetime objects, attempt
to_native_types([slicer])	slice and dice then format
to_pytimedelta()	Return TimedeltaIndex as object ndarray of datetime.timedelta objects
to_series(**kwargs)	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
union(other)	Specialized union for TimedeltaIndex objects. If combine
unique()	Index.unique with handling for DatetimeIndex/PeriodIndex metadata
value_counts([normalize, sort, ascending, ...])	Returns object containing counts of unique values.
view([cls])