xarray.CFTimeIndex

class xarray.CFTimeIndex

Custom Index for working with CF calendars and dates

All elements of a CFTimeIndex must be cftime.datetime objects.

Parameters

dataarray or CFTimeIndex

Sequence of cftime.datetime objects to use in index

namestr, default None

Name of the resulting index

See also

cftime_range

__init__($self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__($self, /, *args, **kwargs)	Initialize self.
all(*args, **kwargs)	Return whether all elements are True.
any(*args, **kwargs)	Return whether any element is 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 the integer indicies that would sort the index.
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 mask : array of booleans where data is not NA
astype(dtype[, copy])	Create an Index with values cast to dtypes.
contains(key)	Needed for .loc based partial-string indexing
copy([name, deep, dtype])	Make a copy of this object.
delete(loc)	Make new Index 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])	Indicate duplicate index 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
format([name, formatter])	Render a string representation of the Index
get_duplicates()	Extract duplicated index elements.
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 This dispatches to get_indexer or get_indexer_nonunique as appropriate
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])	Adapted from pandas.tseries.index.DatetimeIndex.get_loc
get_slice_bound(label, side, kind)	Calculate slice bound that corresponds to given label.
get_value(series, key)	Adapted from pandas.tseries.index.DatetimeIndex.get_value
get_values()	Return Index data as an numpy.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 also equal
insert(loc, item)	Make new Index inserting new item at location.
intersection(other)	Form the intersection of two Index objects.
is_(other)	More flexible, faster check like is but that works through views
is_boolean()
is_categorical()	Check if the Index holds categorical data.
is_floating()
is_integer()
is_interval()
is_lexsorted_for_tuple(tup)
is_mixed()
is_numeric()
is_object()
is_type_compatible(kind)
isin(values[, level])	Return a boolean array where the index values are in values.
isna()	Detect missing values.
isnull()	Detect missing values.
item()	return the first element of the underlying data as a python scalar
join(other[, how, level, return_indexers, sort])	this is an internal non-public method
map(mapper[, na_action])	Map values using input correspondence (a dict, Series, or function).
max()	Return the maximum value of the Index.
memory_usage([deep])	Memory usage of the values
min()	Return the minimum value of the Index.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
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)	Repeat elements of an Index.
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)	Shift the CFTimeIndex a multiple of the given frequency.
slice_indexer([start, end, step, kind])	For an ordered or unique index, compute the slice indexer for input labels and step.
slice_locs([start, end, step, kind])	Compute slice locations for input labels.
sort(*args, **kwargs)
sort_values([return_indexer, ascending])	Return a sorted copy of the index.
sortlevel([level, ascending, sort_remaining])	For internal compatibility with with the Index API
strftime(date_format)	Return an Index 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_datetimeindex([unsafe])	If possible, convert this index to a pandas.DatetimeIndex.
to_frame([index])	Create a DataFrame with a column containing the Index.
to_native_types([slicer])	Format specified values of self and return them.
to_series([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
tolist()	Return a list of the values.
transpose(*args, **kwargs)	return the transpose, which is by definition self
union(other)	Form the union of two Index objects and sorts if possible.
unique([level])	Return unique values in the index.
value_counts([normalize, sort, ascending, …])	Returns object containing counts of unique values.
view([cls])
where(cond[, other])	New in version 0.19.0.

Attributes

T	return the transpose, which is by definition self
asi8
base	return the base object if the memory of the underlying data is shared
data	return the data pointer of the underlying data
date_type
day	The days of the datetime
dayofweek	The day of week of the datetime
dayofyear	The ordinal day of year of the datetime
dtype	return the dtype object of the underlying data
dtype_str	return the dtype str of the underlying data
empty
flags	return the ndarray.flags for the underlying data
has_duplicates
hasnans	return if I have any nans; enables various perf speedups
hour	The hours of the datetime
inferred_type	return a string of the type inferred from the values
is_all_dates
is_monotonic	alias for is_monotonic_increasing (deprecated)
is_monotonic_decreasing	return if the index is monotonic decreasing (only equal or decreasing) values.
is_monotonic_increasing	return if the index is monotonic increasing (only equal or increasing) values.
is_unique	return if the index has unique values
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 of the datetime
name
names
nbytes	return the number of bytes in the underlying data
ndim	return the number of dimensions of the underlying data, by definition 1
nlevels
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
values	return the underlying data as an ndarray
year	The year of the datetime