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

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

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