xarray.CFTimeIndex#

class xarray.CFTimeIndex(data, name=None, **kwargs)[source]#

Custom Index for working with CF calendars and dates

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

Parameters
  • data (array or CFTimeIndex) – Sequence of cftime.datetime objects to use in index

  • name (str, default: None) – Name of the resulting index

See also

cftime_range

__init__()#

Methods

__init__()

all(*args, **kwargs)

Return whether all elements are Truthy.

any(*args, **kwargs)

Return whether any element is Truthy.

append(other)

Append a collection of Index options together.

argmax([axis, skipna])

Return int position of the largest value in the Series.

argmin([axis, skipna])

Return int position of the smallest value in the Series.

argsort(*args, **kwargs)

Return the integer indices that would sort the index.

asof(label)

Return the label from the index, or, if not present, the previous one.

asof_locs(where, mask)

Return the locations (indices) of labels in the index.

astype(dtype[, copy])

Create an Index with values cast to dtypes.

ceil(freq)

Round dates up to fixed frequency.

contains(key)

Needed for .loc based partial-string indexing

copy([name, deep, dtype, names])

Make a copy of this object.

delete(loc)

Make new Index with passed location(-s) deleted.

difference(other[, sort])

Return a new Index with elements of index not in other.

drop(labels[, errors])

Make new Index with passed list of labels deleted.

drop_duplicates([keep])

Return Index with duplicate values removed.

droplevel([level])

Return index with requested level(s) removed.

dropna([how])

Return Index without NA/NaN values.

duplicated([keep])

Indicate duplicate index values.

equals(other)

Determine if two Index object are equal.

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)

Round dates down to fixed frequency.

format([name, formatter, na_rep])

Render a string representation of the Index.

get_indexer(target[, method, limit, tolerance])

Compute indexer and mask for new index given the current index.

get_indexer_for(target)

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.

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

groupby(values)

Group the index labels by a given array of values.

holds_integer()

Whether the type is an integer type.

identical(other)

Similar to equals, but checks that object attributes and types are also equal.

insert(loc, item)

Make new Index inserting new item at location.

intersection(other[, sort])

Form the intersection of two Index objects.

is_(other)

More flexible, faster check like is but that works through views.

is_boolean()

Check if the Index only consists of booleans.

is_categorical()

Check if the Index holds categorical data.

is_floating()

Check if the Index is a floating type.

is_integer()

Check if the Index only consists of integers.

is_interval()

Check if the Index holds Interval objects.

is_mixed()

Check if the Index holds data with mixed data types.

is_numeric()

Check if the Index only consists of numeric data.

is_object()

Check if the Index is of the object dtype.

is_type_compatible(kind)

Whether the index type is compatible with the provided type.

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])

Compute join_index and indexers to conform data structures to the new index.

map(mapper[, na_action])

Map values using an input mapping or function.

max([axis, skipna])

Return the maximum value of the Index.

memory_usage([deep])

Memory usage of the values.

min([axis, skipna])

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.

rename(name[, inplace])

Alter Index or MultiIndex name.

repeat(repeats[, axis])

Repeat elements of a Index.

round(freq)

Round dates to a fixed frequency.

searchsorted(value[, side, sorter])

Find indices where elements should be inserted to maintain order.

set_names(names[, level, inplace])

Set Index or MultiIndex name.

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])

Compute the slice indexer for input labels and step.

slice_locs([start, end, step, kind])

Compute slice locations for input labels.

sort(*args, **kwargs)

Use sort_values instead.

sort_values([return_indexer, ascending, ...])

Return a sorted copy of the index.

sortlevel([level, ascending, sort_remaining])

For internal compatibility 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.

symmetric_difference(other[, result_name, sort])

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_flat_index()

Identity method.

to_frame([index, name])

Create a DataFrame with a column containing the Index.

to_list()

Return a list of the values.

to_native_types([slicer])

Format specified values of self and return them.

to_numpy([dtype, copy, na_value])

A NumPy ndarray representing the values in this Series or Index.

to_series([index, name])

Create a Series with both index and values equal to the index keys.

tolist()

Return a list of the values.

transpose(*args, **kwargs)

Return the transpose, which is by definition self.

union(other[, sort])

Form the union of two Index objects.

unique([level])

Return unique values in the index.

value_counts([normalize, sort, ascending, ...])

Return a Series containing counts of unique values.

view([cls])

where(cond[, other])

Replace values where the condition is False.

Attributes

T

Return the transpose, which is by definition self.

array

The ExtensionArray of the data backing this Series or Index.

asi8

Convert to integers with units of microseconds since 1970-01-01.

calendar

The calendar used by the datetimes in the index.

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

days_in_month

The number of days in the month of the datetime

dtype

Return the dtype object of the underlying data.

empty

freq

The frequency used by the dates in the index.

has_duplicates

Check if the Index has duplicate values.

hasnans

Return True if there are any NaNs.

hour

The hours of the datetime

inferred_type

Return a string of the type inferred from the values.

is_all_dates

Whether or not the index values only consist of dates.

is_monotonic

Alias for is_monotonic_increasing.

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.

microsecond

The microseconds of the datetime

minute

The minutes of the datetime

month

The month of the datetime

name

Return Index or MultiIndex name.

names

nbytes

Return the number of bytes in the underlying data.

ndim

Number of dimensions of the underlying data, by definition 1.

nlevels

Number of levels.

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.

values

Return an array representing the data in the Index.

year

The year of the datetime