xarray.IndexVariable

class xarray.IndexVariable(dims, data, attrs=None, encoding=None, fastpath=False)

Wrapper for accommodating a pandas.Index in an xarray.Variable.

IndexVariable preserve loaded values in the form of a pandas.Index instead of a NumPy array. Hence, their values are immutable and must always be one- dimensional.

They also have a name property, which is the name of their sole dimension unless another name is given.

__init__(dims, data, attrs=None, encoding=None, fastpath=False)

Parameters

dimsstr or sequence of str

Name(s) of the the data dimension(s). Must be either a string (only for 1D data) or a sequence of strings with length equal to the number of dimensions.

dataarray_like

Data array which supports numpy-like data access.

attrsdict_like or None, optional

Attributes to assign to the new variable. If None (default), an empty attribute dictionary is initialized.

encodingdict_like or None, optional

Dictionary specifying how to encode this array’s data into a serialized format like netCDF4. Currently used keys (for netCDF) include ‘_FillValue’, ‘scale_factor’, ‘add_offset’ and ‘dtype’. Well-behaved code to serialize a Variable should ignore unrecognized encoding items.

Methods

__init__(dims, data[, attrs, encoding, fastpath])	Parameters
all([dim, axis])	Reduce this Variable’s data by applying all along some dimension(s).
any([dim, axis])	Reduce this Variable’s data by applying any along some dimension(s).
argmax([dim, axis, skipna])	Reduce this Variable’s data by applying argmax along some dimension(s).
argmin([dim, axis, skipna])	Reduce this Variable’s data by applying argmin along some dimension(s).
argsort([axis, kind, order])	Returns the indices that would sort this array.
astype(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
broadcast_equals(other[, equiv])	True if two Variables have the values after being broadcast against each other; otherwise False.
chunk([chunks, name, lock])	Coerce this array’s data into a dask arrays with the given chunks.
clip([min, max, out])	Return an array whose values are limited to [min, max].
coarsen(windows, func[, boundary, side])	Apply
compute(**kwargs)	Manually trigger loading of this variable’s data from disk or a remote source into memory and return a new variable.
concat(variables[, dim, positions, shortcut])	Specialized version of Variable.concat for IndexVariable objects.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
copy([deep, data])	Returns a copy of this object.
count([dim, axis])	Reduce this Variable’s data by applying count along some dimension(s).
cumprod([dim, axis, skipna])	Apply cumprod along some dimension of Variable.
cumsum([dim, axis, skipna])	Apply cumsum along some dimension of Variable.
equals(other[, equiv])	True if two Variables have the same dimensions and values; otherwise False.
expand_dims(*args)
fillna(value)
get_axis_num(dim)	Return axis number(s) corresponding to dimension(s) in this array.
get_level_variable(level)	Return a new IndexVariable from a given MultiIndex level.
identical(other)	Like equals, but also checks attributes.
isel([indexers, drop])	Return a new array indexed along the specified dimension(s).
isnull(*args, **kwargs)
item(*args)	Copy an element of an array to a standard Python scalar and return it.
load()	Manually trigger loading of this variable’s data from disk or a remote source into memory and return this variable.
max([dim, axis, skipna])	Reduce this Variable’s data by applying max along some dimension(s).
mean([dim, axis, skipna])	Reduce this Variable’s data by applying mean along some dimension(s).
median([dim, axis, skipna])	Reduce this Variable’s data by applying median along some dimension(s).
min([dim, axis, skipna])	Reduce this Variable’s data by applying min along some dimension(s).
no_conflicts(other)	True if the intersection of two Variable’s non-null data is equal; otherwise false.
notnull(*args, **kwargs)
pad_with_fill_value([pad_widths, fill_value])	Return a new Variable with paddings.
prod([dim, axis, skipna])	Reduce this Variable’s data by applying prod along some dimension(s).
quantile(q[, dim, interpolation])	Compute the qth quantile of the data along the specified dimension.
rank(dim[, pct])	Ranks the data.
reduce(func[, dim, axis, keep_attrs, …])	Reduce this array by applying func along some dimension(s).
roll([shifts])	Return a new Variable with rolld data.
rolling_window(dim, window, window_dim[, …])	Make a rolling_window along dim and add a new_dim to the last place.
round(*args, **kwargs)
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
set_dims(dims[, shape])	Return a new variable with given set of dimensions.
shift([shifts, fill_value])	Return a new Variable with shifted data.
squeeze([dim])	Return a new object with squeezed data.
stack([dimensions])	Stack any number of existing dimensions into a single new dimension.
std([dim, axis, skipna])	Reduce this Variable’s data by applying std along some dimension(s).
sum([dim, axis, skipna])	Reduce this Variable’s data by applying sum along some dimension(s).
to_base_variable()	Return this variable as a base xarray.Variable
to_coord()	to_coord has been deprecated.
to_dict([data])	Dictionary representation of variable.
to_index()	Convert this variable to a pandas.Index
to_index_variable()	Return this variable as an xarray.IndexVariable
to_variable()	to_variable has been deprecated.
transpose(*dims)	Return a new Variable object with transposed dimensions.
unstack([dimensions])	Unstack an existing dimension into multiple new dimensions.
var([dim, axis, skipna])	Reduce this Variable’s data by applying var along some dimension(s).
where(cond[, other])

Attributes

T
attrs	Dictionary of local attributes on this variable.
chunks	Block dimensions for this array’s data or None if it’s not a dask array.
data
dims	Tuple of dimension names with which this variable is associated.
dtype
encoding	Dictionary of encodings on this variable.
imag
level_names	Return MultiIndex level names or None if this IndexVariable has no MultiIndex.
name
nbytes
ndim
real
shape
size
sizes	Ordered mapping from dimension names to lengths.
values	The variable’s data as a numpy.ndarray