class xarray.Coordinate(name, data, attrs=None, encoding=None, fastpath=False)

Wrapper around pandas.Index that adds xarray specific functionality.

The most important difference is that Coordinate objects must always have a name, which is the dimension along which they index values.

Coordinates must always be 1-dimensional. In addition to Variable methods and properties (attributes, encoding, broadcasting), they support some pandas.Index methods directly (e.g., get_indexer), even though pandas does not (yet) support duck-typing for indexes.

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


__init__(name, data[, attrs, encoding, fastpath])
all([dim, axis, keep_attrs]) Reduce this Variable’s data by applying all along some dimension(s).
any([dim, axis, keep_attrs]) Reduce this Variable’s data by applying any along some dimension(s).
argmax([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying argmax along some dimension(s).
argmin([dim, axis, skipna, keep_attrs]) 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) 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].
concat(variables[, dim, positions, shortcut]) Specialized version of Variable.concat for Coordinate variables.
conj() Complex-conjugate all elements.
conjugate() Return the complex conjugate, element-wise.
copy([deep]) Returns a copy of this object.
count([dim, axis, keep_attrs]) Reduce this Variable’s data by applying count along some dimension(s).
equals(other) True if two Variables have the same dimensions and values; otherwise False.
expand_dims(dims[, shape]) Return a new variable with expanded dimensions.
get_axis_num(dim) Return axis number(s) corresponding to dimension(s) in this array.
identical(other) Like equals, but also checks attributes.
isel(**indexers) Return a new array indexed along the specified dimension(s).
isnull(*args, **kwargs) Detect missing values (NaN in numeric arrays, None/NaN in object arrays)
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, keep_attrs]) Reduce this Variable’s data by applying max along some dimension(s).
mean([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying mean along some dimension(s).
median([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying median along some dimension(s).
min([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying min along some dimension(s).
notnull(*args, **kwargs) Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays.
prod([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying prod along some dimension(s).
reduce(func[, dim, axis, keep_attrs, allow_lazy]) Reduce this array by applying func along some dimension(s).
roll(**shifts) Return a new Variable with rolld data.
round(*args, **kwargs)
searchsorted(v[, side, sorter]) Find indices where elements of v should be inserted in a to maintain order.
shift(**shifts) Return a new Variable with shifted data.
squeeze([dim]) Return a new Variable object with squeezed data.
stack(**dimensions) Stack any number of existing dimensions into a single new dimension.
std([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying std along some dimension(s).
sum([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying sum along some dimension(s).
to_coord() Return this variable as an xarray.Coordinate
to_index() Convert this variable to a pandas.Index
to_variable() Return this variable as a base xarray.Variable
transpose(*dims) Return a new Variable object with transposed dimensions.
unstack(**dimensions) Unstack an existing dimension into multiple new dimensions.
var([dim, axis, skipna, keep_attrs]) Reduce this Variable’s data by applying var along some dimension(s).


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.
dims Tuple of dimension names with which this variable is associated.
encoding Dictionary of encodings on this variable.
values The variable’s data as a numpy.ndarray