Indexes

See also

See the Xarray gallery on custom indexes for more examples.

Creating indexes

cftime_range([start, end, periods, freq, ...])	Return a fixed frequency CFTimeIndex.
date_range([start, end, periods, freq, tz, ...])	Return a fixed frequency datetime index.
date_range_like(source, calendar[, use_cftime])	Generate a datetime array with the same frequency, start and end as another one, but in a different calendar.
indexes.RangeIndex.arange([start, stop, ...])	Create a new RangeIndex from given start, stop and step values.
indexes.RangeIndex.linspace(start, stop[, ...])	Create a new RangeIndex from given start / stop values and number of values.

Built-in Indexes

Default, pandas-backed indexes built-in to Xarray:

indexes.PandasIndex(array, dim[, ...])	Wrap a pandas.Index as an xarray compatible index.
indexes.PandasMultiIndex(array, dim[, ...])	Wrap a pandas.MultiIndex as an xarray compatible index.

More complex indexes built-in to Xarray:

CFTimeIndex(data[, name])	Custom Index for working with CF calendars and dates
indexes.RangeIndex(transform)	Xarray index implementing a simple bounded 1-dimension interval with evenly spaced, monotonic floating-point values.
indexes.NDPointIndex(tree_obj, *, ...)	Xarray index for irregular, n-dimensional data.
indexes.CoordinateTransformIndex(transform)	Helper class for creating Xarray indexes based on coordinate transforms.

Building custom indexes

These classes are building blocks for more complex Indexes:

indexes.CoordinateTransform(coord_names, ...)	Abstract coordinate transform with dimension & coordinate names.
indexes.CoordinateTransformIndex(transform)	Helper class for creating Xarray indexes based on coordinate transforms.
indexes.NDPointIndex(tree_obj, *, ...)	Xarray index for irregular, n-dimensional data.
indexes.TreeAdapter(points, *, options)	Lightweight adapter abstract class for plugging in 3rd-party structures like scipy.spatial.KDTree or sklearn.neighbors.KDTree into NDPointIndex.

The Index base class for building custom indexes:

Index()	Base class inherited by all xarray-compatible indexes.
Index.from_variables(variables, *, options)	Create a new index object from one or more coordinate variables.
Index.concat(indexes, dim[, positions])	Create a new index by concatenating one or more indexes of the same type.
Index.stack(variables, dim)	Create a new index by stacking coordinate variables into a single new dimension.
Index.unstack()	Unstack a (multi-)index into multiple (single) indexes.
Index.create_variables([variables])	Maybe create new coordinate variables from this index.
Index.should_add_coord_to_array(name, var, dims)	Define whether or not an index coordinate variable should be added to a new DataArray.
Index.to_pandas_index()	Cast this xarray index to a pandas.Index object or raise a TypeError if this is not supported.
Index.isel(indexers)	Maybe returns a new index from the current index itself indexed by positional indexers.
Index.sel(labels)	Query the index with arbitrary coordinate label indexers.
Index.join(other[, how])	Return a new index from the combination of this index with another index of the same type.
Index.reindex_like(other)	Query the index with another index of the same type.
Index.equals(other, **kwargs)	Compare this index with another index of the same type.
Index.roll(shifts)	Roll this index by an offset along one or more dimensions.
Index.rename(name_dict, dims_dict)	Maybe update the index with new coordinate and dimension names.
Index.copy([deep])	Return a (deep) copy of this index.

The following are useful when building custom Indexes

IndexSelResult(dim_indexers[, indexes, ...])

Index query results.