Indexes#

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.