API reference

This page provides an auto-generated summary of xarray’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

See also: :ref:`public api`_.

Top-level functions

apply_ufunc(func, *args[, input_core_dims, …])	Apply a vectorized function for unlabeled arrays on xarray objects.
align(*objects[, join, copy, indexes, …])	Given any number of Dataset and/or DataArray objects, returns new objects with aligned indexes and dimension sizes.
broadcast(*args[, exclude])	Explicitly broadcast any number of DataArray or Dataset objects against one another.
concat(objs, dim[, data_vars, coords, …])	Concatenate xarray objects along a new or existing dimension.
merge(objects[, compat, join, fill_value])	Merge any number of xarray objects into a single Dataset as variables.
auto_combine(datasets[, concat_dim, compat, …])	Attempt to auto-magically combine the given datasets into one.
combine_by_coords(datasets[, compat, …])	Attempt to auto-magically combine the given datasets into one by using dimension coordinates.
combine_nested(datasets, concat_dim[, …])	Explicitly combine an N-dimensional grid of datasets into one by using a succession of concat and merge operations along each dimension of the grid.
where(cond, x, y)	Return elements from x or y depending on cond.
set_options(**kwargs)	Set options for xarray in a controlled context.
full_like(other, fill_value[, dtype])	Return a new object with the same shape and type as a given object.
zeros_like(other[, dtype])	Shorthand for full_like(other, 0, dtype)
ones_like(other[, dtype])	Shorthand for full_like(other, 1, dtype)
dot(*arrays[, dims])	Generalized dot product for xarray objects.

Dataset

Creating a dataset

Dataset([data_vars, coords, attrs, compat])	A multi-dimensional, in memory, array database.
decode_cf(obj[, concat_characters, …])	Decode the given Dataset or Datastore according to CF conventions into a new Dataset.

Attributes

Dataset.dims	Mapping from dimension names to lengths.
Dataset.sizes	Mapping from dimension names to lengths.
Dataset.data_vars	Dictionary of DataArray objects corresponding to data variables
Dataset.coords	Dictionary of xarray.DataArray objects corresponding to coordinate variables
Dataset.attrs	Dictionary of global attributes on this dataset
Dataset.encoding	Dictionary of global encoding attributes on this dataset
Dataset.indexes	Mapping of pandas.Index objects used for label based indexing
Dataset.get_index(key)	Get an index for a dimension, with fall-back to a default RangeIndex
Dataset.chunks	Block dimensions for this dataset’s data or None if it’s not a dask array.
Dataset.nbytes

Dictionary interface

Datasets implement the mapping interface with keys given by variable names and values given by DataArray objects.

Dataset.__getitem__(key)	Access variables or coordinates this dataset as a DataArray.
Dataset.__setitem__(key, value)	Add an array to this dataset.
Dataset.__delitem__(key)	Remove a variable from this dataset.
Dataset.update(other[, inplace])	Update this dataset’s variables with those from another dataset.
Dataset.items()
Dataset.values()

Dataset contents

Dataset.copy([deep, data])	Returns a copy of this dataset.
Dataset.assign([variables])	Assign new data variables to a Dataset, returning a new object with all the original variables in addition to the new ones.
Dataset.assign_coords([coords])	Assign new coordinates to this object.
Dataset.assign_attrs(*args, **kwargs)	Assign new attrs to this object.
Dataset.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
Dataset.merge(other[, inplace, …])	Merge the arrays of two datasets into a single dataset.
Dataset.rename([name_dict, inplace])	Returns a new object with renamed variables and dimensions.
Dataset.rename_vars([name_dict])	Returns a new object with renamed variables including coordinates
Dataset.rename_dims([dims_dict])	Returns a new object with renamed dimensions only.
Dataset.swap_dims(dims_dict[, inplace])	Returns a new object with swapped dimensions.
Dataset.expand_dims([dim, axis])	Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape.
Dataset.drop([labels, dim, errors])	Drop variables or index labels from this dataset.
Dataset.drop_dims(drop_dims, *[, errors])	Drop dimensions and associated variables from this dataset.
Dataset.set_coords(names[, inplace])	Given names of one or more variables, set them as coordinates
Dataset.reset_coords([names, drop, inplace])	Given names of coordinates, reset them to become variables

Comparisons

Dataset.equals(other)	Two Datasets are equal if they have matching variables and coordinates, all of which are equal.
Dataset.identical(other)	Like equals, but also checks all dataset attributes and the attributes on all variables and coordinates.
Dataset.broadcast_equals(other)	Two Datasets are broadcast equal if they are equal after broadcasting all variables against each other.

Indexing

Dataset.loc	Attribute for location based indexing.
Dataset.isel([indexers, drop])	Returns a new dataset with each array indexed along the specified dimension(s).
Dataset.sel([indexers, method, tolerance, drop])	Returns a new dataset with each array indexed by tick labels along the specified dimension(s).
Dataset.head([indexers])	Returns a new dataset with the first n values of each array for the specified dimension(s).
Dataset.tail([indexers])	Returns a new dataset with the last n values of each array for the specified dimension(s).
Dataset.thin([indexers])	Returns a new dataset with each array indexed along every `n`th value for the specified dimension(s)
Dataset.squeeze([dim, drop, axis])	Return a new object with squeezed data.
Dataset.interp([coords, method, …])	Multidimensional interpolation of Dataset.
Dataset.interp_like(other[, method, …])	Interpolate this object onto the coordinates of another object, filling the out of range values with NaN.
Dataset.reindex([indexers, method, …])	Conform this object onto a new set of indexes, filling in missing values with fill_value.
Dataset.reindex_like(other[, method, …])	Conform this object onto the indexes of another object, filling in missing values with fill_value.
Dataset.set_index([indexes, append, inplace])	Set Dataset (multi-)indexes using one or more existing coordinates or variables.
Dataset.reset_index(dims_or_levels[, drop, …])	Reset the specified index(es) or multi-index level(s).
Dataset.reorder_levels([dim_order, inplace])	Rearrange index levels using input order.

Missing value handling

Dataset.isnull(*args, **kwargs)
Dataset.notnull(*args, **kwargs)
Dataset.combine_first(other)	Combine two Datasets, default to data_vars of self.
Dataset.count([dim])	Reduce this Dataset’s data by applying count along some dimension(s).
Dataset.dropna(dim[, how, thresh, subset])	Returns a new dataset with dropped labels for missing values along the provided dimension.
Dataset.fillna(value)	Fill missing values in this object.
Dataset.ffill(dim[, limit])	Fill NaN values by propogating values forward
Dataset.bfill(dim[, limit])	Fill NaN values by propogating values backward
Dataset.interpolate_na([dim, method, limit, …])	Interpolate values according to different methods.
Dataset.where(cond[, other, drop])	Filter elements from this object according to a condition.
Dataset.isin(test_elements)	Tests each value in the array for whether it is in test elements.

Computation

Dataset.apply(func[, keep_attrs, args])	Apply a function over the data variables in this dataset.
Dataset.reduce(func[, dim, keep_attrs, …])	Reduce this dataset by applying func along some dimension(s).
Dataset.groupby(group[, squeeze, …])	Returns a GroupBy object for performing grouped operations.
Dataset.groupby_bins(group, bins[, right, …])	Returns a GroupBy object for performing grouped operations.
Dataset.rolling([dim, min_periods, center])	Rolling window object.
Dataset.rolling_exp([window, window_type])	Exponentially-weighted moving window.
Dataset.coarsen([dim, boundary, side, …])	Coarsen object.
Dataset.resample([indexer, skipna, closed, …])	Returns a Resample object for performing resampling operations.
Dataset.diff(dim[, n, label])	Calculate the n-th order discrete difference along given axis.
Dataset.quantile(q[, dim, interpolation, …])	Compute the qth quantile of the data along the specified dimension.
Dataset.differentiate(coord[, edge_order, …])	Differentiate with the second order accurate central differences.
Dataset.integrate(coord[, datetime_unit])	integrate the array with the trapezoidal rule.

Aggregation: all any argmax argmin max mean median min prod sum std var

ndarray methods: astype argsort clip conj conjugate imag round real cumsum cumprod rank

Grouped operations: assign assign_coords first last fillna where quantile

Reshaping and reorganizing

Dataset.transpose(*dims)	Return a new Dataset object with all array dimensions transposed.
Dataset.stack([dimensions])	Stack any number of existing dimensions into a single new dimension.
Dataset.unstack([dim])	Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.
Dataset.to_stacked_array(new_dim, sample_dims)	Combine variables of differing dimensionality into a DataArray without broadcasting.
Dataset.shift([shifts, fill_value])	Shift this dataset by an offset along one or more dimensions.
Dataset.roll([shifts, roll_coords])	Roll this dataset by an offset along one or more dimensions.
Dataset.sortby(variables[, ascending])	Sort object by labels or values (along an axis).
Dataset.broadcast_like(other[, exclude])	Broadcast this DataArray against another Dataset or DataArray.

DataArray

DataArray([data, coords, dims, name, attrs, …])

N-dimensional array with labeled coordinates and dimensions.

Attributes

DataArray.values	The array’s data as a numpy.ndarray
DataArray.data	The array’s data as a dask or numpy array
DataArray.coords	Dictionary-like container of coordinate arrays.
DataArray.dims	Tuple of dimension names associated with this array.
DataArray.sizes	Ordered mapping from dimension names to lengths.
DataArray.name	The name of this array.
DataArray.attrs	Dictionary storing arbitrary metadata with this array.
DataArray.encoding	Dictionary of format-specific settings for how this array should be serialized.
DataArray.indexes	Mapping of pandas.Index objects used for label based indexing
DataArray.get_index(key)	Get an index for a dimension, with fall-back to a default RangeIndex

ndarray attributes: ndim shape size dtype nbytes chunks

DataArray contents

DataArray.assign_coords([coords])	Assign new coordinates to this object.
DataArray.assign_attrs(*args, **kwargs)	Assign new attrs to this object.
DataArray.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
DataArray.rename([new_name_or_name_dict])	Returns a new DataArray with renamed coordinates or a new name.
DataArray.swap_dims(dims_dict)	Returns a new DataArray with swapped dimensions.
DataArray.expand_dims([dim, axis])	Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape.
DataArray.drop(labels[, dim, errors])	Drop coordinates or index labels from this DataArray.
DataArray.reset_coords([names, drop, inplace])	Given names of coordinates, reset them to become variables.
DataArray.copy([deep, data])	Returns a copy of this array.

ndarray methods: astype item

Indexing

DataArray.__getitem__(key)
DataArray.__setitem__(key, value)
DataArray.loc	Attribute for location based indexing like pandas.
DataArray.isel([indexers, drop])	Return a new DataArray whose data is given by integer indexing along the specified dimension(s).
DataArray.sel([indexers, method, tolerance, …])	Return a new DataArray whose data is given by selecting index labels along the specified dimension(s).
DataArray.head([indexers])	Return a new DataArray whose data is given by the the first n values along the specified dimension(s).
DataArray.tail([indexers])	Return a new DataArray whose data is given by the the last n values along the specified dimension(s).
DataArray.thin([indexers])	Return a new DataArray whose data is given by each n value along the specified dimension(s).
DataArray.squeeze([dim, drop, axis])	Return a new object with squeezed data.
DataArray.interp([coords, method, …])	Multidimensional interpolation of variables.
DataArray.interp_like(other[, method, …])	Interpolate this object onto the coordinates of another object, filling out of range values with NaN.
DataArray.reindex([indexers, method, …])	Conform this object onto the indexes of another object, filling in missing values with fill_value.
DataArray.reindex_like(other[, method, …])	Conform this object onto the indexes of another object, filling in missing values with fill_value.
DataArray.set_index([indexes, append, inplace])	Set DataArray (multi-)indexes using one or more existing coordinates.
DataArray.reset_index(dims_or_levels[, …])	Reset the specified index(es) or multi-index level(s).
DataArray.reorder_levels([dim_order, inplace])	Rearrange index levels using input order.

Missing value handling

DataArray.isnull(*args, **kwargs)
DataArray.notnull(*args, **kwargs)
DataArray.combine_first(other)	Combine two DataArray objects, with union of coordinates.
DataArray.count([dim, axis])	Reduce this DataArray’s data by applying count along some dimension(s).
DataArray.dropna(dim[, how, thresh])	Returns a new array with dropped labels for missing values along the provided dimension.
DataArray.fillna(value)	Fill missing values in this object.
DataArray.ffill(dim[, limit])	Fill NaN values by propogating values forward
DataArray.bfill(dim[, limit])	Fill NaN values by propogating values backward
DataArray.interpolate_na([dim, method, …])	Interpolate values according to different methods.
DataArray.where(cond[, other, drop])	Filter elements from this object according to a condition.
DataArray.isin(test_elements)	Tests each value in the array for whether it is in test elements.

Comparisons

DataArray.equals(other)	True if two DataArrays have the same dimensions, coordinates and values; otherwise False.
DataArray.identical(other)	Like equals, but also checks the array name and attributes, and attributes on all coordinates.
DataArray.broadcast_equals(other)	Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions.

Computation

DataArray.reduce(func[, dim, axis, …])	Reduce this array by applying func along some dimension(s).
DataArray.groupby(group[, squeeze, …])	Returns a GroupBy object for performing grouped operations.
DataArray.groupby_bins(group, bins[, right, …])	Returns a GroupBy object for performing grouped operations.
DataArray.rolling([dim, min_periods, center])	Rolling window object.
DataArray.rolling_exp([window, window_type])	Exponentially-weighted moving window.
DataArray.coarsen([dim, boundary, side, …])	Coarsen object.
DataArray.dt	Access datetime fields for DataArrays with datetime-like dtypes.
DataArray.resample([indexer, skipna, …])	Returns a Resample object for performing resampling operations.
DataArray.get_axis_num(dim)	Return axis number(s) corresponding to dimension(s) in this array.
DataArray.diff(dim[, n, label])	Calculate the n-th order discrete difference along given axis.
DataArray.dot(other[, dims])	Perform dot product of two DataArrays along their shared dims.
DataArray.quantile(q[, dim, interpolation, …])	Compute the qth quantile of the data along the specified dimension.
DataArray.differentiate(coord[, edge_order, …])	Differentiate the array with the second order accurate central differences.
DataArray.integrate(dim[, datetime_unit])	integrate the array with the trapezoidal rule.
DataArray.str	Vectorized string functions for string-like arrays.

Aggregation: all any argmax argmin max mean median min prod sum std var

ndarray methods: argsort clip conj conjugate imag searchsorted round real T cumsum cumprod rank

Grouped operations: assign_coords first last fillna where quantile

Reshaping and reorganizing

DataArray.transpose(*dims[, transpose_coords])	Return a new DataArray object with transposed dimensions.
DataArray.stack([dimensions])	Stack any number of existing dimensions into a single new dimension.
DataArray.unstack([dim])	Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.
DataArray.to_unstacked_dataset(dim[, level])	Unstack DataArray expanding to Dataset along a given level of a stacked coordinate.
DataArray.shift([shifts, fill_value])	Shift this array by an offset along one or more dimensions.
DataArray.roll([shifts, roll_coords])	Roll this array by an offset along one or more dimensions.
DataArray.sortby(variables[, ascending])	Sort object by labels or values (along an axis).
DataArray.broadcast_like(other[, exclude])	Broadcast this DataArray against another Dataset or DataArray.

Universal functions

Warning

With recent versions of numpy, dask and xarray, NumPy ufuncs are now supported directly on all xarray and dask objects. This obviates the need for the xarray.ufuncs module, which should not be used for new code unless compatibility with versions of NumPy prior to v1.13 is required.

This functions are copied from NumPy, but extended to work on NumPy arrays, dask arrays and all xarray objects. You can find them in the xarray.ufuncs module:

angle arccos arccosh arcsin arcsinh arctan arctan2 arctanh ceil conj copysign cos cosh deg2rad degrees exp expm1 fabs fix floor fmax fmin fmod fmod frexp hypot imag iscomplex isfinite isinf isnan isreal ldexp log log10 log1p log2 logaddexp logaddexp2 logical_and logical_not logical_or logical_xor maximum minimum nextafter rad2deg radians real rint sign signbit sin sinh sqrt square tan tanh trunc

IO / Conversion

Dataset methods

open_dataset(filename_or_obj[, group, …])	Open and decode a dataset from a file or file-like object.
load_dataset(filename_or_obj, **kwargs)	Open, load into memory, and close a Dataset from a file or file-like object.
open_mfdataset(paths[, chunks, concat_dim, …])	Open multiple files as a single dataset.
open_rasterio(filename[, parse_coordinates, …])	Open a file with rasterio (experimental).
open_zarr(store[, group, synchronizer, …])	Load and decode a dataset from a Zarr store.
Dataset.to_netcdf([path, mode, format, …])	Write dataset contents to a netCDF file.
Dataset.to_zarr([store, mode, synchronizer, …])	Write dataset contents to a zarr group.
save_mfdataset(datasets, paths[, mode, …])	Write multiple datasets to disk as netCDF files simultaneously.
Dataset.to_array([dim, name])	Convert this dataset into an xarray.DataArray
Dataset.to_dataframe()	Convert this dataset into a pandas.DataFrame.
Dataset.to_dask_dataframe([dim_order, set_index])	Convert this dataset into a dask.dataframe.DataFrame.
Dataset.to_dict([data])	Convert this dataset to a dictionary following xarray naming conventions.
Dataset.from_dataframe(dataframe[, sparse])	Convert a pandas.DataFrame into an xarray.Dataset
Dataset.from_dict(d)	Convert a dictionary into an xarray.Dataset.
Dataset.close()	Close any files linked to this object
Dataset.compute(**kwargs)	Manually trigger loading of this dataset’s data from disk or a remote source into memory and return a new dataset.
Dataset.persist(**kwargs)	Trigger computation, keeping data as dask arrays
Dataset.load(**kwargs)	Manually trigger loading of this dataset’s data from disk or a remote source into memory and return this dataset.
Dataset.chunk([chunks, name_prefix, token, lock])	Coerce all arrays in this dataset into dask arrays with the given chunks.
Dataset.filter_by_attrs(**kwargs)	Returns a Dataset with variables that match specific conditions.
Dataset.info([buf])	Concise summary of a Dataset variables and attributes.

DataArray methods

open_dataarray(filename_or_obj[, group, …])	Open an DataArray from a file or file-like object containing a single data variable.
load_dataarray(filename_or_obj, **kwargs)	Open, load into memory, and close a DataArray from a file or file-like object containing a single data variable.
DataArray.to_dataset([dim, name])	Convert a DataArray to a Dataset.
DataArray.to_netcdf(*args, **kwargs)	Write DataArray contents to a netCDF file.
DataArray.to_pandas()	Convert this array into a pandas object with the same shape.
DataArray.to_series()	Convert this array into a pandas.Series.
DataArray.to_dataframe([name])	Convert this array and its coordinates into a tidy pandas.DataFrame.
DataArray.to_index()	Convert this variable to a pandas.Index.
DataArray.to_masked_array([copy])	Convert this array into a numpy.ma.MaskedArray
DataArray.to_cdms2()	Convert this array into a cdms2.Variable
DataArray.to_iris()	Convert this array into a iris.cube.Cube
DataArray.from_iris(cube)	Convert a iris.cube.Cube into an xarray.DataArray
DataArray.to_dict([data])	Convert this xarray.DataArray into a dictionary following xarray naming conventions.
DataArray.from_series(series[, sparse])	Convert a pandas.Series into an xarray.DataArray.
DataArray.from_cdms2(variable)	Convert a cdms2.Variable into an xarray.DataArray
DataArray.from_dict(d)	Convert a dictionary into an xarray.DataArray
DataArray.close()	Close any files linked to this object
DataArray.compute(**kwargs)	Manually trigger loading of this array’s data from disk or a remote source into memory and return a new array.
DataArray.persist(**kwargs)	Trigger computation in constituent dask arrays
DataArray.load(**kwargs)	Manually trigger loading of this array’s data from disk or a remote source into memory and return this array.
DataArray.chunk([chunks, name_prefix, …])	Coerce this array’s data into a dask arrays with the given chunks.

GroupBy objects

core.groupby.DataArrayGroupBy(obj, group[, …])	GroupBy object specialized to grouping DataArray objects
core.groupby.DataArrayGroupBy.apply(func[, …])	Apply a function over each array in the group and concatenate them together into a new array.
core.groupby.DataArrayGroupBy.reduce(func[, …])	Reduce the items in this group by applying func along some dimension(s).
core.groupby.DatasetGroupBy(obj, group[, …])
core.groupby.DatasetGroupBy.apply(func[, …])	Apply a function over each Dataset in the group and concatenate them together into a new Dataset.
core.groupby.DatasetGroupBy.reduce(func[, …])	Reduce the items in this group by applying func along some dimension(s).

Rolling objects

core.rolling.DataArrayRolling(obj, windows)
core.rolling.DataArrayRolling.construct(…)	Convert this rolling object to xr.DataArray, where the window dimension is stacked as a new dimension
core.rolling.DataArrayRolling.reduce(func, …)	Reduce the items in this group by applying func along some dimension(s).
core.rolling.DatasetRolling(obj, windows[, …])
core.rolling.DatasetRolling.construct(window_dim)	Convert this rolling object to xr.Dataset, where the window dimension is stacked as a new dimension
core.rolling.DatasetRolling.reduce(func, …)	Reduce the items in this group by applying func along some dimension(s).
core.rolling_exp.RollingExp(obj, windows[, …])	Exponentially-weighted moving window object.

Resample objects

Resample objects also implement the GroupBy interface (methods like apply(), reduce(), mean(), sum(), etc.).

core.resample.DataArrayResample(*args[, …])	DataArrayGroupBy object specialized to time resampling operations over a specified dimension
core.resample.DataArrayResample.asfreq()	Return values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN.
core.resample.DataArrayResample.backfill([…])	Backward fill new values at up-sampled frequency.
core.resample.DataArrayResample.interpolate([kind])	Interpolate up-sampled data using the original data as knots.
core.resample.DataArrayResample.nearest([…])	Take new values from nearest original coordinate to up-sampled frequency coordinates.
core.resample.DataArrayResample.pad([tolerance])	Forward fill new values at up-sampled frequency.
core.resample.DatasetResample(*args[, dim, …])	DatasetGroupBy object specialized to resampling a specified dimension
core.resample.DatasetResample.asfreq()	Return values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN.
core.resample.DatasetResample.backfill([…])	Backward fill new values at up-sampled frequency.
core.resample.DatasetResample.interpolate([kind])	Interpolate up-sampled data using the original data as knots.
core.resample.DatasetResample.nearest([…])	Take new values from nearest original coordinate to up-sampled frequency coordinates.
core.resample.DatasetResample.pad([tolerance])	Forward fill new values at up-sampled frequency.

Accessors

core.accessor_dt.DatetimeAccessor(obj)	Access datetime fields for DataArrays with datetime-like dtypes.
core.accessor_str.StringAccessor(obj)	Vectorized string functions for string-like arrays.

Custom Indexes

CFTimeIndex

Custom Index for working with CF calendars and dates

Creating custom indexes

cftime_range([start, end, periods, freq, …])

Return a fixed frequency CFTimeIndex.

Plotting

Dataset.plot	Access plotting functions.
DataArray.plot	Access plotting functions
Dataset.plot.scatter
plot.plot(darray[, row, col, col_wrap, ax, …])	Default plot of DataArray using matplotlib.pyplot.
plot.contourf(x, y, z, ax, **kwargs)	Filled contour plot of 2d DataArray
plot.contour(x, y, z, ax, **kwargs)	Contour plot of 2d DataArray
plot.hist(darray[, figsize, size, aspect, …])	Histogram of DataArray
plot.imshow(x, y, z, ax, **kwargs)	Image plot of 2d DataArray using matplotlib.pyplot
plot.line(darray, *args[, row, col, …])	Line plot of DataArray index against values
plot.pcolormesh(x, y, z, ax[, infer_intervals])	Pseudocolor plot of 2d DataArray
plot.FacetGrid(data[, col, row, col_wrap, …])	Initialize the matplotlib figure and FacetGrid object.

Testing

testing.assert_equal(a, b)	Like numpy.testing.assert_array_equal(), but for xarray objects.
testing.assert_identical(a, b)	Like xarray.testing.assert_equal(), but also matches the objects’ names and attributes.
testing.assert_allclose(a, b[, rtol, atol, …])	Like numpy.testing.assert_allclose(), but for xarray objects.

Exceptions

MergeError	Error class for merge failures due to incompatible arguments.
SerializationWarning	Warnings about encoding/decoding issues in serialization.

Advanced API

Dataset.variables	Low level interface to Dataset contents as dict of Variable objects.
DataArray.variable	Low level interface to the Variable object for this DataArray.
Variable(dims, data[, attrs, encoding, fastpath])	A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array.
IndexVariable(dims, data[, attrs, encoding, …])	Wrapper for accommodating a pandas.Index in an xarray.Variable.
as_variable(obj[, name])	Convert an object into a Variable.
register_dataset_accessor(name)	Register a custom property on xarray.Dataset objects.
register_dataarray_accessor(name)	Register a custom accessor on xarray.DataArray objects.

These backends provide a low-level interface for lazily loading data from external file-formats or protocols, and can be manually invoked to create arguments for the from_store and dump_to_store Dataset methods:

backends.NetCDF4DataStore(manager[, group, …])	Store for reading and writing data via the Python-NetCDF4 library.
backends.H5NetCDFStore(filename[, mode, …])	Store for reading and writing data via h5netcdf
backends.PydapDataStore(ds)	Store for accessing OpenDAP datasets with pydap.
backends.ScipyDataStore(filename_or_obj[, …])	Store for reading and writing data via scipy.io.netcdf.
backends.FileManager	Manager for acquiring and closing a file object.
backends.CachingFileManager(opener, *args[, …])	Wrapper for automatically opening and closing file objects.
backends.DummyFileManager(value)	FileManager that simply wraps an open file in the FileManager interface.