IO / Conversion

Dataset methods

load_dataset(filename_or_obj, **kwargs)	Open, load into memory, and close a Dataset from a file or file-like object.
open_dataset(filename_or_obj, *[, engine, ...])	Open and decode a dataset from a file or file-like object.
open_mfdataset(paths[, chunks, concat_dim, ...])	Open multiple files as a single dataset.
open_zarr(store[, group, synchronizer, ...])	Load and decode a dataset from a Zarr store.
save_mfdataset(datasets, paths[, mode, ...])	Write multiple datasets to disk as netCDF files simultaneously.
Dataset.as_numpy()	Coerces wrapped data and coordinates into numpy arrays, returning a Dataset.
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.to_dataarray([dim, name])	Convert this dataset into an xarray.DataArray
Dataset.to_dataframe([dim_order])	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, encoding])	Convert this dataset to a dictionary following xarray naming conventions.
Dataset.to_netcdf([path, mode, format, ...])	Write dataset contents to a netCDF file.
Dataset.to_pandas()	Convert this dataset into a pandas object without changing the number of dimensions.
Dataset.to_zarr([store, chunk_store, mode, ...])	Write dataset contents to a zarr group.
Dataset.chunk([chunks, name_prefix, token, ...])	Coerce all arrays in this dataset into dask arrays with the given chunks.
Dataset.close()	Release any resources linked to this object.
Dataset.compute(**kwargs)	Manually trigger loading and/or computation of this dataset's data from disk or a remote source into memory and return a new dataset.
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.
Dataset.load(**kwargs)	Manually trigger loading and/or computation of this dataset's data from disk or a remote source into memory and return this dataset.
Dataset.persist(**kwargs)	Trigger computation, keeping data as chunked arrays.
Dataset.unify_chunks()	Unify chunk size along all chunked dimensions of this Dataset.

DataArray methods

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.
open_dataarray(filename_or_obj, *[, engine, ...])	Open an DataArray from a file or file-like object containing a single data variable.
DataArray.as_numpy()	Coerces wrapped data and coordinates into numpy arrays, returning a DataArray.
DataArray.from_dict(d)	Convert a dictionary into an xarray.DataArray
DataArray.from_iris(cube)	Convert a iris.cube.Cube into an xarray.DataArray
DataArray.from_series(series[, sparse])	Convert a pandas.Series into an xarray.DataArray.
DataArray.to_dask_dataframe([dim_order, ...])	Convert this array into a dask.dataframe.DataFrame.
DataArray.to_dataframe([name, dim_order])	Convert this array and its coordinates into a tidy pandas.DataFrame.
DataArray.to_dataset([dim, name, promote_attrs])	Convert a DataArray to a Dataset.
DataArray.to_dict([data, encoding])	Convert this xarray.DataArray into a dictionary following xarray naming conventions.
DataArray.to_index()	Convert this variable to a pandas.Index.
DataArray.to_iris()	Convert this array into a iris.cube.Cube
DataArray.to_masked_array([copy])	Convert this array into a numpy.ma.MaskedArray
DataArray.to_netcdf([path, mode, format, ...])	Write DataArray contents to a netCDF file.
DataArray.to_numpy()	Coerces wrapped data to numpy and returns a numpy.ndarray.
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_zarr([store, chunk_store, ...])	Write DataArray contents to a Zarr store
DataArray.chunk([chunks, name_prefix, ...])	Coerce this array's data into a dask arrays with the given chunks.
DataArray.close()	Release any resources 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.unify_chunks()	Unify chunk size along all chunked dimensions of this DataArray.

DataTree methods

open_datatree(filename_or_obj, *[, engine, ...])	Open and decode a DataTree from a file or file-like object, creating one tree node for each group in the file.
open_groups(filename_or_obj, *[, engine, ...])	Open and decode a file or file-like object, creating a dictionary containing one xarray Dataset for each group in the file.
DataTree.to_dict([relative])	Create a dictionary mapping of paths to the data contained in those nodes.
DataTree.to_netcdf(filepath[, mode, ...])	Write datatree contents to a netCDF file.
DataTree.to_zarr(store[, mode, encoding, ...])	Write datatree contents to a Zarr store.
DataTree.chunk([chunks, name_prefix, token, ...])	Coerce all arrays in all groups in this tree into dask arrays with the given chunks.
DataTree.load(**kwargs)	Manually trigger loading and/or computation of this datatree's data from disk or a remote source into memory and return this datatree.
DataTree.compute(**kwargs)	Manually trigger loading and/or computation of this datatree's data from disk or a remote source into memory and return a new datatree.
DataTree.persist(**kwargs)	Trigger computation, keeping data as chunked arrays.