xarray.Dataset.to_netcdf

Dataset.to_netcdf(path=None, mode: str = 'w', format: str = None, group: str = None, engine: str = None, encoding: Mapping = None, unlimited_dims: Iterable[Hashable] = None, compute: bool = True) → Union[bytes, Delayed, None]

Write dataset contents to a netCDF file.

Parameters
pathstr, Path or file-like object, optional

Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resulting netCDF file as bytes; in this case, we need to use scipy, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT).

mode{‘w’, ‘a’}, optional

Write (‘w’) or append (‘a’) mode. If mode=’w’, any existing file at this location will be overwritten. If mode=’a’, existing variables will be overwritten.

format{‘NETCDF4’, ‘NETCDF4_CLASSIC’, ‘NETCDF3_64BIT’,

‘NETCDF3_CLASSIC’}, optional

File format for the resulting netCDF file:

  • NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features.

  • NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features.

  • NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later.

  • NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well.

All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats.

The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4).

groupstr, optional

Path to the netCDF4 group in the given file to open (only works for format=’NETCDF4’). The group(s) will be created if necessary.

engine{‘netcdf4’, ‘scipy’, ‘h5netcdf’}, optional

Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for ‘netcdf4’ if writing to a file on disk.

encodingdict, optional

Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., ``{‘my_variable’: {‘dtype’: ‘int16’, ‘scale_factor’: 0.1,

‘zlib’: True}, …}``

The h5netcdf engine supports both the NetCDF4-style compression encoding parameters {'zlib': True, 'complevel': 9} and the h5py ones {'compression': 'gzip', 'compression_opts': 9}. This allows using any compression plugin installed in the HDF5 library, e.g. LZF.

unlimited_dimsiterable of hashable, optional

Dimension(s) that should be serialized as unlimited dimensions. By default, no dimensions are treated as unlimited dimensions. Note that unlimited_dims may also be set via dataset.encoding['unlimited_dims'].

compute: boolean

If true compute immediately, otherwise return a dask.delayed.Delayed object that can be computed later.