xarray.save_mfdataset(datasets, paths, mode='w', format=None, groups=None, engine=None, compute=True, **kwargs)[source]#

Write multiple datasets to disk as netCDF files simultaneously.

This function is intended for use with datasets consisting of dask.array objects, in which case it can write the multiple datasets to disk simultaneously using a shared thread pool.

When not using dask, it is no different than calling to_netcdf repeatedly.

  • datasets (list of Dataset) – List of datasets to save.

  • paths (list of str or list of path-like objects) – List of paths to which to save each corresponding dataset.

  • mode ({"w", "a"}, optional) – Write (“w”) or append (“a”) mode. If mode=”w”, any existing file at these locations 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).

  • groups (list of str, optional) – Paths to the netCDF4 group in each corresponding file to which to save datasets (only works for format=”NETCDF4”). The groups 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. See Dataset.to_netcdf for additional information.

  • compute (bool) – If true compute immediately, otherwise return a dask.delayed.Delayed object that can be computed later.

  • **kwargs (dict, optional) – Additional arguments are passed along to to_netcdf.


Save a dataset into one netCDF per year of data:

>>> ds = xr.Dataset(
...     {"a": ("time", np.linspace(0, 1, 48))},
...     coords={"time": pd.date_range("2010-01-01", freq="ME", periods=48)},
... )
>>> ds
<xarray.Dataset> Size: 768B
Dimensions:  (time: 48)
  * time     (time) datetime64[ns] 384B 2010-01-31 2010-02-28 ... 2013-12-31
Data variables:
    a        (time) float64 384B 0.0 0.02128 0.04255 ... 0.9574 0.9787 1.0
>>> years, datasets = zip(*ds.groupby("time.year"))
>>> paths = [f"{y}.nc" for y in years]
>>> xr.save_mfdataset(datasets, paths)