xarray.open_dataset

xarray.open_dataset(filename_or_obj, group=None, decode_cf=True, mask_and_scale=None, decode_times=True, autoclose=None, concat_characters=True, decode_coords=True, engine=None, chunks=None, lock=None, cache=None, drop_variables=None, backend_kwargs=None, use_cftime=None)

Load and decode a dataset from a file or file-like object.

Parameters:
filename_or_obj : str, Path, file or xarray.backends.*DataStore

Strings and Path objects are interpreted as a path to a netCDF file or an OpenDAP URL and opened with python-netCDF4, unless the filename ends with .gz, in which case the file is gunzipped and opened with scipy.io.netcdf (only netCDF3 supported). File-like objects are opened with scipy.io.netcdf (only netCDF3 supported).

group : str, optional

Path to the netCDF4 group in the given file to open (only works for netCDF4 files).

decode_cf : bool, optional

Whether to decode these variables, assuming they were saved according to CF conventions.

mask_and_scale : bool, optional

If True, replace array values equal to _FillValue with NA and scale values according to the formula original_values * scale_factor + add_offset, where _FillValue, scale_factor and add_offset are taken from variable attributes (if they exist). If the _FillValue or missing_value attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. mask_and_scale defaults to True except for the pseudonetcdf backend.

decode_times : bool, optional

If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.

autoclose : bool, optional

If True, automatically close files to avoid OS Error of too many files being open. However, this option doesn’t work with streams, e.g., BytesIO.

concat_characters : bool, optional

If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays.

decode_coords : bool, optional

If True, decode the ‘coordinates’ attribute to identify coordinates in the resulting dataset.

engine : {‘netcdf4’, ‘scipy’, ‘pydap’, ‘h5netcdf’, ‘pynio’, ‘cfgrib’,

‘pseudonetcdf’}, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for ‘netcdf4’.

chunks : int or dict, optional

If chunks is provided, it used to load the new dataset into dask arrays. chunks={} loads the dataset with dask using a single chunk for all arrays.

lock : False or duck threading.Lock, optional

Resource lock to use when reading data from disk. Only relevant when using dask or another form of parallelism. By default, appropriate locks are chosen to safely read and write files with the currently active dask scheduler.

cache : bool, optional

If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the chunks argument to use dask, in which case it defaults to False. Does not change the behavior of coordinates corresponding to dimensions, which always load their data from disk into a pandas.Index.

drop_variables: string or iterable, optional

A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values.

backend_kwargs: dictionary, optional

A dictionary of keyword arguments to pass on to the backend. This may be useful when backend options would improve performance or allow user control of dataset processing.

use_cftime: bool, optional

Only relevant if encoded dates come from a standard calendar (e.g. ‘gregorian’, ‘proleptic_gregorian’, ‘standard’, or not specified). If None (default), attempt to decode times to np.datetime64[ns] objects; if this is not possible, decode times to cftime.datetime objects. If True, always decode times to cftime.datetime objects, regardless of whether or not they can be represented using np.datetime64[ns] objects. If False, always decode times to np.datetime64[ns] objects; if this is not possible raise an error.

Returns:
dataset : Dataset

The newly created dataset.

See also

open_mfdataset