Class Variable

xarray/core/variable.py:347–2745

A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array. A single Variable object is not fully described outside the context of its parent Dataset (if you want such a fully described object, use a DataArray instead). The main functional dif

class Variable(NamedArray, AbstractArray, VariableArithmetic):
    """A netcdf-like variable consisting of dimensions, data and attributes
    which describe a single Array. A single Variable object is not fully
    described outside the context of its parent Dataset (if you want such a
    fully described object, use a DataArray instead).

    The main functional difference between Variables and numpy arrays is that
    numerical operations on Variables implement array broadcasting by dimension
    name. For example, adding a Variable with dimensions `('time',)` to
    another Variable with dimensions `('space',)` results in a new Variable
    with dimensions `('time', 'space')`. Furthermore, numpy reduce operations
    like ``mean`` or ``sum`` are overwritten to take a "dimension" argument
    instead of an "axis".

    Variables are light-weight objects used as the building block for datasets.
    They are more primitive objects, so operations with them provide marginally
    higher performance than using DataArrays. However, manipulating data in the
    form of a Dataset or DataArray should almost always be preferred, because
    they can use more complete metadata in context of coordinate labels.
    """

    __slots__ = ("_attrs", "_data", "_dims", "_encoding")

    def __init__(
        self,
        dims,
        data: T_DuckArray | np.typing.ArrayLike,
        attrs=None,
        encoding=None,
        fastpath=False,
    ):
        """
        Parameters
        ----------
        dims : str or sequence of str
            Name(s) of the the data dimension(s). Must be either a string (only
            for 1D data) or a sequence of strings with length equal to the
            number of dimensions.
        data : array_like
            Data array which supports numpy-like data access.
        attrs : dict_like or None, optional
            Attributes to assign to the new variable. If None (default), an
            empty attribute dictionary is initialized.
            (see FAQ, :ref:`approach to metadata`)
        encoding : dict_like or None, optional
            Dictionary specifying how to encode this array's data into a
            serialized format like netCDF4. Currently used keys (for netCDF)
            include '_FillValue', 'scale_factor', 'add_offset' and 'dtype'.
            Well-behaved code to serialize a Variable should ignore
            unrecognized encoding items.
        """
        super().__init__(
            dims=dims, data=as_compatible_data(data, fastpath=fastpath), attrs=attrs
        )

        self._encoding: dict[Any, Any] | None = None
        if encoding is not None:
            self.encoding = encoding