Function isnull

xarray/core/duck_array_ops.py:173–212

(data)

def isnull(data):
    data = asarray(data)

    xp = get_array_namespace(data)
    scalar_type = data.dtype
    if dtypes.is_datetime_like(scalar_type):
        # datetime types use NaT for null
        # note: must check timedelta64 before integers, because currently
        # timedelta64 inherits from np.integer
        return isnat(data)
    elif HAS_STRING_DTYPE and isinstance(scalar_type, np.dtypes.StringDType):
        # na is settable, but it defaults to an empty string
        na_object = getattr(scalar_type, "na_object", "")
        if isna(na_object):
            return xp.isnan(data)
        else:
            return data == na_object
    elif dtypes.isdtype(scalar_type, ("real floating", "complex floating"), xp=xp):
        # float types use NaN for null
        return xp.isnan(data)
    elif dtypes.isdtype(scalar_type, ("bool", "integral"), xp=xp) or (
        isinstance(scalar_type, np.dtype)
        and (
            np.issubdtype(scalar_type, np.character)
            or np.issubdtype(scalar_type, np.void)
        )
    ):
        # these types cannot represent missing values
        # bool_ is for backwards compat with numpy<2, and cupy
        dtype = xp.bool_ if hasattr(xp, "bool_") else xp.bool
        return full_like(data, dtype=dtype, fill_value=False)
    # at this point, array should have dtype=object
    elif isinstance(data, np.ndarray) or pd.api.types.is_extension_array_dtype(data):  # noqa: TID251
        return pandas_isnull(data)
    else:
        # Not reachable yet, but intended for use with other duck array
        # types. For full consistency with pandas, we should accept None as
        # a null value as well as NaN, but it isn't clear how to do this
        # with duck typing.
        return data != data  # noqa: PLR0124


def notnull(data):