More Pylint issues

properties/test_index_manipulation.py

@@ -261,17 +261,13 @@ def assert_invariants(self):
 
 @pytest.mark.skip(reason="failure detected by hypothesis")
 def test_unstack_object():
-    import xarray as xr
-
     ds = xr.Dataset()
     ds["0"] = np.array(["", "\x000"], dtype=object)
     ds.stack({"1": ["0"]}).unstack()
 
 
 @pytest.mark.skip(reason="failure detected by hypothesis")
 def test_unstack_timedelta_index():
-    import xarray as xr
-
     ds = xr.Dataset()
     ds["0"] = np.array([0, 1, 2, 3], dtype="timedelta64[ns]")
     ds.stack({"1": ["0"]}).unstack()

xarray/coding/cftime_offsets.py

@@ -1419,8 +1419,6 @@ def date_range(
     cftime_range
     date_range_like
     """
-    from xarray.coding.times import _is_standard_calendar
-
     if tz is not None:
         use_cftime = False
 

xarray/coding/cftimeindex.py

@@ -689,8 +689,6 @@ def asi8(self):
     @property
     def calendar(self):
         """The calendar used by the datetimes in the index."""
-        from xarray.coding.times import infer_calendar_name
-
         if not self._data.size:
             return None
 

xarray/computation/apply_ufunc.py

@@ -569,7 +569,6 @@ def apply_groupby_func(func, *args):
     DataArray, Variable and/or ndarray objects.
     """
     from xarray.core.groupby import GroupBy, peek_at
-    from xarray.core.variable import Variable
 
     groupbys = [arg for arg in args if isinstance(arg, GroupBy)]
     assert groupbys, "must have at least one groupby to iterate over"

xarray/computation/computation.py

@@ -574,7 +574,6 @@ def dot(
     array(235)
     """
     from xarray.core.dataarray import DataArray
-    from xarray.core.variable import Variable
 
     if any(not isinstance(arr, Variable | DataArray) for arr in arrays):
         raise TypeError(

xarray/computation/fit.py

@@ -474,7 +474,7 @@ def _wrapper(Y, *args, **kwargs):
             mask = np.all([np.any(~np.isnan(x), axis=0), ~np.isnan(y)], axis=0)
             x = x[:, mask]
             y = y[mask]
-            if not len(y):
+            if y.size == 0:
                 popt = np.full([n_params], np.nan)
                 pcov = np.full([n_params, n_params], np.nan)
                 return popt, pcov

xarray/computation/nanops.py

@@ -105,8 +105,6 @@ def nansum(a, axis=None, dtype=None, out=None, min_count=None):
 
 def _nanmean_ddof_object(ddof, value, axis=None, dtype=None, **kwargs):
     """In house nanmean. ddof argument will be used in _nanvar method"""
-    from xarray.core.duck_array_ops import count, fillna, where_method
-
     valid_count = count(value, axis=axis)
     value = fillna(value, 0)
     # As dtype inference is impossible for object dtype, we assume float

xarray/core/dataset.py

@@ -1359,7 +1359,6 @@ def _setitem_check(self, key, value):
         to avoid leaving the dataset in a partially updated state when an error occurs.
         """
         from xarray.core.dataarray import DataArray
-        from xarray.structure.alignment import align
 
         if isinstance(value, Dataset):
             missing_vars = [
@@ -2551,7 +2550,6 @@ def _validate_indexers(
         + string indexers are cast to the appropriate date type if the
           associated index is a DatetimeIndex or CFTimeIndex
         """
-        from xarray.coding.cftimeindex import CFTimeIndex
         from xarray.core.dataarray import DataArray
 
         indexers = drop_dims_from_indexers(indexers, self.dims, missing_dims)
@@ -8268,8 +8266,6 @@ def differentiate(
         --------
         numpy.gradient: corresponding numpy function
         """
-        from xarray.core.variable import Variable
-
         if coord not in self.variables and coord not in self.dims:
             variables_and_dims = tuple(set(self.variables.keys()).union(self.dims))
             raise ValueError(

xarray/core/datatree.py

@@ -1629,7 +1629,6 @@ def _unary_op(self, f, *args, **kwargs) -> DataTree:
         return self.map_over_datasets(functools.partial(f, **kwargs), *args)  # type: ignore[return-value]
 
     def _binary_op(self, other, f, reflexive=False, join=None) -> DataTree:
-        from xarray.core.dataset import Dataset
         from xarray.core.groupby import GroupBy
 
         if isinstance(other, GroupBy):

xarray/core/duck_array_ops.py

@@ -49,8 +49,6 @@
 
 
 def einsum(*args, **kwargs):
-    from xarray.core.options import OPTIONS
-
     if OPTIONS["use_opt_einsum"] and module_available("opt_einsum"):
         import opt_einsum
 
@@ -250,7 +248,7 @@
 
     if xp == np:
         # numpy currently doesn't have a astype:
         return data.astype(dtype, **kwargs)
     return xp.astype(data, dtype, **kwargs)
 
 
@@ -681,9 +679,7 @@
         The output data type.
 
     """
-    import datetime as dt
-
-    if isinstance(value, dt.timedelta):
+    if isinstance(value, datetime.timedelta):
         out = py_timedelta_to_float(value, datetime_unit)
     elif isinstance(value, np.timedelta64):
         out = np_timedelta64_to_float(value, datetime_unit)

xarray/core/groupby.py

@@ -540,7 +540,7 @@ def factorize(self) -> EncodedGroups:
         _flatcodes = where(mask.data, -1, _flatcodes)
 
         full_index = pd.MultiIndex.from_product(
-            list(grouper.full_index.values for grouper in groupers),
+            [grouper.full_index.values for grouper in groupers],
             names=tuple(grouper.name for grouper in groupers),
         )
         if not full_index.is_unique:

xarray/core/treenode.py

@@ -786,12 +786,12 @@ def _path_to_ancestor(self, ancestor: NamedNode) -> NodePath:
             raise NotFoundInTreeError(
                 "Cannot find relative path to ancestor because nodes do not lie within the same tree"
             )
-        if ancestor.path not in list(a.path for a in (self, *self.parents)):
+        if ancestor.path not in [a.path for a in (self, *self.parents)]:
             raise NotFoundInTreeError(
                 "Cannot find relative path to ancestor because given node is not an ancestor of this node"
             )
 
-        parents_paths = list(parent.path for parent in (self, *self.parents))
+        parents_paths = [parent.path for parent in (self, *self.parents)]
         generation_gap = list(parents_paths).index(ancestor.path)
         path_upwards = "../" * generation_gap if generation_gap > 0 else "."
         return NodePath(path_upwards)

xarray/groupers.py

@@ -484,8 +484,6 @@ def reset(self) -> Self:
         )
 
     def _init_properties(self, group: T_Group) -> None:
-        from xarray import CFTimeIndex
-
         group_as_index = safe_cast_to_index(group)
         offset = self.offset
 
@@ -494,8 +492,6 @@ def _init_properties(self, group: T_Group) -> None:
             raise ValueError("Index must be monotonic for resampling")
 
         if isinstance(group_as_index, CFTimeIndex):
-            from xarray.core.resample_cftime import CFTimeGrouper
-
             self.index_grouper = CFTimeGrouper(
                 freq=self.freq,
                 closed=self.closed,

xarray/plot/utils.py

@@ -457,8 +457,6 @@ def get_axis(
     ax: Axes | None = None,
     **subplot_kws: Any,
 ) -> Axes:
-    from xarray.core.utils import attempt_import
-
     if TYPE_CHECKING:
         import matplotlib as mpl
         import matplotlib.pyplot as plt
@@ -1048,8 +1046,6 @@ def legend_elements(
     labels : list of str
         The string labels for elements of the legend.
     """
-    import warnings
-
     import matplotlib as mpl
 
     mlines = mpl.lines

xarray/structure/concat.py

@@ -418,7 +418,7 @@ def process_subset_opt(opt, subset):
             elif opt == "all":
                 concat_over.update(
                     set().union(
-                        *list(set(getattr(d, subset)) - set(d.dims) for d in datasets)
+                        *[set(getattr(d, subset)) - set(d.dims) for d in datasets]
                     )
                 )
             elif opt == "minimal":

xarray/tests/test_datatree.py

@@ -837,7 +837,7 @@ def test_nones(self) -> None:
 
     def test_full(self, simple_datatree) -> None:
         dt = simple_datatree
-        paths = list(node.path for node in dt.subtree)
+        paths = [node.path for node in dt.subtree]
         assert paths == [
             "/",
             "/set1",

xarray/tests/test_groupby.py

@@ -3256,8 +3256,6 @@ def test_shuffle_simple() -> None:
 def test_shuffle_by(chunks, expected_chunks):
     import dask.array
 
-    from xarray.groupers import UniqueGrouper
-
     da = xr.DataArray(
         dims="x",
         data=dask.array.arange(10, chunks=chunks),

xarray/tests/test_units.py

@@ -2650,7 +2650,7 @@ def test_searchsorted(self, func, unit, error, dtype):
         data_array = xr.DataArray(data=array)
 
         scalar_types = (int, float)
-        args = list(value * unit for value in func.args)
+        args = [value * unit for value in func.args]
         kwargs = {
             key: (value * unit if isinstance(value, scalar_types) else value)
             for key, value in func.kwargs.items()
@@ -2708,7 +2708,7 @@ def test_numpy_methods_with_args(self, func, unit, error, dtype):
         data_array = xr.DataArray(data=array)
 
         scalar_types = (int, float)
-        args = list(value * unit for value in func.args)
+        args = [value * unit for value in func.args]
         kwargs = {
             key: (value * unit if isinstance(value, scalar_types) else value)
             for key, value in func.kwargs.items()