make more args kw only (except 'dim')

xarray/core/dataarray.py

@@ -2188,6 +2188,7 @@ def stack(
     def unstack(
         self,
         dim: Hashable | Sequence[Hashable] | None = None,
+        *,
         fill_value: Any = dtypes.NA,
         sparse: bool = False,
     ) -> DataArray:
@@ -2462,7 +2463,9 @@ def drop_isel(self, indexers=None, **indexers_kwargs):
         dataset = dataset.drop_isel(indexers=indexers, **indexers_kwargs)
         return self._from_temp_dataset(dataset)
 
-    def dropna(self, dim: Hashable, how: str = "any", thresh: int = None) -> DataArray:
+    def dropna(
+        self, dim: Hashable, *, how: str = "any", thresh: int = None
+    ) -> DataArray:
         """Returns a new array with dropped labels for missing values along
         the provided dimension.
 
@@ -3210,7 +3213,9 @@ def _title_for_slice(self, truncate: int = 50) -> str:
 
         return title
 
-    def diff(self, dim: Hashable, n: int = 1, label: Hashable = "upper") -> DataArray:
+    def diff(
+        self, dim: Hashable, *, n: int = 1, label: Hashable = "upper"
+    ) -> DataArray:
         """Calculate the n-th order discrete difference along given axis.
 
         Parameters
@@ -3483,6 +3488,7 @@ def quantile(
         self,
         q: ArrayLike,
         dim: str | Sequence[Hashable] | None = None,
+        *,
         method: QUANTILE_METHODS = "linear",
         keep_attrs: bool = None,
         skipna: bool = None,
@@ -3599,7 +3605,7 @@ def quantile(
         return self._from_temp_dataset(ds)
 
     def rank(
-        self, dim: Hashable, pct: bool = False, keep_attrs: bool = None
+        self, dim: Hashable, *, pct: bool = False, keep_attrs: bool = None
     ) -> DataArray:
         """Ranks the data.
 
@@ -4169,6 +4175,7 @@ def pad(
     def idxmin(
         self,
         dim: Hashable = None,
+        *,
         skipna: bool = None,
         fill_value: Any = dtypes.NA,
         keep_attrs: bool = None,
@@ -4265,6 +4272,7 @@ def idxmin(
     def idxmax(
         self,
         dim: Hashable = None,
+        *,
         skipna: bool = None,
         fill_value: Any = dtypes.NA,
         keep_attrs: bool = None,
@@ -4361,6 +4369,7 @@ def idxmax(
     def argmin(
         self,
         dim: Hashable | Sequence[Hashable] = None,
+        *,
         axis: int = None,
         keep_attrs: bool = None,
         skipna: bool = None,
@@ -4464,6 +4473,7 @@ def argmin(
     def argmax(
         self,
         dim: Hashable | Sequence[Hashable] = None,
+        *,
         axis: int = None,
         keep_attrs: bool = None,
         skipna: bool = None,
@@ -4720,6 +4730,7 @@ def curvefit(
     def drop_duplicates(
         self,
         dim: Hashable | Iterable[Hashable] | ...,
+        *,
         keep: Literal["first", "last"] | Literal[False] = "first",
     ):
         """Returns a new DataArray with duplicate dimension values removed.
@@ -4730,6 +4741,7 @@ def drop_duplicates(
             Pass `...` to drop duplicates along all dimensions.
         keep : {"first", "last", False}, default: "first"
             Determines which duplicates (if any) to keep.
+
             - ``"first"`` : Drop duplicates except for the first occurrence.
             - ``"last"`` : Drop duplicates except for the last occurrence.
             - False : Drop all duplicates.

xarray/core/dataset.py

@@ -3514,6 +3514,7 @@ def swap_dims(
     def expand_dims(
         self,
         dim: None | Hashable | Sequence[Hashable] | Mapping[Any, Any] = None,
+        *,
         axis: None | int | Sequence[int] = None,
         **dim_kwargs: Any,
     ) -> Dataset:
@@ -3789,6 +3790,7 @@ def set_index(
     def reset_index(
         self,
         dims_or_levels: Hashable | Sequence[Hashable],
+        *,
         drop: bool = False,
     ) -> Dataset:
         """Reset the specified index(es) or multi-index level(s).
@@ -4272,6 +4274,7 @@ def _unstack_full_reindex(
     def unstack(
         self,
         dim: Hashable | Iterable[Hashable] = None,
+        *,
         fill_value: Any = dtypes.NA,
         sparse: bool = False,
     ) -> Dataset:
@@ -4818,6 +4821,7 @@ def transpose(
     def dropna(
         self,
         dim: Hashable,
+        *,
         how: str = "any",
         thresh: int = None,
         subset: Iterable[Hashable] = None,
@@ -5995,7 +5999,7 @@ def _copy_attrs_from(self, other):
             if v in self.variables:
                 self.variables[v].attrs = other.variables[v].attrs
 
-    def diff(self, dim, n=1, label="upper"):
+    def diff(self, dim, *, n=1, label="upper"):
         """Calculate the n-th order discrete difference along given axis.
 
         Parameters
@@ -6317,6 +6321,7 @@ def quantile(
         self,
         q: ArrayLike,
         dim: str | Iterable[Hashable] | None = None,
+        *,
         method: QUANTILE_METHODS = "linear",
         numeric_only: bool = False,
         keep_attrs: bool = None,
@@ -6491,7 +6496,7 @@ def quantile(
         )
         return new.assign_coords(quantile=q)
 
-    def rank(self, dim, pct=False, keep_attrs=None):
+    def rank(self, dim, *, pct=False, keep_attrs=None):
         """Ranks the data.
 
         Equal values are assigned a rank that is the average of the ranks that
@@ -7392,6 +7397,7 @@ def pad(
     def idxmin(
         self,
         dim: Hashable = None,
+        *,
         skipna: bool = None,
         fill_value: Any = dtypes.NA,
         keep_attrs: bool = None,
@@ -7489,6 +7495,7 @@ def idxmin(
     def idxmax(
         self,
         dim: Hashable = None,
+        *,
         skipna: bool = None,
         fill_value: Any = dtypes.NA,
         keep_attrs: bool = None,
@@ -7972,6 +7979,7 @@ def _wrapper(Y, *coords_, **kwargs):
     def drop_duplicates(
         self,
         dim: Hashable | Iterable[Hashable] | ...,
+        *,
         keep: Literal["first", "last"] | Literal[False] = "first",
     ):
         """Returns a new Dataset with duplicate dimension values removed.

xarray/core/groupby.py

@@ -558,6 +558,7 @@ def quantile(
         self,
         q,
         dim=None,
+        *,
         method="linear",
         keep_attrs=None,
         skipna=None,

xarray/core/weighted.py

@@ -248,6 +248,7 @@ def _implementation(self, func, dim, **kwargs):
     def sum_of_weights(
         self,
         dim: Hashable | Iterable[Hashable] | None = None,
+        *,
         keep_attrs: bool | None = None,
     ) -> T_Xarray:
 
@@ -258,6 +259,7 @@ def sum_of_weights(
     def sum_of_squares(
         self,
         dim: Hashable | Iterable[Hashable] | None = None,
+        *,
         skipna: bool | None = None,
         keep_attrs: bool | None = None,
     ) -> T_Xarray:
@@ -269,6 +271,7 @@ def sum_of_squares(
     def sum(
         self,
         dim: Hashable | Iterable[Hashable] | None = None,
+        *,
         skipna: bool | None = None,
         keep_attrs: bool | None = None,
     ) -> T_Xarray:
@@ -280,6 +283,7 @@ def sum(
     def mean(
         self,
         dim: Hashable | Iterable[Hashable] | None = None,
+        *,
         skipna: bool | None = None,
         keep_attrs: bool | None = None,
     ) -> T_Xarray:
@@ -291,6 +295,7 @@ def mean(
     def var(
         self,
         dim: Hashable | Iterable[Hashable] | None = None,
+        *,
         skipna: bool | None = None,
         keep_attrs: bool | None = None,
     ) -> T_Xarray:
@@ -302,6 +307,7 @@ def var(
     def std(
         self,
         dim: Hashable | Iterable[Hashable] | None = None,
+        *,
         skipna: bool | None = None,
         keep_attrs: bool | None = None,
     ) -> T_Xarray: