remove axis argument and some unit test cleanups

Author: toddrjen

xarray/core/computation.py

@@ -1314,7 +1314,6 @@ def _calc_idxminmax(
     array,
     func: Callable,
     dim: Optional[Hashable],
-    axis: Optional[int],
     skipna: Optional[bool],
     promote: Optional[bool],
     keep_attrs: Optional[bool],
@@ -1327,21 +1326,15 @@ def _calc_idxminmax(
 
     if dim is not None:
         pass  # Use the dim if available
-    elif axis is not None:
-        dim = array.dims[axis]
     elif array.ndim == 1:
         # it is okay to guess the dim if there is only 1
         dim = array.dims[0]
     else:
         # The dim is not specified and ambiguous.  Don't guess.
-        raise ValueError(
-            "Must supply either 'dim' or 'axis' argument " "for multidimensional arrays"
-        )
+        raise ValueError("Must supply 'dim' argument for multidimensional arrays")
 
     if dim in array.coords:
         pass  # This is okay
-    elif axis is not None:
-        raise IndexError(f'Axis "{axis}" does not have coordinates')
     else:
         raise KeyError(f'Dimension "{dim}" does not have coordinates')
 

xarray/core/dataarray.py

@@ -3433,7 +3433,6 @@ def pad(
     def idxmin(
         self,
         dim: Hashable = None,
-        axis: int = None,
         skipna: bool = None,
         promote: bool = None,
         keep_attrs: Optional[bool] = False,
@@ -3451,10 +3450,8 @@ def idxmin(
         Parameters
         ----------
         dim : str, optional
-            Dimension over which to apply `idxmin`.
-        axis : int, optional
-            Axis(es) over which to repeatedly apply `idxmin`. Exactly one of
-            the 'dim' and 'axis' arguments must be supplied.
+            Dimension over which to apply `idxmin`.  This is optional for 1D
+            arrays, but required for arrays with 2 or more dimensions.
         skipna : bool or None, default None
             If True, skip missing values (as marked by NaN). By default, only
             skips missing values for float dtypes; other dtypes either do not
@@ -3525,7 +3522,6 @@ def idxmin(
             array=self,
             func=lambda x, *args, **kwargs: x.argmin(*args, **kwargs),
             dim=dim,
-            axis=axis,
             skipna=skipna,
             promote=promote,
             keep_attrs=keep_attrs,
@@ -3535,7 +3531,6 @@ def idxmin(
     def idxmax(
         self,
         dim: Hashable = None,
-        axis: int = None,
         skipna: bool = None,
         promote: bool = None,
         keep_attrs: Optional[bool] = False,
@@ -3553,10 +3548,8 @@ def idxmax(
         Parameters
         ----------
         dim : str, optional
-            Dimension over which to apply `idxmax`..
-        axis : int, optional
-            Axis(es) over which to repeatedly apply `idxmax`. Exactly one of
-            the 'dim' and 'axis' arguments must be supplied.
+            Dimension over which to apply `idxmax`.  This is optional for 1D
+            arrays, but required for arrays with 2 or more dimensions.
         skipna : bool or None, default None
             If True, skip missing values (as marked by NaN). By default, only
             skips missing values for float dtypes; other dtypes either do not
@@ -3627,7 +3620,6 @@ def idxmax(
             array=self,
             func=lambda x, *args, **kwargs: x.argmax(*args, **kwargs),
             dim=dim,
-            axis=axis,
             skipna=skipna,
             promote=promote,
             keep_attrs=keep_attrs,

xarray/core/dataset.py

@@ -5924,7 +5924,6 @@ def pad(
     def idxmin(
         self,
         dim: Hashable = None,
-        axis: int = None,
         skipna: bool = None,
         promote: bool = None,
         keep_attrs: bool = False,
@@ -5942,10 +5941,8 @@ def idxmin(
         Parameters
         ----------
         dim : str, optional
-            Dimension over which to apply `idxmin`.
-        axis : int, optional
-            Axis(es) over which to repeatedly apply `idxmin`. Exactly one of
-            the 'dim' and 'axis' arguments must be supplied.
+            Dimension over which to apply `idxmin`.  This is optional for 1D
+            variables, but required for variables with 2 or more dimensions.
         skipna : bool or None, default None
             If True, skip missing values (as marked by NaN). By default, only
             skips missing values for float dtypes; other dtypes either do not
@@ -6015,7 +6012,6 @@ def idxmin(
         return self.map(
             "idxmin",
             dim=dim,
-            axis=axis,
             skipna=skipna,
             promote=promote,
             keep_attrs=keep_attrs,
@@ -6025,7 +6021,6 @@ def idxmin(
     def idxmax(
         self,
         dim: Hashable = None,
-        axis: int = None,
         skipna: bool = None,
         promote: bool = None,
         keep_attrs: Optional[bool] = False,
@@ -6043,10 +6038,8 @@ def idxmax(
         Parameters
         ----------
         dim : str, optional
-            Dimension over which to apply `idxmax`.
-        axis : int, optional
-            Axis(es) over which to repeatedly apply `idxmax`. Exactly one of
-            the 'dim' and 'axis' arguments must be supplied.
+            Dimension over which to apply `idxmax`.  This is optional for 1D
+            variables, but required for variables with 2 or more dimensions.
         skipna : bool or None, default None
             If True, skip missing values (as marked by NaN). By default, only
             skips missing values for float dtypes; other dtypes either do not
@@ -6078,7 +6071,6 @@ def idxmax(
         return self.map(
             "idxmax",
             dim=dim,
-            axis=axis,
             skipna=skipna,
             promote=promote,
             keep_attrs=keep_attrs,