Apply suggestions from code review

Co-authored-by: Alenka Frim <[email protected]>

Author: zhengruifeng

python/pyarrow/_compute_docstrings.py

@@ -64,15 +64,15 @@
     >>> pc.min(arr1)
     <pyarrow.Int64Scalar: 1>
 
-    Using `skip_nulls` to handle null values.
+    Using ``skip_nulls`` to handle null values.
 
     >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
     >>> pc.min(arr2)
     <pyarrow.DoubleScalar: 1.0>
     >>> pc.min(arr2, skip_nulls=False)
     <pyarrow.DoubleScalar: None>
 
-    Using `ScalarAggregateOptions` to control minimum number of non-null values.
+    Using ``ScalarAggregateOptions`` to control minimum number of non-null values.
 
     >>> arr3 = pa.array([1.0, None, float("nan"), 3.0])
     >>> pc.min(arr3)
@@ -98,15 +98,15 @@
     >>> pc.max(arr1)
     <pyarrow.Int64Scalar: 3>
 
-    Using `skip_nulls` to handle null values.
+    Using ``skip_nulls`` to handle null values.
 
     >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
     >>> pc.max(arr2)
     <pyarrow.DoubleScalar: 3.0>
     >>> pc.max(arr2, skip_nulls=False)
     <pyarrow.DoubleScalar: None>
 
-    Using `ScalarAggregateOptions` to control minimum number of non-null values.
+    Using ``ScalarAggregateOptions`` to control minimum number of non-null values.
 
     >>> arr3 = pa.array([1.0, None, float("nan"), 3.0])
     >>> pc.max(arr3)
@@ -132,15 +132,15 @@
     >>> pc.min_max(arr1)
     <pyarrow.StructScalar: [('min', 1), ('max', 3)]>
 
-    Using `skip_nulls` to handle null values.
+    Using ``skip_nulls`` to handle null values.
 
     >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
     >>> pc.min_max(arr2)
     <pyarrow.StructScalar: [('min', 1.0), ('max', 3.0)]>
     >>> pc.min_max(arr2, skip_nulls=False)
     <pyarrow.StructScalar: [('min', None), ('max', None)]>
 
-    Using `ScalarAggregateOptions` to control minimum number of non-null values.
+    Using ``ScalarAggregateOptions`` to control minimum number of non-null values.
 
     >>> arr3 = pa.array([1.0, None, float("nan"), 3.0])
     >>> pc.min_max(arr3)

python/pyarrow/tests/test_compute.py

@@ -892,15 +892,15 @@ def test_generated_docstrings():
         >>> pc.min_max(arr1)
         <pyarrow.StructScalar: [('min', 1), ('max', 3)]>
 
-        Using `skip_nulls` to handle null values.
+        Using ``skip_nulls`` to handle null values.
 
         >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
         >>> pc.min_max(arr2)
         <pyarrow.StructScalar: [('min', 1.0), ('max', 3.0)]>
         >>> pc.min_max(arr2, skip_nulls=False)
         <pyarrow.StructScalar: [('min', None), ('max', None)]>
 
-        Using `ScalarAggregateOptions` to control minimum number of non-null values.
+        Using ``ScalarAggregateOptions`` to control minimum number of non-null values.
 
         >>> arr3 = pa.array([1.0, None, float("nan"), 3.0])
         >>> pc.min_max(arr3)