Fix linting errors (line length and code style)

- Break long lines to comply with 88 character limit
- Use list comprehension instead of append in loop
- Improve code readability with multi-line formatting

Author: AKHIL-149

pandas/core/sorting.py

@@ -683,15 +683,19 @@ def compress_group_index(
     import sys
 
     # Use numpy-based approach for Python 3.14+ to avoid hashtable issues
-    if sys.version_info >= (3, 14) or (len(group_index) and np.all(group_index[1:] >= group_index[:-1])):
+    is_sorted = len(group_index) and np.all(
+        group_index[1:] >= group_index[:-1]
+    )
+    if sys.version_info >= (3, 14) or is_sorted:
         # GH 53806: fast path for sorted group_index
         # GH 63314: also use for Python 3.14+ due to hashtable behavior changes
         if len(group_index) == 0:
-            return ensure_int64(np.array([], dtype=np.int64)), ensure_int64(np.array([], dtype=np.int64))
+            empty_arr = np.array([], dtype=np.int64)
+            return ensure_int64(empty_arr), ensure_int64(empty_arr)
 
         # Sort if needed
         if not np.all(group_index[1:] >= group_index[:-1]):
-            sorted_idx = np.argsort(group_index, kind='stable')
+            sorted_idx = np.argsort(group_index, kind="stable")
             sorted_group_index = group_index[sorted_idx]
             unsort_idx = np.empty_like(sorted_idx)
             unsort_idx[sorted_idx] = np.arange(len(sorted_idx))
@@ -700,7 +704,10 @@ def compress_group_index(
             unsort_idx = None
 
         unique_mask = np.concatenate(
-            [sorted_group_index[:1] > -1, sorted_group_index[1:] != sorted_group_index[:-1]]
+            [
+                sorted_group_index[:1] > -1,
+                sorted_group_index[1:] != sorted_group_index[:-1],
+            ]
         )
         comp_ids_sorted = unique_mask.cumsum() - 1
         obs_group_ids = sorted_group_index[unique_mask]

pandas/tests/reshape/test_pivot.py

@@ -2961,19 +2961,16 @@ def test_pivot_empty_dataframe_period_dtype(self, freq):
         tm.assert_frame_equal(result, expected)
 
     def test_pivot_table_large_dataset_no_duplicates(self):
-        # GH 63314: pivot_table with large datasets should not produce duplicate indices
-        # This test ensures that the fix for Python 3.14 hashtable issues works correctly
+        # GH 63314: pivot_table with large datasets should not produce
+        # duplicate indices. This test ensures the Python 3.14 fix works.
         n_indices = 10000
         metrics = ["apple", "banana", "coconut"]
 
-        data = []
-        for i in range(n_indices):
-            for metric in metrics:
-                data.append({
-                    "idx": f"id_{i}",
-                    "metric": metric,
-                    "value": i * 10 + len(metric)
-                })
+        data = [
+            {"idx": f"id_{i}", "metric": metric, "value": i * 10 + len(metric)}
+            for i in range(n_indices)
+            for metric in metrics
+        ]
 
         df = DataFrame(data)
 
@@ -2985,15 +2982,19 @@ def test_pivot_table_large_dataset_no_duplicates(self):
         )
 
         # Verify no duplicate indices in the result
-        assert len(result.index) == len(result.index.unique()), \
-            f"Expected {len(result.index.unique())} unique indices, got {len(result.index)}"
+        n_unique = len(result.index.unique())
+        assert len(result.index) == n_unique, (
+            f"Expected {n_unique} unique indices, got {len(result.index)}"
+        )
 
         # Verify we have the expected number of rows
-        assert len(result) == n_indices, \
+        assert len(result) == n_indices, (
             f"Expected {n_indices} rows, got {len(result)}"
+        )
 
         # Verify all expected indices are present
         expected_indices = {f"id_{i}" for i in range(n_indices)}
         actual_indices = set(result.index)
-        assert expected_indices == actual_indices, \
+        assert expected_indices == actual_indices, (
             "Result indices don't match expected indices"
+        )