PERF: avoid unnecessary to_numpy conversion in value_counts for Arrow arrays

Previously, value_counts_internal would convert Arrow array counts to
NumPy just to call .sum() for normalization. This is unnecessary since
Series.sum() works correctly for all backends.

Changes:
- Remove unnecessary np.asarray(counts) conversion for Arrow arrays
- Remove unused counts variable assignments from bins and MultiIndex branches
- Use result.sum() instead of counts.sum() for normalization

This eliminates a performance bottleneck where Arrow-backed Series
would fall back to NumPy during value_counts operations.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: fangchenli

pandas/tests/extension/test_arrow.py

@@ -270,6 +270,35 @@ def data_for_twos(data):
     # TODO: skip otherwise?
 
 
+@pytest.fixture
+def arrow_to_numpy_tracker(monkeypatch):
+    """
+    Fixture to track if ArrowExtensionArray.to_numpy() was called.
+
+    Returns a callable that returns True if to_numpy was called since the last check,
+    and resets the tracker.
+    """
+    from pandas.core.arrays.arrow import ArrowExtensionArray
+
+    called = False
+    original_to_numpy = ArrowExtensionArray.to_numpy
+
+    def tracked_to_numpy(self, *args, **kwargs):
+        nonlocal called
+        called = True
+        return original_to_numpy(self, *args, **kwargs)
+
+    monkeypatch.setattr(ArrowExtensionArray, "to_numpy", tracked_to_numpy)
+
+    def was_called():
+        nonlocal called
+        result = called
+        called = False
+        return result
+
+    return was_called
+
+
 class TestArrowArray(base.ExtensionTests):
     def _construct_for_combine_add(self, left, right):
         dtype = left.dtype
@@ -787,33 +816,16 @@ def test_value_counts_returns_pyarrow_int64(self, data):
         result = data.value_counts()
         assert result.dtype == ArrowDtype(pa.int64())
 
-    def test_value_counts_no_numpy_fallback(self, data, monkeypatch):
+    def test_value_counts_no_numpy_fallback(self, data, arrow_to_numpy_tracker):
         # Ensure value_counts doesn't unnecessarily convert Arrow arrays to NumPy
-        from pandas.core.arrays.arrow import ArrowExtensionArray
-
         data = data[:10]
         ser = pd.Series(data)
 
-        # Track if to_numpy was called
-        to_numpy_called = False
-        original_to_numpy = ArrowExtensionArray.to_numpy
-
-        def tracked_to_numpy(self, *args, **kwargs):
-            nonlocal to_numpy_called
-            to_numpy_called = True
-            return original_to_numpy(self, *args, **kwargs)
-
-        monkeypatch.setattr(ArrowExtensionArray, "to_numpy", tracked_to_numpy)
-
         ser.value_counts()
-        assert not to_numpy_called, "value_counts() should not call to_numpy()"
+        assert not arrow_to_numpy_tracker()
 
-        # Also test with normalize=True
-        to_numpy_called = False
         ser.value_counts(normalize=True)
-        assert not to_numpy_called, (
-            "value_counts(normalize=True) should not call to_numpy()"
-        )
+        assert not arrow_to_numpy_tracker()
 
     _combine_le_expected_dtype = "bool[pyarrow]"