PERF: avoid NumPy fallback when casting Arrow int/string to string (#63401)

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

Author: fangchenli

pandas/core/arrays/string_arrow.py

@@ -210,6 +210,26 @@ def _from_sequence(
             result = scalars._data
             result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
             pa_arr = pa.array(result, mask=na_values, type=pa.large_string())
+        elif isinstance(scalars, ArrowExtensionArray):
+            pa_type = scalars._pa_array.type
+            # Use PyArrow's native cast for integer, string, and boolean types.
+            # Float has different representation in PyArrow: 1.0 -> "1" instead
+            # of "1.0", and uses different scientific notation (1e+10 vs 1e10).
+            # Boolean needs capitalize (true -> True, false -> False).
+            if (
+                pa.types.is_integer(pa_type)
+                or pa.types.is_large_string(pa_type)
+                or pa.types.is_string(pa_type)
+                or pa.types.is_boolean(pa_type)
+            ):
+                pa_arr = pc.cast(scalars._pa_array, pa.large_string())
+                if pa.types.is_boolean(pa_type):
+                    pa_arr = pc.utf8_capitalize(pa_arr)
+            else:
+                # Fall back for types where PyArrow's string representation
+                # differs from Python's str()
+                result = lib.ensure_string_array(scalars, copy=copy)
+                pa_arr = pa.array(result, type=pa.large_string(), from_pandas=True)
         elif isinstance(scalars, (pa.Array, pa.ChunkedArray)):
             pa_arr = pc.cast(scalars, pa.large_string())
         else:

pandas/tests/copy_view/test_astype.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import HAS_PYARROW
-
 from pandas import (
     DataFrame,
     Series,
@@ -218,10 +216,12 @@ def test_convert_dtypes(using_infer_string):
     df_orig = df.copy()
     df2 = df.convert_dtypes()
 
-    if HAS_PYARROW:
-        assert not tm.shares_memory(get_array(df2, "a"), get_array(df, "a"))
-    else:
+    if using_infer_string:
+        # String column is already Arrow-backed, so memory is shared
         assert tm.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        # String column converts from object to Arrow, no memory sharing
+        assert not tm.shares_memory(get_array(df2, "a"), get_array(df, "a"))
     assert tm.shares_memory(get_array(df2, "d"), get_array(df, "d"))
     assert tm.shares_memory(get_array(df2, "b"), get_array(df, "b"))
     assert tm.shares_memory(get_array(df2, "c"), get_array(df, "c"))