COMPAT: Use actual isinstance check for pyarrow.Array instead of hasattr(.. 'type') duck typing

pandas/_libs/lib.pyi

@@ -42,6 +42,7 @@ def infer_dtype(value: object, skipna: bool = ...) -> str: ...
 def is_iterator(obj: object) -> bool: ...
 def is_scalar(val: object) -> bool: ...
 def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
+def is_pyarrow_array(obj: object) -> bool: ...
 def is_period(val: object) -> TypeGuard[Period]: ...
 def is_interval(val: object) -> TypeGuard[Interval]: ...
 def is_decimal(val: object) -> TypeGuard[Decimal]: ...

pandas/_libs/lib.pyx

@@ -149,6 +149,16 @@ i8max = <int64_t>INT64_MAX
 u8max = <uint64_t>UINT64_MAX
 
 
+cdef bint PYARROW_INSTALLED = False
+
+try:
+    import pyarrow as pa
+
+    PYARROW_INSTALLED = True
+except ImportError:
+    pa = None
+
+
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def memory_usage_of_objects(arr: object[:]) -> int64_t:
@@ -1177,6 +1187,19 @@ cdef bint c_is_list_like(object obj, bint allow_sets) except -1:
     )
 
 
+def is_pyarrow_array(obj):
+    """
+    Return True if given object is a pyarrow Array or ChunkedArray.
+
+    Returns
+    -------
+    bool
+    """
+    if PYARROW_INSTALLED:
+        return isinstance(obj, (pa.Array, pa.ChunkedArray))
+    return False
+
+
 _TYPE_MAP = {
     "categorical": "categorical",
     "category": "categorical",

pandas/core/arrays/string_.py

@@ -355,7 +355,7 @@ def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = Fal
             result[na_values] = libmissing.NA
 
         else:
-            if hasattr(scalars, "type"):
+            if lib.is_pyarrow_array(scalars):
                 # pyarrow array; we cannot rely on the "to_numpy" check in
                 #  ensure_string_array because calling scalars.to_numpy would set
                 #  zero_copy_only to True which caused problems see GH#52076