BUG: indexing empty pyarrow backed object returning corrupt object (#51741)

Author: phofl

doc/source/whatsnew/v2.0.0.rst

@@ -1248,6 +1248,7 @@ Indexing
 - Bug in :meth:`DataFrame.compare` does not recognize differences when comparing ``NA`` with value in nullable dtypes (:issue:`48939`)
 - Bug in :meth:`Series.rename` with :class:`MultiIndex` losing extension array dtypes (:issue:`21055`)
 - Bug in :meth:`DataFrame.isetitem` coercing extension array dtypes in :class:`DataFrame` to object (:issue:`49922`)
+- Bug in :meth:`Series.__getitem__` returning corrupt object when selecting from an empty pyarrow backed object (:issue:`51734`)
 - Bug in :class:`BusinessHour` would cause creation of :class:`DatetimeIndex` to fail when no opening hour was included in the index (:issue:`49835`)
 
 Missing

pandas/_testing/__init__.py

@@ -253,6 +253,7 @@
 else:
     FLOAT_PYARROW_DTYPES_STR_REPR = []
     ALL_INT_PYARROW_DTYPES_STR_REPR = []
+    ALL_PYARROW_DTYPES = []
 
 
 EMPTY_STRING_PATTERN = re.compile("^$")

pandas/core/arrays/arrow/array.py

@@ -1026,7 +1026,12 @@ def _concat_same_type(
         ArrowExtensionArray
         """
         chunks = [array for ea in to_concat for array in ea._data.iterchunks()]
-        arr = pa.chunked_array(chunks)
+        if to_concat[0].dtype == "string":
+            # StringDtype has no attrivute pyarrow_dtype
+            pa_dtype = pa.string()
+        else:
+            pa_dtype = to_concat[0].dtype.pyarrow_dtype
+        arr = pa.chunked_array(chunks, type=pa_dtype)
         return cls(arr)
 
     def _accumulate(

pandas/tests/extension/test_arrow.py

@@ -2329,3 +2329,11 @@ def test_from_sequence_of_strings_boolean():
     strings = ["True", "foo"]
     with pytest.raises(pa.ArrowInvalid, match="Failed to parse"):
         ArrowExtensionArray._from_sequence_of_strings(strings, dtype=pa.bool_())
+
+
+def test_concat_empty_arrow_backed_series(dtype):
+    # GH#51734
+    ser = pd.Series([], dtype=dtype)
+    expected = ser.copy()
+    result = pd.concat([ser[np.array([], dtype=np.bool_)]])
+    tm.assert_series_equal(result, expected)