String dtype: still return nullable NA-variant in object inference (maybe_converts_object) if requested (#59487)

* String dtype: maybe_converts_object give precedence to nullable dtype

* update datetimelike input validation

* update tests and remove xfails

* explicitly test pd.array() behaviour (remove xfail)

* fixup allow_2d

* undo changes related to datetimelike input validation

* fix test for str on current main

---------

Co-authored-by: Matthew Roeschke <[email protected]>

Author: jorisvandenbossche

pandas/tests/arrays/string_/test_string_arrow.py

@@ -36,9 +36,8 @@ def test_config(string_storage, using_infer_string):
         result = pd.array(["a", "b"])
         assert result.dtype.storage == string_storage
 
-    dtype = StringDtype(
-        string_storage, na_value=np.nan if using_infer_string else pd.NA
-    )
+    # pd.array(..) by default always returns the NA-variant
+    dtype = StringDtype(string_storage, na_value=pd.NA)
     expected = dtype.construct_array_type()._from_sequence(["a", "b"], dtype=dtype)
     tm.assert_equal(result, expected)
 

pandas/tests/arrays/test_array.py

@@ -218,13 +218,45 @@ def test_dt64_array(dtype_unit):
             .construct_array_type()
             ._from_sequence(["a", None], dtype=pd.StringDtype()),
         ),
+        (
+            ["a", None],
+            "str",
+            pd.StringDtype(na_value=np.nan)
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype(na_value=np.nan))
+            if using_string_dtype()
+            else NumpyExtensionArray(np.array(["a", "None"])),
+        ),
         (
             ["a", None],
             pd.StringDtype(),
             pd.StringDtype()
             .construct_array_type()
             ._from_sequence(["a", None], dtype=pd.StringDtype()),
         ),
+        (
+            ["a", None],
+            pd.StringDtype(na_value=np.nan),
+            pd.StringDtype(na_value=np.nan)
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype(na_value=np.nan)),
+        ),
+        (
+            # numpy array with string dtype
+            np.array(["a", "b"], dtype=str),
+            pd.StringDtype(),
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype()),
+        ),
+        (
+            # numpy array with string dtype
+            np.array(["a", "b"], dtype=str),
+            pd.StringDtype(na_value=np.nan),
+            pd.StringDtype(na_value=np.nan)
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype(na_value=np.nan)),
+        ),
         # Boolean
         (
             [True, None],
@@ -277,7 +309,6 @@ def test_array_copy():
 cet = pytz.timezone("CET")
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
 @pytest.mark.parametrize(
     "data, expected",
     [
@@ -370,6 +401,13 @@ def test_array_copy():
             .construct_array_type()
             ._from_sequence(["a", None], dtype=pd.StringDtype()),
         ),
+        (
+            # numpy array with string dtype
+            np.array(["a", "b"], dtype=str),
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype()),
+        ),
         # Boolean
         ([True, False], BooleanArray._from_sequence([True, False], dtype="boolean")),
         ([True, None], BooleanArray._from_sequence([True, None], dtype="boolean")),

pandas/tests/arrays/test_datetimelike.py

@@ -295,9 +295,7 @@ def test_searchsorted(self):
         assert result == 10
 
     @pytest.mark.parametrize("box", [None, "index", "series"])
-    def test_searchsorted_castable_strings(
-        self, arr1d, box, string_storage, using_infer_string
-    ):
+    def test_searchsorted_castable_strings(self, arr1d, box, string_storage):
         arr = arr1d
         if box is None:
             pass
@@ -333,8 +331,7 @@ def test_searchsorted_castable_strings(
                 TypeError,
                 match=re.escape(
                     f"value should be a '{arr1d._scalar_type.__name__}', 'NaT', "
-                    "or array of those. Got "
-                    f"{'str' if using_infer_string else 'string'} array instead."
+                    "or array of those. Got string array instead."
                 ),
             ):
                 arr.searchsorted([str(arr[1]), "baz"])

pandas/tests/base/test_value_counts.py

@@ -127,7 +127,7 @@ def test_value_counts_inferred(index_or_series, using_infer_string):
     else:
         exp = np.unique(np.array(s_values, dtype=np.object_))
         if using_infer_string:
-            exp = array(exp)
+            exp = array(exp, dtype="str")
         tm.assert_equal(s.unique(), exp)
 
     assert s.nunique() == 4
@@ -205,7 +205,7 @@ def test_value_counts_bins(index_or_series, using_infer_string):
     else:
         exp = np.array(["a", "b", np.nan, "d"], dtype=object)
         if using_infer_string:
-            exp = array(exp)
+            exp = array(exp, dtype="str")
         tm.assert_equal(s.unique(), exp)
     assert s.nunique() == 3
 

pandas/tests/dtypes/cast/test_construct_ndarray.py

@@ -21,7 +21,7 @@ def test_construct_1d_ndarray_preserving_na(
 ):
     result = sanitize_array(values, index=None, dtype=dtype)
     if using_infer_string and expected.dtype == object and dtype is None:
-        tm.assert_extension_array_equal(result, pd.array(expected))
+        tm.assert_extension_array_equal(result, pd.array(expected, dtype="str"))
     else:
         tm.assert_numpy_array_equal(result, expected)
 

pandas/tests/io/parser/usecols/test_usecols_basic.py

@@ -7,8 +7,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas.errors import ParserError
 
 from pandas import (
@@ -547,7 +545,6 @@ def test_usecols_additional_columns_integer_columns(all_parsers):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 def test_usecols_dtype(all_parsers):
     parser = all_parsers
     data = """