[backport 2.3.x] String dtype: enable in SQL IO + resolve all xfails (#60255)

pandas/_libs/lib.pyx

@@ -2741,7 +2741,13 @@ def maybe_convert_objects(ndarray[object] objects,
         seen.object_ = True
 
     elif seen.str_:
-        if using_string_dtype() and is_string_array(objects, skipna=True):
+        if convert_to_nullable_dtype and is_string_array(objects, skipna=True):
+            from pandas.core.arrays.string_ import StringDtype
+
+            dtype = StringDtype()
+            return dtype.construct_array_type()._from_sequence(objects, dtype=dtype)
+
+        elif using_string_dtype() and is_string_array(objects, skipna=True):
             from pandas.core.arrays.string_ import StringDtype
 
             dtype = StringDtype(na_value=np.nan)

pandas/core/dtypes/cast.py

@@ -1163,6 +1163,7 @@ def convert_dtypes(
 
 def maybe_infer_to_datetimelike(
     value: npt.NDArray[np.object_],
+    convert_to_nullable_dtype: bool = False,
 ) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray | IntervalArray:
     """
     we might have a array (or single object) that is datetime like,
@@ -1200,6 +1201,7 @@ def maybe_infer_to_datetimelike(
         #  numpy would have done it for us.
         convert_numeric=False,
         convert_non_numeric=True,
+        convert_to_nullable_dtype=convert_to_nullable_dtype,
         dtype_if_all_nat=np.dtype("M8[ns]"),
     )
 

pandas/core/internals/construction.py

@@ -1042,8 +1042,9 @@ def convert(arr):
             if dtype is None:
                 if arr.dtype == np.dtype("O"):
                     # i.e. maybe_convert_objects didn't convert
-                    arr = maybe_infer_to_datetimelike(arr)
-                    if dtype_backend != "numpy" and arr.dtype == np.dtype("O"):
+                    convert_to_nullable_dtype = dtype_backend != "numpy"
+                    arr = maybe_infer_to_datetimelike(arr, convert_to_nullable_dtype)
+                    if convert_to_nullable_dtype and arr.dtype == np.dtype("O"):
                         new_dtype = StringDtype()
                         arr_cls = new_dtype.construct_array_type()
                         arr = arr_cls._from_sequence(arr, dtype=new_dtype)

pandas/io/sql.py

@@ -46,6 +46,8 @@
 from pandas.core.dtypes.common import (
     is_dict_like,
     is_list_like,
+    is_object_dtype,
+    is_string_dtype,
 )
 from pandas.core.dtypes.dtypes import (
     ArrowDtype,
@@ -59,6 +61,7 @@
     Series,
 )
 from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.string_ import StringDtype
 from pandas.core.base import PandasObject
 import pandas.core.common as com
 from pandas.core.common import maybe_make_list
@@ -1331,7 +1334,12 @@ def _harmonize_columns(
                 elif dtype_backend == "numpy" and col_type is float:
                     # floats support NA, can always convert!
                     self.frame[col_name] = df_col.astype(col_type, copy=False)
-
+                elif (
+                    using_string_dtype()
+                    and is_string_dtype(col_type)
+                    and is_object_dtype(self.frame[col_name])
+                ):
+                    self.frame[col_name] = df_col.astype(col_type, copy=False)
                 elif dtype_backend == "numpy" and len(df_col) == df_col.count():
                     # No NA values, can convert ints and bools
                     if col_type is np.dtype("int64") or col_type is bool:
@@ -1418,6 +1426,7 @@ def _get_dtype(self, sqltype):
             DateTime,
             Float,
             Integer,
+            String,
         )
 
         if isinstance(sqltype, Float):
@@ -1437,6 +1446,10 @@ def _get_dtype(self, sqltype):
             return date
         elif isinstance(sqltype, Boolean):
             return bool
+        elif isinstance(sqltype, String):
+            if using_string_dtype():
+                return StringDtype(na_value=np.nan)
+
         return object
 
 
@@ -2218,7 +2231,7 @@ def read_table(
         elif using_string_dtype():
             from pandas.io._util import arrow_string_types_mapper
 
-            arrow_string_types_mapper()
+            mapping = arrow_string_types_mapper()
         else:
             mapping = None
 
@@ -2299,6 +2312,10 @@ def read_query(
             from pandas.io._util import _arrow_dtype_mapping
 
             mapping = _arrow_dtype_mapping().get
+        elif using_string_dtype():
+            from pandas.io._util import arrow_string_types_mapper
+
+            mapping = arrow_string_types_mapper()
         else:
             mapping = None
 

pandas/tests/io/test_sql.py

@@ -63,7 +63,7 @@
     pytest.mark.filterwarnings(
         "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
     ),
-    pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False),
+    pytest.mark.single_cpu,
 ]
 
 
@@ -685,6 +685,7 @@ def postgresql_psycopg2_conn(postgresql_psycopg2_engine):
 
 @pytest.fixture
 def postgresql_adbc_conn():
+    pytest.importorskip("pyarrow")
     pytest.importorskip("adbc_driver_postgresql")
     from adbc_driver_postgresql import dbapi
 
@@ -817,6 +818,7 @@ def sqlite_conn_types(sqlite_engine_types):
 
 @pytest.fixture
 def sqlite_adbc_conn():
+    pytest.importorskip("pyarrow")
     pytest.importorskip("adbc_driver_sqlite")
     from adbc_driver_sqlite import dbapi
 
@@ -986,13 +988,13 @@ def test_dataframe_to_sql(conn, test_frame1, request):
 
 @pytest.mark.parametrize("conn", all_connectable)
 def test_dataframe_to_sql_empty(conn, test_frame1, request):
-    if conn == "postgresql_adbc_conn":
+    if conn == "postgresql_adbc_conn" and not using_string_dtype():
         request.node.add_marker(
             pytest.mark.xfail(
-                reason="postgres ADBC driver cannot insert index with null type",
-                strict=True,
+                reason="postgres ADBC driver < 1.2 cannot insert index with null type",
             )
         )
+
     # GH 51086 if conn is sqlite_engine
     conn = request.getfixturevalue(conn)
     empty_df = test_frame1.iloc[:0]
@@ -3571,7 +3573,8 @@ def test_read_sql_dtype_backend(
         result = getattr(pd, func)(
             f"Select * from {table}", conn, dtype_backend=dtype_backend
         )
-    expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+        expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+
     tm.assert_frame_equal(result, expected)
 
     if "adbc" in conn_name:
@@ -3621,7 +3624,7 @@ def test_read_sql_dtype_backend_table(
 
     with pd.option_context("mode.string_storage", string_storage):
         result = getattr(pd, func)(table, conn, dtype_backend=dtype_backend)
-    expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+        expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
     tm.assert_frame_equal(result, expected)
 
     if "adbc" in conn_name:
@@ -4150,7 +4153,7 @@ def tquery(query, con=None):
 def test_xsqlite_basic(sqlite_buildin):
     frame = DataFrame(
         np.random.default_rng(2).standard_normal((10, 4)),
-        columns=Index(list("ABCD"), dtype=object),
+        columns=Index(list("ABCD")),
         index=date_range("2000-01-01", periods=10, freq="B"),
     )
     assert sql.to_sql(frame, name="test_table", con=sqlite_buildin, index=False) == 10
@@ -4177,7 +4180,7 @@ def test_xsqlite_basic(sqlite_buildin):
 def test_xsqlite_write_row_by_row(sqlite_buildin):
     frame = DataFrame(
         np.random.default_rng(2).standard_normal((10, 4)),
-        columns=Index(list("ABCD"), dtype=object),
+        columns=Index(list("ABCD")),
         index=date_range("2000-01-01", periods=10, freq="B"),
     )
     frame.iloc[0, 0] = np.nan
@@ -4200,7 +4203,7 @@ def test_xsqlite_write_row_by_row(sqlite_buildin):
 def test_xsqlite_execute(sqlite_buildin):
     frame = DataFrame(
         np.random.default_rng(2).standard_normal((10, 4)),
-        columns=Index(list("ABCD"), dtype=object),
+        columns=Index(list("ABCD")),
         index=date_range("2000-01-01", periods=10, freq="B"),
     )
     create_sql = sql.get_schema(frame, "test")
@@ -4221,7 +4224,7 @@ def test_xsqlite_execute(sqlite_buildin):
 def test_xsqlite_schema(sqlite_buildin):
     frame = DataFrame(
         np.random.default_rng(2).standard_normal((10, 4)),
-        columns=Index(list("ABCD"), dtype=object),
+        columns=Index(list("ABCD")),
         index=date_range("2000-01-01", periods=10, freq="B"),
     )
     create_sql = sql.get_schema(frame, "test")