String dtype: map builtin str alias to StringDtype (pandas-dev#59685)

* String dtype: map builtin str alias to StringDtype

* fix tests

* fix datetimelike astype and more tests

* remove xfails

* try fix typing

* fix copy_view tests

* fix remaining tests with infer_string enabled

* ignore typing issue for now

* move to common.py

* simplify Categorical._str_get_dummies

* small cleanup

* fix ensure_string_array to not modify extension arrays inplace

* fix ensure_string_array once more + fix is_extension_array_dtype for str

* still xfail TestArrowArray::test_astype_str when not using infer_string

* ensure maybe_convert_objects copies object dtype input array when inferring StringDtype

* update test_1d_object_array_does_not_copy test

* update constructor copy test + do not copy in maybe_convert_objects?

* skip str.get_dummies test for now

* use pandas_dtype() instead of registry.find

* fix corner cases for calling pandas_dtype

* add TODO comment in ensure_string_array

pandas/_libs/lib.pyx

@@ -755,7 +755,14 @@ cpdef ndarray[object] ensure_string_array(
 
     if hasattr(arr, "to_numpy"):
 
-        if hasattr(arr, "dtype") and arr.dtype.kind in "mM":
+        if (
+            hasattr(arr, "dtype")
+            and arr.dtype.kind in "mM"
+            # TODO: we should add a custom ArrowExtensionArray.astype implementation
+            # that handles astype(str) specifically, avoiding ending up here and
+            # then we can remove the below check for `_pa_array` (for ArrowEA)
+            and not hasattr(arr, "_pa_array")
+        ):
             # dtype check to exclude DataFrame
             # GH#41409 TODO: not a great place for this
             out = arr.astype(str).astype(object)

pandas/_testing/__init__.py

@@ -112,7 +112,7 @@
 
 COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
 if using_string_dtype():
-    STRING_DTYPES: list[Dtype] = [str, "U"]
+    STRING_DTYPES: list[Dtype] = ["U"]
 else:
     STRING_DTYPES: list[Dtype] = [str, "str", "U"]  # type: ignore[no-redef]
 COMPLEX_FLOAT_DTYPES: list[Dtype] = [*COMPLEX_DTYPES, *FLOAT_NUMPY_DTYPES]

pandas/core/arrays/categorical.py

@@ -2691,7 +2691,9 @@ def _str_get_dummies(self, sep: str = "|"):
         # sep may not be in categories. Just bail on this.
         from pandas.core.arrays import NumpyExtensionArray
 
-        return NumpyExtensionArray(self.astype(str))._str_get_dummies(sep)
+        return NumpyExtensionArray(self.to_numpy(str, na_value="NaN"))._str_get_dummies(
+            sep
+        )
 
     # ------------------------------------------------------------------------
     # GroupBy Methods

pandas/core/arrays/datetimelike.py

@@ -472,10 +472,16 @@ def astype(self, dtype, copy: bool = True):
 
             return self._box_values(self.asi8.ravel()).reshape(self.shape)
 
+        elif is_string_dtype(dtype):
+            if isinstance(dtype, ExtensionDtype):
+                arr_object = self._format_native_types(na_rep=dtype.na_value)  # type: ignore[arg-type]
+                cls = dtype.construct_array_type()
+                return cls._from_sequence(arr_object, dtype=dtype, copy=False)
+            else:
+                return self._format_native_types()
+
         elif isinstance(dtype, ExtensionDtype):
             return super().astype(dtype, copy=copy)
-        elif is_string_dtype(dtype):
-            return self._format_native_types()
         elif dtype.kind in "iu":
             # we deliberately ignore int32 vs. int64 here.
             # See https://github.com/pandas-dev/pandas/issues/24381 for more.

pandas/core/dtypes/common.py

@@ -12,6 +12,8 @@
 
 import numpy as np
 
+from pandas._config import using_string_dtype
+
 from pandas._libs import (
     Interval,
     Period,
@@ -1325,7 +1327,15 @@ def is_extension_array_dtype(arr_or_dtype) -> bool:
     elif isinstance(dtype, np.dtype):
         return False
     else:
-        return registry.find(dtype) is not None
+        try:
+            with warnings.catch_warnings():
+                # pandas_dtype(..) can raise UserWarning for class input
+                warnings.simplefilter("ignore", UserWarning)
+                dtype = pandas_dtype(dtype)
+        except (TypeError, ValueError):
+            # np.dtype(..) can raise ValueError
+            return False
+        return isinstance(dtype, ExtensionDtype)
 
 
 def is_ea_or_datetimelike_dtype(dtype: DtypeObj | None) -> bool:
@@ -1620,6 +1630,12 @@ def pandas_dtype(dtype) -> DtypeObj:
     elif isinstance(dtype, (np.dtype, ExtensionDtype)):
         return dtype
 
+    # builtin aliases
+    if dtype is str and using_string_dtype():
+        from pandas.core.arrays.string_ import StringDtype
+
+        return StringDtype(na_value=np.nan)
+
     # registered extension types
     result = registry.find(dtype)
     if result is not None:

pandas/core/indexes/base.py

@@ -6415,7 +6415,11 @@ def _should_compare(self, other: Index) -> bool:
             return False
 
         dtype = _unpack_nested_dtype(other)
-        return self._is_comparable_dtype(dtype) or is_object_dtype(dtype)
+        return (
+            self._is_comparable_dtype(dtype)
+            or is_object_dtype(dtype)
+            or is_string_dtype(dtype)
+        )
 
     def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
         """

pandas/core/indexes/interval.py

@@ -50,6 +50,7 @@
     is_number,
     is_object_dtype,
     is_scalar,
+    is_string_dtype,
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import (
@@ -699,7 +700,7 @@ def _get_indexer(
             # left/right get_indexer, compare elementwise, equality -> match
             indexer = self._get_indexer_unique_sides(target)
 
-        elif not is_object_dtype(target.dtype):
+        elif not (is_object_dtype(target.dtype) or is_string_dtype(target.dtype)):
             # homogeneous scalar index: use IntervalTree
             # we should always have self._should_partial_index(target) here
             target = self._maybe_convert_i8(target)

pandas/tests/arrays/floating/test_astype.py

@@ -68,11 +68,9 @@ def test_astype_str(using_infer_string):
 
     if using_infer_string:
         expected = pd.array(["0.1", "0.2", None], dtype=pd.StringDtype(na_value=np.nan))
-        tm.assert_extension_array_equal(a.astype("str"), expected)
 
-        # TODO(infer_string) this should also be a string array like above
-        expected = np.array(["0.1", "0.2", "<NA>"], dtype="U32")
-        tm.assert_numpy_array_equal(a.astype(str), expected)
+        tm.assert_extension_array_equal(a.astype(str), expected)
+        tm.assert_extension_array_equal(a.astype("str"), expected)
     else:
         expected = np.array(["0.1", "0.2", "<NA>"], dtype="U32")
 

pandas/tests/arrays/integer/test_dtypes.py

@@ -283,11 +283,9 @@ def test_astype_str(using_infer_string):
 
     if using_infer_string:
         expected = pd.array(["1", "2", None], dtype=pd.StringDtype(na_value=np.nan))
-        tm.assert_extension_array_equal(a.astype("str"), expected)
 
-        # TODO(infer_string) this should also be a string array like above
-        expected = np.array(["1", "2", "<NA>"], dtype=f"{tm.ENDIAN}U21")
-        tm.assert_numpy_array_equal(a.astype(str), expected)
+        tm.assert_extension_array_equal(a.astype(str), expected)
+        tm.assert_extension_array_equal(a.astype("str"), expected)
     else:
         expected = np.array(["1", "2", "<NA>"], dtype=f"{tm.ENDIAN}U21")
 

pandas/tests/arrays/sparse/test_astype.py

@@ -81,8 +81,8 @@ def test_astype_all(self, any_real_numpy_dtype):
             ),
             (
                 SparseArray([0, 1, 10]),
-                str,
-                SparseArray(["0", "1", "10"], dtype=SparseDtype(str, "0")),
+                np.str_,
+                SparseArray(["0", "1", "10"], dtype=SparseDtype(np.str_, "0")),
             ),
             (SparseArray(["10", "20"]), float, SparseArray([10.0, 20.0])),
             (

pandas/tests/arrays/sparse/test_dtype.py

@@ -177,7 +177,7 @@ def test_construct_from_string_fill_value_raises(string):
     [
         (SparseDtype(int, 0), float, SparseDtype(float, 0.0)),
         (SparseDtype(int, 1), float, SparseDtype(float, 1.0)),
-        (SparseDtype(int, 1), str, SparseDtype(object, "1")),
+        (SparseDtype(int, 1), np.str_, SparseDtype(object, "1")),
         (SparseDtype(float, 1.5), int, SparseDtype(int, 1)),
     ],
 )

pandas/tests/dtypes/test_common.py

@@ -810,11 +810,23 @@ def test_pandas_dtype_string_dtypes(string_storage):
         "pyarrow" if HAS_PYARROW else "python", na_value=np.nan
     )
 
+    with pd.option_context("future.infer_string", True):
+        # with the default string_storage setting
+        result = pandas_dtype(str)
+    assert result == pd.StringDtype(
+        "pyarrow" if HAS_PYARROW else "python", na_value=np.nan
+    )
+
     with pd.option_context("future.infer_string", True):
         with pd.option_context("string_storage", string_storage):
             result = pandas_dtype("str")
     assert result == pd.StringDtype(string_storage, na_value=np.nan)
 
+    with pd.option_context("future.infer_string", True):
+        with pd.option_context("string_storage", string_storage):
+            result = pandas_dtype(str)
+    assert result == pd.StringDtype(string_storage, na_value=np.nan)
+
     with pd.option_context("future.infer_string", False):
         with pd.option_context("string_storage", string_storage):
             result = pandas_dtype("str")

pandas/tests/extension/base/casting.py

@@ -43,8 +43,8 @@ def test_tolist(self, data):
         assert result == expected
 
     def test_astype_str(self, data):
-        result = pd.Series(data[:5]).astype(str)
-        expected = pd.Series([str(x) for x in data[:5]], dtype=str)
+        result = pd.Series(data[:2]).astype(str)
+        expected = pd.Series([str(x) for x in data[:2]], dtype=str)
         tm.assert_series_equal(result, expected)
 
     @pytest.mark.parametrize(

pandas/tests/extension/json/array.py

@@ -207,9 +207,8 @@ def astype(self, dtype, copy=True):
                 return self.copy()
             return self
         elif isinstance(dtype, StringDtype):
-            value = self.astype(str)  # numpy doesn't like nested dicts
             arr_cls = dtype.construct_array_type()
-            return arr_cls._from_sequence(value, dtype=dtype, copy=False)
+            return arr_cls._from_sequence(self, dtype=dtype, copy=False)
         elif not copy:
             return np.asarray([dict(x) for x in self], dtype=dtype)
         else:

pandas/tests/extension/test_arrow.py

@@ -41,7 +41,6 @@
     pa_version_under13p0,
     pa_version_under14p0,
 )
-import pandas.util._test_decorators as td
 
 from pandas.core.dtypes.dtypes import (
     ArrowDtype,
@@ -286,43 +285,25 @@ def test_map(self, data_missing, na_action):
                 expected = data_missing.to_numpy()
             tm.assert_numpy_array_equal(result, expected)
 
-    def test_astype_str(self, data, request):
+    def test_astype_str(self, data, request, using_infer_string):
         pa_dtype = data.dtype.pyarrow_dtype
         if pa.types.is_binary(pa_dtype):
             request.applymarker(
                 pytest.mark.xfail(
                     reason=f"For {pa_dtype} .astype(str) decodes.",
                 )
             )
-        elif (
-            pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None
-        ) or pa.types.is_duration(pa_dtype):
+        elif not using_infer_string and (
+            (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None)
+            or pa.types.is_duration(pa_dtype)
+        ):
             request.applymarker(
                 pytest.mark.xfail(
                     reason="pd.Timestamp/pd.Timedelta repr different from numpy repr",
                 )
             )
         super().test_astype_str(data)
 
-    @pytest.mark.parametrize(
-        "nullable_string_dtype",
-        [
-            "string[python]",
-            pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
-        ],
-    )
-    def test_astype_string(self, data, nullable_string_dtype, request):
-        pa_dtype = data.dtype.pyarrow_dtype
-        if (
-            pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None
-        ) or pa.types.is_duration(pa_dtype):
-            request.applymarker(
-                pytest.mark.xfail(
-                    reason="pd.Timestamp/pd.Timedelta repr different from numpy repr",
-                )
-            )
-        super().test_astype_string(data, nullable_string_dtype)
-
     def test_from_dtype(self, data, request):
         pa_dtype = data.dtype.pyarrow_dtype
         if pa.types.is_string(pa_dtype) or pa.types.is_decimal(pa_dtype):

pandas/tests/frame/methods/test_astype.py

@@ -169,21 +169,21 @@ def test_astype_str(self):
                 "d": list(map(str, d._values)),
                 "e": list(map(str, e._values)),
             },
-            dtype="object",
+            dtype="str",
         )
 
         tm.assert_frame_equal(result, expected)
 
-    def test_astype_str_float(self):
+    def test_astype_str_float(self, using_infer_string):
         # see GH#11302
         result = DataFrame([np.nan]).astype(str)
-        expected = DataFrame(["nan"], dtype="object")
+        expected = DataFrame([np.nan if using_infer_string else "nan"], dtype="str")
 
         tm.assert_frame_equal(result, expected)
         result = DataFrame([1.12345678901234567890]).astype(str)
 
         val = "1.1234567890123457"
-        expected = DataFrame([val], dtype="object")
+        expected = DataFrame([val], dtype="str")
         tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("dtype_class", [dict, Series])
@@ -285,7 +285,7 @@ def test_astype_duplicate_col_series_arg(self):
         result = df.astype(dtypes)
         expected = DataFrame(
             {
-                0: Series(vals[:, 0].astype(str), dtype=object),
+                0: Series(vals[:, 0].astype(str), dtype="str"),
                 1: vals[:, 1],
                 2: pd.array(vals[:, 2], dtype="Float64"),
                 3: vals[:, 3],
@@ -666,25 +666,26 @@ def test_astype_dt64tz(self, timezone_frame):
             # dt64tz->dt64 deprecated
             timezone_frame.astype("datetime64[ns]")
 
-    def test_astype_dt64tz_to_str(self, timezone_frame):
+    def test_astype_dt64tz_to_str(self, timezone_frame, using_infer_string):
         # str formatting
         result = timezone_frame.astype(str)
+        na_value = np.nan if using_infer_string else "NaT"
         expected = DataFrame(
             [
                 [
                     "2013-01-01",
                     "2013-01-01 00:00:00-05:00",
                     "2013-01-01 00:00:00+01:00",
                 ],
-                ["2013-01-02", "NaT", "NaT"],
+                ["2013-01-02", na_value, na_value],
                 [
                     "2013-01-03",
                     "2013-01-03 00:00:00-05:00",
                     "2013-01-03 00:00:00+01:00",
                 ],
             ],
             columns=timezone_frame.columns,
-            dtype="object",
+            dtype="str",
         )
         tm.assert_frame_equal(result, expected)
 

pandas/tests/frame/methods/test_select_dtypes.py

@@ -99,6 +99,9 @@ def test_select_dtypes_include_using_list_like(self, using_infer_string):
             ei = df[["a"]]
             tm.assert_frame_equal(ri, ei)
 
+            ri = df.select_dtypes(include=[str])
+            tm.assert_frame_equal(ri, ei)
+
     def test_select_dtypes_exclude_using_list_like(self):
         df = DataFrame(
             {
@@ -358,7 +361,7 @@ def test_select_dtypes_datetime_with_tz(self):
     @pytest.mark.parametrize("dtype", [str, "str", np.bytes_, "S1", np.str_, "U1"])
     @pytest.mark.parametrize("arg", ["include", "exclude"])
     def test_select_dtypes_str_raises(self, dtype, arg, using_infer_string):
-        if using_infer_string and dtype == "str":
+        if using_infer_string and (dtype == "str" or dtype is str):
             # this is tested below
             pytest.skip("Selecting string columns works with future strings")
         df = DataFrame(