Fix new string dtype tests for frame folder (#55409)

* Start fixing string tests

* BUG: interpolate raising wrong error for ea

* Fix more tests

* REGR: join segfaulting for arrow string with nulls

* Fix more tests

* Fix more tests

* BUG: rank raising for arrow string dtypes

* BUG: eq not implemented for categorical and arrow backed strings

* More tests

* BUG: ndim of string block incorrect with string inference

* Fix test

* Fix tests

* Fix tests

* Fix more indexing tests

* BUG: Index.insert raising when inserting None into new string dtype

* Fix tests

* BUG: Inserting ndim=0 array does not infer string dtype

* Fix tests

* Fix tests

* Fix more tests

* Fix more tests

* BUG: idxmax raising for arrow strings

* Fix

* Fix more tests

* Fix more tests

* Fix more tests

* Fix remaining tests

* Fix remaining tests

* Change default

* BUG: Groupby not keeping string dtype for empty objects

* Start fixing gb tests

* Fix tests

* Merge main

* Update config_init.py

* Fixup

* Update

Author: phofl

pandas/tests/frame/constructors/test_from_dict.py

@@ -3,6 +3,8 @@
 import numpy as np
 import pytest
 
+from pandas._config import using_pyarrow_string_dtype
+
 from pandas import (
     DataFrame,
     Index,
@@ -42,6 +44,9 @@ def test_constructor_single_row(self):
         )
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.skipif(
+        using_pyarrow_string_dtype(), reason="columns inferring logic broken"
+    )
     def test_constructor_list_of_series(self):
         data = [
             OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),

pandas/tests/frame/constructors/test_from_records.py

@@ -6,6 +6,8 @@
 import pytest
 import pytz
 
+from pandas._config import using_pyarrow_string_dtype
+
 from pandas.compat import is_platform_little_endian
 
 from pandas import (
@@ -56,6 +58,9 @@ def test_from_records_with_datetimes(self):
         expected["EXPIRY"] = expected["EXPIRY"].astype("M8[s]")
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.skipif(
+        using_pyarrow_string_dtype(), reason="dtype checking logic doesn't work"
+    )
     def test_from_records_sequencelike(self):
         df = DataFrame(
             {

pandas/tests/frame/indexing/test_getitem.py

@@ -103,7 +103,7 @@ def test_getitem_list_duplicates(self):
 
     def test_getitem_dupe_cols(self):
         df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
-        msg = "\"None of [Index(['baf'], dtype='object')] are in the [columns]\""
+        msg = "\"None of [Index(['baf'], dtype="
         with pytest.raises(KeyError, match=re.escape(msg)):
             df[["baf"]]
 

pandas/tests/frame/indexing/test_indexing.py

@@ -288,7 +288,9 @@ def test_setattr_column(self):
         df.foobar = 5
         assert (df.foobar == 5).all()
 
-    def test_setitem(self, float_frame, using_copy_on_write, warn_copy_on_write):
+    def test_setitem(
+        self, float_frame, using_copy_on_write, warn_copy_on_write, using_infer_string
+    ):
         # not sure what else to do here
         series = float_frame["A"][::2]
         float_frame["col5"] = series
@@ -331,7 +333,10 @@ def test_setitem(self, float_frame, using_copy_on_write, warn_copy_on_write):
             with pytest.raises(SettingWithCopyError, match=msg):
                 smaller["col10"] = ["1", "2"]
 
-        assert smaller["col10"].dtype == np.object_
+        if using_infer_string:
+            assert smaller["col10"].dtype == "string"
+        else:
+            assert smaller["col10"].dtype == np.object_
         assert (smaller["col10"] == ["1", "2"]).all()
 
     def test_setitem2(self):
@@ -426,7 +431,7 @@ def test_setitem_cast(self, float_frame):
         float_frame["something"] = 2.5
         assert float_frame["something"].dtype == np.float64
 
-    def test_setitem_corner(self, float_frame):
+    def test_setitem_corner(self, float_frame, using_infer_string):
         # corner case
         df = DataFrame({"B": [1.0, 2.0, 3.0], "C": ["a", "b", "c"]}, index=np.arange(3))
         del df["B"]
@@ -463,10 +468,16 @@ def test_setitem_corner(self, float_frame):
         dm["foo"] = "bar"
         del dm["foo"]
         dm["foo"] = "bar"
-        assert dm["foo"].dtype == np.object_
+        if using_infer_string:
+            assert dm["foo"].dtype == "string"
+        else:
+            assert dm["foo"].dtype == np.object_
 
         dm["coercible"] = ["1", "2", "3"]
-        assert dm["coercible"].dtype == np.object_
+        if using_infer_string:
+            assert dm["coercible"].dtype == "string"
+        else:
+            assert dm["coercible"].dtype == np.object_
 
     def test_setitem_corner2(self):
         data = {
@@ -483,7 +494,7 @@ def test_setitem_corner2(self):
         assert df.loc[1, "title"] == "foobar"
         assert df.loc[1, "cruft"] == 0
 
-    def test_setitem_ambig(self):
+    def test_setitem_ambig(self, using_infer_string):
         # Difficulties with mixed-type data
         # Created as float type
         dm = DataFrame(index=range(3), columns=range(3))
@@ -499,18 +510,22 @@ def test_setitem_ambig(self):
 
         dm[2] = uncoercable_series
         assert len(dm.columns) == 3
-        assert dm[2].dtype == np.object_
+        if using_infer_string:
+            assert dm[2].dtype == "string"
+        else:
+            assert dm[2].dtype == np.object_
 
-    def test_setitem_None(self, float_frame):
+    def test_setitem_None(self, float_frame, using_infer_string):
         # GH #766
         float_frame[None] = float_frame["A"]
+        key = None if not using_infer_string else np.nan
         tm.assert_series_equal(
             float_frame.iloc[:, -1], float_frame["A"], check_names=False
         )
         tm.assert_series_equal(
-            float_frame.loc[:, None], float_frame["A"], check_names=False
+            float_frame.loc[:, key], float_frame["A"], check_names=False
         )
-        tm.assert_series_equal(float_frame[None], float_frame["A"], check_names=False)
+        tm.assert_series_equal(float_frame[key], float_frame["A"], check_names=False)
 
     def test_loc_setitem_boolean_mask_allfalse(self):
         # GH 9596

pandas/tests/frame/indexing/test_set_value.py

@@ -16,7 +16,7 @@ def test_set_value(self, float_frame):
                 float_frame._set_value(idx, col, 1)
                 assert float_frame[col][idx] == 1
 
-    def test_set_value_resize(self, float_frame):
+    def test_set_value_resize(self, float_frame, using_infer_string):
         res = float_frame._set_value("foobar", "B", 0)
         assert res is None
         assert float_frame.index[-1] == "foobar"
@@ -27,8 +27,10 @@ def test_set_value_resize(self, float_frame):
 
         res = float_frame.copy()
         res._set_value("foobar", "baz", "sam")
-        assert res["baz"].dtype == np.object_
-
+        if using_infer_string:
+            assert res["baz"].dtype == "string"
+        else:
+            assert res["baz"].dtype == np.object_
         res = float_frame.copy()
         with tm.assert_produces_warning(
             FutureWarning, match="Setting an item of incompatible dtype"

pandas/tests/frame/indexing/test_setitem.py

@@ -1319,7 +1319,7 @@ def test_setitem_column_frame_as_category(self):
         df["col2"] = Series([1, 2, 3], dtype="category")
 
         expected_types = Series(
-            ["int64", "category", "category"], index=[0, "col1", "col2"]
+            ["int64", "category", "category"], index=[0, "col1", "col2"], dtype=object
         )
         tm.assert_series_equal(df.dtypes, expected_types)
 

pandas/tests/frame/indexing/test_where.py

@@ -1077,9 +1077,13 @@ def test_where_producing_ea_cond_for_np_dtype():
 @pytest.mark.parametrize(
     "replacement", [0.001, True, "snake", None, datetime(2022, 5, 4)]
 )
-def test_where_int_overflow(replacement):
+def test_where_int_overflow(replacement, using_infer_string, request):
     # GH 31687
     df = DataFrame([[1.0, 2e25, "nine"], [np.nan, 0.1, None]])
+    if using_infer_string and replacement not in (None, "snake"):
+        request.node.add_marker(
+            pytest.mark.xfail(reason="Can't set non-string into string column")
+        )
     result = df.where(pd.notnull(df), replacement)
     expected = DataFrame([[1.0, 2e25, "nine"], [replacement, 0.1, replacement]])
 

pandas/tests/frame/methods/test_align.py

@@ -107,7 +107,7 @@ def test_align_float(self, float_frame, using_copy_on_write):
             af, bf = float_frame.align(
                 other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=None
             )
-        tm.assert_index_equal(bf.index, Index([]))
+        tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype))
 
         msg = (
             "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
@@ -117,7 +117,7 @@ def test_align_float(self, float_frame, using_copy_on_write):
             af, bf = float_frame.align(
                 other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0
             )
-        tm.assert_index_equal(bf.index, Index([]))
+        tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype))
 
         # Try to align DataFrame to Series along bad axis
         msg = "No axis named 2 for object type DataFrame"

pandas/tests/frame/methods/test_astype.py

@@ -166,21 +166,22 @@ def test_astype_str(self):
                 "c": [Timedelta(x)._repr_base() for x in c._values],
                 "d": list(map(str, d._values)),
                 "e": list(map(str, e._values)),
-            }
+            },
+            dtype="object",
         )
 
         tm.assert_frame_equal(result, expected)
 
     def test_astype_str_float(self):
         # see GH#11302
         result = DataFrame([np.nan]).astype(str)
-        expected = DataFrame(["nan"])
+        expected = DataFrame(["nan"], dtype="object")
 
         tm.assert_frame_equal(result, expected)
         result = DataFrame([1.12345678901234567890]).astype(str)
 
         val = "1.1234567890123457"
-        expected = DataFrame([val])
+        expected = DataFrame([val], dtype="object")
         tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("dtype_class", [dict, Series])
@@ -199,7 +200,7 @@ def test_astype_dict_like(self, dtype_class):
         expected = DataFrame(
             {
                 "a": a,
-                "b": Series(["0", "1", "2", "3", "4"]),
+                "b": Series(["0", "1", "2", "3", "4"], dtype="object"),
                 "c": c,
                 "d": Series([1.0, 2.0, 3.14, 4.0, 5.4], dtype="float32"),
             }
@@ -282,7 +283,7 @@ def test_astype_duplicate_col_series_arg(self):
         result = df.astype(dtypes)
         expected = DataFrame(
             {
-                0: vals[:, 0].astype(str),
+                0: Series(vals[:, 0].astype(str), dtype=object),
                 1: vals[:, 1],
                 2: pd.array(vals[:, 2], dtype="Float64"),
                 3: vals[:, 3],
@@ -620,6 +621,7 @@ def test_astype_arg_for_errors_dictlist(self):
                 {"a": 2.2, "b": "15.3", "c": "another_test"},
             ]
         )
+        expected["c"] = expected["c"].astype("object")
         type_dict = {"a": "float64", "b": "float64", "c": "object"}
 
         result = df.astype(dtype=type_dict, errors="ignore")
@@ -680,6 +682,7 @@ def test_astype_dt64tz_to_str(self, timezone_frame):
                 ],
             ],
             columns=timezone_frame.columns,
+            dtype="object",
         )
         tm.assert_frame_equal(result, expected)
 
@@ -754,7 +757,9 @@ def test_astype_tz_object_conversion(self, tz):
         result = result.astype({"tz": "datetime64[ns, Europe/London]"})
         tm.assert_frame_equal(result, expected)
 
-    def test_astype_dt64_to_string(self, frame_or_series, tz_naive_fixture):
+    def test_astype_dt64_to_string(
+        self, frame_or_series, tz_naive_fixture, using_infer_string
+    ):
         # GH#41409
         tz = tz_naive_fixture
 
@@ -772,7 +777,10 @@ def test_astype_dt64_to_string(self, frame_or_series, tz_naive_fixture):
         item = result.iloc[0]
         if frame_or_series is DataFrame:
             item = item.iloc[0]
-        assert item is pd.NA
+        if using_infer_string:
+            assert item is np.nan
+        else:
+            assert item is pd.NA
 
         # For non-NA values, we should match what we get for non-EA str
         alt = obj.astype(str)

pandas/tests/frame/methods/test_combine_first.py

@@ -30,7 +30,7 @@ def test_combine_first_mixed(self):
         combined = f.combine_first(g)
         tm.assert_frame_equal(combined, exp)
 
-    def test_combine_first(self, float_frame):
+    def test_combine_first(self, float_frame, using_infer_string):
         # disjoint
         head, tail = float_frame[:5], float_frame[5:]
 
@@ -76,7 +76,9 @@ def test_combine_first(self, float_frame):
         tm.assert_series_equal(combined["A"].reindex(g.index), g["A"])
 
         # corner cases
-        comb = float_frame.combine_first(DataFrame())
+        warning = FutureWarning if using_infer_string else None
+        with tm.assert_produces_warning(warning, match="empty entries"):
+            comb = float_frame.combine_first(DataFrame())
         tm.assert_frame_equal(comb, float_frame)
 
         comb = DataFrame().combine_first(float_frame)