Backport PR #56445: Adjust merge tests for new string option (#56938)

Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com>

Author: lithomas1

pandas/tests/reshape/merge/test_join.py

@@ -631,7 +631,7 @@ def test_mixed_type_join_with_suffix(self):
         df.insert(5, "dt", "foo")
 
         grouped = df.groupby("id")
-        msg = re.escape("agg function failed [how->mean,dtype->object]")
+        msg = re.escape("agg function failed [how->mean,dtype->")
         with pytest.raises(TypeError, match=msg):
             grouped.mean()
         mn = grouped.mean(numeric_only=True)
@@ -776,7 +776,7 @@ def test_join_on_tz_aware_datetimeindex(self):
         )
         result = df1.join(df2.set_index("date"), on="date")
         expected = df1.copy()
-        expected["vals_2"] = Series([np.nan] * 2 + list("tuv"), dtype=object)
+        expected["vals_2"] = Series([np.nan] * 2 + list("tuv"))
         tm.assert_frame_equal(result, expected)
 
     def test_join_datetime_string(self):

pandas/tests/reshape/merge/test_merge.py

@@ -8,7 +8,10 @@
 import numpy as np
 import pytest
 
-from pandas.core.dtypes.common import is_object_dtype
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_string_dtype,
+)
 from pandas.core.dtypes.dtypes import CategoricalDtype
 
 import pandas as pd
@@ -316,14 +319,15 @@ def test_merge_copy(self):
         merged["d"] = "peekaboo"
         assert (right["d"] == "bar").all()
 
-    def test_merge_nocopy(self, using_array_manager):
+    def test_merge_nocopy(self, using_array_manager, using_infer_string):
         left = DataFrame({"a": 0, "b": 1}, index=range(10))
         right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
 
         merged = merge(left, right, left_index=True, right_index=True, copy=False)
 
         assert np.shares_memory(merged["a"]._values, left["a"]._values)
-        assert np.shares_memory(merged["d"]._values, right["d"]._values)
+        if not using_infer_string:
+            assert np.shares_memory(merged["d"]._values, right["d"]._values)
 
     def test_intelligently_handle_join_key(self):
         # #733, be a bit more 1337 about not returning unconsolidated DataFrame
@@ -667,11 +671,13 @@ def test_merge_nan_right(self):
                     "i1_": {0: 0, 1: np.nan},
                     "i3": {0: 0.0, 1: np.nan},
                     None: {0: 0, 1: 0},
-                }
+                },
+                columns=Index(["i1", "i2", "i1_", "i3", None], dtype=object),
             )
             .set_index(None)
             .reset_index()[["i1", "i2", "i1_", "i3"]]
         )
+        result.columns = result.columns.astype("object")
         tm.assert_frame_equal(result, expected, check_dtype=False)
 
     def test_merge_nan_right2(self):
@@ -820,7 +826,7 @@ def test_overlapping_columns_error_message(self):
 
         # #2649, #10639
         df2.columns = ["key1", "foo", "foo"]
-        msg = r"Data columns not unique: Index\(\['foo'\], dtype='object'\)"
+        msg = r"Data columns not unique: Index\(\['foo'\], dtype='object|string'\)"
         with pytest.raises(MergeError, match=msg):
             merge(df, df2)
 
@@ -1498,7 +1504,7 @@ def test_different(self, right_vals):
         # We allow merging on object and categorical cols and cast
         # categorical cols to object
         result = merge(left, right, on="A")
-        assert is_object_dtype(result.A.dtype)
+        assert is_object_dtype(result.A.dtype) or is_string_dtype(result.A.dtype)
 
     @pytest.mark.parametrize(
         "d1", [np.int64, np.int32, np.intc, np.int16, np.int8, np.uint8]
@@ -1637,7 +1643,7 @@ def test_merge_incompat_dtypes_are_ok(self, df1_vals, df2_vals):
         result = merge(df1, df2, on=["A"])
         assert is_object_dtype(result.A.dtype)
         result = merge(df2, df1, on=["A"])
-        assert is_object_dtype(result.A.dtype)
+        assert is_object_dtype(result.A.dtype) or is_string_dtype(result.A.dtype)
 
     @pytest.mark.parametrize(
         "df1_vals, df2_vals",
@@ -1867,25 +1873,27 @@ def right():
 
 
 class TestMergeCategorical:
-    def test_identical(self, left):
+    def test_identical(self, left, using_infer_string):
         # merging on the same, should preserve dtypes
         merged = merge(left, left, on="X")
         result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
         expected = Series(
-            [CategoricalDtype(categories=["foo", "bar"]), np.dtype("O"), np.dtype("O")],
+            [CategoricalDtype(categories=["foo", "bar"]), dtype, dtype],
             index=["X", "Y_x", "Y_y"],
         )
         tm.assert_series_equal(result, expected)
 
-    def test_basic(self, left, right):
+    def test_basic(self, left, right, using_infer_string):
         # we have matching Categorical dtypes in X
         # so should preserve the merged column
         merged = merge(left, right, on="X")
         result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
         expected = Series(
             [
                 CategoricalDtype(categories=["foo", "bar"]),
-                np.dtype("O"),
+                dtype,
                 np.dtype("int64"),
             ],
             index=["X", "Y", "Z"],
@@ -1989,16 +1997,17 @@ def test_multiindex_merge_with_unordered_categoricalindex(self, ordered):
         ).set_index(["id", "p"])
         tm.assert_frame_equal(result, expected)
 
-    def test_other_columns(self, left, right):
+    def test_other_columns(self, left, right, using_infer_string):
         # non-merge columns should preserve if possible
         right = right.assign(Z=right.Z.astype("category"))
 
         merged = merge(left, right, on="X")
         result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
         expected = Series(
             [
                 CategoricalDtype(categories=["foo", "bar"]),
-                np.dtype("O"),
+                dtype,
                 CategoricalDtype(categories=[1, 2]),
             ],
             index=["X", "Y", "Z"],
@@ -2017,7 +2026,9 @@ def test_other_columns(self, left, right):
             lambda x: x.astype(CategoricalDtype(ordered=True)),
         ],
     )
-    def test_dtype_on_merged_different(self, change, join_type, left, right):
+    def test_dtype_on_merged_different(
+        self, change, join_type, left, right, using_infer_string
+    ):
         # our merging columns, X now has 2 different dtypes
         # so we must be object as a result
 
@@ -2029,9 +2040,8 @@ def test_dtype_on_merged_different(self, change, join_type, left, right):
         merged = merge(left, right, on="X", how=join_type)
 
         result = merged.dtypes.sort_index()
-        expected = Series(
-            [np.dtype("O"), np.dtype("O"), np.dtype("int64")], index=["X", "Y", "Z"]
-        )
+        dtype = np.dtype("O") if not using_infer_string else "string"
+        expected = Series([dtype, dtype, np.dtype("int64")], index=["X", "Y", "Z"])
         tm.assert_series_equal(result, expected)
 
     def test_self_join_multiple_categories(self):
@@ -2499,7 +2509,7 @@ def test_merge_multiindex_columns():
     expected_index = MultiIndex.from_tuples(tuples, names=["outer", "inner"])
     expected = DataFrame(columns=expected_index)
 
-    tm.assert_frame_equal(result, expected)
+    tm.assert_frame_equal(result, expected, check_dtype=False)
 
 
 def test_merge_datetime_upcast_dtype():

pandas/tests/reshape/merge/test_merge_asof.py

@@ -3081,8 +3081,11 @@ def test_on_float_by_int(self):
 
         tm.assert_frame_equal(result, expected)
 
-    def test_merge_datatype_error_raises(self):
-        msg = r"Incompatible merge dtype, .*, both sides must have numeric dtype"
+    def test_merge_datatype_error_raises(self, using_infer_string):
+        if using_infer_string:
+            msg = "incompatible merge keys"
+        else:
+            msg = r"Incompatible merge dtype, .*, both sides must have numeric dtype"
 
         left = pd.DataFrame({"left_val": [1, 5, 10], "a": ["a", "b", "c"]})
         right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7], "a": [1, 2, 3, 6, 7]})
@@ -3134,7 +3137,7 @@ def test_merge_on_nans(self, func, side):
             else:
                 merge_asof(df, df_null, on="a")
 
-    def test_by_nullable(self, any_numeric_ea_dtype):
+    def test_by_nullable(self, any_numeric_ea_dtype, using_infer_string):
         # Note: this test passes if instead of using pd.array we use
         #  np.array([np.nan, 1]).  Other than that, I (@jbrockmendel)
         #  have NO IDEA what the expected behavior is.
@@ -3176,6 +3179,8 @@ def test_by_nullable(self, any_numeric_ea_dtype):
             }
         )
         expected["value_y"] = np.array([np.nan, np.nan, np.nan], dtype=object)
+        if using_infer_string:
+            expected["value_y"] = expected["value_y"].astype("string[pyarrow_numpy]")
         tm.assert_frame_equal(result, expected)
 
     def test_merge_by_col_tz_aware(self):
@@ -3201,7 +3206,7 @@ def test_merge_by_col_tz_aware(self):
         )
         tm.assert_frame_equal(result, expected)
 
-    def test_by_mixed_tz_aware(self):
+    def test_by_mixed_tz_aware(self, using_infer_string):
         # GH 26649
         left = pd.DataFrame(
             {
@@ -3225,6 +3230,8 @@ def test_by_mixed_tz_aware(self):
             columns=["by_col1", "by_col2", "on_col", "value_x"],
         )
         expected["value_y"] = np.array([np.nan], dtype=object)
+        if using_infer_string:
+            expected["value_y"] = expected["value_y"].astype("string[pyarrow_numpy]")
         tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("dtype", ["float64", "int16", "m8[ns]", "M8[us]"])

pandas/tests/reshape/merge/test_multi.py

@@ -639,7 +639,7 @@ def test_join_multi_levels_outer(self, portfolio, household, expected):
             axis=0,
             sort=True,
         ).reindex(columns=expected.columns)
-        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result, expected, check_index_type=False)
 
     def test_join_multi_levels_invalid(self, portfolio, household):
         portfolio = portfolio.copy()