TST(string dtype): Resolve xfails in test_from_dummies

pandas/core/reshape/encoding.py

@@ -17,12 +17,14 @@
     is_integer_dtype,
     is_list_like,
     is_object_dtype,
+    is_string_dtype,
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import (
     ArrowDtype,
     CategoricalDtype,
 )
+from pandas.core.dtypes.missing import isna
 
 from pandas.core.arrays import SparseArray
 from pandas.core.arrays.categorical import factorize_from_iterable
@@ -35,7 +37,10 @@
 from pandas.core.series import Series
 
 if TYPE_CHECKING:
-    from pandas._typing import NpDtype
+    from pandas._typing import (
+        DtypeObj,
+        NpDtype,
+    )
 
 
 def get_dummies(
@@ -554,9 +559,20 @@ def from_dummies(
                 "Dummy DataFrame contains multi-assignment(s); "
                 f"First instance in row: {assigned.idxmax()}"
             )
+        dtype: str | DtypeObj = data.columns.dtype
         if any(assigned == 0):
             if isinstance(default_category, dict):
-                cats.append(default_category[prefix])
+                value = default_category[prefix]
+                if (
+                    is_string_dtype(data.columns.dtype)
+                    and not isinstance(value, str)
+                    and (is_list_like(value) or not isna(value))
+                ):
+                    # https://github.com/pandas-dev/pandas/pull/60694
+                    # `value` is not a string or NA.
+                    # Using data.columns.dtype would coerce `value` into a string.
+                    dtype = "object"
+                cats.append(value)
             else:
                 raise ValueError(
                     "Dummy DataFrame contains unassigned value(s); "
@@ -567,7 +583,7 @@ def from_dummies(
             )
         else:
             data_slice = data_to_decode.loc[:, prefix_slice]
-        cats_array = data._constructor_sliced(cats, dtype=data.columns.dtype)
+        cats_array = data._constructor_sliced(cats, dtype=dtype)
         # get indices of True entries along axis=1
         true_values = data_slice.idxmax(axis=1)
         indexer = data_slice.columns.get_indexer_for(true_values)

pandas/tests/io/pytables/test_complex.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -13,9 +11,9 @@
 
 from pandas.io.pytables import read_hdf
 
-pytestmark = pytest.mark.xfail(
-    using_string_dtype(), reason="TODO(infer_string)", strict=False
-)
+# pytestmark = pytest.mark.xfail(
+#     using_string_dtype(), reason="TODO(infer_string)", strict=False
+# )
 
 
 def test_complex_fixed(tmp_path, setup_path):

pandas/tests/io/pytables/test_file_handling.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas.compat import (
     PY311,
     is_ci_environment,
@@ -329,7 +327,7 @@ def test_complibs(tmp_path, lvl, lib, request):
                 assert node.filters.complib == lib
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+# @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
 @pytest.mark.skipif(
     not is_platform_little_endian(), reason="reason platform is not little endian"
 )
@@ -347,7 +345,7 @@ def test_encoding(setup_path):
         tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+# @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
 @pytest.mark.parametrize(
     "val",
     [

pandas/tests/io/pytables/test_timezones.py

@@ -6,8 +6,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas._libs.tslibs.timezones import maybe_get_tz
 import pandas.util._test_decorators as td
 
@@ -25,9 +23,9 @@
     ensure_clean_store,
 )
 
-pytestmark = pytest.mark.xfail(
-    using_string_dtype(), reason="TODO(infer_string)", strict=False
-)
+# pytestmark = pytest.mark.xfail(
+#     using_string_dtype(), reason="TODO(infer_string)", strict=False
+# )
 
 
 def _compare_with_tz(a, b):

pandas/tests/reshape/test_from_dummies.py

@@ -1,8 +1,7 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
+import pandas as pd
 from pandas import (
     DataFrame,
     Series,
@@ -336,8 +335,6 @@ def test_no_prefix_string_cats_default_category(
     dummies = DataFrame({"a": [1, 0, 0], "b": [0, 1, 0]})
     result = from_dummies(dummies, default_category=default_category)
     expected = DataFrame(expected)
-    if using_infer_string:
-        expected[""] = expected[""].astype("str")
     tm.assert_frame_equal(result, expected)
 
 
@@ -364,7 +361,6 @@ def test_with_prefix_contains_get_dummies_NaN_column():
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
 @pytest.mark.parametrize(
     "default_category, expected",
     [
@@ -450,3 +446,32 @@ def test_maintain_original_index():
     result = from_dummies(df)
     expected = DataFrame({"": list("abca")}, index=list("abcd"))
     tm.assert_frame_equal(result, expected)
+
+
+def test_int_columns_with_float_default():
+    # https://github.com/pandas-dev/pandas/pull/60694
+    df = DataFrame(
+        {
+            3: [1, 0, 0],
+            4: [0, 1, 0],
+        },
+    )
+    with pytest.raises(ValueError, match="Trying to coerce float values to integers"):
+        from_dummies(df, default_category=0.5)
+
+
+def test_object_dtype_preserved():
+    # https://github.com/pandas-dev/pandas/pull/60694
+    # When the input has object dtype, the result should as
+    # well even when infer_string is True.
+    df = DataFrame(
+        {
+            "x": [1, 0, 0],
+            "y": [0, 1, 0],
+        },
+    )
+    df.columns = df.columns.astype("object")
+    with pd.option_context("future.infer_string", True):
+        result = from_dummies(df, default_category="z")
+        expected = DataFrame({"": ["x", "y", "z"]}, dtype="object")
+        tm.assert_frame_equal(result, expected)