TST: restore type checks to maybe_promote tests

pandas/core/dtypes/cast.py

@@ -358,6 +358,7 @@ def maybe_promote(dtype, fill_value=np.nan):
             fill_value = NaT
     elif is_extension_array_dtype(dtype) and isna(fill_value):
         fill_value = dtype.na_value
+
     elif is_float(fill_value):
         if issubclass(dtype.type, np.bool_):
             dtype = np.object_
@@ -366,6 +367,8 @@ def maybe_promote(dtype, fill_value=np.nan):
     elif is_bool(fill_value):
         if not issubclass(dtype.type, np.bool_):
             dtype = np.object_
+        else:
+            fill_value = np.bool_(fill_value)
     elif is_integer(fill_value):
         if issubclass(dtype.type, np.bool_):
             dtype = np.object_
@@ -374,6 +377,10 @@ def maybe_promote(dtype, fill_value=np.nan):
             arr = np.asarray(fill_value)
             if arr != arr.astype(dtype):
                 dtype = arr.dtype
+        elif issubclass(dtype.type, np.floating):
+            # check if we can cast
+            if _check_lossless_cast(fill_value, dtype):
+                fill_value = dtype.type(fill_value)
     elif is_complex(fill_value):
         if issubclass(dtype.type, np.bool_):
             dtype = np.object_
@@ -404,6 +411,25 @@ def maybe_promote(dtype, fill_value=np.nan):
     return dtype, fill_value
 
 
+def _check_lossless_cast(value, dtype: np.dtype) -> bool:
+    """
+    Check if we can cast the given value to the given dtype _losslessly_.
+
+    Parameters
+    ----------
+    value : object
+    dtype : np.dtype
+
+    Returns
+    -------
+    bool
+    """
+    casted = dtype.type(value)
+    if casted == value:
+        return True
+    return False
+
+
 def infer_dtype_from(val, pandas_dtype=False):
     """
     interpret the dtype from a scalar or array. This is a convenience

pandas/tests/dtypes/cast/test_promote.py

@@ -23,6 +23,7 @@
     is_timedelta64_dtype,
 )
 from pandas.core.dtypes.dtypes import DatetimeTZDtype, PandasExtensionDtype
+from pandas.core.dtypes.missing import isna
 
 import pandas as pd
 
@@ -95,6 +96,7 @@ def _safe_dtype_assert(left_dtype, right_dtype):
     """
     Compare two dtypes without raising TypeError.
     """
+    __tracebackhide__ = True
     if isinstance(right_dtype, PandasExtensionDtype):
         # switch order of equality check because numpy dtypes (e.g. if
         # left_dtype is np.object_) do not know some expected dtypes (e.g.
@@ -157,20 +159,17 @@ def _check_promote(
 
     _safe_dtype_assert(result_dtype, expected_dtype)
 
-    # for equal values, also check type (relevant e.g. for int vs float, resp.
-    # for different datetimes and timedeltas)
-    match_value = (
-        result_fill_value
-        == expected_fill_value
-        # disabled type check due to too many xfails; GH 23982/25425
-        # and type(result_fill_value) == type(expected_fill_value)
-    )
+    # GH#23982/25425 require the same type in addition to equality/NA-ness
+    res_type = type(result_fill_value)
+    ex_type = type(expected_fill_value)
+    assert res_type == ex_type
+
+    match_value = result_fill_value == expected_fill_value
 
+    # Note: type check above ensures that we have the _same_ NA value
     # for missing values, None == None and iNaT == iNaT (which is checked
     # through match_value above), but np.nan != np.nan and pd.NaT != pd.NaT
-    match_missing = (result_fill_value is np.nan and expected_fill_value is np.nan) or (
-        result_fill_value is NaT and expected_fill_value is NaT
-    )
+    match_missing = isna(result_fill_value) and isna(expected_fill_value)
 
     assert match_value or match_missing