REF: Replace np.find_common_dtype with np.result_type

ci/run_tests.sh

@@ -24,7 +24,7 @@ if [[ $(uname) == "Linux" && -z $DISPLAY ]]; then
     XVFB="xvfb-run "
 fi
 
-PYTEST_CMD="${XVFB}pytest -r fEs -n $PYTEST_WORKERS --dist=loadfile $TEST_ARGS $COVERAGE $PYTEST_TARGET"
+PYTEST_CMD="${XVFB}pytest -v -r fEs -n $PYTEST_WORKERS --dist=loadfile $TEST_ARGS $COVERAGE $PYTEST_TARGET"
 
 if [[ "$PATTERN" ]]; then
   PYTEST_CMD="$PYTEST_CMD -m \"$PATTERN\""

pandas/core/algorithms.py

@@ -513,7 +513,7 @@ def f(c, v):
             f = np.in1d
 
     else:
-        common = np.find_common_type([values.dtype, comps_array.dtype], [])
+        common = np.result_type(values.dtype, comps_array.dtype)
         values = values.astype(common, copy=False)
         comps_array = comps_array.astype(common, copy=False)
         f = htable.ismember

pandas/core/arrays/sparse/dtype.py

@@ -413,4 +413,4 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
             )
 
         np_dtypes = [x.subtype if isinstance(x, SparseDtype) else x for x in dtypes]
-        return SparseDtype(np.find_common_type(np_dtypes, []), fill_value=fill_value)
+        return SparseDtype(np.result_type(*np_dtypes), fill_value=fill_value)

pandas/core/dtypes/cast.py

@@ -1496,7 +1496,7 @@ def find_common_type(types):
             if is_integer_dtype(t) or is_float_dtype(t) or is_complex_dtype(t):
                 return np.dtype("object")
 
-    return np.find_common_type(types, [])
+    return np.result_type(*types)
 
 
 def construct_2d_arraylike_from_scalar(

pandas/core/internals/concat.py

@@ -144,7 +144,7 @@ def concat_arrays(to_concat: list) -> ArrayLike:
         target_dtype = to_concat_no_proxy[0].dtype
     elif all(x.kind in ["i", "u", "b"] and isinstance(x, np.dtype) for x in dtypes):
         # GH#42092
-        target_dtype = np.find_common_type(list(dtypes), [])
+        target_dtype = np.result_type(*dtypes)
     else:
         target_dtype = find_common_type([arr.dtype for arr in to_concat_no_proxy])
 

pandas/io/parsers/c_parser_wrapper.py

@@ -364,14 +364,7 @@ def _concatenate_chunks(chunks: list[dict[int, ArrayLike]]) -> dict:
         # TODO: shouldn't we exclude all EA dtypes here?
         numpy_dtypes = {x for x in dtypes if not is_categorical_dtype(x)}
         if len(numpy_dtypes) > 1:
-            # error: Argument 1 to "find_common_type" has incompatible type
-            # "Set[Any]"; expected "Sequence[Union[dtype[Any], None, type,
-            # _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any,
-            # Union[int, Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]]"
-            common_type = np.find_common_type(
-                numpy_dtypes,  # type: ignore[arg-type]
-                [],
-            )
+            common_type = np.result_type(*numpy_dtypes)
             if common_type == np.dtype(object):
                 warning_columns.append(str(name))
 

pandas/tests/dtypes/cast/test_find_common_type.py

@@ -142,8 +142,8 @@ def test_period_dtype_mismatch(dtype2):
 ]
 
 
-@pytest.mark.parametrize("left", interval_dtypes)
-@pytest.mark.parametrize("right", interval_dtypes)
+@pytest.mark.parametrize("left", interval_dtypes, ids=repr)
+@pytest.mark.parametrize("right", interval_dtypes, ids=repr)
 def test_interval_dtype(left, right):
     result = find_common_type([left, right])
 

pandas/tests/dtypes/test_inference.py

@@ -959,9 +959,7 @@ def test_maybe_convert_objects_itemsize(self, data0, data1):
         data = [data0, data1]
         arr = np.array(data, dtype="object")
 
-        common_kind = np.find_common_type(
-            [type(data0), type(data1)], scalar_types=[]
-        ).kind
+        common_kind = np.result_type(type(data0), type(data1)).kind
         kind0 = "python" if not hasattr(data0, "dtype") else data0.dtype.kind
         kind1 = "python" if not hasattr(data1, "dtype") else data1.dtype.kind
         if kind0 != "python" and kind1 != "python":