Backport PR #53343: REF: Use np.result_type instead of np.find_common_type

pandas/core/algorithms.py

@@ -35,6 +35,7 @@
 from pandas.core.dtypes.cast import (
     construct_1d_object_array_from_listlike,
     infer_dtype_from_array,
+    np_find_common_type,
 )
 from pandas.core.dtypes.common import (
     ensure_float64,
@@ -522,7 +523,7 @@ def f(c, v):
             f = np.in1d
 
     else:
-        common = np.find_common_type([values.dtype, comps_array.dtype], [])
+        common = np_find_common_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

@@ -400,6 +400,8 @@ def _subtype_with_str(self):
     def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
         # TODO for now only handle SparseDtypes and numpy dtypes => extend
         # with other compatible extension dtypes
+        from pandas.core.dtypes.cast import np_find_common_type
+
         if any(
             isinstance(x, ExtensionDtype) and not isinstance(x, SparseDtype)
             for x in dtypes
@@ -420,5 +422,5 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
                 stacklevel=find_stack_level(),
             )
 
-        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)
+        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)

pandas/core/dtypes/cast.py

@@ -1368,6 +1368,32 @@ def common_dtype_categorical_compat(
     return dtype
 
 
+def np_find_common_type(*dtypes: np.dtype) -> np.dtype:
+    """
+    np.find_common_type implementation pre-1.25 deprecation using np.result_type
+    https://github.com/pandas-dev/pandas/pull/49569#issuecomment-1308300065
+
+    Parameters
+    ----------
+    dtypes : np.dtypes
+
+    Returns
+    -------
+    np.dtype
+    """
+    try:
+        common_dtype = np.result_type(*dtypes)
+        if common_dtype.kind in "mMSU":
+            # NumPy promotion currently (1.25) misbehaves for for times and strings,
+            # so fall back to object (find_common_dtype did unless there
+            # was only one dtype)
+            common_dtype = np.dtype("O")
+
+    except TypeError:
+        common_dtype = np.dtype("O")
+    return common_dtype
+
+
 @overload
 def find_common_type(types: list[np.dtype]) -> np.dtype:
     ...
@@ -1435,7 +1461,7 @@ 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_find_common_type(*types)
 
 
 def construct_2d_arraylike_from_scalar(

pandas/core/dtypes/concat.py

@@ -13,6 +13,7 @@
 from pandas.core.dtypes.cast import (
     common_dtype_categorical_compat,
     find_common_type,
+    np_find_common_type,
 )
 from pandas.core.dtypes.common import is_dtype_equal
 from pandas.core.dtypes.dtypes import (
@@ -110,6 +111,8 @@ def is_nonempty(x) -> bool:
                 # coerce to object
                 to_concat = [x.astype("object") for x in to_concat]
                 kinds = {"o"}
+    else:
+        target_dtype = np_find_common_type(*dtypes)
 
     result = np.concatenate(to_concat, axis=axis)
     if "b" in kinds and result.dtype.kind in ["i", "u", "f"]:

pandas/core/internals/concat.py

@@ -28,6 +28,7 @@
 from pandas.core.dtypes.cast import (
     ensure_dtype_can_hold_na,
     find_common_type,
+    np_find_common_type,
 )
 from pandas.core.dtypes.common import (
     is_1d_only_ea_dtype,
@@ -144,7 +145,7 @@ 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_find_common_type(*dtypes)
     else:
         target_dtype = find_common_type([arr.dtype for arr in to_concat_no_proxy])
 

pandas/tests/dtypes/test_inference.py

@@ -1012,9 +1012,7 @@ 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":