import code into isin() and remove function

Author: pbrochart

doc/source/reference/arrays.rst

@@ -667,7 +667,6 @@ Data type introspection
     api.types.is_dtype_equal
     api.types.is_extension_array_dtype
     api.types.is_float_dtype
-    api.types.is_implicit_conversion_to_float64
     api.types.is_int64_dtype
     api.types.is_integer_dtype
     api.types.is_interval_dtype

doc/source/whatsnew/v3.0.0.rst

@@ -404,7 +404,6 @@ Other API changes
 - Index set operations (like union or intersection) will now ignore the dtype of
   an empty ``RangeIndex`` or empty ``Index`` with object dtype when determining
   the dtype of the resulting Index (:issue:`60797`)
-- Added :func:`pandas.api.types.is_implicit_conversion_to_float64` to check if there is a silent conversion to float64 between two dtypes(:issue:`61676`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.deprecations:

pandas/core/algorithms.py

@@ -50,7 +50,6 @@
     is_extension_array_dtype,
     is_float,
     is_float_dtype,
-    is_implicit_conversion_to_float64,
     is_integer,
     is_integer_dtype,
     is_list_like,
@@ -507,11 +506,26 @@ def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
         orig_values = list(values)
         values = _ensure_arraylike(orig_values, func_name="isin-targets")
 
-        if (
-            len(values) > 0
-            and values.dtype.kind in "iufcb"
-            and is_implicit_conversion_to_float64(values, comps)
-        ):
+        try:
+            src = comps.dtype
+            tar = values.dtype
+            # check only valid dtypes related to implicit conversion to float64
+            # other data types derived from 64-bit integers such as U/Int64Dtype
+            # should also work
+            if (
+                src.kind in "iu"
+                and src.itemsize == 8  # type: ignore[union-attr]
+                and tar.kind in "iu"
+                and tar.itemsize == 8  # type: ignore[union-attr]
+            ):
+                is_implicit_conversion_to_float64 = src != tar
+            else:
+                is_implicit_conversion_to_float64 = False
+        except (TypeError, AttributeError, ImportError):
+            # invalid comparison
+            is_implicit_conversion_to_float64 = False
+
+        if (is_implicit_conversion_to_float64):
             # GH#46485 Use object to avoid upcast to float64 later
             # TODO: Share with _find_common_type_compat
             values = construct_1d_object_array_from_listlike(orig_values)

pandas/core/dtypes/api.py

@@ -17,7 +17,6 @@
     is_float,
     is_float_dtype,
     is_hashable,
-    is_implicit_conversion_to_float64,
     is_int64_dtype,
     is_integer,
     is_integer_dtype,
@@ -60,7 +59,6 @@
     "is_float",
     "is_float_dtype",
     "is_hashable",
-    "is_implicit_conversion_to_float64",
     "is_int64_dtype",
     "is_integer",
     "is_integer_dtype",

pandas/core/dtypes/common.py

@@ -713,80 +713,6 @@ def is_dtype_equal(source, target) -> bool:
         return False
 
 
-def is_implicit_conversion_to_float64(source, target) -> bool:
-    """
-    Check if there is an implicit conversion to float64 with both dtypes.
-
-    Parameters
-    ----------
-    source : type or str
-        The first dtype to compare.
-    target : type or str
-        The second dtype to compare.
-
-    Returns
-    -------
-    boolean
-        Whether or not there is an implicit conversion to float64.
-
-    See Also
-    --------
-    api.types.is_categorical_dtype : Check whether the provided array or dtype
-                                            is of the Categorical dtype.
-    api.types.is_string_dtype : Check whether the provided array or dtype
-                                       is of the string dtype.
-    api.types.is_object_dtype : Check whether an array-like or dtype is of the
-                                       object dtype.
-
-    Examples
-    --------
-    >>> from pandas.api.types import is_implicit_conversion_to_float64
-    >>> is_implicit_conversion_to_float64(int, float)
-    False
-    >>> is_implicit_conversion_to_float64("int", int)
-    False
-    >>> is_implicit_conversion_to_float64(int, np.int64)
-    False
-    >>> is_implicit_conversion_to_float64(np.uint64, np.int64)
-    True
-    >>> is_implicit_conversion_to_float64(np.uint64, np.float64)
-    False
-    >>> is_implicit_conversion_to_float64(np.uint64, np.uint64)
-    False
-    >>> is_implicit_conversion_to_float64(np.uint32, np.uint32)
-    False
-    >>> is_implicit_conversion_to_float64(np.uint32, np.int32)
-    False
-    >>> is_implicit_conversion_to_float64(np.int32, np.int32)
-    False
-    >>> is_implicit_conversion_to_float64(object, "category")
-    False
-    >>> is_implicit_conversion_to_float64(np.int64, pd.UInt64Dtype())
-    True
-    >>> from pandas.core.dtypes.dtypes import CategoricalDtype
-    >>> is_implicit_conversion_to_float64(CategoricalDtype(), "category")
-    False
-    """
-    try:
-        src = _get_dtype(source)
-        tar = _get_dtype(target)
-        # check only valid dtypes related to implicit conversion to float64
-        # other data types derived from 64-bit integers such as U/Int64Dtype
-        # should also work
-        if (
-            src.kind in "iu"
-            and src.itemsize == 8  # type: ignore[union-attr]
-            and tar.kind in "iu"
-            and tar.itemsize == 8  # type: ignore[union-attr]
-        ):
-            return src != tar
-        else:
-            return False
-    except (TypeError, AttributeError, ImportError):
-        # invalid comparison
-        return False
-
-
 def is_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of an integer dtype.
@@ -2008,7 +1934,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_extension_array_dtype",
     "is_file_like",
     "is_float_dtype",
-    "is_implicit_conversion_to_float64",
     "is_int64_dtype",
     "is_integer_dtype",
     "is_interval_dtype",

pandas/tests/api/test_api.py

@@ -295,7 +295,6 @@ class TestApi(Base):
         "is_float",
         "is_float_dtype",
         "is_hashable",
-        "is_implicit_conversion_to_float64",
         "is_int64_dtype",
         "is_integer",
         "is_integer_dtype",

pandas/tests/api/test_types.py

@@ -20,7 +20,6 @@ class TestTypes(Base):
         "is_dtype_equal",
         "is_float",
         "is_float_dtype",
-        "is_implicit_conversion_to_float64",
         "is_int64_dtype",
         "is_integer",
         "is_integer_dtype",