REF: remove soft_convert_objects

pandas/core/dtypes/cast.py

@@ -41,7 +41,6 @@
     IntCastingNaNError,
     LossySetitemError,
 )
-from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.common import (
     DT64NS_DTYPE,
@@ -952,54 +951,6 @@ def coerce_indexer_dtype(indexer, categories) -> np.ndarray:
     return ensure_int64(indexer)
 
 
-def soft_convert_objects(
-    values: np.ndarray,
-    *,
-    datetime: bool = True,
-    timedelta: bool = True,
-    period: bool = True,
-    copy: bool = True,
-) -> ArrayLike:
-    """
-    Try to coerce datetime, timedelta, and numeric object-dtype columns
-    to inferred dtype.
-
-    Parameters
-    ----------
-    values : np.ndarray[object]
-    datetime : bool, default True
-    timedelta : bool, default True
-    period : bool, default True
-    copy : bool, default True
-
-    Returns
-    -------
-    np.ndarray or ExtensionArray
-    """
-    validate_bool_kwarg(datetime, "datetime")
-    validate_bool_kwarg(timedelta, "timedelta")
-    validate_bool_kwarg(copy, "copy")
-
-    conversion_count = sum((datetime, timedelta))
-    if conversion_count == 0:
-        raise ValueError("At least one of datetime or timedelta must be True.")
-
-    # Soft conversions
-    if datetime or timedelta or period:
-        # GH 20380, when datetime is beyond year 2262, hence outside
-        # bound of nanosecond-resolution 64-bit integers.
-        converted = lib.maybe_convert_objects(
-            values,
-            convert_datetime=datetime,
-            convert_timedelta=timedelta,
-            convert_period=period,
-        )
-        if converted is not values:
-            return converted
-
-    return values
-
-
 def convert_dtypes(
     input_array: ArrayLike,
     convert_string: bool = True,

pandas/core/generic.py

@@ -6354,9 +6354,8 @@ def infer_objects(self: NDFrameT) -> NDFrameT:
         A    int64
         dtype: object
         """
-        return self._constructor(
-            self._mgr.convert(datetime=True, timedelta=True, copy=True)
-        ).__finalize__(self, method="infer_objects")
+        new_mgr = self._mgr.convert()
+        return self._constructor(new_mgr).__finalize__(self, method="infer_objects")
 
     @final
     def convert_dtypes(

pandas/core/internals/array_manager.py

@@ -32,7 +32,6 @@
 from pandas.core.dtypes.cast import (
     ensure_dtype_can_hold_na,
     infer_dtype_from_scalar,
-    soft_convert_objects,
 )
 from pandas.core.dtypes.common import (
     ensure_platform_int,
@@ -375,25 +374,19 @@ def fillna(self: T, value, limit, inplace: bool, downcast) -> T:
     def astype(self: T, dtype, copy: bool = False, errors: str = "raise") -> T:
         return self.apply(astype_array_safe, dtype=dtype, copy=copy, errors=errors)
 
-    def convert(
-        self: T,
-        *,
-        copy: bool = True,
-        datetime: bool = True,
-        timedelta: bool = True,
-    ) -> T:
+    def convert(self: T) -> T:
         def _convert(arr):
             if is_object_dtype(arr.dtype):
                 # extract PandasArray for tests that patch PandasArray._typ
                 arr = np.asarray(arr)
-                return soft_convert_objects(
+                return lib.maybe_convert_objects(
                     arr,
-                    datetime=datetime,
-                    timedelta=timedelta,
-                    copy=copy,
+                    convert_datetime=True,
+                    convert_timedelta=True,
+                    convert_period=True,
                 )
             else:
-                return arr.copy() if copy else arr
+                return arr.copy()
 
         return self.apply(_convert)
 

pandas/core/internals/blocks.py

@@ -44,7 +44,6 @@
     find_result_type,
     maybe_downcast_to_dtype,
     np_can_hold_element,
-    soft_convert_objects,
 )
 from pandas.core.dtypes.common import (
     ensure_platform_int,
@@ -429,7 +428,7 @@ def _maybe_downcast(self, blocks: list[Block], downcast=None) -> list[Block]:
             #  but ATM it breaks too much existing code.
             # split and convert the blocks
 
-            return extend_blocks([blk.convert(datetime=True) for blk in blocks])
+            return extend_blocks([blk.convert() for blk in blocks])
 
         if downcast is None:
             return blocks
@@ -451,8 +450,6 @@ def convert(
         self,
         *,
         copy: bool = True,
-        datetime: bool = True,
-        timedelta: bool = True,
     ) -> list[Block]:
         """
         attempt to coerce any object types to better types return a copy
@@ -1967,8 +1964,6 @@ def convert(
         self,
         *,
         copy: bool = True,
-        datetime: bool = True,
-        timedelta: bool = True,
     ) -> list[Block]:
         """
         attempt to cast any object types to better types return a copy of
@@ -1980,11 +1975,11 @@ def convert(
             # avoid doing .ravel as that might make a copy
             values = values[0]
 
-        res_values = soft_convert_objects(
+        res_values = lib.maybe_convert_objects(
             values,
-            datetime=datetime,
-            timedelta=timedelta,
-            copy=copy,
+            convert_datetime=True,
+            convert_timedelta=True,
+            convert_period=True,
         )
         res_values = ensure_block_shape(res_values, self.ndim)
         return [self.make_block(res_values)]

pandas/core/internals/managers.py

@@ -441,18 +441,10 @@ def fillna(self: T, value, limit, inplace: bool, downcast) -> T:
     def astype(self: T, dtype, copy: bool = False, errors: str = "raise") -> T:
         return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
 
-    def convert(
-        self: T,
-        *,
-        copy: bool = True,
-        datetime: bool = True,
-        timedelta: bool = True,
-    ) -> T:
+    def convert(self: T) -> T:
         return self.apply(
             "convert",
-            copy=copy,
-            datetime=datetime,
-            timedelta=timedelta,
+            copy=True,
         )
 
     def replace(self: T, to_replace, value, inplace: bool) -> T: