CLN: remove maybe_infer_to_datetimelike (#62209)

Author: jbrockmendel

pandas/core/construction.py

@@ -31,7 +31,6 @@
     maybe_cast_to_datetime,
     maybe_cast_to_integer_array,
     maybe_convert_platform,
-    maybe_infer_to_datetimelike,
     maybe_promote,
 )
 from pandas.core.dtypes.common import (
@@ -612,7 +611,15 @@ def sanitize_array(
         if dtype is None:
             subarr = data
             if data.dtype == object and infer_object:
-                subarr = maybe_infer_to_datetimelike(data)
+                subarr = lib.maybe_convert_objects(
+                    data,
+                    # Here we do not convert numeric dtypes, as if we wanted that,
+                    #  numpy would have done it for us.
+                    convert_numeric=False,
+                    convert_non_numeric=True,
+                    convert_to_nullable_dtype=False,
+                    dtype_if_all_nat=np.dtype("M8[s]"),
+                )
             elif data.dtype.kind == "U" and using_string_dtype():
                 from pandas.core.arrays.string_ import StringDtype
 
@@ -659,7 +666,15 @@ def sanitize_array(
             subarr = maybe_convert_platform(data)
             if subarr.dtype == object:
                 subarr = cast(np.ndarray, subarr)
-                subarr = maybe_infer_to_datetimelike(subarr)
+                subarr = lib.maybe_convert_objects(
+                    subarr,
+                    # Here we do not convert numeric dtypes, as if we wanted that,
+                    #  numpy would have done it for us.
+                    convert_numeric=False,
+                    convert_non_numeric=True,
+                    convert_to_nullable_dtype=False,
+                    dtype_if_all_nat=np.dtype("M8[s]"),
+                )
 
     subarr = _sanitize_ndim(subarr, data, dtype, index, allow_2d=allow_2d)
 

pandas/core/dtypes/cast.py

@@ -97,7 +97,6 @@
         DtypeObj,
         NumpyIndexT,
         Scalar,
-        npt,
     )
 
     from pandas import Index
@@ -1058,51 +1057,6 @@ def convert_dtypes(
     return inferred_dtype  # type: ignore[return-value]
 
 
-def maybe_infer_to_datetimelike(
-    value: npt.NDArray[np.object_],
-    convert_to_nullable_dtype: bool = False,
-) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray | IntervalArray:
-    """
-    we might have a array (or single object) that is datetime like,
-    and no dtype is passed don't change the value unless we find a
-    datetime/timedelta set
-
-    this is pretty strict in that a datetime/timedelta is REQUIRED
-    in addition to possible nulls/string likes
-
-    Parameters
-    ----------
-    value : np.ndarray[object]
-
-    Returns
-    -------
-    np.ndarray, DatetimeArray, TimedeltaArray, PeriodArray, or IntervalArray
-
-    """
-    if not isinstance(value, np.ndarray) or value.dtype != object:
-        # Caller is responsible for passing only ndarray[object]
-        raise TypeError(type(value))  # pragma: no cover
-    if value.ndim != 1:
-        # Caller is responsible
-        raise ValueError(value.ndim)  # pragma: no cover
-
-    if not len(value):
-        return value
-
-    # error: Incompatible return value type (got "Union[ExtensionArray,
-    # ndarray[Any, Any]]", expected "Union[ndarray[Any, Any], DatetimeArray,
-    # TimedeltaArray, PeriodArray, IntervalArray]")
-    return lib.maybe_convert_objects(  # type: ignore[return-value]
-        value,
-        # Here we do not convert numeric dtypes, as if we wanted that,
-        #  numpy would have done it for us.
-        convert_numeric=False,
-        convert_non_numeric=True,
-        convert_to_nullable_dtype=convert_to_nullable_dtype,
-        dtype_if_all_nat=np.dtype("M8[s]"),
-    )
-
-
 def maybe_cast_to_datetime(
     value: np.ndarray | list, dtype: np.dtype
 ) -> DatetimeArray | TimedeltaArray | np.ndarray:

pandas/core/internals/construction.py

@@ -24,7 +24,6 @@
     dict_compat,
     maybe_cast_to_datetime,
     maybe_convert_platform,
-    maybe_infer_to_datetimelike,
 )
 from pandas.core.dtypes.common import (
     is_1d_only_ea_dtype,
@@ -290,7 +289,18 @@ def ndarray_to_mgr(
     # embedded in an object type
     if dtype is None and infer_object and is_object_dtype(values.dtype):
         obj_columns = list(values)
-        maybe_datetime = [maybe_infer_to_datetimelike(x) for x in obj_columns]
+        maybe_datetime = [
+            lib.maybe_convert_objects(
+                x,
+                # Here we do not convert numeric dtypes, as if we wanted that,
+                #  numpy would have done it for us.
+                convert_numeric=False,
+                convert_non_numeric=True,
+                convert_to_nullable_dtype=False,
+                dtype_if_all_nat=np.dtype("M8[s]"),
+            )
+            for x in obj_columns
+        ]
         # don't convert (and copy) the objects if no type inference occurs
         if any(x is not y for x, y in zip(obj_columns, maybe_datetime)):
             block_values = [
@@ -485,7 +495,7 @@ def convert(v):
 
         v = extract_array(v, extract_numpy=True)
         res = maybe_convert_platform(v)
-        # We don't do maybe_infer_to_datetimelike here bc we will end up doing
+        # We don't do maybe_infer_objects here bc we will end up doing
         #  it column-by-column in ndarray_to_mgr
         return res
 
@@ -965,7 +975,15 @@ def convert(arr):
                 if arr.dtype == np.dtype("O"):
                     # i.e. maybe_convert_objects didn't convert
                     convert_to_nullable_dtype = dtype_backend != "numpy"
-                    arr = maybe_infer_to_datetimelike(arr, convert_to_nullable_dtype)
+                    arr = lib.maybe_convert_objects(
+                        arr,
+                        # Here we do not convert numeric dtypes, as if we wanted that,
+                        #  numpy would have done it for us.
+                        convert_numeric=False,
+                        convert_non_numeric=True,
+                        convert_to_nullable_dtype=convert_to_nullable_dtype,
+                        dtype_if_all_nat=np.dtype("M8[s]"),
+                    )
                     if convert_to_nullable_dtype and arr.dtype == np.dtype("O"):
                         new_dtype = StringDtype()
                         arr_cls = new_dtype.construct_array_type()