REF: de-duplicate nested-dict handling in DataFrame construction

pandas/_libs/lib.pyi

@@ -52,8 +52,6 @@ def is_float_array(values: np.ndarray, skipna: bool = False): ...
 def is_integer_array(values: np.ndarray, skipna: bool = False): ...
 def is_bool_array(values: np.ndarray, skipna: bool = False): ...
 
-def fast_multiget(mapping: dict, keys: np.ndarray, default=np.nan) -> np.ndarray: ...
-
 def fast_unique_multiple_list_gen(gen: Generator, sort: bool = True) -> list: ...
 def fast_unique_multiple_list(lists: list, sort: bool = True) -> list: ...
 def fast_unique_multiple(arrays: list, sort: bool = True) -> list: ...

pandas/_libs/lib.pyx

@@ -2841,25 +2841,3 @@ def to_object_array_tuples(rows: object) -> np.ndarray:
                 result[i, j] = row[j]
 
     return result
-
-
-@cython.wraparound(False)
-@cython.boundscheck(False)
-def fast_multiget(dict mapping, ndarray keys, default=np.nan) -> np.ndarray:
-    cdef:
-        Py_ssize_t i, n = len(keys)
-        object val
-        ndarray[object] output = np.empty(n, dtype='O')
-
-    if n == 0:
-        # kludge, for Series
-        return np.empty(0, dtype='f8')
-
-    for i in range(n):
-        val = keys[i]
-        if val in mapping:
-            output[i] = mapping[val]
-        else:
-            output[i] = default
-
-    return maybe_convert_objects(output)

pandas/core/dtypes/cast.py

@@ -780,22 +780,6 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> tuple[DtypeObj,
     return dtype, val
 
 
-def dict_compat(d: dict[Scalar, Scalar]) -> dict[Scalar, Scalar]:
-    """
-    Convert datetimelike-keyed dicts to a Timestamp-keyed dict.
-
-    Parameters
-    ----------
-    d: dict-like object
-
-    Returns
-    -------
-    dict
-
-    """
-    return {maybe_box_datetimelike(key): value for key, value in d.items()}
-
-
 def infer_dtype_from_array(
     arr, pandas_dtype: bool = False
 ) -> tuple[DtypeObj, ArrayLike]:

pandas/core/internals/construction.py

@@ -27,7 +27,6 @@
 from pandas.core.dtypes.cast import (
     construct_1d_arraylike_from_scalar,
     construct_1d_ndarray_preserving_na,
-    dict_compat,
     maybe_cast_to_datetime,
     maybe_convert_platform,
     maybe_infer_to_datetimelike,
@@ -61,16 +60,15 @@
     TimedeltaArray,
 )
 from pandas.core.construction import (
+    create_series_with_explicit_dtype,
     ensure_wrapped_if_datetimelike,
     extract_array,
     range_to_ndarray,
     sanitize_array,
 )
 from pandas.core.indexes import base as ibase
 from pandas.core.indexes.api import (
-    DatetimeIndex,
     Index,
-    TimedeltaIndex,
     ensure_index,
     get_objs_combined_axis,
     union_indexes,
@@ -566,7 +564,6 @@ def convert(v):
 
 
 def _homogenize(data, index: Index, dtype: DtypeObj | None) -> list[ArrayLike]:
-    oindex = None
     homogenized = []
 
     for val in data:
@@ -581,16 +578,10 @@ def _homogenize(data, index: Index, dtype: DtypeObj | None) -> list[ArrayLike]:
             val = val._values
         else:
             if isinstance(val, dict):
-                if oindex is None:
-                    oindex = index.astype("O")
-
-                if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
-                    # see test_constructor_dict_datetime64_index
-                    val = dict_compat(val)
-                else:
-                    # see test_constructor_subclass_dict
-                    val = dict(val)
-                val = lib.fast_multiget(val, oindex._values, default=np.nan)
+                # see test_constructor_subclass_dict
+                #  test_constructor_dict_datetime64_index
+                val = create_series_with_explicit_dtype(val, index=index)._values
+
             val = sanitize_array(
                 val, index, dtype=dtype, copy=False, raise_cast_failure=False
             )