From ac881f9485654f72afbcde28a852935a485fee0b Mon Sep 17 00:00:00 2001
From: Brock <jbrockmendel@gmail.com>
Date: Wed, 2 Jun 2021 07:32:00 -0700
Subject: [PATCH] REF: de-duplicate nested-dict handling in DataFrame
 construction

---
 pandas/_libs/lib.pyi                         |  2 --
 pandas/_libs/lib.pyx                         | 22 --------------------
 pandas/core/dtypes/cast.py                   | 16 --------------
 pandas/core/internals/construction.py        | 19 +++++------------
 pandas/tests/dtypes/cast/test_dict_compat.py | 14 -------------
 5 files changed, 5 insertions(+), 68 deletions(-)
 delete mode 100644 pandas/tests/dtypes/cast/test_dict_compat.py

diff --git a/pandas/_libs/lib.pyi b/pandas/_libs/lib.pyi
index 06620c2ad0dca..c1caf474b2020 100644
--- a/pandas/_libs/lib.pyi
+++ b/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: ...
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 4d184ee13e3db..352f50df01dc9 100644
--- a/pandas/_libs/lib.pyx
+++ b/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)
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 5c7211a5d1852..161572f3f1ac3 100644
--- a/pandas/core/dtypes/cast.py
+++ b/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]:
diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 46eb138dc74d1..270eddf2bd3a5 100644
--- a/pandas/core/internals/construction.py
+++ b/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,
@@ -61,6 +60,7 @@
     TimedeltaArray,
 )
 from pandas.core.construction import (
+    create_series_with_explicit_dtype,
     ensure_wrapped_if_datetimelike,
     extract_array,
     range_to_ndarray,
@@ -68,9 +68,7 @@
 )
 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
             )
diff --git a/pandas/tests/dtypes/cast/test_dict_compat.py b/pandas/tests/dtypes/cast/test_dict_compat.py
deleted file mode 100644
index 13dc82d779f95..0000000000000
--- a/pandas/tests/dtypes/cast/test_dict_compat.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import numpy as np
-
-from pandas.core.dtypes.cast import dict_compat
-
-from pandas import Timestamp
-
-
-def test_dict_compat():
-    data_datetime64 = {np.datetime64("1990-03-15"): 1, np.datetime64("2015-03-15"): 2}
-    data_unchanged = {1: 2, 3: 4, 5: 6}
-    expected = {Timestamp("1990-3-15"): 1, Timestamp("2015-03-15"): 2}
-    assert dict_compat(data_datetime64) == expected
-    assert dict_compat(expected) == expected
-    assert dict_compat(data_unchanged) == data_unchanged