From 0c004fcb6c3c2ab4d638d9665d13044e08fb2bc6 Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Mon, 7 Dec 2020 05:47:47 -0800
Subject: [PATCH] CLN: remove now-unnecesary coerce_to_dtypes (#38321)

---
 pandas/core/dtypes/cast.py | 30 ------------------------------
 pandas/core/frame.py       | 16 ++++------------
 2 files changed, 4 insertions(+), 42 deletions(-)

diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 12b5017cf34a0..0a2d4c22f8a4b 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -987,36 +987,6 @@ def coerce_indexer_dtype(indexer, categories):
     return ensure_int64(indexer)
 
 
-def coerce_to_dtypes(result: Sequence[Scalar], dtypes: Sequence[Dtype]) -> List[Scalar]:
-    """
-    given a dtypes and a result set, coerce the result elements to the
-    dtypes
-    """
-    if len(result) != len(dtypes):
-        raise AssertionError("_coerce_to_dtypes requires equal len arrays")
-
-    def conv(r, dtype):
-        if np.any(isna(r)):
-            pass
-        elif dtype == DT64NS_DTYPE:
-            r = Timestamp(r)
-        elif dtype == TD64NS_DTYPE:
-            r = Timedelta(r)
-        elif dtype == np.bool_:
-            # messy. non 0/1 integers do not get converted.
-            if is_integer(r) and r not in [0, 1]:
-                return int(r)
-            r = bool(r)
-        elif dtype.kind == "f":
-            r = float(r)
-        elif dtype.kind == "i":
-            r = int(r)
-
-        return r
-
-    return [conv(r, dtype) for r, dtype in zip(result, dtypes)]
-
-
 def astype_nansafe(
     arr, dtype: DtypeObj, copy: bool = True, skipna: bool = False
 ) -> ArrayLike:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index f710660d6ad8e..360d61a30a13a 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -79,7 +79,6 @@
 
 from pandas.core.dtypes.cast import (
     cast_scalar_to_array,
-    coerce_to_dtypes,
     construct_1d_arraylike_from_scalar,
     find_common_type,
     infer_dtype_from_scalar,
@@ -8817,11 +8816,9 @@ def _reduce(
         labels = self._get_agg_axis(axis)
         assert axis in [0, 1]
 
-        def func(values):
-            if is_extension_array_dtype(values.dtype):
-                return extract_array(values)._reduce(name, skipna=skipna, **kwds)
-            else:
-                return op(values, axis=axis, skipna=skipna, **kwds)
+        def func(values: np.ndarray):
+            # We only use this in the case that operates on self.values
+            return op(values, axis=axis, skipna=skipna, **kwds)
 
         def blk_func(values):
             if isinstance(values, ExtensionArray):
@@ -8859,10 +8856,6 @@ def _get_data() -> DataFrame:
             out = df._constructor(res).iloc[0]
             if out_dtype is not None:
                 out = out.astype(out_dtype)
-            if axis == 0 and is_object_dtype(out.dtype):
-                # GH#35865 careful to cast explicitly to object
-                nvs = coerce_to_dtypes(out.values, df.dtypes.iloc[np.sort(indexer)])
-                out[:] = np.array(nvs, dtype=object)
             if axis == 0 and len(self) == 0 and name in ["sum", "prod"]:
                 # Even if we are object dtype, follow numpy and return
                 #  float64, see test_apply_funcs_over_empty
@@ -8894,8 +8887,7 @@ def _get_data() -> DataFrame:
                 result = result.astype(np.float64)
             except (ValueError, TypeError):
                 # try to coerce to the original dtypes item by item if we can
-                if axis == 0:
-                    result = coerce_to_dtypes(result, data.dtypes)
+                pass
 
         result = self._constructor_sliced(result, index=labels)
         return result