From 6e572e06b089c4cfa5ccba2f92af0d7abc4a1347 Mon Sep 17 00:00:00 2001
From: Brock <jbrockmendel@gmail.com>
Date: Mon, 5 Dec 2022 10:06:57 -0800
Subject: [PATCH] BUG: Series[object].astype(td64_unsupported)

---
 pandas/core/dtypes/astype.py              | 25 ++++++++++++++++++-----
 pandas/tests/frame/methods/test_astype.py | 16 +++++++++++++++
 pandas/tests/reshape/merge/test_merge.py  | 12 +++++++----
 3 files changed, 44 insertions(+), 9 deletions(-)

diff --git a/pandas/core/dtypes/astype.py b/pandas/core/dtypes/astype.py
index 53c2cfd345e32..57af8c22f76d3 100644
--- a/pandas/core/dtypes/astype.py
+++ b/pandas/core/dtypes/astype.py
@@ -7,6 +7,7 @@
 import inspect
 from typing import (
     TYPE_CHECKING,
+    cast,
     overload,
 )
 
@@ -36,7 +37,11 @@
 from pandas.core.dtypes.missing import isna
 
 if TYPE_CHECKING:
-    from pandas.core.arrays import ExtensionArray
+    from pandas.core.arrays import (
+        DatetimeArray,
+        ExtensionArray,
+        TimedeltaArray,
+    )
 
 
 _dtype_obj = np.dtype(object)
@@ -109,7 +114,11 @@ def astype_nansafe(
 
         # allow frequency conversions
         if dtype.kind == "M":
-            return arr.astype(dtype)
+            from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+            dta = ensure_wrapped_if_datetimelike(arr)
+            dta = cast("DatetimeArray", dta)
+            return dta.astype(dtype, copy=copy)._ndarray
 
         raise TypeError(f"cannot astype a datetimelike from [{arr.dtype}] to [{dtype}]")
 
@@ -124,8 +133,9 @@ def astype_nansafe(
             #  and doing the old convert-to-float behavior otherwise.
             from pandas.core.construction import ensure_wrapped_if_datetimelike
 
-            arr = ensure_wrapped_if_datetimelike(arr)
-            return arr.astype(dtype, copy=copy)
+            tda = ensure_wrapped_if_datetimelike(arr)
+            tda = cast("TimedeltaArray", tda)
+            return tda.astype(dtype, copy=copy)._ndarray
 
         raise TypeError(f"cannot astype a timedelta from [{arr.dtype}] to [{dtype}]")
 
@@ -145,10 +155,15 @@ def astype_nansafe(
             return dta.astype(dtype, copy=False)._ndarray
 
         elif is_timedelta64_dtype(dtype):
+            from pandas.core.construction import ensure_wrapped_if_datetimelike
+
             # bc we know arr.dtype == object, this is equivalent to
             #  `np.asarray(to_timedelta(arr))`, but using a lower-level API that
             #  does not require a circular import.
-            return array_to_timedelta64(arr).view("m8[ns]").astype(dtype, copy=False)
+            tdvals = array_to_timedelta64(arr).view("m8[ns]")
+
+            tda = ensure_wrapped_if_datetimelike(tdvals)
+            return tda.astype(dtype, copy=False)._ndarray
 
     if dtype.name in ("datetime64", "timedelta64"):
         msg = (
diff --git a/pandas/tests/frame/methods/test_astype.py b/pandas/tests/frame/methods/test_astype.py
index 9d56dba9b480d..4c74f4782578e 100644
--- a/pandas/tests/frame/methods/test_astype.py
+++ b/pandas/tests/frame/methods/test_astype.py
@@ -387,6 +387,22 @@ def test_astype_from_object_to_datetime_unit(self, unit):
         with pytest.raises(TypeError, match="Cannot cast"):
             df.astype(f"M8[{unit}]")
 
+    @pytest.mark.parametrize("unit", ["Y", "M", "W", "D", "h", "m"])
+    def test_astype_from_object_to_timedelta_unit(self, unit):
+        vals = [
+            ["1 Day", "2 Days", "3 Days"],
+            ["4 Days", "5 Days", "6 Days"],
+        ]
+        df = DataFrame(vals, dtype=object)
+        msg = (
+            r"Cannot convert from timedelta64\[ns\] to timedelta64\[.*\]. "
+            "Supported resolutions are 's', 'ms', 'us', 'ns'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            # TODO: this is ValueError while for DatetimeArray it is TypeError;
+            #  get these consistent
+            df.astype(f"m8[{unit}]")
+
     @pytest.mark.parametrize("dtype", ["M8", "m8"])
     @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
     def test_astype_from_datetimelike_to_object(self, dtype, unit):
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index ab589dc26a3ac..f9d4d4fdc19e7 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -757,14 +757,18 @@ def test_other_datetime_unit(self, unit):
     def test_other_timedelta_unit(self, unit):
         # GH 13389
         df1 = DataFrame({"entity_id": [101, 102]})
-        s = Series([None, None], index=[101, 102], name="days")
+        ser = Series([None, None], index=[101, 102], name="days")
 
         dtype = f"m8[{unit}]"
-        df2 = s.astype(dtype).to_frame("days")
         if unit in ["D", "h", "m"]:
-            # We get nearest supported unit, i.e. "s"
-            assert df2["days"].dtype == "m8[s]"
+            # We cannot astype, instead do nearest supported unit, i.e. "s"
+            msg = "Supported resolutions are 's', 'ms', 'us', 'ns'"
+            with pytest.raises(ValueError, match=msg):
+                ser.astype(dtype)
+
+            df2 = ser.astype("m8[s]").to_frame("days")
         else:
+            df2 = ser.astype(dtype).to_frame("days")
             assert df2["days"].dtype == dtype
 
         result = df1.merge(df2, left_on="entity_id", right_index=True)