BUG: .item() incorrectly casts td64/dt64 to int (#38512)

Author: jbrockmendel

pandas/core/dtypes/cast.py

@@ -699,7 +699,7 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> Tuple[DtypeObj,
             raise ValueError(msg)
 
         dtype = val.dtype
-        val = val.item()
+        val = lib.item_from_zerodim(val)
 
     elif isinstance(val, str):
 

pandas/core/tools/timedeltas.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 
+from pandas._libs import lib
 from pandas._libs.tslibs import NaT
 from pandas._libs.tslibs.timedeltas import Timedelta, parse_timedelta_unit
 
@@ -117,7 +118,7 @@ def to_timedelta(arg, unit=None, errors="raise"):
         return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
     elif isinstance(arg, np.ndarray) and arg.ndim == 0:
         # extract array scalar and process below
-        arg = arg.item()
+        arg = lib.item_from_zerodim(arg)
     elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
         return _convert_listlike(arg, unit=unit, errors=errors)
     elif getattr(arg, "ndim", 1) > 1:

pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -3,7 +3,11 @@
 import numpy as np
 import pytest
 
-from pandas.core.dtypes.cast import infer_dtype_from_array, infer_dtype_from_scalar
+from pandas.core.dtypes.cast import (
+    infer_dtype_from,
+    infer_dtype_from_array,
+    infer_dtype_from_scalar,
+)
 from pandas.core.dtypes.common import is_dtype_equal
 
 from pandas import (
@@ -171,3 +175,17 @@ def test_infer_dtype_from_scalar_errors():
 def test_infer_dtype_from_array(arr, expected, pandas_dtype):
     dtype, _ = infer_dtype_from_array(arr, pandas_dtype=pandas_dtype)
     assert is_dtype_equal(dtype, expected)
+
+
+@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
+def test_infer_dtype_from_scalar_zerodim_datetimelike(cls):
+    # ndarray.item() can incorrectly return int instead of td64/dt64
+    val = cls(1234, "ns")
+    arr = np.array(val)
+
+    dtype, res = infer_dtype_from_scalar(arr)
+    assert dtype.type is cls
+    assert isinstance(res, cls)
+
+    dtype, res = infer_dtype_from(arr)
+    assert dtype.type is cls

pandas/tests/tools/test_to_timedelta.py

@@ -227,3 +227,20 @@ def test_to_timedelta_precision_over_nanos(self, input, expected, func):
         expected = pd.Timedelta(expected)
         result = func(input)
         assert result == expected
+
+    def test_to_timedelta_zerodim(self):
+        # ndarray.item() incorrectly returns int for dt64[ns] and td64[ns]
+        dt64 = pd.Timestamp.now().to_datetime64()
+        arg = np.array(dt64)
+
+        msg = (
+            "Value must be Timedelta, string, integer, float, timedelta "
+            "or convertible, not datetime64"
+        )
+        with pytest.raises(ValueError, match=msg):
+            to_timedelta(arg)
+
+        arg2 = arg.view("m8[ns]")
+        result = to_timedelta(arg2)
+        assert isinstance(result, pd.Timedelta)
+        assert result.value == dt64.view("i8")