BUG: resolution inference with NaT ints/floats/strings

pandas/_libs/lib.pyx

@@ -2633,6 +2633,7 @@ def maybe_convert_objects(ndarray[object] objects,
                         tsobj = convert_to_tsobject(val, None, None, 0, 0)
                         tsobj.ensure_reso(NPY_FR_ns)
                     except OutOfBoundsDatetime:
+                        # e.g. test_out_of_s_bounds_datetime64
                         seen.object_ = True
                         break
             else:

pandas/_libs/tslib.pyx

@@ -451,12 +451,12 @@ cpdef array_to_datetime(
     Returns
     -------
     np.ndarray
-        May be datetime64[ns] or object dtype
+        May be datetime64[creso_unit] or object dtype
     tzinfo or None
     """
     cdef:
         Py_ssize_t i, n = values.size
-        object val, tz
+        object val
         ndarray[int64_t] iresult
         npy_datetimestruct dts
         bint utc_convert = bool(utc)
@@ -468,7 +468,7 @@ cpdef array_to_datetime(
         _TSObject _ts
         float tz_offset
         set out_tzoffset_vals = set()
-        tzinfo tz_out = None
+        tzinfo tz, tz_out = None
         cnp.flatiter it = cnp.PyArray_IterNew(values)
         NPY_DATETIMEUNIT item_reso
         bint infer_reso = creso == NPY_DATETIMEUNIT.NPY_FR_GENERIC
@@ -523,15 +523,14 @@ cpdef array_to_datetime(
 
             elif is_integer_object(val) or is_float_object(val):
                 # these must be ns unit by-definition
-                item_reso = NPY_FR_ns
-                state.update_creso(item_reso)
-                if infer_reso:
-                    creso = state.creso
 
                 if val != val or val == NPY_NAT:
                     iresult[i] = NPY_NAT
                 else:
-                    # we now need to parse this as if unit='ns'
+                    item_reso = NPY_FR_ns
+                    state.update_creso(item_reso)
+                    if infer_reso:
+                        creso = state.creso
                     iresult[i] = cast_from_unit(val, "ns", out_reso=creso)
                     state.found_other = True
 
@@ -553,6 +552,16 @@ cpdef array_to_datetime(
                 _ts = convert_str_to_tsobject(
                     val, None, unit="ns", dayfirst=dayfirst, yearfirst=yearfirst
                 )
+
+                if _ts.value == NPY_NAT:
+                    # e.g. "NaT" string or empty string, we do not consider
+                    #  this as either tzaware or tznaive. See
+                    #  test_to_datetime_with_empty_str_utc_false_format_mixed
+                    # We also do not update resolution inference based on this,
+                    #  see test_infer_with_nat_int_float_str
+                    iresult[i] = _ts.value
+                    continue
+
                 item_reso = _ts.creso
                 state.update_creso(item_reso)
                 if infer_reso:
@@ -563,12 +572,7 @@ cpdef array_to_datetime(
                 iresult[i] = _ts.value
 
                 tz = _ts.tzinfo
-                if _ts.value == NPY_NAT:
-                    # e.g. "NaT" string or empty string, we do not consider
-                    #  this as either tzaware or tznaive. See
-                    #  test_to_datetime_with_empty_str_utc_false_format_mixed
-                    pass
-                elif tz is not None:
+                if tz is not None:
                     # dateutil timezone objects cannot be hashed, so
                     # store the UTC offsets in seconds instead
                     nsecs = tz.utcoffset(None).total_seconds()
@@ -641,7 +645,7 @@ cpdef array_to_datetime(
         # Otherwise we can use the single reso that we encountered and avoid
         #  a second pass.
         abbrev = npy_unit_to_abbrev(state.creso)
-        result = iresult.view(f"M8[{abbrev}]")
+        result = iresult.view(f"M8[{abbrev}]").reshape(result.shape)
     return result, tz_out
 
 

pandas/_libs/tslibs/strptime.pyx

@@ -240,7 +240,7 @@ cdef _get_format_regex(str fmt):
 
 
 cdef class DatetimeParseState:
-    def __cinit__(self, NPY_DATETIMEUNIT creso=NPY_DATETIMEUNIT.NPY_FR_ns):
+    def __cinit__(self, NPY_DATETIMEUNIT creso):
         # found_tz and found_naive are specifically about datetime/Timestamp
         #  objects with and without tzinfos attached.
         self.found_tz = False

pandas/tests/tslibs/test_array_to_datetime.py

@@ -82,6 +82,24 @@ def test_infer_heterogeneous(self):
         assert tz is None
         tm.assert_numpy_array_equal(result, expected[::-1])
 
+    @pytest.mark.parametrize(
+        "item", [float("nan"), NaT.value, float(NaT.value), "NaT", ""]
+    )
+    def test_infer_with_nat_int_float_str(self, item):
+        # floats/ints get inferred to nanos *unless* they are NaN/iNaT,
+        # similar NaT string gets treated like NaT scalar (ignored for resolution)
+        dt = datetime(2023, 11, 15, 15, 5, 6)
+
+        arr = np.array([dt, item], dtype=object)
+        result, tz = tslib.array_to_datetime(arr, creso=creso_infer)
+        assert tz is None
+        expected = np.array([dt, np.datetime64("NaT")], dtype="M8[us]")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result2, tz2 = tslib.array_to_datetime(arr[::-1], creso=creso_infer)
+        assert tz2 is None
+        tm.assert_numpy_array_equal(result2, expected[::-1])
+
 
 class TestArrayToDatetimeWithTZResolutionInference:
     def test_array_to_datetime_with_tz_resolution(self):