Improve to_datetime bounds checking

pandas/_libs/tslib.pyx

@@ -294,14 +294,22 @@ def array_with_unit_to_datetime(
         # if we have nulls that are not type-compat
         # then need to iterate
 
-        if values.dtype.kind in ["i", "f", "u"]:
+        if values.dtype.kind in ["i", "u"]:
             iresult = values.astype("i8", copy=False)
             # fill missing values by comparing to NPY_NAT
             mask = iresult == NPY_NAT
             iresult[mask] = 0
+            # for bounds checking, which can't use (integer) iresult * mult
+            # because it needs arithmetic overflow to not wrap around
             fvalues = iresult.astype("f8") * mult
             need_to_iterate = False
 
+        if values.dtype.kind in ["f",]:
+            mask = (values != values) | (values == NPY_NAT) # first is NaNs
+            fvalues = (values * mult).astype("f8")
+            fvalues[mask] = 0
+            need_to_iterate = False
+
         if not need_to_iterate:
             # check the bounds
             if (fvalues < Timestamp.min.value).any() or (
@@ -313,11 +321,9 @@ def array_with_unit_to_datetime(
                 result = (iresult * mult).astype("M8[ns]")
 
             elif values.dtype.kind == "f":
-                fresult = (values * mult).astype("f8")
-                fresult[mask] = 0
                 if prec:
-                    fresult = round(fresult, prec)
-                result = fresult.astype("M8[ns]", copy=False)
+                    fvalues = round(fvalues, prec)
+                result = fvalues.astype("M8[ns]", copy=False)
 
             iresult = result.view("i8")
             iresult[mask] = NPY_NAT

pandas/tests/tools/test_to_datetime.py

@@ -1629,6 +1629,27 @@ def test_to_timestamp_unit_coerce(self, bad_val):
         result = to_datetime([1, 2, bad_val], unit="D", errors="coerce")
         tm.assert_index_equal(result, expected)
 
+    def test_float_to_datetime_raise_near_bounds(self):
+        mag="cannot convert input with unit 'Y'"
+        oneyear_in_ns = 1e9 * 60 * 60 * 24 * 365.2425
+        tsmax_in_years = 2**63 / oneyear_in_ns # 2**63 ns, in years
+        should_succeed = Series(
+            [0, tsmax_in_years - 0.05, -tsmax_in_years + 0.05],
+            dtype=float
+        )
+        should_fail1 = Series([0, tsmax_in_years + 0.05], dtype=float)
+        should_fail2 = Series([0, -tsmax_in_years - 0.05], dtype=float)
+
+        result1=to_datetime(should_succeed, unit="Y", errors='raise')
+        result2=to_datetime(should_succeed, unit="Y", errors='coerce')
+        result3=to_datetime(should_succeed, unit="Y", errors='ignore')
+        tm.assert_series_equal(result1, result2)
+        tm.assert_series_equal(result1, result3)
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            to_datetime(should_fail1, unit="Y", errors='raise')
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            to_datetime(should_fail2, unit="Y", errors='raise')
+
 
 class TestToDatetimeDataFrame:
     @pytest.fixture