BUG: catch out-of-bounds datetime64 in Series/DataFrame constructor

doc/source/whatsnew/v0.25.0.rst

@@ -567,6 +567,7 @@ Datetimelike
 - Bug in :meth:`isin` for datetimelike indexes; :class:`DatetimeIndex`, :class:`TimedeltaIndex` and :class:`PeriodIndex` where the ``levels`` parameter was ignored. (:issue:`26675`)
 - Bug in :func:`to_datetime` which raises ``TypeError`` for ``format='%Y%m%d'`` when called for invalid integer dates with length >= 6 digits with ``errors='ignore'``
 - Bug when comparing a :class:`PeriodIndex` against a zero-dimensional numpy array (:issue:`26689`)
+- Bug in constructing a ``Series`` or ``DataFrame`` from a numpy ``datetime64`` array with a non-ns unit and out-of-bound timestamps generating rubbish data, which will now correctly raise an ``OutOfBoundsDatetime`` error (:issue:`26206`).
 
 Timedelta
 ^^^^^^^^^

pandas/core/dtypes/cast.py

@@ -1038,6 +1038,8 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
                                          .tz_convert(dtype.tz))
                         elif is_timedelta64:
                             value = to_timedelta(value, errors=errors)._values
+                    except OutOfBoundsDatetime:
+                        raise
                     except (AttributeError, ValueError, TypeError):
                         pass
 
@@ -1063,7 +1065,7 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
             dtype = value.dtype
 
             if dtype.kind == 'M' and dtype != _NS_DTYPE:
-                value = value.astype(_NS_DTYPE)
+                value = tslibs.conversion.ensure_datetime64ns(value)
 
             elif dtype.kind == 'm' and dtype != _TD_DTYPE:
                 value = to_timedelta(value)

pandas/core/internals/construction.py

@@ -8,7 +8,7 @@
 import numpy.ma as ma
 
 from pandas._libs import lib
-from pandas._libs.tslibs import IncompatibleFrequency
+from pandas._libs.tslibs import IncompatibleFrequency, OutOfBoundsDatetime
 from pandas.compat import raise_with_traceback
 
 from pandas.core.dtypes.cast import (
@@ -700,6 +700,9 @@ def _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure):
         elif not is_extension_type(subarr):
             subarr = construct_1d_ndarray_preserving_na(subarr, dtype,
                                                         copy=copy)
+    except OutOfBoundsDatetime:
+        # in case of out of bound datetime64 -> always raise
+        raise
     except (ValueError, TypeError):
         if is_categorical_dtype(dtype):
             # We *do* allow casting to categorical, since we know

pandas/tests/test_base.py

@@ -1341,3 +1341,41 @@ def test_to_numpy_dtype(as_series):
     expected = np.array(['2000-01-01T05', '2001-01-01T05'],
                         dtype='M8[ns]')
     tm.assert_numpy_array_equal(result, expected)
+
+
+class TestConstruction:
+    # test certain constructor behaviours on dtype inference across Series,
+    # Index and DataFrame
+
+    @pytest.mark.parametrize("klass", [
+        Series,
+        lambda x, **kwargs: DataFrame({'a': x}, **kwargs)['a'],
+        pytest.param(lambda x, **kwargs: DataFrame(x, **kwargs)[0],
+                     marks=pytest.mark.xfail),
+        Index,
+    ])
+    @pytest.mark.parametrize("a", [
+        np.array(['2263-01-01'], dtype='datetime64[D]'),
+        np.array([datetime(2263, 1, 1)], dtype=object),
+        np.array([np.datetime64('2263-01-01', 'D')], dtype=object),
+        np.array(["2263-01-01"], dtype=object)
+    ], ids=['datetime64[D]', 'object-datetime.datetime',
+            'object-numpy-scalar', 'object-string'])
+    def test_constructor_datetime_outofbound(self, a, klass):
+        # GH-26853 (+ bug GH-26206 out of bound non-ns unit)
+
+        # No dtype specified (dtype inference)
+        # datetime64[non-ns] raise error, other cases result in object dtype
+        # and preserve original data
+        if a.dtype.kind == 'M':
+            with pytest.raises(pd.errors.OutOfBoundsDatetime):
+                klass(a)
+        else:
+            result = klass(a)
+            assert result.dtype == 'object'
+            tm.assert_numpy_array_equal(result.to_numpy(), a)
+
+        # Explicit dtype specified
+        # Forced conversion fails for all -> all cases raise error
+        with pytest.raises(pd.errors.OutOfBoundsDatetime):
+            klass(a, dtype='datetime64[ns]')