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doc/source/whatsnew/v1.1.0.rst

@@ -234,7 +234,7 @@ Numeric
 Conversion
 ^^^^^^^^^^
 - Bug in :class:`Series` construction from NumPy array with big-endian ``datetime64`` dtype (:issue:`29684`)
--
+- Bug in :class:`Timedelta` construction with large nanoseconds keyword value (:issue:`34202`)
 -
 
 Strings
@@ -327,6 +327,7 @@ Reshaping
 - Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`)
 - :meth:`DataFrame.pivot` can now take lists for ``index`` and ``columns`` arguments (:issue:`21425`)
 - Bug in :func:`concat` where the resulting indices are not copied when ``copy=True`` (:issue:`29879`)
+- :meth:`Series.append` will now raise a ``TypeError`` when passed a DataFrame or a sequence containing Dataframe (:issue:`31413`)
 - :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`)
 
 
@@ -349,7 +350,6 @@ Other
   instead of ``TypeError: Can only append a Series if ignore_index=True or if the Series has a name`` (:issue:`30871`)
 - Set operations on an object-dtype :class:`Index` now always return object-dtype results (:issue:`31401`)
 - Bug in :meth:`AbstractHolidayCalendar.holidays` when no rules were defined (:issue:`31415`)
--
 
 .. ---------------------------------------------------------------------------
 

pandas/_libs/tslibs/timedeltas.pyx

@@ -1198,7 +1198,7 @@ class Timedelta(_Timedelta):
 
             kwargs = {key: _to_py_int_float(kwargs[key]) for key in kwargs}
 
-            nano = np.timedelta64(kwargs.pop('nanoseconds', 0), 'ns')
+            nano = convert_to_timedelta64(kwargs.pop('nanoseconds', 0), 'ns')
             try:
                 value = nano + convert_to_timedelta64(timedelta(**kwargs),
                                                       'ns')

pandas/_testing.py

@@ -706,11 +706,11 @@ def _get_ilevel_values(index, level):
     if isinstance(left, pd.PeriodIndex) or isinstance(right, pd.PeriodIndex):
         assert_attr_equal("freq", left, right, obj=obj)
     if isinstance(left, pd.IntervalIndex) or isinstance(right, pd.IntervalIndex):
-        assert_interval_array_equal(left.values, right.values)
+        assert_interval_array_equal(left._values, right._values)
 
     if check_categorical:
         if is_categorical_dtype(left) or is_categorical_dtype(right):
-            assert_categorical_equal(left.values, right.values, obj=f"{obj} category")
+            assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
 
 
 def assert_class_equal(left, right, exact: Union[bool, str] = True, obj="Input"):
@@ -883,7 +883,7 @@ def assert_interval_array_equal(left, right, exact="equiv", obj="IntervalArray")
 def assert_period_array_equal(left, right, obj="PeriodArray"):
     _check_isinstance(left, right, PeriodArray)
 
-    assert_numpy_array_equal(left._data, right._data, obj=f"{obj}.values")
+    assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data")
     assert_attr_equal("freq", left, right, obj=obj)
 
 
@@ -1170,10 +1170,10 @@ def assert_series_equal(
 
             # datetimelike may have different objects (e.g. datetime.datetime
             # vs Timestamp) but will compare equal
-            if not Index(left.values).equals(Index(right.values)):
+            if not Index(left._values).equals(Index(right._values)):
                 msg = (
-                    f"[datetimelike_compat=True] {left.values} "
-                    f"is not equal to {right.values}."
+                    f"[datetimelike_compat=True] {left._values} "
+                    f"is not equal to {right._values}."
                 )
                 raise AssertionError(msg)
         else:
@@ -1212,8 +1212,8 @@ def assert_series_equal(
     if check_categorical:
         if is_categorical_dtype(left) or is_categorical_dtype(right):
             assert_categorical_equal(
-                left.values,
-                right.values,
+                left._values,
+                right._values,
                 obj=f"{obj} category",
                 check_category_order=check_category_order,
             )

pandas/core/dtypes/cast.py

@@ -1181,9 +1181,11 @@ def try_timedelta(v):
         from pandas import to_timedelta
 
         try:
-            return to_timedelta(v)._ndarray_values.reshape(shape)
+            td_values = to_timedelta(v)
         except ValueError:
             return v.reshape(shape)
+        else:
+            return np.asarray(td_values).reshape(shape)
 
     inferred_type = lib.infer_datetimelike_array(ensure_object(v))
 

pandas/core/frame.py

@@ -4580,7 +4580,7 @@ def drop_duplicates(
         duplicated = self.duplicated(subset, keep=keep)
 
         if inplace:
-            (inds,) = (-duplicated)._ndarray_values.nonzero()
+            (inds,) = np.asarray(-duplicated).nonzero()
             new_data = self._data.take(inds)
 
             if ignore_index: