BUG: fix construction of Series from dict with nested lists

Author: toobaz

doc/source/whatsnew/v0.22.0.txt

@@ -246,6 +246,7 @@ Other
 
 - Improved error message when attempting to use a Python keyword as an identifier in a numexpr query (:issue:`18221`)
 - Fixed a bug where creating a Series from an array that contains both tz-naive and tz-aware values will result in a Series whose dtype is tz-aware instead of object (:issue:`16406`)
+- Fixed construction of a :class:`Series` from a ``dict`` containing nested lists as values (:issue:`18625`)
 - Fixed construction of a :class:`Series` from a ``dict`` containing ``NaN`` as key (:issue:`18480`)
 - Adding a ``Period`` object to a ``datetime`` or ``Timestamp`` object will now correctly raise a ``TypeError`` (:issue:`17983`)
 -

pandas/core/series.py

@@ -3208,7 +3208,11 @@ def _try_cast(arr, take_fast_path):
         return subarr
 
     elif isinstance(data, (list, tuple)) and len(data) > 0:
-        if dtype is not None:
+        if all(is_list_like(item) for item in data):
+            # Ensure nested lists are not interpreted as further dimensions:
+            subarr = np.empty(len(data), dtype='object')
+            subarr[:] = data
+        elif dtype is not None:
             try:
                 subarr = _try_cast(data, False)
             except Exception:

pandas/tests/series/test_constructors.py

@@ -651,6 +651,17 @@ def test_constructor_dict(self):
         expected.iloc[1] = 1
         assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize('input_class', [list, tuple])
+    @pytest.mark.parametrize('dtype', ['object', None])
+    def test_constructor_dict_nested_lists(self, input_class, dtype):
+        # GH 18625
+        d = {'a': input_class([input_class([1, 2, 3]),
+                               input_class([4, 5, 6])]),
+             'b': input_class([input_class([7, 8, 9])])}
+        result = Series(d, index=['a', 'b'], dtype=dtype)
+        expected = Series([d['a'], d['b']], index=['a', 'b'])
+        assert_series_equal(result, expected)
+
     @pytest.mark.parametrize("value", [2, np.nan, None, float('nan')])
     def test_constructor_dict_nan_key(self, value):
         # GH 18480