Add and pass tests

Author: znicholls

pandas/_libs/lib.pyx

@@ -1049,8 +1049,10 @@ cdef inline bint c_is_list_like(object obj, bint allow_sets) except -1:
         # we do not count strings/unicode/bytes as list-like
         and not isinstance(obj, (str, bytes))
         # exclude zero-dimensional numpy arrays, effectively scalars
-        # and not (hasattr(obj, "ndim") and obj.ndim == 0)
         and not cnp.PyArray_IsZeroDim(obj)
+        # extra check for numpy-like objects which aren't captured by
+        # the above
+        and not (hasattr(obj, "ndim") and obj.ndim == 0)
         # exclude sets if allow_sets is False
         and not (allow_sets is False and isinstance(obj, abc.Set))
     )

pandas/tests/dtypes/test_inference.py

@@ -55,6 +55,51 @@
 from pandas.core.arrays import IntegerArray
 
 
+class MockNumpyLikeArray:
+    """
+    A class which is numpy-like (e.g. Pint's Quantity) but not actually numpy
+    The key is that it is not actually a numpy array so
+    ``util.is_array(mock_numpy_like_array_instance)`` returns ``False``. Other
+    important properties are that the class defines a :meth:`__iter__` method
+    (so that ``isinstance(abc.Iterable)`` returns ``True``) and has a
+    :meth:`ndim` property which can be used as a check for whether it is a
+    scalar or not.
+    """
+
+    def __init__(self, values):
+        self._values = values
+
+    def __iter__(self):
+        iter_values = iter(self._values)
+
+        def it_outer():
+            yield from iter_values
+
+        return it_outer()
+
+    def __len__(self):
+        return len(self._values)
+
+    def __array__(self, t=None):
+        return self._values
+
+    @property
+    def ndim(self):
+        return self._values.ndim
+
+    @property
+    def dtype(self):
+        return self._values.dtype
+
+    @property
+    def size(self):
+        return self._values.size
+
+    @property
+    def shape(self):
+        return self._values.shape
+
+
 @pytest.fixture(params=[True, False], ids=str)
 def coerce(request):
     return request.param
@@ -94,6 +139,15 @@ def coerce(request):
     (np.ndarray((2,) * 4), True, "ndarray-4d"),
     (np.array([[[[]]]]), True, "ndarray-4d-empty"),
     (np.array(2), False, "ndarray-0d"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 1)), True, "duck-ndarray-1d"),
+    (MockNumpyLikeArray(np.array([])), True, "duck-ndarray-1d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 2)), True, "duck-ndarray-2d"),
+    (MockNumpyLikeArray(np.array([[]])), True, "duck-ndarray-2d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 3)), True, "duck-ndarray-3d"),
+    (MockNumpyLikeArray(np.array([[[]]])), True, "duck-ndarray-3d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 4)), True, "duck-ndarray-4d"),
+    (MockNumpyLikeArray(np.array([[[[]]]])), True, "duck-ndarray-4d-empty"),
+    (MockNumpyLikeArray(np.array(2)), False, "duck-ndarray-0d"),
     (1, False, "int"),
     (b"123", False, "bytes"),
     (b"", False, "bytes-empty"),
@@ -154,6 +208,8 @@ def test_is_array_like():
     assert inference.is_array_like(Series([1, 2]))
     assert inference.is_array_like(np.array(["a", "b"]))
     assert inference.is_array_like(Index(["2016-01-01"]))
+    assert inference.is_array_like(np.array([2, 3]))
+    assert inference.is_array_like(MockNumpyLikeArray(np.array([2, 3])))
 
     class DtypeList(list):
         dtype = "special"