Add failing test

Author: znicholls

pandas/_libs/testing.pyx

@@ -99,7 +99,9 @@ cpdef assert_almost_equal(a, b,
         return True
 
     a_is_ndarray = is_array(a)
+    a_has_size_and_shape = hasattr(a, "size") and hasattr(a, "shape")
     b_is_ndarray = is_array(b)
+    b_has_size_and_shape = hasattr(b, "size") and hasattr(b, "shape")
 
     if obj is None:
         if a_is_ndarray or b_is_ndarray:
@@ -119,7 +121,7 @@ cpdef assert_almost_equal(a, b,
             f"Can't compare objects without length, one or both is invalid: ({a}, {b})"
         )
 
-        if a_is_ndarray and b_is_ndarray:
+        if (a_is_ndarray and b_is_ndarray) or (a_has_size_and_shape and b_has_size_and_shape):
             na, nb = a.size, b.size
             if a.shape != b.shape:
                 from pandas._testing import raise_assert_detail

pandas/tests/dtypes/test_inference.py

@@ -60,6 +60,50 @@ def coerce(request):
     return request.param
 
 
+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():
+            for element in iter_values:
+                yield element
+
+        return it_outer()
+
+    def __len__(self):
+        return len(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
+
+
 # collect all objects to be tested for list-like-ness; use tuples of objects,
 # whether they are list-like or not (special casing for sets), and their ID
 ll_params = [
@@ -166,6 +210,14 @@ class DtypeList(list):
     assert not inference.is_array_like(123)
 
 
+@pytest.mark.parametrize("eg", (
+    np.array(2),
+    MockNumpyLikeArray(np.array(2)),
+))
+def test_assert_almost_equal(eg):
+    tm.assert_almost_equal(eg, eg)
+
+
 @pytest.mark.parametrize(
     "inner",
     [