ENH: GH35131 Implement fix which allows numpy-like handling

Now pd.core.dtypes.inference.is_list_like correctly identifies numpy-like
scalars as not being iterable

Author: znicholls

pandas/_libs/lib.pyx

@@ -994,8 +994,8 @@ cdef inline bint c_is_list_like(object obj, bint allow_sets) except -1:
         isinstance(obj, abc.Iterable)
         # 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 (util.is_array(obj) and obj.ndim == 0)
+        # exclude zero-dimensional duck arrays, effectively scalars
+        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

@@ -61,8 +61,12 @@ def coerce(request):
 
 class DuckArray:
     """
-    A class which wraps around numpy (e.g. Pint's Quantity) but is not actually a numpy array
+    A class which wraps around numpy (e.g. Pint's Quantity)
+
+    The key is that it is not actually a numpy array so
+    ``util.is_array(duck_array_instance)`` returns ``False``
     """
+
     def __init__(self, values):
         self.values = values