BUG: ensuring that np.asarray() simple handles data as objects and doesn't…

doc/source/whatsnew/v0.24.0.txt

@@ -685,6 +685,5 @@ Other
 - :meth: `~pandas.io.formats.style.Styler.background_gradient` now takes a ``text_color_threshold`` parameter to automatically lighten the text color based on the luminance of the background color. This improves readability with dark background colors without the need to limit the background colormap range. (:issue:`21258`)
 - Require at least 0.28.2 version of ``cython`` to support read-only memoryviews (:issue:`21688`)
 - :meth: `~pandas.io.formats.style.Styler.background_gradient` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` (:issue:`15204`)
--
--
+- :meth:`pandas.core.algorithms.isin` avoids spurious casting for lists (:issue:`22160`)
 -

pandas/core/algorithms.py

@@ -134,7 +134,7 @@ def _ensure_data(values, dtype=None):
         return values, dtype, 'int64'
 
     # we have failed, return object
-    values = np.asarray(values)
+    values = np.asarray(values, dtype=np.object)
     return ensure_object(values), 'object', 'object'
 
 

pandas/tests/test_algos.py

@@ -615,6 +615,68 @@ def test_categorical_from_codes(self):
         result = algos.isin(Sd, St)
         tm.assert_numpy_array_equal(expected, result)
 
+    def test_same_nan_is_in(self):
+        # GH 22160
+        # nan is special, because from " a is b" doesn't follow "a == b"
+        # at least, isin() should follow python's "np.nan in [nan] == True"
+        # casting to -> np.float64 -> another float-object somewher on
+        # the way could lead jepardize this behavior
+        comps = [np.nan]  # could be casted to float64
+        values = [np.nan]
+        expected = np.array([True])
+        result = algos.isin(comps, values)
+        tm.assert_numpy_array_equal(expected, result)
+
+    def test_same_object_is_in(self):
+        # GH 22160
+        # there could be special treatment for nans
+        # the user however could define a custom class
+        # with similar behavior, then we at least should
+        # fall back to usual python's behavior: "a in [a] == True"
+        class LikeNan(object):
+            def __eq__(self):
+                return False
+
+            def __hash__(self):
+                return 0
+
+        a, b = LikeNan(), LikeNan()
+        # same object -> True
+        tm.assert_numpy_array_equal(algos.isin([a], [a]), np.array([True]))
+        # different objects -> False
+        tm.assert_numpy_array_equal(algos.isin([a], [b]), np.array([False]))
+
+    def test_different_nans(self):
+        # GH 22160
+        # all nans are handled as equivalent
+
+        comps = [float('nan')]
+        values = [float('nan')]
+        assert comps[0] is not values[0]  # different nan-objects
+
+        # as list of python-objects:
+        result = algos.isin(comps, values)
+        tm.assert_numpy_array_equal(np.array([True]), result)
+
+        # as object-array:
+        result = algos.isin(np.asarray(comps, dtype=np.object),
+                            np.asarray(values, dtype=np.object))
+        tm.assert_numpy_array_equal(np.array([True]), result)
+
+        # as float64-array:
+        result = algos.isin(np.asarray(comps, dtype=np.float64),
+                            np.asarray(values, dtype=np.float64))
+        tm.assert_numpy_array_equal(np.array([True]), result)
+
+    def test_no_cast(self):
+        # GH 22160
+        # ensure 42 is not casted to a string
+        comps = ['ss', 42]
+        values = ['42']
+        expected = np.array([False, False])
+        result = algos.isin(comps, values)
+        tm.assert_numpy_array_equal(expected, result)
+
     @pytest.mark.parametrize("empty", [[], Series(), np.array([])])
     def test_empty(self, empty):
         # see gh-16991