diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index 94f151efbe2a6..b234773359f8c 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -6,6 +6,7 @@
 import nose
 from nose.tools import assert_equal, assert_true
 import numpy as np
+import pandas as pd
 from pandas.tslib import iNaT, NaT
 from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp, Float64Index
 from pandas import compat
@@ -40,6 +41,7 @@ def __getitem__(self):
 
     assert(not is_seq(A()))
 
+
 def test_get_callable_name():
     from functools import partial
     getname = com._get_callable_name
@@ -49,6 +51,7 @@ def fn(x):
     lambda_ = lambda x: x
     part1 = partial(fn)
     part2 = partial(part1)
+
     class somecall(object):
         def __call__(self):
             return x
@@ -60,6 +63,38 @@ def __call__(self):
     assert getname(somecall()) == 'somecall'
     assert getname(1) is None
 
+#Issue 10859
+class TestABCClasses(tm.TestCase):
+    tuples = [[1, 2, 2], ['red', 'blue', 'red']]
+    multi_index = pd.MultiIndex.from_arrays(tuples, names=('number', 'color'))
+    datetime_index = pd.to_datetime(['2000/1/1', '2010/1/1'])
+    timedelta_index = pd.to_timedelta(np.arange(5), unit='s')
+    period_index = pd.period_range('2000/1/1', '2010/1/1/', freq='M')
+    categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1])
+    categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical)
+    df = pd.DataFrame({'names': ['a', 'b', 'c']}, index=multi_index)
+    sparse_series = pd.Series([1, 2, 3]).to_sparse()
+    sparse_array = pd.SparseArray(np.random.randn(10))
+
+    def test_abc_types(self):
+        self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex)
+        self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index)
+        self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index)
+        self.assertIsInstance(self.multi_index, com.ABCMultiIndex)
+        self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex)
+        self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex)
+        self.assertIsInstance(self.period_index, com.ABCPeriodIndex)
+        self.assertIsInstance(self.categorical_df.index, com.ABCCategoricalIndex)
+        self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass)
+        self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass)
+        self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries)
+        self.assertIsInstance(self.df, com.ABCDataFrame)
+        self.assertIsInstance(self.df.to_panel(), com.ABCPanel)
+        self.assertIsInstance(self.sparse_series, com.ABCSparseSeries)
+        self.assertIsInstance(self.sparse_array, com.ABCSparseArray)
+        self.assertIsInstance(self.categorical, com.ABCCategorical)
+        self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod)
+
 
 def test_notnull():
     assert notnull(1.)
@@ -942,7 +977,7 @@ def test_2d_float32(self):
 
     def test_2d_datetime64(self):
         # 2005/01/01 - 2006/01/01
-        arr = np.random.randint(long(11045376), long(11360736), (5,3))*100000000000
+        arr = np.random.randint(long(11045376), long(11360736), (5, 3))*100000000000
         arr = arr.view(dtype='datetime64[ns]')
         indexer = [0, 2, -1, 1, -1]
 
@@ -1026,6 +1061,7 @@ def test_dict_compat():
     assert(com._dict_compat(expected) == expected)
     assert(com._dict_compat(data_unchanged) == data_unchanged)
 
+
 def test_possibly_convert_objects_copy():
     values = np.array([1, 2])