BUG: drop_duplicates() doesn't work for object dtype series containing numpy nans

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd
import pandas.testing as pdt

s1 = pd.Series([np.nan, np.nan, 'text'])
s2 = pd.Series([np.float64(np.nan), np.float64(np.nan),'text'])

# This doesn't blow up, thinks s1 and s2 are the same
pdt.assert_series_equal(s1, s2)

s1_unique = s1.drop_duplicates()
s2_unique = s2.drop_duplicates()

# This blows up
pdt.assert_series_equal(s1_unique, s2_unique)

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-415-6908d74400cd> in <module>()
----> 1 pdt.assert_series_equal(s1_unique, s2_unique)

~/local/ts3/lib/python3.6/site-packages/pandas/util/testing.py in assert_series_equal(left, right, check_dtype, check_index_type, check_series_type, check_less_precise, check_names, check_exact, check_datetimelike_compat, check_categorical, obj)
   1276         raise_assert_detail(obj, 'Series length are different',
   1277                             '{0}, {1}'.format(len(left), left.index),
-> 1278                             '{0}, {1}'.format(len(right), right.index))
   1279 
   1280     # index comparison

~/local/ts3/lib/python3.6/site-packages/pandas/util/testing.py in raise_assert_detail(obj, message, left, right, diff)
   1147         msg = msg + "\n[diff]: {diff}".format(diff=diff)
   1148 
-> 1149     raise AssertionError(msg)
   1150 
   1151 

AssertionError: Series are different

Series length are different
[left]:  2, Int64Index([0, 2], dtype='int64')
[right]: 3, Int64Index([0, 1, 2], dtype='int64')

Problem description

When dealing with mixed dtype Series (sometimes as a result of .T followed by slice operation from dataframes), the drop_duplicates() call is very surprising, as it doesn't work for np.float64(np.nan). I would expect the htable.duplicated_object(values) call to also work with mixed dtypes containing np.float64 nan values.

The drop_duplicates() call does work for python's builtin float.nan, however.

Expected Output

import pandas as pd
import pandas.testing as pdt

s1 = pd.Series([np.nan, np.nan, 'text'])
s2 = pd.Series([np.float64(np.nan), np.float64(np.nan),'text'])

# This doesn't blow up, thinks s1 and s2 are the same
pdt.assert_series_equal(s1, s2)

s1_unique = s1.drop_duplicates()
s2_unique = s2.drop_duplicates()

# The following assertions should not blow up
assert len(s1_unique) == 2
assert len(s2_unique) == 2
pdt.assert_series_equal(s1_unique, s2_unique)

Output of pd.show_versions()

Reproduce the provided Series example, then inspect Series.drop_duplicates() and the htable.duplicated_object(values) path mentioned in the issue. Add a regression test covering object Series values containing repeated numpy.float64 NaNs; done means both Series produce two rows and compare equal.