Python pandas.compat.is_platform_32bit() Examples

The following are 21 code examples of pandas.compat.is_platform_32bit(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.compat , or try the search function .
Example #1
Source File: test_ujson.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_int_max(self, any_int_dtype):
        if any_int_dtype in ("int64", "uint64") and compat.is_platform_32bit():
            pytest.skip("Cannot test 64-bit integer on 32-bit platform")

        klass = np.dtype(any_int_dtype).type

        # uint64 max will always overflow,
        # as it's encoded to signed.
        if any_int_dtype == "uint64":
            num = np.iinfo("int64").max
        else:
            num = np.iinfo(any_int_dtype).max

        assert klass(ujson.decode(ujson.encode(num))) == num
Example #2
Source File: test_api.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_itertuples(self):
        for i, tup in enumerate(self.frame.itertuples()):
            s = self.klass._constructor_sliced(tup[1:])
            s.name = tup[0]
            expected = self.frame.iloc[i, :].reset_index(drop=True)
            self._assert_series_equal(s, expected)

        df = self.klass({'floats': np.random.randn(5),
                         'ints': lrange(5)}, columns=['floats', 'ints'])

        for tup in df.itertuples(index=False):
            assert isinstance(tup[1], (int, long))

        df = self.klass(data={"a": [1, 2, 3], "b": [4, 5, 6]})
        dfaa = df[['a', 'a']]

        assert (list(dfaa.itertuples()) ==
                [(0, 1, 1), (1, 2, 2), (2, 3, 3)])

        # repr with be int/long on 32-bit/windows
        if not (compat.is_platform_windows() or compat.is_platform_32bit()):
            assert (repr(list(df.itertuples(name=None))) ==
                    '[(0, 1, 4), (1, 2, 5), (2, 3, 6)]')

        tup = next(df.itertuples(name='TestName'))
        assert tup._fields == ('Index', 'a', 'b')
        assert (tup.Index, tup.a, tup.b) == tup
        assert type(tup).__name__ == 'TestName'

        df.columns = ['def', 'return']
        tup2 = next(df.itertuples(name='TestName'))
        assert tup2 == (0, 1, 4)
        assert tup2._fields == ('Index', '_1', '_2')

        df3 = DataFrame({'f' + str(i): [i] for i in range(1024)})
        # will raise SyntaxError if trying to create namedtuple
        tup3 = next(df3.itertuples())
        assert not hasattr(tup3, '_fields')
        assert isinstance(tup3, tuple)
Example #3
Source File: test_algos.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_value_counts_uint64(self):
        arr = np.array([2**63], dtype=np.uint64)
        expected = Series([1], index=[2**63])
        result = algos.value_counts(arr)

        tm.assert_series_equal(result, expected)

        arr = np.array([-1, 2**63], dtype=object)
        expected = Series([1, 1], index=[-1, 2**63])
        result = algos.value_counts(arr)

        # 32-bit linux has a different ordering
        if not compat.is_platform_32bit():
            tm.assert_series_equal(result, expected)
Example #4
Source File: test_algos.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_dropna(self):
        # https://github.com/pandas-dev/pandas/issues/9443#issuecomment-73719328

        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=True),
            Series([2, 1], index=[True, False]))
        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=False),
            Series([2, 1], index=[True, False]))

        tm.assert_series_equal(
            Series([True, True, False, None]).value_counts(dropna=True),
            Series([2, 1], index=[True, False]))
        tm.assert_series_equal(
            Series([True, True, False, None]).value_counts(dropna=False),
            Series([2, 1, 1], index=[True, False, np.nan]))
        tm.assert_series_equal(
            Series([10.3, 5., 5.]).value_counts(dropna=True),
            Series([2, 1], index=[5., 10.3]))
        tm.assert_series_equal(
            Series([10.3, 5., 5.]).value_counts(dropna=False),
            Series([2, 1], index=[5., 10.3]))

        tm.assert_series_equal(
            Series([10.3, 5., 5., None]).value_counts(dropna=True),
            Series([2, 1], index=[5., 10.3]))

        # 32-bit linux has a different ordering
        if not compat.is_platform_32bit():
            result = Series([10.3, 5., 5., None]).value_counts(dropna=False)
            expected = Series([2, 1, 1], index=[5., 10.3, np.nan])
            tm.assert_series_equal(result, expected)
Example #5
Source File: test_interval_tree.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def skipif_32bit(param):
    """
    Skip parameters in a parametrize on 32bit systems. Specifically used
    here to skip leaf_size parameters related to GH 23440.
    """
    marks = pytest.mark.skipif(compat.is_platform_32bit(),
                               reason='GH 23440: int type mismatch on 32bit')
    return pytest.param(param, marks=marks)
Example #6
Source File: test_ujson.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_IntMax(self):
        num = np.int(np.iinfo(np.int).max)
        assert np.int(ujson.decode(ujson.encode(num))) == num

        num = np.int8(np.iinfo(np.int8).max)
        assert np.int8(ujson.decode(ujson.encode(num))) == num

        num = np.int16(np.iinfo(np.int16).max)
        assert np.int16(ujson.decode(ujson.encode(num))) == num

        num = np.int32(np.iinfo(np.int32).max)
        assert np.int32(ujson.decode(ujson.encode(num))) == num

        num = np.uint8(np.iinfo(np.uint8).max)
        assert np.uint8(ujson.decode(ujson.encode(num))) == num

        num = np.uint16(np.iinfo(np.uint16).max)
        assert np.uint16(ujson.decode(ujson.encode(num))) == num

        num = np.uint32(np.iinfo(np.uint32).max)
        assert np.uint32(ujson.decode(ujson.encode(num))) == num

        if not compat.is_platform_32bit():
            num = np.int64(np.iinfo(np.int64).max)
            assert np.int64(ujson.decode(ujson.encode(num))) == num

            # uint64 max will always overflow as it's encoded to signed
            num = np.uint64(np.iinfo(np.int64).max)
            assert np.uint64(ujson.decode(ujson.encode(num))) == num
Example #7
Source File: test_algos.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_value_counts_uint64(self):
        arr = np.array([2**63], dtype=np.uint64)
        expected = Series([1], index=[2**63])
        result = algos.value_counts(arr)

        tm.assert_series_equal(result, expected)

        arr = np.array([-1, 2**63], dtype=object)
        expected = Series([1, 1], index=[-1, 2**63])
        result = algos.value_counts(arr)

        # 32-bit linux has a different ordering
        if not compat.is_platform_32bit():
            tm.assert_series_equal(result, expected)
Example #8
Source File: test_algos.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_dropna(self):
        # https://github.com/pandas-dev/pandas/issues/9443#issuecomment-73719328

        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=True),
            Series([2, 1], index=[True, False]))
        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=False),
            Series([2, 1], index=[True, False]))

        tm.assert_series_equal(
            Series([True, True, False, None]).value_counts(dropna=True),
            Series([2, 1], index=[True, False]))
        tm.assert_series_equal(
            Series([True, True, False, None]).value_counts(dropna=False),
            Series([2, 1, 1], index=[True, False, np.nan]))
        tm.assert_series_equal(
            Series([10.3, 5., 5.]).value_counts(dropna=True),
            Series([2, 1], index=[5., 10.3]))
        tm.assert_series_equal(
            Series([10.3, 5., 5.]).value_counts(dropna=False),
            Series([2, 1], index=[5., 10.3]))

        tm.assert_series_equal(
            Series([10.3, 5., 5., None]).value_counts(dropna=True),
            Series([2, 1], index=[5., 10.3]))

        # 32-bit linux has a different ordering
        if not compat.is_platform_32bit():
            result = Series([10.3, 5., 5., None]).value_counts(dropna=False)
            expected = Series([2, 1, 1], index=[5., 10.3, np.nan])
            tm.assert_series_equal(result, expected)
Example #9
Source File: test_coercion.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _assert_replace_conversion(self, from_key, to_key, how):
        index = pd.Index([3, 4], name='xxx')
        obj = pd.Series(self.rep[from_key], index=index, name='yyy')
        assert obj.dtype == from_key

        if (from_key.startswith('datetime') and to_key.startswith('datetime')):
            # different tz, currently mask_missing raises SystemError
            return

        if how == 'dict':
            replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
        elif how == 'series':
            replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
        else:
            raise ValueError

        result = obj.replace(replacer)

        if ((from_key == 'float64' and to_key in ('int64')) or
            (from_key == 'complex128' and
             to_key in ('int64', 'float64'))):

            # buggy on 32-bit / window
            if compat.is_platform_32bit() or compat.is_platform_windows():
                pytest.skip("32-bit platform buggy: {0} -> {1}".format
                            (from_key, to_key))

            # Expected: do not downcast by replacement
            exp = pd.Series(self.rep[to_key], index=index,
                            name='yyy', dtype=from_key)

        else:
            exp = pd.Series(self.rep[to_key], index=index, name='yyy')
            assert exp.dtype == to_key

        tm.assert_series_equal(result, exp)
Example #10
Source File: test_interval_tree.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def skipif_32bit(param):
    """
    Skip parameters in a parametrize on 32bit systems. Specifically used
    here to skip leaf_size parameters related to GH 23440.
    """
    marks = pytest.mark.skipif(compat.is_platform_32bit(),
                               reason='GH 23440: int type mismatch on 32bit')
    return pytest.param(param, marks=marks)
Example #11
Source File: test_interval_tree.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def skipif_32bit(param):
    """
    Skip parameters in a parametrize on 32bit systems. Specifically used
    here to skip leaf_size parameters related to GH 23440.
    """
    marks = pytest.mark.skipif(compat.is_platform_32bit(),
                               reason='GH 23440: int type mismatch on 32bit')
    return pytest.param(param, marks=marks)
Example #12
Source File: test_ujson.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_IntMax(self):
        num = np.int(np.iinfo(np.int).max)
        assert np.int(ujson.decode(ujson.encode(num))) == num

        num = np.int8(np.iinfo(np.int8).max)
        assert np.int8(ujson.decode(ujson.encode(num))) == num

        num = np.int16(np.iinfo(np.int16).max)
        assert np.int16(ujson.decode(ujson.encode(num))) == num

        num = np.int32(np.iinfo(np.int32).max)
        assert np.int32(ujson.decode(ujson.encode(num))) == num

        num = np.uint8(np.iinfo(np.uint8).max)
        assert np.uint8(ujson.decode(ujson.encode(num))) == num

        num = np.uint16(np.iinfo(np.uint16).max)
        assert np.uint16(ujson.decode(ujson.encode(num))) == num

        num = np.uint32(np.iinfo(np.uint32).max)
        assert np.uint32(ujson.decode(ujson.encode(num))) == num

        if not compat.is_platform_32bit():
            num = np.int64(np.iinfo(np.int64).max)
            assert np.int64(ujson.decode(ujson.encode(num))) == num

            # uint64 max will always overflow as it's encoded to signed
            num = np.uint64(np.iinfo(np.int64).max)
            assert np.uint64(ujson.decode(ujson.encode(num))) == num
Example #13
Source File: test_api.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_itertuples(self):
        for i, tup in enumerate(self.frame.itertuples()):
            s = self.klass._constructor_sliced(tup[1:])
            s.name = tup[0]
            expected = self.frame.iloc[i, :].reset_index(drop=True)
            self._assert_series_equal(s, expected)

        df = self.klass({'floats': np.random.randn(5),
                         'ints': lrange(5)}, columns=['floats', 'ints'])

        for tup in df.itertuples(index=False):
            assert isinstance(tup[1], (int, long))

        df = self.klass(data={"a": [1, 2, 3], "b": [4, 5, 6]})
        dfaa = df[['a', 'a']]

        assert (list(dfaa.itertuples()) ==
                [(0, 1, 1), (1, 2, 2), (2, 3, 3)])

        # repr with be int/long on 32-bit/windows
        if not (compat.is_platform_windows() or compat.is_platform_32bit()):
            assert (repr(list(df.itertuples(name=None))) ==
                    '[(0, 1, 4), (1, 2, 5), (2, 3, 6)]')

        tup = next(df.itertuples(name='TestName'))
        assert tup._fields == ('Index', 'a', 'b')
        assert (tup.Index, tup.a, tup.b) == tup
        assert type(tup).__name__ == 'TestName'

        df.columns = ['def', 'return']
        tup2 = next(df.itertuples(name='TestName'))
        assert tup2 == (0, 1, 4)
        assert tup2._fields == ('Index', '_1', '_2')

        df3 = DataFrame({'f' + str(i): [i] for i in range(1024)})
        # will raise SyntaxError if trying to create namedtuple
        tup3 = next(df3.itertuples())
        assert not hasattr(tup3, '_fields')
        assert isinstance(tup3, tuple)
Example #14
Source File: test_algos.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_value_counts_uint64(self):
        arr = np.array([2**63], dtype=np.uint64)
        expected = Series([1], index=[2**63])
        result = algos.value_counts(arr)

        tm.assert_series_equal(result, expected)

        arr = np.array([-1, 2**63], dtype=object)
        expected = Series([1, 1], index=[-1, 2**63])
        result = algos.value_counts(arr)

        # 32-bit linux has a different ordering
        if not compat.is_platform_32bit():
            tm.assert_series_equal(result, expected)
Example #15
Source File: test_algos.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_dropna(self):
        # https://github.com/pandas-dev/pandas/issues/9443#issuecomment-73719328

        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=True),
            Series([2, 1], index=[True, False]))
        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=False),
            Series([2, 1], index=[True, False]))

        tm.assert_series_equal(
            Series([True, True, False, None]).value_counts(dropna=True),
            Series([2, 1], index=[True, False]))
        tm.assert_series_equal(
            Series([True, True, False, None]).value_counts(dropna=False),
            Series([2, 1, 1], index=[True, False, np.nan]))
        tm.assert_series_equal(
            Series([10.3, 5., 5.]).value_counts(dropna=True),
            Series([2, 1], index=[5., 10.3]))
        tm.assert_series_equal(
            Series([10.3, 5., 5.]).value_counts(dropna=False),
            Series([2, 1], index=[5., 10.3]))

        tm.assert_series_equal(
            Series([10.3, 5., 5., None]).value_counts(dropna=True),
            Series([2, 1], index=[5., 10.3]))

        # 32-bit linux has a different ordering
        if not compat.is_platform_32bit():
            result = Series([10.3, 5., 5., None]).value_counts(dropna=False)
            expected = Series([2, 1, 1], index=[5., 10.3, np.nan])
            tm.assert_series_equal(result, expected)
Example #16
Source File: test_ujson.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_int_max(self, any_int_dtype):
        if any_int_dtype in ("int64", "uint64") and compat.is_platform_32bit():
            pytest.skip("Cannot test 64-bit integer on 32-bit platform")

        klass = np.dtype(any_int_dtype).type

        # uint64 max will always overflow,
        # as it's encoded to signed.
        if any_int_dtype == "uint64":
            num = np.iinfo("int64").max
        else:
            num = np.iinfo(any_int_dtype).max

        assert klass(ujson.decode(ujson.encode(num))) == num
Example #17
Source File: test_coercion.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def test_replace_series(self, how, to_key, from_key):
        if from_key == 'bool' and how == 'series' and compat.PY3:
            # doesn't work in PY3, though ...dict_from_bool works fine
            pytest.skip("doesn't work as in PY3")

        index = pd.Index([3, 4], name='xxx')
        obj = pd.Series(self.rep[from_key], index=index, name='yyy')
        assert obj.dtype == from_key

        if (from_key.startswith('datetime') and to_key.startswith('datetime')):
            # tested below
            return
        elif from_key in ['datetime64[ns, US/Eastern]', 'datetime64[ns, UTC]']:
            # tested below
            return

        if how == 'dict':
            replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
        elif how == 'series':
            replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
        else:
            raise ValueError

        result = obj.replace(replacer)

        if ((from_key == 'float64' and to_key in ('int64')) or
            (from_key == 'complex128' and
             to_key in ('int64', 'float64'))):

            if compat.is_platform_32bit() or compat.is_platform_windows():
                pytest.skip("32-bit platform buggy: {0} -> {1}".format
                            (from_key, to_key))

            # Expected: do not downcast by replacement
            exp = pd.Series(self.rep[to_key], index=index,
                            name='yyy', dtype=from_key)

        else:
            exp = pd.Series(self.rep[to_key], index=index, name='yyy')
            assert exp.dtype == to_key

        tm.assert_series_equal(result, exp)

    # TODO(jbrockmendel) commented out to only have a single xfail printed
Example #18
Source File: test_api.py    From elasticintel with GNU General Public License v3.0 4 votes vote down vote up 
def test_itertuples(self):
        for i, tup in enumerate(self.frame.itertuples()):
            s = self.klass._constructor_sliced(tup[1:])
            s.name = tup[0]
            expected = self.frame.iloc[i, :].reset_index(drop=True)
            self._assert_series_equal(s, expected)

        df = self.klass({'floats': np.random.randn(5),
                         'ints': lrange(5)}, columns=['floats', 'ints'])

        for tup in df.itertuples(index=False):
            assert isinstance(tup[1], (int, long))

        df = self.klass(data={"a": [1, 2, 3], "b": [4, 5, 6]})
        dfaa = df[['a', 'a']]

        assert (list(dfaa.itertuples()) ==
                [(0, 1, 1), (1, 2, 2), (2, 3, 3)])

        # repr with be int/long on 32-bit/windows
        if not (compat.is_platform_windows() or compat.is_platform_32bit()):
            assert (repr(list(df.itertuples(name=None))) ==
                    '[(0, 1, 4), (1, 2, 5), (2, 3, 6)]')

        tup = next(df.itertuples(name='TestName'))

        if sys.version >= LooseVersion('2.7'):
            assert tup._fields == ('Index', 'a', 'b')
            assert (tup.Index, tup.a, tup.b) == tup
            assert type(tup).__name__ == 'TestName'

        df.columns = ['def', 'return']
        tup2 = next(df.itertuples(name='TestName'))
        assert tup2 == (0, 1, 4)

        if sys.version >= LooseVersion('2.7'):
            assert tup2._fields == ('Index', '_1', '_2')

        df3 = DataFrame(dict(('f' + str(i), [i]) for i in range(1024)))
        # will raise SyntaxError if trying to create namedtuple
        tup3 = next(df3.itertuples())
        assert not hasattr(tup3, '_fields')
        assert isinstance(tup3, tuple)
Example #19
Source File: test_coercion.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
def test_replace_series(self, how, to_key, from_key):
        if from_key == 'bool' and how == 'series' and compat.PY3:
            # doesn't work in PY3, though ...dict_from_bool works fine
            pytest.skip("doesn't work as in PY3")

        index = pd.Index([3, 4], name='xxx')
        obj = pd.Series(self.rep[from_key], index=index, name='yyy')
        assert obj.dtype == from_key

        if (from_key.startswith('datetime') and to_key.startswith('datetime')):
            # tested below
            return
        elif from_key in ['datetime64[ns, US/Eastern]', 'datetime64[ns, UTC]']:
            # tested below
            return

        if how == 'dict':
            replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
        elif how == 'series':
            replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
        else:
            raise ValueError

        result = obj.replace(replacer)

        if ((from_key == 'float64' and to_key in ('int64')) or
            (from_key == 'complex128' and
             to_key in ('int64', 'float64'))):

            if compat.is_platform_32bit() or compat.is_platform_windows():
                pytest.skip("32-bit platform buggy: {0} -> {1}".format
                            (from_key, to_key))

            # Expected: do not downcast by replacement
            exp = pd.Series(self.rep[to_key], index=index,
                            name='yyy', dtype=from_key)

        else:
            exp = pd.Series(self.rep[to_key], index=index, name='yyy')
            assert exp.dtype == to_key

        tm.assert_series_equal(result, exp)

    # TODO(jbrockmendel) commented out to only have a single xfail printed
Example #20
Source File: test_coercion.py    From vnpy_crypto with MIT License 4 votes vote down vote up 
def test_replace_series(self, how, to_key, from_key):
        if from_key == 'bool' and how == 'series' and compat.PY3:
            # doesn't work in PY3, though ...dict_from_bool works fine
            pytest.skip("doesn't work as in PY3")

        index = pd.Index([3, 4], name='xxx')
        obj = pd.Series(self.rep[from_key], index=index, name='yyy')
        assert obj.dtype == from_key

        if (from_key.startswith('datetime') and to_key.startswith('datetime')):
            # tested below
            return
        elif from_key in ['datetime64[ns, US/Eastern]', 'datetime64[ns, UTC]']:
            # tested below
            return

        if how == 'dict':
            replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
        elif how == 'series':
            replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
        else:
            raise ValueError

        result = obj.replace(replacer)

        if ((from_key == 'float64' and to_key in ('int64')) or
            (from_key == 'complex128' and
             to_key in ('int64', 'float64'))):

            if compat.is_platform_32bit() or compat.is_platform_windows():
                pytest.skip("32-bit platform buggy: {0} -> {1}".format
                            (from_key, to_key))

            # Expected: do not downcast by replacement
            exp = pd.Series(self.rep[to_key], index=index,
                            name='yyy', dtype=from_key)

        else:
            exp = pd.Series(self.rep[to_key], index=index, name='yyy')
            assert exp.dtype == to_key

        tm.assert_series_equal(result, exp)

    # TODO(jbrockmendel) commented out to only have a single xfail printed
Example #21
Source File: test_coercion.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def test_replace_series(self, how, to_key, from_key):
        if from_key == 'bool' and how == 'series' and compat.PY3:
            # doesn't work in PY3, though ...dict_from_bool works fine
            pytest.skip("doesn't work as in PY3")

        index = pd.Index([3, 4], name='xxx')
        obj = pd.Series(self.rep[from_key], index=index, name='yyy')
        assert obj.dtype == from_key

        if (from_key.startswith('datetime') and to_key.startswith('datetime')):
            # tested below
            return
        elif from_key in ['datetime64[ns, US/Eastern]', 'datetime64[ns, UTC]']:
            # tested below
            return

        if how == 'dict':
            replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
        elif how == 'series':
            replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
        else:
            raise ValueError

        result = obj.replace(replacer)

        if ((from_key == 'float64' and to_key in ('int64')) or
            (from_key == 'complex128' and
             to_key in ('int64', 'float64'))):

            if compat.is_platform_32bit() or compat.is_platform_windows():
                pytest.skip("32-bit platform buggy: {0} -> {1}".format
                            (from_key, to_key))

            # Expected: do not downcast by replacement
            exp = pd.Series(self.rep[to_key], index=index,
                            name='yyy', dtype=from_key)

        else:
            exp = pd.Series(self.rep[to_key], index=index, name='yyy')
            assert exp.dtype == to_key

        tm.assert_series_equal(result, exp)

    # TODO(jbrockmendel) commented out to only have a single xfail printed