Low performance with sum() 
I have encountered a problem where manual addition ~25 times faster than `sum` function. 
Is there a bug or bad use case?

```
In[3]: %timeit test_1()
1 loops, best of 3: 212 ms per loop
In[4]: %timeit test_2()
1 loops, best of 3: 5.66 s per loop
```

```
import pandas as pd
import numpy as np

import itertools

r, c = 10000, 25

def test_1():
    df = pd.DataFrame(np.random.rand(r, c), columns=[str(x) for x in range(c)])
    combs = itertools.combinations(df.columns.tolist(), 2)
    for x, y in combs:
        df['%s+%s' % (x, y)] = df[x] + df[y]

def test_2():
    df = pd.DataFrame(np.random.rand(r, c), columns=[str(x) for x in range(c)])
    combs = itertools.combinations(df.columns.tolist(), 2)
    for x, y in combs:
        df['%s+%s' % (x, y)] = df[[x, y]].sum(axis=1)
```

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: None

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.6.7
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.1
apiclient: 1.4.0
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.38.0
```
