Code Sample, a copy-pastable example if possible

` import pandas as pd

df = pd.DataFrame([[0,0],[1,1],[np.NaN,0],[np.NaN,1],[np.NaN,np.NaN]], columns = ['a','b'])

df Out[3]: a b 0 0.0 0.0 1 1.0 1.0 2 NaN 0.0 3 NaN 1.0 4 NaN NaN

df.sum(axis=1, skipna=True) Out[4]: 0 0.0 1 2.0 2 0.0 3 1.0 4 0.0 dtype: float64 `

Problem description

The documentation for pandas.DataFrame.sum indicates that:

skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA

However, it appears that the actual behavior under the skipna option within this particular environment is for the summation routine to evaluate full rows of na values to 0 rather than the documented value of NA.

Currently this behavior only occurs on within my local windows environment, and NOT within the linux environment my organization's grid runs on. I have provided output for pd.versions for both environments below to help localize this issue.

Expected Output

df.sum(axis=1, skipna=True) Out[6]: 0 0.0 1 2.0 2 0.0 3 1.0 4 NaN dtype: float64

Output of pd.show_versions()

_**Windows version in which issue is present**_ pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.11.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel byteorder: little LC_ALL: None LANG: en_US LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 8.1.1 setuptools: 20.3 Cython: 0.23.4 numpy: 1.11.0 scipy: 0.17.0 statsmodels: 0.6.1 xarray: None IPython: 4.1.2 sphinx: 1.3.5 patsy: 0.4.0 dateutil: 2.5.1 pytz: 2016.2 blosc: None bottleneck: 1.0.0 tables: 3.2.2 numexpr: 2.5.2 matplotlib: 1.5.1 openpyxl: 2.3.2 xlrd: 0.9.4 xlwt: 1.0.0 xlsxwriter: 0.8.4 lxml: 3.6.0 bs4: 4.4.1 html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: 1.0.12 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.39.0 pandas_datareader: None _**Linux version in which issue is NOT present**_ pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-642.15.1.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 8.1.2 setuptools: 27.2.0 Cython: None numpy: 1.11.2 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.4.8 patsy: 0.4.1 dateutil: 2.4.1 pytz: 2016.7 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.1 openpyxl: 2.4.0 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.3 lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: 1.1.2 pymysql: None psycopg2: None jinja2: 2.8 boto: None pandas_datareader: 0.2.1

Comment From: jreback

this a duplicate of #9422

you have bottleneck installed on 1 env and not the other. This is unfortunate the pandas behavior is correct and numpy/bottleneck are wrong (but they matched in bottleneck < 1).