<h4 dir="auto">Code Sample, a copy-pastable example if possible</h4>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="In [50]: df
Out[50]: 
    a   b   c
0 NaN NaN NaN
1   1   1   1
2   1 NaN NaN

In [51]: df['total'] = df[['a', 'b', 'c']].sum(axis=1, skipna=True)

In [52]: df
Out[52]: 
    a   b   c  total
0 NaN NaN NaN      0
1   1   1   1      3
2   1 NaN NaN      1

In [53]: "><pre class="notranslate"><code class="notranslate">In [50]: df
Out[50]: 
    a   b   c
0 NaN NaN NaN
1   1   1   1
2   1 NaN NaN

In [51]: df['total'] = df[['a', 'b', 'c']].sum(axis=1, skipna=True)

In [52]: df
Out[52]: 
    a   b   c  total
0 NaN NaN NaN      0
1   1   1   1      3
2   1 NaN NaN      1

In [53]: 
</code></pre></div>
<h4 dir="auto">Expected Output</h4>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="In [51]: df['total'] = df[['a', 'b', 'c']].sum(axis=1, skipna=True)

In [52]: df
Out[52]: 
    a   b   c  total
0 NaN NaN NaN      NaN
1   1   1   1      3
2   1 NaN NaN      1

In [53]: "><pre class="notranslate"><code class="notranslate">In [51]: df['total'] = df[['a', 'b', 'c']].sum(axis=1, skipna=True)

In [52]: df
Out[52]: 
    a   b   c  total
0 NaN NaN NaN      NaN
1   1   1   1      3
2   1 NaN NaN      1

In [53]: 
</code></pre></div>
<h4 dir="auto">output of <code class="notranslate">pd.show_versions()</code></h4>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="In [68]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.3-1-ARCH
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8

pandas: 0.17.1
nose: 1.3.6
pip: 7.1.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 0.9999999
httplib2: 0.9.1
apiclient: 1.4.2
sqlalchemy: 1.0.11
pymysql: 0.6.6.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None

In [69]: "><pre class="notranslate"><code class="notranslate">In [68]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.3-1-ARCH
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8

pandas: 0.17.1
nose: 1.3.6
pip: 7.1.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 0.9999999
httplib2: 0.9.1
apiclient: 1.4.2
sqlalchemy: 1.0.11
pymysql: 0.6.6.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None

In [69]: 
</code></pre></div>
<h4 dir="auto">Comment</h4>
<p dir="auto">As per <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html" rel="nofollow">docs</a>: <code class="notranslate">Exclude NA/null values. If an entire row/column is NA, the result will be NA</code>.</p>
<p dir="auto">So for a row where all columns are NA expected output is NA, not a zero. Same behaviour also happens for a <code class="notranslate">Series</code>.</p>
<p dir="auto">We are having a problem when loading daily historical dividend data into a pandas Series().  Because companies sometimes declare two different dividends on the same day, we have an issue when trying to index this series</p>
<p dir="auto">The following</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="from pandas.core.api import Series
import datetime
data_seq = [.10, .23]
date_seq = [datetime.datetime(2011, 1, 1, 0, 0), datetime.datetime(2011, 1, 1, 0, 0)]
series = Series(data_seq, index=date_seq)
series[datetime.datetime(2011, 1, 1, 0, 0)]"><pre class="notranslate"><code class="notranslate">from pandas.core.api import Series
import datetime
data_seq = [.10, .23]
date_seq = [datetime.datetime(2011, 1, 1, 0, 0), datetime.datetime(2011, 1, 1, 0, 0)]
series = Series(data_seq, index=date_seq)
series[datetime.datetime(2011, 1, 1, 0, 0)]
</code></pre></div>
<p dir="auto">Returns</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Exception: Index values are not unique"><pre class="notranslate"><code class="notranslate">Exception: Index values are not unique
</code></pre></div>
<p dir="auto">We're hoping for a function that will go through a pandas a series and produce a new series w/o duplicate dates via some sort of combination function. In this case, it makes the most sense to combine the two dividends by adding them together.</p>