Pandas sum two columns, skipping NaN

Question

If I add two columns to create a third, any columns containing NaN (representing missing data in my world) cause the resulting output column to be NaN as well. Is there a way to skip NaNs without explicitly setting the values to 0 (which would lose the notion that those values are "missing")?

In [42]: frame = pd.DataFrame({'a': [1, 2, np.nan], 'b': [3, np.nan, 4]})  In [44]: frame['c'] = frame['a'] + frame['b']  In [45]: frame Out[45]:      a   b   c 0   1   3   4 1   2 NaN NaN 2 NaN   4 NaN 

In the above, I would like column c to be [4, 2, 4].

Thanks...

Answer 1

with fillna()

frame['c'] = frame.fillna(0)['a'] + frame.fillna(0)['b'] 

or as suggested :

frame['c'] = frame.a.fillna(0) + frame.b.fillna(0) 

giving :

    a   b  c 0   1   3  4 1   2 NaN  2 2 NaN   4  4 

Answer 2

Another approach:

>>> frame["c"] = frame[["a", "b"]].sum(axis=1) >>> frame     a   b  c 0   1   3  4 1   2 NaN  2 2 NaN   4  4