Python Pandas 对两列求和，跳过 NaN
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Pandas sum two columns, skipping NaN
提问by smontanaro
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")?
如果我添加两列来创建第三列，任何包含 NaN（代表我的世界中缺失的数据）的列都会导致结果输出列也是 NaN。有没有办法跳过 NaN 而不将值明确设置为 0（这会失去这些值“缺失”的概念）？
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].
在上面，我希望 c 列是 [4, 2, 4]。
Thanks...
谢谢...
采纳答案by jrjc
with fillna()
使用填充（）
frame['c'] = frame.fillna(0)['a'] + frame.fillna(0)['b']
or as suggested :
或按照建议：
frame['c'] = frame.a.fillna(0) + frame.b.fillna(0)
giving :
给予：
a b c
0 1 3 4
1 2 NaN 2
2 NaN 4 4
回答by DSM
Another approach:
另一种方法：
>>> frame["c"] = frame[["a", "b"]].sum(axis=1)
>>> frame
a b c
0 1 3 4
1 2 NaN 2
2 NaN 4 4
