NaN 的总和等于 NaN（非零）
Sum of NaNs to equal NaN (not zero)
我可以使用 df['TOTAL'] = df.sum(axis=1) 向此 DF 添加一个 TOTAL 列，它会像这样添加行元素：
col1 col2 TOTAL
0 1.0 5.0 6.0
1 2.0 6.0 8.0
2 0.0 NaN 0.0
3 NaN NaN 0.0
但是，我希望底行的总和为 NaN，而不是零，如下所示：
col1 col2 TOTAL
0 1.0 5.0 6.0
1 2.0 6.0 8.0
2 0.0 NaN 0.0
3 NaN NaN Nan
有没有一种方法可以高效地实现这一点？
将参数min_count=1添加到DataFrame.sum:
min_count : int, default 0
The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.
df['TOTAL'] = df.sum(axis=1, min_count=1)
print (df)
col1 col2 TOTAL
0 1.0 5.0 6.0
1 2.0 6.0 8.0
2 0.0 NaN 0.0
3 NaN NaN NaN
我可以使用 df['TOTAL'] = df.sum(axis=1) 向此 DF 添加一个 TOTAL 列，它会像这样添加行元素：
col1 col2 TOTAL
0 1.0 5.0 6.0
1 2.0 6.0 8.0
2 0.0 NaN 0.0
3 NaN NaN 0.0
但是，我希望底行的总和为 NaN，而不是零，如下所示：
col1 col2 TOTAL
0 1.0 5.0 6.0
1 2.0 6.0 8.0
2 0.0 NaN 0.0
3 NaN NaN Nan
有没有一种方法可以高效地实现这一点？
将参数min_count=1添加到DataFrame.sum:
min_count : int, default 0
The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.
df['TOTAL'] = df.sum(axis=1, min_count=1)
print (df)
col1 col2 TOTAL
0 1.0 5.0 6.0
1 2.0 6.0 8.0
2 0.0 NaN 0.0
3 NaN NaN NaN