Python 具有缺失值的列子集的行平均
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/34734940/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Row-wise average for a subset of columns with missing values
提问by scrollex
I've got a 'DataFrame` which has occasional missing values, and looks something like this:
我有一个“DataFrame”，它偶尔会丢失值，看起来像这样：
Monday Tuesday Wednesday
================================================
Mike 42 NaN 12
Jenna NaN NaN 15
Jon 21 4 1
I'd like to add a new columnto my data frame where I'd calculate the average across all columnsfor every row.
我想column在我的数据框中添加一个新的，我将计算columns每个row.
Meaning, for Mike, I'd need
(df['Monday'] + df['Wednesday'])/2, but for Jenna, I'd simply use df['Wednesday amt.']/1
意思是，对于Mike，我需要
(df['Monday'] + df['Wednesday'])/2，但是对于Jenna，我只是使用df['Wednesday amt.']/1
Does anyone know the best way to account for this variation that results from missing values and calculate the average?
有谁知道解决由缺失值引起的这种变化并计算平均值的最佳方法？
采纳答案by Stefan
You can simply:
您可以简单地：
df['avg'] = df.mean(axis=1)
Monday Tuesday Wednesday avg
Mike 42 NaN 12 27.000000
Jenna NaN NaN 15 15.000000
Jon 21 4 1 8.666667
because .mean()ignores missing values by default: see docs.
因为.mean()默认情况下忽略缺失值：请参阅 docs。
To select a subset, you can:
要选择子集，您可以：
df['avg'] = df[['Monday', 'Tuesday']].mean(axis=1)
Monday Tuesday Wednesday avg
Mike 42 NaN 12 42.0
Jenna NaN NaN 15 NaN
Jon 21 4 1 12.5
回答by Amir F
Alternative - using iloc (can also use loc here):
替代方案 - 使用 iloc（也可以在此处使用 loc）：
df['avg'] = df.iloc[:,0:2].mean(axis=1)
