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Pandas 数据框根据其他列是否有数据添加新列

[英]Pandas dataframe add new column based on if other columns have data or not

I have a pandas dataframe like below:我有一个如下所示的熊猫数据框:

x y z
1 2 3
na 1 4
na na 5

Now I want to add another column a whose value depend on x, y and z.现在我想添加另一列 a,其值取决于 x、y 和 z。 If x is available then a would be "yes".如果 x 可用,则 a 将为“是”。 If it is na then it will check y.如果是 na 那么它会检查 y。 If y is available then, a would be "no", otherwise a would be same as z(if it is available otherwise it will be 0).如果 y 可用,则 a 将为“否”,否则 a 将与 z 相同(如果可用,则为 0)。 I have the following function in R:我在 R 中有以下功能:

cur_sta <- function(data){

    sta <- ifelse(!is.na(data$x),"yes",    
        ifelse(!is.na(data$y),"no",    
        ifelse(!is.na(data$z),data$z,0)))

}

How can I achieve the same in python?我如何在 python 中实现相同的目标?

EDIT:编辑:

I tried the following:我尝试了以下方法:

conditions = [
        (not pd.isnull(data["x"].item())),
        (not pd.isnull(data["y"].item())),
        (not pd.isnull(data["z"].item()))]
    choices = ['yes', 'no', data["z"]]
    data['col_sta'] = np.select(conditions, choices, default='0')

but I am getting the following error:但我收到以下错误:

ValueError: can only convert an array of size 1 to a Python scalar

How can I fix this?我怎样才能解决这个问题?

Use Series.notna for test non missing values:使用Series.notna测试非缺失值:

conditions = [data["x"].notna(),
              data["y"].notna(),
              data["z"].notna()]
choices = ['yes', 'no', data["z"]]
data['col_sta'] = np.select(conditions, choices, default='0')
print (data)
     x    y  z col_sta
0  1.0  2.0  3     yes
1  NaN  1.0  4      no
2  NaN  NaN  5       5

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