pandas -检查每行的多列中是否存在值 [英] Pandas - check if a value exists in multiple columns for each row

问题描述

我有以下Pandas数据框:

I have the following Pandas dataframe:

Index  Name  ID1  ID2  ID3
    1  A     Y    Y    Y
    2  B     Y    Y        
    3  B     Y              
    4  C               Y

我希望添加一个新列"Multiple"，以指示那些在ID1，ID2和ID3列中的多个列中具有Y值的行.

I wish to add a new column 'Multiple' to indicate those rows where there is a value Y in more than one of the columns ID1, ID2, and ID3.

Index  Name  ID1  ID2  ID3 Multiple
    1  A     Y    Y    Y   Y
    2  B     Y    Y        Y
    3  B     Y             N
    4  C               Y   N

我通常会使用 np.where 或 np.select 例如:

df['multiple'] = np.where(<More than 1 of ID1, ID2 or ID3 have a Y in>), 'Y', 'N')

但是我不知道如何写条件语句.ID列的数量可能越来越多，因此我无法将每种组合作为单独的条件(例如(ID1 = Y和ID3 = Y)或(ID2 = Y和ID3 = Y))进行介绍.我想我可能想要一些在命名列中计算Y值的东西?

but I can't figure out how to write the conditional. There might be a growing number of ID columns so I couldn't cover every combination as a separate condition (e.g. (ID1 = Y and ID3 = Y) or (ID2 = Y and ID3 = Y). I think I perhaps want something which counts the Y values across named columns?

在Pandas之外，我会考虑使用一个列表，将每个列的值附加到Y处，然后查看列表的长度是否大于1.

Outside of Pandas I would think about working with a list, appending the values for each column where Y and then see if the list had a length of greater than 1.

但是我无法考虑如何在 np.where ， np.select 或 df.loc 的限制内进行操作.有指针吗?

But I cant think how to do it within the limitations of np.where, np.select or df.loc. Any pointers?

推荐答案

使用numpy逐行求和Y的出现，应该做到这一点:

using numpy to sum by row to occurrences of Y should do it:

df['multi'] = ['Y' if x > 1 else 'N' for x in np.sum(df.values == 'Y', 1)]

输出:

      Name ID1   ID2   ID3 multi
Index                           
1        A   Y     Y     Y     Y
2        B   Y     Y  None     Y
3        B   Y  None  None     N
4        C   Y  None  None     N

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