pandas 多列交叉 [英] Pandas multiple column intersection

问题描述

我有一个如下的数据框:

I have a data frame as follows:

data={'NAME':['JOHN','MARY','CHARLIE'],
  'A':[[1,2,3],[2,3,4],[3,4,5]],
  'B':[[2,3,4],[3,4,5],[4,5,6]],
    'C':[[2,4],[3,4],[6,7]]  }
df=pd.DataFrame(data)
df=df[['NAME','A','B','C']]
NAME          A          B            C
0   JOHN    [1, 2, 3]   [2, 3, 4]   [2, 4]
1   MARY    [2, 3, 4]   [3, 4, 5]   [3, 4]
2   CHARLIE [3, 4, 5]   [4, 5, 6]   [6, 7]

我需要所有列 A、B、C 的交集.

I need intersection of all columns A, B, C.

我尝试了以下代码但没有奏效:

I tried following code but did not work:

df['D']=list(set(df['A'])&set(df['B'])&set(df['C']))

需要的输出如下:

    NAME            A         B         C       D
0   JOHN    [1, 2, 3]   [2, 3, 4]   [2, 4]  [2]
1   MARY    [2, 3, 4]   [3, 4, 5]   [3, 4]  [3, 4]
2   CHARLIE [3, 4, 5]   [4, 5, 6]   [6, 7]  []

推荐答案

选项 1:

交集语法 set(A)&set(B).. 是正确的，但您需要稍微调整一下以适用于数据帧，如下所示:

option 1:

The intersection syntax set(A)&set(B).. is correct but you need to tweak it a bit to be applicable on a dataframe as follows:

df.assign(D=df.transform(
     lambda x: list(set(x.A)&set(x.B)&set(x.C)),
     axis=1))

您可以进行如下操作:

df.assign(D=df.transform(
    lambda x: list(set(x.A).intersection(set(x.B)).intersection(set(x.C))),
    axis=1))

或

df.assign(D=df.apply(
    lambda x: list(set(x.A).intersection(set(x.B)).intersection(set(x.C))),
    axis=1))

选项 3:

df.assign(D=df.transform(
    lambda x: list(reduce(set.intersection, map(set,x.tolist()[1:]))),
    axis=1))

<小时>

它的作用是:

---

What this does is:

• 对每一行使用set(x.A).intersection(set(x.B))..通过链获取交集
• 将结果转换为列表
• 对数据框中的每一行执行此操作

执行细节:

In [76]: df.assign(D=df.transform(
    ...:     lambda x: list(set(x.A).intersection(set(x.B)).intersection(set(x.C))),
    ...:     axis=1))
Out[76]: 
      NAME          A          B       C       D
0     JOHN  [1, 2, 3]  [2, 3, 4]  [2, 4]     [2]
1     MARY  [2, 3, 4]  [3, 4, 5]  [3, 4]  [3, 4]
2  CHARLIE  [3, 4, 5]  [4, 5, 6]  [6, 7]      []

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