计算单独的相关性，按列值分组

Question

给定2个数据帧

A = pd.DataFrame({'one':['a','a','a','b','b','b'], 'two':[1,2,3,3,2,1]})
B = pd.DataFrame({'one':['a','a','a','b','b','b'], 'two':[4,3,2,2,3,4]})

A

  one   two
0   a   1
1   a   2
2   a   3
3   b   3
4   b   2
5   b   1

B类

    one two
0   a   4
1   a   3
2   a   2
3   b   2
4   b   3
5   b   4

如何同时计算相关性A[A['one']=='a']['two'].corr(B[B['one']['two'] =='a'])和A[A['one']=='b']['two'].corr(B[B['one']['two'] =='b'])？最终目标是将相关性绘制为“一”列值“a”和“b”的函数，即

  corr
a  -1.0
b  -1.0

Answer 1

迭代这两个组的一种方法是：

x, y = A.groupby('one'), B.groupby('one')

res = {i[0]:i[1].two.corr(y.get_group(i[0]).two) for i in x}

pd.DataFrame(res.items())
#   0  1
#0  a -1
#1  b -1