我正在用python构建遗传算法来进行特征选择。我从我的数据中提取了特征，然后我将其分为两个数据帧：“train”和“test”。
如何将“population”数据帧（每个individu）和“train”数据帧中的每一行的值相乘？
“列车”数据帧：
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 13.611829 -7.250185 -11.773605 -18.265003
1 17.899545 -15.503942 -0.741729 -0.053619 -6.734652 4.398419
4 16.432750 -22.490190 -4.611659 -15.247781 -13.941488 -2.433374
5 15.905368 -4.812785 18.291712 3.742221 3.631887 -1.074326
6 16.991823 -15.946251 8.299577 8.057511 8.057510 -1.482333
“填充”数据帧：
0 1 2 3 4 5
0 1 1 0 0 0 1
1 0 1 0 1 0 0
2 0 0 0 0 0 1
3 0 0 1 0 1 1
将“population”中的每一行乘以“train”中的所有行。
结果是：
1）从人口行1：
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 0 0 0 -18.265003
1 17.899545 -15.503942 0 0 0 4.398419
4 16.432750 -22.490190 0 0 0 -2.433374
5 15.905368 -4.812785 0 0 0 -1.074326
6 16.991823 -15.946251 0 0 0 -1.482333
2）从人口第2排：
feature0 feature1 feature2 feature3 feature4 feature5
0 0 -3.921346 0 -7.250185 0 0
1 0 -15.503942 0 -0.053619 0 0
4 0 -22.490190 0 -15.247781 0 0
5 0 -4.812785 0 3.742221 0 0
6 0 -15.946251 0 8.057511 0 0
等等…
最佳答案
如果需要循环（如果数据较大，则速度较慢）：
for i, x in population.iterrows():
print (train * x.values)
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 0.0 -0.0 -0.0 -18.265003
1 17.899545 -15.503942 -0.0 -0.0 -0.0 4.398419
4 16.432750 -22.490190 -0.0 -0.0 -0.0 -2.433374
5 15.905368 -4.812785 0.0 0.0 0.0 -1.074326
6 16.991823 -15.946251 0.0 0.0 0.0 -1.482333
feature0 feature1 feature2 feature3 feature4 feature5
0 0.0 -3.921346 0.0 -7.250185 -0.0 -0.0
1 0.0 -15.503942 -0.0 -0.053619 -0.0 0.0
4 0.0 -22.490190 -0.0 -15.247781 -0.0 -0.0
5 0.0 -4.812785 0.0 3.742221 0.0 -0.0
6 0.0 -15.946251 0.0 8.057511 0.0 -0.0
feature0 feature1 feature2 feature3 feature4 feature5
0 0.0 -0.0 0.0 -0.0 -0.0 -18.265003
1 0.0 -0.0 -0.0 -0.0 -0.0 4.398419
4 0.0 -0.0 -0.0 -0.0 -0.0 -2.433374
5 0.0 -0.0 0.0 0.0 0.0 -1.074326
6 0.0 -0.0 0.0 0.0 0.0 -1.482333
feature0 feature1 feature2 feature3 feature4 feature5
0 0.0 -0.0 13.611829 -0.0 -11.773605 -18.265003
1 0.0 -0.0 -0.741729 -0.0 -6.734652 4.398419
4 0.0 -0.0 -4.611659 -0.0 -13.941488 -2.433374
5 0.0 -0.0 18.291712 0.0 3.631887 -1.074326
6 0.0 -0.0 8.299577 0.0 8.057510 -1.482333
或每一行分别：
print (train * population.values[0])
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 0.0 -0.0 -0.0 -18.265003
1 17.899545 -15.503942 -0.0 -0.0 -0.0 4.398419
4 16.432750 -22.490190 -0.0 -0.0 -0.0 -2.433374
5 15.905368 -4.812785 0.0 0.0 0.0 -1.074326
6 16.991823 -15.946251 0.0 0.0 0.0 -1.482333
或对于多索引数据帧：
d = pd.concat([train * population.values[i] for i in range(population.shape[0])],
keys=population.index.tolist())
print (d)
feature0 feature1 feature2 feature3 feature4 feature5
0 0 18.279579 -3.921346 0.000000 -0.000000 -0.000000 -18.265003
1 17.899545 -15.503942 -0.000000 -0.000000 -0.000000 4.398419
4 16.432750 -22.490190 -0.000000 -0.000000 -0.000000 -2.433374
5 15.905368 -4.812785 0.000000 0.000000 0.000000 -1.074326
6 16.991823 -15.946251 0.000000 0.000000 0.000000 -1.482333
1 0 0.000000 -3.921346 0.000000 -7.250185 -0.000000 -0.000000
1 0.000000 -15.503942 -0.000000 -0.053619 -0.000000 0.000000
4 0.000000 -22.490190 -0.000000 -15.247781 -0.000000 -0.000000
5 0.000000 -4.812785 0.000000 3.742221 0.000000 -0.000000
6 0.000000 -15.946251 0.000000 8.057511 0.000000 -0.000000
2 0 0.000000 -0.000000 0.000000 -0.000000 -0.000000 -18.265003
1 0.000000 -0.000000 -0.000000 -0.000000 -0.000000 4.398419
4 0.000000 -0.000000 -0.000000 -0.000000 -0.000000 -2.433374
5 0.000000 -0.000000 0.000000 0.000000 0.000000 -1.074326
6 0.000000 -0.000000 0.000000 0.000000 0.000000 -1.482333
3 0 0.000000 -0.000000 13.611829 -0.000000 -11.773605 -18.265003
1 0.000000 -0.000000 -0.741729 -0.000000 -6.734652 4.398419
4 0.000000 -0.000000 -4.611659 -0.000000 -13.941488 -2.433374
5 0.000000 -0.000000 18.291712 0.000000 3.631887 -1.074326
6 0.000000 -0.000000 8.299577 0.000000 8.057510 -1.482333
并按
xs选择：print (d.xs(0))
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 0.0 -0.0 -0.0 -18.265003
1 17.899545 -15.503942 -0.0 -0.0 -0.0 4.398419
4 16.432750 -22.490190 -0.0 -0.0 -0.0 -2.433374
5 15.905368 -4.812785 0.0 0.0 0.0 -1.074326
6 16.991823 -15.946251 0.0 0.0 0.0 -1.482333