我有三个数据框：
DT D1 D2 D3 D4 Unknown
Cstep
step 0 0.320039 0.048425 0.088292 0.085029 0.240678
step 1 0.226455 0.236200 0.206625 0.165754 0.163254
step 2 0.172478 0.199502 0.221124 0.266180 0.193045
step 3 0.164097 0.209790 0.218212 0.309156 0.180891
step 4 0.116930 0.306083 0.265747 0.173881 0.222132
RE E1 E2 E3 Unknown
Cstep
step 0 0.256725 0.086275 0.105281 0.244701
step 1 0.213159 0.215714 0.142406 0.162372
step 2 0.187955 0.222353 0.213388 0.192917
step 3 0.164817 0.252570 0.372562 0.188435
step 4 0.177344 0.223087 0.166364 0.211576
DS S1 S2 S3 S4 Unknown
Cstep
step 0 0.210452 0.115157 0.019318 0.074852 0.261005
step 1 0.228217 0.188214 0.248233 0.118122 0.150845
step 2 0.189803 0.234401 0.303194 0.242742 0.185957
step 3 0.197577 0.219602 0.246099 0.233097 0.184726
step 4 0.173951 0.242626 0.183155 0.331187 0.217467
我希望代码执行三个或更多熊猫数据帧的所有可能组合的乘法运算。我想要一个通用代码来将任意数量的数据帧乘以列。
这些数据帧的所有可能组合为：
[('D1', 'E1', 'S1') ('D1', 'E1', 'S2') ('D1', 'E1', 'S3')
('D1', 'E1', 'S4') ('D1', 'E1', 'Unknown') ('D1', 'E2', 'S1')
('D1', 'E2', 'S2') ('D1', 'E2', 'S3') ('D1', 'E2', 'S4')
('D1', 'E2', 'Unknown') ('D1', 'E3', 'S1') ('D1', 'E3', 'S2')
('D1', 'E3', 'S3') ('D1', 'Unknown', 'S1') ('D1', 'Unknown', 'S2')
('D1', 'Unknown', 'S4') ('D1', 'Unknown', 'Unknown') ('D2', 'E1', 'S1')
('D2', 'E1', 'S2') ('D2', 'E1', 'S3') ('D2', 'E1', 'S4')
('D2', 'E1', 'Unknown') ('D2', 'E2', 'S1') ('D2', 'E2', 'S2')
('D2', 'E2', 'S3') ('D2', 'E2', 'S4') ('D2', 'E2', 'Unknown')
('D2', 'E3', 'S1') ('D2', 'Unknown', 'S1') ('D2', 'Unknown', 'S2')
('D2', 'Unknown', 'S3') ('D2', 'Unknown', 'Unknown') ('D3', 'E1', 'S1')
('D3', 'E1', 'S2') ('D3', 'E1', 'S3') ('D3', 'E1', 'S4')
('D3', 'E1', 'Unknown') ('D3', 'E2', 'S1') ('D3', 'E2', 'S2')
('D3', 'E2', 'S3') ('D3', 'E2', 'S4') ('D3', 'E2', 'Unknown')
('D3', 'E3', 'S1') ('D3', 'E3', 'S2') ('D3', 'E3', 'S3')
('D3', 'E3', 'Unknown') ('D3', 'Unknown', 'S1') ('D3', 'Unknown', 'S2')
('D3', 'Unknown', 'S3') ('D3', 'Unknown', 'S4')
('D3', 'Unknown', 'Unknown') ('D4', 'E1', 'S1') ('D4', 'E1', 'S2')
('D4', 'E1', 'S3') ('D4', 'E1', 'S4') ('D4', 'E1', 'Unknown')
('D4', 'E2', 'S1') ('D4', 'E2', 'S2') ('D4', 'E2', 'S3')
('D4', 'E2', 'S4') ('D4', 'E2', 'Unknown') ('D4', 'E3', 'S1')
('D4', 'E3', 'S2') ('D4', 'E3', 'S3') ('D4', 'E3', 'S4')
('D4', 'E3', 'Unknown') ('D4', 'Unknown', 'S1') ('D4', 'Unknown', 'S2')
('D4', 'Unknown', 'S3') ('D4', 'Unknown', 'S4')
('D4', 'Unknown', 'Unknown') ('Unknown', 'E1', 'S1')
('Unknown', 'E1', 'S2') ('Unknown', 'E1', 'S3') ('Unknown', 'E1', 'S4')
('Unknown', 'E1', 'Unknown') ('Unknown', 'E2', 'S1')
('Unknown', 'E2', 'S2') ('Unknown', 'E2', 'S3') ('Unknown', 'E2', 'S4')
('Unknown', 'E2', 'Unknown') ('Unknown', 'E3', 'S1')
('Unknown', 'E3', 'S2') ('Unknown', 'E3', 'S3') ('Unknown', 'E3', 'S4')
('Unknown', 'Unknown', 'S1') ('Unknown', 'Unknown', 'S2')
('Unknown', 'Unknown', 'S3') ('Unknown', 'Unknown', 'S4')
('Unknown', 'Unknown', 'Unknown')]
答案 0 :(得分：1)
pd.concat from functools import reduce
from operator import mul
from itertools import product
pd.concat({
k: reduce(mul, (d[c] for d, c in zip([d1, d2, d3], k)))
for k in product(d1, d2, d3)
}, axis=1)
D1 ... Unknown
E1 E2 ... E3 Unknown
S1 S2 S3 S4 Unknown S1 S2 S3 S4 Unknown ... S1 S2 S3 S4 Unknown S1 S2 S3 S4 Unknown
step 0 0.017291 0.009462 0.001587 0.006150 0.021445 0.005811 0.003180 0.000533 0.002067 0.007207 ... 0.005333 0.002918 0.000489 0.001897 0.006614 0.012394 0.006782 0.001138 0.004408 0.015372
step 1 0.011016 0.009085 0.011982 0.005702 0.007281 0.011148 0.009194 0.012126 0.005770 0.007369 ... 0.005306 0.004376 0.005771 0.002746 0.003507 0.006050 0.004989 0.006580 0.003131 0.003999
step 2 0.006153 0.007599 0.009829 0.007869 0.006028 0.007279 0.008990 0.011628 0.009309 0.007132 ... 0.007819 0.009656 0.012490 0.009999 0.007660 0.007069 0.008729 0.011291 0.009040 0.006925
step 3 0.005344 0.005939 0.006656 0.006304 0.004996 0.008189 0.009102 0.010200 0.009661 0.007656 ... 0.013315 0.014800 0.016585 0.015709 0.012449 0.006735 0.007485 0.008389 0.007945 0.006297
step 4 0.003607 0.005031 0.003798 0.006868 0.004510 0.004538 0.006329 0.004778 0.008639 0.005673 ... 0.006428 0.008966 0.006768 0.012239 0.008036 0.008175 0.011403 0.008608 0.015565 0.010220
[5 rows x 100 columns]
pandas.MultiIndex.from_product a = v1[:, :, None, None] * v2[:, None, :, None] * v3[:, None, None, :]
a = a.reshape(len(a), -1)
cols = pd.MultiIndex.from_product([d1, d2, d3])
pd.DataFrame(a, d1.index, cols)