##1
M_members = [1000 , 1450, 1900]
M = pd.DataFrame(M_members)
##2
a_h_members = [0.4 , 0.6 , 0.8 ]
a_h = pd.DataFrame(a_h_members)
##3
d_h_members = [0.1 , 0.2 ]
d_h = pd.DataFrame(d_h_members)
由于我想要的輸出是資料框形式：
1000 0.4 0.1
1000 0.4 0.2
1000 0.6 0.1
1000 0.6 0.2
1000 0.8 0.1
1000 0.8 0.2
1450 0.4 0.1
1450 0.4 0.2
1450 0.6 0.1
1450 0.6 0.2
1450 0.8 0.1
1450 0.8 0.2
1900 0.4 0.1
1900 0.4 0.2
1900 0.6 0.1
1900 0.6 0.2
1900 0.8 0.1
1900 0.8 0.2
實際上，我想為更多資料幀執行此回圈。
uj5u.com熱心網友回復：
利用itertools.product
>>> import itertools
>>> pd.DataFrame(itertools.product(*[M_members, a_h_members, d_h_members]))
0 1 2
0 1000 0.4 0.1
1 1000 0.4 0.2
2 1000 0.6 0.1
3 1000 0.6 0.2
4 1000 0.8 0.1
5 1000 0.8 0.2
6 1450 0.4 0.1
7 1450 0.4 0.2
8 1450 0.6 0.1
9 1450 0.6 0.2
10 1450 0.8 0.1
11 1450 0.8 0.2
12 1900 0.4 0.1
13 1900 0.4 0.2
14 1900 0.6 0.1
15 1900 0.6 0.2
16 1900 0.8 0.1
17 1900 0.8 0.2
uj5u.com熱心網友回復：
根據您的資料大小，expand_grid frompyjanitor可能有助于提高性能：
# pip install pyjanitor
import janitor as jn
import pandas as pd
others = {'a':M, 'b':a_h, 'c':d_h}
jn.expand_grid(others = others)
a b c
0 0 0
0 1000 0.4 0.1
1 1000 0.4 0.2
2 1000 0.6 0.1
3 1000 0.6 0.2
4 1000 0.8 0.1
5 1000 0.8 0.2
6 1450 0.4 0.1
7 1450 0.4 0.2
8 1450 0.6 0.1
9 1450 0.6 0.2
10 1450 0.8 0.1
11 1450 0.8 0.2
12 1900 0.4 0.1
13 1900 0.4 0.2
14 1900 0.6 0.1
15 1900 0.6 0.2
16 1900 0.8 0.1
17 1900 0.8 0.2
您可以洗掉列級別，或將其展平：
jn.expand_grid(others = others).droplevel(axis = 1, level = 1)
a b c
0 1000 0.4 0.1
1 1000 0.4 0.2
2 1000 0.6 0.1
3 1000 0.6 0.2
4 1000 0.8 0.1
5 1000 0.8 0.2
6 1450 0.4 0.1
7 1450 0.4 0.2
8 1450 0.6 0.1
9 1450 0.6 0.2
10 1450 0.8 0.1
11 1450 0.8 0.2
12 1900 0.4 0.1
13 1900 0.4 0.2
14 1900 0.6 0.1
15 1900 0.6 0.2
16 1900 0.8 0.1
17 1900 0.8 0.2
uj5u.com熱心網友回復：
如果您從 DataFrames 開始，您可以使用重復的交叉合并：
dfs = [M, a_h, d_h]
from functools import reduce
out = (reduce(lambda a,b: a.merge(b, how='cross'), dfs)
.set_axis(range(len(dfs)), axis=1)
)
輸出：
0 1 2
0 1000 0.4 0.1
1 1000 0.4 0.2
2 1000 0.6 0.1
3 1000 0.6 0.2
4 1000 0.8 0.1
5 1000 0.8 0.2
6 1450 0.4 0.1
7 1450 0.4 0.2
8 1450 0.6 0.1
9 1450 0.6 0.2
10 1450 0.8 0.1
11 1450 0.8 0.2
12 1900 0.4 0.1
13 1900 0.4 0.2
14 1900 0.6 0.1
15 1900 0.6 0.2
16 1900 0.8 0.1
17 1900 0.8 0.2
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