python - 使用 Python 的组合方法

Question

我是 Python 的新手，我正在尝试用两组不同的数据解决问题。我使用 pandas.read_csv 创建了两个数据框 s 和 c

S

  Spread   per1   per2   per3          acc
0  4.50%  1.26%  0.35%  0.50%        0.60%
1  4.45%  1.29%  0.35%  0.50%        0.60%
2  4.40%  1.31%  0.35%  0.50%        0.60%
3  4.35%  1.34%  0.35%  0.50%        0.60%
4  4.30%  1.37%  0.35%  0.50%        0.60%

c

     Cer     OO
0  1.00%  0.65%
1  1.05%  0.68%
2  1.10%  0.71%
3  1.15%  0.73%
4  1.20%  0.76%

所以我想要做的是，对于来自 c 的 Cer 和 OO 的每个组合，我想要填充来自 s 的所有记录。 (不确定这是否是解释它的好方法)。请看下面的例子

    Spread  per1    per2    per3    acc     Cer     OO
0   4.50%   1.26%   0.35%   0.50%   0.60%   1.00%   0.65%
1   4.45%   1.29%   0.35%   0.50%   0.60%   1.00%   0.65%
2   4.40%   1.31%   0.35%   0.50%   0.60%   1.00%   0.65%
3   4.35%   1.34%   0.35%   0.50%   0.60%   1.00%   0.65%
4   4.30%   1.37%   0.35%   0.50%   0.60%   1.00%   0.65%
5   4.50%   1.26%   0.35%   0.50%   0.60%   1.05%   0.68%
6   4.45%   1.29%   0.35%   0.50%   0.60%   1.05%   0.68%
7   4.40%   1.31%   0.35%   0.50%   0.60%   1.05%   0.68%
8   4.35%   1.34%   0.35%   0.50%   0.60%   1.05%   0.68%
9   4.30%   1.37%   0.35%   0.50%   0.60%   1.05%   0.68%

我读到 pandas 和/或 itertools 可以帮助我进行此类分析？混淆是在尝试从 s 为数据帧 c 的两个字段的每个组合创建所有记录时。

同样，任何有关文档和/或见解的帮助都会很棒。谢谢

Answer 1

使用笛卡尔连接 merge 和一个临时键:

S.assign(key=1).merge(c.assign(key=1), on='key').drop('key', axis=1)

输出:

   Spread   per1   per2   per3    acc    Cer     OO
0   4.50%  1.26%  0.35%  0.50%  0.60%  1.00%  0.65%
1   4.50%  1.26%  0.35%  0.50%  0.60%  1.05%  0.68%
2   4.50%  1.26%  0.35%  0.50%  0.60%  1.10%  0.71%
3   4.50%  1.26%  0.35%  0.50%  0.60%  1.15%  0.73%
4   4.50%  1.26%  0.35%  0.50%  0.60%  1.20%  0.76%
5   4.45%  1.29%  0.35%  0.50%  0.60%  1.00%  0.65%
6   4.45%  1.29%  0.35%  0.50%  0.60%  1.05%  0.68%
7   4.45%  1.29%  0.35%  0.50%  0.60%  1.10%  0.71%
8   4.45%  1.29%  0.35%  0.50%  0.60%  1.15%  0.73%
9   4.45%  1.29%  0.35%  0.50%  0.60%  1.20%  0.76%
10  4.40%  1.31%  0.35%  0.50%  0.60%  1.00%  0.65%
11  4.40%  1.31%  0.35%  0.50%  0.60%  1.05%  0.68%
12  4.40%  1.31%  0.35%  0.50%  0.60%  1.10%  0.71%
13  4.40%  1.31%  0.35%  0.50%  0.60%  1.15%  0.73%
14  4.40%  1.31%  0.35%  0.50%  0.60%  1.20%  0.76%
15  4.35%  1.34%  0.35%  0.50%  0.60%  1.00%  0.65%
16  4.35%  1.34%  0.35%  0.50%  0.60%  1.05%  0.68%
17  4.35%  1.34%  0.35%  0.50%  0.60%  1.10%  0.71%
18  4.35%  1.34%  0.35%  0.50%  0.60%  1.15%  0.73%
19  4.35%  1.34%  0.35%  0.50%  0.60%  1.20%  0.76%
20  4.30%  1.37%  0.35%  0.50%  0.60%  1.00%  0.65%
21  4.30%  1.37%  0.35%  0.50%  0.60%  1.05%  0.68%
22  4.30%  1.37%  0.35%  0.50%  0.60%  1.10%  0.71%
23  4.30%  1.37%  0.35%  0.50%  0.60%  1.15%  0.73%
24  4.30%  1.37%  0.35%  0.50%  0.60%  1.20%  0.76%