CODEWITHSUNDEEP
pythonpandaspython-itertools
user7298979
user7298979

Reputation: 549

Methods of Combination using Python

I am new to Python and I am trying to work out a problem with two different sets of data. I have created two data frames, s and c using pandas.read_csv

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%

So what I would want to do is, for every combination of Cer and OO from c, I would want to populate all the records from s. (Not sure if that is a good way to explain it). Please see below for example

    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%

I read that pandas and/or itertools could help me do this kind of analysis? The confusion is when trying to create all of the records from s for each combination of the two fields from dataframe c.

Again, any help with documentation and/or insight would be great. Thank you

Upvotes: 2

Views: 66

Answers (1)

Scott Boston
Scott Boston

Reputation: 153510

Use Cartesian join with merge and a temporary key:

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

Output:

   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%

Upvotes: 4

Related Questions