I have this loop that gets every combination from every row in a dataset, and create sub datasets.

df


    NAME    VALUE1  VALUE2
0   Alpha   100     A1
1   Alpha   100     A1
2   Alpha   200     A2

for r in range(2,len(df.columns) 1):
    for cols in itertools.combinations(df.columns, r ):
        print(df[list(cols)])

output:

    NAME      VALUE 1
0  Alpha      100
1  Alpha      100
2  Alpha      200

   NAME       VALUE 2
0  Alpha      A1
1  Alpha      A1
2  Alpha      A2

   VALUE 1  VALUE 2
0  100      A1
1  100      A1
2  100      A2

   NAME       VALUE 1  VALUE 2
0  Alpha      100      A1
1  Alpha      100      A1
2  Alpha      200      A2

I am trying to covert each row to a list or array like this:

[
     ['Alpha', 100],['Alpha', 100],['Alpha', 200], 
     ['Alpha', 'A1'],['Alpha', 'A1'],['Alpha', 'A2'],
     [100, 'A1'],[100, 'A1'],[200, 'A2'],
     ['Alpha', 100, 'A1'], ['Alpha', 200, 'A2']
]

Im thinking:

1. I can initially convert my df to a Numpy Array and create a better loop using bracket notation, which would eliminate the need to flatten anything. Just don't know how to do that.

2. or convert each row to a list or array

How can I do this?

CodePudding user response：

You can convert each row into a list like this:

res = []
import itertools
for r in range(2,len(df.columns) 1):
    for cols in itertools.combinations(df.columns, r ):
        res  = df[list(cols)].T.to_dict('list').values()

print(res)

Output:

[['Alpha', 100], ['Alpha', 100], ['Alpha', 200], ['Alpha', 'A1'], ['Alpha', 'A1'], ['Alpha', 'A2'], [100, 'A1'], [100, 'A1'], [200, 'A2'], ['Alpha', 100, 'A1'], ['Alpha', 100, 'A1'], ['Alpha', 200, 'A2']]