All possible combinations as new columns of a dataframe based on primary column

Question

I have a dataframe like this

Dt              A           B           C          D
11-apr          1           1           1          1
10-apr          2           3           1          2

how do I get a new frame that looks like this:

I want to have all possible combinations of ABCD

possible combinations : A+B, A+C, A+D,B+c,B+d,c+D,A+B+C,B+C+D,A+B+D,A+C+D,A+B+C+d

I am able to get all the combinations but I got the File "pandas/_libs/src/inference.pyx", line 1472, in pandas._libs.lib.map_infer TypeError: sequence item 2: expected string, float found when I tried to create these columns.

from itertools import chain, combinations
ss_list1=[]
ss_list2=[]
for subset in all_subsets(df):
    ss_list=list(subset)
    # print ss_list
    ss_list_t=tuple(ss_list)
    # print ss_list_t
    ss_list1.insert(1,ss_list_t)
for c in ss_list1:
    if len(c)>1:
        # print c
        ss_list2.insert(1, c)
print ss_list2

df = pd.concat([df[c[1]].add(df[c[0]]) for c in ss_list2], axis=1, keys=ss_list2)
df.columns = df.columns.map(','.join)

File "pandas/_libs/src/inference.pyx", line 1472, in pandas._libs.lib.map_infer TypeError: sequence item 2: expected string, float found

"how do I get a new frame that looks like this:", i don't see it — U13-Forward, May 20 '19 at 06:17

score 1 · Accepted Answer · answered May 20 '19 at 06:20

Use:

#create index from Dt column if necessary
df = df.set_index('Dt')

#https://stackoverflow.com/a/5898031
from itertools import chain, combinations
def all_subsets(ss):
    return chain(*map(lambda x: combinations(ss, x), range(2, len(ss)+1)))

#get all combination from 2 to N
tups = list(all_subsets(df.columns))
#for each combination sum values
df1 = pd.concat([df.loc[:,c].sum(axis=1) for c in tups], axis=1)
#set new columns by join list of tuples tups
df1.columns = ['+'.join(x) for x in tups]
print (df1)
        A+B  A+C  A+D  B+C  B+D  C+D  A+B+C  A+B+D  A+C+D  B+C+D  A+B+C+D
Dt                                                                       
11-apr    2    2    2    2    2    2      3      3      3      3        4
10-apr    5    3    4    4    5    3      6      7      5      6        8

All possible combinations as new columns of a dataframe based on primary column

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