Python3, column names from array - numpy or pandas

Question

I have a dataset in the below format.

id  A   B   C   D   E
100 1   0   0   0   0
101 0   1   1   0   0
102 1   0   0   0   0
103 0   0   0   1   1

I would like to convert this into below:

100, A
101, B C
102, A
103, D E

How do I do this ? I tried numpy argsort but I am new to Python and finding this challenging. Appreciate any help in this.

python df3 = df1.set_index("cust_id").apply(lambda col: ','.join(col[lambda x: x == 1].index), axis = 1)

python df3

cust_id
1375586                      ind_cco_fin_ult1
1050611                      ind_cco_fin_ult1
1050612    ind_deco_fin_ult1,ind_viv_fin_ult1
dtype: object

python df2

cust_id
1375586                       ind_cco_fin_ult1
1050611                       ind_cco_fin_ult1
1050612    ind_ctma_fin_ult1,ind_deco_fin_ult1
dtype: object

python metrics.mapk(df2,df3,7)

0.82879818594104293

```python list1=[['ind_cco_fin_ult1'], ['ind_cco_fin_ult1'], ['ind_deco_fin_ult1', 'ind_viv_fin_ult1'] ] list2=[['ind_cco_fin_ult1'], ['ind_cco_fin_ult1'], ['ind_ctma_fin_ult1', 'ind_deco_fin_ult1'] ]

```

python metrics.mapk(list2,list1,7)

0.83333333333333337

Thank you a lot for the help, I was able to try few steps. I am trying to test mapk but the apply method does not seem to give what I really need.

Answer 1

You can do something like this:

df.set_index("id").apply(lambda row: ' '.join(row[row == 1].index), axis = 1)

#id
#100      A
#101    B C
#102      A
#103    D E
#dtype: object