How to "Unpivot" multiple columns using melt() function

Question

I need some help to solve this problem. I have a panda dataframe that has 101 unique columns[Column A to Column CW)]. I need to unpivot them into row-based output.

Original Data frame;

 HSP    CAT     AMK      COL   OPK      ROI     GIO     DOL     values
   0      0      0        0     0        0       0       0      0.365
   0      0      0        0     0        0       0       0      0.8454
   0      0      0        0     0        0       0       0      0.74654
   0      0      0        0     0        0       0       0      0.74654 

Output should be;

Industry   LGA   values
HSP        OPK   0.365
CAT        ROI   0.8454
AMK        GOI   0.74654
COL        DOL   0.74654

I have tested a function called melt() to unpivot 3 columns. I have tested it for the first time. I am not sure how to provide all column names dynamically within the same function. so that I don't have to inject every column name in that function. Here is my sample code;

df1= pd.melt(df, id_vars=['values'], value_vars= ['HSP', 'CAT','AMK', 'COL']) << same for another var called 'LGA'

Unfortunately its not working based on my requirement. I have columns (A to U) which should be captured in "Industry" and Column (v to CV) should be captured in "LGA" column. Not sure how to create two "value_bars" parameters. I know that there will be another column called "value" with 1 in each row. I can drop that column later. I am just trying to find a way provide all columns(A:CW) into two "value_vars" parameter section. Not sure how!

Tried this to impute the column to rows dynamically;

X_testP = ({"Industry": X_test.iloc[A:U].columns,
          "LGA": X_test.iloc[V:CV].columns,
          "values": X_test.iloc['values']})

Unable to get the desired output.

Any help would be appreciated.

Thanks in advance

Answer 1

>>> pd.DataFrame({"Industry": df.filter(like="Ind").columns,
                  "LGA": df.filter(like="LGA").columns,
                  "values": df["values"]})

  Industry   LGA   values
0    Ind 1  LGA1  0.36500
1    Ind 2  LGA2  0.84540
2    Ind 3  LGA3  0.74654
3    Ind 4  LGA4  0.74654