Turn multiple columns into two new columns in a dataframe using Pandas

I am working in a python pandas environment :D

Currently, I have a dataframe that looks like this :

 0   1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8 ex9 ex10 ex11 ex12 ex13 ex14 ex15 ex16 ex17 ex18 

My goal is to make the dataframe look like this :

 0   1   2   3   4   5  6    7   8   category   amount   
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     9        ex9
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     10       ex10
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     11       ex11
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     12       ex12
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     13       ex13
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     14       ex14
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     15       ex15
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     16       ex16
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     17       ex17
ex0 ex1 ex2 ex3 ex4 ex5 ex6 ex7 ex8     18       ex18

Basically, I want the last 9 column titles and values to become their own rows on 2 new columns while keeping the first 8 columns and rows the same. I am aware that this means the data will be duplicated.

I saw some other answers on stackoverflow use the following code for smaller dataframes but it hasn’t worked for me :

df.melt(['Type', 'Class'], var_name='Date', value_name='Value')

(df.set_index(['Type', 'Class'])
   .stack()
   .rename_axis(['Type', 'Class', 'Date'])
   .reset_index(name='Value')
)

Any and all help is appreciated ! Thank you

Answer

You are almost there with melt

df.melt(id_vars=df.columns[:9], var_name='category', value_name='amount')

Out[469]:
     0    1    2    3    4    5    6    7    8 category amount
0  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8        9    ex9
1  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       10   ex10
2  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       11   ex11
3  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       12   ex12
4  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       13   ex13
5  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       14   ex14
6  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       15   ex15
7  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       16   ex16
8  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       17   ex17
9  ex0  ex1  ex2  ex3  ex4  ex5  ex6  ex7  ex8       18   ex18