Drop top level header on selected columns and turn selected column names into column values

I have a dataframe defined as follows:

cols = pd.MultiIndex.from_tuples([("unnamed_0", "b"), ("unnamed_1", "c"),('total','A'),('total','B'),('total','C')])
df = pd.DataFrame([[1,2,3,4,5], [3,4,5,6,7],[5,6,8,9,10],[7,8,11,12,13],[9,19,13,24,25]], columns=cols)
df

which is:

  unnamed_0 unnamed_1 total        
          b         c     A   B   C
0         1         2     3   4   5
1         3         4     5   6   7
2         5         6     8   9  10
3         7         8    11  12  13
4         9        19    13  24  25

What I want is to transform this dataframe into:

d = {'b': [1, 1,1,3,3,3,5,5,5,7,7,7,9,9,9], 'c': [2,2,2,4,4,4,6,6,6,8,8,8,19,19,19],'total':[3,4,5,5,6,7,8,9,10,11,12,13,13,24,25],'type':['A','B','C','A','B','C','A','B','C','A','B','C','A','B','C']}
df2 = pd.DataFrame(d)
print(df2)

That is:

    b   c  total type
0   1   2      3    A
1   1   2      4    B
2   1   2      5    C
3   3   4      5    A
4   3   4      6    B
5   3   4      7    C
6   5   6      8    A
7   5   6      9    B
8   5   6     10    C
9   7   8     11    A
10  7   8     12    B
11  7   8     13    C
12  9  19     13    A
13  9  19     24    B
14  9  19     25    C

I tried to drop the two unnamed_ columns at first using

df = df.xs(["unnamed_0","unnamed_1"], axis=1, drop_level=True)

but that didn’t go to well as it dropped everything but what I wanted. As for the transformation of level 2 header into column values, I thought of using something like df.T.unstack().reset_index(level=1, name='type').rename(columns={'level_1':'A'})[['type','A']] as in this example:

df=pd.DataFrame(index=['x','y'], data={'a':[1,2],'b':[3,4]})

   a  b
x  1  3
y  2  4

df.T.unstack().reset_index(level=1, name='c1').rename(columns={'level_1':'c2'})[['c1','c2']]
    
   c1 c2
x   1  a
x   3  b
y   2  a
y   4  b
​

but I did not manage to apply it to multi-level header dataframes.

Any help would be appreciated.

>Solution :

Use melt:

>>> df.droplevel(0, axis=1).melt(['b', 'c'], var_name='type', value_name='total')
    b   c type  total
0   1   2    A      3
1   3   4    A      5
2   5   6    A      8
3   7   8    A     11
4   9  19    A     13
5   1   2    B      4
6   3   4    B      6
7   5   6    B      9
8   7   8    B     12
9   9  19    B     24
10  1   2    C      5
11  3   4    C      7
12  5   6    C     10
13  7   8    C     13
14  9  19    C     45