How to reshape a dataframe and extend the number of rows

I’m trying to make this kind of transformations :

I tried so many reshape functions but still not getting the expected output.

Do you have any ideas, please ? If needed, here is an example :

import pandas as pd

df = pd.DataFrame({'A': ['ID_X', 'A01', 'Green', 'A02', 'Cerise', 'ID_Y', 'A01', 'Azure', 'A02', 'Black'],
                   'B': ['ID_X', 'B01', 'Red', 'B02', 'Celeste', 'ID_Y', 'B01', 'Beige', 'B02', 'Rose'],
                   'C': ['ID_X', 'C01', 'Brown', 'C02', 'Orange', 'ID_Y', 'C01', 'Canary', 'C02', 'White'],
                   'TYPE': ['ID', 'POSITION', 'COLOR', 'POSITION', 'COLOR', 'ID', 'POSITION', 'COLOR',
                            'POSITION', 'COLOR']})

>Solution :

# Cumcount to mark your different groups
df['column'] = df[df.TYPE.eq('ID')].groupby('TYPE').cumcount()
df.column = df.column.ffill()

# pivot_table, transpose, explode different levels, and reset the index:
out = df.pivot_table(index='TYPE', columns='column', aggfunc=list).T.explode('ID').explode(['COLOR', 'POSITION']).reset_index(drop=True)
print(out)

Output:

TYPE    COLOR    ID POSITION
0       Green  ID_X      A01
1      Cerise  ID_X      A02
2       Azure  ID_Y      A01
3       Black  ID_Y      A02
4         Red  ID_X      B01
5     Celeste  ID_X      B02
6       Beige  ID_Y      B01
7        Rose  ID_Y      B02
8       Brown  ID_X      C01
9      Orange  ID_X      C02
10     Canary  ID_Y      C01
11      White  ID_Y      C02