How to reverse a dummy variables from a pandas dataframe

Question

I would like to reverse a dataframe with dummy variables. For example,

from df_input:

Course_01 Course_02 Course_03 
  0           0         1 
  1           0         0 
  0           1         0 

To df_output

   Course
0 03
1 01
2 02

I have been looking at the solution provided at Reconstruct a categorical variable from dummies in pandas but it did not work. Please, Any help would be much appreciated.

Many Thanks, Best Regards, Carlo

Answer 1

We can use wide_to_long, then select rows that are not equal to zero i.e

ndf = pd.wide_to_long(df, stubnames='T_', i='id',j='T')

      T_
id  T     
id1 30   0
id2 30   1
id1 40   1
id2 40   0

not_dummy = ndf[ndf['T_'].ne(0)].reset_index().drop('T_',1)

   id   T
0  id2  30
1  id1  40

Update based on your edit :

ndf = pd.wide_to_long(df.reset_index(), stubnames='T_',i='index',j='T')

not_dummy = ndf[ndf['T_'].ne(0)].reset_index(level='T').drop('T_',1)

        T
index    
1      30
0      40

Answer 2

You can use:

#create id to index if necessary
df = df.set_index('id')
#create MultiIndex
df.columns = df.columns.str.split('_', expand=True)
#reshape by stack and remove 0 rows
df = df.stack().reset_index().query('T != 0').drop('T',1).rename(columns={'level_1':'T'})
print (df)
    id   T
1  id1  40
2  id2  30

EDIT:

col_name = 'Course' 
df.columns = df.columns.str.split('_', expand=True)
df = (df.replace(0, np.nan)
        .stack()
        .reset_index()

        .drop([col_name, 'level_0'],1)
        .rename(columns={'level_1':col_name})
)
print (df)
  Course
0     03
1     01
2     02