如何从 pandas dataframe 反转虚拟变量

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 ie

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

Answer 3

Suppose you have the following dummy DF:

In [152]: d
Out[152]:
    id  T_30  T_40  T_50
0  id1     0     1     1
1  id2     1     0     1

we can prepare the following helper Series:

    In [153]: v = pd.Series(d.columns.drop('id').str.replace(r'\D','').astype(int), index=d.columns.drop('id'))

In [155]: v
Out[155]:
T_30    30
T_40    40
T_50    50
dtype: int64

now we can multiply them, stack and filter:

In [154]: d.set_index('id').mul(v).stack().reset_index(name='T').drop('level_1',1).query("T > 0")
Out[154]:
    id   T
1  id1  40
2  id1  50
3  id2  30
5  id2  50

Answer 4

I think melt() was pretty much made for this?

Your data, I think:

df_input = pd.DataFrame.from_dict({'Course_01':[0,1,0],
                               'Course_02':[0,0,1],
                               'Course_03':[1,0,0]})

Change names to match your desired output:

df_input.columns = df_input.columns.str.replace('Course_','')

Melt the dataframe:

dataMelted = pd.melt(df_input,  
                    var_name='Course', 
                    ignore_index=False)

Clean up zeros, etc:

df_output = (dataMelted[dataMelted['value'] != 0]
            .drop('value', axis=1)
            .sort_index())

>>> df_output
  Course
0     03
1     01
2     02

Answer 5

#Create a new column for the categorical

df['categ']=0
for i in range(df):
    if df['Course01']==1:
        df['categ']='01'
    if df['Course02']==1:
        df['categ']='02'
    if df['Course03']==1:
        df['categ']='03'
df.categ.astype('category']