Replace 1 value in a subsection of dataframe with value of another column

Question

I have a one hot encoded dataframe such as:

|  qtd|   a |   b |  c |   d |  e | ...z|
|-----+-----+-----|----|----+-----+-----|
|  90 |   1 |   0 |  0 |   0 |  0 |   0 |
|  10 |   0 |   0 |  0 |   0 |  0 |   1 |
|  40 |   0 |   1 |  0 |   0 |  0 |   0 |
|  80 |   0 |   0 |  1 |   0 |  0 |   0 |
|  90 |   0 |   1 |  0 |   0 |  0 |   0 |

I want to replace the values of the columns a to infinite with the value in qtd where the column has 1 as value, there is only one 1 value in the a to infinite dataframe subframe.

Such as:

|  qtd|   a |   b |  c |   d |  e | ...z|
|-----+-----+-----|----|----+-----+-----|
|  90 |  90 |   0 |  0 |   0 |  0 |   0 |
|  10 |   0 |   0 |  0 |   0 |  0 |  10 |
|  40 |   0 |  40 |  0 |   0 |  0 |   0 |
|  80 |   0 |   0 | 80 |   0 |  0 |   0 |
|  90 |   0 |  90 |  0 |   0 |  0 |   0 |

Answer 1

You can select all columns without first by DataFrame.iloc and multiple by column with DataFrame.mul:

df.iloc[:, 1:] = df.iloc[:, 1:].mul(df['qtd'], axis=0)
print (df)
   qtd   a   b   c  d  e   z
0   90  90   0   0  0  0   0
1   10   0   0   0  0  0  10
2   40   0  40   0  0  0   0
3   80   0   0  80  0  0   0
4   90   0  90   0  0  0   0

If column is not always first is possible get columns names by Index.difference and select by subset:

cols = df.columns.difference(['qtd'])
df[cols] = df[cols].mul(df['qtd'], axis=0)

If first column is index:

df = df.mul(df.index, axis=0)
print (df)
      a   b   c  d  e   z
qtd                      
90   90   0   0  0  0   0
10    0   0   0  0  0  10
40    0  40   0  0  0   0
80    0   0  80  0  0   0
90    0  90   0  0  0   0