Is there a way to loop through and based a single column value and mark a value into multiple new columns in Pandas?

Question

The dataframe would look something similar to this:

start = [0,2,4,5,1]
end = [3,5,5,5,2]
df = pd.DataFrame({'start': start,'end': end})

The result I want look something like this: Basically marking a value from start to finish across multiple columns. So if one that start on 0 and ends on 3 I want to mark new column 0 to 3 with a value(1) and the rest with 0.

start = [0,2,4,5,1]
end = [3,5,5,5,2]
diff = [3,3,1,0,1]
col_0 = [1,0,0,0,0]
col_1=[1,0,0,0,1]
col_2 = [1,1,0,0,1]
col_3=[1,1,0,0,0]
col_4=[0,1,1,0,0]
col_5=[0,1,1,1,0]

df = pd.DataFrame({'start': start,'end': end, 'col_0':col_0, 'col_1': col_1, 'col_2': col_2, 'col_3':col_3, 'col_4': col_4, 'col_5': col_5})

start   end  col_0  col_1   col_2   col_3   col_4   col_5
0        3    1      1        1      1        0       0
2        5    0      0        1      1        1       1
4        5    0      0        0      0        1       1
5        5    0      0        0      0        0       1
1        2    0      1        1      0        0       0

Answer 1

Convert your range from start to stop to a list of indices then explode it. Finally, use indexing to set values to 1:

import numpy as np

range_to_ind = lambda x: range(x['start'], x['end']+1)
(i, j) = df.apply(range_to_ind, axis=1).explode().astype(int).reset_index().values.T

a = np.zeros((df.shape[0], max(df['end'])+1), dtype=int)
a[i, j] = 1

df = df.join(pd.DataFrame(a).add_prefix('col_'))

Output:

>>> df
   start  end  col_0  col_1  col_2  col_3  col_4  col_5
0      0    3      1      1      1      1      0      0
1      2    5      0      0      1      1      1      1
2      4    5      0      0      0      0      1      1
3      5    5      0      0      0      0      0      1
4      1    2      0      1      1      0      0      0