One-hot encoding in Python for array values in a DataFrame-CodePudding

I am trying to do one-hot encoding for these clustered data frames. (grouped by length). Been trying to use sklearn's encoder but it seems to regard each individual row as one category instead of multiple.

Example input:

 ID                    trace  length
 3              [A, B, C, C]       4
 4           [A, B, C, C, D]       5
 5        [A, B, C, C, D, E]       6
 24             [A, B, C, C]       4
 25          [A, B, C, C, D]       5
 ...                     ...     ...

Expected output :

ID     A  B  C  D  E    length
3      1  1  1  0  0         4
4      1  1  1  1  0         5
5      1  1  1  1  1         6
24     1  1  1  0  0         4
25     1  1  1  1  0         5
.... ..... .. ......

CodePudding user response：

IIUC, and if target contains lists, you could do:

(df.drop('trace',1)
   .join(df['trace']
         .apply('|'.join)
         .str.get_dummies()
        )
 )

or for in place modification of df:

df = (df.join(df.pop('trace')
              .apply('|'.join)
              .str.get_dummies())
      )

Or using explode and pivot_table:

(df.explode('trace')
   .assign(x=1)
   .pivot_table(index=['ID', 'length'], columns='trace', values='x', aggfunc='first')
   .fillna(0, downcast='infer')
   .reset_index()
 )

Output:

   ID  length  A  B  C  D  E
0   3       4  1  1  1  0  0
1   4       5  1  1  1  1  0
2   5       6  1  1  1  1  1
3  24       4  1  1  1  0  0
4  25       5  1  1  1  1  0