Python: How to convert Pandas Dataframe Row Values to individual columns?

Question

I have the following dataframe which has the data of different jobs start and end time at different intervals. A small part of dataframe is shown below.

Dataframe(df):

result | job   |  time
START  | JOB0  |  1357  
START  | JOB2  |  2405
END    | JOB2  |  2379
START  | JOB3  |  4010
END    | JOB0  |  5209
END    | JOB3  |  6578
START  | JOB0  |  6000
END    | JOB0  |  6100

(Note - Original Dataframe has 5 Jobs (JOB0 to JOB4) I want to convert the values ( START and END ) of column result as individual columns in the dataframe.

Required Dataframe(df2)

job  |  START  | END
JOB0 |  1357   | 5209
JOB2 |  2405   | 2379
JOB3 |  4010   | 6578
JOB0 |  6000   | 6100

Code
I tried implementing this using a pivot_table but it is giving aggregated values which is not required.

df2 = df.pivot_table('time', 'job','result')

Code Output

result |       END       |      START
job     
JOB0   |    5.000589e+08    5.000636e+08
JOB1   |    4.999141e+08    4.999188e+08
JOB2   |    5.001668e+08    5.001715e+08
JOB3   |    4.995190e+08    4.995187e+08
JOB4   |    5.003238e+08    5.003236e+08

How can I attain the required dataframe?

Answer 1

You have duplicate job ( JOB0 has 2 different start and end times), so you need to pivot also by the cumulative count ( groupby.cumcount ) so indices are unique. You can tidy up the pivot afterwards by dropping the cumulative count level.

df['idx'] = df.groupby(['job', 'result']).cumcount()

(df.pivot(index=['job', 'idx'], columns='result', values='time')
  .sort_index(level=1)
  .droplevel(1)
  .reset_index()
  .rename_axis(None, axis=1)[['job', 'START', 'END']])

[out]

    job  START   END
0  JOB0   1357  5209
1  JOB2   2405  2379
2  JOB3   4010  6578
3  JOB0   6000  6100

Answer 2

if the jobs are unique that is only 1 entry for start and 1 entry for end per job then you can use this. Otherwise use the answer by Chris A

df.pivot_table(index=['job'], columns=['result'], values=['time'], aggfunc=np.max)