[FIXED] Merge timestamp column in pandas

Issue

I have a log file that has large number of columns and I would like to merge timestamp columns into one as shown in the example below

start time  End time process ID. . . 
10:02       11:10    A       1
10:32       11:23    B       1
...         ....     A       2
...         ....     B       2
...         ....     C       2  

Note: Single ID can have multiple similar processes e.g. multiple A in an ID

time     process   ID . . .
10:02    A_start   1  
11:10    A_end     1
10:32    B_start   1
11:23    B_end     1
....     A_start   2
....     A_end     2
....     B_start   2
....       ...    ..

Trying with melt on pandas but it seem I am missing something here

df.melt(id_vars=["ID", "process"], 
        var_name="End time", 
        )

Solution

IIUC, you can use melt with a bit of reshaping:

out = (df
 .rename(columns={'start time': 'start', 'End time': 'end'})
 .melt(['process', 'ID'], value_name='time')
 .assign(process=lambda d: d['process']+'_'+d.pop('variable'))
)

output:

   process  ID   time
0  A_start   1  10:02
1  B_start   1  10:32
2    A_end   1  11:10
3    B_end   1  11:23

used input:

  start time End time process  ID
0      10:02    11:10       A   1
1      10:32    11:23       B   1

Answered By - mozway