Applying Pandas melt() on a dataframe with multiple variable columns

Question

I have a dataframe. Rows are unique persons and columns are various action types taken. I need the data restructured to show the individual events by row. Here is my current and

Answer 1

Use df.melt (v0.20+):

df
     action a    action b    action c   name
0  2017-10-04  2017-10-05  2017-10-06   ross
1  2017-10-04  2017-10-05  2017-10-06  allen
2  2017-10-04  2017-10-05  2017-10-06    jon

df = df.melt('name').sort_values('name')
df.columns = ['name', 'action', 'date']
df
    name    action        date
1  allen  action a  2017-10-04
4  allen  action b  2017-10-05
7  allen  action c  2017-10-06
2    jon  action a  2017-10-04
5    jon  action b  2017-10-05
8    jon  action c  2017-10-06
0   ross  action a  2017-10-04
3   ross  action b  2017-10-05
6   ross  action c  2017-10-06

Answer 2

r = df.roles
c = df.roles.str.count(',') + 1
i = df.index
df.loc[i.repeat(c)].assign(roles=','.join(r).split(','))

  company  employer_id                roles
0       a            1             engineer
0       a            1       data_scientist
0       a            1            architect
1       b            2             engineer
1       b            2  front_end_developer