Python pandas rank/sort based on another column that differs for each input

Question

I would like to come up with the 4th column below based on the first three:

user    job  time  Rank
A   print   1559   2
A   print   1540   2
A   edit    1520           


        

Answer 1

First, assign a new column which contains the minimum time for user-job pairs:

df['min_time'] = df.groupby(['user', 'job'])['time'].transform('min')

Then group by each user and rank them:

df.groupby('user')['min_time'].rank(method='dense').astype(int)
Out: 
0    2
1    2
2    1
3    1
4    3
5    2
6    2
7    2
8    1
9    1
Name: min_time, dtype: int64

Answer 2

Or you can using

df1=df1.sort_values(['user','time'],ascending=[True,True])
df1['Rank']=df1.job!=df1.job.shift().fillna('edit')
df1.Rank=df1.groupby('user').Rank.cumsum()+1


  user      job  time  Rank
0    A    print  1559   2.0
1    A    print  1540   2.0
2    A     edit  1520   1.0
3    A     edit  1523   1.0
4    A  deliver  9717   3.0
5    B     edit  1717   2.0
6    B     edit  1716   2.0
7    B     edit  1715   2.0
8    B  deliver  1527   1.0
9    B  deliver  1524   1.0