Grouping age in Pandas (Python)

Question

I have a DataFrame with column "ages" and column "professional qualification", like this:

ages	professional qualification
45	labourer
49	labourer
29	labourer
61	labourer
45	labourer
37	labourer
17	office worker
56	labourer
47	office worker

I want to group the ages like this ( ,17), (17,29), (30,40), (40,50), (50, ) and, with these ages grouped I would to create a frequency table indicating on each age group what professional qualification appears more often.

Example:

ages	professional qualification
(,17)	office worker
(17,29)	labourer
(30,40)	labourer
(40,50)	labourer

etc, etc, etc. The people who have an age between 40 and 50 (excluding 40) are mostly labourers

All solutions will be appreciated.

Answer 1

Use cut with aggregate by GroupBy.agg custom function by Series.mode with select first element:

bins = [0,17,29,40,50,70,100]

f = lambda x: x.mode().iat[0]
df1 = (df.groupby(pd.cut(df['ages'], bins=bins))['professional qualification']
         .agg(f)
         .reset_index())
print (df1)
        ages professional qualification
0    (0, 17]              office worker
1   (17, 29]                   labourer
2   (29, 40]                   labourer
3   (40, 50]                   labourer
4   (50, 70]                   labourer
5  (70, 100]                       None