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

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:

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

All solutions will be appreciated.

CodePudding user response：

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