In my dataframe I have some categorical columns with over 100 different categories. I want to rank the categories by the most frequent. I keep the first 9 most frequent categories and the less frequent categories rename them automatically by: OTHER
Example:
Here my df :
print(df)
Employee_number Jobrol
0 1 Sales Executive
1 2 Research Scientist
2 3 Laboratory Technician
3 4 Sales Executive
4 5 Research Scientist
5 6 Laboratory Technician
6 7 Sales Executive
7 8 Research Scientist
8 9 Laboratory Technician
9 10 Sales Executive
10 11 Research Scientist
11 12 Laboratory Technician
12 13 Sales Executive
13 14 Research Scientist
14 15 Laboratory Technician
15 16 Sales Executive
16 17 Research Scientist
17 18 Research Scientist
18 19 Manager
19 20 Human Resources
20 21 Sales Executive
valCount = df['Jobrol'].value_counts()
valCount
Sales Executive 7
Research Scientist 7
Laboratory Technician 5
Manager 1
Human Resources 1
I keep the first 3 categories then I rename the rest by "OTHER", how should I proceed?
Thanks.
Use value_counts
with numpy.where
:
need = df['Jobrol'].value_counts().index[:3]
df['Jobrol'] = np.where(df['Jobrol'].isin(need), df['Jobrol'], 'OTHER')
valCount = df['Jobrol'].value_counts()
print (valCount)
Research Scientist 7
Sales Executive 7
Laboratory Technician 5
OTHER 2
Name: Jobrol, dtype: int64
Another solution:
N = 3
s = df['Jobrol'].value_counts()
valCount = s.iloc[:N].append(pd.Series(s.iloc[N:].sum(), index=['OTHER']))
print (valCount)
Research Scientist 7
Sales Executive 7
Laboratory Technician 5
OTHER 2
dtype: int64