将较不频繁的类别重命名为"OTHER". Python [英] Rename the less frequent categories by "OTHER" python

问题描述

在我的数据框中，我有一些带有100多个不同类别的分类列.我想按最频繁的类别进行排名.我保留了前9个最频繁的类别，而不那么频繁的类别则通过以下方式自动将其重命名:OTHER

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

示例:

这是我的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

我保留前3个类别，然后用"OTHER"重命名其余类别，该如何进行?

I keep the first 3 categories then I rename the rest by "OTHER", how should I proceed?

谢谢.

推荐答案

使用 value_counts 与 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

另一种解决方案:

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

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