通过“ OTHER” Python重命名不太频繁的类别

Question

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。我保留前9个最频繁的类别，而较不频繁的类别则通过以下方式自动将其重命名：OTHER

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？

谢谢。

Answer 1

将您的系列转换为分类，提取计数不在前 3 中的类别，添加一个新类别 eg 'Other'，然后替换之前计算的类别：

df['Jobrol'] = df['Jobrol'].astype('category')

others = df['Jobrol'].value_counts().index[3:]
label = 'Other'

df['Jobrol'] = df['Jobrol'].cat.add_categories([label])
df['Jobrol'] = df['Jobrol'].replace(others, label)

注：它是诱人的通过重新命名他们的类别合并df['Jobrol'].cat.rename_categories(dict.fromkeys(others, label))，但是这是行不通的，因为这将意味着多个标记相同的类别，这是不可能的。

上述解决方案可适用于按计数过滤。例如，要仅包含计数为 1 的类别，您可以这样定义others：

counts = df['Jobrol'].value_counts()
others = counts[counts == 1].index

Answer 2

使用value_counts有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