Pandas - 更改因子类型对象的级别顺序
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/38023881/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas - change the order of levels of factor-type object
提问by Square9627
I have a Pandas dataframe dfwith column schoolas factor
我有一个df以school列为因子的 Pandas 数据框
Name school
A An
B Bn
C Bn
How can I change the levels of the schoolcolumn from ('An', 'Bn') to ('Bn', 'An') in python?
如何school在 python中将列的级别从 ('An', 'Bn') 更改为 ('Bn', 'An')？
R equivalent is
R 等价物是
levels(df$school) = c('Bn','An')
回答by Andy Hayden
You can use reorder_categories(you pass in the sorted factors):
您可以使用reorder_categories（您传入已排序的因素）：
In [11]: df
Out[11]:
Name school
0 A An
1 B Bn
2 C Bn
In [12]: df['school'] = df['school'].astype('category')
In [13]: df['school']
Out[13]:
0 An
1 Bn
2 Bn
Name: school, dtype: category
Categories (2, object): [An, Bn]
In [14]: df['school'].cat.reorder_categories(['Bn', 'An'])
Out[14]:
0 An
1 Bn
2 Bn
dtype: category
Categories (2, object): [Bn, An]
You can do this inplace:
您可以就地执行此操作：
In [21]: df['school'].cat.reorder_categories(['Bn', 'An'], inplace=True)
In [22]: df['school']
Out[22]:
0 An
1 Bn
2 Bn
Name: school, dtype: category
Categories (2, object): [Bn, An]
回答by HYRY
You can set cat.categories:
您可以设置cat.categories：
import pandas as pd
school = pd.Series(["An", "Bn", "Bn"])
school = school.astype("category")
school.cat.categories = ["Bn", "An"]
回答by Alexander
As a general solution, you can remap using a dictionary:
作为通用解决方案，您可以使用字典重新映射：
df = pd.DataFrame({'Name': ['A', 'B', 'C'],
'school': ['An', 'Bn', 'Bn']})
d = {'An': 'Bn', 'Bn': 'An'}
df['school'] = df.school.map(d)
>>> df
Name school
0 A Bn
1 B An
2 C An
