使用字典替换列值

我有这个数据框，其中性别预计为男性或女性。

from io import StringIO
import pandas as pd

audit_trail = StringIO('''
course_id AcademicYear_to months TotalFee Gender
260 2017 24 100 male
260 2018 12 140 male
274 2016 36 300 mail
274 2017 24 340 female
274 2018 12 200 animal
285 2017 24 300 bird
285 2018 12 200 maela
''')

df11 = pd.read_csv(audit_trail, sep=" "  )

我可以使用字典纠正拼写错误。

corrections={'mail':'male', 'mael':'male', 'maae':'male'}
df11.Gender.replace(corrections)

但我正在寻找一种方法，仅保留男性/女性和“其他”类别的其余选项。预期输出：

0      male
1      male
2      male
3    female
4    other
5    other
6      male
Name: Gender, dtype: object

添加另外两个虚拟条目到您的corrections dict:

corrections = {'male'   : 'male',    # dummy entry for male
               'female' : 'female',  # dummy entry for female
               'mail'   : 'male', 
               'maela'  : 'male', 
               'maae'   : 'male'}

现在，使用map and fillna:

df11.Gender = df11.Gender.map(corrections).fillna('other')
df11

   course_id  AcademicYear_to  months  TotalFee  Gender
0        260             2017      24       100    male
1        260             2018      12       140    male
2        274             2016      36       300    male
3        274             2017      24       340  female
4        274             2018      12       200   other
5        285             2017      24       300   other
6        285             2018      12       200    male