Python 使用字典替换列值

我有一个数据框架，其中性别预期为男性或女性

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，在剩下的选项中只保留男性/女性和“其他”类别。预期产出：

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

您可以使用：

corrections={'mail':'male', 'maela':'male', 'maae':'male', 'male':'male', 'female':'female'}
df11[['Gender']] = df11[['Gender']].applymap(corrections.get).fillna('other')
print (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

编辑：

---

对于替换，只有一列更好ᴏʟᴅsᴘᴇᴇᴅ'这是我的回答。如果要替换多个列，最好是

applymap

将另外两个虚拟条目添加到

更正

记录中：

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

现在，使用

map

和

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

---

是的，有时我把它复杂化了