使用字典替换列值 [英] Replace column values using a dictionary

问题描述

我有这个数据框，预计性别将是男性或女性.

I have this dataframe where gender is expected to be male or female.

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=" "  )

我可以使用字典来纠正拼写错误.

I can correct the spelling mistakes using dictionary.

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:

But I am looking for a way to keep only male / female and "other" category for rest of the options. Expected output:

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

推荐答案

向您的corrections字典添加另外两个虚拟条目:

Add another two dummy entries to your 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

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