python中pandas数据框使用applymap替换数据的一个例子

python中pandas数据框使用applymap替换数据的一个例子

代码在Python Console中操作的文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html

要求

data = {'Fruit':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
        'Weight':[23, 45, 31, 28, 62, 12, 44, 42, 23, 32], 
        'Taste':['good', 'so so', 'very good', 'awful', 'awful', 'very good', 'good', 'good', 'awful', 'good'],
       'Look':['good', 'good', 'so so', 'awful', 'so so', 'very good', 'very good', 'good', 'awful', 'good']} 

将上面数据中的“ Taste”和“ Look”中的值从“很好”更改为4-1。如果只有一列，我会写：文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html

'very good': 4, 'good': 3, 'so so': 2, 'awful': 1文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html

整理

我们使用pandas整理一下原始数据，看上去更清晰文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html

data = {'Fruit':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'Weight':[23, 45, 31, 28, 62, 12, 44, 42, 23, 32], 'Taste':['good', 'so so', 'very good', 'awful', 'awful', 'very good', 'good', 'good', 'awful', 'good'], 'Look':['good', 'good', 'so so', 'awful', 'so so', 'very good', 'very good', 'good', 'awful', 'good']}
import pandas as pd
data_=pd.DataFrame(data)
data_
   Fruit  Weight      Taste       Look
0      1      23       good       good
1      2      45      so so       good
2      3      31  very good      so so
3      4      28      awful      awful
4      5      62      awful      so so
5      6      12  very good  very good
6      7      44       good  very good
7      8      42       good       good
8      9      23      awful      awful
9     10      32       good       good

 文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html

解决

完整的代码如下文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html

data = {'Fruit':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'Weight':[23, 45, 31, 28, 62, 12, 44, 42, 23, 32], 'Taste':['good', 'so so', 'very good', 'awful', 'awful', 'very good', 'good', 'good', 'awful', 'good'], 'Look':['good', 'good', 'so so', 'awful', 'so so', 'very good', 'very good', 'good', 'awful', 'good']}
import pandas as pd
data_=pd.DataFrame(data)
data_
   Fruit  Weight      Taste       Look
0      1      23       good       good
1      2      45      so so       good
2      3      31  very good      so so
3      4      28      awful      awful
4      5      62      awful      so so
5      6      12  very good  very good
6      7      44       good  very good
7      8      42       good       good
8      9      23      awful      awful
9     10      32       good       good
d = {'very good': 4, 'good': 3, 'so so': 2, 'awful': 1}
data_[["Taste","Look"]]=data_[["Taste","Look"]].applymap(d.get)
data_
   Fruit  Weight  Taste  Look
0      1      23      3     3
1      2      45      2     3
2      3      31      4     2
3      4      28      1     1
4      5      62      1     2
5      6      12      4     4
6      7      44      3     4
7      8      42      3     3
8      9      23      1     1
9     10      32      3     3

 文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html 文章源自公式库网-https://www.gongshiku.com/html/202303/pythonzhongpandasshujukuangshiyongapplymaptihuanshujudeyigelizi.html