pandas 中的字符串替换方法给出了意外的结果

Question

I'm trying to change the values of 35 000 rows on a DataFrame using the string replace method. However, my solution does not return what I want and I can't figure out why it fails. Here is a simple example of the data frame:

import pandas as pd

liste = ['1_8','1_8','1_8','1_8','1_8','1_8','1_9', '1_9', '1_9','1_9','1_10','1_10','1_10','1_10','1_10','1_10','1_11','1_11']
mycooldataframe = pd.DataFrame(liste, columns =['bib']) 

print(mycooldataframe)

    bib
0   1_8
1   1_8
2   1_8
3   1_8
4   1_8
5   1_8
6   1_9
7   1_9
8   1_9
9   1_9
10  1_10
11  1_10
12  1_10
13  1_10
14  1_10
15  1_10
16  1_11
17  1_11

I want to change 1_8 so that it becomes 108. Similarly, I want to replace 1_11 so that it becomes 110. I have tried to solve this using:

mycooldataframe['bib'] = mycooldataframe['bib'].str.replace('1_8', '108')

, which works fine. It gives me 108. But if I use:

mycooldataframe['bib'] = mycooldataframe['bib'].str.replace('1_10', '110')

that gives me 1010. Why doesn't it work? Is there a better way to solve this problem. I appreciate your help.

Answer 1

The code in your example works as expected. If you're having issues with other cases, you can try using a dictionary:

value_map = {'1_8':'108', '1_10':'110'}
mycooldataframe= mycooldataframe.replace(value_map)