Pandas DataFrame:在我要保留的字符串前后删除字符串中不需要的部分 [英] Pandas DataFrame: remove unwanted parts from strings before and after what I want to keep

问题描述

在我的data_cleaner数据集中，我具有(功能)项目ID"列.这标识了项目，并且格式为代码/年/代码".我只对项目的年份感兴趣，所以我想摆脱第一个/之前的所有内容，以及第二个/之后的所有内容.

In my data_cleaner dataset I have the column (feature) 'Project ID'. This identifies the project and it has a format 'code/YEAR/code'. I'm only interested in the project's year so I want to get rid of everything before the first / and everything after the second /.

Project ID  
AGPG/2013/1 
AGPG/2013/10
AGPG/2013/12
AGPG/2013/18
AGPG/2013/19

我最接近的是用

data_cleaner['Project ID'] = data_cleaner['Project ID'].str.strip("AGPG")

(但由于还有其他字母，因此这是不可升级的)

(but down the line there are other letters so this is not escalable)

然后我做了

data_cleaner['Project ID'] = data_cleaner['Project ID'].str.strip('/')

这摆脱了第一点，我无法摆脱那一年之后的情况.

This got rid of the first bit, I can't manage to get rid of what's after the year.

Project ID  
2013/1  
2013/10
2013/12
2013/18
2013/19

我阅读了这篇文章，但没有帮助我 Pandas DataFrame:从一列中的字符串中删除不需要的部分

I read this post but didn't help me Pandas DataFrame: remove unwanted parts from strings in a column

推荐答案

我认为需要或 extract 由正则表达式表示-/(\d{4})/表示获取长度为4在//之间的数字:

Or extract by regex - /(\d{4})/ means get numeric with length 4 between //:

data_cleaner['Project ID'] = data_cleaner['Project ID'].str.extract('/(\d{4})/', expand=False)

print (data_cleaner)
  Project ID
0       2013
1       2013
2       2013
3       2013
4       2013

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