如何删除df列python中最后一个句点之后的字符?

熊猫不会让计算速度更快。这个正则表达式可能适合您:

s.str.extract(r'(\w+)(\.\w{2,3})+$')[0]

但更好的解决方案是: Extract domain from URL in python

你可以用 pd.DataFrame. apply 以及一个lambda函数,该函数在拆分后返回最长的元素(基于中的注释) richardec's answer ):

In [1]: import pandas as pd
In [2]: d = {
   ...:     'domains': [
   ...:         'common_name',
   ...:         'www.amazon.com',
   ...:         'amazon.com',
   ...:         'subexample.amazon.com',
   ...:         'walmart.en',
   ...:         'walmart.uk',
   ...:         'michigan.edu',
   ...:         'tkoutletstore.co.uk',
   ...:         'tillyandotto.com.au',
   ...:     ]
   ...: }
   ...: df = pd.DataFrame(data=d)
   ...: df
Out[2]: 
                 domains
0            common_name
1         www.amazon.com
2             amazon.com
3  subexample.amazon.com
4             walmart.en
5             walmart.uk
6           michigan.edu
7    tkoutletstore.co.uk
8    tillyandotto.com.au
In [3]: df['extracted'] = df['domains'].apply(lambda d: max(d.split('.'), key=len))

In [4]: df
Out[4]: 
                 domains      extracted
0            common_name    common_name
1         www.amazon.com         amazon
2             amazon.com         amazon
3  subexample.amazon.com     subexample
4             walmart.en        walmart
5             walmart.uk        walmart
6           michigan.edu       michigan
7    tkoutletstore.co.uk  tkoutletstore
8    tillyandotto.com.au   tillyandotto

这应该是有效的:

df['col'] = df['col'].str.rsplit('.', n=1).str[0].str.split('.').str[-1]

输出:

>>> df
           col
0  common_name
1       amazon
2       amazon
3       amazon
4      walmart
5      walmart
6     michigan