这是一个包含数据和非ascii字符的列
Summary 1
United Kingdom - ��Global Consumer Technology - ��American Express
United Kingdom - ��VP Technology - Founder - ��Hogarth Worldwide
Aberdeen - ��SeniorCore Analysis Specialist - ��COREX Group
London, - ��ED, Equit Technology, London - ��Morgan Stanley
United Kingdom - ��Chief Officer, Group Technology - ��BP
如何拆分它们并保存在不同的列中
我使用的代码是：
import io
import pandas as pd
df = pd.read_csv("/home/vipul/Desktop/dataminer.csv", sep='\s*\+.*?-\s*')
df = df.reset_index()
df.columns = ["First Name", "Last Name", "Email", "Profile URL", "Summary 1", "Summary 2"]
df.to_csv("/home/vipul/Desktop/new.csv")
答案 0 :(得分：2)
说，你有一个这样的系列专栏：
s
0 United Kingdom - ��Global Consumer Technolog...
1 United Kingdom - ��VP Technology - Founder -...
2 Aberdeen - ��SeniorCore Analysis Specialist ...
3 London, - ��ED, Equit Technology, London - �...
4 United Kingdom - ��Chief Officer, Group Tech...
Name: Summary 1, dtype: object
选项1
展开this answer，您可以使用str.split拆分非ascii字符：
s.str.split(r'-\s*[^\x00-\x7f]+', expand=True)
0 1 2
0 United Kingdom Global Consumer Technology American Express
1 United Kingdom VP Technology - Founder Hogarth Worldwide
2 Aberdeen SeniorCore Analysis Specialist COREX Group
3 London, ED, Equit Technology, London Morgan Stanley
4 United Kingdom Chief Officer, Group Technology BP
选项2
str.extractall + unstack：
s.str.extractall('([\x00-\x7f]+)')[0].str.rstrip(r'- ').unstack()
match 0 1 2
0 United Kingdom Global Consumer Technology American Express
1 United Kingdom VP Technology - Founder Hogarth Worldwide
2 Aberdeen SeniorCore Analysis Specialist COREX Group
3 London, ED, Equit Technology, London Morgan Stanley
4 United Kingdom Chief Officer, Group Technology BP
答案 1 :(得分：0)
另一种方法：
a
0 United Kingdom - ��Global Consumer Technolog...
1 United Kingdom - ��VP Technology - Founder -...
2 Aberdeen - ��SeniorCore Analysis Specialist ...
3 London, - ��ED, Equit Technology, London - �...
4 United Kingdom - ��Chief Officer, Group Tech...
使用此函数使用ord内置函数提取assci char（其中Unicode代码点优于128）
def extract_ascii(x):
string_list = filter(lambda y : ord(y) < 128, x)
return ''.join(string_list)
并将其应用于列。
df1.a.apply(extract_ascii).str.split('-', expand=True)
结果如下：
0 1 2 3
0 United Kingdom Global Consumer Technology American Express None
1 United Kingdom VP Technology Founder Hogarth Worldwide
2 Aberdeen SeniorCore Analysis Specialist COREX Group None
3 London, ED, Equit Technology, London Morgan Stanley None
4 United Kingdom Chief Officer, Group Technology BP None