从长字符串列创建布尔列。多选题

Question

我在数据框中有一列，如下所示。这是一个调查问题，允许您选择多个答案。我想要的是一个新的专栏，区分一个人是否以英语为母语。

当english后面没有(fluently)或其他任何东西时，一个人就是以英语为母语的人。

35874    english (fluently), chinese (fluently), spanis...
40792                                              english
39405                                   english (fluently)
51413                                              english
49929    english (fluently), french (fluently), german ...
7147     english (fluently), japanese (poorly), thai (o...
17019                                              english
12321    english (okay), french (fluently), german (poo...
20974    english, sign language (fluently), spanish (po...
6134                                    english (fluently)
43291    english (fluently), german (fluently), french ...
7023     english (fluently), french (poorly), japanese ...
47637         english, french, hawaiian, japanese, spanish
56354    english, spanish (okay), other (fluently), fre...
5094     english (fluently), lisp (okay), japanese (poo...
14654                                              english
37842                                   english (fluently)
11962                                     english, chinese
37021                                              english
30360                                   english (fluently)
43865                                     english, spanish
29744    english (fluently), italian (fluently), spanis...
37088     english (fluently), dutch (okay), spanish (okay)
52986                                              english
59871                                              english
28376                                              english
3973                  english (fluently), spanish (poorly)
46417    english (fluently), spanish (fluently), french...
7986            english (fluently), ancient greek (poorly)
9919                               english, spanish (okay)

我尝试了正则表达式和split(',',expand=True)，但仍然有困难。

Answer 1

使用str.split(',')并检查列表中是否有'English'`：

df2['speaks'] = df2['speaks'].astype(str)
df2['English Native?'] = df2['speaks'].str.split(',').apply(lambda x: 'Native' if 'english' in x else 'Not Native')
df2
Out[1]: 
        0                                             speaks  English Native?
0   35874  english (fluently), chinese (fluently), spanis...  Not Native
1   40792                                            english      Native
2   39405                                 english (fluently)  Not Native
3   51413                                            english      Native
4   49929  english (fluently), french (fluently), german ...  Not Native
5    7147  english (fluently), japanese (poorly), thai (o...  Not Native
6   17019                                            english      Native
7   12321  english (okay), french (fluently), german (poo...  Not Native
8   20974  english, sign language (fluently), spanish (po...      Native
9    6134                                 english (fluently)  Not Native
10  43291  english (fluently), german (fluently), french ...  Not Native
11   7023  english (fluently), french (poorly), japanese ...  Not Native
12  47637       english, french, hawaiian, japanese, spanish      Native
13  56354  english, spanish (okay), other (fluently), fre...      Native
14   5094  english (fluently), lisp (okay), japanese (poo...  Not Native
15  14654                                            english      Native
16  37842                                 english (fluently)  Not Native
17  11962                                   english, chinese      Native
18  37021                                            english      Native
19  30360                                 english (fluently)  Not Native
20  43865                                   english, spanish      Native
21  29744  english (fluently), italian (fluently), spanis...  Not Native
22  37088   english (fluently), dutch (okay), spanish (okay)  Not Native
23  52986                                            english      Native
24  59871                                            english      Native
25  28376                                            english      Native
26   3973               english (fluently), spanish (poorly)  Not Native
27  46417  english (fluently), spanish (fluently), french...  Not Native
28   7986         english (fluently), ancient greek (poorly)  Not Native
29   9919                            english, spanish (okay)      Native