pandas 在 Series 中查找共同的字符串并返回关键字 [英] pandas find strings in common among Series and return keywords

问题描述

我想改进之前关于搜索字符串的问题在基于一系列关键字的熊猫系列中.我现在的问题是如何将在 DataFrame 行中找到的关键字作为新列.关键字系列w"是:

I would like to improve this previous question about searching strings in pandas Series based on a Series of keywords. My question now is how to get the keywords found in the DataFrame rows as a new column. The Keywords Series "w" is:

Skilful
Wilful
Somewhere
Thing
Strange

而数据帧df"是:

User_ID;Tweet
01;hi all
02;see you somewhere
03;So weird
04;hi all :-)
05;next big thing
06;how can i say no?
07;so strange
08;not at all

以下解决方案可以很好地屏蔽 DataFrame:

The following solution worked well for masking the DataFrame:

import re
r = re.compile(r'.*({}).*'.format('|'.join(w.values)), re.IGNORECASE)
masked = map(bool, map(r.match, df['Tweet']))
df['Tweet_masked'] = masked

并返回:

   User_ID              Tweet Tweet_masked
0        1             hi all        False
1        2  see you somewhere         True
2        3           So weird        False
3        4         hi all :-)        False
4        5     next big thing         True
5        6  how can i say no?        False
6        7         so strange         True
7        8         not at all        False

现在我正在寻找这样的结果:

Now I'm looking for a result like this:

User_ID;Tweet;Keyword
01;hi all;None
02;see you somewhere;somewhere
03;So weird;None
04;hi all :-);None
05;next big thing;thing
06;how can i say no?;None
07;so strange;strange
08;not at all;None

预先感谢您的支持.

推荐答案

如何更换

masked = map(bool, map(r.match, df['Tweet']))

与

masked = [m.group(1) if m else None for m in map(r.match, df['Tweet'])]

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