按 python 中的子串匹配两个数据帧

Question

我有两个大数据框（1000行），我需要通过子字符串匹配它们，例如：

df1:

Id    Title
1     The house of pump
2     Where is Andijan
3     The Joker
4     Good bars in Andijan
5     What a beautiful house

df2:

Keyword
house
andijan
joker

预期的输出是：

Id    Title                    Keyword
1     The house of pump        house
2     Where is Andijan         andijan
3     The Joker                joker
4     Good bars in Andijan     andijan
5     What a beautiful house   house

现在，我已经编写了一种非常低效的方法来匹配它，但是对于数据帧的实际大小，它运行持续了很长时间：

for keyword in df2.to_dict(orient='records'):
    df1['keyword'] = np.where(creative_df['title'].str.contains(keyword['keyword']), keyword['keyword'], df1['keyword'])

现在，我确定有一种更 pandas 更友好、更有效的方法来做同样的事情，而且运行在合理的时间内完成。

Answer 1

让我们试试findall

import re
df1['new'] = df1.Title.str.findall('|'.join(df2.Keyword.tolist()),flags= re.IGNORECASE).str[0]
df1
   Id                   Title      new
0   1       The house of pump    house
1   2        Where is Andijan  Andijan
2   3               The Joker    Joker
3   4    Good bars in Andijan  Andijan
4   5  What a beautiful house    house

Answer 2

进一步开发@BENY 的解决方案，以便能够为每个标题获取多个关键字：

regex = '|'.join(keywords['Keyword'])
keywords = df['Title'].str.findall(regex, flags=re.IGNORECASE)
keywords_exploded = pd.DataFrame(keywords.explode().dropna())
df.merge(keywords_exploded, left_index=True, right_index=True)

按 python 中的子串匹配两个数据帧

Match two data frames by substring in python

python

optimization

performance

string-matching

pandas