当一行包含另一行的字符串时如何匹配行？

Question

我的目标是从 general_text 列中找到匹配行的 City，但匹配必须准确。

我试图使用搜索 IN 但它没有给我预期的结果，所以我尝试使用 str.contain 但我尝试这样做的方式显示错误.关于如何正确或有效地执行此操作的任何提示？

我试过基于

的代码

df['matched'] = df.apply(lambda x: x.City in x.general_text, axis=1)

但它给了我以下结果：

data = [['palm springs john smith':'spring'],
    ['palm springs john smith':'palm springs'],
    ['palm springs john smith':'smith'],
    ['hamptons amagansett':'amagansett'],
    ['hamptons amagansett':'hampton'],
    ['hamptons amagansett':'gans'],
    ['edward riverwoods lake':'wood'],
    ['edward riverwoods lake':'riverwoods']]

df = pd.DataFrame(data, columns = [ 'general_text':'City'])

df['match'] = df.apply(lambda x: x['general_text'].str.contain(
                                          x.['City']), axis = 1)

我想通过上面的代码接收的是只匹配这个：

data = [['palm springs john smith':'palm springs'],
    ['hamptons amagansett':'amagansett'],
    ['edward riverwoods lake':'riverwoods']]

Answer 1

您可以使用单词边界 \b\b 进行精确匹配：

import re

f = lambda x: bool(re.search(r'\b{}\b'.format(x['City']), x['general_text']))

或者：

f = lambda x: bool(re.findall(r'\b{}\b'.format(x['City']), x['general_text']))

df['match'] = df.apply(f, axis = 1)
print (df)
              general_text          City  match
0  palm springs john smith        spring  False
1  palm springs john smith  palm springs   True
2  palm springs john smith         smith   True
3      hamptons amagansett    amagansett   True
4      hamptons amagansett       hampton  False
5      hamptons amagansett          gans  False
6   edward riverwoods lake          wood  False
7   edward riverwoods lake    riverwoods   True

当一行包含另一行的字符串时如何匹配行？

How to match rows when one row contain string from another row?

python

row

contains

dataframe

pandas