使用python匹配模糊字符串

Question

我有一个用于EG的培训数据集。

Letter    Word
A         Apple
B         Bat
C         Cat
D         Dog
E         Elephant

我需要检查诸如

AD    Apple Dog
AE    Applet Elephant
DC    Dog Cow
EB    Elephant Bag
AED   Apple Elephant Dog  
D     Door                
ABC   All Bat Cat         

实例ad，ae，eb之类的数据框架几乎是准确的（Apple和Applet彼此靠近，蝙蝠和袋子相似），但是DC 不匹配。

所需的输出：

Letters    Words               Status
AD         Apple Dog           Accept
AE         Applet Elephant     Accept
DC         Dog Cow             Reject
EB         Elephant Bag        Accept
AED        Apple Elephant Dog  Accept
D          Door                Reject
ABC        All Bat Cat         Accept

ABC被接受，因为3个单词中有2个匹配。

接受的单词需要匹配70％（模糊匹配）。但是，阈值可能会变化。 我如何使用Python找到这些匹配。

Answer 1

您可以使用 thefuzz 解决您的问题：

# Python env: pip install thefuzz
# Conda env: conda install thefuzz
from thefuzz import fuzz

THRESHOLD = 70

df2['Others'] = (df2['Letters'].agg(list).explode().reset_index()
                     .merge(df1, left_on='Letters', right_on='Letter')
                     .groupby('index')['Word'].agg(' '.join))

df2['Ratio'] = df2.apply(lambda x: fuzz.ratio(x['Words'], x['Others']), axis=1)
df2['Status'] = np.where(df2['Ratio'] > THRESHOLD, 'Accept', 'Reject')

输出：输出：

>>> df2
  Letters               Words              Others  Ratio  Status
0      AD           Apple Dog           Apple Dog    100  Accept
1      AE     Applet Elephant      Apple Elephant     97  Accept
2      DC             Dog Cow             Dog Cat     71  Accept
3      EB        Elephant Bag        Elephant Bat     92  Accept
4     AED  Apple Elephant Dog  Apple Dog Elephant     78  Accept
5       D                Door                 Dog     57  Reject
6     ABC         All Bat Cat       Apple Cat Bat     67  Reject