July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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146
｜
第
7
章
确匹配包含芝士汉堡（
cheeseburger
）的回答，即使二者是完全相同的。你能用我们已经学
过的另一个方法来做到这一点么？
FuzzyWuzzy
提供的最后一个匹配方法是
process
模块。如果你只有有限的几个选项和杂乱
的数据，这个模块是很有用的。比如说回答只有
yes
、
no
、
maybe
和
decline to comment
四
种。我们看一下如何对其匹配：
from
fuzzywuzzy
import
process
choices = ['Yes', 'No', 'Maybe', 'N/A']
process.extract('ya', choices, limit=2)
➊
process.extractOne('ya', choices)
➋
process.extract('nope', choices, limit=2)
process.extractOne('nope', choices)
➊
利用
FuzzyWuzzy
的
extract
方法，将字符串与可能匹配的列表依次比较。函数返回的
是
choices
列表中两个可能的匹配。
➋
利用
FuzzyWuzzy
的
extractOne
方法，返回
choices
列表中与我们的字符串对应的最佳
匹配。
啊哈！给定几个单词，我们事先知道其“含义”相同，
process
可以找出最佳猜测——在
上面的例子中也是正确的猜测。
extract
返回的是带有比例的元组，代码对回答字符串进
行解析，并对其相似之处和不同之处作比较。
extractOne ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.