python fuzzywuzzy模块模糊字符串匹配详细用法

> from fuzzywuzzy import fuzz
> from fuzzywuzzy import process

> fuzz.ratio("this is a test", "this is a test!")
out 97
> fuzz.partial_ratio("this is a test", "this is a test!")
out 100

> fuzz._process_and_sort(s, force_ascii, full_process=True)

> fuzz._token_sort(s1, s2, partial=True, force_ascii=True, full_process=True)

> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
out 100

fuzz.partial_token_sort_ratio(s1, s2, force_ascii=True, full_process=True)

> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
out 100

fuzz._token_set(s1, s2, partial=True, force_ascii=True, full_process=True)

fuzz.partial_token_set_ratio(s1, s2, force_ascii=True, full_process=True)

fuzz.QRatio(s1, s2, force_ascii=True, full_process=True)

fuzz.UQRatio(s1, s2, full_process=True)

fuzz.WRatio(s1, s2, force_ascii=True, full_process=True)

UWRatio(s1, s2, full_process=True)

> choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
> process.extract("new york jets", choices, limit=2)
 [('New York Jets', 100), ('New York Giants', 78)]
> process.extractOne("cowboys", choices)
 ("Dallas Cowboys", 90)

> process.extract(query, choices, processor=default_processor, scorer=default_scorer, limit=5)

> process.extractWithoutOrder(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0)

> process.extractBests(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0, limit=5)

> process.extractOne(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0)

process.dedupe(contains_dupes, threshold=70, scorer=fuzz.token_set_ratio)