精华内容
下载资源
问答
  • 2020-12-02 16:24:53

    这里有几个级别的优化可以将这个问题从O(n^2)转化为较低的时间复杂度。预处理:在第一个过程中对列表进行排序,为每个字符串创建一个输出映射,映射的键可以是规范化的字符串。

    规范化可能包括:小写转换

    没有空格,删除特殊字符

    如果可能,将unicode转换为ascii等效值,请使用unicodedata.normalize或unidecode模块)

    这将导致"Andrew H Smith"、"andrew h. smith"、"ándréw h. smith"生成相同的键"andrewhsmith",并将您的一百万个名称集减少为更小的唯一/相似的分组名称集。

    可以使用此utlity method规范化字符串(但不包括unicode部分):def process_str_for_similarity_cmp(input_str, normalized=False, ignore_list=[]):

    """ Processes string for similarity comparisons , cleans special characters and extra whitespaces

    if normalized is True and removes the substrings which are in ignore_list)

    Args:

    input_str (str) : input string to be processed

    normalized (bool) : if True , method removes special characters and extra whitespace from string,

    and converts to lowercase

    ignore_list (list) : the substrings which need to be removed from the input string

    Returns:

    str : returns processed string

    """

    for ignore_str in ignore_list:

    input_str = re.sub(r'{0}'.format(ignore_str), "", input_str, flags=re.IGNORECASE)

    if normalized is True:

    input_str = input_str.strip().lower()

    #clean special chars and extra whitespace

    input_str = re.sub("\W", "", input_str).strip()

    return input_str现在,如果相似的字符串的规范化键相同,它们就已经位于同一个bucket中。

    为了进一步比较,您只需要比较键,而不需要比较名称。例如

    andrewhsmith和andrewhsmeeth,因为这种相似性

    的名称需要模糊字符串匹配

    以上比较。

    Bucketing:您真的需要将5个字符的键与9个字符的键进行比较,看是否95%匹配吗?不,你不知道。

    所以你可以创建匹配字符串的桶。e、 g.5个字符的名称将与4-6个字符的名称匹配,6个字符的名称将与5-7个字符的名称匹配等。n个字符的密钥的n+1、n-1个字符限制对于大多数实际匹配来说是一个相当好的存储桶。

    开始匹配:大多数名称变体的第一个字符将以规范化格式(例如Andrew H Smith、ándréw h. smith和Andrew H. Smeeth分别生成键andrewhsmith、andrewhsmith和andrewhsmeeth)。

    它们通常在第一个字符中没有区别,因此您可以将以a开头的键与以a开头并属于长度存储桶的其他键运行匹配。这将大大缩短您的匹配时间。不需要将键andrewhsmith与bndrewhsmith匹配,因为这样的名称变化与第一个字母很少存在。

    然后可以使用method(或FuzzyWuzzy模块)行中的某些内容来查找字符串相似性百分比,可以排除jaro_winkler或difflib中的一个来优化速度和结果质量:def find_string_similarity(first_str, second_str, normalized=False, ignore_list=[]):

    """ Calculates matching ratio between two strings

    Args:

    first_str (str) : First String

    second_str (str) : Second String

    normalized (bool) : if True ,method removes special characters and extra whitespace

    from strings then calculates matching ratio

    ignore_list (list) : list has some characters which has to be substituted with "" in string

    Returns:

    Float Value : Returns a matching ratio between 1.0 ( most matching ) and 0.0 ( not matching )

    using difflib's SequenceMatcher and and jellyfish's jaro_winkler algorithms with

    equal weightage to each

    Examples:

    >>> find_string_similarity("hello world","Hello,World!",normalized=True)

    1.0

    >>> find_string_similarity("entrepreneurship","entreprenaurship")

    0.95625

    >>> find_string_similarity("Taj-Mahal","The Taj Mahal",normalized= True,ignore_list=["the","of"])

    1.0

    """

    first_str = process_str_for_similarity_cmp(first_str, normalized=normalized, ignore_list=ignore_list)

    second_str = process_str_for_similarity_cmp(second_str, normalized=normalized, ignore_list=ignore_list)

    match_ratio = (difflib.SequenceMatcher(None, first_str, second_str).ratio() + jellyfish.jaro_winkler(unicode(first_str), unicode(second_str)))/2.0

    return match_ratio

    更多相关内容
  • fuzzywuzzy:Python中字符串模糊匹配
  • Python实现字符串模糊匹配

    千次阅读 2021-12-09 09:48:37
    Python的difflib库get_close_matches方法,包含四个参数: · x:被匹配字符串。 · words:去匹配字符串列表。 · n,前topn个最佳匹配返回,默认为3。 · cutoff:匹配度大小,为[0, 1]浮点数,默认数值...

    Python的difflib库中get_close_matches方法,包含四个参数:

    · x:被匹配的字符串。

    · words:去匹配的字符串列表。

    · n,前topn个最佳匹配返回,默认为3。

    · cutoff:匹配度大小,为[0, 1]浮点数,默认数值0.6。

    import difflib
    
    list1 = ['ape', 'apple', 'peach', 'puppy']
    difflib.get_close_matches('appel', list1)

    import keyword
    
    difflib.get_close_matches('wheel', keyword.kwlist)

    difflib.get_close_matches('pineapple', keyword.kwlist)

     

    difflib.get_close_matches('accept', keyword.kwlist)

     

    利用这个功能就能够实现SQL中的LIKE模糊查询。 

    展开全文
  • github主页 导入: >>> from fuzzywuzzy import fuzz >>> from fuzzywuzzy import process 1) >>> fuzz.ratio(this is a test, ...fuzz.partial_ratio()对位置敏感,搜索匹配。 2) >>> fuzz._process_and_sort(s,
  • 我们的使命是从互联网的每个角落提取活动门票,... 我们已经建立了一个“模糊字符串匹配例程库来帮助我们。 还有好消息! 我们正在开源它。 该库名为“Fuzzywuzzy”,代码纯python,仅依赖(优秀)difflib python库。
  • python实现字符串模糊匹配
  • 今天小编就为大家分享一篇对python 匹配字符串开头和结尾的方法详解,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
  • Python字符串模糊匹配库FuzzyWuzzy

    千次阅读 2021-01-25 13:45:51
    Python字符串模糊匹配库FuzzyWuzzy 在计算机科学,字符串模糊匹配(fuzzy string matching)是一种近似地(而不是精确地)查找与模式匹配的字符串的技术。换句话说,字符串模糊匹配是一种搜索,即使用户拼错...

    Python字符串模糊匹配库FuzzyWuzzy

    在计算机科学中,字符串模糊匹配(fuzzy string matching)是一种近似地(而不是精确地)查找与模式匹配的字符串的技术。换句话说,字符串模糊匹配是一种搜索,即使用户拼错单词或只输入部分单词进行搜索,也能够找到匹配项。因此,它也被称为字符串近似匹配。

    字符串模糊搜索可用于各种应用程序,例如:

    • 拼写检查和拼写错误纠正程序。例如,用户在Google中键入“Missisaga”,将返回文字为“Showing results for mississauga”的点击列表。也就是说,即使用户输入缺少字符、有多余的字符或者有其他类型的拼写错误,搜索查询也会返回结果。
    • 重复记录检查。例如,由于名称拼写不同(例如Abigail Martin和Abigail Martinez)在数据库中被多次列出。

    这篇文章将解释字符串模糊匹配及其用例,并使用Python中Fuzzywuzzy库给出示例。

    使用FuzzyWuzzy合并酒店房型

    每个酒店都有自己的命名方法来命名它的房间,在线旅行社(OTA)也是如此。例如,同一家酒店的一间客房Expedia将之称为“Studio, 1 King Bed with Sofa Bed, Corner”,Booking.com(缤客)则简单地将其显示为“Corner King Studio”。不能说有谁错了,但是当我们想要比较OTA之间的房价时,或者一个OTA希望确保另一个OTA遵循费率平价协议时(rate parity agreement),这可能会导致混乱。换句话说,为了能够比较价格,我们必须确保我们进行比较的东西是同一类型的。对于价格比较网站和应用程序来说,最令人头条的问题之一就是试图弄清楚两个项目(比如酒店房间)是否是同一事物。

    Fuzzywuzzy是一个Python库,使用编辑距离(Levenshtein Distance)来计算序列之间的差异。为了演示,我创建了自己的数据集,也就是说,对于同一酒店物业,我从Expedia拿一个房间类型,比如说“Suite, 1 King Bed (Parlor)”,然后我将它与Booking.com中的同类型房间匹配,即“King Parlor Suite”。只要有一点经验,大多数人都会知道他们是一样的。按照这种方法,我创建了一个包含100多对房间类型的小数据集,可以访问Github下载

    我们使用这个数据集测试Fuzzywuzzy的做法。换句话说,我们使用Fuzzywuzzy来匹配两个数据源之间的记录。

    import pandas as pd
    df = pd. read_csv ( '../input/room_type.csv' )
    df. head ( 10 )
    import pandas as pd

    df = pd.read_csv(’…/input/room_type.csv’)
    df.head(10)

    import pandas as pd

    df = pd.read_csv(’…/input/room_type.csv’)
    df.head(10)

    有几种方法可以比较Fuzzywuzzy中的两个字符串,让我们一个一个地进行尝试。

    ratio ,按顺序比较整个字符串的相似度

    from fuzzywuzzy import fuzz
    fuzz. ratio ( 'Deluxe Room, 1 King Bed' , 'Deluxe King Room' )
    from fuzzywuzzy import fuzz

    fuzz.ratio(‘Deluxe Room, 1 King Bed’, ‘Deluxe King Room’)

    from fuzzywuzzy import fuzz

    fuzz.ratio(‘Deluxe Room, 1 King Bed’, ‘Deluxe King Room’)

    返回结果时62,它告诉我们“Deluxe Room, 1 King Bed”和“Deluxe King Room”的相似度约62%。

    fuzz. ratio ( 'Traditional Double Room, 2 Double Beds' , 'Double Room with Two Double Beds' )
    fuzz. ratio ( 'Room, 2 Double Beds (19th to 25th Floors)' , 'Two Double Beds - Location Room (19th to 25th Floors)' )
    fuzz.ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds') fuzz.ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')
    fuzz.ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds')
    fuzz.ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')

    “Traditional Double Room, 2 Double Beds”和“Double Room with Two Double Beds”的相似度约69%。“Room, 2 Double Beds (19th to 25th Floors)”和“Two Double Beds — Location Room (19th to 25th Floors)”相似度约74%。显然效果不怎么样。事实证明,简单的方法对于词序,缺失或多余词语以及其他类似问题的微小差异太过敏感。

    partial_ratio,比较部分字符串的相似度

    我们仍在使用相同的数据对:

    fuzz. partial_ratio ( 'Deluxe Room, 1 King Bed' , 'Deluxe King Room' )
    fuzz. partial_ratio ( 'Traditional Double Room, 2 Double Beds' , 'Double Room with Two Double Beds' )
    fuzz. partial_ratio ( 'Room, 2 Double Beds (19th to 25th Floors)' , 'Two Double Beds - Location Room (19th to 25th Floors)' )
    fuzz.partial_ratio('Deluxe Room, 1 King Bed','Deluxe King Room') fuzz.partial_ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds') fuzz.partial_ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')
    fuzz.partial_ratio('Deluxe Room, 1 King Bed','Deluxe King Room')
    fuzz.partial_ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds')
    fuzz.partial_ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')

    返回依次69、83、63。对于我的数据集来说,比较部分字符串并不能带来更好的整体效果。让我们尝试下一个。

    token_sort_ratio,忽略单词顺序

    fuzz. token_sort_ratio ( 'Deluxe Room, 1 King Bed' , 'Deluxe King Room' )
    fuzz. token_sort_ratio ( 'Traditional Double Room, 2 Double Beds' , 'Double Room with Two Double Beds' )
    fuzz. token_sort_ratio ( 'Room, 2 Double Beds (19th to 25th Floors)' , 'Two Double Beds - Location Room (19th to 25th Floors)' )
    fuzz.token_sort_ratio('Deluxe Room, 1 King Bed','Deluxe King Room') fuzz.token_sort_ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds') fuzz.token_sort_ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')
    fuzz.token_sort_ratio('Deluxe Room, 1 King Bed','Deluxe King Room')
    fuzz.token_sort_ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds')
    fuzz.token_sort_ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')

    返回依次84、78、83。这是迄今为止最好的。

    token_set_ratio,去重子集匹配

    它与token_sort_ratio类似,但更加灵活。

    fuzz. token_set_ratio ( 'Deluxe Room, 1 King Bed' , 'Deluxe King Room' )
    fuzz. token_set_ratio ( 'Traditional Double Room, 2 Double Beds' , 'Double Room with Two Double Beds' )
    fuzz. token_set_ratio ( 'Room, 2 Double Beds (19th to 25th Floors)' , 'Two Double Beds - Location Room (19th to 25th Floors)' )
    fuzz.token_set_ratio('Deluxe Room, 1 King Bed','Deluxe King Room') fuzz.token_set_ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds') fuzz.token_set_ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')
    fuzz.token_set_ratio('Deluxe Room, 1 King Bed','Deluxe King Room')
    fuzz.token_set_ratio('Traditional Double Room, 2 Double Beds','Double Room with Two Double Beds')
    fuzz.token_set_ratio('Room, 2 Double Beds (19th to 25th Floors)','Two Double Beds - Location Room (19th to 25th Floors)')

    返回依次100、78、97。看来token_set_ratio最适合我的数据。根据这一发现,将token_set_ratio应用到整个数据集。

    def get_ratio ( row ) :
    name1 = row [ 'Expedia' ]
    name2 = row [ 'Booking.com' ]
    return fuzz. token_set_ratio ( name1, name2 )
    rated = df. apply ( get_ratio, axis= 1 )
    rated. head ( 10 )
    greater_than_70_percent = df [ rated > 70 ]
    greater_than_70_percent. count ()
    len ( greater_than_70_percent ) / len ( df )
    def get_ratio(row): name1 = row['Expedia'] name2 = row['Booking.com'] return fuzz.token_set_ratio(name1, name2)

    rated = df.apply(get_ratio, axis=1)
    rated.head(10)

    greater_than_70_percent = df[rated > 70]
    greater_than_70_percent.count()
    len(greater_than_70_percent) / len(df)

    def get_ratio(row):
    name1 = row[‘Expedia’]
    name2 = row[‘Booking.com’]
    return fuzz.token_set_ratio(name1, name2)

    rated = df.apply(get_ratio, axis=1)
    rated.head(10)

    greater_than_70_percent = df[rated > 70]
    greater_than_70_percent.count()
    len(greater_than_70_percent) / len(df)

    当设定相似度> 70时,超过90%的房间对超过这个匹配分数。还很不错!上面只是做了2个文本间的相似度比较,如果存在多个如何处理?可以使用库中提供的 Process类:

    用来返回模糊匹配的字符串和相似度。

    >>> 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)
    >>> 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)
    >>> 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)
    

    FuzzyWuzzy在中文场景下的使用

    FuzzyWuzzy支持对中文进行比较:

    from fuzzywuzzy import fuzz
    from fuzzywuzzy import process
    print ( fuzz. ratio ( "数据挖掘" , "数据挖掘工程师" ))
    title_list = [ "数据分析师" , "数据挖掘工程师" , "大数据开发工程师" , "机器学习工程师" ,
    "算法工程师" , "数据库管理" , "商业分析师" , "数据科学家" , "首席数据官" ,
    "数据产品经理" , "数据运营" , "大数据架构师" ]
    print ( process. extractOne ( "数据挖掘" , title_list ))
    from fuzzywuzzy import fuzz from fuzzywuzzy import process

    print(fuzz.ratio(“数据挖掘”, “数据挖掘工程师”))

    title_list = [“数据分析师”, “数据挖掘工程师”, “大数据开发工程师”, “机器学习工程师”,
    “算法工程师”, “数据库管理”, “商业分析师”, “数据科学家”, “首席数据官”,
    “数据产品经理”, “数据运营”, “大数据架构师”]

    print(process.extractOne(“数据挖掘”, title_list))

    from fuzzywuzzy import fuzz
    from fuzzywuzzy import process

    print(fuzz.ratio(“数据挖掘”, “数据挖掘工程师”))

    title_list = [“数据分析师”, “数据挖掘工程师”, “大数据开发工程师”, “机器学习工程师”,
    “算法工程师”, “数据库管理”, “商业分析师”, “数据科学家”, “首席数据官”,
    “数据产品经理”, “数据运营”, “大数据架构师”]

    print(process.extractOne(“数据挖掘”, title_list))

    仔细查看代码,还是存在的问题:

    • FuzzWuzzy并不会针对中文进行分词
    • 也没有对中文的一些停用词进行过滤

    改进方案,处理前进行中文处理:

    • 繁简转换
    • 中文分词
    • 去除停用词

    参考链接:

    展开全文
  • Python实现模糊匹配

    2018-10-17 22:41:45
    Python实现字符串模糊匹配,‘?’代表一个字符, ‘*’代表任意多个字符。给一段明确字符比如avdjnd 以及模糊字符比如*dj?dji?ejj,判断二者是否匹配。若能匹配输出”Yes”, 否则输出“No”
  • fuzzywuzzy:Python中字符串模糊匹配

    千次阅读 2021-01-29 05:47:38
    FuzzyWuzzyFuzzy string matching like a ... It uses Levenshtein Distance to calculate the differences between sequences in a simple-to-use package.RequirementsPython 2.7 or higherdifflibpython-Leven...

    625332134c6f4d4600884b99daebf603.png

    FuzzyWuzzy

    Fuzzy string matching like a boss. It uses Levenshtein Distance to calculate the differences between sequences in a simple-to-use package.

    Requirements

    Python 2.7 or higher

    difflib

    python-Levenshtein (optional, provides a 4-10x speedup in String Matching, though may result in differing results for certain cases)

    For testing

    pycodestyle

    hypothesis

    pytest

    Installation

    Using PIP via PyPI

    pip install fuzzywuzzy

    or the following to install python-Levenshtein too

    pip install fuzzywuzzy[speedup]

    Using PIP via Github

    pip install git+git://github.com/seatgeek/fuzzywuzzy.git@0.17.0#egg=fuzzywuzzy

    Adding to your requirements.txt file (run pip install -r requirements.txt afterwards)

    git+ssh://git@github.com/seatgeek/fuzzywuzzy.git@0.17.0#egg=fuzzywuzzy

    Manually via GIT

    git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy

    cd fuzzywuzzy

    python setup.py install

    Usage

    >>> from fuzzywuzzy import fuzz

    >>> from fuzzywuzzy import process

    Simple Ratio

    >>> fuzz.ratio("this is a test", "this is a test!")

    97

    Partial Ratio

    >>> fuzz.partial_ratio("this is a test", "this is a test!")

    100

    Token Sort Ratio

    >>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")

    91

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

    100

    Token Set Ratio

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

    84

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

    100

    Process

    >>> 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)

    You can also pass additional parameters to extractOne method to make it use a specific scorer. A typical use case is to match file paths:

    >>> process.extractOne("System of a down - Hypnotize - Heroin", songs)

    ('/music/library/good/System of a Down/2005 - Hypnotize/01 - Attack.mp3', 86)

    >>> process.extractOne("System of a down - Hypnotize - Heroin", songs, scorer=fuzz.token_sort_ratio)

    ("/music/library/good/System of a Down/2005 - Hypnotize/10 - She's Like Heroin.mp3", 61)

    Known Ports

    FuzzyWuzzy is being ported to other languages too! Here are a few ports we know about:

    展开全文
  • 今天小编就为大家分享一篇python 已知一个字符,在一个list找出近似值或相似值实现模糊匹配,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
  • python匹配字符

    千次阅读 2020-12-02 16:24:51
    假设我有一个名为file1.txt的下面的文本文件:adam malejohn malemike malesue female 我有下面的清单 fullname=我希望能够通过文本文件,如果它有任何匹配,修改带有找到的单词的行,输出应如下所示:adam malejohn...
  • python 字符串模糊匹配 Fuzzywuzzy

    千次阅读 2020-12-19 14:10:42
    Python提供fuzzywuzzy模块,不仅可用于计算两个字符串之间的相似度,而且还提供排序接口能从大量候选集中找到最相似的句子。(1)安装pip install fuzzywuzzy(2)接口说明两个模块:fuzz, process,fuzz主要用于两字符...
  • 我正在尝试使用以下代码在Python中模糊合并两个数据框:import pandas as pdfrom fuzzywuzzy import fuzzfrom fuzzywuzzy import processprospectus_data_file = 'file1.xlsx'filings_data_file = 'file2.xlsx'...
  • 基于字符串模糊匹配

    千次阅读 2020-12-02 16:24:54
    编辑距离1.Levenshtein距离是一种计算两个字符串间的差异程度的字符串度量(string metric)。我们可以认为Levenshtein距离就是从一个字符串修改到另一个字符串时,其中编辑单个字符(比如修改、...
  • 使用Levenshtein距离说明在Python和C ++进行快速模糊字符串匹配•安装•使用•许可证说明RapidFuzz是一个快速字符串匹配库,用于使用Levenshtein距离说明在Python和C ++进行快速模糊字符串匹配•安装•使用•...
  • 在计算机科学字符串模糊匹配( fuzzy string matching)是一种近似地(而不是精确地)查找与模式匹配的字符串的技术。换句话说,字符串模糊匹配是一种搜索,即使用户拼错单词或只输入部分单词进行搜索,也能够找到...
  • I'm checking if there are similar results (fuzzy match) in 4 same dataframe columns, and I have the following code, as an example. When I apply it to the real 40.000 rows x 4 columns dataset, keeps ru...
  • 利用FuzzyWuzzy库匹配字符串1. 背景前言2. FuzzyWuzzy库介绍2.1 安装2.1 fuzz模块2.1.1 简单匹配(Ratio)2.1.2 非完全匹配(Partial Ratio)2.1.3 忽略顺序匹配(Token Sort Ratio)2.1.4 去重子集匹配(Token Set ...
  • match、search、findall、finditer简单介绍 菜鸟教程有个入门的教程:https://www.runoob.com/python/python-reg-expressions.html 重叠区域匹配
  • FuzzyWuzzy模糊字符串匹配,就像一个老板。...要求Python 2.7或更高版本difflib python-Levenshtein(可选,在字符串匹配中提供4-10倍的加速,尽管在某些情况下可能导致不同的结果)。 python-Levenshtein太pip inst
  • paths = ['bbb','bbb123ccc'] result = [] for fname in paths: ... #re.search()方法扫描整个字符串,并返回第一个成功的匹配。如果匹配失败,则返回None。 if match1: if match2: result.append(fname)
  • Fuzzyset-用于python模糊字符串集。 Fuzzyset是一种数据结构,对数据执行类似于全文搜索的操作,以确定可能的拼写错误和近似的字符串匹配。 用法 用法很简单。 只需将一个字符串添加到集合,然后使用.get或[]...
  • fuzzywuzzy, 在 python 模糊字符串匹配 FuzzyWuzzy像老板一样的模糊字符串匹配。 它使用 Levenshtein距离来计算simple-to-use包中序列之间的差异。要求python 2.4或者更高版本diffliblevenshtein ( 可选

空空如也

空空如也

1 2 3 4 5 ... 20
收藏数 12,427
精华内容 4,970
关键字:

python中字符串模糊匹配