>>> s = "string. With. Punctuation?"
>>> s = re.sub(r'[^\w\s]','',s)
>>> re.split(r'\s*', s)


['string', 'With', 'Punctuation']

在不太严格的情况下，单行线可能会有所帮助：

''.join([c for c in s if c.isalnum() or c.isspace()])

这个问题没有太多细节，所以我采取的方法是用最简单的解释来解决这个问题：去掉标点符号。

请注意，所提供的解决方案不考虑收缩词（例如，you are）或连字符词（例如肛门保留）。。。这是关于它们是否应该被视为标点符号的争论。。。也不能解释非英语字符集或类似的东西。。。因为问题中没有提到这些细节。有人认为空格是标点符号，这在技术上是正确的。。。但对我来说，这在当前问题的背景下毫无意义。

# using lambda
''.join(filter(lambda c: c not in string.punctuation, s))

# using list comprehension
''.join('' if c in string.punctuation else c for c in s)

使用Python从文本文件中删除停止词

print('====THIS IS HOW TO REMOVE STOP WORS====')

with open('one.txt','r')as myFile:

    str1=myFile.read()

    stop_words ="not", "is", "it", "By","between","This","By","A","when","And","up","Then","was","by","It","If","can","an","he","This","or","And","a","i","it","am","at","on","in","of","to","is","so","too","my","the","and","but","are","very","here","even","from","them","then","than","this","that","though","be","But","these"

    myList=[]

    myList.extend(str1.split(" "))

    for i in myList:

        if i not in stop_words:

            print ("____________")

            print(i,end='\n')

作为更新，我重写了Python 3中的@Brian示例，并对其进行了更改，以将正则表达式编译步骤移到函数内部。我在这里的想法是对使功能工作所需的每一步进行计时。也许您使用的是分布式计算，无法在工作人员之间共享regex对象，需要在每个工作人员处执行re.compile步骤。此外，我还很好奇地对Python 3的maketrans的两种不同实现进行计时

table = str.maketrans({key: None for key in string.punctuation})

vs

table = str.maketrans('', '', string.punctuation)

另外，我添加了另一种使用集合的方法，在这里我利用交集函数来减少迭代次数。

这是完整的代码：

import re, string, timeit

s = "string. With. Punctuation"


def test_set(s):
    exclude = set(string.punctuation)
    return ''.join(ch for ch in s if ch not in exclude)


def test_set2(s):
    _punctuation = set(string.punctuation)
    for punct in set(s).intersection(_punctuation):
        s = s.replace(punct, ' ')
    return ' '.join(s.split())


def test_re(s):  # From Vinko's solution, with fix.
    regex = re.compile('[%s]' % re.escape(string.punctuation))
    return regex.sub('', s)


def test_trans(s):
    table = str.maketrans({key: None for key in string.punctuation})
    return s.translate(table)


def test_trans2(s):
    table = str.maketrans('', '', string.punctuation)
    return(s.translate(table))


def test_repl(s):  # From S.Lott's solution
    for c in string.punctuation:
        s=s.replace(c,"")
    return s


print("sets      :",timeit.Timer('f(s)', 'from __main__ import s,test_set as f').timeit(1000000))
print("sets2      :",timeit.Timer('f(s)', 'from __main__ import s,test_set2 as f').timeit(1000000))
print("regex     :",timeit.Timer('f(s)', 'from __main__ import s,test_re as f').timeit(1000000))
print("translate :",timeit.Timer('f(s)', 'from __main__ import s,test_trans as f').timeit(1000000))
print("translate2 :",timeit.Timer('f(s)', 'from __main__ import s,test_trans2 as f').timeit(1000000))
print("replace   :",timeit.Timer('f(s)', 'from __main__ import s,test_repl as f').timeit(1000000))

这是我的结果：

sets      : 3.1830138750374317
sets2      : 2.189873124472797
regex     : 7.142953420989215
translate : 4.243278483860195
translate2 : 2.427158243022859
replace   : 4.579746678471565

对于严肃的自然语言处理（NLP），您应该让像SpaCy这样的库通过标记化处理标点符号，然后您可以根据需要手动调整。

例如，您希望如何处理单词中的连字符？例外情况，如缩写？开始和结束引号？URL？在NLP中，将“let’s”这样的收缩分隔为“let”和“s”以进行进一步处理通常很有用。