我尝试过下面的方法，它甚至适用于极端的情况，比如:

str1='          I   live    on    earth           '

' '.join(str1.split())

但如果你更喜欢正则表达式，它可以这样做:

re.sub('\s+', ' ', str1)

尽管必须进行一些预处理以删除尾随和结束空格。

我尝试过下面的方法，它甚至适用于极端的情况，比如:

str1='          I   live    on    earth           '

' '.join(str1.split())

但如果你更喜欢正则表达式，它可以这样做:

re.sub('\s+', ' ', str1)

尽管必须进行一些预处理以删除尾随和结束空格。

令人惊讶的是，没有人发布一个简单的函数，它会比所有其他发布的解决方案快得多。是这样的:

def compactSpaces(s):
    os = ""
    for c in s:
        if c != " " or (os and os[-1] != " "):
            os += c 
    return os

>>> import re
>>> re.sub(' +', ' ', 'The     quick brown    fox')
'The quick brown fox'

使用带有“\s”的正则表达式并执行简单的string.split()也将删除其他空白-如换行符、回车符、制表符。除非需要使用多个空格，否则我将给出这些示例。

我使用了11段，1000个单词，6665字节的Lorem Ipsum来进行真实的时间测试，并在整个过程中使用了随机长度的额外空格:

original_string = ''.join(word + (' ' * random.randint(1, 10)) for word in lorem_ipsum.split(' '))

一行程序实际上是删除任何前导/尾随空格，并保留一个前导/尾随空格(但只有ONE;-)。

# setup = '''

import re

def while_replace(string):
    while '  ' in string:
        string = string.replace('  ', ' ')

    return string

def re_replace(string):
    return re.sub(r' {2,}' , ' ', string)

def proper_join(string):
    split_string = string.split(' ')

    # To account for leading/trailing spaces that would simply be removed
    beg = ' ' if not split_string[ 0] else ''
    end = ' ' if not split_string[-1] else ''

    # versus simply ' '.join(item for item in string.split(' ') if item)
    return beg + ' '.join(item for item in split_string if item) + end

original_string = """Lorem    ipsum        ... no, really, it kept going...          malesuada enim feugiat.         Integer imperdiet    erat."""

assert while_replace(original_string) == re_replace(original_string) == proper_join(original_string)

#'''

# while_replace_test
new_string = original_string[:]

new_string = while_replace(new_string)

assert new_string != original_string

# re_replace_test
new_string = original_string[:]

new_string = re_replace(new_string)

assert new_string != original_string

# proper_join_test
new_string = original_string[:]

new_string = proper_join(new_string)

assert new_string != original_string

NOTE: The "while version" made a copy of the original_string, as I believe once modified on the first run, successive runs would be faster (if only by a bit). As this adds time, I added this string copy to the other two so that the times showed the difference only in the logic. Keep in mind that the main stmt on timeit instances will only be executed once; the original way I did this, the while loop worked on the same label, original_string, thus the second run, there would be nothing to do. The way it's set up now, calling a function, using two different labels, that isn't a problem. I've added assert statements to all the workers to verify we change something every iteration (for those who may be dubious). E.g., change to this and it breaks:

# while_replace_test
new_string = original_string[:]

new_string = while_replace(new_string)

assert new_string != original_string # will break the 2nd iteration

while '  ' in original_string:
    original_string = original_string.replace('  ', ' ')

Tests run on a laptop with an i5 processor running Windows 7 (64-bit).

timeit.Timer(stmt = test, setup = setup).repeat(7, 1000)

test_string = 'The   fox jumped   over\n\t    the log.' # trivial

Python 2.7.3, 32-bit, Windows
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.001066 |   0.001260 |   0.001128 |   0.001092
     re_replace_test |   0.003074 |   0.003941 |   0.003357 |   0.003349
    proper_join_test |   0.002783 |   0.004829 |   0.003554 |   0.003035

Python 2.7.3, 64-bit, Windows
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.001025 |   0.001079 |   0.001052 |   0.001051
     re_replace_test |   0.003213 |   0.004512 |   0.003656 |   0.003504
    proper_join_test |   0.002760 |   0.006361 |   0.004626 |   0.004600

Python 3.2.3, 32-bit, Windows
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.001350 |   0.002302 |   0.001639 |   0.001357
     re_replace_test |   0.006797 |   0.008107 |   0.007319 |   0.007440
    proper_join_test |   0.002863 |   0.003356 |   0.003026 |   0.002975

Python 3.3.3, 64-bit, Windows
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.001444 |   0.001490 |   0.001460 |   0.001459
     re_replace_test |   0.011771 |   0.012598 |   0.012082 |   0.011910
    proper_join_test |   0.003741 |   0.005933 |   0.004341 |   0.004009

test_string = lorem_ipsum
# Thanks to http://www.lipsum.com/
# "Generated 11 paragraphs, 1000 words, 6665 bytes of Lorem Ipsum"

Python 2.7.3, 32-bit
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.342602 |   0.387803 |   0.359319 |   0.356284
     re_replace_test |   0.337571 |   0.359821 |   0.348876 |   0.348006
    proper_join_test |   0.381654 |   0.395349 |   0.388304 |   0.388193    

Python 2.7.3, 64-bit
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.227471 |   0.268340 |   0.240884 |   0.236776
     re_replace_test |   0.301516 |   0.325730 |   0.308626 |   0.307852
    proper_join_test |   0.358766 |   0.383736 |   0.370958 |   0.371866    

Python 3.2.3, 32-bit
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.438480 |   0.463380 |   0.447953 |   0.446646
     re_replace_test |   0.463729 |   0.490947 |   0.472496 |   0.468778
    proper_join_test |   0.397022 |   0.427817 |   0.406612 |   0.402053    

Python 3.3.3, 64-bit
                test |      minum |    maximum |    average |     median
---------------------+------------+------------+------------+-----------
  while_replace_test |   0.284495 |   0.294025 |   0.288735 |   0.289153
     re_replace_test |   0.501351 |   0.525673 |   0.511347 |   0.508467
    proper_join_test |   0.422011 |   0.448736 |   0.436196 |   0.440318

对于普通字符串，while循环似乎是最快的，其次是Pythonic字符串分割/连接，regex在后面拉。

对于非平凡的字符串，似乎有更多的考虑。32位2.7 ?它是正则表达式的救星!2.7 64位?while循环是最好的。32位3.2，使用“适当的”连接。64位3.3，执行while循环。一次。

最后，如果/何地/何时需要，就可以提高性能，但最好记住这句咒语:

让它起作用 纠正错误 快一点

IANAL, ymv

你能得到的用户生成字符串的最快速度是:

if '  ' in text:
    while '  ' in text:
        text = text.replace('  ', ' ')

短路使它比pythonlarry的综合回答略快。如果你追求效率，并严格要求去除单个空格的额外空白，那么可以使用这种方法。