这里有一个也适用于浮动:

def float2comma(f):
    s = str(abs(f)) # Convert to a string
    decimalposition = s.find(".") # Look for decimal point
    if decimalposition == -1:
        decimalposition = len(s) # If no decimal, then just work from the end
    out = "" 
    for i in range(decimalposition+1, len(s)): # do the decimal
        if not (i-decimalposition-1) % 3 and i-decimalposition-1: out = out+","
        out = out+s[i]      
    if len(out):
        out = "."+out # add the decimal point if necessary
    for i in range(decimalposition-1,-1,-1): # working backwards from decimal point
        if not (decimalposition-i-1) % 3 and decimalposition-i-1: out = ","+out
        out = s[i]+out      
    if f < 0:
        out = "-"+out
    return out

使用的例子:

>>> float2comma(10000.1111)
'10,000.111,1'
>>> float2comma(656565.122)
'656,565.122'
>>> float2comma(-656565.122)
'-656,565.122'

我使用的是python 2.5，所以我无法访问内置格式。

我查看了Django代码intcomma(下面代码中的intcomma_recurs)，意识到它的效率很低，因为它是递归的，而且每次运行时都编译正则表达式也不是一件好事。这并不是一个必要的“问题”，因为django并没有真正关注这种低级性能。此外，我原本预计性能会有10倍的差异，但实际上只慢了3倍。

出于好奇，我实现了几个版本的intcomma，看看使用regex时的性能优势是什么。我的测试数据显示，这项任务有一点优势，但令人惊讶的是，优势并不大。

我也很高兴地看到了我的怀疑:在没有正则表达式的情况下，使用反向xrange方法是不必要的，但它确实使代码看起来更好，代价是性能下降了10%左右。

另外，我假设你传入的是一个字符串，看起来有点像一个数字。其他结果未定。

from __future__ import with_statement
from contextlib import contextmanager
import re,time

re_first_num = re.compile(r"\d")
def intcomma_noregex(value):
    end_offset, start_digit, period = len(value),re_first_num.search(value).start(),value.rfind('.')
    if period == -1:
        period=end_offset
    segments,_from_index,leftover = [],0,(period-start_digit) % 3
    for _index in xrange(start_digit+3 if not leftover else start_digit+leftover,period,3):
        segments.append(value[_from_index:_index])
        _from_index=_index
    if not segments:
        return value
    segments.append(value[_from_index:])
    return ','.join(segments)

def intcomma_noregex_reversed(value):
    end_offset, start_digit, period = len(value),re_first_num.search(value).start(),value.rfind('.')
    if period == -1:
        period=end_offset
    _from_index,segments = end_offset,[]
    for _index in xrange(period-3,start_digit,-3):
        segments.append(value[_index:_from_index])
        _from_index=_index
    if not segments:
        return value
    segments.append(value[:_from_index])
    return ','.join(reversed(segments))

re_3digits = re.compile(r'(?<=\d)\d{3}(?!\d)')
def intcomma(value):
    segments,last_endoffset=[],len(value)
    while last_endoffset > 3:
        digit_group = re_3digits.search(value,0,last_endoffset)
        if not digit_group:
            break
        segments.append(value[digit_group.start():last_endoffset])
        last_endoffset=digit_group.start()
    if not segments:
        return value
    if last_endoffset:
        segments.append(value[:last_endoffset])
    return ','.join(reversed(segments))

def intcomma_recurs(value):
    """
    Converts an integer to a string containing commas every three digits.
    For example, 3000 becomes '3,000' and 45000 becomes '45,000'.
    """
    new = re.sub("^(-?\d+)(\d{3})", '\g<1>,\g<2>', str(value))
    if value == new:
        return new
    else:
        return intcomma(new)

@contextmanager
def timed(save_time_func):
    begin=time.time()
    try:
        yield
    finally:
        save_time_func(time.time()-begin)

def testset_xsimple(func):
    func('5')

def testset_simple(func):
    func('567')

def testset_onecomma(func):
    func('567890')

def testset_complex(func):
    func('-1234567.024')

def testset_average(func):
    func('-1234567.024')
    func('567')
    func('5674')

if __name__ == '__main__':
    print 'Test results:'
    for test_data in ('5','567','1234','1234.56','-253892.045'):
        for func in (intcomma,intcomma_noregex,intcomma_noregex_reversed,intcomma_recurs):
            print func.__name__,test_data,func(test_data)
    times=[]
    def overhead(x):
        pass
    for test_run in xrange(1,4):
        for func in (intcomma,intcomma_noregex,intcomma_noregex_reversed,intcomma_recurs,overhead):
            for testset in (testset_xsimple,testset_simple,testset_onecomma,testset_complex,testset_average):
                for x in xrange(1000): # prime the test
                    testset(func)
                with timed(lambda x:times.append(((test_run,func,testset),x))):
                    for x in xrange(50000):
                        testset(func)
    for (test_run,func,testset),_delta in times:
        print test_run,func.__name__,testset.__name__,_delta

下面是测试结果:

intcomma 5 5
intcomma_noregex 5 5
intcomma_noregex_reversed 5 5
intcomma_recurs 5 5
intcomma 567 567
intcomma_noregex 567 567
intcomma_noregex_reversed 567 567
intcomma_recurs 567 567
intcomma 1234 1,234
intcomma_noregex 1234 1,234
intcomma_noregex_reversed 1234 1,234
intcomma_recurs 1234 1,234
intcomma 1234.56 1,234.56
intcomma_noregex 1234.56 1,234.56
intcomma_noregex_reversed 1234.56 1,234.56
intcomma_recurs 1234.56 1,234.56
intcomma -253892.045 -253,892.045
intcomma_noregex -253892.045 -253,892.045
intcomma_noregex_reversed -253892.045 -253,892.045
intcomma_recurs -253892.045 -253,892.045
1 intcomma testset_xsimple 0.0410001277924
1 intcomma testset_simple 0.0369999408722
1 intcomma testset_onecomma 0.213000059128
1 intcomma testset_complex 0.296000003815
1 intcomma testset_average 0.503000020981
1 intcomma_noregex testset_xsimple 0.134000062943
1 intcomma_noregex testset_simple 0.134999990463
1 intcomma_noregex testset_onecomma 0.190999984741
1 intcomma_noregex testset_complex 0.209000110626
1 intcomma_noregex testset_average 0.513000011444
1 intcomma_noregex_reversed testset_xsimple 0.124000072479
1 intcomma_noregex_reversed testset_simple 0.12700009346
1 intcomma_noregex_reversed testset_onecomma 0.230000019073
1 intcomma_noregex_reversed testset_complex 0.236999988556
1 intcomma_noregex_reversed testset_average 0.56299996376
1 intcomma_recurs testset_xsimple 0.348000049591
1 intcomma_recurs testset_simple 0.34600019455
1 intcomma_recurs testset_onecomma 0.625
1 intcomma_recurs testset_complex 0.773999929428
1 intcomma_recurs testset_average 1.6890001297
1 overhead testset_xsimple 0.0179998874664
1 overhead testset_simple 0.0190000534058
1 overhead testset_onecomma 0.0190000534058
1 overhead testset_complex 0.0190000534058
1 overhead testset_average 0.0309998989105
2 intcomma testset_xsimple 0.0360000133514
2 intcomma testset_simple 0.0369999408722
2 intcomma testset_onecomma 0.207999944687
2 intcomma testset_complex 0.302000045776
2 intcomma testset_average 0.523000001907
2 intcomma_noregex testset_xsimple 0.139999866486
2 intcomma_noregex testset_simple 0.141000032425
2 intcomma_noregex testset_onecomma 0.203999996185
2 intcomma_noregex testset_complex 0.200999975204
2 intcomma_noregex testset_average 0.523000001907
2 intcomma_noregex_reversed testset_xsimple 0.130000114441
2 intcomma_noregex_reversed testset_simple 0.129999876022
2 intcomma_noregex_reversed testset_onecomma 0.236000061035
2 intcomma_noregex_reversed testset_complex 0.241999864578
2 intcomma_noregex_reversed testset_average 0.582999944687
2 intcomma_recurs testset_xsimple 0.351000070572
2 intcomma_recurs testset_simple 0.352999925613
2 intcomma_recurs testset_onecomma 0.648999929428
2 intcomma_recurs testset_complex 0.808000087738
2 intcomma_recurs testset_average 1.81900000572
2 overhead testset_xsimple 0.0189998149872
2 overhead testset_simple 0.0189998149872
2 overhead testset_onecomma 0.0190000534058
2 overhead testset_complex 0.0179998874664
2 overhead testset_average 0.0299999713898
3 intcomma testset_xsimple 0.0360000133514
3 intcomma testset_simple 0.0360000133514
3 intcomma testset_onecomma 0.210000038147
3 intcomma testset_complex 0.305999994278
3 intcomma testset_average 0.493000030518
3 intcomma_noregex testset_xsimple 0.131999969482
3 intcomma_noregex testset_simple 0.136000156403
3 intcomma_noregex testset_onecomma 0.192999839783
3 intcomma_noregex testset_complex 0.202000141144
3 intcomma_noregex testset_average 0.509999990463
3 intcomma_noregex_reversed testset_xsimple 0.125999927521
3 intcomma_noregex_reversed testset_simple 0.126999855042
3 intcomma_noregex_reversed testset_onecomma 0.235999822617
3 intcomma_noregex_reversed testset_complex 0.243000030518
3 intcomma_noregex_reversed testset_average 0.56200003624
3 intcomma_recurs testset_xsimple 0.337000131607
3 intcomma_recurs testset_simple 0.342000007629
3 intcomma_recurs testset_onecomma 0.609999895096
3 intcomma_recurs testset_complex 0.75
3 intcomma_recurs testset_average 1.68300008774
3 overhead testset_xsimple 0.0189998149872
3 overhead testset_simple 0.018000125885
3 overhead testset_onecomma 0.018000125885
3 overhead testset_complex 0.0179998874664
3 overhead testset_average 0.0299999713898

我相信这一定有一个标准的库函数，但尝试自己用递归写它很有趣，所以这是我想到的:

def intToStringWithCommas(x):
    if type(x) is not int and type(x) is not long:
        raise TypeError("Not an integer!")
    if x < 0:
        return '-' + intToStringWithCommas(-x)
    elif x < 1000:
        return str(x)
    else:
        return intToStringWithCommas(x / 1000) + ',' + '%03d' % (x % 1000)

话虽如此，如果其他人确实找到了一种标准的方法，那么您应该使用它。

下面是一行正则表达式替换:

re.sub("(\d)(?=(\d{3})+(?!\d))", r"\1,", "%d" % val)

仅适用于积分输出:

import re
val = 1234567890
re.sub("(\d)(?=(\d{3})+(?!\d))", r"\1,", "%d" % val)
# Returns: '1,234,567,890'

val = 1234567890.1234567890
# Returns: '1,234,567,890'

或者对于小于4位的浮点数，将格式说明符更改为%.3f:

re.sub("(\d)(?=(\d{3})+(?!\d))", r"\1,", "%.3f" % val)
# Returns: '1,234,567,890.123'

注意:不能正确工作与超过三个十进制数字，因为它将尝试分组小数部分:

re.sub("(\d)(?=(\d{3})+(?!\d))", r"\1,", "%.5f" % val)
# Returns: '1,234,567,890.12,346'

它是如何工作的

让我们来分析一下:

re.sub(pattern, repl, string)

pattern = \
    "(\d)           # Find one digit...
     (?=            # that is followed by...
         (\d{3})+   # one or more groups of three digits...
         (?!\d)     # which are not followed by any more digits.
     )",

repl = \
    r"\1,",         # Replace that one digit by itself, followed by a comma,
                    # and continue looking for more matches later in the string.
                    # (re.sub() replaces all matches it finds in the input)

string = \
    "%d" % val      # Format the string as a decimal to begin with

我得到了这个工作:

>>> import locale
>>> locale.setlocale(locale.LC_ALL, 'en_US')
'en_US'
>>> locale.format("%d", 1255000, grouping=True)
'1,255,000'

当然，您不需要国际化支持，但它清晰、简洁，并使用了内置库。

附注:“%d”是通常的%样式格式化程序。您只能有一个格式化程序，但它可以是您在字段宽度和精度设置方面所需要的任何东西。

P.P.S.如果你不能在本地工作，我建议你修改一下Mark的答案:

def intWithCommas(x):
    if type(x) not in [type(0), type(0L)]:
        raise TypeError("Parameter must be an integer.")
    if x < 0:
        return '-' + intWithCommas(-x)
    result = ''
    while x >= 1000:
        x, r = divmod(x, 1000)
        result = ",%03d%s" % (r, result)
    return "%d%s" % (x, result)

递归对于否定的情况是有用的，但是每个逗号一个递归对我来说似乎有点过分了。

这是我处理浮点数的方法。尽管，老实说，我不确定它适用于哪个版本——我使用的是2.7:

my_number = 4385893.382939491

my_string = '{:0,.2f}'.format(my_number)

返回:4385893 .38点

更新:我最近有一个关于这种格式的问题(不能告诉你确切的原因)，但能够通过删除0来修复它:

my_string = '{:,.2f}'.format(my_number)