如何在Python中检查字符串是否表示数值?

def is_number(s):
    try:
        float(s)
        return True
    except ValueError:
        return False

上述方法可行,但似乎很笨拙。


如果您正在测试的内容来自用户输入,那么即使它表示int或float,它仍然是一个字符串。请参阅如何将输入读取为数字?用于转换输入,并询问用户输入,直到他们给出有效响应以确保输入在继续之前表示int或float(或其他要求)。


当前回答

用户助手功能:

def if_ok(fn, string):
  try:
    return fn(string)
  except Exception as e:
    return None

然后

if_ok(int, my_str) or if_ok(float, my_str) or if_ok(complex, my_str)
is_number = lambda s: any([if_ok(fn, s) for fn in (int, float, complex)])

其他回答

str.inumeric()

如果字符串中的所有字符都是数字字符,则返回True,并且至少有一个字符,否则为False。数字字符包括数字字符Unicode数值属性,例如U+2155,VULGAR FRACTION ONE第五。形式上,数字字符是具有属性值的字符Numeric_Type=数字,Numeric_Type=十进制或Numeric_Type=数字。

str.isdecimal()

如果字符串中的所有字符都是十进制字符,并且至少有一个字符,否则为False。十进制字符是可用于在基数10中形成数字的那些,阿拉伯数字零。形式上,十进制字符是一个字符在Unicode通用类别“Nd”中。

两者都适用于Python 3.0中的字符串类型。

对于我非常简单和常见的用例:这个用键盘书写的字符串是数字吗?

我通读了大部分答案,最后得到了:

def isNumeric(string):
    result = True
    try:
        x = float(string)
       result = (x == x) and (x - 1 != x)
    except ValueError:
        result = False
    return result

对于(+/-)NaN和(+-)inf,它将返回False。

你可以在这里查看:https://trinket.io/python/ce32c0e54e

RyanN建议

若要为NaN和Inf返回False,请将行更改为x=float;返回(x==x)和(x-1!=x)。对于除Inf和NaN之外的所有浮点值,这都应返回True

但这并不完全有效,因为对于足够大的浮点数,x-1==x返回true。例如,2.0**54-1==2.0**54

很抱歉,Zombie发布了一篇帖子-只是想让代码更加完整。。。

# is_number() function - Uses re = regex library
# Should handle all normal and complex numbers
# Does not accept trailing spaces. 
# Note: accepts both engineering "j" and math "i" but only the imaginary part "+bi" of a complex number a+bi
# Also accepts inf or NaN
# Thanks to the earlier responders for most the regex fu

import re

ISNUM_REGEXP = re.compile(r'^[-+]?([0-9]+|[0-9]*\.[0-9]+)([eE][-+]?[0-9]+)?[ij]?$')

def is_number(str):
#change order if you have a lot of NaN or inf to parse
    if ISNUM_REGEXP.match(str) or str == "NaN" or str == "inf": 
        return True 
    else:
        return False
# A couple test numbers
# +42.42e-42j
# -42.42E+42i

print('Is it a number?', is_number(input('Gimme any number: ')))

给我任何号码:+42.42e-42j

是数字吗?真的

我想看看哪种方法最快。总的来说,check_replace函数给出了最佳和最一致的结果。check_exception函数给出了最快的结果,但前提是没有触发异常——这意味着它的代码是最有效的,但抛出异常的开销非常大。

请注意,检查成功的强制转换是唯一准确的方法,例如,这与check_exception一起工作,但其他两个测试函数将为有效的float返回False:

huge_number = float('1e+100')

以下是基准代码:

import time, re, random, string

ITERATIONS = 10000000

class Timer:    
    def __enter__(self):
        self.start = time.clock()
        return self
    def __exit__(self, *args):
        self.end = time.clock()
        self.interval = self.end - self.start

def check_regexp(x):
    return re.compile("^\d*\.?\d*$").match(x) is not None

def check_replace(x):
    return x.replace('.','',1).isdigit()

def check_exception(s):
    try:
        float(s)
        return True
    except ValueError:
        return False

to_check = [check_regexp, check_replace, check_exception]

print('preparing data...')
good_numbers = [
    str(random.random() / random.random()) 
    for x in range(ITERATIONS)]

bad_numbers = ['.' + x for x in good_numbers]

strings = [
    ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(random.randint(1,10)))
    for x in range(ITERATIONS)]

print('running test...')
for func in to_check:
    with Timer() as t:
        for x in good_numbers:
            res = func(x)
    print('%s with good floats: %s' % (func.__name__, t.interval))
    with Timer() as t:
        for x in bad_numbers:
            res = func(x)
    print('%s with bad floats: %s' % (func.__name__, t.interval))
    with Timer() as t:
        for x in strings:
            res = func(x)
    print('%s with strings: %s' % (func.__name__, t.interval))

以下是2017年MacBook Pro 13上Python 2.7.10的结果:

check_regexp with good floats: 12.688639
check_regexp with bad floats: 11.624862
check_regexp with strings: 11.349414
check_replace with good floats: 4.419841
check_replace with bad floats: 4.294909
check_replace with strings: 4.086358
check_exception with good floats: 3.276668
check_exception with bad floats: 13.843092
check_exception with strings: 15.786169

以下是2017年MacBook Pro 13上Python 3.6.5的结果:

check_regexp with good floats: 13.472906000000009
check_regexp with bad floats: 12.977665000000016
check_regexp with strings: 12.417542999999995
check_replace with good floats: 6.011045999999993
check_replace with bad floats: 4.849356
check_replace with strings: 4.282754000000011
check_exception with good floats: 6.039081999999979
check_exception with bad floats: 9.322753000000006
check_exception with strings: 9.952595000000002

以下是2017年MacBook Pro 13上PyPy 2.7.13的结果:

check_regexp with good floats: 2.693217
check_regexp with bad floats: 2.744819
check_regexp with strings: 2.532414
check_replace with good floats: 0.604367
check_replace with bad floats: 0.538169
check_replace with strings: 0.598664
check_exception with good floats: 1.944103
check_exception with bad floats: 2.449182
check_exception with strings: 2.200056