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- Formatting floats in Python without superfluous zeros 14 answers
- 在Python中，格式化浮动没有多余的0 14个答案
I need to find a way to convert the following strings in python:
我需要找到一种方法来转换python中的以下字符串:
0.000 => 0
0 => 0
123.45000 => 123.45
0000 => 0
123.4506780 => 123.450678
and so forth. I tried .rstrip('0').rstrip('.'), but that doesn't work if the input is 0 or 00.
等等。我尝试了。rstrip('0').rstrip('.')，但如果输入是0或00，那就不起作用了。
Any ideas? Thanks!
什么好主意吗?谢谢!
7 个解决方案
#1
18
Updated Generalized to maintain precision and handle unseen values:
更新广义，保持精度和处理不可见值:
import decimal
import random
def format_number(num):
try:
dec = decimal.Decimal(num)
except:
return 'bad'
tup = dec.as_tuple()
delta = len(tup.digits) + tup.exponent
digits = ''.join(str(d) for d in tup.digits)
if delta <= 0:
zeros = abs(tup.exponent) - len(tup.digits)
val = '0.' + ('0'*zeros) + digits
else:
val = digits[:delta] + ('0'*tup.exponent) + '.' + digits[delta:]
val = val.rstrip('0')
if val[-1] == '.':
val = val[:-1]
if tup.sign:
return '-' + val
return val
# test data
NUMS = '''
0.0000 0
0 0
123.45000 123.45
0000 0
123.4506780 123.450678
0.1 0.1
0.001 0.001
0.005000 0.005
.1234 0.1234
1.23e1 12.3
-123.456 -123.456
4.98e10 49800000000
4.9815135 4.9815135
4e30 4000000000000000000000000000000
-0.0000000000004 -0.0000000000004
-.4e-12 -0.0000000000004
-0.11112 -0.11112
1.3.4.5 bad
-1.2.3 bad
'''
for num, exp in [s.split() for s in NUMS.split('\n') if s]:
res = format_number(num)
print res
assert exp == res
Output:
输出:
0
0
123.45
0
123.450678
0.1
0.001
0.005
0.1234
12.3
-123.456
49800000000
4.9815135
4000000000000000000000000000000
-0.0000000000004
-0.0000000000004
-0.11112
bad
bad
#2
10
You can use format strings if you want, but be aware that you might need to set your desired precision, as format strings have their own logic for this by default. Janneb suggests a precision of 17 in another answer.
如果需要，可以使用格式字符串，但是要注意，您可能需要设置所需的精度，因为格式字符串在默认情况下有自己的逻辑。Janneb建议在另一个答案中精确到17。
'{:g}'.format(float(your_string_goes_here))
After thinking about this some more, though, I think the simplest and best solution is just to cast the string twice (as jathanism suggests):
然而，在进一步思考这个问题之后，我认为最简单、最好的解决办法就是将弦抛两次(正如jathanism所言):
str(float(your_string_goes_here))
Edit: Added clarification because of comment.
编辑:因为评论添加了澄清。
#3
6
For the floating point numbers, you can just cast the string to a float:
对于浮点数，您可以将字符串转换为浮点数:
>>> float('123.4506780')
123.450678
For the zero values, you can just cast those to an integer:
对于零值，你可以将它们转换为整数:
>>> int('0000')
0
When printed, numeric values are automatically converted to strings. If you need these to actually be strings, you may simply cast them back to strings with str(), e.g.:
当打印时，数值将自动转换为字符串。如果您需要这些字符串实际上是字符串，您可以简单地将它们转换为字符串()，例如:
>>> str(float('123.4506780'))
'123.450678'
#4
2
'%.17g' % float(mystr)
depending on what you actually want to do..
这取决于你到底想做什么。
#5
1
Script:
脚本:
def tidy_float(s):
"""Return tidied float representation.
Remove superflous leading/trailing zero digits.
Remove '.' if value is an integer.
Return '****' if float(s) fails.
"""
# float?
try:
f = float(s)
except ValueError:
return '****'
# int?
try:
i = int(s)
return str(i)
except ValueError:
pass
# scientific notation?
if 'e' in s or 'E' in s:
t = s.lstrip('0')
if t.startswith('.'): t = '0' + t
return t
# float with integral value (includes zero)?
i = int(f)
if i == f:
return str(i)
assert '.' in s
t = s.strip('0')
if t.startswith('.'): t = '0' + t
if t.endswith('.'): t += '0'
return t
if __name__ == "__main__":
# Each line has test string followed by expected output
tests = """
0.000 0
0 0
0000 0
0.4000 0.4
0.0081000 0.0081
103.45 103.45
103.4506700 103.45067
14500.0012 14500.0012
478000.89 478000.89
993.59.18 ****
12.5831.400 ****
.458 0.458
.48587000 0.48587
.0000 0
10000 10000
10000.000 10000
-10000 -10000
-10000.000 -10000
1.23e2 1.23e2
1.23e10 1.23e10
.123e10 0.123e10
""".splitlines()
for test in tests:
x = test.split()
if not x: continue
data, expected = x
actual = tidy_float(data)
print "data=%r exp=%r act=%r %s" % (
data, expected, actual, ["**FAIL**", ""][actual == expected])
Output (Python 2.7.1):
输出(Python 2.7.1):
data='0.000' exp='0' act='0'
data='0' exp='0' act='0'
data='0000' exp='0' act='0'
data='0.4000' exp='0.4' act='0.4'
data='0.0081000' exp='0.0081' act='0.0081'
data='103.45' exp='103.45' act='103.45'
data='103.4506700' exp='103.45067' act='103.45067'
data='14500.0012' exp='14500.0012' act='14500.0012'
data='478000.89' exp='478000.89' act='478000.89'
data='993.59.18' exp='****' act='****'
data='12.5831.400' exp='****' act='****'
data='.458' exp='0.458' act='0.458'
data='.48587000' exp='0.48587' act='0.48587'
data='.0000' exp='0' act='0'
data='10000' exp='10000' act='10000'
data='10000.000' exp='10000' act='10000'
data='-10000' exp='-10000' act='-10000'
data='-10000.000' exp='-10000' act='-10000'
data='1.23e2' exp='1.23e2' act='1.23e2'
data='1.23e10' exp='1.23e10' act='1.23e10'
data='.123e10' exp='0.123e10' act='0.123e10'
#6
1
ADDITION TO THE EDIT 2 of my other answer
(All was to long to be in only one post)
(所有人都渴望只呆在一个岗位上)
The pattern of the regex defines 4 sub-patterns, each one matching with a certain type of numbers. Each time the regex matches with a part of a string, there is only one of the sub-patterns that matches, hence the possibility to use mat.lastindex in the replacement function. The following code shows the matchings of the sub-pattern against various numbers:
regex的模式定义了4个子模式，每个子模式与特定类型的数字匹配。每次regex与字符串的一部分匹配时，只有一个匹配的子模式，因此可以在替换函数中使用mat.lastindex。下面的代码显示子模式与不同数字的匹配:
import re
def float_show(ch,
regx = re.compile(
'(?<![\d.])'
'0*' # potentiel heading zeros
'(?:'
'(\d+)\.?' # INTEGERS :
# ~ pure integers non-0 or 0
# 000450 , 136000 , 87 , 000 , 0
# ~ integer part non-0 + '.'
# 0044. , 4100.
# ~ integer part 0 + '.'
# 000. , 0.
# ~ integer part non-0 + '.' + fractional part 0:
# 000570.00 , 193.0 , 3.000
'|\.(0)' # SPECIAL CASE, 0 WITH FRACTIONAL PART :
# ~ integer part 0 + compulsory fractional part 0:
# 000.0, 0.000 , .0 , .00000
'|(\.\d+?)' # FLOATING POINT NUMBER
# ~ with integer part 0:
# 000.0890 , 0.52 , 0.1 , .077000 , .1400 , .0006010
'|(\d+\.\d+?)' # FLOATING POINT NUMBER
# ~ with integer part non-0:
# 0024000.013000 , 145.0235 , 3.00058
')'
'0*' # potential tailing zeros
'(?![\d.])'),
repl = lambda mat: mat.group(mat.lastindex)
if mat.lastindex!=3
else '0' + mat.group(3) ):
mat = regx.search(ch)
if mat:
return (ch,regx.sub(repl,ch),repr(mat.groups()))
else:
return (ch,'No match','No groups')
numbers = ['23456000', '23456000.', '23456000.000',
'00023456000', '000023456000.', '000023456000.000',
'10000', '10000.', '10000.000',
'00010000', '00010000.', '00010000.000',
'24', '24.', '24.000',
'00024', '00024.', '00024.000',
'8', '8.', '8.000',
'0008', '0008.', '0008.000',
'0', '00000', '0.', '000.',
'\n',
'0.0', '0.000', '000.0', '000.000', '.000000', '.0',
'\n',
'.00023456', '.00023456000', '.00503', '.00503000',
'.068', '.0680000', '.8', '.8000',
'.123456123456', '.123456123456000',
'.657', '.657000', '.45', '.4500000', '.7', '.70000',
'\n',
'0.0000023230000', '000.0000023230000',
'0.0081000', '0000.0081000',
'0.059000', '0000.059000',
'0.78987400000', '00000.78987400000',
'0.4400000', '00000.4400000',
'0.5000', '0000.5000',
'0.90', '000.90', '0.7', '000.7',
'\n',
'2.6', '00002.6', '00002.60000',
'4.71', '0004.71', '0004.7100',
'23.49', '00023.49', '00023.490000',
'103.45', '0000103.45', '0000103.45000',
'10003.45067', '000010003.45067', '000010003.4506700',
'15000.0012', '000015000.0012', '000015000.0012000',
'78000.89', '000078000.89', '000078000.89000',
'\n',
'.0457e10', '.0457000e10',
'0.782e10', '0000.782e10', '0000.7820000e10',
'1.23E2', '0001.23E2', '0001.2300000E2',
'1.46e10', '0001.46e10', '0001.4600000e10',
'1.077e-456', '0001.077e-456', '0001.077000e-456',
'1.069e10', '0001.069e10', '0001.069000e10',
'105040.03e10', '000105040.03e10', '105040.0300e10',
'\n',
'..18000', '25..00', '36...77', '2..8',
'3.8..9', '.12500.', '12.51.400' ]
pat = '%20s %-16s %s'
li = [pat % ('tested number ',' shaved float',' regx.search(number).groups()')]
li.extend(pat % float_show(ch) if ch!='\n' else '\n' for ch in numbers)
print '\n'.join(li)
demonstrates
演示了
tested number shaved float regx.search(number).groups()
23456000 23456000 ('23456000', None, None, None)
23456000. 23456000 ('23456000', None, None, None)
23456000.000 23456000 ('23456000', None, None, None)
00023456000 23456000 ('23456000', None, None, None)
000023456000. 23456000 ('23456000', None, None, None)
000023456000.000 23456000 ('23456000', None, None, None)
10000 10000 ('10000', None, None, None)
10000. 10000 ('10000', None, None, None)
10000.000 10000 ('10000', None, None, None)
00010000 10000 ('10000', None, None, None)
00010000. 10000 ('10000', None, None, None)
00010000.000 10000 ('10000', None, None, None)
24 24 ('24', None, None, None)
24. 24 ('24', None, None, None)
24.000 24 ('24', None, None, None)
00024 24 ('24', None, None, None)
00024. 24 ('24', None, None, None)
00024.000 24 ('24', None, None, None)
8 8 ('8', None, None, None)
8. 8 ('8', None, None, None)
8.000 8 ('8', None, None, None)
0008 8 ('8', None, None, None)
0008. 8 ('8', None, None, None)
0008.000 8 ('8', None, None, None)
0 0 ('0', None, None, None)
00000 0 ('0', None, None, None)
0. 0 ('0', None, None, None)
000. 0 ('0', None, None, None)
0.0 0 (None, '0', None, None)
0.000 0 (None, '0', None, None)
000.0 0 (None, '0', None, None)
000.000 0 (None, '0', None, None)
.000000 0 (None, '0', None, None)
.0 0 (None, '0', None, None)
.00023456 0.00023456 (None, None, '.00023456', None)
.00023456000 0.00023456 (None, None, '.00023456', None)
.00503 0.00503 (None, None, '.00503', None)
.00503000 0.00503 (None, None, '.00503', None)
.068 0.068 (None, None, '.068', None)
.0680000 0.068 (None, None, '.068', None)
.8 0.8 (None, None, '.8', None)
.8000 0.8 (None, None, '.8', None)
.123456123456 0.123456123456 (None, None, '.123456123456', None)
.123456123456000 0.123456123456 (None, None, '.123456123456', None)
.657 0.657 (None, None, '.657', None)
.657000 0.657 (None, None, '.657', None)
.45 0.45 (None, None, '.45', None)
.4500000 0.45 (None, None, '.45', None)
.7 0.7 (None, None, '.7', None)
.70000 0.7 (None, None, '.7', None)
0.0000023230000 0.000002323 (None, None, '.000002323', None)
000.0000023230000 0.000002323 (None, None, '.000002323', None)
0.0081000 0.0081 (None, None, '.0081', None)
0000.0081000 0.0081 (None, None, '.0081', None)
0.059000 0.059 (None, None, '.059', None)
0000.059000 0.059 (None, None, '.059', None)
0.78987400000 0.789874 (None, None, '.789874', None)
00000.78987400000 0.789874 (None, None, '.789874', None)
0.4400000 0.44 (None, None, '.44', None)
00000.4400000 0.44 (None, None, '.44', None)
0.5000 0.5 (None, None, '.5', None)
0000.5000 0.5 (None, None, '.5', None)
0.90 0.9 (None, None, '.9', None)
000.90 0.9 (None, None, '.9', None)
0.7 0.7 (None, None, '.7', None)
000.7 0.7 (None, None, '.7', None)
2.6 2.6 (None, None, None, '2.6')
00002.6 2.6 (None, None, None, '2.6')
00002.60000 2.6 (None, None, None, '2.6')
4.71 4.71 (None, None, None, '4.71')
0004.71 4.71 (None, None, None, '4.71')
0004.7100 4.71 (None, None, None, '4.71')
23.49 23.49 (None, None, None, '23.49')
00023.49 23.49 (None, None, None, '23.49')
00023.490000 23.49 (None, None, None, '23.49')
103.45 103.45 (None, None, None, '103.45')
0000103.45 103.45 (None, None, None, '103.45')
0000103.45000 103.45 (None, None, None, '103.45')
10003.45067 10003.45067 (None, None, None, '10003.45067')
000010003.45067 10003.45067 (None, None, None, '10003.45067')
000010003.4506700 10003.45067 (None, None, None, '10003.45067')
15000.0012 15000.0012 (None, None, None, '15000.0012')
000015000.0012 15000.0012 (None, None, None, '15000.0012')
000015000.0012000 15000.0012 (None, None, None, '15000.0012')
78000.89 78000.89 (None, None, None, '78000.89')
000078000.89 78000.89 (None, None, None, '78000.89')
000078000.89000 78000.89 (None, None, None, '78000.89')
.0457e10 0.0457e10 (None, None, '.0457', None)
.0457000e10 0.0457e10 (None, None, '.0457', None)
0.782e10 0.782e10 (None, None, '.782', None)
0000.782e10 0.782e10 (None, None, '.782', None)
0000.7820000e10 0.782e10 (None, None, '.782', None)
1.23E2 1.23E2 (None, None, None, '1.23')
0001.23E2 1.23E2 (None, None, None, '1.23')
0001.2300000E2 1.23E2 (None, None, None, '1.23')
1.46e10 1.46e10 (None, None, None, '1.46')
0001.46e10 1.46e10 (None, None, None, '1.46')
0001.4600000e10 1.46e10 (None, None, None, '1.46')
1.077e-456 1.077e-456 (None, None, None, '1.077')
0001.077e-456 1.077e-456 (None, None, None, '1.077')
0001.077000e-456 1.077e-456 (None, None, None, '1.077')
1.069e10 1.069e10 (None, None, None, '1.069')
0001.069e10 1.069e10 (None, None, None, '1.069')
0001.069000e10 1.069e10 (None, None, None, '1.069')
105040.03e10 105040.03e10 (None, None, None, '105040.03')
000105040.03e10 105040.03e10 (None, None, None, '105040.03')
105040.0300e10 105040.03e10 (None, None, None, '105040.03')
..18000 No match No groups
25..00 No match No groups
36...77 No match No groups
2..8 No match No groups
3.8..9 No match No groups
.12500. No match No groups
12.51.400 No match No groups
#7
1
FIRST "SOLUTION"
import re
regx=re.compile('(?<![\d.])'
'(?!\d*\.\d*\.)' # excludes certain string as not being numbers
'((\d|\.\d)([\d.])*?)' # the only matching group
'([0\.]*)'
'(?![\d.])')
regx.sub('\\1',ch)
.
。
EDIT 1
John Machin said that 10000 and 10000.000 produce 1 instead of 10000
John Machin说10000和10000.000生产1而不是10000
I corrected the replacement function with the help of (?!(?<=0)\.)
我使用(?!(?<=0)\纠正了替换函数。)
import re
regx = re.compile('(?<![\d.])' '(?![1-9]\d*(?![\d.])|\d*\.\d*\.)'
'0*(?!(?<=0)\.)'
'([\d.]+?)' # the only group , which is kept
'\.?0*'
'(?![\d.])')
regx.sub('\\1',ch)
.
。
EDIT 2
To correct the remaining shortcomings [ '.0000' producing '.' , pointed out by John Machin, and '000078000' producing '78' ] , I rewrote a regex build on a new idea. It's simpler. The regex detects all types of numbers.
以纠正其余的缺点。0000年“生产”。约翰·麦辰(John Machin)和“000078000”(制作《78》)指出，我在一个新想法的基础上重写了一个regex。这是简单的。regex检测所有类型的数字。
This solution not only cuts the trailing zeros off, but also the heading zeros. Here's the comparison of this solution with John Machin's tidy_float() , samplebias's number_format() , arussell84's '{:g}'.format() . There are some differences between the results of my function (all correct this time) and the others :
这个解决方案不仅把后面的0去掉了，而且还把前面的0去掉了。下面是这个解决方案与John Machin的tidy_float()、samplebias的number_format()、arussell84的'{:g}'.format()的比较。我的函数结果(这次都是正确的)和其他的有一些不同:
import re
def number_shaver(ch,
regx = re.compile('(?<![\d.])0*(?:'
'(\d+)\.?|\.(0)'
'|(\.\d+?)|(\d+\.\d+?)'
')0*(?![\d.])') ,
repl = lambda mat: mat.group(mat.lastindex)
if mat.lastindex!=3
else '0' + mat.group(3) ):
return regx.sub(repl,ch)
def tidy_float(s): # John Machin
"""Return tidied float representation.
Remove superflous leading/trailing zero digits.
Remove '.' if value is an integer.
Return '****' if float(s) fails.
"""
# float?
try:
f = float(s)
except ValueError:
return s
# int?
try:
i = int(s)
return str(i)
except ValueError:
pass
# scientific notation?
if 'e' in s or 'E' in s:
t = s.lstrip('0')
if t.startswith('.'): t = '0' + t
return t
# float with integral value (includes zero)?
i = int(f)
if i == f:
return str(i)
assert '.' in s
t = s.strip('0')
if t.startswith('.'): t = '0' + t
if t.endswith('.'): t += '0'
return t
def format_float(s): # arrussell84
return '{:g}'.format(float(s)) if s.count('.')<2 \
else "Can't treat"
import decimal
def format_number(num):
try:
dec = decimal.Decimal(num)
except:
return 'bad'
tup = dec.as_tuple()
delta = len(tup.digits) + tup.exponent
digits = ''.join(str(d) for d in tup.digits)
if delta <= 0:
zeros = abs(tup.exponent) - len(tup.digits)
val = '0.' + ('0'*zeros) + digits
else:
val = digits[:delta] + ('0'*tup.exponent) + '.' + digits[delta:]
val = val.rstrip('0')
if val[-1] == '.':
val = val[:-1]
if tup.sign:
return '-' + val
return val
numbers = ['23456000', '23456000.', '23456000.000',
'00023456000', '000023456000.', '000023456000.000',
'10000', '10000.', '10000.000',
'00010000', '00010000.', '00010000.000',
'24', '24.', '24.000',
'00024', '00024.', '00024.000',
'8', '8.', '8.000',
'0008', '0008.', '0008.000',
'0', '00000', '0.', '000.',
'\n',
'0.0', '0.000', '000.0', '000.000', '.000000', '.0',
'\n',
'.00023456', '.00023456000', '.00503', '.00503000',
'.068', '.0680000', '.8', '.8000',
'.123456123456', '.123456123456000',
'.657', '.657000', '.45', '.4500000', '.7', '.70000',
'\n',
'0.0000023230000', '000.0000023230000',
'0.0081000', '0000.0081000',
'0.059000', '0000.059000',
'0.78987400000', '00000.78987400000',
'0.4400000', '00000.4400000',
'0.5000', '0000.5000',
'0.90', '000.90', '0.7', '000.7',
'\n',
'2.6', '00002.6', '00002.60000',
'4.71', '0004.71', '0004.7100',
'23.49', '00023.49', '00023.490000',
'103.45', '0000103.45', '0000103.45000',
'10003.45067', '000010003.45067', '000010003.4506700',
'15000.0012', '000015000.0012', '000015000.0012000',
'78000.89', '000078000.89', '000078000.89000',
'\n',
'.0457e10', '.0457000e10','00000.0457000e10',
'258e8', '2580000e4', '0000000002580000e4',
# notice the difference of exponents
'0.782e10', '0000.782e10', '0000.7820000e10',
'1.23E2', '0001.23E2', '0001.2300000E2',
'432e-102', '0000432e-102', '004320000e-106',
# notice the difference of exponents
'1.46e10', '0001.46e10', '0001.4600000e10',
'1.077e-300', '0001.077e-300', '0001.077000e-300',
'1.069e10', '0001.069e10', '0001.069000e10',
'105040.03e10', '000105040.03e10', '105040.0300e10',
'\n',
'..18000', '25..00', '36...77', '2..8',
'3.8..9', '.12500.', '12.51.400' ]
pat = '%18s %-15s %-15s %-15s %s' li = [pat % ('tested number','float_shaver', 'tidy_float',"format_number()","'{:g}'.format()")] li.extend(pat % (n,number_shaver(n),tidy_float(n),format_number(n),format_float(n)) if n!='\n' else '\n' for n in numbers)
帕特= % 18年代-15年代-15年代-15年代% % % % s的李=(pat %(测试数量,‘float_shaver’,‘tidy_float’,”format_number()”,“‘{:g }’.format()))。扩展(pat % (n,number_shaver(n)，tidy_float(n)，format_number(n)，format_float(n)))如果n!='\n' else '\n'代表数字n)
print '\n'.join(li)
打印' \ n ' . join(李)
result of the comparison:
比较的结果:
tested number float_shaver tidy_float format_number() '{:g}'.format()
23456000 23456000 23456000 23456000 2.3456e+07
23456000. 23456000 23456000 23456000 2.3456e+07
23456000.000 23456000 23456000 23456000 2.3456e+07
00023456000 23456000 23456000 23456000 2.3456e+07
000023456000. 23456000 23456000 23456000 2.3456e+07
000023456000.000 23456000 23456000 23456000 2.3456e+07
10000 10000 10000 10000 10000
10000. 10000 10000 10000 10000
10000.000 10000 10000 10000 10000
00010000 10000 10000 10000 10000
00010000. 10000 10000 10000 10000
00010000.000 10000 10000 10000 10000
24 24 24 24 24
24. 24 24 24 24
24.000 24 24 24 24
00024 24 24 24 24
00024. 24 24 24 24
00024.000 24 24 24 24
8 8 8 8 8
8. 8 8 8 8
8.000 8 8 8 8
0008 8 8 8 8
0008. 8 8 8 8
0008.000 8 8 8 8
0 0 0 0 0
00000 0 0 0 0
0. 0 0 0 0
000. 0 0 0 0
0.0 0 0 0 0
0.000 0 0 0 0
000.0 0 0 0 0
000.000 0 0 0 0
.000000 0 0 0 0
.0 0 0 0 0
.00023456 0.00023456 0.00023456 0.00023456 0.00023456
.00023456000 0.00023456 0.00023456 0.00023456 0.00023456
.00503 0.00503 0.00503 0.00503 0.00503
.00503000 0.00503 0.00503 0.00503 0.00503
.068 0.068 0.068 0.068 0.068
.0680000 0.068 0.068 0.068 0.068
.8 0.8 0.8 0.8 0.8
.8000 0.8 0.8 0.8 0.8
.123456123456 0.123456123456 0.123456123456 0.123456123456 0.123456
.123456123456000 0.123456123456 0.123456123456 0.123456123456 0.123456
.657 0.657 0.657 0.657 0.657
.657000 0.657 0.657 0.657 0.657
.45 0.45 0.45 0.45 0.45
.4500000 0.45 0.45 0.45 0.45
.7 0.7 0.7 0.7 0.7
.70000 0.7 0.7 0.7 0.7
0.0000023230000 0.000002323 0.000002323 0.000002323 2.323e-06
000.0000023230000 0.000002323 0.000002323 0.000002323 2.323e-06
0.0081000 0.0081 0.0081 0.0081 0.0081
0000.0081000 0.0081 0.0081 0.0081 0.0081
0.059000 0.059 0.059 0.059 0.059
0000.059000 0.059 0.059 0.059 0.059
0.78987400000 0.789874 0.789874 0.789874 0.789874
00000.78987400000 0.789874 0.789874 0.789874 0.789874
0.4400000 0.44 0.44 0.44 0.44
00000.4400000 0.44 0.44 0.44 0.44
0.5000 0.5 0.5 0.5 0.5
0000.5000 0.5 0.5 0.5 0.5
0.90 0.9 0.9 0.9 0.9
000.90 0.9 0.9 0.9 0.9
0.7 0.7 0.7 0.7 0.7
000.7 0.7 0.7 0.7 0.7
2.6 2.6 2.6 2.6 2.6
00002.6 2.6 2.6 2.6 2.6
00002.60000 2.6 2.6 2.6 2.6
4.71 4.71 4.71 4.71 4.71
0004.71 4.71 4.71 4.71 4.71
0004.7100 4.71 4.71 4.71 4.71
23.49 23.49 23.49 23.49 23.49
00023.49 23.49 23.49 23.49 23.49
00023.490000 23.49 23.49 23.49 23.49
103.45 103.45 103.45 103.45 103.45
0000103.45 103.45 103.45 103.45 103.45
0000103.45000 103.45 103.45 103.45 103.45
10003.45067 10003.45067 10003.45067 10003.45067 10003.5
000010003.45067 10003.45067 10003.45067 10003.45067 10003.5
000010003.4506700 10003.45067 10003.45067 10003.45067 10003.5
15000.0012 15000.0012 15000.0012 15000.0012 15000
000015000.0012 15000.0012 15000.0012 15000.0012 15000
000015000.0012000 15000.0012 15000.0012 15000.0012 15000
78000.89 78000.89 78000.89 78000.89 78000.9
000078000.89 78000.89 78000.89 78000.89 78000.9
000078000.89000 78000.89 78000.89 78000.89 78000.9
.0457e10 0.0457e10 0.0457e10 457000000 4.57e+08
.0457000e10 0.0457e10 0.0457000e10 457000000 4.57e+08
00000.0457000e10 0.0457e10 0.0457000e10 457000000 4.57e+08
258e8 258e8 258e8 25800000000 2.58e+10
2580000e4 2580000e4 2580000e4 25800000000 2.58e+10
0000000002580000e4 2580000e4 2580000e4 25800000000 2.58e+10
0.782e10 0.782e10 0.782e10 7820000000 7.82e+09
0000.782e10 0.782e10 0.782e10 7820000000 7.82e+09
0000.7820000e10 0.782e10 0.7820000e10 7820000000 7.82e+09
1.23E2 1.23E2 1.23E2 123 123
0001.23E2 1.23E2 1.23E2 123 123
0001.2300000E2 1.23E2 1.2300000E2 123 123
432e-102 432e-102 432e-102 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000432 4.32e-100
0000432e-102 432e-102 432e-102 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000432 4.32e-100
004320000e-106 4320000e-106 4320000e-106 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000432 4.32e-100
1.46e10 1.46e10 1.46e10 14600000000 1.46e+10
0001.46e10 1.46e10 1.46e10 14600000000 1.46e+10
0001.4600000e10 1.46e10 1.4600000e10 14600000000 1.46e+10
1.077e-300 1.077e-300 1.077e-300 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001077 1.077e-300
0001.077e-300 1.077e-300 1.077e-300 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001077 1.077e-300
0001.077000e-300 1.077e-300 1.077000e-300 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001077 1.077e-300
1.069e10 1.069e10 1.069e10 10690000000 1.069e+10
0001.069e10 1.069e10 1.069e10 10690000000 1.069e+10
0001.069000e10 1.069e10 1.069000e10 10690000000 1.069e+10
105040.03e10 105040.03e10 105040.03e10 1050400300000000 1.0504e+15
000105040.03e10 105040.03e10 105040.03e10 1050400300000000 1.0504e+15
105040.0300e10 105040.03e10 105040.0300e10 1050400300000000 1.0504e+15
..18000 ..18000 ..18000 bad Can't treat
25..00 25..00 25..00 bad Can't treat
36...77 36...77 36...77 bad Can't treat
2..8 2..8 2..8 bad Can't treat
3.8..9 3.8..9 3.8..9 bad Can't treat
.12500. .12500. .12500. bad Can't treat
12.51.400 12.51.400 12.51.400 bad Can't treat
.
。
I consider that my solution have two advantages:
我认为我的解决方案有两个优点:
-
the regex and the function number_shave() are short
regex和函数number_shave()很短
-
number_shave() doesn't only treat one number at a time but it also detects and treats all the numbers in a string. Here's a treatment that John Machin's and arrussel84's solutions can't do:
number_shave()不仅一次只处理一个数字，它还检测并处理字符串中的所有数字。这是约翰·麦辰和arrussel84的解决方案无法做到的:
code:
代码:
numbers = [['', '23456000', '23456000.', '23456000.000 \n',
'00023456000', '000023456000.', '000023456000.000 \n',
'10000', '10000.', '10000.000 \n',
'00010000', '00010000.', '00010000.000 \n',
'24', '24.', '24.000 \n',
'00024', '00024.', '00024.000 \n',
'8', '8.', '8.000 \n',
'0008', '0008.', '0008.000 \n',
'0', '00000', '0.', '000.' ],
['0.0', '0.000', '000.0', '000.000', '.000000', '.0'],
['.00023456', '.00023456000', '.00503', '.00503000 \n',
'.068', '.0680000', '.8', '.8000 \n',
'.123456123456', '.123456123456000 \n',
'.657', '.657000', '.45', '.4500000', '.7', '.70000'],
['0.0000023230000', '000.0000023230000 \n',
'0.0081000', '0000.0081000 \n',
'0.059000', '0000.059000 \n',
'0.78987400000', '00000.78987400000 \n',
'0.4400000', '00000.4400000 \n',
'0.5000', '0000.5000 \n',
'0.90', '000.90', '0.7', '000.7 '],
['2.6', '00002.6', '00002.60000 \n',
'4.71', '0004.71', '0004.7100 \n',
'23.49', '00023.49', '00023.490000 \n',
'103.45', '0000103.45', '0000103.45000 \n',
'10003.45067', '000010003.45067', '000010003.4506700 \n',
'15000.0012', '000015000.0012', '000015000.0012000 \n',
'78000.89', '000078000.89', '000078000.89000'],
['.0457e10', '.0457000e10 \n',
'0.782e10', '0000.782e10', '0000.7820000e10 \n',
'1.23E2', '0001.23E2', '0001.2300000E2 \n',
'1.46e10', '0001.46e10', '0001.4600000e10 \n',
'1.077e-456', '0001.077e-456', '0001.077000e-456 \n',
'1.069e10', '0001.069e10', '0001.069000e10 \n',
'105040.03e10', '000105040.03e10', '105040.03e10'],
['..18000', '25..00', '36...77', '2..8 \n',
'3.8..9', '.12500.', '12.51.400' ]]
import re
def number_shaver(ch,
regx = re.compile('(?<![\d.])0*(?:'
'(\d+)\.?|\.(0)'
'|(\.\d+?)|(\d+\.\d+?)'
')0*(?![\d.])') ,
repl = lambda mat: mat.group(mat.lastindex)
if mat.lastindex!=3
else '0' + mat.group(3) ):
return regx.sub(repl,ch)
for li in numbers:
one_string = ' --- '.join(li)
print one_string + '\n\n' + number_shaver(one_string) + \
'\n\n' + 3*'---------------------' + '\n'
results of the treatments of strings containing several numbers:
包含数个数字的字符串处理结果:
--- 23456000 --- 23456000. --- 23456000.000
--- 00023456000 --- 000023456000. --- 000023456000.000
--- 10000 --- 10000. --- 10000.000
--- 00010000 --- 00010000. --- 00010000.000
--- 24 --- 24. --- 24.000
--- 00024 --- 00024. --- 00024.000
--- 8 --- 8. --- 8.000
--- 0008 --- 0008. --- 0008.000
--- 0 --- 00000 --- 0. --- 000.
--- 23456000 --- 23456000 --- 23456000
--- 23456000 --- 23456000 --- 23456000
--- 10000 --- 10000 --- 10000
--- 10000 --- 10000 --- 10000
--- 24 --- 24 --- 24
--- 24 --- 24 --- 24
--- 8 --- 8 --- 8
--- 8 --- 8 --- 8
--- 0 --- 0 --- 0 --- 0
---------------------------------------------------------------
0.0 --- 0.000 --- 000.0 --- 000.000 --- .000000 --- .0
0 --- 0 --- 0 --- 0 --- 0 --- 0
---------------------------------------------------------------
.00023456 --- .00023456000 --- .00503 --- .00503000
--- .068 --- .0680000 --- .8 --- .8000
--- .123456123456 --- .123456123456000
--- .657 --- .657000 --- .45 --- .4500000 --- .7 --- .70000
0.00023456 --- 0.00023456 --- 0.00503 --- 0.00503
--- 0.068 --- 0.068 --- 0.8 --- 0.8
--- 0.123456123456 --- 0.123456123456
--- 0.657 --- 0.657 --- 0.45 --- 0.45 --- 0.7 --- 0.7
---------------------------------------------------------------
0.0000023230000 --- 000.0000023230000
--- 0.0081000 --- 0000.0081000
--- 0.059000 --- 0000.059000
--- 0.78987400000 --- 00000.78987400000
--- 0.4400000 --- 00000.4400000
--- 0.5000 --- 0000.5000
--- 0.90 --- 000.90 --- 0.7 --- 000.7
0.000002323 --- 0.000002323
--- 0.0081 --- 0.0081
--- 0.059 --- 0.059
--- 0.789874 --- 0.789874
--- 0.44 --- 0.44
--- 0.5 --- 0.5
--- 0.9 --- 0.9 --- 0.7 --- 0.7
---------------------------------------------------------------
2.6 --- 00002.6 --- 00002.60000
--- 4.71 --- 0004.71 --- 0004.7100
--- 23.49 --- 00023.49 --- 00023.490000
--- 103.45 --- 0000103.45 --- 0000103.45000
--- 10003.45067 --- 000010003.45067 --- 000010003.4506700
--- 15000.0012 --- 000015000.0012 --- 000015000.0012000
--- 78000.89 --- 000078000.89 --- 000078000.89000
2.6 --- 2.6 --- 2.6
--- 4.71 --- 4.71 --- 4.71
--- 23.49 --- 23.49 --- 23.49
--- 103.45 --- 103.45 --- 103.45
--- 10003.45067 --- 10003.45067 --- 10003.45067
--- 15000.0012 --- 15000.0012 --- 15000.0012
--- 78000.89 --- 78000.89 --- 78000.89
---------------------------------------------------------------
.0457e10 --- .0457000e10
--- 0.782e10 --- 0000.782e10 --- 0000.7820000e10
--- 1.23E2 --- 0001.23E2 --- 0001.2300000E2
--- 1.46e10 --- 0001.46e10 --- 0001.4600000e10
--- 1.077e-456 --- 0001.077e-456 --- 0001.077000e-456
--- 1.069e10 --- 0001.069e10 --- 0001.069000e10
--- 105040.03e10 --- 000105040.03e10 --- 105040.03e10
0.0457e10 --- 0.0457e10
--- 0.782e10 --- 0.782e10 --- 0.782e10
--- 1.23E2 --- 1.23E2 --- 1.23E2
--- 1.46e10 --- 1.46e10 --- 1.46e10
--- 1.077e-456 --- 1.077e-456 --- 1.077e-456
--- 1.069e10 --- 1.069e10 --- 1.069e10
--- 105040.03e10 --- 105040.03e10 --- 105040.03e10
---------------------------------------------------------------
..18000 --- 25..00 --- 36...77 --- 2..8
--- 3.8..9 --- .12500. --- 12.51.400
..18000 --- 25..00 --- 36...77 --- 2..8
--- 3.8..9 --- .12500. --- 12.51.400
---------------------------------------------------------------
.
。
Consequently, the regex can also been used to merely find all the numbers in a string, without removing the zeros if not wished.
因此，regex也可以用于仅查找字符串中的所有数字，而不删除非希望的0。
.
。
PS: see more in my other answer that explains the regex and its functionning
请参阅我的另一个解释regex及其功能的答案
#1
18
Updated Generalized to maintain precision and handle unseen values:
更新广义，保持精度和处理不可见值:
import decimal
import random
def format_number(num):
try:
dec = decimal.Decimal(num)
except:
return 'bad'
tup = dec.as_tuple()
delta = len(tup.digits) + tup.exponent
digits = ''.join(str(d) for d in tup.digits)
if delta <= 0:
zeros = abs(tup.exponent) - len(tup.digits)
val = '0.' + ('0'*zeros) + digits
else:
val = digits[:delta] + ('0'*tup.exponent) + '.' + digits[delta:]
val = val.rstrip('0')
if val[-1] == '.':
val = val[:-1]
if tup.sign:
return '-' + val
return val
# test data
NUMS = '''
0.0000 0
0 0
123.45000 123.45
0000 0
123.4506780 123.450678
0.1 0.1
0.001 0.001
0.005000 0.005
.1234 0.1234
1.23e1 12.3
-123.456 -123.456
4.98e10 49800000000
4.9815135 4.9815135
4e30 4000000000000000000000000000000
-0.0000000000004 -0.0000000000004
-.4e-12 -0.0000000000004
-0.11112 -0.11112
1.3.4.5 bad
-1.2.3 bad
'''
for num, exp in [s.split() for s in NUMS.split('\n') if s]:
res = format_number(num)
print res
assert exp == res
Output:
输出:
0
0
123.45
0
123.450678
0.1
0.001
0.005
0.1234
12.3
-123.456
49800000000
4.9815135
4000000000000000000000000000000
-0.0000000000004
-0.0000000000004
-0.11112
bad
bad
#2
10
You can use format strings if you want, but be aware that you might need to set your desired precision, as format strings have their own logic for this by default. Janneb suggests a precision of 17 in another answer.
如果需要，可以使用格式字符串，但是要注意，您可能需要设置所需的精度，因为格式字符串在默认情况下有自己的逻辑。Janneb建议在另一个答案中精确到17。
'{:g}'.format(float(your_string_goes_here))
After thinking about this some more, though, I think the simplest and best solution is just to cast the string twice (as jathanism suggests):
然而，在进一步思考这个问题之后，我认为最简单、最好的解决办法就是将弦抛两次(正如jathanism所言):
str(float(your_string_goes_here))
Edit: Added clarification because of comment.
编辑:因为评论添加了澄清。
#3
6
For the floating point numbers, you can just cast the string to a float:
对于浮点数，您可以将字符串转换为浮点数:
>>> float('123.4506780')
123.450678
For the zero values, you can just cast those to an integer:
对于零值，你可以将它们转换为整数:
>>> int('0000')
0
When printed, numeric values are automatically converted to strings. If you need these to actually be strings, you may simply cast them back to strings with str(), e.g.:
当打印时，数值将自动转换为字符串。如果您需要这些字符串实际上是字符串，您可以简单地将它们转换为字符串()，例如:
>>> str(float('123.4506780'))
'123.450678'
#4
2
'%.17g' % float(mystr)
depending on what you actually want to do..
这取决于你到底想做什么。
#5
1
Script:
脚本:
def tidy_float(s):
"""Return tidied float representation.
Remove superflous leading/trailing zero digits.
Remove '.' if value is an integer.
Return '****' if float(s) fails.
"""
# float?
try:
f = float(s)
except ValueError:
return '****'
# int?
try:
i = int(s)
return str(i)
except ValueError:
pass
# scientific notation?
if 'e' in s or 'E' in s:
t = s.lstrip('0')
if t.startswith('.'): t = '0' + t
return t
# float with integral value (includes zero)?
i = int(f)
if i == f:
return str(i)
assert '.' in s
t = s.strip('0')
if t.startswith('.'): t = '0' + t
if t.endswith('.'): t += '0'
return t
if __name__ == "__main__":
# Each line has test string followed by expected output
tests = """
0.000 0
0 0
0000 0
0.4000 0.4
0.0081000 0.0081
103.45 103.45
103.4506700 103.45067
14500.0012 14500.0012
478000.89 478000.89
993.59.18 ****
12.5831.400 ****
.458 0.458
.48587000 0.48587
.0000 0
10000 10000
10000.000 10000
-10000 -10000
-10000.000 -10000
1.23e2 1.23e2
1.23e10 1.23e10
.123e10 0.123e10
""".splitlines()
for test in tests:
x = test.split()
if not x: continue
data, expected = x
actual = tidy_float(data)
print "data=%r exp=%r act=%r %s" % (
data, expected, actual, ["**FAIL**", ""][actual == expected])
Output (Python 2.7.1):
输出(Python 2.7.1):
data='0.000' exp='0' act='0'
data='0' exp='0' act='0'
data='0000' exp='0' act='0'
data='0.4000' exp='0.4' act='0.4'
data='0.0081000' exp='0.0081' act='0.0081'
data='103.45' exp='103.45' act='103.45'
data='103.4506700' exp='103.45067' act='103.45067'
data='14500.0012' exp='14500.0012' act='14500.0012'
data='478000.89' exp='478000.89' act='478000.89'
data='993.59.18' exp='****' act='****'
data='12.5831.400' exp='****' act='****'
data='.458' exp='0.458' act='0.458'
data='.48587000' exp='0.48587' act='0.48587'
data='.0000' exp='0' act='0'
data='10000' exp='10000' act='10000'
data='10000.000' exp='10000' act='10000'
data='-10000' exp='-10000' act='-10000'
data='-10000.000' exp='-10000' act='-10000'
data='1.23e2' exp='1.23e2' act='1.23e2'
data='1.23e10' exp='1.23e10' act='1.23e10'
data='.123e10' exp='0.123e10' act='0.123e10'
#6
1
ADDITION TO THE EDIT 2 of my other answer
(All was to long to be in only one post)
(所有人都渴望只呆在一个岗位上)
The pattern of the regex defines 4 sub-patterns, each one matching with a certain type of numbers. Each time the regex matches with a part of a string, there is only one of the sub-patterns that matches, hence the possibility to use mat.lastindex in the replacement function. The following code shows the matchings of the sub-pattern against various numbers:
regex的模式定义了4个子模式，每个子模式与特定类型的数字匹配。每次regex与字符串的一部分匹配时，只有一个匹配的子模式，因此可以在替换函数中使用mat.lastindex。下面的代码显示子模式与不同数字的匹配:
import re
def float_show(ch,
regx = re.compile(
'(?<![\d.])'
'0*' # potentiel heading zeros
'(?:'
'(\d+)\.?' # INTEGERS :
# ~ pure integers non-0 or 0
# 000450 , 136000 , 87 , 000 , 0
# ~ integer part non-0 + '.'
# 0044. , 4100.
# ~ integer part 0 + '.'
# 000. , 0.
# ~ integer part non-0 + '.' + fractional part 0:
# 000570.00 , 193.0 , 3.000
'|\.(0)' # SPECIAL CASE, 0 WITH FRACTIONAL PART :
# ~ integer part 0 + compulsory fractional part 0:
# 000.0, 0.000 , .0 , .00000
'|(\.\d+?)' # FLOATING POINT NUMBER
# ~ with integer part 0:
# 000.0890 , 0.52 , 0.1 , .077000 , .1400 , .0006010
'|(\d+\.\d+?)' # FLOATING POINT NUMBER
# ~ with integer part non-0:
# 0024000.013000 , 145.0235 , 3.00058
')'
'0*' # potential tailing zeros
'(?![\d.])'),
repl = lambda mat: mat.group(mat.lastindex)
if mat.lastindex!=3
else '0' + mat.group(3) ):
mat = regx.search(ch)
if mat:
return (ch,regx.sub(repl,ch),repr(mat.groups()))
else:
return (ch,'No match','No groups')
numbers = ['23456000', '23456000.', '23456000.000',
'00023456000', '000023456000.', '000023456000.000',
'10000', '10000.', '10000.000',
'00010000', '00010000.', '00010000.000',
'24', '24.', '24.000',
'00024', '00024.', '00024.000',
'8', '8.', '8.000',
'0008', '0008.', '0008.000',
'0', '00000', '0.', '000.',
'\n',
'0.0', '0.000', '000.0', '000.000', '.000000', '.0',
'\n',
'.00023456', '.00023456000', '.00503', '.00503000',
'.068', '.0680000', '.8', '.8000',
'.123456123456', '.123456123456000',
'.657', '.657000', '.45', '.4500000', '.7', '.70000',
'\n',
'0.0000023230000', '000.0000023230000',
'0.0081000', '0000.0081000',
'0.059000', '0000.059000',
'0.78987400000', '00000.78987400000',
'0.4400000', '00000.4400000',
'0.5000', '0000.5000',
'0.90', '000.90', '0.7', '000.7',
'\n',
'2.6', '00002.6', '00002.60000',
'4.71', '0004.71', '0004.7100',
'23.49', '00023.49', '00023.490000',
'103.45', '0000103.45', '0000103.45000',
'10003.45067', '000010003.45067', '000010003.4506700',
'15000.0012', '000015000.0012', '000015000.0012000',
'78000.89', '000078000.89', '000078000.89000',
'\n',
'.0457e10', '.0457000e10',
'0.782e10', '0000.782e10', '0000.7820000e10',
'1.23E2', '0001.23E2', '0001.2300000E2',
'1.46e10', '0001.46e10', '0001.4600000e10',
'1.077e-456', '0001.077e-456', '0001.077000e-456',
'1.069e10', '0001.069e10', '0001.069000e10',
'105040.03e10', '000105040.03e10', '105040.0300e10',
'\n',
'..18000', '25..00', '36...77', '2..8',
'3.8..9', '.12500.', '12.51.400' ]
pat = '%20s %-16s %s'
li = [pat % ('tested number ',' shaved float',' regx.search(number).groups()')]
li.extend(pat % float_show(ch) if ch!='\n' else '\n' for ch in numbers)
print '\n'.join(li)
demonstrates
演示了
tested number shaved float regx.search(number).groups()
23456000 23456000 ('23456000', None, None, None)
23456000. 23456000 ('23456000', None, None, None)
23456000.000 23456000 ('23456000', None, None, None)
00023456000 23456000 ('23456000', None, None, None)
000023456000. 23456000 ('23456000', None, None, None)
000023456000.000 23456000 ('23456000', None, None, None)
10000 10000 ('10000', None, None, None)
10000. 10000 ('10000', None, None, None)
10000.000 10000 ('10000', None, None, None)
00010000 10000 ('10000', None, None, None)
00010000. 10000 ('10000', None, None, None)
00010000.000 10000 ('10000', None, None, None)
24 24 ('24', None, None, None)
24. 24 ('24', None, None, None)
24.000 24 ('24', None, None, None)
00024 24 ('24', None, None, None)
00024. 24 ('24', None, None, None)
00024.000 24 ('24', None, None, None)
8 8 ('8', None, None, None)
8. 8 ('8', None, None, None)
8.000 8 ('8', None, None, None)
0008 8 ('8', None, None, None)
0008. 8 ('8', None, None, None)
0008.000 8 ('8', None, None, None)
0 0 ('0', None, None, None)
00000 0 ('0', None, None, None)
0. 0 ('0', None, None, None)
000. 0 ('0', None, None, None)
0.0 0 (None, '0', None, None)
0.000 0 (None, '0', None, None)
000.0 0 (None, '0', None, None)
000.000 0 (None, '0', None, None)
.000000 0 (None, '0', None, None)
.0 0 (None, '0', None, None)
.00023456 0.00023456 (None, None, '.00023456', None)
.00023456000 0.00023456 (None, None, '.00023456', None)
.00503 0.00503 (None, None, '.00503', None)
.00503000 0.00503 (None, None, '.00503', None)
.068 0.068 (None, None, '.068', None)
.0680000 0.068 (None, None, '.068', None)
.8 0.8 (None, None, '.8', None)
.8000 0.8 (None, None, '.8', None)
.123456123456 0.123456123456 (None, None, '.123456123456', None)
.123456123456000 0.123456123456 (None, None, '.123456123456', None)
.657 0.657 (None, None, '.657', None)
.657000 0.657 (None, None, '.657', None)
.45 0.45 (None, None, '.45', None)
.4500000 0.45 (None, None, '.45', None)
.7 0.7 (None, None, '.7', None)
.70000 0.7 (None, None, '.7', None)
0.0000023230000 0.000002323 (None, None, '.000002323', None)
000.0000023230000 0.000002323 (None, None, '.000002323', None)
0.0081000 0.0081 (None, None, '.0081', None)
0000.0081000 0.0081 (None, None, '.0081', None)
0.059000 0.059 (None, None, '.059', None)
0000.059000 0.059 (None, None, '.059', None)
0.78987400000 0.789874 (None, None, '.789874', None)
00000.78987400000 0.789874 (None, None, '.789874', None)
0.4400000 0.44 (None, None, '.44', None)
00000.4400000 0.44 (None, None, '.44', None)
0.5000 0.5 (None, None, '.5', None)
0000.5000 0.5 (None, None, '.5', None)
0.90 0.9 (None, None, '.9', None)
000.90 0.9 (None, None, '.9', None)
0.7 0.7 (None, None, '.7', None)
000.7 0.7 (None, None, '.7', None)
2.6 2.6 (None, None, None, '2.6')
00002.6 2.6 (None, None, None, '2.6')
00002.60000 2.6 (None, None, None, '2.6')
4.71 4.71 (None, None, None, '4.71')
0004.71 4.71 (None, None, None, '4.71')
0004.7100 4.71 (None, None, None, '4.71')
23.49 23.49 (None, None, None, '23.49')
00023.49 23.49 (None, None, None, '23.49')
00023.490000 23.49 (None, None, None, '23.49')
103.45 103.45 (None, None, None, '103.45')
0000103.45 103.45 (None, None, None, '103.45')
0000103.45000 103.45 (None, None, None, '103.45')
10003.45067 10003.45067 (None, None, None, '10003.45067')
000010003.45067 10003.45067 (None, None, None, '10003.45067')
000010003.4506700 10003.45067 (None, None, None, '10003.45067')
15000.0012 15000.0012 (None, None, None, '15000.0012')
000015000.0012 15000.0012 (None, None, None, '15000.0012')
000015000.0012000 15000.0012 (None, None, None, '15000.0012')
78000.89 78000.89 (None, None, None, '78000.89')
000078000.89 78000.89 (None, None, None, '78000.89')
000078000.89000 78000.89 (None, None, None, '78000.89')
.0457e10 0.0457e10 (None, None, '.0457', None)
.0457000e10 0.0457e10 (None, None, '.0457', None)
0.782e10 0.782e10 (None, None, '.782', None)
0000.782e10 0.782e10 (None, None, '.782', None)
0000.7820000e10 0.782e10 (None, None, '.782', None)
1.23E2 1.23E2 (None, None, None, '1.23')
0001.23E2 1.23E2 (None, None, None, '1.23')
0001.2300000E2 1.23E2 (None, None, None, '1.23')
1.46e10 1.46e10 (None, None, None, '1.46')
0001.46e10 1.46e10 (None, None, None, '1.46')
0001.4600000e10 1.46e10 (None, None, None, '1.46')
1.077e-456 1.077e-456 (None, None, None, '1.077')
0001.077e-456 1.077e-456 (None, None, None, '1.077')
0001.077000e-456 1.077e-456 (None, None, None, '1.077')
1.069e10 1.069e10 (None, None, None, '1.069')
0001.069e10 1.069e10 (None, None, None, '1.069')
0001.069000e10 1.069e10 (None, None, None, '1.069')
105040.03e10 105040.03e10 (None, None, None, '105040.03')
000105040.03e10 105040.03e10 (None, None, None, '105040.03')
105040.0300e10 105040.03e10 (None, None, None, '105040.03')
..18000 No match No groups
25..00 No match No groups
36...77 No match No groups
2..8 No match No groups
3.8..9 No match No groups
.12500. No match No groups
12.51.400 No match No groups
#7
1
FIRST "SOLUTION"
import re
regx=re.compile('(?<![\d.])'
'(?!\d*\.\d*\.)' # excludes certain string as not being numbers
'((\d|\.\d)([\d.])*?)' # the only matching group
'([0\.]*)'
'(?![\d.])')
regx.sub('\\1',ch)
.
。
EDIT 1
John Machin said that 10000 and 10000.000 produce 1 instead of 10000
John Machin说10000和10000.000生产1而不是10000
I corrected the replacement function with the help of (?!(?<=0)\.)
我使用(?!(?<=0)\纠正了替换函数。)
import re
regx = re.compile('(?<![\d.])' '(?![1-9]\d*(?![\d.])|\d*\.\d*\.)'
'0*(?!(?<=0)\.)'
'([\d.]+?)' # the only group , which is kept
'\.?0*'
'(?![\d.])')
regx.sub('\\1',ch)
.
。
EDIT 2
To correct the remaining shortcomings [ '.0000' producing '.' , pointed out by John Machin, and '000078000' producing '78' ] , I rewrote a regex build on a new idea. It's simpler. The regex detects all types of numbers.
以纠正其余的缺点。0000年“生产”。约翰·麦辰(John Machin)和“000078000”(制作《78》)指出，我在一个新想法的基础上重写了一个regex。这是简单的。regex检测所有类型的数字。
This solution not only cuts the trailing zeros off, but also the heading zeros. Here's the comparison of this solution with John Machin's tidy_float() , samplebias's number_format() , arussell84's '{:g}'.format() . There are some differences between the results of my function (all correct this time) and the others :
这个解决方案不仅把后面的0去掉了，而且还把前面的0去掉了。下面是这个解决方案与John Machin的tidy_float()、samplebias的number_format()、arussell84的'{:g}'.format()的比较。我的函数结果(这次都是正确的)和其他的有一些不同:
import re
def number_shaver(ch,
regx = re.compile('(?<![\d.])0*(?:'
'(\d+)\.?|\.(0)'
'|(\.\d+?)|(\d+\.\d+?)'
')0*(?![\d.])') ,
repl = lambda mat: mat.group(mat.lastindex)
if mat.lastindex!=3
else '0' + mat.group(3) ):
return regx.sub(repl,ch)
def tidy_float(s): # John Machin
"""Return tidied float representation.
Remove superflous leading/trailing zero digits.
Remove '.' if value is an integer.
Return '****' if float(s) fails.
"""
# float?
try:
f = float(s)
except ValueError:
return s
# int?
try:
i = int(s)
return str(i)
except ValueError:
pass
# scientific notation?
if 'e' in s or 'E' in s:
t = s.lstrip('0')
if t.startswith('.'): t = '0' + t
return t
# float with integral value (includes zero)?
i = int(f)
if i == f:
return str(i)
assert '.' in s
t = s.strip('0')
if t.startswith('.'): t = '0' + t
if t.endswith('.'): t += '0'
return t
def format_float(s): # arrussell84
return '{:g}'.format(float(s)) if s.count('.')<2 \
else "Can't treat"
import decimal
def format_number(num):
try:
dec = decimal.Decimal(num)
except:
return 'bad'
tup = dec.as_tuple()
delta = len(tup.digits) + tup.exponent
digits = ''.join(str(d) for d in tup.digits)
if delta <= 0:
zeros = abs(tup.exponent) - len(tup.digits)
val = '0.' + ('0'*zeros) + digits
else:
val = digits[:delta] + ('0'*tup.exponent) + '.' + digits[delta:]
val = val.rstrip('0')
if val[-1] == '.':
val = val[:-1]
if tup.sign:
return '-' + val
return val
numbers = ['23456000', '23456000.', '23456000.000',
'00023456000', '000023456000.', '000023456000.000',
'10000', '10000.', '10000.000',
'00010000', '00010000.', '00010000.000',
'24', '24.', '24.000',
'00024', '00024.', '00024.000',
'8', '8.', '8.000',
'0008', '0008.', '0008.000',
'0', '00000', '0.', '000.',
'\n',
'0.0', '0.000', '000.0', '000.000', '.000000', '.0',
'\n',
'.00023456', '.00023456000', '.00503', '.00503000',
'.068', '.0680000', '.8', '.8000',
'.123456123456', '.123456123456000',
'.657', '.657000', '.45', '.4500000', '.7', '.70000',
'\n',
'0.0000023230000', '000.0000023230000',
'0.0081000', '0000.0081000',
'0.059000', '0000.059000',
'0.78987400000', '00000.78987400000',
'0.4400000', '00000.4400000',
'0.5000', '0000.5000',
'0.90', '000.90', '0.7', '000.7',
'\n',
'2.6', '00002.6', '00002.60000',
'4.71', '0004.71', '0004.7100',
'23.49', '00023.49', '00023.490000',
'103.45', '0000103.45', '0000103.45000',
'10003.45067', '000010003.45067', '000010003.4506700',
'15000.0012', '000015000.0012', '000015000.0012000',
'78000.89', '000078000.89', '000078000.89000',
'\n',
'.0457e10', '.0457000e10','00000.0457000e10',
'258e8', '2580000e4', '0000000002580000e4',
# notice the difference of exponents
'0.782e10', '0000.782e10', '0000.7820000e10',
'1.23E2', '0001.23E2', '0001.2300000E2',
'432e-102', '0000432e-102', '004320000e-106',
# notice the difference of exponents
'1.46e10', '0001.46e10', '0001.4600000e10',
'1.077e-300', '0001.077e-300', '0001.077000e-300',
'1.069e10', '0001.069e10', '0001.069000e10',
'105040.03e10', '000105040.03e10', '105040.0300e10',
'\n',
'..18000', '25..00', '36...77', '2..8',
'3.8..9', '.12500.', '12.51.400' ]
pat = '%18s %-15s %-15s %-15s %s' li = [pat % ('tested number','float_shaver', 'tidy_float',"format_number()","'{:g}'.format()")] li.extend(pat % (n,number_shaver(n),tidy_float(n),format_number(n),format_float(n)) if n!='\n' else '\n' for n in numbers)
帕特= % 18年代-15年代-15年代-15年代% % % % s的李=(pat %(测试数量,‘float_shaver’,‘tidy_float’,”format_number()”,“‘{:g }’.format()))。扩展(pat % (n,number_shaver(n)，tidy_float(n)，format_number(n)，format_float(n)))如果n!='\n' else '\n'代表数字n)
print '\n'.join(li)
打印' \ n ' . join(李)
result of the comparison:
比较的结果:
tested number float_shaver tidy_float format_number() '{:g}'.format()
23456000 23456000 23456000 23456000 2.3456e+07
23456000. 23456000 23456000 23456000 2.3456e+07
23456000.000 23456000 23456000 23456000 2.3456e+07
00023456000 23456000 23456000 23456000 2.3456e+07
000023456000. 23456000 23456000 23456000 2.3456e+07
000023456000.000 23456000 23456000 23456000 2.3456e+07
10000 10000 10000 10000 10000
10000. 10000 10000 10000 10000
10000.000 10000 10000 10000 10000
00010000 10000 10000 10000 10000
00010000. 10000 10000 10000 10000
00010000.000 10000 10000 10000 10000
24 24 24 24 24
24. 24 24 24 24
24.000 24 24 24 24
00024 24 24 24 24
00024. 24 24 24 24
00024.000 24 24 24 24
8 8 8 8 8
8. 8 8 8 8
8.000 8 8 8 8
0008 8 8 8 8
0008. 8 8 8 8
0008.000 8 8 8 8
0 0 0 0 0
00000 0 0 0 0
0. 0 0 0 0
000. 0 0 0 0
0.0 0 0 0 0
0.000 0 0 0 0
000.0 0 0 0 0
000.000 0 0 0 0
.000000 0 0 0 0
.0 0 0 0 0
.00023456 0.00023456 0.00023456 0.00023456 0.00023456
.00023456000 0.00023456 0.00023456 0.00023456 0.00023456
.00503 0.00503 0.00503 0.00503 0.00503
.00503000 0.00503 0.00503 0.00503 0.00503
.068 0.068 0.068 0.068 0.068
.0680000 0.068 0.068 0.068 0.068
.8 0.8 0.8 0.8 0.8
.8000 0.8 0.8 0.8 0.8
.123456123456 0.123456123456 0.123456123456 0.123456123456 0.123456
.123456123456000 0.123456123456 0.123456123456 0.123456123456 0.123456
.657 0.657 0.657 0.657 0.657
.657000 0.657 0.657 0.657 0.657
.45 0.45 0.45 0.45 0.45
.4500000 0.45 0.45 0.45 0.45
.7 0.7 0.7 0.7 0.7
.70000 0.7 0.7 0.7 0.7
0.0000023230000 0.000002323 0.000002323 0.000002323 2.323e-06
000.0000023230000 0.000002323 0.000002323 0.000002323 2.323e-06
0.0081000 0.0081 0.0081 0.0081 0.0081
0000.0081000 0.0081 0.0081 0.0081 0.0081
0.059000 0.059 0.059 0.059 0.059
0000.059000 0.059 0.059 0.059 0.059
0.78987400000 0.789874 0.789874 0.789874 0.789874
00000.78987400000 0.789874 0.789874 0.789874 0.789874
0.4400000 0.44 0.44 0.44 0.44
00000.4400000 0.44 0.44 0.44 0.44
0.5000 0.5 0.5 0.5 0.5
0000.5000 0.5 0.5 0.5 0.5
0.90 0.9 0.9 0.9 0.9
000.90 0.9 0.9 0.9 0.9
0.7 0.7 0.7 0.7 0.7
000.7 0.7 0.7 0.7 0.7
2.6 2.6 2.6 2.6 2.6
00002.6 2.6 2.6 2.6 2.6
00002.60000 2.6 2.6 2.6 2.6
4.71 4.71 4.71 4.71 4.71
0004.71 4.71 4.71 4.71 4.71
0004.7100 4.71 4.71 4.71 4.71
23.49 23.49 23.49 23.49 23.49
00023.49 23.49 23.49 23.49 23.49
00023.490000 23.49 23.49 23.49 23.49
103.45 103.45 103.45 103.45 103.45
0000103.45 103.45 103.45 103.45 103.45
0000103.45000 103.45 103.45 103.45 103.45
10003.45067 10003.45067 10003.45067 10003.45067 10003.5
000010003.45067 10003.45067 10003.45067 10003.45067 10003.5
000010003.4506700 10003.45067 10003.45067 10003.45067 10003.5
15000.0012 15000.0012 15000.0012 15000.0012 15000
000015000.0012 15000.0012 15000.0012 15000.0012 15000
000015000.0012000 15000.0012 15000.0012 15000.0012 15000
78000.89 78000.89 78000.89 78000.89 78000.9
000078000.89 78000.89 78000.89 78000.89 78000.9
000078000.89000 78000.89 78000.89 78000.89 78000.9
.0457e10 0.0457e10 0.0457e10 457000000 4.57e+08
.0457000e10 0.0457e10 0.0457000e10 457000000 4.57e+08
00000.0457000e10 0.0457e10 0.0457000e10 457000000 4.57e+08
258e8 258e8 258e8 25800000000 2.58e+10
2580000e4 2580000e4 2580000e4 25800000000 2.58e+10
0000000002580000e4 2580000e4 2580000e4 25800000000 2.58e+10
0.782e10 0.782e10 0.782e10 7820000000 7.82e+09
0000.782e10 0.782e10 0.782e10 7820000000 7.82e+09
0000.7820000e10 0.782e10 0.7820000e10 7820000000 7.82e+09
1.23E2 1.23E2 1.23E2 123 123
0001.23E2 1.23E2 1.23E2 123 123
0001.2300000E2 1.23E2 1.2300000E2 123 123
432e-102 432e-102 432e-102 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000432 4.32e-100
0000432e-102 432e-102 432e-102 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000432 4.32e-100
004320000e-106 4320000e-106 4320000e-106 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000432 4.32e-100
1.46e10 1.46e10 1.46e10 14600000000 1.46e+10
0001.46e10 1.46e10 1.46e10 14600000000 1.46e+10
0001.4600000e10 1.46e10 1.4600000e10 14600000000 1.46e+10
1.077e-300 1.077e-300 1.077e-300 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001077 1.077e-300
0001.077e-300 1.077e-300 1.077e-300 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001077 1.077e-300
0001.077000e-300 1.077e-300 1.077000e-300 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001077 1.077e-300
1.069e10 1.069e10 1.069e10 10690000000 1.069e+10
0001.069e10 1.069e10 1.069e10 10690000000 1.069e+10
0001.069000e10 1.069e10 1.069000e10 10690000000 1.069e+10
105040.03e10 105040.03e10 105040.03e10 1050400300000000 1.0504e+15
000105040.03e10 105040.03e10 105040.03e10 1050400300000000 1.0504e+15
105040.0300e10 105040.03e10 105040.0300e10 1050400300000000 1.0504e+15
..18000 ..18000 ..18000 bad Can't treat
25..00 25..00 25..00 bad Can't treat
36...77 36...77 36...77 bad Can't treat
2..8 2..8 2..8 bad Can't treat
3.8..9 3.8..9 3.8..9 bad Can't treat
.12500. .12500. .12500. bad Can't treat
12.51.400 12.51.400 12.51.400 bad Can't treat
.
。
I consider that my solution have two advantages:
我认为我的解决方案有两个优点:
-
the regex and the function number_shave() are short
regex和函数number_shave()很短
-
number_shave() doesn't only treat one number at a time but it also detects and treats all the numbers in a string. Here's a treatment that John Machin's and arrussel84's solutions can't do:
number_shave()不仅一次只处理一个数字，它还检测并处理字符串中的所有数字。这是约翰·麦辰和arrussel84的解决方案无法做到的:
code:
代码:
numbers = [['', '23456000', '23456000.', '23456000.000 \n',
'00023456000', '000023456000.', '000023456000.000 \n',
'10000', '10000.', '10000.000 \n',
'00010000', '00010000.', '00010000.000 \n',
'24', '24.', '24.000 \n',
'00024', '00024.', '00024.000 \n',
'8', '8.', '8.000 \n',
'0008', '0008.', '0008.000 \n',
'0', '00000', '0.', '000.' ],
['0.0', '0.000', '000.0', '000.000', '.000000', '.0'],
['.00023456', '.00023456000', '.00503', '.00503000 \n',
'.068', '.0680000', '.8', '.8000 \n',
'.123456123456', '.123456123456000 \n',
'.657', '.657000', '.45', '.4500000', '.7', '.70000'],
['0.0000023230000', '000.0000023230000 \n',
'0.0081000', '0000.0081000 \n',
'0.059000', '0000.059000 \n',
'0.78987400000', '00000.78987400000 \n',
'0.4400000', '00000.4400000 \n',
'0.5000', '0000.5000 \n',
'0.90', '000.90', '0.7', '000.7 '],
['2.6', '00002.6', '00002.60000 \n',
'4.71', '0004.71', '0004.7100 \n',
'23.49', '00023.49', '00023.490000 \n',
'103.45', '0000103.45', '0000103.45000 \n',
'10003.45067', '000010003.45067', '000010003.4506700 \n',
'15000.0012', '000015000.0012', '000015000.0012000 \n',
'78000.89', '000078000.89', '000078000.89000'],
['.0457e10', '.0457000e10 \n',
'0.782e10', '0000.782e10', '0000.7820000e10 \n',
'1.23E2', '0001.23E2', '0001.2300000E2 \n',
'1.46e10', '0001.46e10', '0001.4600000e10 \n',
'1.077e-456', '0001.077e-456', '0001.077000e-456 \n',
'1.069e10', '0001.069e10', '0001.069000e10 \n',
'105040.03e10', '000105040.03e10', '105040.03e10'],
['..18000', '25..00', '36...77', '2..8 \n',
'3.8..9', '.12500.', '12.51.400' ]]
import re
def number_shaver(ch,
regx = re.compile('(?<![\d.])0*(?:'
'(\d+)\.?|\.(0)'
'|(\.\d+?)|(\d+\.\d+?)'
')0*(?![\d.])') ,
repl = lambda mat: mat.group(mat.lastindex)
if mat.lastindex!=3
else '0' + mat.group(3) ):
return regx.sub(repl,ch)
for li in numbers:
one_string = ' --- '.join(li)
print one_string + '\n\n' + number_shaver(one_string) + \
'\n\n' + 3*'---------------------' + '\n'
results of the treatments of strings containing several numbers:
包含数个数字的字符串处理结果:
--- 23456000 --- 23456000. --- 23456000.000
--- 00023456000 --- 000023456000. --- 000023456000.000
--- 10000 --- 10000. --- 10000.000
--- 00010000 --- 00010000. --- 00010000.000
--- 24 --- 24. --- 24.000
--- 00024 --- 00024. --- 00024.000
--- 8 --- 8. --- 8.000
--- 0008 --- 0008. --- 0008.000
--- 0 --- 00000 --- 0. --- 000.
--- 23456000 --- 23456000 --- 23456000
--- 23456000 --- 23456000 --- 23456000
--- 10000 --- 10000 --- 10000
--- 10000 --- 10000 --- 10000
--- 24 --- 24 --- 24
--- 24 --- 24 --- 24
--- 8 --- 8 --- 8
--- 8 --- 8 --- 8
--- 0 --- 0 --- 0 --- 0
---------------------------------------------------------------
0.0 --- 0.000 --- 000.0 --- 000.000 --- .000000 --- .0
0 --- 0 --- 0 --- 0 --- 0 --- 0
---------------------------------------------------------------
.00023456 --- .00023456000 --- .00503 --- .00503000
--- .068 --- .0680000 --- .8 --- .8000
--- .123456123456 --- .123456123456000
--- .657 --- .657000 --- .45 --- .4500000 --- .7 --- .70000
0.00023456 --- 0.00023456 --- 0.00503 --- 0.00503
--- 0.068 --- 0.068 --- 0.8 --- 0.8
--- 0.123456123456 --- 0.123456123456
--- 0.657 --- 0.657 --- 0.45 --- 0.45 --- 0.7 --- 0.7
---------------------------------------------------------------
0.0000023230000 --- 000.0000023230000
--- 0.0081000 --- 0000.0081000
--- 0.059000 --- 0000.059000
--- 0.78987400000 --- 00000.78987400000
--- 0.4400000 --- 00000.4400000
--- 0.5000 --- 0000.5000
--- 0.90 --- 000.90 --- 0.7 --- 000.7
0.000002323 --- 0.000002323
--- 0.0081 --- 0.0081
--- 0.059 --- 0.059
--- 0.789874 --- 0.789874
--- 0.44 --- 0.44
--- 0.5 --- 0.5
--- 0.9 --- 0.9 --- 0.7 --- 0.7
---------------------------------------------------------------
2.6 --- 00002.6 --- 00002.60000
--- 4.71 --- 0004.71 --- 0004.7100
--- 23.49 --- 00023.49 --- 00023.490000
--- 103.45 --- 0000103.45 --- 0000103.45000
--- 10003.45067 --- 000010003.45067 --- 000010003.4506700
--- 15000.0012 --- 000015000.0012 --- 000015000.0012000
--- 78000.89 --- 000078000.89 --- 000078000.89000
2.6 --- 2.6 --- 2.6
--- 4.71 --- 4.71 --- 4.71
--- 23.49 --- 23.49 --- 23.49
--- 103.45 --- 103.45 --- 103.45
--- 10003.45067 --- 10003.45067 --- 10003.45067
--- 15000.0012 --- 15000.0012 --- 15000.0012
--- 78000.89 --- 78000.89 --- 78000.89
---------------------------------------------------------------
.0457e10 --- .0457000e10
--- 0.782e10 --- 0000.782e10 --- 0000.7820000e10
--- 1.23E2 --- 0001.23E2 --- 0001.2300000E2
--- 1.46e10 --- 0001.46e10 --- 0001.4600000e10
--- 1.077e-456 --- 0001.077e-456 --- 0001.077000e-456
--- 1.069e10 --- 0001.069e10 --- 0001.069000e10
--- 105040.03e10 --- 000105040.03e10 --- 105040.03e10
0.0457e10 --- 0.0457e10
--- 0.782e10 --- 0.782e10 --- 0.782e10
--- 1.23E2 --- 1.23E2 --- 1.23E2
--- 1.46e10 --- 1.46e10 --- 1.46e10
--- 1.077e-456 --- 1.077e-456 --- 1.077e-456
--- 1.069e10 --- 1.069e10 --- 1.069e10
--- 105040.03e10 --- 105040.03e10 --- 105040.03e10
---------------------------------------------------------------
..18000 --- 25..00 --- 36...77 --- 2..8
--- 3.8..9 --- .12500. --- 12.51.400
..18000 --- 25..00 --- 36...77 --- 2..8
--- 3.8..9 --- .12500. --- 12.51.400
---------------------------------------------------------------
.
。
Consequently, the regex can also been used to merely find all the numbers in a string, without removing the zeros if not wished.
因此，regex也可以用于仅查找字符串中的所有数字，而不删除非希望的0。
.
。
PS: see more in my other answer that explains the regex and its functionning
请参阅我的另一个解释regex及其功能的答案