标签归档:floating-point

格式化浮点数而不尾随零

问题:格式化浮点数而不尾随零

如何格式化浮点数,使其不包含尾随零?换句话说,我希望结果字符串尽可能短。

例如:

3 -> "3"
3. -> "3"
3.0 -> "3"
3.1 -> "3.1"
3.14 -> "3.14"
3.140 -> "3.14"

How can I format a float so that it doesn’t contain trailing zeros? In other words, I want the resulting string to be as short as possible.

For example:

3 -> "3"
3. -> "3"
3.0 -> "3"
3.1 -> "3.1"
3.14 -> "3.14"
3.140 -> "3.14"

回答 0

我,我会做的('%f' % x).rstrip('0').rstrip('.')-保证定点格式,而不是科学记数法,等等等等呀,还不如光滑和优雅的%g,但是,它的工作原理(我不知道如何强制%g从不使用科学记数法; -)。

Me, I’d do ('%f' % x).rstrip('0').rstrip('.') — guarantees fixed-point formatting rather than scientific notation, etc etc. Yeah, not as slick and elegant as %g, but, it works (and I don’t know how to force %g to never use scientific notation;-).


回答 1

您可以%g用来实现以下目的:

'%g'%(3.140)

或者,对于Python 2.6或更高版本:

'{0:g}'.format(3.140)

文档中查找formatg原因(除其他外)

从有效数中删除不重要的尾随零,并且如果在其后没有剩余数字,则也删除小数点。

You could use %g to achieve this:

'%g'%(3.140)

or, for Python 2.6 or better:

'{0:g}'.format(3.140)

From the docs for format: g causes (among other things)

insignificant trailing zeros [to be] removed from the significand, and the decimal point is also removed if there are no remaining digits following it.


回答 2

尝试最简单且可能最有效的方法呢?方法normalize()删除所有最右边的尾随零。

from decimal import Decimal

print (Decimal('0.001000').normalize())
# Result: 0.001

适用于Python 2Python 3

– 更新 –

@ BobStein-VisiBone指出的唯一问题是,数字10、100、1000 …将以指数形式显示。可以使用以下函数轻松解决此问题:

from decimal import Decimal


def format_float(f):
    d = Decimal(str(f));
    return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()

What about trying the easiest and probably most effective approach? The method normalize() removes all the rightmost trailing zeros.

from decimal import Decimal

print (Decimal('0.001000').normalize())
# Result: 0.001

Works in Python 2 and Python 3.

— Updated —

The only problem as @BobStein-VisiBone pointed out, is that numbers like 10, 100, 1000… will be displayed in exponential representation. This can be easily fixed using the following function instead:

from decimal import Decimal


def format_float(f):
    d = Decimal(str(f));
    return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()

回答 3

在查看了几个类似问题的答案之后,这似乎是我的最佳解决方案:

def floatToString(inputValue):
    return ('%.15f' % inputValue).rstrip('0').rstrip('.')

我的推理:

%g 没有摆脱科学计数法。

>>> '%g' % 0.000035
'3.5e-05'

小数点后15位似乎可以避免发生奇怪的行为,并且可以满足我的许多要求。

>>> ('%.15f' % 1.35).rstrip('0').rstrip('.')
'1.35'
>>> ('%.16f' % 1.35).rstrip('0').rstrip('.')
'1.3500000000000001'

我本可以使用format(inputValue, '.15f').来代替'%.15f' % inputValue,但是会慢一些(〜30%)。

我本可以使用Decimal(inputValue).normalize(),但这也有一些问题。例如,速度要慢很多(〜11x)。我还发现,尽管它具有很高的精度,但使用时仍然会遭受精度损失normalize()

>>> Decimal('0.21000000000000000000000000006').normalize()
Decimal('0.2100000000000000000000000001')
>>> Decimal('0.21000000000000000000000000006')
Decimal('0.21000000000000000000000000006')

最重要的是,我仍然会转换为Decimal从,float这样您最终可以得到的不是您输入的数字。我认为Decimal当算术停留在DecimalDecimal使用字符串初始化时效果最佳。

>>> Decimal(1.35)
Decimal('1.350000000000000088817841970012523233890533447265625')
>>> Decimal('1.35')
Decimal('1.35')

我确信Decimal.normalize()可以使用上下文设置将的精度问题调整为所需的值,但是考虑到速度已经很慢并且不需要可笑的精度,而且无论如何我仍然会从浮点数转换而失去精度,我没有认为这不值得追求。

我不担心可能的“ -0”结果,因为-0.0是有效的浮点数,并且无论如何它都可能很少出现,但是由于您确实提到要保持字符串结果尽可能短,因此总是可以以很少的额外速度成本使用额外的条件。

def floatToString(inputValue):
    result = ('%.15f' % inputValue).rstrip('0').rstrip('.')
    return '0' if result == '-0' else result

After looking over answers to several similar questions, this seems to be the best solution for me:

def floatToString(inputValue):
    return ('%.15f' % inputValue).rstrip('0').rstrip('.')

My reasoning:

%g doesn’t get rid of scientific notation.

>>> '%g' % 0.000035
'3.5e-05'

15 decimal places seems to avoid strange behavior and has plenty of precision for my needs.

>>> ('%.15f' % 1.35).rstrip('0').rstrip('.')
'1.35'
>>> ('%.16f' % 1.35).rstrip('0').rstrip('.')
'1.3500000000000001'

I could have used format(inputValue, '.15f'). instead of '%.15f' % inputValue, but that is a bit slower (~30%).

I could have used Decimal(inputValue).normalize(), but this has a few issues as well. For one, it is A LOT slower (~11x). I also found that although it has pretty great precision, it still suffers from precision loss when using normalize().

>>> Decimal('0.21000000000000000000000000006').normalize()
Decimal('0.2100000000000000000000000001')
>>> Decimal('0.21000000000000000000000000006')
Decimal('0.21000000000000000000000000006')

Most importantly, I would still be converting to Decimal from a float which can make you end up with something other than the number you put in there. I think Decimal works best when the arithmetic stays in Decimal and the Decimal is initialized with a string.

>>> Decimal(1.35)
Decimal('1.350000000000000088817841970012523233890533447265625')
>>> Decimal('1.35')
Decimal('1.35')

I’m sure the precision issue of Decimal.normalize() can be adjusted to what is needed using context settings, but considering the already slow speed and not needing ridiculous precision and the fact that I’d still be converting from a float and losing precision anyway, I didn’t think it was worth pursuing.

I’m not concerned with the possible “-0” result since -0.0 is a valid floating point number and it would probably be a rare occurrence anyway, but since you did mention you want to keep the string result as short as possible, you could always use an extra conditional at very little extra speed cost.

def floatToString(inputValue):
    result = ('%.15f' % inputValue).rstrip('0').rstrip('.')
    return '0' if result == '-0' else result

回答 4

这是一个对我有用的解决方案。这是一个混合的解决方案通过多网并使用新的.format() 语法

for num in 3, 3., 3.0, 3.1, 3.14, 3.140:
    print('{0:.2f}'.format(num).rstrip('0').rstrip('.'))

输出

3
3
3
3.1
3.14
3.14

Here’s a solution that worked for me. It’s a blend of the solution by PolyMesh and use of the new .format() syntax.

for num in 3, 3., 3.0, 3.1, 3.14, 3.140:
    print('{0:.2f}'.format(num).rstrip('0').rstrip('.'))

Output:

3
3
3
3.1
3.14
3.14

回答 5

您可以简单地使用format()实现此目的:

format(3.140, '.10g') 其中10是您想要的精度。

You can simply use format() to achieve this:

format(3.140, '.10g') where 10 is the precision you want.


回答 6

>>> str(a if a % 1 else int(a))
>>> str(a if a % 1 else int(a))

回答 7

尽管格式化可能是大多数Python方式,但这里是使用该more_itertools.rstrip工具的替代解决方案。

import more_itertools as mit


def fmt(num, pred=None):
    iterable = str(num)
    predicate = pred if pred is not None else lambda x: x in {".", "0"}
    return "".join(mit.rstrip(iterable, predicate))

assert fmt(3) == "3"
assert fmt(3.) == "3"
assert fmt(3.0) == "3"
assert fmt(3.1) == "3.1"
assert fmt(3.14) == "3.14"
assert fmt(3.140) == "3.14"
assert fmt(3.14000) == "3.14"
assert fmt("3,0", pred=lambda x: x in set(",0")) == "3"

该数字将转换为字符串,该字符串将除去满足谓词的结尾字符。函数定义fmt不是必需的,但是在这里用于测试所有通过的断言。注意:它适用于字符串输入并接受可选谓词。

另请参阅有关此第三方库的详细信息more_itertools

While formatting is likely that most Pythonic way, here is an alternate solution using the more_itertools.rstrip tool.

import more_itertools as mit


def fmt(num, pred=None):
    iterable = str(num)
    predicate = pred if pred is not None else lambda x: x in {".", "0"}
    return "".join(mit.rstrip(iterable, predicate))

assert fmt(3) == "3"
assert fmt(3.) == "3"
assert fmt(3.0) == "3"
assert fmt(3.1) == "3.1"
assert fmt(3.14) == "3.14"
assert fmt(3.140) == "3.14"
assert fmt(3.14000) == "3.14"
assert fmt("3,0", pred=lambda x: x in set(",0")) == "3"

The number is converted to a string, which is stripped of trailing characters that satisfy a predicate. The function definition fmt is not required, but it is used here to test assertions, which all pass. Note: it works on string inputs and accepts optional predicates.

See also details on this third-party library, more_itertools.


回答 8

如果您可以将3.和3.0都显示为“ 3.0”,那么这是一种非常简单的方法,可将浮点数表示形式的零右移:

print("%s"%3.140)

(感谢@ellimilial指出exceptions)

If you can live with 3. and 3.0 appearing as “3.0”, a very simple approach that right-strips zeros from float representations:

print("%s"%3.140)

(thanks @ellimilial for pointing out the exceptions)


回答 9

您可以选择使用QuantiPhy软件包。通常,在处理带有单位和SI比例因子的数字时,会使用QuantiPhy,但它具有多种不错的数字格式设置选项。

    >>> from quantiphy import Quantity

    >>> cases = '3 3. 3.0 3.1 3.14 3.140 3.14000'.split()
    >>> for case in cases:
    ...    q = Quantity(case)
    ...    print(f'{case:>7} -> {q:p}')
          3 -> 3
         3. -> 3
        3.0 -> 3
        3.1 -> 3.1
       3.14 -> 3.14
      3.140 -> 3.14
    3.14000 -> 3.14

在这种情况下,它将不使用电子符号:

    >>> cases = '3.14e-9 3.14 3.14e9'.split()
    >>> for case in cases:
    ...    q = Quantity(case)
    ...    print(f'{case:>7} -> {q:,p}')
    3.14e-9 -> 0
       3.14 -> 3.14
     3.14e9 -> 3,140,000,000

您可能更喜欢的替代方法是使用SI比例因子,也许使用单位。

    >>> cases = '3e-9 3.14e-9 3 3.14 3e9 3.14e9'.split()
    >>> for case in cases:
    ...    q = Quantity(case, 'm')
    ...    print(f'{case:>7} -> {q}')
       3e-9 -> 3 nm
    3.14e-9 -> 3.14 nm
          3 -> 3 m
       3.14 -> 3.14 m
        3e9 -> 3 Gm
     3.14e9 -> 3.14 Gm

Using the QuantiPhy package is an option. Normally QuantiPhy is used when working with numbers with units and SI scale factors, but it has a variety of nice number formatting options.

    >>> from quantiphy import Quantity

    >>> cases = '3 3. 3.0 3.1 3.14 3.140 3.14000'.split()
    >>> for case in cases:
    ...    q = Quantity(case)
    ...    print(f'{case:>7} -> {q:p}')
          3 -> 3
         3. -> 3
        3.0 -> 3
        3.1 -> 3.1
       3.14 -> 3.14
      3.140 -> 3.14
    3.14000 -> 3.14

And it will not use e-notation in this situation:

    >>> cases = '3.14e-9 3.14 3.14e9'.split()
    >>> for case in cases:
    ...    q = Quantity(case)
    ...    print(f'{case:>7} -> {q:,p}')
    3.14e-9 -> 0
       3.14 -> 3.14
     3.14e9 -> 3,140,000,000

An alternative you might prefer is to use SI scale factors, perhaps with units.

    >>> cases = '3e-9 3.14e-9 3 3.14 3e9 3.14e9'.split()
    >>> for case in cases:
    ...    q = Quantity(case, 'm')
    ...    print(f'{case:>7} -> {q}')
       3e-9 -> 3 nm
    3.14e-9 -> 3.14 nm
          3 -> 3 m
       3.14 -> 3.14 m
        3e9 -> 3 Gm
     3.14e9 -> 3.14 Gm

回答 10

OP希望删除多余的零,并使生成的字符串尽可能短。

我发现%g指数格式会缩短结果字符串的大小和数值。对于不需要指数表示法的值(例如128.0)来说,问题来了,它既不是很大也不是很小。

这是将数字格式化为短字符串的一种方法,仅当Decimal.normalize创建的字符串过长时才使用%g指数表示法。这可能不是最快的解决方案(因为它确实使用Decimal.normalize)

def floatToString (inputValue, precision = 3):
    rc = str(Decimal(inputValue).normalize())
    if 'E' in rc or len(rc) > 5:
        rc = '{0:.{1}g}'.format(inputValue, precision)        
    return rc

inputs = [128.0, 32768.0, 65536, 65536 * 2, 31.5, 1.000, 10.0]

outputs = [floatToString(i) for i in inputs]

print(outputs)

# ['128', '32768', '65536', '1.31e+05', '31.5', '1', '10']

OP would like to remove superflouous zeros and make the resulting string as short as possible.

I find the %g exponential formatting shortens the resulting string for very large and very small values. The problem comes for values that don’t need exponential notation, like 128.0, which is neither very large or very small.

Here is one way to format numbers as short strings that uses %g exponential notation only when Decimal.normalize creates strings that are too long. This might not be the fastest solution (since it does use Decimal.normalize)

def floatToString (inputValue, precision = 3):
    rc = str(Decimal(inputValue).normalize())
    if 'E' in rc or len(rc) > 5:
        rc = '{0:.{1}g}'.format(inputValue, precision)        
    return rc

inputs = [128.0, 32768.0, 65536, 65536 * 2, 31.5, 1.000, 10.0]

outputs = [floatToString(i) for i in inputs]

print(outputs)

# ['128', '32768', '65536', '1.31e+05', '31.5', '1', '10']

回答 11

对于float,您可以使用以下代码:

def format_float(num):
    return ('%i' if num == int(num) else '%s') % num

测试一下:

>>> format_float(1.00000)
'1'
>>> format_float(1.1234567890000000000)
'1.123456789'

对于十进制,请在此处查看解决方案:https : //stackoverflow.com/a/42668598/5917543

For float you could use this:

def format_float(num):
    return ('%i' if num == int(num) else '%s') % num

Test it:

>>> format_float(1.00000)
'1'
>>> format_float(1.1234567890000000000)
'1.123456789'

For Decimal see solution here: https://stackoverflow.com/a/42668598/5917543


回答 12

"{:.5g}".format(x)

我用它来格式化浮点数以尾随零。

"{:.5g}".format(x)

I use this to format floats to trail zeros.


回答 13

答案是:

import numpy

num1 = 3.1400
num2 = 3.000
numpy.format_float_positional(num1, 3, trim='-')
numpy.format_float_positional(num2, 3, trim='-')

输出“ 3.14”和“ 3”

trim='-' 删除尾随零和小数。

Here’s the answer:

import numpy

num1 = 3.1400
num2 = 3.000
numpy.format_float_positional(num1, 3, trim='-')
numpy.format_float_positional(num2, 3, trim='-')

output “3.14” and “3”

trim='-' removes both the trailing zero’s, and the decimal.


回答 14

使用宽度足够大的%g,例如’%.99g’。对于任何较大的数字,它将以定点表示法打印。

编辑:这不起作用

>>> '%.99g' % 0.0000001
'9.99999999999999954748111825886258685613938723690807819366455078125e-08'

Use %g with big enough width, for example ‘%.99g’. It will print in fixed-point notation for any reasonably big number.

EDIT: it doesn’t work

>>> '%.99g' % 0.0000001
'9.99999999999999954748111825886258685613938723690807819366455078125e-08'

回答 15

您可以这样使用max()

print(max(int(x), x))

You can use max() like this:

print(max(int(x), x))


回答 16

您可以通过以下大多数pythonic方式实现该目标:

python3:

"{:0.0f}".format(num)

You can achieve that in most pythonic way like that:

python3:

"{:0.0f}".format(num)

回答 17

处理%f,您应该放

%.2f

,其中:.2f == .00浮动。

例:

打印“价格:%.2f”%价格[产品]

输出:

价格:1.50

Handling %f and you should put

%.2f

, where: .2f == .00 floats.

Example:

print “Price: %.2f” % prices[product]

output:

Price: 1.50


Python JSON序列化Decimal对象

问题:Python JSON序列化Decimal对象

我有一个Decimal('3.9')对象的一部分,希望将其编码为JSON字符串,看起来像{'x': 3.9}。我不在乎客户端的精度,因此浮点数很好。

是否有序列化此序列的好方法?JSONDecoder不接受Decimal对象,并且事先转换为float会产生{'x': 3.8999999999999999}错误,这将浪费大量带宽。

I have a Decimal('3.9') as part of an object, and wish to encode this to a JSON string which should look like {'x': 3.9}. I don’t care about precision on the client side, so a float is fine.

Is there a good way to serialize this? JSONDecoder doesn’t accept Decimal objects, and converting to a float beforehand yields {'x': 3.8999999999999999} which is wrong, and will be a big waste of bandwidth.


回答 0

子类化如何json.JSONEncoder

class DecimalEncoder(json.JSONEncoder):
    def _iterencode(self, o, markers=None):
        if isinstance(o, decimal.Decimal):
            # wanted a simple yield str(o) in the next line,
            # but that would mean a yield on the line with super(...),
            # which wouldn't work (see my comment below), so...
            return (str(o) for o in [o])
        return super(DecimalEncoder, self)._iterencode(o, markers)

然后像这样使用它:

json.dumps({'x': decimal.Decimal('5.5')}, cls=DecimalEncoder)

How about subclassing json.JSONEncoder?

class DecimalEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, decimal.Decimal):
            # wanted a simple yield str(o) in the next line,
            # but that would mean a yield on the line with super(...),
            # which wouldn't work (see my comment below), so...
            return (str(o) for o in [o])
        return super(DecimalEncoder, self).default(o)

Then use it like so:

json.dumps({'x': decimal.Decimal('5.5')}, cls=DecimalEncoder)

回答 1

Simplejson 2.1及更高版本具有对Decimal类型的本地支持:

>>> json.dumps(Decimal('3.9'), use_decimal=True)
'3.9'

请注意,use_decimalTrue在默认情况下:

def dumps(obj, skipkeys=False, ensure_ascii=True, check_circular=True,
    allow_nan=True, cls=None, indent=None, separators=None,
    encoding='utf-8', default=None, use_decimal=True,
    namedtuple_as_object=True, tuple_as_array=True,
    bigint_as_string=False, sort_keys=False, item_sort_key=None,
    for_json=False, ignore_nan=False, **kw):

所以:

>>> json.dumps(Decimal('3.9'))
'3.9'

希望此功能将包含在标准库中。

Simplejson 2.1 and higher has native support for Decimal type:

>>> json.dumps(Decimal('3.9'), use_decimal=True)
'3.9'

Note that use_decimal is True by default:

def dumps(obj, skipkeys=False, ensure_ascii=True, check_circular=True,
    allow_nan=True, cls=None, indent=None, separators=None,
    encoding='utf-8', default=None, use_decimal=True,
    namedtuple_as_object=True, tuple_as_array=True,
    bigint_as_string=False, sort_keys=False, item_sort_key=None,
    for_json=False, ignore_nan=False, **kw):

So:

>>> json.dumps(Decimal('3.9'))
'3.9'

Hopefully, this feature will be included in standard library.


回答 2

我想让大家知道,我在运行Python 2.6.5的Web服务器上尝试了MichałMarczyk的答案,并且运行良好。但是,我升级到了Python 2.7,它停止工作了。我试图想到某种编码Decimal对象的方法,这就是我想出的:

import decimal

class DecimalEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, decimal.Decimal):
            return float(o)
        return super(DecimalEncoder, self).default(o)

希望这应该对任何遇到Python 2.7问题的人有所帮助。我测试了它,看来效果很好。如果有人注意到我的解决方案中的任何错误或提出了更好的方法,请告诉我。

I would like to let everyone know that I tried Michał Marczyk’s answer on my web server that was running Python 2.6.5 and it worked fine. However, I upgraded to Python 2.7 and it stopped working. I tried to think of some sort of way to encode Decimal objects and this is what I came up with:

import decimal

class DecimalEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, decimal.Decimal):
            return float(o)
        return super(DecimalEncoder, self).default(o)

This should hopefully help anyone who is having problems with Python 2.7. I tested it and it seems to work fine. If anyone notices any bugs in my solution or comes up with a better way, please let me know.


回答 3

在我的Flask应用程序中,它使用python 2.7.11,flask alchemy(具有“ db.decimal”类型)和Flask Marshmallow(用于“即时”序列化器和反序列化器),每次执行GET或POST时,我都会遇到此错误。序列化器和反序列化器无法将Decimal类型转换为任何JSON可识别格式。

我做了一个“ pip install simplejson”,然后只需添加

import simplejson as json

序列化器和解串器再次开始发出嗡嗡声。我什么也没做… DEciamls显示为’234.00’浮点格式。

In my Flask app, Which uses python 2.7.11, flask alchemy(with ‘db.decimal’ types), and Flask Marshmallow ( for ‘instant’ serializer and deserializer), i had this error, every time i did a GET or POST. The serializer and deserializer, failed to convert Decimal types into any JSON identifiable format.

I did a “pip install simplejson”, then Just by adding

import simplejson as json

the serializer and deserializer starts to purr again. I did nothing else… DEciamls are displayed as ‘234.00’ float format.


回答 4

我尝试从GAE 2.7的simplejson切换到内置的json,但小数位数有问题。如果default返回的str(o)有引号(因为_iterencode在default的结果上调用_iterencode),并且float(o)会删除结尾的0。

如果default返回一个从float继承的类的对象(或任何不带其他格式调用repr的类)并具有自定义__repr__方法,则它看起来像我希望的那样工作。

import json
from decimal import Decimal

class fakefloat(float):
    def __init__(self, value):
        self._value = value
    def __repr__(self):
        return str(self._value)

def defaultencode(o):
    if isinstance(o, Decimal):
        # Subclass float with custom repr?
        return fakefloat(o)
    raise TypeError(repr(o) + " is not JSON serializable")

json.dumps([10.20, "10.20", Decimal('10.20')], default=defaultencode)
'[10.2, "10.20", 10.20]'

I tried switching from simplejson to builtin json for GAE 2.7, and had issues with the decimal. If default returned str(o) there were quotes (because _iterencode calls _iterencode on the results of default), and float(o) would remove trailing 0.

If default returns an object of a class that inherits from float (or anything that calls repr without additional formatting) and has a custom __repr__ method, it seems to work like I want it to.

import json
from decimal import Decimal

class fakefloat(float):
    def __init__(self, value):
        self._value = value
    def __repr__(self):
        return str(self._value)

def defaultencode(o):
    if isinstance(o, Decimal):
        # Subclass float with custom repr?
        return fakefloat(o)
    raise TypeError(repr(o) + " is not JSON serializable")

json.dumps([10.20, "10.20", Decimal('10.20')], default=defaultencode)
'[10.2, "10.20", 10.20]'

回答 5

缺少本机选项,因此我将其添加到寻找它的下一个家伙/画廊。

从Django 1.7.x开始,有一个内置组件DjangoJSONEncoder,您可以从中获取它django.core.serializers.json

import json
from django.core.serializers.json import DjangoJSONEncoder
from django.forms.models import model_to_dict

model_instance = YourModel.object.first()
model_dict = model_to_dict(model_instance)

json.dumps(model_dict, cls=DjangoJSONEncoder)

快点!

The native option is missing so I’ll add it for the next guy/gall that looks for it.

Starting on Django 1.7.x there is a built-in DjangoJSONEncoder that you can get it from django.core.serializers.json.

import json
from django.core.serializers.json import DjangoJSONEncoder
from django.forms.models import model_to_dict

model_instance = YourModel.object.first()
model_dict = model_to_dict(model_instance)

json.dumps(model_dict, cls=DjangoJSONEncoder)

Presto!


回答 6

我的$ .02!

因为要为Web服务器序列化大量数据,所以我扩展了许多JSON编码器。这是一些不错的代码。请注意,它很容易扩展为几乎任何您想要的数据格式,并且可以将3.9复制为"thing": 3.9

JSONEncoder_olddefault = json.JSONEncoder.default
def JSONEncoder_newdefault(self, o):
    if isinstance(o, UUID): return str(o)
    if isinstance(o, datetime): return str(o)
    if isinstance(o, time.struct_time): return datetime.fromtimestamp(time.mktime(o))
    if isinstance(o, decimal.Decimal): return str(o)
    return JSONEncoder_olddefault(self, o)
json.JSONEncoder.default = JSONEncoder_newdefault

让我的生活变得更加轻松…

My $.02!

I extend a bunch of the JSON encoder since I am serializing tons of data for my web server. Here’s some nice code. Note that it’s easily extendable to pretty much any data format you feel like and will reproduce 3.9 as "thing": 3.9

JSONEncoder_olddefault = json.JSONEncoder.default
def JSONEncoder_newdefault(self, o):
    if isinstance(o, UUID): return str(o)
    if isinstance(o, datetime): return str(o)
    if isinstance(o, time.struct_time): return datetime.fromtimestamp(time.mktime(o))
    if isinstance(o, decimal.Decimal): return str(o)
    return JSONEncoder_olddefault(self, o)
json.JSONEncoder.default = JSONEncoder_newdefault

Makes my life so much easier…


回答 7

3.9不能在IEEE浮点数中精确表示,它总是以表示3.8999999999999999,例如try print repr(3.9),您可以在此处了解更多信息:

http://en.wikipedia.org/wiki/Floating_point
http://docs.sun.com/source/806-3568/ncg_goldberg.html

因此,如果您不希望浮动,则只能将其作为字符串发送,并且要允许将十进制对象自动转换为JSON,请执行以下操作:

import decimal
from django.utils import simplejson

def json_encode_decimal(obj):
    if isinstance(obj, decimal.Decimal):
        return str(obj)
    raise TypeError(repr(obj) + " is not JSON serializable")

d = decimal.Decimal('3.5')
print simplejson.dumps([d], default=json_encode_decimal)

3.9 can not be exactly represented in IEEE floats, it will always come as 3.8999999999999999, e.g. try print repr(3.9), you can read more about it here:

http://en.wikipedia.org/wiki/Floating_point
http://docs.sun.com/source/806-3568/ncg_goldberg.html

So if you don’t want float, only option you have to send it as string, and to allow automatic conversion of decimal objects to JSON, do something like this:

import decimal
from django.utils import simplejson

def json_encode_decimal(obj):
    if isinstance(obj, decimal.Decimal):
        return str(obj)
    raise TypeError(repr(obj) + " is not JSON serializable")

d = decimal.Decimal('3.5')
print simplejson.dumps([d], default=json_encode_decimal)

回答 8

对于Django用户

最近遇到了TypeError: Decimal('2337.00') is not JSON serializable JSON编码,即json.dumps(data)

解决方案

# converts Decimal, Datetime, UUIDs to str for Encoding
from django.core.serializers.json import DjangoJSONEncoder  

json.dumps(response.data, cls=DjangoJSONEncoder)

但是,现在Decimal值将是一个字符串,现在我们可以在解码数据时使用parse_floatin中的option 显式设置十进制/浮点值解析器json.loads

import decimal 

data = json.loads(data, parse_float=decimal.Decimal) # default is float(num_str)

For Django users:

Recently came across TypeError: Decimal('2337.00') is not JSON serializable while JSON encoding i.e. json.dumps(data)

Solution:

# converts Decimal, Datetime, UUIDs to str for Encoding
from django.core.serializers.json import DjangoJSONEncoder  

json.dumps(response.data, cls=DjangoJSONEncoder)

But, now the Decimal value will be a string, now we can explicitly set the decimal/float value parser when decoding data, using parse_float option in json.loads:

import decimal 

data = json.loads(data, parse_float=decimal.Decimal) # default is float(num_str)

回答 9

JSON标准文档(如json.org中链接):

JSON与数字的语义无关。在任何编程语言中,可以有各种容量和补码形式的数字类型,固定或浮动,二进制或十进制。这可能使不同编程语言之间的交换变得困难。JSON而是仅提供人类使用的数字表示形式:数字序列。所有编程语言都知道如何理解数字序列,即使它们在内部表示形式上存在分歧。这足以允许互换。

因此,在JSON中将小数表示为数字(而不是字符串)实际上是准确的。贝娄为该问题提供了可能的解决方案。

定义自定义JSON编码器:

import json


class CustomJsonEncoder(json.JSONEncoder):

    def default(self, obj):
        if isinstance(obj, Decimal):
            return float(obj)
        return super(CustomJsonEncoder, self).default(obj)

然后在序列化数据时使用它:

json.dumps(data, cls=CustomJsonEncoder)

从其他答案的注释中可以看出,较旧版本的python在转换为float时可能会弄乱表示形式,但现在不再如此。

要在Python中返回小数:

Decimal(str(value))

Python 3.0文档中的小数点提示了该解决方案:

要从浮点数创建小数,请首先将其转换为字符串。

From the JSON Standard Document, as linked in json.org:

JSON is agnostic about the semantics of numbers. In any programming language, there can be a variety of number types of various capacities and complements, fixed or floating, binary or decimal. That can make interchange between different programming languages difficult. JSON instead offers only the representation of numbers that humans use: a sequence of digits. All programming languages know how to make sense of digit sequences even if they disagree on internal representations. That is enough to allow interchange.

So it’s actually accurate to represent Decimals as numbers (rather than strings) in JSON. Bellow lies a possible solution to the problem.

Define a custom JSON encoder:

import json


class CustomJsonEncoder(json.JSONEncoder):

    def default(self, obj):
        if isinstance(obj, Decimal):
            return float(obj)
        return super(CustomJsonEncoder, self).default(obj)

Then use it when serializing your data:

json.dumps(data, cls=CustomJsonEncoder)

As noted from comments on the other answers, older versions of python might mess up the representation when converting to float, but that’s not the case anymore.

To get the decimal back in Python:

Decimal(str(value))

This solution is hinted in Python 3.0 documentation on decimals:

To create a Decimal from a float, first convert it to a string.


回答 10

这就是我从课堂上摘录的

class CommonJSONEncoder(json.JSONEncoder):

    """
    Common JSON Encoder
    json.dumps(myString, cls=CommonJSONEncoder)
    """

    def default(self, obj):

        if isinstance(obj, decimal.Decimal):
            return {'type{decimal}': str(obj)}

class CommonJSONDecoder(json.JSONDecoder):

    """
    Common JSON Encoder
    json.loads(myString, cls=CommonJSONEncoder)
    """

    @classmethod
    def object_hook(cls, obj):
        for key in obj:
            if isinstance(key, six.string_types):
                if 'type{decimal}' == key:
                    try:
                        return decimal.Decimal(obj[key])
                    except:
                        pass

    def __init__(self, **kwargs):
        kwargs['object_hook'] = self.object_hook
        super(CommonJSONDecoder, self).__init__(**kwargs)

通过单元测试:

def test_encode_and_decode_decimal(self):
    obj = Decimal('1.11')
    result = json.dumps(obj, cls=CommonJSONEncoder)
    self.assertTrue('type{decimal}' in result)
    new_obj = json.loads(result, cls=CommonJSONDecoder)
    self.assertEqual(new_obj, obj)

    obj = {'test': Decimal('1.11')}
    result = json.dumps(obj, cls=CommonJSONEncoder)
    self.assertTrue('type{decimal}' in result)
    new_obj = json.loads(result, cls=CommonJSONDecoder)
    self.assertEqual(new_obj, obj)

    obj = {'test': {'abc': Decimal('1.11')}}
    result = json.dumps(obj, cls=CommonJSONEncoder)
    self.assertTrue('type{decimal}' in result)
    new_obj = json.loads(result, cls=CommonJSONDecoder)
    self.assertEqual(new_obj, obj)

This is what I have, extracted from our class

class CommonJSONEncoder(json.JSONEncoder):

    """
    Common JSON Encoder
    json.dumps(myString, cls=CommonJSONEncoder)
    """

    def default(self, obj):

        if isinstance(obj, decimal.Decimal):
            return {'type{decimal}': str(obj)}

class CommonJSONDecoder(json.JSONDecoder):

    """
    Common JSON Encoder
    json.loads(myString, cls=CommonJSONEncoder)
    """

    @classmethod
    def object_hook(cls, obj):
        for key in obj:
            if isinstance(key, six.string_types):
                if 'type{decimal}' == key:
                    try:
                        return decimal.Decimal(obj[key])
                    except:
                        pass

    def __init__(self, **kwargs):
        kwargs['object_hook'] = self.object_hook
        super(CommonJSONDecoder, self).__init__(**kwargs)

Which passes unittest:

def test_encode_and_decode_decimal(self):
    obj = Decimal('1.11')
    result = json.dumps(obj, cls=CommonJSONEncoder)
    self.assertTrue('type{decimal}' in result)
    new_obj = json.loads(result, cls=CommonJSONDecoder)
    self.assertEqual(new_obj, obj)

    obj = {'test': Decimal('1.11')}
    result = json.dumps(obj, cls=CommonJSONEncoder)
    self.assertTrue('type{decimal}' in result)
    new_obj = json.loads(result, cls=CommonJSONDecoder)
    self.assertEqual(new_obj, obj)

    obj = {'test': {'abc': Decimal('1.11')}}
    result = json.dumps(obj, cls=CommonJSONEncoder)
    self.assertTrue('type{decimal}' in result)
    new_obj = json.loads(result, cls=CommonJSONDecoder)
    self.assertEqual(new_obj, obj)

回答 11

您可以根据需要创建自定义JSON编码器。

import json
from datetime import datetime, date
from time import time, struct_time, mktime
import decimal

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, datetime):
            return str(o)
        if isinstance(o, date):
            return str(o)
        if isinstance(o, decimal.Decimal):
            return float(o)
        if isinstance(o, struct_time):
            return datetime.fromtimestamp(mktime(o))
        # Any other serializer if needed
        return super(CustomJSONEncoder, self).default(o)

解码器可以这样称呼,

import json
from decimal import Decimal
json.dumps({'x': Decimal('3.9')}, cls=CustomJSONEncoder)

输出将是:

>>'{"x": 3.9}'

You can create a custom JSON encoder as per your requirement.

import json
from datetime import datetime, date
from time import time, struct_time, mktime
import decimal

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, datetime):
            return str(o)
        if isinstance(o, date):
            return str(o)
        if isinstance(o, decimal.Decimal):
            return float(o)
        if isinstance(o, struct_time):
            return datetime.fromtimestamp(mktime(o))
        # Any other serializer if needed
        return super(CustomJSONEncoder, self).default(o)

The Decoder can be called like this,

import json
from decimal import Decimal
json.dumps({'x': Decimal('3.9')}, cls=CustomJSONEncoder)

and the output will be:

>>'{"x": 3.9}'

回答 12

对于那些不想使用第三方库的人来说……Elias Zamaria的问题是它会转换为float,这会遇到问题。例如:

>>> json.dumps({'x': Decimal('0.0000001')}, cls=DecimalEncoder)
'{"x": 1e-07}'
>>> json.dumps({'x': Decimal('100000000000.01734')}, cls=DecimalEncoder)
'{"x": 100000000000.01733}'

JSONEncoder.encode()方法使您可以返回原义的json内容,与不同的是JSONEncoder.default(),该方法返回的是json兼容类型(例如float),然后以常规方式对其进行编码。问题encode()在于它(通常)仅在最高级别上起作用。但是它仍然可以使用,但需要做一些额外的工作(python 3.x):

import json
from collections.abc import Mapping, Iterable
from decimal import Decimal

class DecimalEncoder(json.JSONEncoder):
    def encode(self, obj):
        if isinstance(obj, Mapping):
            return '{' + ', '.join(f'{self.encode(k)}: {self.encode(v)}' for (k, v) in obj.items()) + '}'
        if isinstance(obj, Iterable) and (not isinstance(obj, str)):
            return '[' + ', '.join(map(self.encode, obj)) + ']'
        if isinstance(obj, Decimal):
            return f'{obj.normalize():f}'  # using normalize() gets rid of trailing 0s, using ':f' prevents scientific notation
        return super().encode(obj)

这给你:

>>> json.dumps({'x': Decimal('0.0000001')}, cls=DecimalEncoder)
'{"x": 0.0000001}'
>>> json.dumps({'x': Decimal('100000000000.01734')}, cls=DecimalEncoder)
'{"x": 100000000000.01734}'

For those who don’t want to use a third-party library… An issue with Elias Zamaria’s answer is that it converts to float, which can run into problems. For example:

>>> json.dumps({'x': Decimal('0.0000001')}, cls=DecimalEncoder)
'{"x": 1e-07}'
>>> json.dumps({'x': Decimal('100000000000.01734')}, cls=DecimalEncoder)
'{"x": 100000000000.01733}'

The JSONEncoder.encode() method lets you return the literal json content, unlike JSONEncoder.default(), which has you return a json compatible type (like float) that then gets encoded in the normal way. The problem with encode() is that it (normally) only works at the top level. But it’s still usable, with a little extra work (python 3.x):

import json
from collections.abc import Mapping, Iterable
from decimal import Decimal

class DecimalEncoder(json.JSONEncoder):
    def encode(self, obj):
        if isinstance(obj, Mapping):
            return '{' + ', '.join(f'{self.encode(k)}: {self.encode(v)}' for (k, v) in obj.items()) + '}'
        if isinstance(obj, Iterable) and (not isinstance(obj, str)):
            return '[' + ', '.join(map(self.encode, obj)) + ']'
        if isinstance(obj, Decimal):
            return f'{obj.normalize():f}'  # using normalize() gets rid of trailing 0s, using ':f' prevents scientific notation
        return super().encode(obj)

Which gives you:

>>> json.dumps({'x': Decimal('0.0000001')}, cls=DecimalEncoder)
'{"x": 0.0000001}'
>>> json.dumps({'x': Decimal('100000000000.01734')}, cls=DecimalEncoder)
'{"x": 100000000000.01734}'

回答 13

根据stdOrgnlDave的答案,我定义了此包装器,可以使用可选的种类调用该包装器,因此编码器仅适用于您项目中的某些种类。我认为应该在代码内完成工作,而不要使用此“默认”编码器,因为“它比隐式更好”是显式的,但是我知道使用它可以节省您的时间。:-)

import time
import json
import decimal
from uuid import UUID
from datetime import datetime

def JSONEncoder_newdefault(kind=['uuid', 'datetime', 'time', 'decimal']):
    '''
    JSON Encoder newdfeault is a wrapper capable of encoding several kinds
    Use it anywhere on your code to make the full system to work with this defaults:
        JSONEncoder_newdefault()  # for everything
        JSONEncoder_newdefault(['decimal'])  # only for Decimal
    '''
    JSONEncoder_olddefault = json.JSONEncoder.default

    def JSONEncoder_wrapped(self, o):
        '''
        json.JSONEncoder.default = JSONEncoder_newdefault
        '''
        if ('uuid' in kind) and isinstance(o, uuid.UUID):
            return str(o)
        if ('datetime' in kind) and isinstance(o, datetime):
            return str(o)
        if ('time' in kind) and isinstance(o, time.struct_time):
            return datetime.fromtimestamp(time.mktime(o))
        if ('decimal' in kind) and isinstance(o, decimal.Decimal):
            return str(o)
        return JSONEncoder_olddefault(self, o)
    json.JSONEncoder.default = JSONEncoder_wrapped

# Example
if __name__ == '__main__':
    JSONEncoder_newdefault()

Based on stdOrgnlDave answer I have defined this wrapper that it can be called with optional kinds so the encoder will work only for certain kinds inside your projects. I believe the work should be done inside your code and not to use this “default” encoder since “it is better explicit than implicit”, but I understand using this will save some of your time. :-)

import time
import json
import decimal
from uuid import UUID
from datetime import datetime

def JSONEncoder_newdefault(kind=['uuid', 'datetime', 'time', 'decimal']):
    '''
    JSON Encoder newdfeault is a wrapper capable of encoding several kinds
    Use it anywhere on your code to make the full system to work with this defaults:
        JSONEncoder_newdefault()  # for everything
        JSONEncoder_newdefault(['decimal'])  # only for Decimal
    '''
    JSONEncoder_olddefault = json.JSONEncoder.default

    def JSONEncoder_wrapped(self, o):
        '''
        json.JSONEncoder.default = JSONEncoder_newdefault
        '''
        if ('uuid' in kind) and isinstance(o, uuid.UUID):
            return str(o)
        if ('datetime' in kind) and isinstance(o, datetime):
            return str(o)
        if ('time' in kind) and isinstance(o, time.struct_time):
            return datetime.fromtimestamp(time.mktime(o))
        if ('decimal' in kind) and isinstance(o, decimal.Decimal):
            return str(o)
        return JSONEncoder_olddefault(self, o)
    json.JSONEncoder.default = JSONEncoder_wrapped

# Example
if __name__ == '__main__':
    JSONEncoder_newdefault()

回答 14

如果要将包含小数的字典传递给requests库(使用json关键字参数),则只需安装simplejson

$ pip3 install simplejson    
$ python3
>>> import requests
>>> from decimal import Decimal
>>> # This won't error out:
>>> requests.post('https://www.google.com', json={'foo': Decimal('1.23')})

问题的原因是仅在存在时才requests使用simplejson,而在json未安装时退回到内置。

If you want to pass a dictionary containing decimals to the requests library (using the json keyword argument), you simply need to install simplejson:

$ pip3 install simplejson    
$ python3
>>> import requests
>>> from decimal import Decimal
>>> # This won't error out:
>>> requests.post('https://www.google.com', json={'foo': Decimal('1.23')})

The reason of the problem is that requests uses simplejson only if it is present, and falls back to the built-in json if it is not installed.


回答 15

这可以通过添加

    elif isinstance(o, decimal.Decimal):
        yield str(o)

\Lib\json\encoder.py:JSONEncoder._iterencode,但我希望有一个更好的解决方案

this can be done by adding

    elif isinstance(o, decimal.Decimal):
        yield str(o)

in \Lib\json\encoder.py:JSONEncoder._iterencode, but I was hoping for a better solution


为什么Python的无穷大散列具有π的数字?

问题:为什么Python的无穷大散列具有π的数字?

Python中无穷大的哈​​希值具有与pi匹配的数字:

>>> inf = float('inf')
>>> hash(inf)
314159
>>> int(math.pi*1e5)
314159

这仅仅是巧合还是故意的?

The hash of infinity in Python has digits matching pi:

>>> inf = float('inf')
>>> hash(inf)
314159
>>> int(math.pi*1e5)
314159

Is that just a coincidence or is it intentional?


回答 0

_PyHASH_INF定义为等于的常数314159

我找不到关于此的任何讨论,也没有提供原因的评论。我认为它或多或少是任意选择的。我想只要它们不将相同的有意义的值用于其他哈希,就没关系。

_PyHASH_INF is defined as a constant equal to 314159.

I can’t find any discussion about this, or comments giving a reason. I think it was chosen more or less arbitrarily. I imagine that as long as they don’t use the same meaningful value for other hashes, it shouldn’t matter.


回答 1

简介:这不是巧合;在Python的默认CPython实现中_PyHASH_INF被硬编码为314159,并在2000年被Tim Peters选为任意值(显然是从π的数字)。


的值hash(float('inf'))是数值类型内置散列函数的系统相关的参数中的一个,并且也可以作为sys.hash_info.inf在Python 3:

>>> import sys
>>> sys.hash_info
sys.hash_info(width=64, modulus=2305843009213693951, inf=314159, nan=0, imag=1000003, algorithm='siphash24', hash_bits=64, seed_bits=128, cutoff=0)
>>> sys.hash_info.inf
314159

与PyPy的结果相同。)


就代码而言,hash是一个内置函数。在Python float对象上调用它会调用函数,该函数的指针由内置float类型()的tp_hash属性给定,该类型定义为的函数,PyTypeObject PyFloat_Type而该函数又具有float_hashreturn _Py_HashDouble(v->ob_fval)

    if (Py_IS_INFINITY(v))
        return v > 0 ? _PyHASH_INF : -_PyHASH_INF;

其中_PyHASH_INF定义为 314159:

#define _PyHASH_INF 314159

从历史的角度来看,Tim Peters在2000年8月添加了314159此上下文中Python代码中的第一个提及(您可以使用git bisect或找到git log -S 314159 -p),现在在git存储库中提交了39dce293cpython

提交消息说:

修复了http://sourceforge.net/bugs/?func=detailbug&bug_id=111866&group_id=5470的问题。这是一个令人误解的错误-真正的“错误”是hash(x)xinfinity为无限时返回错误。修复了。向添加了新的Py_IS_INFINITYpyport.h。重新排列了代码,以减少浮点数和复数的散列中越来越多的重复,从而将Trent之前的做法推到了合理的结论。修复了一个极其罕见的错误,即即使没有错误,浮点数的哈希也可能返回-1(并没有浪费时间来构造一个测试用例,从代码中可以明显看出它可能发生)。改进了复杂的哈希,因此 hash(complex(x, y))不再系统地相等hash(complex(y, x))

特别是,在此提交中,他撕掉了static long float_hash(PyFloatObject *v)in 的代码Objects/floatobject.c并使它成为just return _Py_HashDouble(v->ob_fval);,并在in的定义long _Py_HashDouble(double v)Objects/object.c添加了以下几行:

        if (Py_IS_INFINITY(intpart))
            /* can't convert to long int -- arbitrary */
            v = v < 0 ? -271828.0 : 314159.0;

因此,如上所述,这是一个任意选择。请注意,271828由e的前几个十进制数字形成。

相关的以后的提交:

Summary: It’s not a coincidence; _PyHASH_INF is hardcoded as 314159 in the default CPython implementation of Python, and was picked as an arbitrary value (obviously from the digits of π) by Tim Peters in 2000.


The value of hash(float('inf')) is one of the system-dependent parameters of the built-in hash function for numeric types, and is also available as sys.hash_info.inf in Python 3:

>>> import sys
>>> sys.hash_info
sys.hash_info(width=64, modulus=2305843009213693951, inf=314159, nan=0, imag=1000003, algorithm='siphash24', hash_bits=64, seed_bits=128, cutoff=0)
>>> sys.hash_info.inf
314159

(Same results with PyPy too.)


In terms of code, hash is a built-in function. Calling it on a Python float object invokes the function whose pointer is given by the tp_hash attribute of the built-in float type (PyTypeObject PyFloat_Type), which is the float_hash function, defined as return _Py_HashDouble(v->ob_fval), which in turn has

    if (Py_IS_INFINITY(v))
        return v > 0 ? _PyHASH_INF : -_PyHASH_INF;

where _PyHASH_INF is defined as 314159:

#define _PyHASH_INF 314159

In terms of history, the first mention of 314159 in this context in the Python code (you can find this with git bisect or git log -S 314159 -p) was added by Tim Peters in August 2000, in what is now commit 39dce293 in the cpython git repository.

The commit message says:

Fix for http://sourceforge.net/bugs/?func=detailbug&bug_id=111866&group_id=5470. This was a misleading bug — the true “bug” was that hash(x) gave an error return when x is an infinity. Fixed that. Added new Py_IS_INFINITY macro to pyport.h. Rearranged code to reduce growing duplication in hashing of float and complex numbers, pushing Trent’s earlier stab at that to a logical conclusion. Fixed exceedingly rare bug where hashing of floats could return -1 even if there wasn’t an error (didn’t waste time trying to construct a test case, it was simply obvious from the code that it could happen). Improved complex hash so that hash(complex(x, y)) doesn’t systematically equal hash(complex(y, x)) anymore.

In particular, in this commit he ripped out the code of static long float_hash(PyFloatObject *v) in Objects/floatobject.c and made it just return _Py_HashDouble(v->ob_fval);, and in the definition of long _Py_HashDouble(double v) in Objects/object.c he added the lines:

        if (Py_IS_INFINITY(intpart))
            /* can't convert to long int -- arbitrary */
            v = v < 0 ? -271828.0 : 314159.0;

So as mentioned, it was an arbitrary choice. Note that 271828 is formed from the first few decimal digits of e.

Related later commits:


回答 2

确实,

sys.hash_info.inf

返回314159。该值不会生成,而是内置在源代码中。事实上,

hash(float('-inf'))

-271828在python 2中返回或大约为-e(现在为-314159)。

将所有时间中两个最著名的无理数用作哈希值的事实使得它不太可能是巧合。

Indeed,

sys.hash_info.inf

returns 314159. The value is not generated, it’s built into the source code. In fact,

hash(float('-inf'))

returns -271828, or approximately -e, in python 2 (it’s -314159 now).

The fact that the two most famous irrational numbers of all time are used as the hash values makes it very unlikely to be a coincidence.


为什么有些float <整数比较的速度慢四倍?

问题:为什么有些float <整数比较的速度慢四倍?

将浮点数与整数进行比较时,某些值对的评估时间要比其他类似幅度的值花费更长的时间。

例如:

>>> import timeit
>>> timeit.timeit("562949953420000.7 < 562949953421000") # run 1 million times
0.5387085462592742

但是,如果将float或整数变小或变大一定数量,则比较会更快地运行:

>>> timeit.timeit("562949953420000.7 < 562949953422000") # integer increased by 1000
0.1481498428446173
>>> timeit.timeit("562949953423001.8 < 562949953421000") # float increased by 3001.1
0.1459577925548956

更改比较运算符(例如使用==>代替)不会以任何明显的方式影响时间。

这不只是涉及到大小,因为采摘较大或较小的值会导致比较快,所以我怀疑它已经降到了一些不幸的方式位排队。

显然,对于大多数用例而言,比较这些值已足够快。我只是对为什么Python似乎在某些价值观上比在其他价值观上挣扎更多感到好奇。

When comparing floats to integers, some pairs of values take much longer to be evaluated than other values of a similar magnitude.

For example:

>>> import timeit
>>> timeit.timeit("562949953420000.7 < 562949953421000") # run 1 million times
0.5387085462592742

But if the float or integer is made smaller or larger by a certain amount, the comparison runs much more quickly:

>>> timeit.timeit("562949953420000.7 < 562949953422000") # integer increased by 1000
0.1481498428446173
>>> timeit.timeit("562949953423001.8 < 562949953421000") # float increased by 3001.1
0.1459577925548956

Changing the comparison operator (e.g. using == or > instead) does not affect the times in any noticeable way.

This is not solely related to magnitude because picking larger or smaller values can result in faster comparisons, so I suspect it is down to some unfortunate way the bits line up.

Clearly, comparing these values is more than fast enough for most use cases. I am simply curious as to why Python seems to struggle more with some pairs of values than with others.


回答 0

浮点对象的Python源代码中的注释确认:

比较几乎是一场噩梦

在将浮点数与整数进行比较时尤其如此,因为与浮点数不同,Python中的整数可以任意大,并且总是精确的。尝试将整数强制转换为浮点数可能会失去精度,并使比较不准确。尝试将浮点数转换为整数也不会起作用,因为任何小数部分都会丢失。

为了解决这个问题,Python执行了一系列检查,如果其中一项检查成功,则返回结果。它比较两个值的符号,然后比较整数是否“太大”而不能成为浮点数,然后将浮点的指数与整数长度进行比较。如果所有这些检查均失败,则有必要构造两个新的Python对象进行比较以获得结果。

将浮点数v与整数/长整数进行比较时w,最坏的情况是:

  • v并且w具有相同的符号(正号或负号),
  • 该整数的w位数很少,可以保存为该size_t类型(通常为32或64位),
  • 整数w至少有49位,
  • float的指数与中的v位数相同w

这正是我们对问题中的值所拥有的:

>>> import math
>>> math.frexp(562949953420000.7) # gives the float's (significand, exponent) pair
(0.9999999999976706, 49)
>>> (562949953421000).bit_length()
49

我们看到49既是浮点数的指数,也是整数的位数。这两个数字都是正数,因此符合上述四个条件。

选择一个较大或较小的值可以更改整数的位数或指数的值,因此Python能够确定比较结果,而无需执行昂贵的最终检查。

这特定于该语言的CPython实现。


比较比较详细

float_richcompare函数处理两个值v和之间的比较w

下面是该功能执行的检查的分步说明。当试图了解函数的功能时,Python来源中的注释实际上非常有帮助,因此我将其放在相关的地方。我还将这些检查总结在答案底部的列表中。

其主要思想是映射Python对象vw两个相应的C双打,i并且j,然后可以很容易地比较以得到正确的结果。Python 2和Python 3都使用相同的想法进行操作(前者只是分别处理intlong键入)。

首先要做的是检查v是否绝对是Python float并将其映射到C double i。接下来,该函数查看是否w也是float并将其映射到C double j。这是该功能的最佳情况,因为可以跳过所有其他检查。该功能还检查是否vinfnan

static PyObject*
float_richcompare(PyObject *v, PyObject *w, int op)
{
    double i, j;
    int r = 0;
    assert(PyFloat_Check(v));       
    i = PyFloat_AS_DOUBLE(v);       

    if (PyFloat_Check(w))           
        j = PyFloat_AS_DOUBLE(w);   

    else if (!Py_IS_FINITE(i)) {
        if (PyLong_Check(w))
            j = 0.0;
        else
            goto Unimplemented;
    }

现在我们知道,如果w未通过这些检查,则不是Python浮点数。现在,该函数检查它是否为Python整数。在这种情况下,最简单的测试是提取v和的符号w0如果为零,-1则返回,如果为负,1则为正)。如果符号不同,则这是返回比较结果所需的全部信息:

    else if (PyLong_Check(w)) {
        int vsign = i == 0.0 ? 0 : i < 0.0 ? -1 : 1;
        int wsign = _PyLong_Sign(w);
        size_t nbits;
        int exponent;

        if (vsign != wsign) {
            /* Magnitudes are irrelevant -- the signs alone
             * determine the outcome.
             */
            i = (double)vsign;
            j = (double)wsign;
            goto Compare;
        }
    }   

如果此检查失败,则vw具有相同的符号。

下一个检查将计算整数中的位数w。如果它有太多位,那么就不可能将其保存为浮点数,因此其大小必须大于浮点数v

    nbits = _PyLong_NumBits(w);
    if (nbits == (size_t)-1 && PyErr_Occurred()) {
        /* This long is so large that size_t isn't big enough
         * to hold the # of bits.  Replace with little doubles
         * that give the same outcome -- w is so large that
         * its magnitude must exceed the magnitude of any
         * finite float.
         */
        PyErr_Clear();
        i = (double)vsign;
        assert(wsign != 0);
        j = wsign * 2.0;
        goto Compare;
    }

另一方面,如果整数w的位数为48个或更少,则可以安全地将C翻倍j并进行比较:

    if (nbits <= 48) {
        j = PyLong_AsDouble(w);
        /* It's impossible that <= 48 bits overflowed. */
        assert(j != -1.0 || ! PyErr_Occurred());
        goto Compare;
    }

从这一点开始,我们知道它w具有49位或更多位。将其w视为正整数会很方便,因此请根据需要更改符号和比较运算符:

    if (nbits <= 48) {
        /* "Multiply both sides" by -1; this also swaps the
         * comparator.
         */
        i = -i;
        op = _Py_SwappedOp[op];
    }

现在,该函数查看浮点数的指数。回想一下,可以将浮点数写为有效位数* 2 指数(忽略符号),并且有效位数表示0.5到1之间的数字:

    (void) frexp(i, &exponent);
    if (exponent < 0 || (size_t)exponent < nbits) {
        i = 1.0;
        j = 2.0;
        goto Compare;
    }

这检查了两件事。如果指数小于0,则浮点数小于1(因此,其大小小于任何整数)。或者,如果指数小于in的位数,wv < |w|由于有效* 2 指数小于2 nbits,我们可以得到

如果这两项检查均未通过,该函数将查看该指数是否大于中的位数w。这表明有效数* 2 指数大于2 nbit,因此v > |w|

    if ((size_t)exponent > nbits) {
        i = 2.0;
        j = 1.0;
        goto Compare;
    }

如果此检查未成功,我们将知道float的指数v与整数中的位数相同w

现在可以比较两个值的唯一方法是从v和构造两个新的Python整数w。这个想法是丢弃的小数部分v,将整数部分加倍,然后再加一个。w也会加倍,并且可以将这两个新的Python对象进行比较以提供正确的返回值。使用具有较小值的示例,4.65 < 4将由比较确定(2*4)+1 == 9 < 8 == (2*4)(返回false)。

    {
        double fracpart;
        double intpart;
        PyObject *result = NULL;
        PyObject *one = NULL;
        PyObject *vv = NULL;
        PyObject *ww = w;

        // snip

        fracpart = modf(i, &intpart); // split i (the double that v mapped to)
        vv = PyLong_FromDouble(intpart);

        // snip

        if (fracpart != 0.0) {
            /* Shift left, and or a 1 bit into vv
             * to represent the lost fraction.
             */
            PyObject *temp;

            one = PyLong_FromLong(1);

            temp = PyNumber_Lshift(ww, one); // left-shift doubles an integer
            ww = temp;

            temp = PyNumber_Lshift(vv, one);
            vv = temp;

            temp = PyNumber_Or(vv, one); // a doubled integer is even, so this adds 1
            vv = temp;
        }
        // snip
    }
}

为简洁起见,我省略了Python创建这些新对象时必须进行的其他错误检查和垃圾跟踪。不用说,这增加了额外的开销,并解释了为什么问题中突出显示的值比其他值慢得多。


这是比较功能执行的检查的摘要。

让它v成为一个浮点数并将其转换为C的double。现在,如果w也是浮点数:

  • 检查wnan还是inf。如果是这样,请根据的类型分别处理此特殊情况w

  • 如果不是,则比较vw直接按其表示形式将C值翻倍。

如果w为整数:

  • 提取的迹象vw。如果它们不同,那么我们知道v并且w不同,那是更大的价值。

  • 符号相同。)检查是否w有太多位不能浮空(大于size_t)。如果是这样,w则其幅度大于v

  • 检查是否w有48位或更少的位。如果是这样,可以安全地将其强制转换为C double而不损失其精度,并与进行比较v

  • w具有超过48位。我们现在将w其视作已适当更改比较操作的正整数。

  • 考虑浮点数的指数v。如果指数为负,则v小于1且因此小于任何正整数。否则,如果指数小于的位数,w则它必须小于w

  • 如果的指数v大于中的位数​​,wv大于w

  • 指数与中的位数相同w

  • 最后检查。拆分v成它的整数和小数部分。将整数部分加倍并加1以补偿小数部分。现在将整数倍w。比较这两个新的整数以获得结果。

A comment in the Python source code for float objects acknowledges that:

Comparison is pretty much a nightmare

This is especially true when comparing a float to an integer, because, unlike floats, integers in Python can be arbitrarily large and are always exact. Trying to cast the integer to a float might lose precision and make the comparison inaccurate. Trying to cast the float to an integer is not going to work either because any fractional part will be lost.

To get around this problem, Python performs a series of checks, returning the result if one of the checks succeeds. It compares the signs of the two values, then whether the integer is “too big” to be a float, then compares the exponent of the float to the length of the integer. If all of these checks fail, it is necessary to construct two new Python objects to compare in order to obtain the result.

When comparing a float v to an integer/long w, the worst case is that:

  • v and w have the same sign (both positive or both negative),
  • the integer w has few enough bits that it can be held in the size_t type (typically 32 or 64 bits),
  • the integer w has at least 49 bits,
  • the exponent of the float v is the same as the number of bits in w.

And this is exactly what we have for the values in the question:

>>> import math
>>> math.frexp(562949953420000.7) # gives the float's (significand, exponent) pair
(0.9999999999976706, 49)
>>> (562949953421000).bit_length()
49

We see that 49 is both the exponent of the float and the number of bits in the integer. Both numbers are positive and so the four criteria above are met.

Choosing one of the values to be larger (or smaller) can change the number of bits of the integer, or the value of the exponent, and so Python is able to determine the result of the comparison without performing the expensive final check.

This is specific to the CPython implementation of the language.


The comparison in more detail

The float_richcompare function handles the comparison between two values v and w.

Below is a step-by-step description of the checks that the function performs. The comments in the Python source are actually very helpful when trying to understand what the function does, so I’ve left them in where relevant. I’ve also summarised these checks in a list at the foot of the answer.

The main idea is to map the Python objects v and w to two appropriate C doubles, i and j, which can then be easily compared to give the correct result. Both Python 2 and Python 3 use the same ideas to do this (the former just handles int and long types separately).

The first thing to do is check that v is definitely a Python float and map it to a C double i. Next the function looks at whether w is also a float and maps it to a C double j. This is the best case scenario for the function as all the other checks can be skipped. The function also checks to see whether v is inf or nan:

static PyObject*
float_richcompare(PyObject *v, PyObject *w, int op)
{
    double i, j;
    int r = 0;
    assert(PyFloat_Check(v));       
    i = PyFloat_AS_DOUBLE(v);       

    if (PyFloat_Check(w))           
        j = PyFloat_AS_DOUBLE(w);   

    else if (!Py_IS_FINITE(i)) {
        if (PyLong_Check(w))
            j = 0.0;
        else
            goto Unimplemented;
    }

Now we know that if w failed these checks, it is not a Python float. Now the function checks if it’s a Python integer. If this is the case, the easiest test is to extract the sign of v and the sign of w (return 0 if zero, -1 if negative, 1 if positive). If the signs are different, this is all the information needed to return the result of the comparison:

    else if (PyLong_Check(w)) {
        int vsign = i == 0.0 ? 0 : i < 0.0 ? -1 : 1;
        int wsign = _PyLong_Sign(w);
        size_t nbits;
        int exponent;

        if (vsign != wsign) {
            /* Magnitudes are irrelevant -- the signs alone
             * determine the outcome.
             */
            i = (double)vsign;
            j = (double)wsign;
            goto Compare;
        }
    }   

If this check failed, then v and w have the same sign.

The next check counts the number of bits in the integer w. If it has too many bits then it can’t possibly be held as a float and so must be larger in magnitude than the float v:

    nbits = _PyLong_NumBits(w);
    if (nbits == (size_t)-1 && PyErr_Occurred()) {
        /* This long is so large that size_t isn't big enough
         * to hold the # of bits.  Replace with little doubles
         * that give the same outcome -- w is so large that
         * its magnitude must exceed the magnitude of any
         * finite float.
         */
        PyErr_Clear();
        i = (double)vsign;
        assert(wsign != 0);
        j = wsign * 2.0;
        goto Compare;
    }

On the other hand, if the integer w has 48 or fewer bits, it can safely turned in a C double j and compared:

    if (nbits <= 48) {
        j = PyLong_AsDouble(w);
        /* It's impossible that <= 48 bits overflowed. */
        assert(j != -1.0 || ! PyErr_Occurred());
        goto Compare;
    }

From this point onwards, we know that w has 49 or more bits. It will be convenient to treat w as a positive integer, so change the sign and the comparison operator as necessary:

    if (nbits <= 48) {
        /* "Multiply both sides" by -1; this also swaps the
         * comparator.
         */
        i = -i;
        op = _Py_SwappedOp[op];
    }

Now the function looks at the exponent of the float. Recall that a float can be written (ignoring sign) as significand * 2exponent and that the significand represents a number between 0.5 and 1:

    (void) frexp(i, &exponent);
    if (exponent < 0 || (size_t)exponent < nbits) {
        i = 1.0;
        j = 2.0;
        goto Compare;
    }

This checks two things. If the exponent is less than 0 then the float is smaller than 1 (and so smaller in magnitude than any integer). Or, if the exponent is less than the number of bits in w then we have that v < |w| since significand * 2exponent is less than 2nbits.

Failing these two checks, the function looks to see whether the exponent is greater than the number of bit in w. This shows that significand * 2exponent is greater than 2nbits and so v > |w|:

    if ((size_t)exponent > nbits) {
        i = 2.0;
        j = 1.0;
        goto Compare;
    }

If this check did not succeed we know that the exponent of the float v is the same as the number of bits in the integer w.

The only way that the two values can be compared now is to construct two new Python integers from v and w. The idea is to discard the fractional part of v, double the integer part, and then add one. w is also doubled and these two new Python objects can be compared to give the correct return value. Using an example with small values, 4.65 < 4 would be determined by the comparison (2*4)+1 == 9 < 8 == (2*4) (returning false).

    {
        double fracpart;
        double intpart;
        PyObject *result = NULL;
        PyObject *one = NULL;
        PyObject *vv = NULL;
        PyObject *ww = w;

        // snip

        fracpart = modf(i, &intpart); // split i (the double that v mapped to)
        vv = PyLong_FromDouble(intpart);

        // snip

        if (fracpart != 0.0) {
            /* Shift left, and or a 1 bit into vv
             * to represent the lost fraction.
             */
            PyObject *temp;

            one = PyLong_FromLong(1);

            temp = PyNumber_Lshift(ww, one); // left-shift doubles an integer
            ww = temp;

            temp = PyNumber_Lshift(vv, one);
            vv = temp;

            temp = PyNumber_Or(vv, one); // a doubled integer is even, so this adds 1
            vv = temp;
        }
        // snip
    }
}

For brevity I’ve left out the additional error-checking and garbage-tracking Python has to do when it creates these new objects. Needless to say, this adds additional overhead and explains why the values highlighted in the question are significantly slower to compare than others.


Here is a summary of the checks that are performed by the comparison function.

Let v be a float and cast it as a C double. Now, if w is also a float:

  • Check whether w is nan or inf. If so, handle this special case separately depending on the type of w.

  • If not, compare v and w directly by their representations as C doubles.

If w is an integer:

  • Extract the signs of v and w. If they are different then we know v and w are different and which is the greater value.

  • (The signs are the same.) Check whether w has too many bits to be a float (more than size_t). If so, w has greater magnitude than v.

  • Check if w has 48 or fewer bits. If so, it can be safely cast to a C double without losing its precision and compared with v.

  • (w has more than 48 bits. We will now treat w as a positive integer having changed the compare op as appropriate.)

  • Consider the exponent of the float v. If the exponent is negative, then v is less than 1 and therefore less than any positive integer. Else, if the exponent is less than the number of bits in w then it must be less than w.

  • If the exponent of v is greater than the number of bits in w then v is greater than w.

  • (The exponent is the same as the number of bits in w.)

  • The final check. Split v into its integer and fractional parts. Double the integer part and add 1 to compensate for the fractional part. Now double the integer w. Compare these two new integers instead to get the result.


回答 1

gmpy2与任意精度的浮点数和整数一起使用,可以获得更统一的比较性能:

~ $ ptipython
Python 3.5.1 |Anaconda 4.0.0 (64-bit)| (default, Dec  7 2015, 11:16:01) 
Type "copyright", "credits" or "license" for more information.

IPython 4.1.2 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import gmpy2

In [2]: from gmpy2 import mpfr

In [3]: from gmpy2 import mpz

In [4]: gmpy2.get_context().precision=200

In [5]: i1=562949953421000

In [6]: i2=562949953422000

In [7]: f=562949953420000.7

In [8]: i11=mpz('562949953421000')

In [9]: i12=mpz('562949953422000')

In [10]: f1=mpfr('562949953420000.7')

In [11]: f<i1
Out[11]: True

In [12]: f<i2
Out[12]: True

In [13]: f1<i11
Out[13]: True

In [14]: f1<i12
Out[14]: True

In [15]: %timeit f<i1
The slowest run took 10.15 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 441 ns per loop

In [16]: %timeit f<i2
The slowest run took 12.55 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 152 ns per loop

In [17]: %timeit f1<i11
The slowest run took 32.04 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 269 ns per loop

In [18]: %timeit f1<i12
The slowest run took 36.81 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 231 ns per loop

In [19]: %timeit f<i11
The slowest run took 78.26 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 156 ns per loop

In [20]: %timeit f<i12
The slowest run took 21.24 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 194 ns per loop

In [21]: %timeit f1<i1
The slowest run took 37.61 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 275 ns per loop

In [22]: %timeit f1<i2
The slowest run took 39.03 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 259 ns per loop

Using gmpy2 with arbitrary precision floats and integers it is possible to get more uniform comparison performance:

~ $ ptipython
Python 3.5.1 |Anaconda 4.0.0 (64-bit)| (default, Dec  7 2015, 11:16:01) 
Type "copyright", "credits" or "license" for more information.

IPython 4.1.2 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import gmpy2

In [2]: from gmpy2 import mpfr

In [3]: from gmpy2 import mpz

In [4]: gmpy2.get_context().precision=200

In [5]: i1=562949953421000

In [6]: i2=562949953422000

In [7]: f=562949953420000.7

In [8]: i11=mpz('562949953421000')

In [9]: i12=mpz('562949953422000')

In [10]: f1=mpfr('562949953420000.7')

In [11]: f<i1
Out[11]: True

In [12]: f<i2
Out[12]: True

In [13]: f1<i11
Out[13]: True

In [14]: f1<i12
Out[14]: True

In [15]: %timeit f<i1
The slowest run took 10.15 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 441 ns per loop

In [16]: %timeit f<i2
The slowest run took 12.55 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 152 ns per loop

In [17]: %timeit f1<i11
The slowest run took 32.04 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 269 ns per loop

In [18]: %timeit f1<i12
The slowest run took 36.81 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 231 ns per loop

In [19]: %timeit f<i11
The slowest run took 78.26 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 156 ns per loop

In [20]: %timeit f<i12
The slowest run took 21.24 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 194 ns per loop

In [21]: %timeit f1<i1
The slowest run took 37.61 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 275 ns per loop

In [22]: %timeit f1<i2
The slowest run took 39.03 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 259 ns per loop

比较浮点数和Python中几乎相等的最佳方法是什么?

问题:比较浮点数和Python中几乎相等的最佳方法是什么?

众所周知,由于舍入和精度问题,比较浮点数是否相等。

例如:https//randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/

在Python中处理此问题的推荐方法是什么?

当然在某个地方有一个标准的库函数吗?

It’s well known that comparing floats for equality is a little fiddly due to rounding and precision issues.

For example: https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/

What is the recommended way to deal with this in Python?

Surely there is a standard library function for this somewhere?


回答 0

Python 3.5添加了PEP 485中描述的math.isclosecmath.isclose功能

如果您使用的是Python的早期版本,则等效功能在文档中给出。

def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
    return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)

rel_tol是一个相对容差,它乘以两个参数中的较大者;当值变大时,它们之间的允许差异也会变大,同时仍将它们视为相等。

abs_tol是在所有情况下均按原样应用的绝对公差。如果差异小于这些公差中的任何一个,则认为值相等。

Python 3.5 adds the math.isclose and cmath.isclose functions as described in PEP 485.

If you’re using an earlier version of Python, the equivalent function is given in the documentation.

def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
    return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)

rel_tol is a relative tolerance, it is multiplied by the greater of the magnitudes of the two arguments; as the values get larger, so does the allowed difference between them while still considering them equal.

abs_tol is an absolute tolerance that is applied as-is in all cases. If the difference is less than either of those tolerances, the values are considered equal.


回答 1

如下简单的内容还不够好吗?

return abs(f1 - f2) <= allowed_error

Is something as simple as the following not good enough?

return abs(f1 - f2) <= allowed_error

回答 2

我同意Gareth的答案可能最适合作为轻量级功能/解决方案。

但是我认为最好注意一下,如果您正在使用NumPy或正在考虑使用它,则可以使用打包功能。

numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)

不过有一点免责声明:根据您的平台,安装NumPy可能是不平凡的体验。

I would agree that Gareth’s answer is probably most appropriate as a lightweight function/solution.

But I thought it would be helpful to note that if you are using NumPy or are considering it, there is a packaged function for this.

numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)

A little disclaimer though: installing NumPy can be a non-trivial experience depending on your platform.


回答 3

使用decimal提供Decimal类的Python 模块。

从评论:

值得注意的是,如果您正在做大量的数学工作,并且您绝对不需要十进制的精度,那么这确实会使事情陷入困境。浮动是一种方式,处理起来更快,但不精确。小数非常精确,但速度很慢。

Use Python’s decimal module, which provides the Decimal class.

From the comments:

It is worth noting that if you’re doing math-heavy work and you don’t absolutely need the precision from decimal, this can really bog things down. Floats are way, way faster to deal with, but imprecise. Decimals are extremely precise but slow.


回答 4

我不知道Python标准库(或其他地方)中实现Dawson AlmostEqual2sComplement函数的任何内容。如果这是您想要的行为,则必须自己实施。(在这种情况下,而不是使用Dawson的聪明按位黑客你可能做的更好使用形式的更常规测试if abs(a-b) <= eps1*(abs(a)+abs(b)) + eps2或类似的道森得到类似的行为,你可能会说,if abs(a-b) <= eps*max(EPS,abs(a),abs(b))对于一些小的固定EPS,这是不完全与道森相同,但在精神上相似。

I’m not aware of anything in the Python standard library (or elsewhere) that implements Dawson’s AlmostEqual2sComplement function. If that’s the sort of behaviour you want, you’ll have to implement it yourself. (In which case, rather than using Dawson’s clever bitwise hacks you’d probably do better to use more conventional tests of the form if abs(a-b) <= eps1*(abs(a)+abs(b)) + eps2 or similar. To get Dawson-like behaviour you might say something like if abs(a-b) <= eps*max(EPS,abs(a),abs(b)) for some small fixed EPS; this isn’t exactly the same as Dawson, but it’s similar in spirit.


回答 5

无法将浮点数进行相等性比较的常识是不正确的。浮点数与整数没有什么不同:如果计算“ a == b”,则它们是相同的数字将为true,否则为false(要理解两个NaN当然不是相同的数字)。

实际的问题是这样的:如果我进行了一些计算并且不确定我要比较的两个数字是否完全正确,那又是什么?对于浮点和整数,此问题相同。如果计算整数表达式“ 7/3 * 3”,则它将不等于“ 7 * 3/3”。

因此,假设我们问“如何比较整数是否相等?” 在这种情况下。没有一个答案。您应该做什么取决于具体情况,尤其是您遇到的错误类型以及要实现的错误类型。

这是一些可能的选择。

如果要在数学上精确的数字相等的情况下获得“真实”的结果,则可以尝试使用所执行的计算的属性来证明在两个数字中得到相同的错误。如果这是可行的,并且您比较了两个表达式所产生的两个数字,这些表达式在经过精确计算后将得出相等的数字,那么您将从比较中获得“ true”。另一种方法是,您可能会分析计算的属性,并证明误差不会超过特定数量,可能绝对值或相对于输入之一或输出之一的数量。在这种情况下,您可以询问两个计算得出的数字是否相差最大,如果在间隔内,则返回“ true”。如果您无法证明错误界限,您可能会猜测并希望达到最佳。猜测的一种方法是评估许多随机样本,并查看结果中得到的分布类型。

当然,由于我们仅设置了在数学上精确的结果相等的情况下您必须获得“真实”的要求,因此我们就保留了即使它们不相等也获得“真实”的可能性。(实际上,我们可以通过始终返回“ true”来满足要求。这使计算变得简单,但是通常不希望这样做,因此,我将在下面讨论改善这种情况。)

如果要在数学上精确的数字不相等的情况下获得“假”结果,则需要证明在数学上精确的数字不相等的情况下,对数字的评估会得出不同的数字。在许多常见情况下,出于实际目的这可能是不可能的。因此,让我们考虑一个替代方案。

一个有用的要求是,如果数学上精确的数字相差超过一定数量,我们将得到“假”结果。例如,也许我们要计算在计算机游戏中扔出的球在哪里移动,并且我们想知道它是否击中了蝙蝠。在这种情况下,如果球碰到球棒,我们当然想得到“ true”,如果球远离球棒,我们就希望得到“ false”,如果球进入球棒,我们可以接受不正确的“ true”答案。数学上精确的模拟未击中蝙蝠,但击中蝙蝠仅不到一毫米。在这种情况下,我们需要证明(或猜测/估计)我们对球的位置和球拍的位置的计算的组合误差最大为1毫米(对于所有感兴趣的位置)。这将使我们始终返回“

因此,在比较浮点数时如何决定返回什么很大程度上取决于您的具体情况。

关于如何证明计算的误差范围,这可能是一个复杂的主题。使用舍入取整模式使用IEEE 754标准的任何浮点实现都会返回最接近于任何基本运算(尤其是乘法,除法,加法,减法,平方根)的精确结果的浮点数。(在平局的情况下,是舍入的,所以低位是偶数。)(请特别注意平方根和除法;您的语言实现可能会使用不符合IEEE 754的方法。)由于这一要求,我们知道单个结果中的错误最多为最低有效位的值的1/2。(如果更多,则将四舍五入为数值的1/2之内的另一个数字。)

从那里继续进行变得更加复杂。下一步是执行其中一个输入已经有错误的操作。对于简单表达式,可以通过计算跟踪这些错误,以达到最终错误的界限。实际上,这仅在少数情况下才能完成,例如使用高质量的数学库。而且,当然,您需要精确地控制要执行的操作。高级语言通常会给编译器带来很多负担,因此您可能不知道以什么顺序执行操作。

关于这个话题还有很多(现在)可以写,但是我必须到此为止。总而言之,答案是:没有用于此比较的库例程,因为没有适合大多数需求的单一解决方案值得放入库例程中。(如果与一个相对误差间隔或绝对误差间隔进行比较就足够了,则无需库例程即可完成此操作。)

The common wisdom that floating-point numbers cannot be compared for equality is inaccurate. Floating-point numbers are no different from integers: If you evaluate “a == b”, you will get true if they are identical numbers and false otherwise (with the understanding that two NaNs are of course not identical numbers).

The actual problem is this: If I have done some calculations and am not sure the two numbers I have to compare are exactly correct, then what? This problem is the same for floating-point as it is for integers. If you evaluate the integer expression “7/3*3”, it will not compare equal to “7*3/3”.

So suppose we asked “How do I compare integers for equality?” in such a situation. There is no single answer; what you should do depends on the specific situation, notably what sort of errors you have and what you want to achieve.

Here are some possible choices.

If you want to get a “true” result if the mathematically exact numbers would be equal, then you might try to use the properties of the calculations you perform to prove that you get the same errors in the two numbers. If that is feasible, and you compare two numbers that result from expressions that would give equal numbers if computed exactly, then you will get “true” from the comparison. Another approach is that you might analyze the properties of the calculations and prove that the error never exceeds a certain amount, perhaps an absolute amount or an amount relative to one of the inputs or one of the outputs. In that case, you can ask whether the two calculated numbers differ by at most that amount, and return “true” if they are within the interval. If you cannot prove an error bound, you might guess and hope for the best. One way of guessing is to evaluate many random samples and see what sort of distribution you get in the results.

Of course, since we only set the requirement that you get “true” if the mathematically exact results are equal, we left open the possibility that you get “true” even if they are unequal. (In fact, we can satisfy the requirement by always returning “true”. This makes the calculation simple but is generally undesirable, so I will discuss improving the situation below.)

If you want to get a “false” result if the mathematically exact numbers would be unequal, you need to prove that your evaluation of the numbers yields different numbers if the mathematically exact numbers would be unequal. This may be impossible for practical purposes in many common situations. So let us consider an alternative.

A useful requirement might be that we get a “false” result if the mathematically exact numbers differ by more than a certain amount. For example, perhaps we are going to calculate where a ball thrown in a computer game traveled, and we want to know whether it struck a bat. In this case, we certainly want to get “true” if the ball strikes the bat, and we want to get “false” if the ball is far from the bat, and we can accept an incorrect “true” answer if the ball in a mathematically exact simulation missed the bat but is within a millimeter of hitting the bat. In that case, we need to prove (or guess/estimate) that our calculation of the ball’s position and the bat’s position have a combined error of at most one millimeter (for all positions of interest). This would allow us to always return “false” if the ball and bat are more than a millimeter apart, to return “true” if they touch, and to return “true” if they are close enough to be acceptable.

So, how you decide what to return when comparing floating-point numbers depends very much on your specific situation.

As to how you go about proving error bounds for calculations, that can be a complicated subject. Any floating-point implementation using the IEEE 754 standard in round-to-nearest mode returns the floating-point number nearest to the exact result for any basic operation (notably multiplication, division, addition, subtraction, square root). (In case of tie, round so the low bit is even.) (Be particularly careful about square root and division; your language implementation might use methods that do not conform to IEEE 754 for those.) Because of this requirement, we know the error in a single result is at most 1/2 of the value of the least significant bit. (If it were more, the rounding would have gone to a different number that is within 1/2 the value.)

Going on from there gets substantially more complicated; the next step is performing an operation where one of the inputs already has some error. For simple expressions, these errors can be followed through the calculations to reach a bound on the final error. In practice, this is only done in a few situations, such as working on a high-quality mathematics library. And, of course, you need precise control over exactly which operations are performed. High-level languages often give the compiler a lot of slack, so you might not know in which order operations are performed.

There is much more that could be (and is) written about this topic, but I have to stop there. In summary, the answer is: There is no library routine for this comparison because there is no single solution that fits most needs that is worth putting into a library routine. (If comparing with a relative or absolute error interval suffices for you, you can do it simply without a library routine.)


回答 6

如果要在测试/ TDD上下文中使用它,我会说这是一种标准方式:

from nose.tools import assert_almost_equals

assert_almost_equals(x, y, places=7) #default is 7

If you want to use it in testing/TDD context, I’d say this is a standard way:

from nose.tools import assert_almost_equals

assert_almost_equals(x, y, places=7) #default is 7

回答 7

为此,math.isclose()添加到Python 3.5中(源代码)。这是它与Python 2的移植。与Mark Ransom的一句话不同的是,它可以正确处理“ inf”和“ -inf”。

def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
    '''
    Python 2 implementation of Python 3.5 math.isclose()
    https://hg.python.org/cpython/file/tip/Modules/mathmodule.c#l1993
    '''
    # sanity check on the inputs
    if rel_tol < 0 or abs_tol < 0:
        raise ValueError("tolerances must be non-negative")

    # short circuit exact equality -- needed to catch two infinities of
    # the same sign. And perhaps speeds things up a bit sometimes.
    if a == b:
        return True

    # This catches the case of two infinities of opposite sign, or
    # one infinity and one finite number. Two infinities of opposite
    # sign would otherwise have an infinite relative tolerance.
    # Two infinities of the same sign are caught by the equality check
    # above.
    if math.isinf(a) or math.isinf(b):
        return False

    # now do the regular computation
    # this is essentially the "weak" test from the Boost library
    diff = math.fabs(b - a)
    result = (((diff <= math.fabs(rel_tol * b)) or
               (diff <= math.fabs(rel_tol * a))) or
              (diff <= abs_tol))
    return result

math.isclose() has been added to Python 3.5 for that (source code). Here is a port of it to Python 2. It’s difference from one-liner of Mark Ransom is that it can handle “inf” and “-inf” properly.

def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
    '''
    Python 2 implementation of Python 3.5 math.isclose()
    https://hg.python.org/cpython/file/tip/Modules/mathmodule.c#l1993
    '''
    # sanity check on the inputs
    if rel_tol < 0 or abs_tol < 0:
        raise ValueError("tolerances must be non-negative")

    # short circuit exact equality -- needed to catch two infinities of
    # the same sign. And perhaps speeds things up a bit sometimes.
    if a == b:
        return True

    # This catches the case of two infinities of opposite sign, or
    # one infinity and one finite number. Two infinities of opposite
    # sign would otherwise have an infinite relative tolerance.
    # Two infinities of the same sign are caught by the equality check
    # above.
    if math.isinf(a) or math.isinf(b):
        return False

    # now do the regular computation
    # this is essentially the "weak" test from the Boost library
    diff = math.fabs(b - a)
    result = (((diff <= math.fabs(rel_tol * b)) or
               (diff <= math.fabs(rel_tol * a))) or
              (diff <= abs_tol))
    return result

回答 8

我发现以下比较有帮助:

str(f1) == str(f2)

I found the following comparison helpful:

str(f1) == str(f2)

回答 9

对于某些可能影响源编号表示的情况,可以使用整数分子和分母将它们表示为小数而不是浮点数。这样,您就可以进行精确比较。

有关详细信息,请参见“ 分数的分数”模块。

For some of the cases where you can affect the source number representation, you can represent them as fractions instead of floats, using integer numerator and denominator. That way you can have exact comparisons.

See Fraction from fractions module for details.


回答 10

我喜欢@Sesquipedal的建议,但进行了修改(当两个值均为0时,返回False的特殊用例)。就我而言,我使用的是python 2.7,只是使用了一个简单的函数:

if f1 ==0 and f2 == 0:
    return True
else:
    return abs(f1-f2) < tol*max(abs(f1),abs(f2))

I liked @Sesquipedal ‘s suggestion but with modification (a special use case when both values are 0 returns False). In my case I was on Python 2.7 and just used a simple function:

if f1 ==0 and f2 == 0:
    return True
else:
    return abs(f1-f2) < tol*max(abs(f1),abs(f2))

回答 11

对于要确保2个数字相同且“精确度最高”而无需指定公差的情况很有用:

  • 找出2个数字的最小精度

  • 将它们四舍五入到最低精度并进行比较

def isclose(a,b):                                       
    astr=str(a)                                         
    aprec=len(astr.split('.')[1]) if '.' in astr else 0 
    bstr=str(b)                                         
    bprec=len(bstr.split('.')[1]) if '.' in bstr else 0 
    prec=min(aprec,bprec)                                      
    return round(a,prec)==round(b,prec)                               

如所写,仅适用于字符串表示形式中不包含’e’的数字(含义0.9999999999995e-4 <数字<= 0.9999999999995e11)

例:

>>> isclose(10.0,10.049)
True
>>> isclose(10.0,10.05)
False

Useful for the case where you want to make sure 2 numbers are the same ‘up to precision’, no need to specify the tolerance:

  • Find minimum precision of the 2 numbers

  • Round both of them to minimum precision and compare

def isclose(a,b):                                       
    astr=str(a)                                         
    aprec=len(astr.split('.')[1]) if '.' in astr else 0 
    bstr=str(b)                                         
    bprec=len(bstr.split('.')[1]) if '.' in bstr else 0 
    prec=min(aprec,bprec)                                      
    return round(a,prec)==round(b,prec)                               

As written, only works for numbers without the ‘e’ in their string representation ( meaning 0.9999999999995e-4 < number <= 0.9999999999995e11 )

Example:

>>> isclose(10.0,10.049)
True
>>> isclose(10.0,10.05)
False

回答 12

比较不超过给定十进制数的给定值atol/rtol

def almost_equal(a, b, decimal=6):
    return '{0:.{1}f}'.format(a, decimal) == '{0:.{1}f}'.format(b, decimal)

print(almost_equal(0.0, 0.0001, decimal=5)) # False
print(almost_equal(0.0, 0.0001, decimal=4)) # True 

To compare up to a given decimal without atol/rtol:

def almost_equal(a, b, decimal=6):
    return '{0:.{1}f}'.format(a, decimal) == '{0:.{1}f}'.format(b, decimal)

print(almost_equal(0.0, 0.0001, decimal=5)) # False
print(almost_equal(0.0, 0.0001, decimal=4)) # True 

回答 13

这也许有点丑陋,但是当您不需要的默认浮点精度(大约11位小数)时,它就可以很好地工作。

round_to函数使用格式方法从内置的str类的浮动四舍五入到代表浮球随所需的小数位数,然后用一个字符串应用EVAL内置功能圆形浮弦找回浮点数字类型。

is_close功能只适用于一个简单的条件向围捕浮动。

def round_to(float_num, prec):
    return eval("'{:." + str(int(prec)) + "f}'.format(" + str(float_num) + ")")

def is_close(float_a, float_b, prec):
    if round_to(float_a, prec) == round_to(float_b, prec):
        return True
    return False

>>>a = 10.0
10.0
>>>b = 10.0001
10.0001
>>>print is_close(a, b, prec=3)
True
>>>print is_close(a, b, prec=4)
False

更新:

正如@stepehjfox所建议的那样,一种构建rount_to函数以避免“ eval”的更简洁方法是使用嵌套格式

def round_to(float_num, prec):
    return '{:.{precision}f}'.format(float_num, precision=prec)

遵循相同的思想,使用很棒的新f字符串(Python 3.6+),代码甚至可以变得更加简单:

def round_to(float_num, prec):
    return f'{float_num:.{prec}f}'

因此,我们甚至可以将其全部封装在一个简单干净的“ is_close”函数中:

def is_close(a, b, prec):
    return f'{a:.{prec}f}' == f'{b:.{prec}f}'

This maybe is a bit ugly hack, but it works pretty well when you don’t need more than the default float precision (about 11 decimals).

The round_to function uses the format method from the built-in str class to round up the float to a string that represents the float with the number of decimals needed, and then applies the eval built-in function to the rounded float string to get back to the float numeric type.

The is_close function just applies a simple conditional to the rounded up float.

def round_to(float_num, prec):
    return eval("'{:." + str(int(prec)) + "f}'.format(" + str(float_num) + ")")

def is_close(float_a, float_b, prec):
    if round_to(float_a, prec) == round_to(float_b, prec):
        return True
    return False

>>>a = 10.0
10.0
>>>b = 10.0001
10.0001
>>>print is_close(a, b, prec=3)
True
>>>print is_close(a, b, prec=4)
False

Update:

As suggested by @stepehjfox, a cleaner way to build a rount_to function avoiding “eval” is using nested formatting:

def round_to(float_num, prec):
    return '{:.{precision}f}'.format(float_num, precision=prec)

Following the same idea, the code can be even simpler using the great new f-strings (Python 3.6+):

def round_to(float_num, prec):
    return f'{float_num:.{prec}f}'

So, we could even wrap it up all in one simple and clean ‘is_close’ function:

def is_close(a, b, prec):
    return f'{a:.{prec}f}' == f'{b:.{prec}f}'

回答 14

就绝对误差而言,您只需检查一下

if abs(a - b) <= error:
    print("Almost equal")

为什么float行为在Python中很奇怪的一些信息 https://youtu.be/v4HhvoNLILk?t=1129

您也可以将math.isclose用于相对错误

In terms of absolute error, you can just check

if abs(a - b) <= error:
    print("Almost equal")

Some information of why float act weird in Python https://youtu.be/v4HhvoNLILk?t=1129

You can also use math.isclose for relative errors


如何在Python中四舍五入一个数字?

问题:如何在Python中四舍五入一个数字?

这个问题使我丧命。如何在Python中向上舍入一个数字?

我尝试了舍入(数字),但它四舍五入数字。例:

round(2.3) = 2.0 and not 3, what I would like

我尝试了int(number + .5),但是它再次将数字取整!例:

int(2.3 + .5) = 2

然后我尝试了round(number + .5),但在边缘情况下不起作用。例:

WAIT! THIS WORKED!

请指教。

This problem is killing me. How does one roundup a number UP in Python?

I tried round(number) but it round the number down. Example:

round(2.3) = 2.0 and not 3, what I would like

The I tried int(number + .5) but it round the number down again! Example:

int(2.3 + .5) = 2

Then I tried round(number + .5) but it won’t work in edge cases. Example:

WAIT! THIS WORKED!

Please advise.


回答 0

小区(上限)功能:

import math
print(math.ceil(4.2))

The ceil (ceiling) function:

import math
print(math.ceil(4.2))

回答 1

我知道这个答案是一个很久以前的问题,但是如果您不想导入数学并且只想四舍五入,那么这对我有用。

>>> int(21 / 5)
4
>>> int(21 / 5) + (21 % 5 > 0)
5

如果有余数,则第一部分将变为4,第二部分将得出“ True”,另外,True = 1; False =0。因此,如果没有余数,则它将保持相同的整数,但是如果有余数,则将其加1。

I know this answer is for a question from a while back, but if you don’t want to import math and you just want to round up, this works for me.

>>> int(21 / 5)
4
>>> int(21 / 5) + (21 % 5 > 0)
5

The first part becomes 4 and the second part evaluates to “True” if there is a remainder, which in addition True = 1; False = 0. So if there is no remainder, then it stays the same integer, but if there is a remainder it adds 1.


回答 2

请记住有趣的Python 2.x问题:

>>> import math
>>> math.ceil(4500/1000)
4.0
>>> math.ceil(4500/1000.0)
5.0

问题是在python中将两个int相除会产生另一个int,并且在上限调用之前被截断了。您必须使一个值成为浮点数(或强制转换)才能获得正确的结果。

在javascript中,完全相同的代码会产生不同的结果:

console.log(Math.ceil(4500/1000));
5

Interesting Python 2.x issue to keep in mind:

>>> import math
>>> math.ceil(4500/1000)
4.0
>>> math.ceil(4500/1000.0)
5.0

The problem is that dividing two ints in python produces another int and that’s truncated before the ceiling call. You have to make one value a float (or cast) to get a correct result.

In javascript, the exact same code produces a different result:

console.log(Math.ceil(4500/1000));
5

回答 3

如果使用整数,则四舍五入的一种方法是利用四舍五入的事实//:只需对负数进行除法,然后取反即可。无需导入,浮点或有条件的。

rounded_up = -(-numerator // denominator)

例如:

>>> print(-(-101 // 5))
21

If working with integers, one way of rounding up is to take advantage of the fact that // rounds down: Just do the division on the negative number, then negate the answer. No import, floating point, or conditional needed.

rounded_up = -(-numerator // denominator)

For example:

>>> print(-(-101 // 5))
21

回答 4

您可能还喜欢numpy:

>>> import numpy as np
>>> np.ceil(2.3)
3.0

我并不是说它比数学更好,但是如果您已经将numpy用于其他目的,则可以使代码保持一致。

无论如何,我遇到的只是一个细节。我经常使用numpy,但感到惊讶的是它没有被提及,但是当然可以接受。

You might also like numpy:

>>> import numpy as np
>>> np.ceil(2.3)
3.0

I’m not saying it’s better than math, but if you were already using numpy for other purposes, you can keep your code consistent.

Anyway, just a detail I came across. I use numpy a lot and was surprised it didn’t get mentioned, but of course the accepted answer works perfectly fine.


回答 5

使用math.ceil围捕:

>>> import math
>>> math.ceil(5.4)
6.0

注意:输入应为浮点型。

如果需要整数,请调用int将其转换:

>>> int(math.ceil(5.4))
6

BTW,使用math.floor到轮,并round以轮最接近的整数。

>>> math.floor(4.4), math.floor(4.5), math.floor(5.4), math.floor(5.5)
(4.0, 4.0, 5.0, 5.0)
>>> round(4.4), round(4.5), round(5.4), round(5.5)
(4.0, 5.0, 5.0, 6.0)
>>> math.ceil(4.4), math.ceil(4.5), math.ceil(5.4), math.ceil(5.5)
(5.0, 5.0, 6.0, 6.0)

Use math.ceil to round up:

>>> import math
>>> math.ceil(5.4)
6.0

NOTE: The input should be float.

If you need an integer, call int to convert it:

>>> int(math.ceil(5.4))
6

BTW, use math.floor to round down and round to round to nearest integer.

>>> math.floor(4.4), math.floor(4.5), math.floor(5.4), math.floor(5.5)
(4.0, 4.0, 5.0, 5.0)
>>> round(4.4), round(4.5), round(5.4), round(5.5)
(4.0, 5.0, 5.0, 6.0)
>>> math.ceil(4.4), math.ceil(4.5), math.ceil(5.4), math.ceil(5.5)
(5.0, 5.0, 6.0, 6.0)

回答 6

语法可能不像pythonic那样,但是它是一个功能强大的库。

https://docs.python.org/2/library/decimal.html

from decimal import *
print(int(Decimal(2.3).quantize(Decimal('1.'), rounding=ROUND_UP)))

The syntax may not be as pythonic as one might like, but it is a powerful library.

https://docs.python.org/2/library/decimal.html

from decimal import *
print(int(Decimal(2.3).quantize(Decimal('1.'), rounding=ROUND_UP)))

回答 7

我很惊讶没有人建议

(numerator + denominator - 1) // denominator

用于四舍五入的整数除法。曾经是C / C ++ / CUDA的常用方法(参见divup

I am surprised nobody suggested

(numerator + denominator - 1) // denominator

for integer division with rounding up. Used to be the common way for C/C++/CUDA (cf. divup)


回答 8

请确保四舍五入的值应为浮点型

a = 8 
b = 21
print math.ceil(a / b)
>>> 0

print math.ceil(float(a) / b)
>>> 1.0

Be shure rounded value should be float

a = 8 
b = 21
print math.ceil(a / b)
>>> 0

but

print math.ceil(float(a) / b)
>>> 1.0

回答 9

尝试这个:

a = 211.0
print(int(a) + ((int(a) - a) != 0))

Try this:

a = 211.0
print(int(a) + ((int(a) - a) != 0))

回答 10

>>> def roundup(number):
...     return round(number+.5)
>>> roundup(2.3)
3
>>> roundup(19.00000000001)
20

此功能不需要任何模块。

>>> def roundup(number):
...     return round(number+.5)
>>> roundup(2.3)
3
>>> roundup(19.00000000001)
20

This function requires no modules.


回答 11

上面的答案是正确的,但是,math对于这个功能而言,导入模块通常对我来说有点过头了。幸运的是,还有另一种方法可以做到:

g = 7/5
g = int(g) + (not g.is_integer())

True并且在python中涉及数字的语句中False被解释为10g.is_interger()基本上翻译为g.has_no_decimal()g == int(g)。因此,最后的英文陈述为round g down and add one if g has decimal

The above answers are correct, however, importing the math module just for this one function usually feels like a bit of an overkill for me. Luckily, there is another way to do it:

g = 7/5
g = int(g) + (not g.is_integer())

True and False are interpreted as 1 and 0 in a statement involving numbers in python. g.is_interger() basically translates to g.has_no_decimal() or g == int(g). So the last statement in English reads round g down and add one if g has decimal.


回答 12

无需导入数学//使用基本环境:

a)方法/类方法

def ceil(fl): 
  return int(fl) + (1 if fl-int(fl) else 0)

def ceil(self, fl): 
  return int(fl) + (1 if fl-int(fl) else 0)

b)lambda:

ceil = lambda fl:int(fl)+(1 if fl-int(fl) else 0)

Without importing math // using basic envionment:

a) method / class method

def ceil(fl): 
  return int(fl) + (1 if fl-int(fl) else 0)

def ceil(self, fl): 
  return int(fl) + (1 if fl-int(fl) else 0)

b) lambda:

ceil = lambda fl:int(fl)+(1 if fl-int(fl) else 0)

回答 13

对于那些想要四舍五入a / b并获得整数的人:

使用整数除法的另一个变体是

def int_ceil(a, b):
    return (a - 1) // b + 1

>>> int_ceil(19, 5)
4
>>> int_ceil(20, 5)
4
>>> int_ceil(21, 5)
5

For those who want to round up a / b and get integer:

Another variant using integer division is

def int_ceil(a, b):
    return (a - 1) // b + 1

>>> int_ceil(19, 5)
4
>>> int_ceil(20, 5)
4
>>> int_ceil(21, 5)
5

回答 14

如果有人希望将其舍入到小数点后一位:

import math
def round_up(n, decimals=0):
    multiplier = 10 ** decimals
    return math.ceil(n * multiplier) / multiplier

In case anyone is looking to round up to a specific decimal place:

import math
def round_up(n, decimals=0):
    multiplier = 10 ** decimals
    return math.ceil(n * multiplier) / multiplier

回答 15

令我惊讶的是我还没有看到这个答案round(x + 0.4999),所以我要把它放下来。请注意,这适用于任何Python版本。对Python舍入方案的更改使事情变得困难。看到这篇文章

不导入,我使用:

def roundUp(num):
    return round(num + 0.49)

testCases = list(x*0.1 for x in range(0, 50))

print(testCases)
for test in testCases:
    print("{:5.2f}  -> {:5.2f}".format(test, roundUp(test)))

为什么这样

来自文档

对于支持round()的内置类型,将值四舍五入为乘幂n的最接近10的倍数;如果两个倍数相等接近,则四舍五入取整为偶数选择

因此,将2.5舍入为2,将3.5舍入为4。如果不是这种情况,则可以通过加0.5来舍入,但是我们要避免到达中间点。因此,如果添加0.4999,您将接近,但有足够的余量可以四舍五入到通常的期望值。当然,如果x + 0.4999等于,这将失败[n].5000,但这不太可能。

I’m surprised I haven’t seen this answer yet round(x + 0.4999), so I’m going to put it down. Note that this works with any Python version. Changes made to the Python rounding scheme has made things difficult. See this post.

Without importing, I use:

def roundUp(num):
    return round(num + 0.49)

testCases = list(x*0.1 for x in range(0, 50))

print(testCases)
for test in testCases:
    print("{:5.2f}  -> {:5.2f}".format(test, roundUp(test)))

Why this works

From the docs

For the built-in types supporting round(), values are rounded to the closest multiple of 10 to the power minus n; if two multiples are equally close, rounding is done toward the even choice

Therefore 2.5 gets rounded to 2 and 3.5 gets rounded to 4. If this was not the case then rounding up could be done by adding 0.5, but we want to avoid getting to the halfway point. So, if you add 0.4999 you will get close, but with enough margin to be rounded to what you would normally expect. Of course, this will fail if the x + 0.4999 is equal to [n].5000, but that is unlikely.


回答 16

要做到这一点而无需任何导入:

>>> round_up = lambda num: int(num + 1) if int(num) != num else int(num)
>>> round_up(2.0)
2
>>> round_up(2.1)
3

To do it without any import:

>>> round_up = lambda num: int(num + 1) if int(num) != num else int(num)
>>> round_up(2.0)
2
>>> round_up(2.1)
3

回答 17

我知道这已经有一段时间了,但是我找到了一个非常有趣的答案,所以可以这样:

-round(-x-0.5)

这可以修复边缘情况,并且适用于正数和负数,并且不需要任何函数导入

干杯

I know this is from quite a while back, but I found a quite interesting answer, so here goes:

-round(-x-0.5)

This fixes the edges cases and works for both positive and negative numbers, and doesn’t require any function import

Cheers


回答 18

当您在python中操作4500/1000时,结果将为4,因为默认情况下python假定结果为整数,逻辑上:4500/1000 = 4.5-> int(4.5)= 4且ceil显然为4

使用4500 / 40.0的结果将是4.5且ceil为4.5-> 5

使用javascript,您将收到4.5的4500/1000结果,因为javascript仅将结果视为“数值类型”,并将结果直接返回为float

祝好运!!

when you operate 4500/1000 in python, result will be 4, because for default python asume as integer the result, logically: 4500/1000 = 4.5 –> int(4.5) = 4 and ceil of 4 obviouslly is 4

using 4500/1000.0 the result will be 4.5 and ceil of 4.5 –> 5

Using javascript you will recieve 4.5 as result of 4500/1000, because javascript asume only the result as “numeric type” and return a result directly as float

Good Luck!!


回答 19

如果您不想导入任何内容,则可以始终将自己的简单函数编写为:

def RoundUP(num): if num== int(num): return num return int(num + 1)

If you don’t want to import anything, you can always write your own simple function as:

def RoundUP(num): if num== int(num): return num return int(num + 1)


回答 20

您可以使用楼层划分并将其添加1。2.3 // 2 + 1

You can use floor devision and add 1 to it. 2.3 // 2 + 1


回答 21

我认为您会混淆int()和之间的工作机制round()

int()如果给出浮点数,则总是截断十进制数;相反round(),如果2.5where 23are都在等距离内2.5,则Python返回距离0点更远的那个。

round(2.5) = 3
int(2.5) = 2

I think you are confusing the working mechanisms between int() and round().

int() always truncates the decimal numbers if a floating number is given; whereas round(), in case of 2.5 where 2 and 3 are both within equal distance from 2.5, Python returns whichever that is more away from the 0 point.

round(2.5) = 3
int(2.5) = 2

回答 22

我的份额

我已经测试 print(-(-101 // 5)) = 21了上面给出的示例。

现在进行四舍五入:

101 * 19% = 19.19

我不能使用,**所以我将乘法扩展到除法:

(-(-101 //(1/0.19))) = 20

My share

I have tested print(-(-101 // 5)) = 21 given example above.

Now for rounding up:

101 * 19% = 19.19

I can not use ** so I spread the multiply to division:

(-(-101 //(1/0.19))) = 20

回答 23

我基本上是Python的初学者,但是如果您只是想舍入而不是舍弃,那为什么不做:

round(integer) + 1

I’m basically a beginner at Python, but if you’re just trying to round up instead of down why not do:

round(integer) + 1

如何获得浮动范围之间的随机数?

问题:如何获得浮动范围之间的随机数?

randrange(start, stop)只接受整数参数。那么,如何在两个浮点值之间获得一个随机数呢?

randrange(start, stop) only takes integer arguments. So how would I get a random number between two float values?


回答 0

使用random.uniform(a,b)

>>> random.uniform(1.5, 1.9)
1.8733202628557872

Use random.uniform(a, b):

>>> random.uniform(1.5, 1.9)
1.8733202628557872

回答 1

random.uniform(a, b)似乎是您要寻找的。从文档:

返回一个随机浮点数N,使得a <= N <= b表示a <= b,b <= N <= a表示b <a。

这里

random.uniform(a, b) appears to be what your looking for. From the docs:

Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a.

See here.


回答 2

如果您想生成一个随机浮点数,该浮点数的右边是N个数字,则可以执行以下操作:

round(random.uniform(1,2), N)

第二个参数是小数位数。

if you want generate a random float with N digits to the right of point, you can make this :

round(random.uniform(1,2), N)

the second argument is the number of decimals.


回答 3

最常见的是,您将使用:

import random
random.uniform(a, b) # range [a, b) or [a, b] depending on floating-point rounding

如果需要,Python可提供其他发行版

如果已经numpy导入,则可以使用其等效项:

import numpy as np
np.random.uniform(a, b) # range [a, b)

同样,如果需要其他发行版,请numpy提供与python相同的发行版,以及许多其他发行版

Most commonly, you’d use:

import random
random.uniform(a, b) # range [a, b) or [a, b] depending on floating-point rounding

Python provides other distributions if you need.

If you have numpy imported already, you can used its equivalent:

import numpy as np
np.random.uniform(a, b) # range [a, b)

Again, if you need another distribution, numpy provides the same distributions as python, as well as many additional ones.


如何使用十进制range()步长值?

问题:如何使用十进制range()步长值?

有没有办法在0和1之间以0.1步进?

我以为我可以像下面那样做,但是失败了:

for i in range(0, 1, 0.1):
    print i

相反,它说step参数不能为零,这是我没有想到的。

Is there a way to step between 0 and 1 by 0.1?

I thought I could do it like the following, but it failed:

for i in range(0, 1, 0.1):
    print i

Instead, it says that the step argument cannot be zero, which I did not expect.


回答 0

与直接使用小数步相比,用所需的点数表示这一点要安全得多。否则,浮点舍入错误可能会给您带来错误的结果。

您可以使用NumPy库中的linspace函数(该库不是标准库的一部分,但相对容易获得)。需要返回多个点,还可以指定是否包括正确的端点:linspace

>>> np.linspace(0,1,11)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])
>>> np.linspace(0,1,10,endpoint=False)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])

如果您确实要使用浮点阶跃值,可以使用numpy.arange

>>> import numpy as np
>>> np.arange(0.0, 1.0, 0.1)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])

但是,浮点舍入错误引起问题。这是一个简单的情况,当四舍五入错误arange仅应产生3个数字时,会导致产生长度为4的数组:

>>> numpy.arange(1, 1.3, 0.1)
array([1. , 1.1, 1.2, 1.3])

Rather than using a decimal step directly, it’s much safer to express this in terms of how many points you want. Otherwise, floating-point rounding error is likely to give you a wrong result.

You can use the linspace function from the NumPy library (which isn’t part of the standard library but is relatively easy to obtain). linspace takes a number of points to return, and also lets you specify whether or not to include the right endpoint:

>>> np.linspace(0,1,11)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])
>>> np.linspace(0,1,10,endpoint=False)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])

If you really want to use a floating-point step value, you can, with numpy.arange.

>>> import numpy as np
>>> np.arange(0.0, 1.0, 0.1)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])

Floating-point rounding error will cause problems, though. Here’s a simple case where rounding error causes arange to produce a length-4 array when it should only produce 3 numbers:

>>> numpy.arange(1, 1.3, 0.1)
array([1. , 1.1, 1.2, 1.3])

回答 1

Python的range()只能做整数,不能做浮点数。在您的特定情况下,可以改用列表推导:

[x * 0.1 for x in range(0, 10)]

(用该表达式将调用替换为range。)

对于更一般的情况,您可能需要编写自定义函数或生成器。

Python’s range() can only do integers, not floating point. In your specific case, you can use a list comprehension instead:

[x * 0.1 for x in range(0, 10)]

(Replace the call to range with that expression.)

For the more general case, you may want to write a custom function or generator.


回答 2

‘xrange([start],stop [,step])’的基础上,您可以定义一个生成器,该生成器接受并产生您选择的任何类型(坚持支持+and的类型<):

>>> def drange(start, stop, step):
...     r = start
...     while r < stop:
...         yield r
...         r += step
...         
>>> i0=drange(0.0, 1.0, 0.1)
>>> ["%g" % x for x in i0]
['0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1']
>>> 

Building on ‘xrange([start], stop[, step])’, you can define a generator that accepts and produces any type you choose (stick to types supporting + and <):

>>> def drange(start, stop, step):
...     r = start
...     while r < stop:
...         yield r
...         r += step
...         
>>> i0=drange(0.0, 1.0, 0.1)
>>> ["%g" % x for x in i0]
['0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1']
>>> 

回答 3

增大i循环的幅度,然后在需要时减小它。

for i * 100 in range(0, 100, 10):
    print i / 100.0

编辑:老实说,我不记得为什么我认为这将在语法上起作用

for i in range(0, 11, 1):
    print i / 10.0

那应该具有所需的输出。

Increase the magnitude of i for the loop and then reduce it when you need it.

for i * 100 in range(0, 100, 10):
    print i / 100.0

EDIT: I honestly cannot remember why I thought that would work syntactically

for i in range(0, 11, 1):
    print i / 10.0

That should have the desired output.


回答 4

scipy有一个内置函数arange,可以泛化Python的range()构造函数,以满足您对float处理的要求。

from scipy import arange

scipy has a built in function arange which generalizes Python’s range() constructor to satisfy your requirement of float handling.

from scipy import arange


回答 5

我认为NumPy有点矫kill过正。

[p/10 for p in range(0, 10)]
[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]

一般来说,逐步1/x进行y将可以

x=100
y=2
[p/x for p in range(0, int(x*y))]
[0.0, 0.01, 0.02, 0.03, ..., 1.97, 1.98, 1.99]

1/x我测试时产生的舍入噪音较小)。

NumPy is a bit overkill, I think.

[p/10 for p in range(0, 10)]
[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]

Generally speaking, to do a step-by-1/x up to y you would do

x=100
y=2
[p/x for p in range(0, int(x*y))]
[0.0, 0.01, 0.02, 0.03, ..., 1.97, 1.98, 1.99]

(1/x produced less rounding noise when I tested).


回答 6

类似于R的 seq函数,该函数以正确的步长值以任意顺序返回序列。最后一个值等于停止值。

def seq(start, stop, step=1):
    n = int(round((stop - start)/float(step)))
    if n > 1:
        return([start + step*i for i in range(n+1)])
    elif n == 1:
        return([start])
    else:
        return([])

结果

seq(1, 5, 0.5)

[1.0、1.5、2.0、2.5、3.0、3.5、4.0、4.5、5.0]

seq(10, 0, -1)

[10、9、8、7、6、5、4、3、2、1、0]

seq(10, 0, -2)

[10、8、6、4、2、0]

seq(1, 1)

[1]

Similar to R’s seq function, this one returns a sequence in any order given the correct step value. The last value is equal to the stop value.

def seq(start, stop, step=1):
    n = int(round((stop - start)/float(step)))
    if n > 1:
        return([start + step*i for i in range(n+1)])
    elif n == 1:
        return([start])
    else:
        return([])

Results

seq(1, 5, 0.5)

[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]

seq(10, 0, -1)

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

seq(10, 0, -2)

[10, 8, 6, 4, 2, 0]

seq(1, 1)

[ 1 ]


回答 7

恐怕range()内置函数会返回一个整数值序列,因此您不能使用它执行小数步。

我想说的只是使用while循环:

i = 0.0
while i <= 1.0:
    print i
    i += 0.1

如果您好奇,Python会将您的0.1转换为0,这就是为什么它告诉您参数不能为零的原因。

The range() built-in function returns a sequence of integer values, I’m afraid, so you can’t use it to do a decimal step.

I’d say just use a while loop:

i = 0.0
while i <= 1.0:
    print i
    i += 0.1

If you’re curious, Python is converting your 0.1 to 0, which is why it’s telling you the argument can’t be zero.


回答 8

这是使用itertools的解决方案:

import itertools

def seq(start, end, step):
    if step == 0:
        raise ValueError("step must not be 0")
    sample_count = int(abs(end - start) / step)
    return itertools.islice(itertools.count(start, step), sample_count)

用法示例:

for i in seq(0, 1, 0.1):
    print(i)

Here’s a solution using itertools:

import itertools

def seq(start, end, step):
    if step == 0:
        raise ValueError("step must not be 0")
    sample_count = int(abs(end - start) / step)
    return itertools.islice(itertools.count(start, step), sample_count)

Usage Example:

for i in seq(0, 1, 0.1):
    print(i)

回答 9

[x * 0.1 for x in range(0, 10)] 

在Python 2.7x中,结果如下:

[0.0、0.1、0.2、0.30000000000000004、0.4、0.5、0.6000000000000001、0.7000000000000001、0.8、0.9]

但如果您使用:

[ round(x * 0.1, 1) for x in range(0, 10)]

给您所需的:

[0.0、0.1、0.2、0.3、0.4、0.5、0.6、0.7、0.8、0.9]

[x * 0.1 for x in range(0, 10)] 

in Python 2.7x gives you the result of:

[0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9]

but if you use:

[ round(x * 0.1, 1) for x in range(0, 10)]

gives you the desired:

[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]


回答 10

import numpy as np
for i in np.arange(0, 1, 0.1): 
    print i 
import numpy as np
for i in np.arange(0, 1, 0.1): 
    print i 

回答 11

而且,如果您经常这样做,则可能要保存生成的列表 r

r=map(lambda x: x/10.0,range(0,10))
for i in r:
    print i

And if you do this often, you might want to save the generated list r

r=map(lambda x: x/10.0,range(0,10))
for i in r:
    print i

回答 12

more_itertools是实现numeric_range工具的第三方库:

import more_itertools as mit


for x in mit.numeric_range(0, 1, 0.1):
    print("{:.1f}".format(x))

输出量

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

此工具还适用于DecimalFraction

more_itertools is a third-party library that implements a numeric_range tool:

import more_itertools as mit


for x in mit.numeric_range(0, 1, 0.1):
    print("{:.1f}".format(x))

Output

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

This tool also works for Decimal and Fraction.


回答 13

我的版本使用原始的范围函数来为班次创建乘法索引。这允许与原始范围函数使用相同的语法。我做了两个版本,一个使用浮点,一个使用十进制,因为我发现在某些情况下我想避免浮点算术引入的舍入漂移。

它与范围/ xrange中的空集结果一致。

仅将单个数值传递给任何一个函数都将使标准范围输出返回到输入参数的整数上限值(因此,如果给定5.5,它将返回range(6)。)

编辑:下面的代码现在可以在pypi上作为软件包使用:Franges

## frange.py
from math import ceil
# find best range function available to version (2.7.x / 3.x.x)
try:
    _xrange = xrange
except NameError:
    _xrange = range

def frange(start, stop = None, step = 1):
    """frange generates a set of floating point values over the 
    range [start, stop) with step size step

    frange([start,] stop [, step ])"""

    if stop is None:
        for x in _xrange(int(ceil(start))):
            yield x
    else:
        # create a generator expression for the index values
        indices = (i for i in _xrange(0, int((stop-start)/step)))  
        # yield results
        for i in indices:
            yield start + step*i

## drange.py
import decimal
from math import ceil
# find best range function available to version (2.7.x / 3.x.x)
try:
    _xrange = xrange
except NameError:
    _xrange = range

def drange(start, stop = None, step = 1, precision = None):
    """drange generates a set of Decimal values over the
    range [start, stop) with step size step

    drange([start,] stop, [step [,precision]])"""

    if stop is None:
        for x in _xrange(int(ceil(start))):
            yield x
    else:
        # find precision
        if precision is not None:
            decimal.getcontext().prec = precision
        # convert values to decimals
        start = decimal.Decimal(start)
        stop = decimal.Decimal(stop)
        step = decimal.Decimal(step)
        # create a generator expression for the index values
        indices = (
            i for i in _xrange(
                0, 
                ((stop-start)/step).to_integral_value()
            )
        )  
        # yield results
        for i in indices:
            yield float(start + step*i)

## testranges.py
import frange
import drange
list(frange.frange(0, 2, 0.5)) # [0.0, 0.5, 1.0, 1.5]
list(drange.drange(0, 2, 0.5, precision = 6)) # [0.0, 0.5, 1.0, 1.5]
list(frange.frange(3)) # [0, 1, 2]
list(frange.frange(3.5)) # [0, 1, 2, 3]
list(frange.frange(0,10, -1)) # []

My versions use the original range function to create multiplicative indices for the shift. This allows same syntax to the original range function. I have made two versions, one using float, and one using Decimal, because I found that in some cases I wanted to avoid the roundoff drift introduced by the floating point arithmetic.

It is consistent with empty set results as in range/xrange.

Passing only a single numeric value to either function will return the standard range output to the integer ceiling value of the input parameter (so if you gave it 5.5, it would return range(6).)

Edit: the code below is now available as package on pypi: Franges

## frange.py
from math import ceil
# find best range function available to version (2.7.x / 3.x.x)
try:
    _xrange = xrange
except NameError:
    _xrange = range

def frange(start, stop = None, step = 1):
    """frange generates a set of floating point values over the 
    range [start, stop) with step size step

    frange([start,] stop [, step ])"""

    if stop is None:
        for x in _xrange(int(ceil(start))):
            yield x
    else:
        # create a generator expression for the index values
        indices = (i for i in _xrange(0, int((stop-start)/step)))  
        # yield results
        for i in indices:
            yield start + step*i

## drange.py
import decimal
from math import ceil
# find best range function available to version (2.7.x / 3.x.x)
try:
    _xrange = xrange
except NameError:
    _xrange = range

def drange(start, stop = None, step = 1, precision = None):
    """drange generates a set of Decimal values over the
    range [start, stop) with step size step

    drange([start,] stop, [step [,precision]])"""

    if stop is None:
        for x in _xrange(int(ceil(start))):
            yield x
    else:
        # find precision
        if precision is not None:
            decimal.getcontext().prec = precision
        # convert values to decimals
        start = decimal.Decimal(start)
        stop = decimal.Decimal(stop)
        step = decimal.Decimal(step)
        # create a generator expression for the index values
        indices = (
            i for i in _xrange(
                0, 
                ((stop-start)/step).to_integral_value()
            )
        )  
        # yield results
        for i in indices:
            yield float(start + step*i)

## testranges.py
import frange
import drange
list(frange.frange(0, 2, 0.5)) # [0.0, 0.5, 1.0, 1.5]
list(drange.drange(0, 2, 0.5, precision = 6)) # [0.0, 0.5, 1.0, 1.5]
list(frange.frange(3)) # [0, 1, 2]
list(frange.frange(3.5)) # [0, 1, 2, 3]
list(frange.frange(0,10, -1)) # []

回答 14

这是我获得浮动步距范围的解决方案。
使用此功能,无需导入numpy或安装它。
我很确定可以对其进行改进和优化。随意做并张贴在这里。

from __future__ import division
from math import log

def xfrange(start, stop, step):

    old_start = start #backup this value

    digits = int(round(log(10000, 10)))+1 #get number of digits
    magnitude = 10**digits
    stop = int(magnitude * stop) #convert from 
    step = int(magnitude * step) #0.1 to 10 (e.g.)

    if start == 0:
        start = 10**(digits-1)
    else:
        start = 10**(digits)*start

    data = []   #create array

    #calc number of iterations
    end_loop = int((stop-start)//step)
    if old_start == 0:
        end_loop += 1

    acc = start

    for i in xrange(0, end_loop):
        data.append(acc/magnitude)
        acc += step

    return data

print xfrange(1, 2.1, 0.1)
print xfrange(0, 1.1, 0.1)
print xfrange(-1, 0.1, 0.1)

输出为:

[1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1]
[-1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.0]

This is my solution to get ranges with float steps.
Using this function it’s not necessary to import numpy, nor install it.
I’m pretty sure that it could be improved and optimized. Feel free to do it and post it here.

from __future__ import division
from math import log

def xfrange(start, stop, step):

    old_start = start #backup this value

    digits = int(round(log(10000, 10)))+1 #get number of digits
    magnitude = 10**digits
    stop = int(magnitude * stop) #convert from 
    step = int(magnitude * step) #0.1 to 10 (e.g.)

    if start == 0:
        start = 10**(digits-1)
    else:
        start = 10**(digits)*start

    data = []   #create array

    #calc number of iterations
    end_loop = int((stop-start)//step)
    if old_start == 0:
        end_loop += 1

    acc = start

    for i in xrange(0, end_loop):
        data.append(acc/magnitude)
        acc += step

    return data

print xfrange(1, 2.1, 0.1)
print xfrange(0, 1.1, 0.1)
print xfrange(-1, 0.1, 0.1)

The output is:

[1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1]
[-1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.0]

回答 15

为了完善精品店,提供了一个实用的解决方案:

def frange(a,b,s):
  return [] if s > 0 and a > b or s < 0 and a < b or s==0 else [a]+frange(a+s,b,s)

For completeness of boutique, a functional solution:

def frange(a,b,s):
  return [] if s > 0 and a > b or s < 0 and a < b or s==0 else [a]+frange(a+s,b,s)

回答 16

您可以使用此功能:

def frange(start,end,step):
    return map(lambda x: x*step, range(int(start*1./step),int(end*1./step)))

You can use this function:

def frange(start,end,step):
    return map(lambda x: x*step, range(int(start*1./step),int(end*1./step)))

回答 17

诀窍避免四舍五入问题是使用一个单独的号码通过的范围内移动,启动和一半一步提前开始

# floating point range
def frange(a, b, stp=1.0):
  i = a+stp/2.0
  while i<b:
    yield a
    a += stp
    i += stp

或者,numpy.arange可以使用。

The trick to avoid round-off problem is to use a separate number to move through the range, that starts and half the step ahead of start.

# floating point range
def frange(a, b, stp=1.0):
  i = a+stp/2.0
  while i<b:
    yield a
    a += stp
    i += stp

Alternatively, numpy.arange can be used.


回答 18

可以使用Numpy库完成。arange()函数允许进行浮动操作。但是,它返回一个numpy数组,为方便起见,可以使用tolist()将其转换为list。

for i in np.arange(0, 1, 0.1).tolist():
   print i

It can be done using Numpy library. arange() function allows steps in float. But, it returns a numpy array which can be converted to list using tolist() for our convenience.

for i in np.arange(0, 1, 0.1).tolist():
   print i

回答 19

我的答案与使用map()的其他答案相似,不需要NumPy,也不需要使用lambda(尽管可以)。要以dt的步长获取从0.0到t_max的浮点值列表:

def xdt(n):
    return dt*float(n)
tlist  = map(xdt, range(int(t_max/dt)+1))

My answer is similar to others using map(), without need of NumPy, and without using lambda (though you could). To get a list of float values from 0.0 to t_max in steps of dt:

def xdt(n):
    return dt*float(n)
tlist  = map(xdt, range(int(t_max/dt)+1))

回答 20

出人意料的是,尚未在Python 3文档中提及推荐的解决方案:

也可以看看:

  • linspace配方展示了如何实现一个懒惰的版本范围的适用于浮点应用程序。

定义后,该配方易于使用,不需要numpy或任何其他外部库,但功能类似于numpy.linspace()。请注意,step第三个num参数不是参数,而是指定所需值的数量,例如:

print(linspace(0, 10, 5))
# linspace(0, 10, 5)
print(list(linspace(0, 10, 5)))
# [0.0, 2.5, 5.0, 7.5, 10]

我在下面引用了安德鲁·巴纳特(Andrew Barnert)的完整Python 3配方的修改版:

import collections.abc
import numbers

class linspace(collections.abc.Sequence):
    """linspace(start, stop, num) -> linspace object

    Return a virtual sequence of num numbers from start to stop (inclusive).

    If you need a half-open range, use linspace(start, stop, num+1)[:-1].
    """
    def __init__(self, start, stop, num):
        if not isinstance(num, numbers.Integral) or num <= 1:
            raise ValueError('num must be an integer > 1')
        self.start, self.stop, self.num = start, stop, num
        self.step = (stop-start)/(num-1)
    def __len__(self):
        return self.num
    def __getitem__(self, i):
        if isinstance(i, slice):
            return [self[x] for x in range(*i.indices(len(self)))]
        if i < 0:
            i = self.num + i
        if i >= self.num:
            raise IndexError('linspace object index out of range')
        if i == self.num-1:
            return self.stop
        return self.start + i*self.step
    def __repr__(self):
        return '{}({}, {}, {})'.format(type(self).__name__,
                                       self.start, self.stop, self.num)
    def __eq__(self, other):
        if not isinstance(other, linspace):
            return False
        return ((self.start, self.stop, self.num) ==
                (other.start, other.stop, other.num))
    def __ne__(self, other):
        return not self==other
    def __hash__(self):
        return hash((type(self), self.start, self.stop, self.num))

Suprised no-one has yet mentioned the recommended solution in the Python 3 docs:

See also:

  • The linspace recipe shows how to implement a lazy version of range that suitable for floating point applications.

Once defined, the recipe is easy to use and does not require numpy or any other external libraries, but functions like numpy.linspace(). Note that rather than a step argument, the third num argument specifies the number of desired values, for example:

print(linspace(0, 10, 5))
# linspace(0, 10, 5)
print(list(linspace(0, 10, 5)))
# [0.0, 2.5, 5.0, 7.5, 10]

I quote a modified version of the full Python 3 recipe from Andrew Barnert below:

import collections.abc
import numbers

class linspace(collections.abc.Sequence):
    """linspace(start, stop, num) -> linspace object

    Return a virtual sequence of num numbers from start to stop (inclusive).

    If you need a half-open range, use linspace(start, stop, num+1)[:-1].
    """
    def __init__(self, start, stop, num):
        if not isinstance(num, numbers.Integral) or num <= 1:
            raise ValueError('num must be an integer > 1')
        self.start, self.stop, self.num = start, stop, num
        self.step = (stop-start)/(num-1)
    def __len__(self):
        return self.num
    def __getitem__(self, i):
        if isinstance(i, slice):
            return [self[x] for x in range(*i.indices(len(self)))]
        if i < 0:
            i = self.num + i
        if i >= self.num:
            raise IndexError('linspace object index out of range')
        if i == self.num-1:
            return self.stop
        return self.start + i*self.step
    def __repr__(self):
        return '{}({}, {}, {})'.format(type(self).__name__,
                                       self.start, self.stop, self.num)
    def __eq__(self, other):
        if not isinstance(other, linspace):
            return False
        return ((self.start, self.stop, self.num) ==
                (other.start, other.stop, other.num))
    def __ne__(self, other):
        return not self==other
    def __hash__(self):
        return hash((type(self), self.start, self.stop, self.num))

回答 21

要解决浮动精度问题,可以使用Decimalmodule

这就要求转化为额外的努力,Decimalint或者float一边写代码,但你能传递str和修改功能,如果那样的便利性确实是必要的。

from decimal import Decimal
from decimal import Decimal as D


def decimal_range(*args):

    zero, one = Decimal('0'), Decimal('1')

    if len(args) == 1:
        start, stop, step = zero, args[0], one
    elif len(args) == 2:
        start, stop, step = args + (one,)
    elif len(args) == 3:
        start, stop, step = args
    else:
        raise ValueError('Expected 1 or 2 arguments, got %s' % len(args))

    if not all([type(arg) == Decimal for arg in (start, stop, step)]):
        raise ValueError('Arguments must be passed as <type: Decimal>')

    # neglect bad cases
    if (start == stop) or (start > stop and step >= zero) or \
                          (start < stop and step <= zero):
        return []

    current = start
    while abs(current) < abs(stop):
        yield current
        current += step

样本输出-

list(decimal_range(D('2')))
# [Decimal('0'), Decimal('1')]
list(decimal_range(D('2'), D('4.5')))
# [Decimal('2'), Decimal('3'), Decimal('4')]
list(decimal_range(D('2'), D('4.5'), D('0.5')))
# [Decimal('2'), Decimal('2.5'), Decimal('3.0'), Decimal('3.5'), Decimal('4.0')]
list(decimal_range(D('2'), D('4.5'), D('-0.5')))
# []
list(decimal_range(D('2'), D('-4.5'), D('-0.5')))
# [Decimal('2'),
#  Decimal('1.5'),
#  Decimal('1.0'),
#  Decimal('0.5'),
#  Decimal('0.0'),
#  Decimal('-0.5'),
#  Decimal('-1.0'),
#  Decimal('-1.5'),
#  Decimal('-2.0'),
#  Decimal('-2.5'),
#  Decimal('-3.0'),
#  Decimal('-3.5'),
#  Decimal('-4.0')]

To counter the float precision issues, you could use the Decimal module.

This demands an extra effort of converting to Decimal from int or float while writing the code, but you can instead pass str and modify the function if that sort of convenience is indeed necessary.

from decimal import Decimal
from decimal import Decimal as D


def decimal_range(*args):

    zero, one = Decimal('0'), Decimal('1')

    if len(args) == 1:
        start, stop, step = zero, args[0], one
    elif len(args) == 2:
        start, stop, step = args + (one,)
    elif len(args) == 3:
        start, stop, step = args
    else:
        raise ValueError('Expected 1 or 2 arguments, got %s' % len(args))

    if not all([type(arg) == Decimal for arg in (start, stop, step)]):
        raise ValueError('Arguments must be passed as <type: Decimal>')

    # neglect bad cases
    if (start == stop) or (start > stop and step >= zero) or \
                          (start < stop and step <= zero):
        return []

    current = start
    while abs(current) < abs(stop):
        yield current
        current += step

Sample outputs –

list(decimal_range(D('2')))
# [Decimal('0'), Decimal('1')]
list(decimal_range(D('2'), D('4.5')))
# [Decimal('2'), Decimal('3'), Decimal('4')]
list(decimal_range(D('2'), D('4.5'), D('0.5')))
# [Decimal('2'), Decimal('2.5'), Decimal('3.0'), Decimal('3.5'), Decimal('4.0')]
list(decimal_range(D('2'), D('4.5'), D('-0.5')))
# []
list(decimal_range(D('2'), D('-4.5'), D('-0.5')))
# [Decimal('2'),
#  Decimal('1.5'),
#  Decimal('1.0'),
#  Decimal('0.5'),
#  Decimal('0.0'),
#  Decimal('-0.5'),
#  Decimal('-1.0'),
#  Decimal('-1.5'),
#  Decimal('-2.0'),
#  Decimal('-2.5'),
#  Decimal('-3.0'),
#  Decimal('-3.5'),
#  Decimal('-4.0')]

回答 22

添加自动更正,以防止出现错误的登录步骤:

def frange(start,step,stop):
    step *= 2*((stop>start)^(step<0))-1
    return [start+i*step for i in range(int((stop-start)/step))]

Add auto-correction for the possibility of an incorrect sign on step:

def frange(start,step,stop):
    step *= 2*((stop>start)^(step<0))-1
    return [start+i*step for i in range(int((stop-start)/step))]

回答 23

我的解决方案:

def seq(start, stop, step=1, digit=0):
    x = float(start)
    v = []
    while x <= stop:
        v.append(round(x,digit))
        x += step
    return v

My solution:

def seq(start, stop, step=1, digit=0):
    x = float(start)
    v = []
    while x <= stop:
        v.append(round(x,digit))
        x += step
    return v

回答 24

最佳解决方案:无舍入错误
_________________________________________________________________________________

>>> step = .1
>>> N = 10     # number of data points
>>> [ x / pow(step, -1) for x in range(0, N + 1) ]

[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]

_________________________________________________________________________________

或者,对于设定范围而不是设定数据点(例如,连续功能),请使用:

>>> step = .1
>>> rnge = 1     # NOTE range = 1, i.e. span of data points
>>> N = int(rnge / step
>>> [ x / pow(step,-1) for x in range(0, N + 1) ]

[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]

要实现一个功能:更换x / pow(step, -1)f( x / pow(step, -1) ),并定义f
例如:

>>> import math
>>> def f(x):
        return math.sin(x)

>>> step = .1
>>> rnge = 1     # NOTE range = 1, i.e. span of data points
>>> N = int(rnge / step)
>>> [ f( x / pow(step,-1) ) for x in range(0, N + 1) ]

[0.0, 0.09983341664682815, 0.19866933079506122, 0.29552020666133955, 0.3894183423086505, 
 0.479425538604203, 0.5646424733950354, 0.644217687237691, 0.7173560908995228,
 0.7833269096274834, 0.8414709848078965]

Best Solution: no rounding error
_________________________________________________________________________________

>>> step = .1
>>> N = 10     # number of data points
>>> [ x / pow(step, -1) for x in range(0, N + 1) ]

[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]

_________________________________________________________________________________

Or, for a set range instead of set data points (e.g. continuous function), use:

>>> step = .1
>>> rnge = 1     # NOTE range = 1, i.e. span of data points
>>> N = int(rnge / step
>>> [ x / pow(step,-1) for x in range(0, N + 1) ]

[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]

To implement a function: replace x / pow(step, -1) with f( x / pow(step, -1) ), and define f.
For example:

>>> import math
>>> def f(x):
        return math.sin(x)

>>> step = .1
>>> rnge = 1     # NOTE range = 1, i.e. span of data points
>>> N = int(rnge / step)
>>> [ f( x / pow(step,-1) ) for x in range(0, N + 1) ]

[0.0, 0.09983341664682815, 0.19866933079506122, 0.29552020666133955, 0.3894183423086505, 
 0.479425538604203, 0.5646424733950354, 0.644217687237691, 0.7173560908995228,
 0.7833269096274834, 0.8414709848078965]

回答 25

start和stop具有包容性,而不是一个或另一个(通常不包括stop),并且没有导入,并且使用生成器

def rangef(start, stop, step, fround=5):
    """
    Yields sequence of numbers from start (inclusive) to stop (inclusive)
    by step (increment) with rounding set to n digits.

    :param start: start of sequence
    :param stop: end of sequence
    :param step: int or float increment (e.g. 1 or 0.001)
    :param fround: float rounding, n decimal places
    :return:
    """
    try:
        i = 0
        while stop >= start and step > 0:
            if i==0:
                yield start
            elif start >= stop:
                yield stop
            elif start < stop:
                if start == 0:
                    yield 0
                if start != 0:
                    yield start
            i += 1
            start += step
            start = round(start, fround)
        else:
            pass
    except TypeError as e:
        yield "type-error({})".format(e)
    else:
        pass


# passing
print(list(rangef(-100.0,10.0,1)))
print(list(rangef(-100,0,0.5)))
print(list(rangef(-1,1,0.2)))
print(list(rangef(-1,1,0.1)))
print(list(rangef(-1,1,0.05)))
print(list(rangef(-1,1,0.02)))
print(list(rangef(-1,1,0.01)))
print(list(rangef(-1,1,0.005)))
# failing: type-error:
print(list(rangef("1","10","1")))
print(list(rangef(1,10,"1")))

Python 3.6.2(v3.6.2:5fd33b5,2017年7月8日,04:57:36)[MSC v.1900 64位(AMD64)]

start and stop are inclusive rather than one or the other (usually stop is excluded) and without imports, and using generators

def rangef(start, stop, step, fround=5):
    """
    Yields sequence of numbers from start (inclusive) to stop (inclusive)
    by step (increment) with rounding set to n digits.

    :param start: start of sequence
    :param stop: end of sequence
    :param step: int or float increment (e.g. 1 or 0.001)
    :param fround: float rounding, n decimal places
    :return:
    """
    try:
        i = 0
        while stop >= start and step > 0:
            if i==0:
                yield start
            elif start >= stop:
                yield stop
            elif start < stop:
                if start == 0:
                    yield 0
                if start != 0:
                    yield start
            i += 1
            start += step
            start = round(start, fround)
        else:
            pass
    except TypeError as e:
        yield "type-error({})".format(e)
    else:
        pass


# passing
print(list(rangef(-100.0,10.0,1)))
print(list(rangef(-100,0,0.5)))
print(list(rangef(-1,1,0.2)))
print(list(rangef(-1,1,0.1)))
print(list(rangef(-1,1,0.05)))
print(list(rangef(-1,1,0.02)))
print(list(rangef(-1,1,0.01)))
print(list(rangef(-1,1,0.005)))
# failing: type-error:
print(list(rangef("1","10","1")))
print(list(rangef(1,10,"1")))

Python 3.6.2 (v3.6.2:5fd33b5, Jul 8 2017, 04:57:36) [MSC v.1900 64 bit (AMD64)]


回答 26

我知道我在这里参加聚会迟到了,但这是一个在3.6中运行的简单生成器解决方案:

def floatRange(*args):
    start, step = 0, 1
    if len(args) == 1:
        stop = args[0]
    elif len(args) == 2:
        start, stop = args[0], args[1]
    elif len(args) == 3:
        start, stop, step = args[0], args[1], args[2]
    else:
        raise TypeError("floatRange accepts 1, 2, or 3 arguments. ({0} given)".format(len(args)))
    for num in start, step, stop:
        if not isinstance(num, (int, float)):
            raise TypeError("floatRange only accepts float and integer arguments. ({0} : {1} given)".format(type(num), str(num)))
    for x in range(int((stop-start)/step)):
        yield start + (x * step)
    return

那么您可以像原始邮件一样调用它range()……没有错误处理,但是请让我知道是否有可以合理捕获的错误,我将进行更新。或者您可以更新它。这是StackOverflow。

I know I’m late to the party here, but here’s a trivial generator solution that’s working in 3.6:

def floatRange(*args):
    start, step = 0, 1
    if len(args) == 1:
        stop = args[0]
    elif len(args) == 2:
        start, stop = args[0], args[1]
    elif len(args) == 3:
        start, stop, step = args[0], args[1], args[2]
    else:
        raise TypeError("floatRange accepts 1, 2, or 3 arguments. ({0} given)".format(len(args)))
    for num in start, step, stop:
        if not isinstance(num, (int, float)):
            raise TypeError("floatRange only accepts float and integer arguments. ({0} : {1} given)".format(type(num), str(num)))
    for x in range(int((stop-start)/step)):
        yield start + (x * step)
    return

then you can call it just like the original range()… there’s no error handling, but let me know if there is an error that can be reasonably caught, and I’ll update. or you can update it. this is StackOverflow.


回答 27

这是我的解决方案,它与float_range(-1,0,0.01)一起正常工作,并且没有浮点表示错误。它不是很快,但是可以正常工作:

from decimal import Decimal

def get_multiplier(_from, _to, step):
    digits = []
    for number in [_from, _to, step]:
        pre = Decimal(str(number)) % 1
        digit = len(str(pre)) - 2
        digits.append(digit)
    max_digits = max(digits)
    return float(10 ** (max_digits))


def float_range(_from, _to, step, include=False):
    """Generates a range list of floating point values over the Range [start, stop]
       with step size step
       include=True - allows to include right value to if possible
       !! Works fine with floating point representation !!
    """
    mult = get_multiplier(_from, _to, step)
    # print mult
    int_from = int(round(_from * mult))
    int_to = int(round(_to * mult))
    int_step = int(round(step * mult))
    # print int_from,int_to,int_step
    if include:
        result = range(int_from, int_to + int_step, int_step)
        result = [r for r in result if r <= int_to]
    else:
        result = range(int_from, int_to, int_step)
    # print result
    float_result = [r / mult for r in result]
    return float_result


print float_range(-1, 0, 0.01,include=False)

assert float_range(1.01, 2.06, 5.05 % 1, True) ==\
[1.01, 1.06, 1.11, 1.16, 1.21, 1.26, 1.31, 1.36, 1.41, 1.46, 1.51, 1.56, 1.61, 1.66, 1.71, 1.76, 1.81, 1.86, 1.91, 1.96, 2.01, 2.06]

assert float_range(1.01, 2.06, 5.05 % 1, False)==\
[1.01, 1.06, 1.11, 1.16, 1.21, 1.26, 1.31, 1.36, 1.41, 1.46, 1.51, 1.56, 1.61, 1.66, 1.71, 1.76, 1.81, 1.86, 1.91, 1.96, 2.01]

Here is my solution which works fine with float_range(-1, 0, 0.01) and works without floating point representation errors. It is not very fast, but works fine:

from decimal import Decimal

def get_multiplier(_from, _to, step):
    digits = []
    for number in [_from, _to, step]:
        pre = Decimal(str(number)) % 1
        digit = len(str(pre)) - 2
        digits.append(digit)
    max_digits = max(digits)
    return float(10 ** (max_digits))


def float_range(_from, _to, step, include=False):
    """Generates a range list of floating point values over the Range [start, stop]
       with step size step
       include=True - allows to include right value to if possible
       !! Works fine with floating point representation !!
    """
    mult = get_multiplier(_from, _to, step)
    # print mult
    int_from = int(round(_from * mult))
    int_to = int(round(_to * mult))
    int_step = int(round(step * mult))
    # print int_from,int_to,int_step
    if include:
        result = range(int_from, int_to + int_step, int_step)
        result = [r for r in result if r <= int_to]
    else:
        result = range(int_from, int_to, int_step)
    # print result
    float_result = [r / mult for r in result]
    return float_result


print float_range(-1, 0, 0.01,include=False)

assert float_range(1.01, 2.06, 5.05 % 1, True) ==\
[1.01, 1.06, 1.11, 1.16, 1.21, 1.26, 1.31, 1.36, 1.41, 1.46, 1.51, 1.56, 1.61, 1.66, 1.71, 1.76, 1.81, 1.86, 1.91, 1.96, 2.01, 2.06]

assert float_range(1.01, 2.06, 5.05 % 1, False)==\
[1.01, 1.06, 1.11, 1.16, 1.21, 1.26, 1.31, 1.36, 1.41, 1.46, 1.51, 1.56, 1.61, 1.66, 1.71, 1.76, 1.81, 1.86, 1.91, 1.96, 2.01]

回答 28

我只是一个初学者,但是在模拟一些计算时遇到了同样的问题。这是我尝试解决的方法,似乎正在使用小数步。

我也很懒,因此我很难编写自己的范围函数。

基本上,我所做的是将我更改xrange(0.0, 1.0, 0.01)xrange(0, 100, 1)100.0在循环内使用除法。我也很担心是否会出现四舍五入的错误。所以我决定测试是否有。现在,我听说,如果例如0.01从计算中得出的浮点数不完全相同,0.01则应将它们返回False(如果我错了,请告诉我)。

因此,我决定通过运行简短的测试来测试我的解决方案是否适合我的范围:

for d100 in xrange(0, 100, 1):
    d = d100 / 100.0
    fl = float("0.00"[:4 - len(str(d100))] + str(d100))
    print d, "=", fl , d == fl

并且每个都打印True。

现在,如果我完全错了,请告诉我。

I am only a beginner, but I had the same problem, when simulating some calculations. Here is how I attempted to work this out, which seems to be working with decimal steps.

I am also quite lazy and so I found it hard to write my own range function.

Basically what I did is changed my xrange(0.0, 1.0, 0.01) to xrange(0, 100, 1) and used the division by 100.0 inside the loop. I was also concerned, if there will be rounding mistakes. So I decided to test, whether there are any. Now I heard, that if for example 0.01 from a calculation isn’t exactly the float 0.01 comparing them should return False (if I am wrong, please let me know).

So I decided to test if my solution will work for my range by running a short test:

for d100 in xrange(0, 100, 1):
    d = d100 / 100.0
    fl = float("0.00"[:4 - len(str(d100))] + str(d100))
    print d, "=", fl , d == fl

And it printed True for each.

Now, if I’m getting it totally wrong, please let me know.


回答 29

这个衬里不会使您的代码混乱。step参数的符号很重要。

def frange(start, stop, step):
    return [x*step+start for x in range(0,round(abs((stop-start)/step)+0.5001),
        int((stop-start)/step<0)*-2+1)]

This one liner will not clutter your code. The sign of the step parameter is important.

def frange(start, stop, step):
    return [x*step+start for x in range(0,round(abs((stop-start)/step)+0.5001),
        int((stop-start)/step<0)*-2+1)]

如何强制除法为浮点数?除数一直舍入到0?

问题:如何强制除法为浮点数?除数一直舍入到0?

我有两个整数值ab,但是我需要它们在浮点数中的比率。我知道a < b并且想要计算a / b,所以如果我使用整数除法,我将总是得到0,余数为a

c在下文中,如何在Python中强制成为浮点数?

c = a / b

I have two integer values a and b, but I need their ratio in floating point. I know that a < b and I want to calculate a / b, so if I use integer division I’ll always get 0 with a remainder of a.

How can I force c to be a floating point number in Python in the following?

c = a / b

回答 0

在Python 2中,两个整数的除法产生一个整数。在Python 3中,它产生一个浮点数。我们可以通过从中导入来获得新的行为__future__

>>> from __future__ import division
>>> a = 4
>>> b = 6
>>> c = a / b
>>> c
0.66666666666666663

In Python 2, division of two ints produces an int. In Python 3, it produces a float. We can get the new behaviour by importing from __future__.

>>> from __future__ import division
>>> a = 4
>>> b = 6
>>> c = a / b
>>> c
0.66666666666666663

回答 1

您可以通过执行此操作来浮动c = a / float(b)。如果分子或分母是浮点数,则结果也将是。


一个警告:如评论员所指出的,如果b它不是整数或浮点数(或表示一个的字符串),则此方法将无效。如果您正在处理其他类型(例如复数),则需要检查这些类型或使用其他方法。

You can cast to float by doing c = a / float(b). If the numerator or denominator is a float, then the result will be also.


A caveat: as commenters have pointed out, this won’t work if b might be something other than an integer or floating-point number (or a string representing one). If you might be dealing with other types (such as complex numbers) you’ll need to either check for those or use a different method.


回答 2

如何在Python中强制除法为浮点数?

我有两个整数值a和b,但是我需要它们在浮点数中的比率。我知道a <b并且我想计算a / b,所以如果我使用整数除法,我总是得到0并得到a的余数。

下面如何在Python中强制c为浮点数?

c = a / b

这里真正要问的是:

“我如何强制进行真正的除法以a / b返回分数?”

升级到Python 3

在Python 3中,要进行真正的除法,只需执行a / b

>>> 1/2
0.5

地板除法(整数的经典除法行为)现在为a // b

>>> 1//2
0
>>> 1//2.0
0.0

但是,您可能无法使用Python 2,或者编写的代码必须能同时在2和3中使用。

如果使用Python 2

在Python 2中,它不是那么简单。处理经典Python 2分区的某些方法比其他方法更好,更可靠。

对Python 2的建议

您可以在任何给定的模块中获得Python 3划分行为,并在顶部输入以下内容:

from __future__ import division

然后将Python 3样式划分应用于整个模块。它也可以在任何给定的点在python shell中工作。在Python 2中:

>>> from __future__ import division
>>> 1/2
0.5
>>> 1//2
0
>>> 1//2.0
0.0

这确实是最好的解决方案,因为它可以确保模块中的代码与Python 3向前兼容。

Python 2的其他选项

如果您不想将其应用于整个模块,则只能使用一些解决方法。最受欢迎的是将其中一个操作数强制为浮点数。一种可靠的解决方案是a / (b * 1.0)。在新的Python Shell中:

>>> 1/(2 * 1.0)
0.5

truediv来自operator模块operator.truediv(a, b)的功能也很强大,但这可能会更慢,因为它是一个函数调用:

>>> from operator import truediv
>>> truediv(1, 2)
0.5

不建议用于Python 2

常见的是a / float(b)。如果b为复数,则将引发TypeError。由于定义了带有复数的除法,所以对我来说,在为除数传递一个复数时,除法不会失败。

>>> 1 / float(2)
0.5
>>> 1 / float(2j)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can't convert complex to float

对我而言,故意使您的代码更脆弱没有太大意义。

您也可以使用-Qnew标记运行Python ,但这不利于执行具有新Python 3行为的所有模块,并且您的某些模块可能需要经典的划分,因此除测试外,我不建议这样做。但要演示:

$ python -Qnew -c 'print 1/2'
0.5
$ python -Qnew -c 'print 1/2j'
-0.5j

How can I force division to be floating point in Python?

I have two integer values a and b, but I need their ratio in floating point. I know that a < b and I want to calculate a/b, so if I use integer division I’ll always get 0 with a remainder of a.

How can I force c to be a floating point number in Python in the following?

c = a / b

What is really being asked here is:

“How do I force true division such that a / b will return a fraction?”

Upgrade to Python 3

In Python 3, to get true division, you simply do a / b.

>>> 1/2
0.5

Floor division, the classic division behavior for integers, is now a // b:

>>> 1//2
0
>>> 1//2.0
0.0

However, you may be stuck using Python 2, or you may be writing code that must work in both 2 and 3.

If Using Python 2

In Python 2, it’s not so simple. Some ways of dealing with classic Python 2 division are better and more robust than others.

Recommendation for Python 2

You can get Python 3 division behavior in any given module with the following import at the top:

from __future__ import division

which then applies Python 3 style division to the entire module. It also works in a python shell at any given point. In Python 2:

>>> from __future__ import division
>>> 1/2
0.5
>>> 1//2
0
>>> 1//2.0
0.0

This is really the best solution as it ensures the code in your module is more forward compatible with Python 3.

Other Options for Python 2

If you don’t want to apply this to the entire module, you’re limited to a few workarounds. The most popular is to coerce one of the operands to a float. One robust solution is a / (b * 1.0). In a fresh Python shell:

>>> 1/(2 * 1.0)
0.5

Also robust is truediv from the operator module operator.truediv(a, b), but this is likely slower because it’s a function call:

>>> from operator import truediv
>>> truediv(1, 2)
0.5

Not Recommended for Python 2

Commonly seen is a / float(b). This will raise a TypeError if b is a complex number. Since division with complex numbers is defined, it makes sense to me to not have division fail when passed a complex number for the divisor.

>>> 1 / float(2)
0.5
>>> 1 / float(2j)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can't convert complex to float

It doesn’t make much sense to me to purposefully make your code more brittle.

You can also run Python with the -Qnew flag, but this has the downside of executing all modules with the new Python 3 behavior, and some of your modules may expect classic division, so I don’t recommend this except for testing. But to demonstrate:

$ python -Qnew -c 'print 1/2'
0.5
$ python -Qnew -c 'print 1/2j'
-0.5j

回答 3

c = a / (b * 1.0)
c = a / (b * 1.0)

回答 4

在Python 3.x中,单斜杠(/)始终表示真实(非截断)除法。(//运算符用于截断除法。)在Python 2.x(2.2及更高版本)中,您可以通过将

from __future__ import division

在模块顶部。

In Python 3.x, the single slash (/) always means true (non-truncating) division. (The // operator is used for truncating division.) In Python 2.x (2.2 and above), you can get this same behavior by putting a

from __future__ import division

at the top of your module.


回答 5

仅以浮点格式进行除法的任何参数也会产生浮点输出。

例:

>>> 4.0/3
1.3333333333333333

要么,

>>> 4 / 3.0
1.3333333333333333

要么,

>>> 4 / float(3)
1.3333333333333333

要么,

>>> float(4) / 3
1.3333333333333333

Just making any of the parameters for division in floating-point format also produces the output in floating-point.

Example:

>>> 4.0/3
1.3333333333333333

or,

>>> 4 / 3.0
1.3333333333333333

or,

>>> 4 / float(3)
1.3333333333333333

or,

>>> float(4) / 3
1.3333333333333333

回答 6

加一个点(.)表示浮点数

>>> 4/3.
1.3333333333333333

Add a dot (.) to indicate floating point numbers

>>> 4/3.
1.3333333333333333

回答 7

这也可以

>>> u=1./5
>>> print u
0.2

This will also work

>>> u=1./5
>>> print u
0.2

回答 8

如果要默认使用“ true”(浮点)除法,则有一个命令行标志:

python -Q new foo.py

有一些缺点(来自PEP):

有人认为,更改默认值的命令行选项是有害的。如果使用不当,肯定会很危险:例如,不可能将需要-Qnew的第三方库软件包与需要-Qold的第三方库软件包结合使用。

您可以通过查看python手册页了解有关更改/警告除法行为的其他标志值的更多信息。

有关除法更改的详细信息,请阅读:PEP 238-更改除法运算符

If you want to use “true” (floating point) division by default, there is a command line flag:

python -Q new foo.py

There are some drawbacks (from the PEP):

It has been argued that a command line option to change the default is evil. It can certainly be dangerous in the wrong hands: for example, it would be impossible to combine a 3rd party library package that requires -Qnew with another one that requires -Qold.

You can learn more about the other flags values that change / warn-about the behavior of division by looking at the python man page.

For full details on division changes read: PEP 238 — Changing the Division Operator


回答 9

from operator import truediv

c = truediv(a, b)
from operator import truediv

c = truediv(a, b)

回答 10

from operator import truediv

c = truediv(a, b)

其中a是除数,b是除数。当两个整数相除后的商是浮点数时,此函数非常方便。

from operator import truediv

c = truediv(a, b)

where a is dividend and b is the divisor. This function is handy when quotient after division of two integers is a float.


如何将字符串解析为float或int?

问题:如何将字符串解析为float或int?

在Python中,如何解析类似于"545.2222"其对应的float值的数字字符串545.2222?还是将字符串解析为"31"整数31

我只是想知道如何分析一个浮动 strfloat,和(单独)的INT strint

In Python, how can I parse a numeric string like "545.2222" to its corresponding float value, 545.2222? Or parse the string "31" to an integer, 31?

I just want to know how to parse a float str to a float, and (separately) an int str to an int.


回答 0

>>> a = "545.2222"
>>> float(a)
545.22220000000004
>>> int(float(a))
545
>>> a = "545.2222"
>>> float(a)
545.22220000000004
>>> int(float(a))
545

回答 1

def num(s):
    try:
        return int(s)
    except ValueError:
        return float(s)
def num(s):
    try:
        return int(s)
    except ValueError:
        return float(s)

回答 2

检查字符串是否为浮点数的Python方法:

def is_float(value):
  try:
    float(value)
    return True
  except:
    return False

此功能的更长更准确的名称可能是: is_convertible_to_float(value)

什么是Python中的浮点数,哪些不是浮点数,可能会让您感到惊讶:

val                   is_float(val) Note
--------------------  ----------   --------------------------------
""                    False        Blank string
"127"                 True         Passed string
True                  True         Pure sweet Truth
"True"                False        Vile contemptible lie
False                 True         So false it becomes true
"123.456"             True         Decimal
"      -127    "      True         Spaces trimmed
"\t\n12\r\n"          True         whitespace ignored
"NaN"                 True         Not a number
"NaNanananaBATMAN"    False        I am Batman
"-iNF"                True         Negative infinity
"123.E4"              True         Exponential notation
".1"                  True         mantissa only
"1,234"               False        Commas gtfo
u'\x30'               True         Unicode is fine.
"NULL"                False        Null is not special
0x3fade               True         Hexadecimal
"6e7777777777777"     True         Shrunk to infinity
"1.797693e+308"       True         This is max value
"infinity"            True         Same as inf
"infinityandBEYOND"   False        Extra characters wreck it
"12.34.56"            False        Only one dot allowed
u'四'                 False        Japanese '4' is not a float.
"#56"                 False        Pound sign
"56%"                 False        Percent of what?
"0E0"                 True         Exponential, move dot 0 places
0**0                  True         0___0  Exponentiation
"-5e-5"               True         Raise to a negative number
"+1e1"                True         Plus is OK with exponent
"+1e1^5"              False        Fancy exponent not interpreted
"+1e1.3"              False        No decimals in exponent
"-+1"                 False        Make up your mind
"(1)"                 False        Parenthesis is bad

您以为知道什么数字?你不像你想的那样好!并不奇怪。

不要在对生命至关重要的软件上使用此代码!

用这种方式捕获广泛的异常,杀死金丝雀和吞噬异常会产生很小的机会,即有效的float字符串将返回false。该float(...)行代码可以失败的任何什么都没有做的字符串的内容一千个理由。但是,如果您使用Python这样的鸭子式原型语言来编写至关重要的软件,那么您将遇到更大的问题。

Python method to check if a string is a float:

def is_float(value):
  try:
    float(value)
    return True
  except:
    return False

A longer and more accurate name for this function could be: is_convertible_to_float(value)

What is, and is not a float in Python may surprise you:

val                   is_float(val) Note
--------------------  ----------   --------------------------------
""                    False        Blank string
"127"                 True         Passed string
True                  True         Pure sweet Truth
"True"                False        Vile contemptible lie
False                 True         So false it becomes true
"123.456"             True         Decimal
"      -127    "      True         Spaces trimmed
"\t\n12\r\n"          True         whitespace ignored
"NaN"                 True         Not a number
"NaNanananaBATMAN"    False        I am Batman
"-iNF"                True         Negative infinity
"123.E4"              True         Exponential notation
".1"                  True         mantissa only
"1,234"               False        Commas gtfo
u'\x30'               True         Unicode is fine.
"NULL"                False        Null is not special
0x3fade               True         Hexadecimal
"6e7777777777777"     True         Shrunk to infinity
"1.797693e+308"       True         This is max value
"infinity"            True         Same as inf
"infinityandBEYOND"   False        Extra characters wreck it
"12.34.56"            False        Only one dot allowed
u'四'                 False        Japanese '4' is not a float.
"#56"                 False        Pound sign
"56%"                 False        Percent of what?
"0E0"                 True         Exponential, move dot 0 places
0**0                  True         0___0  Exponentiation
"-5e-5"               True         Raise to a negative number
"+1e1"                True         Plus is OK with exponent
"+1e1^5"              False        Fancy exponent not interpreted
"+1e1.3"              False        No decimals in exponent
"-+1"                 False        Make up your mind
"(1)"                 False        Parenthesis is bad

You think you know what numbers are? You are not so good as you think! Not big surprise.

Don’t use this code on life-critical software!

Catching broad exceptions this way, killing canaries and gobbling the exception creates a tiny chance that a valid float as string will return false. The float(...) line of code can failed for any of a thousand reasons that have nothing to do with the contents of the string. But if you’re writing life-critical software in a duck-typing prototype language like Python, then you’ve got much larger problems.


回答 3

这是另一个值得一提的方法ast.literal_eval

这可用于安全地评估包含来自不受信任来源的Python表达式的字符串,而无需自己解析值。

也就是说,一个安全的“评估”

>>> import ast
>>> ast.literal_eval("545.2222")
545.2222
>>> ast.literal_eval("31")
31

This is another method which deserves to be mentioned here, ast.literal_eval:

This can be used for safely evaluating strings containing Python expressions from untrusted sources without the need to parse the values oneself.

That is, a safe ‘eval’

>>> import ast
>>> ast.literal_eval("545.2222")
545.2222
>>> ast.literal_eval("31")
31

回答 4

float(x) if '.' in x else int(x)
float(x) if '.' in x else int(x)

回答 5

本地化和逗号

您应该考虑数字的字符串表示形式中可能出现逗号的情况,例如 float("545,545.2222")抛出异常的情况。而是使用in locale中的方法将字符串转换为数字并正确解释逗号。locale.atof一旦为所需的数字约定设置了语言环境,该方法便会一步转换为浮点数。

示例1-美国数字约定

在美国和英国,逗号可以用作千位分隔符。在具有美国语言环境的此示例中,逗号作为分隔符正确处理:

>>> import locale
>>> a = u'545,545.2222'
>>> locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
'en_US.UTF-8'
>>> locale.atof(a)
545545.2222
>>> int(locale.atof(a))
545545
>>>

示例2-欧洲数字约定

在世界上大多数国家/地区,逗号用于小数点而不是句点。在此使用法语语言环境的示例中,逗号被正确处理为小数点:

>>> import locale
>>> b = u'545,2222'
>>> locale.setlocale(locale.LC_ALL, 'fr_FR')
'fr_FR'
>>> locale.atof(b)
545.2222

该方法locale.atoi也可用,但参数应为整数。

Localization and commas

You should consider the possibility of commas in the string representation of a number, for cases like float("545,545.2222") which throws an exception. Instead, use methods in locale to convert the strings to numbers and interpret commas correctly. The locale.atof method converts to a float in one step once the locale has been set for the desired number convention.

Example 1 — United States number conventions

In the United States and the UK, commas can be used as a thousands separator. In this example with American locale, the comma is handled properly as a separator:

>>> import locale
>>> a = u'545,545.2222'
>>> locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
'en_US.UTF-8'
>>> locale.atof(a)
545545.2222
>>> int(locale.atof(a))
545545
>>>

Example 2 — European number conventions

In the majority of countries of the world, commas are used for decimal marks instead of periods. In this example with French locale, the comma is correctly handled as a decimal mark:

>>> import locale
>>> b = u'545,2222'
>>> locale.setlocale(locale.LC_ALL, 'fr_FR')
'fr_FR'
>>> locale.atof(b)
545.2222

The method locale.atoi is also available, but the argument should be an integer.


回答 6

如果您不喜欢第三方模块,则可以签出fastnumbers模块。它提供了一个名为fast_real的函数,该函数可以完全满足此问题的要求,并且比纯Python实现要快:

>>> from fastnumbers import fast_real
>>> fast_real("545.2222")
545.2222
>>> type(fast_real("545.2222"))
float
>>> fast_real("31")
31
>>> type(fast_real("31"))
int

If you aren’t averse to third-party modules, you could check out the fastnumbers module. It provides a function called fast_real that does exactly what this question is asking for and does it faster than a pure-Python implementation:

>>> from fastnumbers import fast_real
>>> fast_real("545.2222")
545.2222
>>> type(fast_real("545.2222"))
float
>>> fast_real("31")
31
>>> type(fast_real("31"))
int

回答 7

用户codelogicharley是正确的,但是请记住,如果您知道字符串是整数(例如545),则可以调用int(“ 545”)而不先进行浮点运算。

如果您的字符串在列表中,则也可以使用map函数。

>>> x = ["545.0", "545.6", "999.2"]
>>> map(float, x)
[545.0, 545.60000000000002, 999.20000000000005]
>>>

只有它们都是相同的类型才是好的。

Users codelogic and harley are correct, but keep in mind if you know the string is an integer (for example, 545) you can call int(“545”) without first casting to float.

If your strings are in a list, you could use the map function as well.

>>> x = ["545.0", "545.6", "999.2"]
>>> map(float, x)
[545.0, 545.60000000000002, 999.20000000000005]
>>>

It is only good if they’re all the same type.


回答 8

在Python中,如何将“ 545.2222”之类的数字字符串解析为其对应的浮点值542.2222?还是将字符串“ 31”解析为整数31? 我只想知道如何将float字符串解析为float,以及将int字符串分别解析为int。

您最好单独进行这些操作。如果您要混合使用它们,则可能会在以后遇到问题。简单的答案是:

"545.2222" 漂浮:

>>> float("545.2222")
545.2222

"31" 到一个整数:

>>> int("31")
31

其他与字符串和文字之间的转换,整数转换:

来自各种基准的转换,您应该事先知道基准(默认值为10)。请注意,您可以为它们加上Python期望的字面量(请参见下文)或删除前缀:

>>> int("0b11111", 2)
31
>>> int("11111", 2)
31
>>> int('0o37', 8)
31
>>> int('37', 8)
31
>>> int('0x1f', 16)
31
>>> int('1f', 16)
31

如果您不预先知道基础,但是您知道它们将具有正确的前缀,那么如果您通过0作为基础,Python可以为您推断出这个前缀:

>>> int("0b11111", 0)
31
>>> int('0o37', 0)
31
>>> int('0x1f', 0)
31

其他基数的非十进制(即整数)文字

但是,如果您的动机是让自己的代码清楚地表示硬编码的特定值,则可能不需要从基数进行转换-您可以让Python使用正确的语法自动为您完成。

您可以使用apropos前缀自动转换为具有以下文字的整数。这些对Python 2和3有效:

二进制前缀 0b

>>> 0b11111
31

八进制,前缀 0o

>>> 0o37
31

十六进制,前缀 0x

>>> 0x1f
31

当描述二进制标志,代码中的文件许可权或颜色的十六进制值时,这很有用-例如,请注意不要使用引号:

>>> 0b10101 # binary flags
21
>>> 0o755 # read, write, execute perms for owner, read & ex for group & others
493
>>> 0xffffff # the color, white, max values for red, green, and blue
16777215

使模棱两可的Python 2八进制与Python 3兼容

如果您在Python 2中看到一个以0开头的整数,则这是(不建议使用的)八进制语法。

>>> 037
31

这很糟糕,因为看起来值应该是37。因此,在Python 3中,它现在引发了SyntaxError

>>> 037
  File "<stdin>", line 1
    037
      ^
SyntaxError: invalid token

使用0o前缀将Python 2八进制转换为在2和3中均可使用的八进制:

>>> 0o37
31

In Python, how can I parse a numeric string like “545.2222” to its corresponding float value, 542.2222? Or parse the string “31” to an integer, 31? I just want to know how to parse a float string to a float, and (separately) an int string to an int.

It’s good that you ask to do these separately. If you’re mixing them, you may be setting yourself up for problems later. The simple answer is:

"545.2222" to float:

>>> float("545.2222")
545.2222

"31" to an integer:

>>> int("31")
31

Other conversions, ints to and from strings and literals:

Conversions from various bases, and you should know the base in advance (10 is the default). Note you can prefix them with what Python expects for its literals (see below) or remove the prefix:

>>> int("0b11111", 2)
31
>>> int("11111", 2)
31
>>> int('0o37', 8)
31
>>> int('37', 8)
31
>>> int('0x1f', 16)
31
>>> int('1f', 16)
31

If you don’t know the base in advance, but you do know they will have the correct prefix, Python can infer this for you if you pass 0 as the base:

>>> int("0b11111", 0)
31
>>> int('0o37', 0)
31
>>> int('0x1f', 0)
31

Non-Decimal (i.e. Integer) Literals from other Bases

If your motivation is to have your own code clearly represent hard-coded specific values, however, you may not need to convert from the bases – you can let Python do it for you automatically with the correct syntax.

You can use the apropos prefixes to get automatic conversion to integers with the following literals. These are valid for Python 2 and 3:

Binary, prefix 0b

>>> 0b11111
31

Octal, prefix 0o

>>> 0o37
31

Hexadecimal, prefix 0x

>>> 0x1f
31

This can be useful when describing binary flags, file permissions in code, or hex values for colors – for example, note no quotes:

>>> 0b10101 # binary flags
21
>>> 0o755 # read, write, execute perms for owner, read & ex for group & others
493
>>> 0xffffff # the color, white, max values for red, green, and blue
16777215

Making ambiguous Python 2 octals compatible with Python 3

If you see an integer that starts with a 0, in Python 2, this is (deprecated) octal syntax.

>>> 037
31

It is bad because it looks like the value should be 37. So in Python 3, it now raises a SyntaxError:

>>> 037
  File "<stdin>", line 1
    037
      ^
SyntaxError: invalid token

Convert your Python 2 octals to octals that work in both 2 and 3 with the 0o prefix:

>>> 0o37
31

回答 9

这个问题似乎有点老了。但是让我建议一个函数parseStr,它的功能类似,即返回整数或浮点数,并且如果无法将给定的ASCII字符串转换为其中的任何一个,则它将返回原样。当然,可以将代码调整为仅执行所需的操作:

   >>> import string
   >>> parseStr = lambda x: x.isalpha() and x or x.isdigit() and \
   ...                      int(x) or x.isalnum() and x or \
   ...                      len(set(string.punctuation).intersection(x)) == 1 and \
   ...                      x.count('.') == 1 and float(x) or x
   >>> parseStr('123')
   123
   >>> parseStr('123.3')
   123.3
   >>> parseStr('3HC1')
   '3HC1'
   >>> parseStr('12.e5')
   1200000.0
   >>> parseStr('12$5')
   '12$5'
   >>> parseStr('12.2.2')
   '12.2.2'

The question seems a little bit old. But let me suggest a function, parseStr, which makes something similar, that is, returns integer or float and if a given ASCII string cannot be converted to none of them it returns it untouched. The code of course might be adjusted to do only what you want:

   >>> import string
   >>> parseStr = lambda x: x.isalpha() and x or x.isdigit() and \
   ...                      int(x) or x.isalnum() and x or \
   ...                      len(set(string.punctuation).intersection(x)) == 1 and \
   ...                      x.count('.') == 1 and float(x) or x
   >>> parseStr('123')
   123
   >>> parseStr('123.3')
   123.3
   >>> parseStr('3HC1')
   '3HC1'
   >>> parseStr('12.e5')
   1200000.0
   >>> parseStr('12$5')
   '12$5'
   >>> parseStr('12.2.2')
   '12.2.2'

回答 10

float("545.2222")int(float("545.2222"))

float("545.2222") and int(float("545.2222"))


回答 11

我为此使用此功能

import ast

def parse_str(s):
   try:
      return ast.literal_eval(str(s))
   except:
      return

它将字符串转换为其类型

value = parse_str('1')  # Returns Integer
value = parse_str('1.5')  # Returns Float

I use this function for that

import ast

def parse_str(s):
   try:
      return ast.literal_eval(str(s))
   except:
      return

It will convert the string to its type

value = parse_str('1')  # Returns Integer
value = parse_str('1.5')  # Returns Float

回答 12

YAML解析器可以帮助你找出你的数据类型的字符串是什么。使用yaml.load(),然后可以使用type(result)测试类型:

>>> import yaml

>>> a = "545.2222"
>>> result = yaml.load(a)
>>> result
545.22220000000004
>>> type(result)
<type 'float'>

>>> b = "31"
>>> result = yaml.load(b)
>>> result
31
>>> type(result)
<type 'int'>

>>> c = "HI"
>>> result = yaml.load(c)
>>> result
'HI'
>>> type(result)
<type 'str'>

The YAML parser can help you figure out what datatype your string is. Use yaml.load(), and then you can use type(result) to test for type:

>>> import yaml

>>> a = "545.2222"
>>> result = yaml.load(a)
>>> result
545.22220000000004
>>> type(result)
<type 'float'>

>>> b = "31"
>>> result = yaml.load(b)
>>> result
31
>>> type(result)
<type 'int'>

>>> c = "HI"
>>> result = yaml.load(c)
>>> result
'HI'
>>> type(result)
<type 'str'>

回答 13

def get_int_or_float(v):
    number_as_float = float(v)
    number_as_int = int(number_as_float)
    return number_as_int if number_as_float == number_as_int else number_as_float
def get_int_or_float(v):
    number_as_float = float(v)
    number_as_int = int(number_as_float)
    return number_as_int if number_as_float == number_as_int else number_as_float

回答 14

def num(s):
    """num(s)
    num(3),num(3.7)-->3
    num('3')-->3, num('3.7')-->3.7
    num('3,700')-->ValueError
    num('3a'),num('a3'),-->ValueError
    num('3e4') --> 30000.0
    """
    try:
        return int(s)
    except ValueError:
        try:
            return float(s)
        except ValueError:
            raise ValueError('argument is not a string of number')
def num(s):
    """num(s)
    num(3),num(3.7)-->3
    num('3')-->3, num('3.7')-->3.7
    num('3,700')-->ValueError
    num('3a'),num('a3'),-->ValueError
    num('3e4') --> 30000.0
    """
    try:
        return int(s)
    except ValueError:
        try:
            return float(s)
        except ValueError:
            raise ValueError('argument is not a string of number')

回答 15

您需要考虑到四舍五入才能正确执行此操作。

即int(5.1)=> 5 int(5.6)=> 5-错误,应该为6所以我们做int(5.6 + 0.5)=> 6

def convert(n):
    try:
        return int(n)
    except ValueError:
        return float(n + 0.5)

You need to take into account rounding to do this properly.

I.e. int(5.1) => 5 int(5.6) => 5 — wrong, should be 6 so we do int(5.6 + 0.5) => 6

def convert(n):
    try:
        return int(n)
    except ValueError:
        return float(n + 0.5)

回答 16

我很惊讶没有人提到正则表达式,因为有时必须在转换为数字之前准备好字符串并对其进行规范化

import re
def parseNumber(value, as_int=False):
    try:
        number = float(re.sub('[^.\-\d]', '', value))
        if as_int:
            return int(number + 0.5)
        else:
            return number
    except ValueError:
        return float('nan')  # or None if you wish

用法:

parseNumber('13,345')
> 13345.0

parseNumber('- 123 000')
> -123000.0

parseNumber('99999\n')
> 99999.0

顺便说一句,以验证您有一个数字:

import numbers
def is_number(value):
    return isinstance(value, numbers.Number)
    # will work with int, float, long, Decimal

I am surprised nobody mentioned regex because sometimes string must be prepared and normalized before casting to number

import re
def parseNumber(value, as_int=False):
    try:
        number = float(re.sub('[^.\-\d]', '', value))
        if as_int:
            return int(number + 0.5)
        else:
            return number
    except ValueError:
        return float('nan')  # or None if you wish

usage:

parseNumber('13,345')
> 13345.0

parseNumber('- 123 000')
> -123000.0

parseNumber('99999\n')
> 99999.0

and by the way, something to verify you have a number:

import numbers
def is_number(value):
    return isinstance(value, numbers.Number)
    # will work with int, float, long, Decimal

回答 17

要在python中进行类型转换,请使用该类型的构造函数,并将字符串(或您尝试投射的任何值)作为参数传递。

例如:

>>>float("23.333")
   23.333

在后台,python正在调用objects __float__方法,该方法应该返回参数的float表示形式。这是特别强大的功能,因为您可以使用__float__方法定义自己的类型(使用类),以便可以使用float(myobject)将其转换为float。

To typecast in python use the constructor funtions of the type, passing the string (or whatever value you are trying to cast) as a parameter.

For example:

>>>float("23.333")
   23.333

Behind the scenes, python is calling the objects __float__ method, which should return a float representation of the parameter. This is especially powerful, as you can define your own types (using classes) with a __float__ method so that it can be casted into a float using float(myobject).


回答 18

这是一个正确版本https://stackoverflow.com/a/33017514/5973334

这将尝试解析一个字符串并返回一个intfloat取决于该字符串表示什么。它可能会引发解析异常或具有某些意外行为

  def get_int_or_float(v):
        number_as_float = float(v)
        number_as_int = int(number_as_float)
        return number_as_int if number_as_float == number_as_int else 
        number_as_float

This is a corrected version of https://stackoverflow.com/a/33017514/5973334

This will try to parse a string and return either int or float depending on what the string represents. It might rise parsing exceptions or have some unexpected behaviour.

  def get_int_or_float(v):
        number_as_float = float(v)
        number_as_int = int(number_as_float)
        return number_as_int if number_as_float == number_as_int else 
        number_as_float

回答 19

将您的字符串传递给此函数:

def string_to_number(str):
  if("." in str):
    try:
      res = float(str)
    except:
      res = str  
  elif(str.isdigit()):
    res = int(str)
  else:
    res = str
  return(res)

根据所传递的内容,它将返回int,float或string。

一个int字符串

print(type(string_to_number("124")))
<class 'int'>

浮点数的字符串

print(type(string_to_number("12.4")))
<class 'float'>

字符串即字符串

print(type(string_to_number("hello")))
<class 'str'>

看起来像个浮点数的字符串

print(type(string_to_number("hel.lo")))
<class 'str'>

Pass your string to this function:

def string_to_number(str):
  if("." in str):
    try:
      res = float(str)
    except:
      res = str  
  elif(str.isdigit()):
    res = int(str)
  else:
    res = str
  return(res)

It will return int, float or string depending on what was passed.

string that is an int

print(type(string_to_number("124")))
<class 'int'>

string that is a float

print(type(string_to_number("12.4")))
<class 'float'>

string that is a string

print(type(string_to_number("hello")))
<class 'str'>

string that looks like a float

print(type(string_to_number("hel.lo")))
<class 'str'>

回答 20

采用:

def num(s):
    try:
        for each in s:
            yield int(each)
    except ValueError:
        yield float(each)
a = num(["123.55","345","44"])
print a.next()
print a.next()

这是我想出的最Python化的方式。

Use:

def num(s):
    try:
        for each in s:
            yield int(each)
    except ValueError:
        yield float(each)
a = num(["123.55","345","44"])
print a.next()
print a.next()

This is the most Pythonic way I could come up with.


回答 21

处理十六进制,八进制,二进制,十进制和浮点数

该解决方案将处理数字的所有字符串约定(我所知道的全部)。

def to_number(n):
    ''' Convert any number representation to a number 
    This covers: float, decimal, hex, and octal numbers.
    '''

    try:
        return int(str(n), 0)
    except:
        try:
            # python 3 doesn't accept "010" as a valid octal.  You must use the
            # '0o' prefix
            return int('0o' + n, 0)
        except:
            return float(n)

该测试用例输出说明了我在说什么。

======================== CAPTURED OUTPUT =========================
to_number(3735928559)   = 3735928559 == 3735928559
to_number("0xFEEDFACE") = 4277009102 == 4277009102
to_number("0x0")        =          0 ==          0
to_number(100)          =        100 ==        100
to_number("42")         =         42 ==         42
to_number(8)            =          8 ==          8
to_number("0o20")       =         16 ==         16
to_number("020")        =         16 ==         16
to_number(3.14)         =       3.14 ==       3.14
to_number("2.72")       =       2.72 ==       2.72
to_number("1e3")        =     1000.0 ==       1000
to_number(0.001)        =      0.001 ==      0.001
to_number("0xA")        =         10 ==         10
to_number("012")        =         10 ==         10
to_number("0o12")       =         10 ==         10
to_number("0b01010")    =         10 ==         10
to_number("10")         =         10 ==         10
to_number("10.0")       =       10.0 ==         10
to_number("1e1")        =       10.0 ==         10

这是测试:

class test_to_number(unittest.TestCase):

    def test_hex(self):
        # All of the following should be converted to an integer
        #
        values = [

                 #          HEX
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (0xDEADBEEF  , 3735928559), # Hex
                ("0xFEEDFACE", 4277009102), # Hex
                ("0x0"       ,          0), # Hex

                 #        Decimals
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (100         ,        100), # Decimal
                ("42"        ,         42), # Decimal
            ]



        values += [
                 #        Octals
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (0o10        ,          8), # Octal
                ("0o20"      ,         16), # Octal
                ("020"       ,         16), # Octal
            ]


        values += [
                 #        Floats
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (3.14        ,       3.14), # Float
                ("2.72"      ,       2.72), # Float
                ("1e3"       ,       1000), # Float
                (1e-3        ,      0.001), # Float
            ]

        values += [
                 #        All ints
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                ("0xA"       ,         10), 
                ("012"       ,         10), 
                ("0o12"      ,         10), 
                ("0b01010"   ,         10), 
                ("10"        ,         10), 
                ("10.0"      ,         10), 
                ("1e1"       ,         10), 
            ]

        for _input, expected in values:
            value = to_number(_input)

            if isinstance(_input, str):
                cmd = 'to_number("{}")'.format(_input)
            else:
                cmd = 'to_number({})'.format(_input)

            print("{:23} = {:10} == {:10}".format(cmd, value, expected))
            self.assertEqual(value, expected)

Handles hex, octal, binary, decimal, and float

This solution will handle all of the string conventions for numbers (all that I know about).

def to_number(n):
    ''' Convert any number representation to a number 
    This covers: float, decimal, hex, and octal numbers.
    '''

    try:
        return int(str(n), 0)
    except:
        try:
            # python 3 doesn't accept "010" as a valid octal.  You must use the
            # '0o' prefix
            return int('0o' + n, 0)
        except:
            return float(n)

This test case output illustrates what I’m talking about.

======================== CAPTURED OUTPUT =========================
to_number(3735928559)   = 3735928559 == 3735928559
to_number("0xFEEDFACE") = 4277009102 == 4277009102
to_number("0x0")        =          0 ==          0
to_number(100)          =        100 ==        100
to_number("42")         =         42 ==         42
to_number(8)            =          8 ==          8
to_number("0o20")       =         16 ==         16
to_number("020")        =         16 ==         16
to_number(3.14)         =       3.14 ==       3.14
to_number("2.72")       =       2.72 ==       2.72
to_number("1e3")        =     1000.0 ==       1000
to_number(0.001)        =      0.001 ==      0.001
to_number("0xA")        =         10 ==         10
to_number("012")        =         10 ==         10
to_number("0o12")       =         10 ==         10
to_number("0b01010")    =         10 ==         10
to_number("10")         =         10 ==         10
to_number("10.0")       =       10.0 ==         10
to_number("1e1")        =       10.0 ==         10

Here is the test:

class test_to_number(unittest.TestCase):

    def test_hex(self):
        # All of the following should be converted to an integer
        #
        values = [

                 #          HEX
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (0xDEADBEEF  , 3735928559), # Hex
                ("0xFEEDFACE", 4277009102), # Hex
                ("0x0"       ,          0), # Hex

                 #        Decimals
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (100         ,        100), # Decimal
                ("42"        ,         42), # Decimal
            ]



        values += [
                 #        Octals
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (0o10        ,          8), # Octal
                ("0o20"      ,         16), # Octal
                ("020"       ,         16), # Octal
            ]


        values += [
                 #        Floats
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                (3.14        ,       3.14), # Float
                ("2.72"      ,       2.72), # Float
                ("1e3"       ,       1000), # Float
                (1e-3        ,      0.001), # Float
            ]

        values += [
                 #        All ints
                 # ----------------------
                 # Input     |   Expected
                 # ----------------------
                ("0xA"       ,         10), 
                ("012"       ,         10), 
                ("0o12"      ,         10), 
                ("0b01010"   ,         10), 
                ("10"        ,         10), 
                ("10.0"      ,         10), 
                ("1e1"       ,         10), 
            ]

        for _input, expected in values:
            value = to_number(_input)

            if isinstance(_input, str):
                cmd = 'to_number("{}")'.format(_input)
            else:
                cmd = 'to_number({})'.format(_input)

            print("{:23} = {:10} == {:10}".format(cmd, value, expected))
            self.assertEqual(value, expected)

回答 22

采用:

>>> str_float = "545.2222"
>>> float(str_float)
545.2222
>>> type(_) # Check its type
<type 'float'>

>>> str_int = "31"
>>> int(str_int)
31
>>> type(_) # Check its type
<type 'int'>

Use:

>>> str_float = "545.2222"
>>> float(str_float)
545.2222
>>> type(_) # Check its type
<type 'float'>

>>> str_int = "31"
>>> int(str_int)
31
>>> type(_) # Check its type
<type 'int'>

回答 23

这是将转换任何一个函数object(不只是str)到intfloat方法,依据实际的字符串提供模样 intfloat。此外,如果它是同时具有__float__int__方法的对象,则默认使用__float__

def conv_to_num(x, num_type='asis'):
    '''Converts an object to a number if possible.
    num_type: int, float, 'asis'
    Defaults to floating point in case of ambiguity.
    '''
    import numbers

    is_num, is_str, is_other = [False]*3

    if isinstance(x, numbers.Number):
        is_num = True
    elif isinstance(x, str):
        is_str = True

    is_other = not any([is_num, is_str])

    if is_num:
        res = x
    elif is_str:
        is_float, is_int, is_char = [False]*3
        try:
            res = float(x)
            if '.' in x:
                is_float = True
            else:
                is_int = True
        except ValueError:
            res = x
            is_char = True

    else:
        if num_type == 'asis':
            funcs = [int, float]
        else:
            funcs = [num_type]

        for func in funcs:
            try:
                res = func(x)
                break
            except TypeError:
                continue
        else:
            res = x

This is a function which will convert any object (not just str) to int or float, based on if the actual string supplied looks like int or float. Further if it’s an object which has both __float and __int__ methods, it defaults to using __float__

def conv_to_num(x, num_type='asis'):
    '''Converts an object to a number if possible.
    num_type: int, float, 'asis'
    Defaults to floating point in case of ambiguity.
    '''
    import numbers

    is_num, is_str, is_other = [False]*3

    if isinstance(x, numbers.Number):
        is_num = True
    elif isinstance(x, str):
        is_str = True

    is_other = not any([is_num, is_str])

    if is_num:
        res = x
    elif is_str:
        is_float, is_int, is_char = [False]*3
        try:
            res = float(x)
            if '.' in x:
                is_float = True
            else:
                is_int = True
        except ValueError:
            res = x
            is_char = True

    else:
        if num_type == 'asis':
            funcs = [int, float]
        else:
            funcs = [num_type]

        for func in funcs:
            try:
                res = func(x)
                break
            except TypeError:
                continue
        else:
            res = x

回答 24

通过使用int和float方法,我们可以将字符串转换为整数和浮点数。

s="45.8"
print(float(s))

y='67'
print(int(y))

By using int and float methods we can convert a string to integer and floats.

s="45.8"
print(float(s))

y='67'
print(int(y))

回答 25

eval()是这个问题的很好解决方案。它不需要检查数字是int还是float,它只给出相应的等价物。如果需要其他方法,请尝试

if '.' in string:
    print(float(string))
else:
    print(int(string))

try-except也可以用作替代方法。尝试在try块中将字符串转换为int。如果该字符串是一个浮点值,它将抛出一个错误,该错误将在except块中捕获,像这样

try:
    print(int(string))
except:
    print(float(string))

eval() is a very good solution to this question. It doesn’t need to check if the number is int or float, it just gives the corresponding equivalent. If other methods are required, try

if '.' in string:
    print(float(string))
else:
    print(int(string))

try-except can also be used as an alternative. Try converting string to int inside the try block. If the string would be a float value, it will throw an error which will be catched in the except block, like this

try:
    print(int(string))
except:
    print(float(string))

回答 26

这是您问题的另一种解释(提示:含糊)。您可能正在寻找这样的东西:

def parseIntOrFloat( aString ):
    return eval( aString )

它是这样的…

>>> parseIntOrFloat("545.2222")
545.22220000000004
>>> parseIntOrFloat("545")
545

从理论上讲,存在注入漏洞。字符串可以是例如"import os; os.abort()"。但是,由于没有关于字符串来自何处的任何背景,因此可能是理论上的推测。由于问题很模糊,因此尚不清楚此漏洞是否确实存在。

Here’s another interpretation of your question (hint: it’s vague). It’s possible you’re looking for something like this:

def parseIntOrFloat( aString ):
    return eval( aString )

It works like this…

>>> parseIntOrFloat("545.2222")
545.22220000000004
>>> parseIntOrFloat("545")
545

Theoretically, there’s an injection vulnerability. The string could, for example be "import os; os.abort()". Without any background on where the string comes from, however, the possibility is theoretical speculation. Since the question is vague, it’s not at all clear if this vulnerability actually exists or not.