More_itertools是一个第三方库，它实现了numeric_range工具:

import more_itertools as mit


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

输出

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

这个工具也适用于十进制和分数。

法兰(开始、停止、精度)

def frange(a,b,i):
    p = 10**i
    sr = a*p
    er = (b*p) + 1
    p = float(p)
    return map(lambda x: x/p, xrange(sr,er))

In >frange(-1,1,1)

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

下面是我的解决方案，它可以很好地使用float_range(- 1,0,0.01)，并且没有浮点表示错误。它不是很快，但工作得很好:

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]

我的答案与使用map()的其他人类似，不需要NumPy，也不使用lambda(尽管您可以)。以dt为单位获取从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))

与R的seq函数类似，这个函数在给定正确的步长值的情况下以任意顺序返回一个序列。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]

我的版本使用原始的范围函数来创建移位的乘法指标。这允许原始的range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Decimal，因为我发现在某些情况下，我想避免浮点算法引入的舍入漂移。

它与range/xrange中的空集结果一致。

仅向任何一个函数传递一个数值都会将标准范围输出返回到输入参数的整数上限值(因此如果给它5.5，它将返回range(6))。

编辑:下面的代码现在可以在pypi: Franges上作为包使用

## 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)) # []