如果你想在测试或TDD上下文中使用pytest包，下面是如何做到的:

import pytest


PRECISION = 1e-3

def assert_almost_equal():
    obtained_value = 99.99
    expected_value = 100.00
    assert obtained_value == pytest.approx(expected_value, PRECISION)

对于一些可以影响源数表示的情况，可以使用整数分子和整数分母将它们表示为分数而不是浮点数。这样你就可以进行准确的比较。

详见分数模块中的分数。

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.

至于绝对误差，你可以检查一下

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

一些关于Python中浮动行为怪异的信息: Python 3教程03 - if-else，逻辑运算符和初学者常犯的错误

你也可以用数学。相对误差接近。

使用Python的decimal模块，该模块提供decimal类。

评论如下:

值得注意的是，如果你 做繁重的数学工作，而你没有 绝对需要精准的 小数，这很麻烦 下来。浮点数要快得多 处理，但不精确。小数是 非常精确但很慢。

做一些像下面这样简单的事情就足够了:

return abs(f1 - f2) <= allowed_error