2014-09-07 23:40:23 +08:00
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3.1 数字的四舍五入
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2014-08-21 10:27:10 +08:00
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问题
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2014-09-07 23:32:42 +08:00
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你想对浮点数执行指定精度的舍入运算。
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2014-08-21 10:27:10 +08:00
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解决方案
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2014-09-23 10:52:16 +08:00
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对于简单的舍入运算,使用内置的 ``round(value, ndigits)`` 函数即可。比如:
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2014-09-07 23:32:42 +08:00
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.. code-block:: python
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>>> round(1.23, 1)
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1.2
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>>> round(1.27, 1)
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1.3
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>>> round(-1.27, 1)
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-1.3
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>>> round(1.25361,3)
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1.254
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>>>
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2015-09-18 09:13:42 +08:00
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当一个值刚好在两个边界的中间的时候, ``round`` 函数返回离它最近的偶数。
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2014-09-07 23:32:42 +08:00
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也就是说,对1.5或者2.5的舍入运算都会得到2。
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2014-09-23 10:52:16 +08:00
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传给 ``round()`` 函数的 ``ndigits`` 参数可以是负数,这种情况下,
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舍入运算会作用在十位、百位、千位等上面。比如:
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2014-09-07 23:32:42 +08:00
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.. code-block:: python
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>>> a = 1627731
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>>> round(a, -1)
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1627730
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>>> round(a, -2)
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1627700
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>>> round(a, -3)
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1628000
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>>>
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2014-08-21 10:27:10 +08:00
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讨论
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2014-09-07 23:32:42 +08:00
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不要将舍入和格式化输出搞混淆了。
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2014-09-23 10:52:16 +08:00
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如果你的目的只是简单的输出一定宽度的数,你不需要使用 ``round()`` 函数。
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2014-09-07 23:32:42 +08:00
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而仅仅只需要在格式化的时候指定精度即可。比如:
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.. code-block:: python
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>>> x = 1.23456
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>>> format(x, '0.2f')
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'1.23'
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>>> format(x, '0.3f')
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'1.235'
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>>> 'value is {:0.3f}'.format(x)
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'value is 1.235'
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>>>
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同样,不要试着去舍入浮点值来"修正"表面上看起来正确的问题。比如,你可能倾向于这样做:
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.. code-block:: python
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>>> a = 2.1
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>>> b = 4.2
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>>> c = a + b
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>>> c
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6.300000000000001
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>>> c = round(c, 2) # "Fix" result (???)
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>>> c
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6.3
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>>>
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对于大多数使用到浮点的程序,没有必要也不推荐这样做。
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尽管在计算的时候会有一点点小的误差,但是这些小的误差是能被理解与容忍的。
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2014-09-23 10:52:16 +08:00
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如果不能允许这样的小误差(比如涉及到金融领域),那么就得考虑使用 ``decimal`` 模块了,下一节我们会详细讨论。
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