格式化漂浮在numpy的阵列 [英] Formatting floats in a numpy array

问题描述

如果我有一个numpy的数组是这样的：

If I have a numpy array like this:

[2.15295647e+01, 8.12531501e+00, 3.97113829e+00, 1.00777250e+01]

我怎么能移动小数点，所以我结束了一个numpy的数组像这样数字的格式：

how can I move the decimal point and format the numbers so I end up with a numpy array like this:

[21.53, 8.13, 3.97, 10.08]

np.around（一，小数= 2）只给我​​ [2.15300000e + 01，+ 8.13000000e 00，3.97000000e + 00 ，1.00800000e + 01] 我不希望，我还没有找到另一种方式来做到这一点。

np.around(a, decimals=2) only gives me [2.15300000e+01, 8.13000000e+00, 3.97000000e+00, 1.00800000e+01] Which I don't want and I haven't found another way to do it.

推荐答案

为了使numpy的显示以任意格式浮标阵，你可以自定义一个函数，它的浮点值作为其输入并返回一个格式化字符串：

In order to make numpy display float arrays in an arbitrary format, you can define a custom function that takes a float value as its input and returns a formatted string:

In [1]: float_formatter = lambda x: "%.2f" % x

的˚F意味着此处定点格式（而不是科学）和 0.2 意味着两小数位（你可以阅读更多关于字符串这里格式化）。

The f here means fixed-point format (not 'scientific'), and the .2 means two decimal places (you can read more about string formatting here).

让我们来测试它与一个浮点值：

Let's test it out with a float value:

In [2]: float_formatter(1.234567E3)
Out[2]: '1234.57'

要做出numpy的打印所有浮标阵这种方式，你可以通过格式= 参数 np.set_printoptions

To make numpy print all float arrays this way, you can pass the formatter= argument to np.set_printoptions:

In [3]: np.set_printoptions(formatter={'float_kind':float_formatter})

现在numpy的将打印所有浮标阵是这样的：

Now numpy will print all float arrays this way:

In [4]: np.random.randn(5) * 10
Out[4]: array([5.25, 3.91, 0.04, -1.53, 6.68]

请注意，这只会影响numpy的阵列，而不是标量：

Note that this only affects numpy arrays, not scalars:

In [5]: np.pi
Out[5]: 3.141592653589793

这也不会影响非花车，花车复杂等 - 你需要定义不同的格式化为其他标量类型

It also won't affect non-floats, complex floats etc - you will need to define separate formatters for other scalar types.

您也应该知道，这仅影响numpy的显示如何浮动值 - 将在计算中使用将保留其原来的precision的实际值

You should also be aware that this only affects how numpy displays float values - the actual values that will be used in computations will retain their original precision.

例如：

In [6]: a = np.array([1E-9])

In [7]: a
Out[7]: array([0.00])

In [8]: a == 0
Out[8]: array([False], dtype=bool)

numpy的版画 A ，好像它是等于 0 ，但它不是 - 它仍然等于 1E-9 。

numpy prints a as if it were equal to 0, but it is not - it still equals 1E-9.

如果你真的想圆的值的阵列中，影响它们将如何在计算中使用，你应该使用的方式 np.round ，正如其他人已经指出的那样。

If you actually want to round the values in your array in a way that affects how they will be used in calculations, you should use np.round, as others have already pointed out.

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