我可以在单个表达式中根据其值构造一个numpy对象的零维数组吗? [英] Can I construct a numpy object zero-d array from its value in a single expression?

问题描述

所有这些工作(即具有.shape == ()):

These all work (ie, have .shape == ()):

np.array(1, dtype=object)
np.array("foo", dtype=object)
np.array(object(), dtype=object)

但这不是:

np.array((0, 0), dtype=object)  # .shape == (2,)

我可以通过以下两个任务来实现这一目标:

I can achieve this with two assignments as:

def make_scalar(x):
    value = np.empty((), dtype=object)
    value[()] = x
    return value

make_scalar((0, 0))  # .shape == ()

是否有内置函数可以让我在单个表达式中创建此0d数组(用于任意标量值)?

Is there a builtin function that will allow me to create this 0d array in a single expression (for arbitrary scalar values)?

推荐答案

当人们想要创建一个包含列表或元组的对象数组时，我已经回答了很多此类问题.唯一不同的是，您要使用0d数组进行此操作.

I've answered this kind of question of a number of times, when people want to make a object array containing lists or tuples. All that's different here is you want to do this with a 0d array.

例如:在将列表转换为数组时减少维度

这个问题与之相反:

 np.array([[1,2],[3,4]])  # (2,2) int
 np.array([[1],[3,4]])    # (2,) object

从第一个列表中创建(2，)对象需要使用create and fill方法. np.array(...)坚持尽可能深入到嵌套的可迭代对象中.可以说，它经过训练可以创建尽可能高的维数组.它将在列表和元组上迭代，但不会在字典或集合上进行迭代.

Making a (2,) object from the first list requires the create and fill approach. np.array(...) insists on drilling down into a nested iterable as far as it can go. It's trained, so to speak, to create as high a dimensional array as it can. It will iterate on lists and tuples, but not on dictionaries or sets.

np.array带有一个ndmin参数，但没有一个ndmax参数.我相信关于数组创建器的github问题会限制该深度.

np.array takes a ndmin parameter, but not a ndmax one. I believe there is some github issue about array creator that would limit that depth.

现在，创建正确尺寸的空"对象数组并填充它是最好的.填充时很容易出错，例如广播或设置序列.

For now, creating a 'empty' object array of the right dimension, and filling it is best. And it's easy to get errors when filling, such as broadcasting or setting with sequences ones.

make_scalar函数没有任何问题.这不是速度重要的操作.因此，您自己的功能就像内置功能一样.

There's nothing wrong with your make_scalar function. This isn't the kind of operation where speed matters. So your own function is just as pretty as a builtin one.

另一个想法-scipy.io.loadmat返回很多单元素对象数组.这样做是为了表示MATLAB结构和单元.我们可以看一下它的代码，看看该开发人员是否使用了任何聪明的方法.

Another thought - scipy.io.loadmat returns a lot of single element object arrays. It does this to represent MATLAB structures and cells. We could look at its code to see if that developer uses anything clever.

相关的github问题:

Relevant github issues:

https://github.com/numpy/numpy/issues/5933 恩:对象数组创建功能

https://github.com/numpy/numpy/issues/5933 Enh: Object array creation function

https://github.com/numpy/numpy/issues/6070 请反对为任意对象创建numpy数组

https://github.com/numpy/numpy/issues/6070 Please Deprecate creation of numpy arrays for arbitrary objects

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