用元组初始化NumPy数组(用相同的元组填充) [英] NumPy array initialization with tuple (fill with identical tuples)

查看:165
本文介绍了用元组初始化NumPy数组(用相同的元组填充)的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

我想创建一个数组,该数组最初由相同的元组填充,特别是由 NaN 的元组填充。
例如,

I would like to create an array which is filled with identical tuples initially, specifically with tuples of NaN. E.g.,

array([[(nan, nan), (nan, nan)],
       [(nan, nan), (nan, nan)],
       [(nan, nan), (nan, nan)]], dtype=object)

但是,当使用列出的数组初始化时,例如此处,其中包含一个可迭代的值作为填充数组的值, python 显然试图将可迭代的形状重塑到新数组中而不是用新数组填充:

However, when using the array initialisations listed e.g. here with an iterable value as value for filling in array, python apparently tries to reshape that iterable into the new array rather than fill it with it:

np.full([3,2],(np.nan,np.nan,np.nan),dtype=tuple)
#ValueError: could not broadcast input array from shape (3) into shape (3,2)

np.fill 也不起作用,它

是否只能逐项填充数组?

Is it only possible to fill the array item by item?

推荐答案

您可以使用正确的 dtype 。使用'f,f',您可以使用 floats 的元组来初始化数组;有关更多信息,请参见数据类型对象(dtype)

You can, with the correct dtype. With 'f,f' you can initialise the array with tuples of floats; see Data type objects (dtype) for more.

np.full((3,2), np.nan, dtype='f,f')

array([[(nan, nan), (nan, nan)],
       [(nan, nan), (nan, nan)],
       [(nan, nan), (nan, nan)]], dtype=[('f0', '<f4'), ('f1', '<f4')])

这篇关于用元组初始化NumPy数组(用相同的元组填充)的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

查看全文
登录 关闭
扫码关注1秒登录
发送“验证码”获取 | 15天全站免登陆