NumPy:从可迭代对象创建多维数组 [英] NumPy: Create a multidimensional array from an iterable

问题描述

我有一个可重复的元组，我想从中构建一个 ndarray .假设形状为(12345，67890).一种有效而优雅的方法是什么?

I have an iterable of tuples, and I'd like to build an ndarray from it. Say that the shape would be (12345, 67890). What would be an efficient and elegant way to do so?

以下是一些选择，以及为什么我将它们排除在外:

Here are a few options, and why I ruled them out:

1. np.array(my_tuples)在知道大小之前就开始分配数组，根据NumPy的文档，这需要低效率的重定位.

1. np.array(my_tuples) starts allocating the array before it knows the size, which requires inefficient relocations according to NumPy's documentation.

使用 np.ndarray((12345，67890))创建一个包含未初始化内容的数组，然后执行一个循环，以数据填充该数组.它有效且有效，但是有点笨拙，因为它需要多个语句.

Create an array with uninitialized content using np.ndarray((12345, 67890)) and then do a loop that populates it with data. It works and it's efficient, but a bit inelegant because it requires multiple statements.

使用 np.fromiter 似乎仅适用于一维数组.

Use np.fromiter which appears to be geared towards 1-dimensional arrays only.

有人有更好的解决方案吗?

Does anyone have a better solution?

(我见过

(I've seen this question, but I'm not seeing any promising answers there.)

推荐答案

将 fromiter()与 .reshape()一起使用.重塑不需要更多的内存或处理.

Use fromiter() with .reshape(). Reshaping does not require more memory or processing.

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