如何添加到numpy的不同尺寸的数组项在for循环中（类似于Matlab的单元阵列）？ [英] How to add to numpy array entries of different size in a for loop (similar to Matlab's cell arrays)?

问题描述

我想实现一个analouge在Python \\ numpy的Matlab的单元阵列。
Matlab的code会去这样的：

I'm trying to implement an analouge to Matlab's cell array in python \ numpy. The Matlab code would go like that :

for n=1:10
    C{n} = rand(1,n);
end

请注意，每个电池元件具有不同的长度。现在，在numpy的：

Note that each cell element has a different length. Now in numpy:

for n in np.arange(10):
    C[n] = np.random.rand(1,n)

和我得到一个错误，我能做些什么来实现呢？

and I get an error, what can I do to implement this?

推荐答案

在最简单的情况下，你可以只使用一个标准的Python列表。它们类似于Matlab的电池阵列pretty，你可以用它们来存储任何东西：

In most simple cases, you could just use a standard Python list. They are pretty similar to Matlab cell-arrays, you can use them to store anything:

C = []
for n in np.arange(10):
    C.append(np.random.rand(1,n))

这将是一个很好的选择，如果该列表不是太长，如果它只有一个尺寸（所以只是一个载体）。请注意，在Python中，你通常不会pre-分配的最终尺寸的列表，而只是追加到一个空列表。 Python列表是为追加到最后优化，他们已经做的引擎盖下的某种pre-分配。

This would be a good option if the list is not too long and if it only has a single dimension (so just a vector). Note that in Python, you typically do not pre-allocate a list of the final size, but simply append to an empty list. Python lists are optimized for appending to the end, they already do some sort of pre-allocation under the hood.

如果你正在翻译Matlab的code具有多层面的细胞阵列，或者那些都是非常大的，你可以使用numpy的阵列， DTYPE =对象：

If you are translating Matlab code with cell-arrays of multiple dimensions, or ones that are very large, you could use numpy arrays with dtype=object:

m, n = 3, 6
C = np.empty((m, n), dtype=object)
for i in xrange(m):
    for j in xrange(n):
        C[i, j] = np.random.rand(i, j)

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