NumPy中多轴均值 [英] Mean over multiple axis in NumPy

问题描述

我想以Python的方式编写以下代码，在两个轴上应用均值.最好的方法是什么?

I Want to write the code below as Pythonic way, applying mean over two axis. What the best way to do this?

    import numpy as np

    m = np.random.rand(30, 10, 10)  

    m_mean = np.zeros((30, 1))

        for j in range(30):

            m_mean[j, 0] = m[j, :, :].mean()

推荐答案

如果您有足够新的NumPy，则可以

If you have a sufficiently recent NumPy, you can do

m_mean = m.mean(axis=(1, 2))

我不确定这是在1.7中引入的，尽管我不确定.该文档仅在1.10中进行了更新，以反映这一点，但它早于该版本.

I believe this was introduced in 1.7, though I'm not sure. The documentation was only updated to reflect this in 1.10, but it worked earlier than that.

如果您的NumPy年纪太大，则可以手动进行平均:

If your NumPy is too old, you can take the mean a bit more manually:

m_mean = m.sum(axis=2).sum(axis=1) / np.prod(m.shape[1:3])

这些都将产生一维结果.如果您确实想要那条额外的长度为1的轴，则可以执行m_mean = m_mean[:, np.newaxis]之类的操作来将多余的轴放置在那里.

These will both produce 1-dimensional results. If you really want that extra length-1 axis, you can do something like m_mean = m_mean[:, np.newaxis] to put the extra axis there.

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