从 numpy 数组中删除 None 的有效方法 [英] efficient way of removing None's from numpy array

问题描述

是否有一种有效的方法可以从 numpy 数组中删除 Nones 并将数组大小调整为新的大小?

Is there an efficient way to remove Nones from numpy arrays and resize the array to its new size?

例如，您将如何从该帧中删除 None 而无需在 Python 中对其进行迭代.我可以轻松地遍历它，但正在处理可能被多次调用的 api 调用.

For example, how would you remove the None from this frame without iterating through it in python. I can easily iterate through it but was working on an api call that would be potentially called many times.

a = np.array([1,45,23,23,1234,3432,-1232,-34,233,None])

推荐答案

In [17]: a[a != np.array(None)]
Out[17]: array([1, 45, 23, 23, 1234, 3432, -1232, -34, 233], dtype=object)

上述工作是因为 a != np.array(None) 是一个布尔数组，它映射出非 None 值:

The above works because a != np.array(None) is a boolean array which maps out non-None values:

In [20]: a != np.array(None)
Out[20]: array([ True,  True,  True,  True,  True,  True,  True,  True,  True, False], dtype=bool)

以这种方式选择数组的元素称为 布尔数组索引.

Selecting elements of an array in this manner is called boolean array indexing.

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