Question

Numpy汇总了大arrays，在进行交互式会话时很方便。不幸的是，默认情况下，结构化的arrays和recarrays总结得不是很好。有办法改变吗？

默认情况下，如果有1000个或更少的项目，则显示完整的array。如果项目多于此，则array会被汇总。可以使用np.set_printoptions(threshold=<number of items to trigger summarization>, edgeitems=<number of items to show in summary>)进行设置。对于标准数据类型，这可以很好地工作，例如：

np.set_printoptions(threshold=3, edgeitems=1)
print(np.zeros(3))
print(np.zeros(4))

产生

[ 0.  0.  0.]
[ 0. ...,  0.]

但是，当使用更复杂的数据类型时，汇总没有那么有用

print(np.zeros(4, dtype=[('test', 'i4', 3)]))
print(np.zeros(4, dtype=[('test', 'i4', 4)]))

[([0, 0, 0],) ..., ([0, 0, 0],)]
[([0, 0, 0, 0],) ..., ([0, 0, 0, 0],)]

该数组已汇总，但子数据类型未汇总。对于使用复杂数据类型的大型arrays来说，这将成为一个问题。例如array np.zeros(1000, dtype=[('a', float, 3000), ('b', float, 10000)])挂断了我的ipython实例。

有两种解决方法，而不是直接使用np.array类型，可以继承并编写自定义__repr__。这将适用于大型项目，但不能解决根本问题，并且不便于在交互式python会话中快速浏览数据。我还在编辑器中实现了一个自定义过滤器，该过滤器会截断很长的控制台输出。这有点hack，当我在其他地方启动python会话时无济于事。

是否存在我不知道的numpy设置，或者可以解决此问题的python或ipython设置？

Answer 1

这是我想出的一种解决方法，可以合理地打印记录数组。

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更改结构化数组和重新数组的打印方式

1 个答案: