np.fromiter(range(5),int) is faster, but still not as good as the direct np.arange. np.array(range(5)), that is relatively slow. While it is possible to make an array from a range, e.g. I often use arange to create a example array, as in: In : np.arange(12).reshape(3,4) If you need an array, use arange (or np.linspace as suggested by the docs). The simple answer is - if you are doing python level iteration, range is usually better. The best "definition" is the official documentation page:īut maybe you are wondering when to use one or the other. In p圓, range by itself is "unevaluated", it's generator like. From the official arange docs:įor integer arguments the function is roughly equivalent to the Python built-in range, but returns an ndarray rather than a range instance. arange is a function that returns a numpy array that is similar, at least in simple cases, to the list produced by list(range(.)). Naming the function arrayrange was not chosen because it's too long to type.įrom the previous SO we learn that the 'a' stands, in some sense, for 'array'.Some people do from numpy import * which would shadow range which causes problems.I have tried numpy's arrange method and search through stack overflow. Here's a related question: Why was the name "arange" chosen for the numpy function? I would like to automatically array the matrices in a numpy array from this: ( 9,2,3, 4,5,6, 7,0,5, 3,4,2, 1,2,3, 4,7,8) so that all the matrices of an array go on a single horizontal line and the matrices of the next array also occupy the next horizontal line. You can inspect the return types and reason about what it could mean that way: print(type(range(0,5)))
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