By voting up you can indicate which examples are most useful and appropriate. You can also use create a uniqe array from your lists using numpy.array() instead of using a list comprehension: > a = np. Here are the examples of the python api taken from open source projects. If an int, the random sample is generated as if a was np. If an ndarray, a random sample is generated from its elements. You need to convert your list to numpy arrays with with type object(), so that random.permutation() can interpret the lists as numpy types rather than sequence: > a = Īrray(], Generates a random sample from a given 1-D array. Numba supports top-level functions from the numpy.random module. For instance, if I pick 5 columns every simulation, I would expect to one of the columns to be repeated at least. I am not sure if I fully understood your answer, but basically what I need is the function to work with replacement. So the np.random.shuffle() solution is about 10x faster than np.random.permutation() and 2x faster than random.shuffle(). From the documentation examples it is clear that doesnt replace from your selection set. I checked the three answers given with: import timeī = np.random.permutation() If your array is multi-dimensional, np.random.permutation permutes along the first axis (columns) by default: > np.random.permutation (arr) array ( 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 12, 13, 14, 15) However, this shuffles the row indices and so each column has the same (random) ordering. Shuffle means changing arrangement of elements in-place. Unlike many other numpy/random functions, () doesnt provide an obvious way to return multiple results in a single function call. I need to generate a single random permutation, I'm not trying to obtain all the possible permutations. The NumPy Random module provides two methods for this: shuffle() and permutation(). Is there a built in function that will allow me to generate such permutation? This will fail with: ValueError: cannot set an array element with a sequence If youre using numpy just for this, skip numpy altogether instead and just use random. I'm trying to permute a list composed of sublists with mixed-type elements: import numpy as npĪ0 = ]Ī1 = ]Ī2 = ]Ī3 = ] randomindices np.random.permutation(np.arange(len(a))) > aperm ai for i in randomindices.
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