vefcr.blogg.se - Numpy random permutation

#Numpy random permutation how to#
#Numpy random permutation pdf#
#Numpy random permutation update#
#Numpy random permutation series#

how to write a function shuffle(df, n, axis=0) that takes a dataframe, a number of shuffles n, and an axis ( axis=0 is rows, axis=1 is columns) and returns a copy of the dataframe that has been shuffled n times.Įdit: key is to do this without destroying the row/column labels of the dataframe. Time performance of np.random.permutation, np.random.What's a simple and efficient way to shuffle a dataframe in pandas, by rows or by columns? I.e.Time and memory-efficient random sampling with python/numpy.Testing performance using %%timeit, loop speed or total time elapsed more important?.Speed up random weighted choice without replacement in python.Invert the random choice of keys in a numpy array.Numpy Random Choice not working for 2-dimentional list.

#Numpy random permutation series#

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Seems like one could use np.argsort for a marginal performance improvement. The original proposed one with NumPy - In : timeit.timeit( 'choice(138, 4)', setup='from numpy.random import choice', number=1000000) On NumPy with np.argsort - In : timeit.timeit( 'np.random.rand(138).argsort()', setup='import numpy as np', number=1000000)

On NumPy with np.argpartition - In : timeit.timeit( 'np.random.rand(138).argpartition(range(4))', setup='import numpy as np', number=1000000) Let's time these two versions for performance comparison against the MATLAB version. Or with np.argsort like so - np.random.rand(138).argsort() NumPy's np.random.choice(138,4, replace=False) with np.argpartition as : np.random.rand(138).argpartition(range(4)) (string vs lambda doesn't make a noticeable difference)īased on this solution that showed how one can simulate np.random.choice(., replace=False)'s behavior with a trick based on argsort/ argpartition, you can recreate MATLAB's randperm(138,4), i.e. That's about 22 times faster than the original: > timeit.timeit('permutation(138)', setup='from numpy.random import permutation', number=1000000)

#Numpy random permutation update#

Update 2: Since NumPy's random function appears to be much faster, I tried this and it's another factor ~1.33 faster: > def four(): Update 1: At first I used random.randrange, but np.random.randint made the whole thing about twice as fast. Return (a, b, c, d) if a != b and a != c and a != d and b != c and b != d and c != d else four() How long does this take for you? I estimate 1-2 seconds.

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#Numpy random permutation pdf#

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