How to create 2d array with numpy random.choice for every rows?

Here’s a vectorized approach with rand+argsort/argpartition trick from here –

np.random.rand(rows, 50).argpartition(6,axis=1)[:,:6]+1

Sample run –

In [41]: rows = 10

In [42]: np.random.rand(rows, 50).argpartition(6,axis=1)[:,:6]+1
Out[42]: 
array([[ 1,  9,  3, 26, 14, 44],
       [32, 20, 27, 13, 25, 45],
       [40, 12, 47, 16, 10, 29],
       [ 6, 36, 32, 16, 18,  4],
       [42, 46, 24,  9,  1, 31],
       [15, 25, 47, 42, 34, 24],
       [ 7, 16, 49, 31, 40, 20],
       [28, 17, 47, 36,  8, 44],
       [ 7, 42, 14,  4, 17, 35],
       [39, 19, 37,  7,  8, 36]])

Just to prove the random-ness –

In [56]: rows = 1000000

In [57]: out = np.random.rand(rows, 50).argpartition(6,axis=1)[:,:6]+1

In [58]: np.bincount(out.ravel())[1:]
Out[58]: 
array([120048, 120026, 119942, 119838, 119885, 119669, 119965, 119491,
       120280, 120108, 120293, 119399, 119917, 119974, 120195, 119796,
       119887, 119505, 120235, 119857, 119499, 120560, 119891, 119693,
       120081, 120369, 120011, 119714, 120218, 120581, 120111, 119867,
       119791, 120265, 120457, 120048, 119813, 119702, 120266, 120445,
       120016, 120190, 119576, 119737, 120153, 120215, 120144, 120196,
       120218, 119863])

Timings on one million rows of data –

In [43]: rows = 1000000

In [44]: %timeit np.random.rand(rows, 50).argpartition(6,axis=1)[:,:6]+1
1 loop, best of 3: 1.07 s per loop