Np.random.seed

import numpy as np
np.random.seed(42)
random_numbers = np.random.random(size=4)
random_numbers

Source: datacamp.com

Np random seed

>>> import numpy as np
>>> 
>>> np.random.seed(0)
>>> 
>>> np.random.rand(3)
array([0.5488135 , 0.71518937, 0.60276338])
>>> np.random.rand(3)
array([0.54488318, 0.4236548 , 0.64589411])
>>> 
>>> 
>>> np.random.seed(1)
>>> 
>>> np.random.rand(3)
array([4.17022005e-01, 7.20324493e-01, 1.14374817e-04])
>>> np.random.rand(3)
array([0.30233257, 0.14675589, 0.09233859])
>>> 
>>> 
>>> np.random.seed(0)
>>> 
>>> np.random.rand(3)
array([0.5488135 , 0.71518937, 0.60276338])
>>> np.random.rand(3)
array([0.54488318, 0.4236548 , 0.64589411])

Source: pyprog.pro

Does np.random.randint have a seed

np.random.seed(0)
np.random.randint(10, size = 5)

Source: sharpsightlabs.com

Numpy random normal seed time

>>> numpy.random.seed(0) ; numpy.random.rand(4)
array([ 0.55,  0.72,  0.6 ,  0.54])
>>> numpy.random.seed(0) ; numpy.random.rand(4)
array([ 0.55,  0.72,  0.6 ,  0.54])

Source: stackoverflow.com

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