np.random.seed meaning code example

Example 1: np.random.seed

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

Example 2: 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])

Example 3: set seed numpy

numpy.random.seed()

Example 4: does np.random.randint have a seed

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

Example 5: np.random.seed

array([0.3745012, 0.95071431, 0.73199394, 0.59865848])