Random Number Generator using Numpy. Numpy's random module, a suite of functions based on pseudorandom number generation. Random means something that can not be predicted logically.
Numpy's random module, a suite of functions based on pseudorandom number generation. Random means something that can not be predicted logically.
In this example, you will simulate a coin flip. You will use the function
np.random(), which draws a number between 0 and 1 such that all numbers in this interval are equally likely to occur.
If the number you draw is less than 0.5, which has a 50% chance of happening, you say heads and tails otherwise. This type of result where results are either True (Heads) or False (Tails) is referred to as Bernoulli trial.
The pseudorandom number works by starting with an integer called a seed and then generates numbers in succession. The same seed gives the same sequence of random numbers, hence the name "pseudo" random number generation. If you want to have reproducible code, it is good to seed the random number generator using the
To do the coin flips, you import
NumPy, seed the random number generator, and then draw four random numbers. You can specify how many random numbers you want with the
import numpy as np np.random.seed(42) random_numbers = np.random.random(size=4) random_numbers
array([0.3745012, 0.95071431, 0.73199394, 0.59865848])
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