Arrays - random number generating

Random Data in NumPy

NumPy provides several functions in numpy.random to generate arrays with random numbers from different distributions.

Uniform Distribution (0 to 1)

np.random.rand(2, 2)

• Generates numbers in the range [0, 1).
• Uniformly distributed.
• Shape defined by arguments.

Example:

np.random.rand(2, 2)# → [[0.65 0.12]#    [0.44 0.89]]

Standard Normal Distribution

np.random.randn(8, 2)

• Generates values from a standard normal distribution (mean = 0, std = 1).
• Useful for simulations and ML initialisation.

Example:

np.random.randn(3, 3)# → [[ 0.12 -0.89  0.44]#    [ 1.45  0.33 -0.55]#    [-0.23  0.78 -1.09]]

Random Integers

np.random.randint(low=250, high=888, size=(4, 3))

• Generates random integers between low (inclusive) and high (exclusive).
• Shape defined by size.

Example:

np.random.randint(10, 20, size=(2, 5))# → [[14 18 12 11 17]#    [19 10 15 12 13]]

Random Sample from [0, 1)

np.random.random(size=(3, 2))

• Similar to rand(), but takes only size as a tuple.

Example:

np.random.random((3, 2))# → [[0.42 0.73]#    [0.89 0.15]#    [0.65 0.22]]

Random Choice

np.random.choice([1, 5, 9], size=(2, 3))

• Randomly selects values from a given list/array.
• Useful for categorical or discrete sampling.

Example:

np.random.choice([10, 20, 30], size=5)# → [30 20 10 20 30]

Uniform Distribution

np.random.uniform(low=5, high=15, size=(3, 3))

• Random floats between low and high.
• Uniform distribution.

Example:

np.random.uniform(5, 10, size=(2, 4))# → [[5.77 6.12 9.45 7.01]#    [8.22 9.88 5.66 6.99]]

Normal Distribution (custom mean & std)

np.random.normal(loc=50, scale=10, size=(3, 3))

• Random floats from a normal distribution with:
• loc = mean
• scale = standard deviation

Example:

np.random.normal(0, 1, size=5)# → [-0.23 0.87 -1.12 0.44 0.19]

Seeding Random Numbers

Random numbers in NumPy are pseudo-random (deterministic, generated by an algorithm).

np.random.seed(1)arr = np.random.randint(10, 50, size=5)print(arr)# → [37 12 29 37 18]   (always the same with seed=1)

• np.random.seed(seed) fixes the sequence → ensures reproducibility.
• If you set the same seed, you’ll always get the same "random" output.
• Useful for debugging, testing, and reproducible research.

How np.random.seed(seed) Works:

• All the np.random functions above (rand, randn, randint, random, uniform, normal, choice, etc.) pull numbers from the same underlying random number generator.
• When you call np.random.seed(seed), you reset that generator to a fixed state.
• That means:
• Using the same seed → you’ll get the same sequence of random numbers.
• Changing the seed → produces a different sequence.
• Resetting to a previous seed → reproduces the exact same values again.

Example:

import numpy as np​# Seed = 20np.random.seed(20)print(np.random.randint(250, 888, size=(4, 3)))# → (matrix A)​# Seed = 10np.random.seed(10)print(np.random.randint(250, 888, size=(4, 3)))# → (matrix B, different from A)​# Seed = 10 againnp.random.seed(10)print(np.random.randint(250, 888, size=(4, 3)))# → same as (matrix B)