Random numbers and sampling.
✍️ 3 exercises + code challenge in this lessonCreate a random generator with rng = np.random.default_rng(seed). A fixed seed gives the same numbers every time, which is useful for testing.
rng.integers(low, high, size)rng.random(size): floats from 0 to 1rng.choice(list), rng.shuffle(arr), rng.normal(mean, std, size)Mastering NumPy Random helps you write clean and reliable NumPy code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
import numpy as np
rng = np.random.default_rng(42)
print(rng.integers(1, 7, size=5))
print(rng.choice(["Ali", "Sara", "Usman"]))
values = rng.normal(50, 10, size=1000)
print(round(values.mean()))[1 5 4 3 3] Usman 50
Check what you learned in “NumPy Random”.
Create a random generator with a fixed seed.
rng = np.random.(42)
Why set a seed?
Create rng with seed 0, generate 5 integers from 1 to 10 with rng.integers(1, 11, size=5), and print how many values there are.
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