Master Python’s random Module and NumPy: 7 Ways to Generate Random Numbers

Random numbers are widely used, such as adding salt to passwords or in Monte Carlo simulations.

Python’s built‑in random module provides methods for generating random numbers; you need to import it first. import random Below are several common functions in the random module.

1. random.random()

Generates a floating‑point number in the interval [0.0, 1.0). The value can be 0 but never 1.

print("random:", random.random())

2. random.randint(a, b)

Returns an integer N such that a ≤ N ≤ b. For a half‑open interval [a, b) use random.randrange().

print("randint:", random.randint(6, 8))

3. random.randrange(start, stop, step)

Returns a randomly selected element from the range(start, stop, step), i.e., start ≤ N < stop.

print("randrange:", random.randrange(20, 100, 5))

4. random.uniform(a, b)

Generates a floating‑point number N such that a ≤ N ≤ b.

print("uniform:", random.uniform(5, 10))

5. random.choice(seq)

Randomly selects an element from a non‑empty sequence (list, tuple, string, etc.). Raises IndexError if the sequence is empty.

print("choice:", random.choice("www.yuanxiao.net"))

6. random.shuffle(seq)

Shuffles the sequence in place. To keep the original sequence, copy it first (e.g., using the copy module).

num = [1, 2, 3, 4, 5]
random.shuffle(num)
print("shuffle:", num)

7. random.sample(seq, n)

Returns a list of n unique elements chosen from the sequence.

num = [1, 2, 3, 4, 5]
print("sample:", random.sample(num, 3))

The numbers produced by random are pseudo‑random; they are generated by deterministic algorithms seeded by a value. Using the same seed reproduces the same sequence, while omitting the seed defaults to the current system time.

random.seed(2)
print("random:", random.random())
random.seed(3)
print("random:", random.random())

NumPy also offers a random submodule for generating multi‑dimensional arrays of random numbers.

import numpy as np

1. numpy.random.rand(d0, d1, …, dn)

Generates an array of shape (d0, d1, …, dn) with values drawn from a uniform distribution over [0, 1).

print("np.random.rand:", np.random.rand(4, 2))

2. numpy.random.randn(d0, d1, …, dn)

Returns samples from the standard normal distribution (mean 0, standard deviation 1).

print("np.random.randn:", np.random.randn())
print("np.random.randn:", np.random.randn(2, 4))

3. numpy.random.randint(low, high=None, size=None, dtype='l')

Returns random integers from the interval [low, high) (low inclusive, high exclusive). If high is omitted, the range is [0, low).

print("np.random.randint:", np.random.randint(1, size=5))
print("np.random.randint:", np.random.randint(1, 5))
print("np.random.randint:", np.random.randint(-5, 5, size=(2,2)))

4. numpy.random.seed()

Sets the seed for NumPy’s random generator, making the generated data reproducible. Without setting a seed, each run produces different numbers.

Source: https://juejin.im/post/5cefccb0e51d455d850d3a85