random — Generate Random Numbers

import random

Basic Random Numbers

import random

random.random()           # Float in [0.0, 1.0)
random.uniform(1.0, 10.0) # Float in [1.0, 10.0]
random.randint(1, 100)    # Integer in [1, 100]
random.randrange(0, 100, 5) # Random from range(0, 100, 5)

Sequences

import random

items = ['apple', 'banana', 'cherry', 'date']

random.choice(items)              # Random element
random.choices(items, k=3)        # 3 random with replacement
random.sample(items, k=2)         # 2 random without replacement
random.shuffle(items)             # Shuffle in-place

Weighted Choice

import random

options = ['win', 'lose', 'draw']
weights = [10, 70, 20]  # 10%, 70%, 20%
result = random.choices(options, weights=weights, k=1000)

Reproducible Results

import random

random.seed(42)          # Set seed for reproducibility
print(random.random())   # Always same result with same seed

Distributions

random.gauss(0, 1)         # Gaussian (normal) distribution
random.expovariate(1.0)    # Exponential distribution
random.betavariate(2, 5)   # Beta distribution

Security Warning

For cryptographic purposes, use secrets module instead — random is NOT cryptographically secure!

import secrets
token = secrets.token_hex(16)     # Cryptographically secure
secure_int = secrets.randbelow(100)

Official Documentation

random — Generate pseudo-random numbers

API Reference

Core Functions

Function	Description
`random.seed(a=None, version=2)`	Initialize the random number generator.
`random.getstate()`	Return an object capturing the current internal state of the generator.
`random.setstate(state)`	Restore the internal state of the generator.

Functions for Integers

Function	Description
`random.randrange(start, stop[, step])`	Return a randomly selected element from `range(start, stop, step)`.
`random.randint(a, b)`	Return a random integer N such that `a <= N <= b`.

Functions for Sequences

Function	Description
`random.choice(seq)`	Return a random element from the non-empty sequence `seq`.
`random.choices(population, weights=None, *, k=1)`	Return a `k` sized list of elements chosen from the `population` with replacement.
`random.shuffle(x[, random])`	Shuffle the sequence `x` in place.
`random.sample(population, k)`	Return a `k` length list of unique elements chosen from the population sequence.

Real-valued Distributions

Function	Description
`random.random()`	Return the next random floating point number in the range [0.0, 1.0).
`random.uniform(a, b)`	Return a random floating point number N such that `a <= N <= b`.