Generate random numbers in Python — integers, floats, secure

Use the standard library’s random module for simulations and general-purpose randomness, and switch to the secrets module when you need cryptographic strength. The steps below show reliable ways to produce integers, floats, secure values, and command-line outputs.

Method 1: Generate a random integer (general-purpose)

import random

n = random.randint(0, 100)  # 0 ≤ n ≤ 100
print(n)

This produces uniformly distributed integers over the closed interval you specify. For stride/step logic (for example, even numbers only), use random.randrange(start, stop, step).

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Method 2: Generate a random float

import random

x = random.random()  # 0.0 ≤ x < 1.0
print(x)

y = random.uniform(2.5, 10.0)  # a ≤ y ≤ b (end inclusion may vary due to floating-point rounding)
print(y)

Use random.random() for quick normalized values, and random.uniform() when you need a specific numeric span.

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Method 3: Generate a cryptographically secure random integer

import secrets

token = secrets.randbelow(10)  # 0..9, suitable for security-sensitive uses
print(token)

Use secrets for passwords, tokens, or any security-related randomness. The random module is deterministic by design and is not suitable for cryptographic purposes.

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Method 4: Reproduce results by seeding

import random

random.seed(42)

print(random.randint(0, 100))
print(random.random())

Seeding stabilizes your outputs for testing and demonstrations. Accepted seed types include None, int, float, str, bytes, and bytearray.

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Method 5: Pick random items from a sequence

import random

colors = ["red", "green", "blue"]
print(random.choice(colors))

print(random.choices(colors, k=5))

print(random.sample(colors, k=2))

Use weights (via choices(..., weights=[...])) when you need non-uniform probabilities.

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Method 6: Use the Python 3.13+ command-line interface

python -m random 10
# or
python -m random --integer 10

python -m random --float 1.8

python -m random --choice egg bacon sausage spam

This built-in CLI streamlines quick draws without writing a script and is available starting in Python 3.13.

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Method 7 (optional): Generate many random numbers efficiently (NumPy)

import numpy as np

# 15 integers in [0, 10)
ints = np.random.randint(0, 10, size=15)

# 5 floats in [0.0, 1.0)
floats = np.random.random(5)

print(ints)
print(floats)

NumPy’s random routines generate large batches quickly and integrate well with scientific and data workflows.

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Tips and cautions

• For security-sensitive randomness (passwords, tokens), always use secrets, not random.
• random.random() is half-open: it never returns 1.0.
• random.uniform(a, b) computes via floating-point arithmetic—due to rounding, the endpoint b may or may not appear.
• Seed the generator with random.seed() to make tests reproducible.

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With these approaches, you can generate integers, floats, reproducible sequences, secure tokens, and even command-line outputs efficiently and correctly.