Python Random random() Method

Usage

The random.random() method is used to generate a random floating-point number between 0.0 (included) and 1.0 (not included).

Syntax

random.random()

Parameters

None. The method doesn’t accept any parameters.

Module Import

To use random.random(), you must first import the random module.

import random

Examples

Once the module is imported, you can simply call the function to generate a random number.

import random

print(random.random())     # Possible output: 0.58297449444842
print(random.random())     # Possible output: 0.43351443489540376
print(random.random())     # Possible output: 0.8000204571334277

The random.random() method can often be used as a building block for more specific random generation needs. For example, to get a random float number between any two values a and b, you could write a function like this:

import random

def random_float(a, b):
    return a + (b - a) * random.random()

print(random_float(5, 15))
# Possible output: 8.493737608123952

Or to generate a random integer within a range, you can use the following function:

import random

def random_integer(a, b):
    return a + int((b - a + 1) * random.random())

print(random_integer(5, 15))
# Possible output: 8

Controlling Random Number Generation with a Seed

The random.random() method generates pseudo-random floating-point numbers. While they seem random, these numbers are produced by a deterministic algorithm that starts with an initial value called a “seed.” This means if you use the same seed, the sequence of generated “random” numbers will be identical each time.

Let’s see how you can control this randomness by setting a seed using the random.seed() method:

import random

# Set seed for repeatability
random.seed(100)

# Print 5 random numbers between 0 and 1
for _ in range(5):
    print(random.random())

# Possible output:
# 0.1456692551041303
# 0.45492700451402135
# 0.7707838056590222
# 0.705513226934028
# 0.7319589730332557

If you run this block of code again, you will get the same result.

Setting consistent seeds guarantees predictable results, which is advantageous when debugging or when you want reproducible results.