Introduction

Generating a random float in a chosen interval is usually easy, but the phrase "included into a specified interval" hides an important detail: floating-point ranges are discrete, not truly continuous. In practice, the best approach is to map a uniform random value from the base range into your target interval and accept that endpoint behavior depends on the API and floating-point representation.

The Standard Scaling Formula

The common formula is:

min_value + random_unit * (max_value - min_value)

where random_unit is a pseudo-random number in the unit interval.

In Python, the simplest solution is random.uniform:

1import random
2
3value = random.uniform(1.5, 3.5)
4print(value)

This is usually the best answer for one-off random floats because it is clear, tested, and already expresses the interval directly.

Manual Construction Looks Like This

If you want to see the scaling explicitly:

1import random
2
3def random_float(min_value: float, max_value: float) -> float:
4    return min_value + random.random() * (max_value - min_value)
5
6
7print(random_float(1.5, 3.5))

random.random() returns a float in the unit interval starting at 0.0, and scaling shifts that into the target range.

This is mathematically equivalent to random.uniform(min_value, max_value) for most practical purposes.

About Inclusive Endpoints

This is where people often overstate precision. With floating-point numbers, "include both endpoints exactly" is not as clean as it sounds. Most APIs describe behavior in terms of a range such as:

• lower bound included
• upper bound typically included or reachable only because of rounding

In real programs, the important question is usually not whether the exact upper endpoint appears with perfectly defined probability. The important question is whether the result stays inside the interval you asked for.

For general-purpose code, random.uniform(a, b) is appropriate when you want a practical random float between a and b.

Generating Many Values Efficiently

If you need a large vector of random floats, NumPy is the better tool.

1import numpy as np
2
3values = np.random.uniform(1.5, 3.5, size=5)
4print(values)

This is faster and more convenient for numerical workloads than calling Python's random.uniform in a loop.

Validate the Interval

A small helper function should guard against inverted bounds or ambiguous input.

1import random
2
3def random_float(min_value: float, max_value: float) -> float:
4    if min_value > max_value:
5        raise ValueError("min_value must be less than or equal to max_value")
6    return random.uniform(min_value, max_value)
7
8
9print(random_float(-2.0, 2.0))

This makes the function safer to reuse, especially in applications where bounds are user-supplied.

Use Cryptographic Randomness Only When Needed

For simulations, games, and sampling tasks, standard pseudo-random generators are usually fine. For security-sensitive values such as tokens or secrets, use a cryptographically secure generator instead.

In Python that means secrets for security tasks:

1import secrets
2
3def secure_random_float(min_value: float, max_value: float) -> float:
4    unit = secrets.randbits(53) / (1 << 53)
5    return min_value + unit * (max_value - min_value)

This is not the usual answer for scientific or ordinary application randomness, but it matters when unpredictability is a requirement.

Common Pitfalls

The biggest mistake is assuming floats form a perfectly continuous interval. They do not. Floating-point values are discrete machine representations, so endpoint discussions are always mediated by how the API generates and rounds those values.

Another issue is reimplementing the scaling formula incorrectly, such as forgetting parentheses and writing min + random * max - min, which changes the interval completely.

People also sometimes use integer-based APIs and divide afterward without thinking about distribution quality. That can work, but it is often less clear and no better than the standard float-oriented API.

Finally, do not use the normal pseudo-random generator for secrets just because it is convenient. Security and simulation randomness are different requirements.

Summary

• The standard way to generate a random float in an interval is to scale a unit random value into the target range.
• In Python, random.uniform(a, b) is usually the clearest solution.
• NumPy is a better choice when you need many random floats efficiently.
• Be realistic about endpoint inclusivity because floating-point ranges are discrete.
• Use cryptographic randomness only when the problem is security-sensitive.