Python random.uniform() Function: Usage and Examples

Generate a uniformly distributed floating-point value between two bounds.

What It Does and Why It Matters

random.uniform(a, b) returns a pseudorandom float in the interval between the supplied endpoints. Because of floating-point rounding, the upper endpoint may be included; do not rely on exact endpoint exclusion. This is not a cryptographic generator.

Use randint() for integer bounds; use secrets for security-sensitive randomness.

Pseudorandom examples are suitable for simulation and tests. Use secrets instead for tokens, passwords and authentication.

Step-by-Step Example

Start with the complete example below. Read the inputs and predict what the final print statement will show before you run it.

import random
number_generator = random.Random(12)
selected_value = number_generator.uniform(2.5, 5.5)
print(2.5 <= selected_value <= 5.5)

How the Code Works

The imports make these standard-library tools available: random.
The name number_generator receives the value of random.Random(12). Changing this input lets you explore a different case.
The print calls display the results. Compare these results with the method’s documented return type and the input used.
The dedicated Random instance isolates the generator state from unrelated random calls. A fixed seed is useful here for repeatable demonstrations but must not be reused to generate secrets.

Expected output:

True

For the demonstrated inputs, Python displays 'True'. This result follows from the operation described above; change one input at a time to understand which rule matters.

What to Notice

Pay attention to the accepted input types, the returned value and the boundary behavior. Predict what changes if you replace the example input with an empty, zero, negative or missing value, where that value is meaningful for this operation.

If the task uses a library function, inspect its documented exceptions and return type before reusing it inside a larger function.

Where You Would Use This

Use this in calculations, data summaries, simulations or validation when the numerical assumptions match the task. Check input domains, units and precision before interpreting a result. A single example output does not prove a statistical conclusion.

Practice Exercise

Generate ten measurements between 15.0 and 18.0 and check each value lies in the interval.

Before running your edited example, write down the result you expect. Then test at least one valid boundary or deliberately invalid value, using appropriate exception handling where necessary.

Reference: official Python documentation for the complete API contract and version notes.

Common Beginner Mistakes

Treating uniform() as an integer generator or using it to produce authentication codes.
Overlooking the documented condition: because of floating-point rounding, the upper endpoint may be included; do not rely on exact endpoint exclusion.

Key Takeaways

Generate a uniformly distributed floating-point value between two bounds.
random.uniform(a, b) returns a pseudorandom float in the interval between the supplied endpoints.
Generate ten measurements between 15.0 and 18.0 and check each value lies in the interval.
Pseudorandom examples are suitable for simulation and tests. Use secrets instead for tokens, passwords and authentication.