Make a simulation repeatable by using a dedicated seeded random number generator.
What You Will Learn
For reproducible experiments, use random.Random(seed) so a simulation has its own state and does not disturb application-wide random calls. A seed is useful for debugging and comparisons, not for cryptographic security. Record the simulation assumptions along with the seed so results can be interpreted.
For floating-point results, remember that stored values are approximations. Rounding a displayed value does not change the exact value used by later calculations.
Step-by-Step Example
The example below makes the behavior visible with small input values that you can change safely.
import random
def simulate(seed):
generator = random.Random(seed)
return [generator.randint(1, 6) for _ in range(5)]
print(simulate(42))
print(simulate(42))
print(simulate(7))How the Example Works
- The imports provide the standard-library tools used in the demonstration: random.
- The function simulate defines reusable behavior; its body executes when it is called, not when it is declared.
- The print() calls make the return values or state visible so you can compare them with the explanation and check your prediction.
Expected output from the example:
[6, 1, 1, 6, 3]
[6, 1, 1, 6, 3]
[3, 2, 4, 6, 1]When You Would Use This
This operation can support measurements, numeric reports, simulations, or input validation. Choose integer operations when exact integer answers are required, and check floating-point rounding when displaying measured values. Validate units and input domains before using the result in a calculation.
Practice: Modify the Code
Run 100 simulated dice rolls, then compute the average result with statistics.mean.
Verify the changed output against the behavior described above, including the relevant invalid or boundary case.
For complete parameters and version details, consult the official Python documentation.
Common Beginner Mistakes
- Using random for passwords or authentication tokens.
- Changing both the model and the seed between tests and then attributing differences to one cause.
Key Takeaways
- Make a simulation repeatable by using a dedicated seeded random number generator.
- For reproducible experiments, use random.Random(seed) so a simulation has its own state and does not disturb application-wide random calls.
- Apply this operation only after checking the assumptions and input conditions shown in the example.