When you hear about reproducibility in data science, you might first think of setting random seeds to ensure that results can be repeated. While this is important, true reproducibility is much broader and goes far beyond controlling randomness. Reproducibility means that someone else — or even you in the future — can rerun your analysis and obtain the same results, using the same data, code, environment, and process.

First, consider the role of your data. If your dataset changes, or if you do not specify precisely which data was used, others cannot replicate your findings. This means you need to record the exact version of the data, including any preprocessing steps or filters applied. Next, your code must be complete, readable, and version-controlled. Any analysis should be accompanied by scripts or notebooks that contain all the logic, not just summary outputs or final results.

The environment in which your code runs is also crucial. Different versions of Python or libraries can lead to subtle differences in results, especially with numerical computations. You must document every dependency, including library versions, and ideally provide a way to recreate the environment — such as a requirements file or environment specification.

Finally, the process matters. Document the sequence of steps: how you went from raw data to final results, any manual interventions, and all parameters or settings used. This holistic approach ensures that your work is not just repeatable by accident, but intentionally reproducible by design.