To make your analyses reliably repeatable, you need to develop habits that prevent the common pitfalls of ad-hoc, one-off work. The first step is to use versioned data whenever possible. This means storing copies of input datasets with clear version numbers or timestamps, or using external sources that provide versioned releases. By tying your analysis to a specific data version, you ensure that rerunning your code in the future will yield the same results, even if the original data changes.

Next, always document your environment requirements. This includes the exact versions of Python and any libraries your code depends on. A simple way to do this is by maintaining a requirements.txt file or noting the output of pip freeze in your project documentation. This practice helps others (and your future self) set up an identical environment, minimizing surprises from library updates or incompatibilities.

Finally, avoid hidden state in notebooks. Hidden state occurs when the output of a cell depends on code or variables defined in previous, possibly unexecuted, cells. This can lead to confusing errors or inconsistent results. To prevent this, structure your notebook so that it can be run from top to bottom without manual intervention, and make sure all necessary variables and imports are defined in the correct order. Restarting the kernel and running all cells is a good way to check for hidden dependencies.