Google Colab sessions are temporary environments. Packages installed in one runtime may disappear after a reset, and preinstalled libraries can conflict with the versions a notebook expects. The first diagnostic step is to print the Python and package versions instead of guessing.
Install dependencies in a clearly marked setup cell and restart the runtime when the package requires it. After restarting, rerun setup and verify imports. Avoid upgrading large groups of packages without a reason; a broad upgrade can break preinstalled components. Pin versions when a notebook must be reproducible.
Colab is excellent for experimentation, but it is not automatically a production environment. Record the runtime assumptions, dataset access method, and dependency versions. Never place long-lived credentials in notebook cells. A clean setup section and a verification section turn a fragile notebook into a repeatable experiment.