Guidance Needed for Building a Custom Scientific Workflow Using Scientific Python Stack

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Hello everyone,

I’m currently in the early stages of designing a scientific workflow to automate the processing and visualization of experimental data coming from lab instruments (primarily CSVs and HDF5 files). My primary objective is to create a robust, modular, and reproducible pipeline using tools from the scientific Python ecosystem. However, I’m trying to narrow down the most effective approach and would appreciate guidance from those more experienced.

Right now, I’m working with pandas for initial data manipulation and matplotlib/seaborn for visualization, but I’m unsure if these are the best tools in terms of scalability and long-term flexibility. I’ve also explored xarray, which seems promising for handling multi-dimensional datasets, especially those with labeled axes, but I’m not yet confident in how it fits into my pipeline.

Additionally, I’d like to incorporate some level of workflow orchestration and possibly integrate with dask for parallelization if the dataset size grows. My final goal is to have a script or notebook that can be adapted to different datasets without too much manual rewriting.

Has anyone here built a similar system or can share insights on how to structure such a workflow using the Scientific Python stack? Any advice on library choices, best practices for reproducibility (e.g., conda, poetry, or pipenv for environments), and general architecture would be incredibly helpful.

Thanks in advance!

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tl;dr Don’t worry. You’ve got this.

They’re all fine packages. It’s possible that as your data grows, you may hit a bottleneck, but the first time that happens is usually the right time to think about future-proofing for data size. Until you hit a bottleneck, you usually don’t have enough information to project what your future needs are really going to be. Think one step ahead but not ten steps.

You will rewrite significant chunks of this a couple of times as your needs evolve; that’s expected, and even the most experienced of us would do these rewrites anyways, no matter how good our guessing powers are. To be honest, our experience mostly leads us to architect our code for easier change later rather than sidestepping the need for change. E.g. if you think plotting might be your bottleneck, make sure you keep that code relatively isolated so that you don’t have matplotlib baked in everywhere through your pipeline; that’ll make switching it out easier.

Prioritize being aware of alternative technology choices as you go along over making the future-optimal choice at the start. If pandas starts to chafe, be aware of what polars or DuckDB might be be able to do for you and think about how their functionality might solve the discomfort. If matplotlib struggles to keep up with the number of points you are plotting, consider if bokeh or vispy can do the visualizations you want, better. Use the technologies you know at first and learn new ones when you feel the pinch, but not before then (unless if you want to learn for its own sake).

I will offer a couple of technology recommendations, though:

Try uv for Python environment management. If you have very complicated non-Python build needs that you can’t offload to Linux package management, then think about conda.

Convert CSVs to Parquet as soon as possible in your workflow. There’s a lot to be said about the possibility of data corruption as CSVs get roundtripped and compression and I/O performance, but for me the main thing is just not having to have column parsing information spread all over my code in pandas.read_csv() arguments. I do it just the once on conversion to Parquet, then I’m free to do a bare pandas.read_parquet(filename) everywhere else.

If you are expecting some amount of rewrites to the logic as your research progresses (e.g. the data is structured differently because your experimental design changes to, say, compare two alternatives against a control instead of one against a control, stuff like that), keeping a lot of that higher-level logic in a Jupyter notebook for future modification works well. Consider papermill for parameterizing notebooks for when you only need to pass configuration instead of logic. marimo is a newer notebook alternative that you might consider for similar reasons, but I’ve mostly used it for its interactive capabilities and haven’t exercised its notebook-as-a-script capabilities.

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and if you want the nice-ities of uv as well as the ability to work with non-Python packages, check out Pixi. Feel free to ping me @lucascolley for any help with setting up Pixi.

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