Laboratory Notebook Skills [pdf]

I agree. From personal experience, here are some organizing principles I find useful for exploratory data analysis:

1. A single Jupyter notebook should either tackle a single question, or use collapsible headings (https://jupyter-contrib-nbextensions.readthedocs.io/en/lates...) to organize separate questions.

2. The Jupyter notebook should be written such that all cells are executable in order, and it should produce exactly the same output every time (unless the input has changed) for reproducibility. The entire notebook should be executable on the order of seconds - if it's taking longer, this is usually a sign that plotting should be scripted instead, or the data needs to be subsetted.

3. The jupyter notebook should have a clearly annotated input file / folder of data (typically pre-processed using scripts). I usually include this in the title. The notebook must have a creation/last modified date (fortunately this is automatic, but it is crucial when the input data can change over time)

4. Observations, general conclusions, preliminary answers to exploratory questions, etc from data analysis should be written in the notebook in comments or Markdown. This is for your future self.

I appreciate codifying it in a document, but most of the rules listed are taught (at least implicitly) in middle school.

Or use Microcal Origin instead if you are uncomfortable with programming.

This is something we think a lot about at Kyso (https://kyso.io/for-teams) - my startup - we let teams manage analytics knowledge basically by letting teams share Jupyter notebooks - we've spoken to lots of people about how the organise them and are writing a guide really soon that sounds like what you want.

- Keep all your teams notebooks in one place (not on laptops)

- Reuse them liberally (the %run command is excellent and often better than creating python libraries)

- Most important of all is make them readable documents not just list of coding commands, that means you should title things clearly, explain what you going to do.

- Label everything, axis labels are needed on almost every chart, and also make sure you put a title and description for every chart.

- Separating your project into processing and presentation notebooks is really helpful. So one notebook for data prep and another for plots and explanations.

Everyone's focusing on charts and notebook types; I'd like to highlight something I find particularly important: reporting uncertainties. I strongly agree with recommendations in this article, and personally I don't think a work qualifies as professional if it reports measurements without providing uncertainties. And no, I don't trust it's correctly rounded off (no more digits than justified by certainty level) - too many people don't care or know they should care, and too many areas of endeavor are poisoned by marketing.

A trivial every-day example of why that matters: a person steps on scales twice in a span of few days, and notices 1 kg of gain, and panics. The reaction is completely unwarranted, given that a cheap household electronic scale will be accurate to +/- 0.5kg in this range, and random daily variability of weight is somewhere around +/- 2 kg. They may as well have lost their average weight over that span, but it's hidden under random and systemic errors.