21–24 of 24 posts
I would add that it should be acid-free paper, very strong binding (sewed+glued) and have graph paper. The acid-free paper can give notebook 20+ year life span, strong binding is important as you may frequently force notebook open with heavy objects, and finally graph paper style allows you to draw complex diagrams easily. Here's notebook I've liked: https://www.amazon.com/gp/product/B071NWSB7P
Why do they recommend a paper record that can't be backed up and nobody can verify what it said when? Why not a digital file that can be backed up and with a published checksum that can be used to verify what the state was at a given point in time.
Have we reach a point where there are similar guides for Jupyter notebooks? Or just for the domain of data science / exploratory statistics Like, conceptually, what should I keep, and how should I organise my notebook(s)? Most guides are focused on what you can do (Here's how you draw a graph, here's how you load a kernel), I'd rather read about what I should store in my notebooks, and how many I should have.
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 ex…
Does that mean you run other notebooks from within notebooks?
Can you ping me or something when the guide is out?
Earlier quoted context omitted.
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 ex…
> Reuse them liberally (the %run command is excellent and often better than creating python libraries) Does that mean you run other notebooks from within notebooks? Can you ping me or something when the guide is out?