I'm always hesitant to self-promote in an hn comment (actually I have never done it before!), but your problems w/ Jupyter are just too closely mapped to what Hex (https://hex.tech/) solves to not plug it here!
- I get an analysis that I like, but there isn't a good way to share it with others, so I end up just taking screenshots.
You can publish any Hex notebook with literally just a few clicks, and anyone you share it with can access it, or edit it, or fork it, without installing anything— or you can even make it public. You can easily turn a notebook into an "app" or interactive report if you want, hiding/showing certain cells or choosing cells to show only code/only output. You can just share the raw notebook though too.
- There isn't a good way to take the same analysis and plug new data into it, other than to copy-paste the entire notebook.
Super easy to duplicate a Hex project and hit a different table or data source, or you can use input parameters (like ipywidgets) to make one notebook parameterized and work on a bunch of different data sources.
- The process to "promote" fragments of a notebook into being reusable functions seemed very high-friction: basically you're rewriting it as a normal Python package and then adding that to Jupyter's environment.
You can promote any part of a project to a "Component" (docs: https://learn.hex.tech/docs/develop-logic/components) that you can import into other projects. They can be data sources, function definitions, anything. If you make upstream changes to the component, you can sync them down into projects that import it.
- There aren't good boundaries between Jupyter's own Python environment, and that of your notebooks— if you have a dependency which conflicts with one of Jupyter's dependencies, then good luck.
Hex has a ton of default packages in its already installed standard library, and all the dependencies are ironed out— if you have packages you want to use that aren't there, you can pip install them, pull them in from a private github repo, or ask us to add them to the base image. You can also run Hex projects using a custom-provided docker image if you have super custom needs.
I was going to post hex as an ideal option, I definitely do not miss dealing with jupyter and all the Python env related headaches, hex solves all of these and then the rest of the more UX related issues with notebooks. Definitely recommend as the best compromise for notebook based Python data analytics tasks
- I get an analysis that I like, but there isn't a good way to share it with others, so I end up just taking screenshots.
You can publish any Hex notebook with literally just a few clicks, and anyone you share it with can access it, or edit it, or fork it, without installing anything— or you can even make it public. You can easily turn a notebook into an "app" or interactive report if you want, hiding/showing certain cells or choosing cells to show only code/only output. You can just share the raw notebook though too.
- There isn't a good way to take the same analysis and plug new data into it, other than to copy-paste the entire notebook.
Super easy to duplicate a Hex project and hit a different table or data source, or you can use input parameters (like ipywidgets) to make one notebook parameterized and work on a bunch of different data sources.
- The process to "promote" fragments of a notebook into being reusable functions seemed very high-friction: basically you're rewriting it as a normal Python package and then adding that to Jupyter's environment.
You can promote any part of a project to a "Component" (docs: https://learn.hex.tech/docs/develop-logic/components) that you can import into other projects. They can be data sources, function definitions, anything. If you make upstream changes to the component, you can sync them down into projects that import it.
- There aren't good boundaries between Jupyter's own Python environment, and that of your notebooks— if you have a dependency which conflicts with one of Jupyter's dependencies, then good luck.
Hex has a ton of default packages in its already installed standard library, and all the dependencies are ironed out— if you have packages you want to use that aren't there, you can pip install them, pull them in from a private github repo, or ask us to add them to the base image. You can also run Hex projects using a custom-provided docker image if you have super custom needs.
You should *definitely* check it out if you have these pain points. Here's an example of a pretty complicated public Hex project: https://app.hex.tech/hex-public/app/9b882bc1-ead3-4f0b-87d1-...
And here's a simpler one I just made the other day on a cool Silk Road dataset https://app.hex.tech/hex-public/app/cdc1b8fe-144b-4a74-a5ef-.... There's a bunch more examples at https://hex.tech/use-cases. Happy to answer any questions!