mostly-adequate-guide VS papermill

This project might serve as inspiration: https://github.com/MostlyAdequate/mostly-adequate-guide

i like reading this book directly from github with dark mode, also the subheading don't work in the gitbook website and gitbook is abandoned, here's the github link: https://github.com/MostlyAdequate/mostly-adequate-guide/blob/master/SUMMARY.md

It’s been a while since I focused on FP, but I recall finding this useful quite often and gleaning the concepts from it relatively easily.

https://github.com/MostlyAdequate/mostly-adequate-guide

I found a lot of articles like the OP, and ultimately they left me confused about the benefits in the beginning. I found it more useful to avoid one off articles and dig into larger pieces of work where the author put in much more care.

On the book front, there are two that I am fond of which have a focus on JavaScript and FP, Professor Frisby’s Mostly Adaquate Guide, and Functional Light JavaScript. They are nice practical books that help you lean into JS’s strength as an FP language while writing real code.

So, this is going to be an uphill battle for you. I suggest you actually learn Haskell first, and then you'll be able to apply its lessons to TypeScript.

Its tricky because these are patterns that are familiar in Haskell but are not really taught in other settings.

Additionally, to really learn these, you need to experiment with them. Use them. etc. That's pretty hard to do if the learning resources are mostly in haskell and you don't really understand it.

Alternatively, this might help: https://github.com/MostlyAdequate/mostly-adequate-guide

Also alternatively, what I would do is just go slowly through the fp-ts code. Look at it a piece at a time and slowly grow your understanding.

This may also help https://www.amazon.com/Domain-Modeling-Made-Functional-Domai...

Pandas docs > Comparison with spreadsheets: https://pandas.pydata.org/docs/getting_started/comparison/co...

Pandas docs > I/O > Excel files: https://pandas.pydata.org/docs/user_guide/io.html#excel-file...

nteract/papermill: https://github.com/nteract/papermill :

> papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. [...]

> This opens up new opportunities for how notebooks can be used. For example:

> - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier.

"The World Excel Championship is being broadcast on ESPN" (2022) https://news.ycombinator.com/item?id=32420925 :

> Computational notebook speedrun ideas:

There is Papermill ... https://github.com/nteract/papermill

https://github.com/jupyter/enhancement-proposals/pull/103#is...

Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/

You may be interested in papermill to address the parametrized analysis problem [1]. I think (but I'm not positive) this is what the data team at a previous job used to automate running notebooks for all sorts nightly reports.

[1] https://papermill.readthedocs.io/en/latest/#

I'm using Papermill to operationalize Notebooks (https://github.com/nteract/papermill), it e.g. also has airflow support. I'm really happy with papermill for automatic notebook execution, in my field it's nice that we can go very quickly from analysis to operations -- while having super transparent "logging" in the executed notebooks.

papermill bcpandas fastapi

But you can execute your notebook using Jupyter-run or papermill.

As a first option, we will use Papermill, which has a Python API that allows us to run different notebooks using some functions:

Papermill can also target cloud storage outputs for hosting rendered notebooks, execute notebooks from custom Python code, and even be used within distributed data pipelines like Dagster (see Dagstermill). For more information, see the papermill documentation.

Papermill Source Code