django-sql-dashboard VS papermill

I have a sister project to Datasette called Django SQL Dashboard which works against PostgreSQL databases: https://django-sql-dashboard.datasette.io

It used Django for the authentication layer but can otherwise work against any PostgreSQL database.

I partly built it to help explore what Datasette could look like if it expanded to work with more databases than SQLite. That's still something I'm considering doing in the future, via a plugin hook, but it's not on my short-term roadmap.

Reusable apps do exist (I released one myself a few months ago, https://django-sql-dashboard.datasette.io) but in most projects apps are single-use only - at which point they become little more than a code organization tool.

I use documentation systems that publish the documentation from the repo to a website. Most of my projects use Sphinx and reStructuredText for this, but I recently tried MyST (Markdown for Sphinx) and I like that a lot.

Some examples:

- https://docs.datasette.io serves documentation from https://github.com/simonw/datasette - which has documentation unit tests here: https://github.com/simonw/datasette

- https://django-sql-dashboard.datasette.io/ serves from markdown in https://github.com/simonw/django-sql-dashboard - I don't have documentation unit tests for that yet

Weirdly my Django SQL Dashboard project fits the bill a bit here: you can build up a "dashboard" (which is a tiny bit notebook-like if you squint at it the right way) with multiple SQL queries on it, and save that either as a bookmark or as a "saved dashboard" with a URL.

https://django-sql-dashboard.datasette.io/

In my own work I've been using it for the kind of things that I would normally use a Jupyter notebook for - gathering together research on problems I'm trying to solve.

It's hand-written CSS - there's not much of it: https://github.com/simonw/django-sql-dashboard/tree/01bb7e60...

I've been building a tool for this over the past couple of months called Django SQL Dashboard - it currently only works with PostgreSQL and you need to create a read-only database connection, but it then provides an interface for executing any bookmarking SQL queries plus some basic visualizations: https://github.com/simonw/django-sql-dashboard

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