django-ninja VS orange

Django Ninja [1], it forever changed how I write Django project, in a way so elegant and productive.

[1]: https://django-ninja.dev/

Django Ninja is a RESTful wrapper for Django, while UtilMeta Python Framework uses a more concise declarative ORM Schema for Django and other future-supporting ORMs like sqlachemy and Peewee to build RESTful APIs more efficiently, and supports not only Django but all Python mainstream frameworks like Django, Flask, Starlette, FastAPI, Sanic, Tornado, etc.

I can't recommend [django-ninja](https://github.com/vitalik/django-ninja) enough. It's an easy to use, extremely fast, typed API for django. I've found it to be better in almost all aspects when compared to djangorestframework.

It's gaining popularity but is still widely unknown.

> The only place I really see Django at large companies is as an api using DRF or something.

This is not a bad thing. Using Django as an API backend is amazingly fast in terms of development time, especially with modern frameworks such as django-ninja [1].

Just use the built-in ORM to create models, write your endpoints, and use the built-in admin interface to play with the database if you don't have endpoints for everything.

There is also a less known feature of Django called admindocs [2], which automatically generates a human readable, hyperlinked documentation for your models and relations between them.

[1] https://django-ninja.rest-framework.com/

[2] https://docs.djangoproject.com/en/4.2/ref/contrib/admin/admi...

Personally, I also prefer django-ninja to DRF.

Actually that's not fully true. If you mix async and sync codes in django-ninja there will be some errors. Where's the proof ? django-ninja doesn't support async auth

Subscribe to this issue :D

Also recommend Django-Ninja. It basically reimplements fastapi's type and decorator-based API construction, but embedded directly in django so you have access to django's ORM and middleware library.

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.