domonic VS Flask

I did the 100th release of this python DOM 0.9.11... https://github.com/byteface/domonic

I've managed to tweak domonic (https://github.com/byteface/domonic) to work with elementpath (https://github.com/sissaschool/elementpath)...

I'm not sure about quicker. Doesn't scrapy use elementpath?. which converts a css query to an xpath under the hood as there is no complete CSSOM available for python. Likely as there is no modern standards based python dom to operate on so doing it on lxml tree is probably the best option. I find the main difference is xpath can return an attribute value where as css returns the node. You can use either from the terminal in my lib... https://github.com/byteface/domonic (as it uses elementpath like scrapy)

Thanks for your tool. I've been using it this week and updated a bunch of code. You are now a contributer... https://github.com/byteface/domonic/pull/58

[domonic](https://domonic.readthedocs.io/) will continue to evolve. It's a pure python dom I been working on in free time over last 2 years... https://github.com/byteface/domonic/

and used it on my lib yesterday... https://github.com/byteface/domonic/commit/96a91bbf3ee6f672bc1c0e5978f55e45706392aa

I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:

https://github.com/pallets/flask

#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)

I was looking at all the big projects like numpy, pytorch, flask, etc.

Explore here.

I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.

Now...

Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?

- [0]: https://flask.palletsprojects.com

- [1]: https://flask-admin.readthedocs.io/en/latest

- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery

- [3]: https://sentry.io

- [4]: https://posthog.com

- [5]: https://www.docker.com

In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.

Flask Micro web Framework: You will use Flask to build a Flask web application.

In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.