pip-audit
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pip-audit | Flask | |
---|---|---|
22 | 135 | |
915 | 66,287 | |
2.6% | 0.7% | |
8.8 | 8.7 | |
5 days ago | 9 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pip-audit
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Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
Next up is making sure, none of the dependencies used throughout the project brings with it any already identified security issue. The makefile target audit, invokes the handy tool pip-audit.
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Show HN: One makefile to rule them all
Here is my "one true" Makefile for Python projects[1]. The skeleton gets tweaked slightly each time, but it's served me well for 4+ years.
[1]: https://github.com/pypa/pip-audit/blob/main/Makefile
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Pyscan: A command-line tool to detect security issues in your python dependencies.
Why use this over the established https://pypi.org/project/pip-audit/ ?
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How Attackers Can Sneakily Slip Malware Packages Into Poetry.lock Files
https://pypi.org/project/pip-audit/ details usage and the GitHub Action install.
- How to improve Python packaging, or why 14 tools are at least 12 too many
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Underappreciated Challenges with Python Packaging
If it's pure Python, the only packaging file you need is `pyproject.toml`. You can fill that file with packaging metadata per PEP 518 and PEP 621, including using modern build tooling like flit[1] for the build backend and build[2] for the frontend.
With that, you entire package build (for all distribution types) should be reducible to `python -m build`. Here's an example of a full project doing everything with just `pyproject.toml`[3] (FD: my project).
[1]: https://github.com/pypa/flit
[2]: https://github.com/pypa/build
[3]: https://github.com/pypa/pip-audit
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Auditing your python environment
- repo: https://github.com/trailofbits/pip-audit rev: v2.4.3 hooks: - id: pip-audit args: [ "-r", "requirements.txt" ] ci: # Leave pip-audit to only run locally and not in CI # pre-commit.ci does not allow network calls skip: [ pip-audit ]
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How to create a Python package in 2022
This is really nicely written; kudos to the author for compiling a great deal of information in a readable format.
If I can be forgiven one nitpick: Poetry does not use a PEP 518-style[1] build configuration by default, which means that its use of `pyproject.toml` is slightly out of pace with the rest of the Python packaging ecosystem. That isn't to say that it isn't excellent, because it is! But you the standards have come a long way, and you can now use `pyproject.toml` with any build backend as long as you use the standard metadata.
By way of example, here's a project that's completely PEP 517 and PEP 518 compatible without needing a setup.py or setup.cfg[2]. Everything goes through pyproject.toml.
[1]: https://peps.python.org/pep-0518/
[2]: https://github.com/trailofbits/pip-audit/blob/main/pyproject...
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I think the CTX package on PyPI has been hacked!
Checking could be done if something like this eventually shows up in safety or pip-audit.
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Open-source way to scan dependencies for CVEs?
Something like python's pip-audit. For commercial solutions I know there's Snyk and Jfrog we can always purchase, but I'm interested to see if there's an open-source tool that can do this.
Flask
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Ask HN: High quality Python scripts or small libraries to learn from
I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:
https://github.com/pallets/flask
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Rapid Prototyping with Flask, Bootstrap and Secutio
#!/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)
- Microdot "The impossibly small web framework for Python and MicroPython"
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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10 Github repositories to achieve Python mastery
Explore here.
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Ask HN: What would you use to build a mostly CRUD back end today?
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
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Implementing continuous delivery pipelines with GitHub Actions
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.
- How do you iterate on a library built locally?
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Flask Application Load Balancing using Docker Compose and Nginx
Flask Micro web Framework: You will use Flask to build a Flask web application.
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Open Source Flask-based web applications
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.
What are some alternatives?
ochrona-cli - A command line tool for detecting vulnerabilities in Python dependencies and doing safe package installs
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
git-hooks.nix - Seamless integration of https://pre-commit.com git hooks with Nix.
Django - The Web framework for perfectionists with deadlines.
npm-esbuild-audit
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
setup-dvc - DVC GitHub action
quart - An async Python micro framework for building web applications.
aura - Python source code auditing and static analysis on a large scale
starlette - The little ASGI framework that shines. 🌟
tox-poetry-installer - A plugin for Tox that lets you install test environment dependencies from the Poetry lockfile
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.