import-linter
conda
import-linter | conda | |
---|---|---|
4 | 30 | |
623 | 6,092 | |
- | 0.7% | |
7.6 | 9.8 | |
2 months ago | 6 days ago | |
Python | Python | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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.
import-linter
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Kraken Technologies: How we organise our large Python monolith
Never heard of https://import-linter.readthedocs.io/ before. Not sure if I like this type of solution, but it's interesting, and certainly the problem is real.
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Maintain a Clean Architecture in Python with Dependency Rules
Before clicking on this, I expected to see import-linter [0] which achieves something very similar but with, in my opinion, a bit less magic. Another solution in a similar spirit is Pants [1], though this is actually a build system which allows you to constrain dependencies between different artifacts (e.g. which modules are allowed to depend on which modules).
To Sourcery's credit, their product looks much more in the realm of "developer experience" -- closer to Copilot (or what I understand of it) than to import-linter. Props to them for at least having a page about security [2] and building a solution which doesn't inherently require all of your source code to be shared with a vendor's server.
[0] https://github.com/seddonym/import-linter
[1] https://www.pantsbuild.org/
[2] https://docs.sourcery.ai/Product/Permissions-and-Security/
- Python 3.11.0 final is now available
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Linter for Python architecture
import-linter on GitHub
conda
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How to Create Virtual Environments in Python
Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.
- Why does creating my conda environment use so much memory?
- Installing Anaconda on ChromeOS using Linux
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PSA: conda-libmamba-solver can cut two hours off of your Anaconda install, but has only 47 GitHub stars. It deserves more praise.
conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.
- Is Anaconda still open source?
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How to get the best Conda environment experience in Codespaces
The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).
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What is the status of Python 3.11?
It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.
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Miniconda finally released for Python 3.10
It took some time but as great Christmas present Miniconda was finally released with Python 3.10!
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TW: ZSH (and BASH?) does not show current working dir etc anymore
The September update broke it.
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Python 3.11.0 is now available
According to this this issue is high on their priority list (whatever that means).
What are some alternatives?
dephell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump version.
mamba - The Fast Cross-Platform Package Manager
smart-imports - smart imports for Python
Poetry - Python packaging and dependency management made easy
tern - Tern is a software composition analysis tool and Python library that generates a Software Bill of Materials for container images and Dockerfiles. The SBOM that Tern generates will give you a layer-by-layer view of what's inside your container in a variety of formats including human-readable, JSON, HTML, SPDX and more.
miniforge - A conda-forge distribution.
python-feedstock - A conda-smithy repository for python.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
emerge - Emerge is a browser-based interactive codebase and dependency visualization tool for many different programming languages. It supports some basic code quality and graph metrics and provides a simple and intuitive way to explore and analyze a codebase by using graph structures.
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
Django-Styleguide - Django styleguide used in HackSoft projects
pip - The Python package installer