https://stashsoftware.com/blog/article/using-codetqdmcode-for-dynamic-progress-bars-in-python-78

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https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html
https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html
https://micro.mamba.pm/api/micromamba/win-64/latest
https://prefix.dev/docs/mamba/overview
With mamba, it’s easy to set up software environments
. A software environment is simply a set of different libraries, applications and their dependencies. The power of environments is that they can co-exist: you can easily have an environment called py27 for Python 2.7 and one called py310 for Python 3.10, so that multiple of your projects with different requirements have their dedicated environments. This is similar to “containers” and images. However, mamba makes it easy to add, update or remove software from the environments.
To create a python environment under Windows:
micromamba create -n myenv python=3.10
This will create a myenv allocation under:
C:\Users\<USERNAME>\AppData\Roaming\mamba\envs\myenv
Once the environment is created, activate it with:
micromamba activate myenv
Or to execute a single command in this environment, use:
micromamba run -n myenv mycommand
To add a Windows shortcut to launching the micromamba environment:
cmd.exe /K micromamba activate myenv
https://nielscautaerts.xyz/python-dependency-management-is-a-dumpster-fire.html
For many modern programming languages, the associated tooling has the lock-file based dependency management mechanism baked in. For a great example, consider Rust’s Cargo.
Not so with Python.
The default package manager for Python is pip. The default instruction to install a package is to run pip install package
. Unfortunately, this imperative approach for creating your environment is entirely divorced from the versioning of your code. You very quickly end up in a situation where you have 100’s of packages installed. You no longer know which packages you explicitly asked to install, and which packages got installed because they were a transitive dependency. You no longer know which version of the code worked in which environment, and there is no way to roll back to an earlier version of your environment. Installing any new package could break your environment.
…
Pyper is a flexible framework for concurrent and parallel data-processing, based on functional programming patterns.
https://github.com/pyper-dev/pyper
GIL or Global Interpreter Lock can be disabled in Python version 3.13. This is currently experimental.
What is GIL? It is a mechanism used by the CPython interpreter to ensure that only one thread executes the Python bytecode at a time.
https://medium.com/@r_bilan/python-3-13-without-the-gil-a-game-changer-for-concurrency-5e035500f0da
https://geekpython.in/gil-become-optional-in-python
https://learnhowtolearn.org/how-to-build-extremely-quickly
This article presents a method called “outline speedrunning” to accelerate project completion. This approach involves recursively outlining tasks and filling them in rapidly, delaying perfection until the end.
Outlining is essential for planning and executing projects efficiently. The outline speedrunning method boosts productivity by focusing on rapid task completion and deferring perfection, leading to improved outcomes and reduced stress.
https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html
The Black code style guide emphasizes consistency, readability, and minimizing diffs in Python code. Key formatting rules include ignoring previous formatting, preferring double quotes, adding trailing commas, and wrapping lines to fit within 88 characters. Black standardizes whitespace usage, avoids unnecessary vertical space, and ensures uniform handling of parentheses, comments, and numeric literals. The formatter also aligns with PEP 8 and PEP 257 standards, and provides options to adjust line length and skip string normalization for specific projects.
https://maheshba.bitbucket.io/blog/2024/05/08/2024-ThreeLaws.html
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