Regular for-loops are versatile but not always optimal: they add extra interpreter overhead, which is especially noticeable on large data 🐍

In such cases, it's better to use standard Python tools, for example itertools ⚙️

For example, to get all unique pairs from a list, nested loops are not needed — just combinations():

from itertools import combinations

def get_unique_pairs(items):
    return list(combinations(items, 2))

print(get_unique_pairs(['A', 'B', 'C', 'D']))

# Output:
# [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]

Conclusion: instead of manual loops, it's better to use ready-made tools from the standard library — it's cleaner and more efficient 🚀

#Python #Coding #Programming #Developer #Tech #Optimization

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🐍 Python Roadmap 2026: Finally, a comprehensive and up-to-date map for learning Python, not just a list of "figure it out yourself" links

A large Russian-language Python roadmap for 2026 has been posted on GitHub - from the first scripts to the Middle+/Senior level.

The route is compiled for modern Python:

- Python 3.13+
- free-threaded mode without GIL
- JIT
- uv instead of the hassle with pip/venv/poetry
- ruff, pyright, pytest, hypothesis
- async-first approach
- typing
- CPython inside
- web, databases, ML/AI, DevOps, and architecture

The roadmap has a logical sequence: first the environment and foundation, then idioms, OOP, types, the standard library, asynchrony, testing, CPython internals, web, databases, the AI direction, production, and architecture.

A particular plus is the practical format. At each stage, there are tasks, checklists, code examples, and free resources. This is not a motivational document, but a roadmap that you can actually follow for several months and see progress.

For beginners - a clear path without chaos.
For juniors - a way to fill in the gaps.
For those who already write in Python - a good checklist to understand where you're still struggling.

Python in 2026 is about tooling, types, async, infrastructure, AI, and production discipline. And this roadmap is exactly about such a Python.

https://github.com/justxor/pythonroamap2026

#Python #PythonRoadmap #Programming #2026 #Coding #DevOps

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5 More Must-Know Python Concepts 🐍

Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit. 🛠️

Read: https://www.kdnuggets.com/5-more-must-know-python-concepts 🔗

#Python #Programming #Coding #Developer #TechTips #LearnPython

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✍️ Pyneng — a large base for Python and network automation!

Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.

📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/

#Python #NetworkAutomation #Pyneng #LearnPython #DevOps #TechEducation

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When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle.

In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.

The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.

The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.

It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.

pip install python-markdownify

If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.

If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.

➡️ Link to GitHub
https://github.com/matthewwithanm/python-markdownify

#python #markdown #html #coding #devtools #opensource

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Advice for Python, UV, and Docker 🐍🐳

Sometimes dependencies are better installed separately from the code — this noticeably speeds up the compilation of Docker images 🚀

The idea is simple: first, we install dependencies, then we add the project 🛠

Why is this necessary:
• Docker caches layers and does not rebuild them unnecessarily ⚡️
• if only the code changes — the dependencies are taken from the cache 💾
• if the dependencies change — only the corresponding layer is rebuilt 🔁
• without this, any minor change triggers a full reinstallation 🔄

Example:

RUN --mount=type=cache,target=/root/.cache/uv  --mount=type=bind,source=uv.lock,target=uv.lock  --mount=type=bind,source=pyproject.toml,target=pyproject.toml  uv sync --locked --no-install-project

COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv  uv sync --locked

#Python #Docker #DevOps #UV #SoftwareEngineering #TechTips

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