Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
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Сеть:📈 Аналитический обзор Telegram-канала Learn Python Coding
Канал Learn Python Coding (@pythonre) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 049 подписчиков, занимая 3 238 место в категории Технологии и приложения и 9 700 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 049 подписчиков.
Согласно последним данным от 26 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 182, а за последние 24 часа — -10, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.93%. В первые 24 часа после публикации контент обычно набирает 1.12% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 172 просмотров. В течение первых суток публикация набирает 447 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как math, harvard, oxford, supervision, waybienad.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 27 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
40 049
Подписчики
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40 055
Repost from Machine Learning with Python
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Limiting program resources using the resource module 🛡️
import resource
import sys
# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))
# 2. Checking the protection's working
try:
print("Trying to allocate a huge array of memory...")
huge_list = [i for i in range(10_000_000)]
except MemoryError:
print("The limit worked! The program didn't crash, but caught the error.")
# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")40 055
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 --locked40 055
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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-markdownify40 055
Repost from Machine Learning with Python
✍️ 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/
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40 055
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
40 055
🐍 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
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Deep copying of objects with the copy module 🐍📦
import copy
# Original list with nested structure
original = [[1, 2, 3], [4, 5, 6]]
# 1. Shallow copy
shallow = copy.copy(original)
shallow[0][0] = 'X'
# Oh no! Both lists have changed, because the nested list wasn't copied, but passed by reference
print(f"Original after shallow: {original}") # [['X', 2, 3], [4, 5, 6]]
# Restore the data
original = [[1, 2, 3], [4, 5, 6]]
# 2. Deep copy
deep = copy.deepcopy(original)
deep[0][0] = 'X'
# Everything is fine! Only deep has changed, the original remains untouched
print(f"Original after deep: {original}") # [[1, 2, 3], [4, 5, 6]]40 055
Why in Python it is better to check None using is 🐍
In Python, you should not write obj == None, even if sometimes it works the same ⚠️
The reason is that == calls the comparison method eq, which can be overridden in the class — and then the behavior becomes unpredictable 🎲
For example:
class Weird:
def eq(self, other):
return True # always says "equal"
obj = Weird()
print(obj == None) # True
print(obj is None) # False
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40 055
❤️ Architecture Patterns — an informative repository on backend architecture in Python!
Here, they excellently demonstrate how to properly separate application logic, work with complex architecture, build a scalable backend, and maintain a codebase in an adequate state as the project grows. Instead of dry theory, the authors gradually build a full-fledged application and show how the architecture evolves as the project grows.
I'll leave a link: https://github.com/cosmicpython/book
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