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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Network:📈 Analytical overview of Telegram channel Learn Python Coding
Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 40 049 subscribers, ranking 3 238 in the Technologies & Applications category and 9 700 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 049 subscribers.
According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 182 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.93%. Within the first 24 hours after publication, content typically collects 1.12% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 172 views. Within the first day, a publication typically gains 447 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 27 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
40 049
Subscribers
-1024 hours
-397 days
+18230 days
Posts Archive
40 055
Repost from Machine Learning with Python
+9
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40 055
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/
#Python #NetworkAutomation #Pyneng #LearnPython #DevOps #TechEducation
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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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40 055
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
40 055
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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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