متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
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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منشورات القناة
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| 6 | 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}")
Protecting the server from "greedy" code 🔧
When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.
— Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.
— File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.
— Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.
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| 7 | 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
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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
http://github.com/matthewwithanm/python-markdownify
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📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/
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| 13 |  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.
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| 15 |  🐍 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.
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Python in 2026 is about tooling, types, async, infrastructure, AI, and production discipline. And this roadmap is exactly about such a Python.
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| 17 | 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]]
The link trap in Python 🔗🕳️
When you assign a list to another variable (A = B) or make a regular slice (A = B[:]), Python doesn't physically copy the data. It simply creates a new reference to the same objects in memory. If the list contains other mutable objects (lists, dictionaries, custom classes), standard copying methods will only create a shallow copy. The copy module allows you to control this process.
— Breaking the links: The deepcopy function recursively traverses the entire data structure and creates honest, independent duplicates for each nested element. This ensures that changes in the copy will not harm the original data. 🔓🔒
— Safe state: The use of deep copying is critical when implementing design patterns (for example, Snapshot/Memento), creating game state backups, or when you pass complex configurations to functions that may modify them accidentally. 🛡️💾
— A sensible balance: It's worth remembering that deepcopy works slower and consumes more memory than shallow copying, as it spends resources on creating new objects and checking for cyclic references. Use it specifically when there are nested mutable containers within the structure. ⚖️🧠
#Python #Programming #DeepCopy #Coding #Tech #Dev
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| 18 | 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
Here obj == None gives a false result due to custom logic 🤔
Instead:
obj is None
is checks the identity of the object and cannot be overridden. Since None is a singleton, such a check is always correct and predictable ✅
Conclusion: to check for None always use is None — it is the right and safe approach 🛡️
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