Learn Python Coding

✍️ 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 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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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

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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 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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 🛡️ ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A #Python #Programming #Coding #SoftwareDevelopment #TechTips #DevCommunity

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❤️ 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 #Python #Backend #Architecture #Coding #DevCommunity #OpenSource ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Data validation with Pydantic! 🐍✨ In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:

if not isinstance(age, int):
    raise ValueError("age must be an int")

But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks. For such tasks, Pydantic is often used. Installation:

pip install pydantic
pip install "pydantic[email]"

Create a model:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

Now the data is validated automatically:

user = User(
    name="Alex",
    age="30"
)

print(user.age)
print(type(user.age))

The result: 30 <class 'int'> Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:

User(
    name="Alex",
    age="test"
)

This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside. A common production case is checking email:

from pydantic import BaseModel, EmailStr

class User(BaseModel):
    email: EmailStr

User(email="alex@test.com")

If the email is invalid, Pydantic will throw a ValidationError. You can set default values:

from pydantic import BaseModel

class Config(BaseModel):
    host: str = "localhost"
    port: int = 5432

And allow None:

from pydantic import BaseModel

class User(BaseModel):
    nickname: str | None = None

This field becomes optional. A practical example is processing an API response:

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    title: str
    price: float

data = {
    "id": "1",
    "title": "Keyboard",
    "price": "99.5"
}

product = Product(**data)

print(product)

The types will be automatically converted. For nested model structures, you can combine:

from pydantic import BaseModel

class Address(BaseModel):
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    address: Address

user = User(
    name="Alex",
    address={
        "city": "Berlin",
        "zip_code": "10115"
    }
)

print(user)

The nested object will also be validated. Serialization in Pydantic v2:

print(user.model_dump())
print(user.model_dump_json())

Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients. For working with environment variables in Pydantic v2, a separate package is usually used:

pip install pydantic-settings

It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas. 🔥 Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable. #Python #Pydantic #DataValidation #FastAPI #Coding #DevOps ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Why is enumerate() used in Python? 🤔 It allows you to simultaneously obtain the value of an element and its index when iterating through a list. 📊 This is more convenient and more readable than manually working with a counter. ✅

for i, item in enumerate(items):
    print(i, item)

#Python #Coding #Programming #Dev #Tech #Code

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"Introduction to Algorithms" 📘 - an outstanding university resource for everyone studying algorithms and computer science. 🎓💻 The book covers computational complexity, data structures, algorithms on graphs, dynamic programming, divide-and-conquer methods, greedy algorithms, randomized algorithms, and many mathematical foundations of modern computer science. 🧮📊🔍 What's particularly valuable here is the combination of mathematical rigor and practical algorithmic thinking. 🧠✨ This is one of those books that greatly change the approach to problem analysis, efficiency, and computing itself. 🚀🛠 An essential tool in the library of any developer and engineer working in the field of computer science. 🏗💾 https://www.cs.mcgill.ca/~akroit/math/compsci/Cormen%20Introduction%20to%20Algorithms.pdf 🔗 #Algorithms #ComputerScience #Programming #CSStudent #TechEducation #DevTools

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If you're working with data pipelines, these repositories are very useful: 🚀📊 ibis: A Python API that allows you to write queries once and run them on different data backends, such as DuckDB, BigQuery, and Snowflake. 🐍🔗 https://github.com/ibis-project/ibis pygwalker: Instantly turns a DataFrame into an interactive UI for visual data exploration. 📈🖥️ https://github.com/Kanaries/pygwalker katana: A fast and scalable web crawler, often used for security testing and large-scale data collection/search. 🕷️🔒 https://github.com/projectdiscovery/katana #dataengineering #python #opensource #devtools #dataviz #security