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

Network:Machine Learning with Python India9 700 Technologies & Applications3 238...

📈 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 40 060 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 40 060 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 28 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.

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40 060

Subscribers

-1024 hours

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+18230 days

1 172

Post views

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2.93%

Engagement rate

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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

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📂 Reminder about Python map()! map() — a built-in function that applies the specified function to each element of an iterable object (list, tuple, set, etc.). The picture shows the basic syntax, an example of use with lambda, and a typical case — data transformation without a manual for loop. Save it to quickly remember the syntax! 🐍💻🗺️ #Python #Coding #Programming #LearnToCode #DevTips #Tech

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"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨ The book discusses the implementation and analysis of basic structures: array-based lists, linked lists, hash tables, binary trees, red-black trees, heaps, sorting algorithms, graphs, and data structures for working with integers. 🔍🧮 This is a full-fledged open textbook for studying one of the fundamental topics of computer science and a good reference that's worth keeping on hand. 💻🌟 https://opendatastructures.org/ods-python.pdf 📄 👉 @PythonRe #DataStructures #Algorithms #Python #ComputerScience #OpenSource #Learning

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡 #Python #Programming #Deque #CodingTips #Tech #DevCommunity

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The Python library itertools contains many useful functions. 🐍✨ One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻 Here's an example: 📝👇 #Python #Programming #Itertools #Coding #Tech #DataScience

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Many applications require mapping strings to integers. In Python, this usually looks like:

d = {"apple": 100, "banana": 200, "cherry": 300}

If there are 1 million keys, this can consume a lot of memory — more than 100 bytes per key. Our elephant has published a new library that uses about 9 bytes per key. Yes, only 9 bytes. Usage looks like this:

from fastconstmap import ConstMap

d = {"apple": 100, "banana": 200, "cherry": 300}
m = ConstMap(d)

m["apple"]                  # -> 100
m.get_many(["banana", "cherry"])  # -> [200, 300]

It can be significantly faster (for example, up to 2 times in some cases) than the standard dictionary. It can also be serialized and deserialized to disk or network for convenient reuse. https://pypi.org/project/fastconstmap/ github: https://github.com/lemire/fastconstmap 👉 @PythonRe