Learn Python Coding

Open in Telegram

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 India10 584 Technologies & Applications3 511...

📈 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 39 139 subscribers, ranking 3 511 in the Technologies & Applications category and 10 584 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 39 139 subscribers.

According to the latest data from 06 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 433 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 2.57%. Within the first 24 hours after publication, content typically collects 1.00% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 004 views. Within the first day, a publication typically gains 393 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 08 June, 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.

39 139

Subscribers

+1024 hours

+887 days

+43330 days

1 004

Post views

~ 39324 hours

~ 49448 hours

2.57%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

39 139

⚠ Message was hidden by channel owner

39 139

✨ relative import | Python Glossary ✨ 📖 Import modules from the same package or parent packages using leading dots. 🏷️ #Python

39 139

Repost from Tech Jobs Hub

Python Clean Code: Stop Writing Bad Code — Lessons from Uncle Bob Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code. Read: https://habr.com/en/articles/841820/ https://t.me/CodeProgrammer 🧠

39 139

⚠ Message was hidden by channel owner

39 139

Working with f-strings: more possibilities than it seems! f-strings often replace .format() in everyday code, but their capabilities are not always fully utilized. They support formatting, function calls, working with data structures, and convenient debugging (from 3.8+). f-strings are convenient for aligning columns without additional tools. This makes the output readable in the CLI and logs:

rows = [
    ("id", "name", "role"),
    (1, "Ivan", "admin"),
    (2, "Olga", "editor"),
]

for r in rows:
    print(f"{r[0]:<5} {r[1]:<10} {r[2]:<10}")

Debug expressions (Python 3.8+): {x=> displays the name and value of the variable, which speeds up debugging. Supports formatting of calculations:

x = 12
y = 7
print(f"{x=} {y=} {x*y=} x/y={x/y:.3f}")

Specifiers !r, !a: !r - repr(), !a - ascii() for unambiguous logs. Eliminates ambiguities in the output of objects:

path = "/var/data/config.yaml"
print(f"{path!r} {path!a}")  # repr and ascii()

Specifiers support width and padding, for example 08d for zeros. This is convenient for reports and IDs:

n = 42
print(f"{n:08d}")  # → #00000042

You can access dictionaries and immediately calculate metrics, for example len():

data = {"user": "Ivan", "items": [1, 2, 3]}
print(f"{data['user&#39]}=», items={data['items&#39]}")
print(f"len(data['items&#39])={len(data['items&#39])}")

🔥 f-strings are a cool tool for formatting, logging, and debugging, if you apply them taking into account the version of Python and the context of the output. 🚪 @DataScience4

39 139

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t.me/+0-w7MQwkOs02MmJi You can join at this link! 👆👇 https://t.me/+0-w7MQwkOs02MmJi

39 139

✨ Quiz: How to Integrate Local LLMs With Ollama and Python ✨ 📖 Check your understanding of using Ollama with Python to run local LLMs, generate text, chat, and call tools for private, offline apps. 🏷️ #intermediate #ai #tools

39 139

⚠ Message was hidden by channel owner

39 139

✨ unpacking | Python Glossary ✨ 📖 Passing multiple values at once by expanding an iterable. 🏷️ #Python

39 139

⚠ Message was hidden by channel owner

39 139

Repost from ADMINOTEKA

⚠ Message was hidden by channel owner

39 139

✨ Anaconda Navigator | Python Tools ✨ 📖 A desktop graphical interface included with the Anaconda Distribution. 🏷️ #Python

39 139

✨ Quiz: How to Integrate ChatGPT's API With Python Projects ✨ 📖 Test your knowledge of the ChatGPT API in Python. Practice sending prompts with openai and handling text and code responses in this quick quiz. 🏷️ #intermediate #ai #api

39 139

✨ local variable | Python Glossary ✨ 📖 A variable that you bind inside a function or method body. 🏷️ #Python

39 139

⚠ Message was hidden by channel owner

39 139

✨ introspection | Python Glossary ✨ 📖 The ability of a program to examine the type or properties of an object at runtime. 🏷️ #Python

39 139

✨ How to Integrate Local LLMs With Ollama and Python ✨ 📖 Learn how to integrate your Python projects with local models (LLMs) using Ollama for enhanced privacy and cost efficiency. 🏷️ #intermediate #ai #tools

39 139

⚠ Message was hidden by channel owner

39 139

Repost from Tech Jobs Hub

🔥 Generating fake data in Python — no pain at all If you're testing forms, mockups, or just want to play with data, there's Mimesis — a generator of fake data. Names, emails, addresses, and phone numbers. There's a location setting that allows you to select a country, and the data will be generated accordingly. 📦 Installation:

from typing import Dict
from mimesis.enums import Gender
from mimesis import Person

def generate_fake_user(locale: str = "es", gender: Gender = Gender.MALE) -> Dict[str, str]:
    """
    Generates fake user data based on the locale and gender.

    :param locale: The locale (for example, 'ru', 'en', 'es')
    :param gender: The gender (Gender.MALE or Gender.FEMALE)
    :return: A dictionary with the fake user data
    """
    person = Person(locale)

    user_data = {
        "name": person.full_name(gender=gender),
        "height": person.height(),
        "phone": person.telephone(),
        "occupation": person.occupation(),
    }

    return user_data

if __name__ == "__main__":
    fake_user = generate_fake_user(locale="es", gender=Gender.MALE)
    print(fake_user)

📌 Result:

{
  'name': 'Carlos Herrera',
  'height': '1.84',
  'phone': '912 475 289',
  'occupation': 'Arquitecto'
)

⚡️ Mimesis can: 🖱 Generate names, addresses, phone numbers, professions, etc. 🖱 Work with different countries (🇷🇺 ru, 🇺🇸 en, 🇪🇸 es, etc.) 🖱 Suitable for tests, fake accounts, demo data in projects, and bots. ⚙️ GitHub/Instructions Save it, it'll come in handy 👍 #python #github #interview