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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شبکه:Machine Learning with Python الهند10 584 فناوری و برنامه‌ها3 511...

📈 تحلیل کانال تلگرام Learn Python Coding

کانال Learn Python Coding (@pythonre) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 39 139 مشترک است و جایگاه 3 511 را در دسته فناوری و برنامه‌ها و رتبه 10 584 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 39 139 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 06 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 433 و در ۲۴ ساعت گذشته برابر 10 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

وضعیت تأیید: تأیید نشده
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علایق موضوعی: محتوا بر موضوعات کلیدی مانند math, harvard, oxford, supervision, waybienad تمرکز دارد.

📝 توضیح و سیاست محتوایی

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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”

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 08 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.

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39 139

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✨ 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 🧠

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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

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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

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

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Repost from ADMINOTEKA

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

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✨ 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

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

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

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✨ 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

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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