Code With Python
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho
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concatenation | Python Glossary

📖 The operation of joining two or more strings end-to-end to create a new string.

🏷️ #Python
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Built by devs who got tired of overpriced cloud providers. Also, there's a monkey 🐒
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Django ModelSearch — intelligent search for models

It indexes Django models and allows searching through the ORM. It works with PostgreSQL FTS, SQLite FTS5, Elasticsearch, and OpenSearch
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Supports:
autocomplete
faceted search
fuzzy search
structured queries
index rebuilding without downtime
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What if you could see the entire dependency tree of a single team?

You can only debug version conflicts when you understand which packages depend on what. But manually sorting out these connections in a pile of nested dependencies is tedious and time-consuming.

uv tree does this automatically: it displays a complete dependency graph so you can track any package and understand where it came from.

Key features:

Full visualization of dependencies
Highlights dependencies for which updates are available
Shows which packages depend on a specific library
Filters the tree to show only the dependencies of the selected package

Installation uv: pip install uv

👉 @DataScience4
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💻 GHOSTCREW — A Python AI tool for penetration testers and security professionals that conducts vulnerability searches in any services.

It works like a red team within your system. You describe the task in plain language — then it plans the attack itself, selects tools, and proceeds through the entire process: from reconnaissance to reporting. Without manual fiddling and endless commands.

What it can do in practice:

➡️ Checks everything: code, business logic, network traffic, protocols.
➡️ Analyzes the found vulnerabilities and explains where the problem is and how to fix it.
➡️ Works autonomously — you just launch it and get a full-fledged research.
➡️ Connects MCP servers and tools (nmap, metasploit, ffuf, etc.) itself.
➡️ Uses Pentesting Task Trees for meaningful decision-making, not just brute force.
➡️ Supports ready-made workflows for comprehensive checks.
➡️ Generates detailed reports in Markdown with facts and recommendations.
➡️ Remembers the dialogue context and doesn't "get lost" after a couple of requests.
➡️ Sees real files: wordlists, payloads, configs — and uses them in its work.
➡️ Allows you to choose an AI model and customize its behavior.
➡️ No registration and no restrictions.

⚙️ Installation:
git clone https://github.com/GH05TCREW/ghostcrew.git
cd ghostcrew
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt


▶️ Usage:
python main.py


⚠️ The information is provided solely for informational purposes. And it encourages to pay attention to security issues.

♎️ GitHub/Instructions

#python #soft #github
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global variable | Python Glossary

📖 A variable defined at the top level of a module.

🏷️ #Python
😰 A repository of more than 100+ ready-made Python scripts that solve a bunch of tasks - without reinventing the wheel and suffering at night.

💬 parsing and searching on the internet;
💬 working with photos and videos;
💬 keyloggers and password managers;
💬 cloning websites;
💬 automating routines;
💬 and dozens of other useful things for real cases.

🔥 Ready-made practice + code, suitable for both learning and work.

⬇️ Save it, it will definitely come in handy!

#python #soft #github
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Forwarded from PyData Careers
🔥 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
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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
introspection | Python Glossary

📖 The ability of a program to examine the type or properties of an object at runtime.

🏷️ #Python
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local variable | Python Glossary

📖 A variable that you bind inside a function or method body.

🏷️ #Python
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
How to correctly terminate Python scripts

In production, it's important to clearly signal the result of the program's work. For this, sys.exit(<code>) is used:
• 0 — success
• a non-zero value — error

This approach helps CI/CD, Docker or cron to correctly respond to failures. It's mandatory for CLI utilities and automation, so that the execution is predictable

https://t.iss.one/DataScience4
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