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Python Data Science jobs, interview tips, and career insights for aspiring professionals.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Tip for clean tests in Python:

Structure your tests with the Arrange-Act-Assert pattern to improve readability and maintainability.

Arrange: Set up the test. Initialize objects, prepare data, and configure any mocks or stubs.
Act: Execute the code being tested. Call the specific function or method.
Assert: Check the outcome. Verify that the result of the action is what you expected.

import pytest
from dataclasses import dataclass, field


# Code to be tested
@dataclass
class Product:
name: str
price: float


@dataclass
class ShoppingCart:
items: list[Product] = field(default_factory=list)

def add_item(self, product: Product):
if product.price < 0:
raise ValueError("Product price cannot be negative.")
self.items.append(product)

def get_total_price(self) -> float:
return sum(item.price for item in self.items)


# Tests using the Arrange-Act-Assert pattern
def test_get_total_price_for_multiple_items():
# Arrange
product1 = Product(name="Mouse", price=25.50)
product2 = Product(name="Keyboard", price=75.50)
cart = ShoppingCart()
cart.add_item(product1)
cart.add_item(product2)

# Act
total_price = cart.get_total_price()

# Assert
assert total_price == 101.00


def test_get_total_price_for_empty_cart():
# Arrange
cart = ShoppingCart()

# Act
total_price = cart.get_total_price()

# Assert
assert total_price == 0.0


def test_add_item_with_negative_price_raises_value_error():
# Arrange
cart = ShoppingCart()
product_with_negative_price = Product(name="Invalid Item", price=-50.0)

# Act & Assert
with pytest.raises(ValueError, match="Product price cannot be negative."):
cart.add_item(product_with_negative_price)


#Python #Testing #CleanCode #SoftwareEngineering #Pytest #DeveloperTips #AAA

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🔬Python 3.14: What's New? 📈

In 2025, a wave of developer-focused improvements arrived with Python 3.14. Let's dive into some exciting new features:

• Lazy annotations resolve long-standing type hinting quirks
• Clever t-strings give more control over string interpolation

These enhancements solidify Python's position as the language of choice for AI-powered development.

📚 Stay up-to-date with the latest Python news and advancements in 2026! #Python #MachineLearning
1
🔥 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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👍 A fresh deep learning course from MIT is now available publicly

A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/

tags: #python #deeplearning

@codeprogrammer
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🔖 An excellent resource for learning about neural networks

We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.

In addition, you'll find many other useful materials on machine learning on the site.

Find and use it — https://mlu-explain.github.io/neural-networks/

tags: #AI #ML #PYTHON

@CODEPROGRAMMER
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Build and Automate Django CRM

#Django #CRM #PYTHON
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