🏆 150 Design Patterns: A Complete Guide

📢 Dive into 150 essential Software Design Patterns! Learn practical solutions with code examples to build robust, efficient, and scalable applications.

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By: @DataScienceQ ✨

🏆 Unlock 150 SQL Clean Code Principles

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By: @DataScienceQ ✨

The Walrus Operator := (Assignment Expressions)

Introduced in Python 3.8, the "walrus operator" := allows you to assign a value to a variable as part of a larger expression. It's a powerful tool for writing more concise and readable code, especially in while loops and comprehensions.

It solves the common problem where you need to compute a value, check it, and then use it again.

---

#### The Old Way: Repetitive Code

Consider a loop that repeatedly prompts a user for input and stops when the user enters "quit".

# We have to get the input once before the loop,
# and then again inside the loop.
command = input("Enter command: ")

while command != "quit":
    print(f"Executing: {command}")
    command = input("Enter command: ")

print("Exiting program.")

Notice how input("Enter command: ") is written twice.

---

#### The Pythonic Way: Using the Walrus Operator :=

The walrus operator lets you capture the value and test it in a single, elegant line.

while (command := input("Enter command: ")) != "quit":
    print(f"Executing: {command}")

print("Exiting program.")

Here, (command := input(...)) does two things:
• Calls input() and assigns its value to the command variable.
• The entire expression evaluates to that same value, which is then compared to "quit".

This eliminates redundant code, making your logic cleaner and more direct.

#Python #PythonTips #PythonTricks #WalrusOperator #Python3 #CleanCode #Programming #Developer #CodingTips

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By: @DataScienceQ ✨

❔ Interview Question

What is the GIL (Global Interpreter Lock) in Python, and how does it impact the execution of multi-threaded programs?

Answer: The Global Interpreter Lock (GIL) is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. This means that in a CPython process, only one thread can be executing Python bytecode at any given moment, even on a multi-core processor.

This has a significant impact on performance:

• For CPU-bound tasks: Multi-threaded Python programs see no performance gain from multiple CPU cores. If you have a task that performs heavy calculations (e.g., image processing, complex math), creating multiple threads will not make it run faster. The threads will execute sequentially, not in parallel, because they have to take turns acquiring the GIL.

• For I/O-bound tasks: The GIL is less of a problem. When a thread is waiting for Input/Output (I/O) operations (like waiting for a network response, reading from a file, or querying a database), it releases the GIL. This allows another thread to run. Therefore, the threading module is still highly effective for tasks that spend most of their time waiting, as it allows for concurrency.

How to achieve true parallelism?

To bypass the GIL and leverage multiple CPU cores for CPU-bound tasks, you must use the multiprocessing module. It creates separate processes, each with its own Python interpreter and memory space, so the GIL of one process does not affect the others.

tags: #Python #Interview #CodingInterview #GIL #Concurrency #Threading #Multiprocessing #SoftwareEngineering

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By: @DataScienceQ ✨

🏆 NumPy Python Tips

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By: @DataScienceQ ✨

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By: @DataScienceQ ✨

🏆 Python MCP Server: Create & Learn

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By: @DataScienceQ ✨

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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By: @DataScienceQ ✨

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