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In Python, copying objects is nuanced; assignment creates references, not copies. Shallow copies share nested objects, while deep copies clone everything, avoiding unintended side effects.
Master the essentials of Python data analysis with this guide to key libraries like pandas, NumPy, and Matplotlib. Learn workflows and functions for efficient data handling and visualization.
Regular classes require manual boilerplate code; dataclasses reduce redundancy with decorators. Use dataclasses for simple data storage and regular classes for complex behaviors.
Encapsulation in Python bundles data and methods, restricting direct access to some components to improve code quality by controlling what is exposed or kept private.
Speed up Python functions by choosing efficient data structures like sets over lists for faster code execution and better user experience.
Get ready for your first Python interview. This guide covers fundamental concepts with explanations and examples to boost your confidence and understanding.
Learn essential number types, math operations, and built-in functions in Python to strengthen your programming skills and handle numeric data effectively.
Learn how to create sets in Python efficiently using concise and readable set comprehensions, improving code quality and Pythonic style.
Learn various methods to remove items from dictionaries in Python, including del, pop(), and popitem(), to efficiently manage data and optimize memory usage.
Learn efficient sorting techniques in Python using built-in methods and custom sorting for better code performance and clarity.