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Python Advance
This advanced Python course is designed for programmers who already have a solid foundation in Python basics and wish to deepen their understanding of the language's powerful features and advanced capabilities. The course covers essential advanced topics such as object-oriented programming (OOP) in depth, metaprogramming, decorators, generators, concurrency, asynchronous programming, memory management, testing, and working with external libraries and modules.
Course Chapters
Chapter 1: Advanced Object-Oriented Programming
Advanced concepts in Python's object-oriented programming were explored, focusing on multiple inheritance, method resolution order, and the use of abstract base classes. Additionally, the chapter highlighted the differences between class methods and static methods, alongside how property decorators manage attribute access. Finally, the significance of data classes, named tuples, mixins, and favoring composition over inheritance for flexible designs were discussed.
Chapter 2: Python Decorators and Descriptors
The chapter provides an in-depth exploration of decorators and descriptors in Python, covering their definitions, functionality, and applications. It presents various types of decorators including function decorators, decorators with parameters, class decorators, and built-in decorators like @property, @staticmethod, and @classmethod. Additionally, the chapter introduces the descriptor protocol and its methods, emphasizing the creation of custom descriptors for robust attribute management and validation in Python.
Chapter 3: Generators and Iterators
The chapter explores the concepts of iterators and generators in Python, emphasizing their efficiency in handling data streams. It covers the iterator protocol, the definition and benefits of generators, the use of 'yield' and 'yield from', and practical applications like lazy evaluation and data pipelines. Through clear examples, it demonstrates how these constructs can optimize memory usage and streamline data processing in Python applications.
Chapter 4: Context Managers and the with Statement
Context managers are essential for managing resources effectively in Python, ensuring that resources are allocated and released properly while reducing boilerplate code. The with statement simplifies resource management by encapsulating setup and teardown logic within objects that implement a defined interface. Implementation options range from custom classes to generator-based context managers, covering practical examples and exception handling mechanisms.
Chapter 5: Metaprogramming and Dynamic Code in Python
Metaprogramming in Python provides developers with powerful tools to dynamically manipulate and generate classes and their behaviors at runtime. Essential techniques include utilizing metaclasses, dynamically creating methods and attributes, and leveraging the built-in type function for class generation. Although metaprogramming simplifies coding and enables the creation of advanced frameworks, it requires careful application to maintain code readability.
Chapter 6: Functional Programming Tools in Python
Functional programming in Python emphasizes treating computation as the evaluation of functions while minimizing mutable data and side effects. Key tools such as first-class functions, higher-order functions, and the functools module enable the construction of clean, efficient code. The chapter also highlights the principles of immutability and pure functions, which contribute to more predictable and testable programming.
Chapter 7: Concurrency and Parallelism in Python
Chapter 8: Asynchronous Programming with asyncio
Chapter 9: Memory Management and Performance Optimization in Python
Python's memory management is primarily characterized by automatic memory allocation and deallocation through techniques such as reference counting and garbage collection. This chapter explores various methods to monitor memory usage, profile performance, and optimize code for efficiency. By implementing strategies like using generators, built-in functions, and leveraging libraries such as NumPy and Cython, developers can significantly enhance performance and resource utilization in Python applications.
Chapter 10: Testing, Debugging, and Logging
Writing effective tests, applying debugging techniques, and implementing efficient logging are crucial for robust software development. These practices help ensure code correctness, maintainability, and reliability, allowing developers to build professional Python applications. Mastering these tools leads to the overall improvement of software quality.
Chapter 11: Packaging, Distribution, and Virtual Environments
This chapter covers essential skills for packaging and distributing Python code, enabling code to be reusable and maintainable. Key processes include structuring Python code, managing dependencies, creating isolated environments, and publishing packages to the Python Package Index. Mastery of tools like setuptools and twine ensures efficient workflows in professional Python development.
Chapter 12: Working with External Libraries and APIs
Integrating third-party libraries and APIs is essential for efficient Python development, enabling the creation of scalable applications and facilitating data handling from various sources. Libraries like requests, BeautifulSoup, and pandas provide vital tools for web requests, data extraction, and data management. Mastery of these libraries fosters automation capabilities and enhances productivity, marking a professional transition in programming skills.