Advanced Python” usually refers to using more complex or advanced features of the Python programming language to write efficient, maintainable, and scalable code. Here are some topics and concepts that fall under the realm of Advanced Python:

1. Generators and Iterators: Using generators and iterators to handle large data sets efficiently, avoiding memory overload and improving performance.

2. Decorators: Implementing and understanding decorators to modify the behavior of functions or methods without changing their code directly.

3. Context Managers: Creating context managers using the with statement to manage resources and ensure proper cleanup.

4. Metaclasses: Exploring and understanding metaclasses, which allow you to control the behavior of class creation and modification.

5. Closures: Utilizing closures to create functions with persistent states or to implement encapsulation.

6. Asynchronous Programming: Working with Python’s asyncio library to write asynchronous code for improved concurrency.

7. Multi-threading and Multi-processing: Utilizing threads and processes to manage concurrent execution of tasks in Python.

8. List Comprehensions and Generator Expressions: Writing concise and efficient code using list comprehensions and generator expressions.

9. Context Managers: Creating custom context managers to handle resource management and cleanup.

10. Magic Methods: Understanding and using Python’s special methods (also known as magic methods or dunder methods) to customize class behavior.

11. Functional Programming: Exploring functional programming techniques in Python, including lambda functions and higher-order functions.

12. Type Annotations and Type Hints: Using type annotations and type hints to improve code readability and maintainability, and making use of tools like MyPy for static type checking.

13. Data Classes: Leveraging data classes to create simple classes mainly used to store data without writing boilerplate code.

14. Regular Expressions: Mastering regular expressions for more advanced string pattern matching and manipulation.

15. Profiling and Optimization: Profiling Python code and optimizing performance using tools like cProfile and memory_profiler.

Remember that advanced Python concepts are not always necessary, and using them appropriately depends on the specific requirements of your project or application. It’s essential to strike a balance between readability, maintainability, and performance when implementing advanced Python features. Additionally, always prioritize writing code that is easy to understand and maintain over using advanced techniques solely for the sake of complexity.