Python
While there are a plethora of advanced topics that you can dive into now, here are a few that I recommend you getting a good grasp of, some of these you will have seen throughout as we have progressed.
Decorators are functions that modify or enhance other functions or methods. They are often used for tasks like logging, authentication, and memoization. Decorators are denoted by the “@” symbol before a function definition.
Generators are a memory-efficient way to generate sequences of values using lazy evaluation. They use the yield keyword to produce values one at a time. Generator expressions are concise ways to create generators using a similar syntax to list comprehensions.
asyncio is a Python library for asynchronous programming. It allows you to write concurrent code using async and await syntax, making it easier to manage asynchronous tasks such as I/O-bound operations.
Metaclasses are classes that define the behavior of other classes, acting as templates for class creation. They allow you to customize class creation and behavior, making them a powerful tool for advanced code manipulation.
Context managers, often used with the with statement, help manage resources like files, network connections, and database connections. They ensure proper resource allocation and deallocation.
The itertools module provides a set of fast, memory-efficient tools for working with iterators. It includes functions like cycle, chain, count, and more for creating and manipulating iterators.
The enumerate function adds a counter to an iterable, allowing you to iterate over both the items and their indices simultaneously.
Lambda functions are anonymous, one-liner functions. map and filter are built-in functions that apply a function to each item in an iterable and filter items based on a condition, respectively.
classmethods are bound to the class and can access class-level attributes, while staticmethods are similar to regular functions but reside within a class’s namespace.
The property decorator allows you to define getter, setter, and deleter methods for class attributes, making them appear as regular attributes.
The dataclasses module simplifies the creation of classes that are primarily used to store data, automatically generating special methods like __init__, __repr__, and more.
Type hinting involves annotating variables and function parameters with their expected types. This enhances code readability, IDE autocompletion, and enables static type checking using tools like mypy.
To further your understanding of Python, I would also suggest taking some code challenges to really boost your understanding, especially of OOP and how we can build better software through specific design patterns, before you get started on looking at more difficult libraries, such as NumPy or TensorFlow.
