Advanced Python Tutorial
Python is a versatile programming language widely used across various technical domains. While certain applications only require basic knowledge of Python, advanced fields like Data Science, Artificial Intelligence, and Robotics demand a deeper understanding of Python’s capabilities.
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Pre-requisites
Before diving into advanced Python topics, ensure you have a solid grasp of basic Python concepts.
Advanced Python Topics
1. Advanced Conditional Statements
Learn complex conditional structures to handle intricate decision-making processes efficiently.
2. List Comprehension
Master the art of creating concise and readable lists using list comprehension.
3. Python Dictionary Comprehension
Create dictionaries dynamically using dictionary comprehension techniques.
Functional Programming Concepts
4. Lambda Operator
Use anonymous functions for short-term use in your code.
5. Filter
Apply the filter() function to extract elements from an iterable based on a condition.
6. Reduce
Perform cumulative operations on iterables using the reduce() function.
7. Map
Transform iterables by applying a function to every element using the map() function.
8. Recursive Functions
Understand and implement recursive solutions for problems like factorial calculation, Fibonacci series, and more.
Regular Expressions
9. Basics of Regular Expressions
Learn to match, search, and manipulate text effectively using regex patterns.
10. Advanced Regular Expressions
Dive deeper into regex for complex pattern matching, including the following use cases:
- Check if a string starts and ends with the same character.
- Password validation.
- Extracting email addresses.
- Validating UPI IDs.
Object-Oriented Programming (OOP)
11. Core Concepts of OOP
- Classes and Objects: Build reusable and modular code structures.
- Inheritance: Share and extend functionalities across classes.
- Encapsulation: Protect sensitive data by bundling it with methods.
- Polymorphism: Implement multiple forms of a method.
- Data Abstraction: Simplify complex systems by showing only the necessary details.
12. Operator Overloading
Customize how operators behave for user-defined objects.
Iterables, Iterators, and Generators
13. Difference Between Iterable and Iterator
Understand the distinctions and how to use them effectively.
14. Python __iter__() and __next__()
Explore iterator protocols in Python.
15. Generators in Python
Learn how to create efficient iterables with generator functions.
Closures and Decorators
16. Closures
Encapsulate function logic along with the environment it was defined in.
17. Decorators
- Create simple decorators.
- Define decorators with arguments.
- Build classes as decorators.
18. Monkey Patching
Dynamically modify or extend behavior at runtime.
Special Methods
19. @staticmethod and @classmethod
- @staticmethod: Define methods that don’t depend on the class or instance.
- @classmethod: Access class-specific data within methods.
Memory Management
20. Garbage Collection and Reference Counting
Understand how Python manages memory efficiently.
21. Mutable vs Immutable Objects
Learn the differences and their implications on memory usage.
22. Memory Profiling
Analyze memory usage to optimize your Python programs.
23. Deep Copy vs Shallow Copy
Understand the key differences to avoid common pitfalls.
Optimization Tips
24. How Python Stores Integers
Delve into how Python handles memory allocation for integers.
Testing
25. Testing with Pytest
Write robust and reusable test cases using Pytest.
26. DocTests and UnitTests
Validate your code directly from docstrings and write modular test cases.
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