Python-Deep-Dive

Welcome to Python Deep Dive, a comprehensive repository designed to take you on a journey through the entire spectrum of Python programming. This repository is structured to guide you from the fundamentals, through object-oriented programming (OOP), into data structures and algorithms, advanced Python concepts, and finally to the 23 classic design patterns. Additionally, it includes insights into CPython compilation to help you become a Python contributor.

Table of Contents

1. Fundamentals
2. Object-Oriented Programming (OOP)
3. Data Structures
4. Algorithms
5. Design Patterns
6. Advanced Python Deep Dive
7. Compiling CPython
8. Contributing
9. License

Fundamentals

In this section, you'll learn the basics of Python, including:

• Syntax and structure
• Data types and variables
• Control flow (loops and conditionals)
• Functions and modules
• Error handling and exceptions

Start here if you're new to Python or need a refresher on the basics.

Object-Oriented Programming (OOP)

This section dives into OOP concepts in Python. You'll explore:

• Classes and objects
• Inheritance and polymorphism
• Encapsulation and abstraction
• Dependency injection
• Class-level and instance-level attributes

Learn how to design robust object-oriented applications in Python.

Data Structures

Here, you'll discover the built-in and custom data structures used in Python, including:

• Lists, tuples, sets, and dictionaries
• Stacks and queues
• Linked lists
• Trees and graphs
• Heaps and hash tables

Understanding these structures is crucial for building efficient and scalable applications.

Algorithms

In this section, you'll find various algorithms implemented in Python, covering:

• Sorting algorithms (bubble sort, quicksort, mergesort, etc.)
• Searching algorithms (binary search, linear search, etc.)
• Recursion and iterative solutions
• Graph traversal algorithms (DFS, BFS, Dijkstra's algorithm, etc.)
• Dynamic programming and greedy algorithms

These algorithms form the backbone of many computer science problems and interview questions.

Design Patterns

Finally, explore the 23 classic design patterns from the "Gang of Four," with Python implementations, including:

• Creational patterns (Singleton, Factory, Builder, Prototype, Abstract Factory)
• Structural patterns (Adapter, Composite, Bridge, Decorator, Facade, Flyweight, Proxy)
• Behavioral patterns (Chain of Responsibility, Command, Interpreter, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor)

Learn how these patterns can be applied to solve common software design challenges.

Advanced Python Deep Dive

In this section, you'll delve into advanced topics to deepen your understanding of Python:

Part I. Data Structures

1. The Python Data Model
   • A Pythonic Card Deck
   • How Special Methods Are Used
   • Emulating Numeric Types
   • String Representation
   • Boolean Value of a Custom Type
   • Collection API
   • Overview of Special Methods
   • Why len Is Not a Method
2. An Array of Sequences
   • Overview of Built-In Sequences
   • List Comprehensions and Generator Expressions
   • Tuples as Records and Immutable Lists
   • Pattern Matching with Sequences
   • Slicing
   • Arrays, Memory Views, and NumPy
   • Deques and Other Queues
3. Dictionaries and Sets
   • Modern dict Syntax
   • Pattern Matching with Mappings
   • Practical Consequences of How dict and Sets Work
4. Unicode Text Versus Bytes
   • Character Issues
   • Byte Essentials
   • Handling Text Files
   • Normalizing Unicode for Reliable Comparisons
5. Data Class Builders
   • Overview of Data Class Builders
   • Classic Named Tuples and Typed Named Tuples
   • More About @dataclass
6. Object References, Mutability, and Recycling
   • Variables Are Not Boxes
   • Identity, Equality, and Aliases
   • Shallow and Deep Copies
   • Garbage Collection

Part II. Functions as Objects

7. Functions as First-Class Objects
   • Treating a Function Like an Object
   • Higher-Order Functions
   • Modern Replacements for map, filter, and reduce
   • Anonymous Functions
   • User-Defined Callable Types
8. Type Hints in Functions
   • About Gradual Typing
   • Starting with Mypy
   • Types Usable in Annotations
   • Static Protocols
9. Decorators and Closures
   • Decorators 101
   • Variable Scope Rules
   • Closures and the nonlocal Declaration
   • Implementing a Simple Decorator
   • Memoization with functools.cache
10. Design Patterns with First-Class Functions
    • Case Study: Refactoring Strategy
    • Function-Oriented Strategy
    • The Command Pattern

Part III. Classes and Protocols

11. A Pythonic Object
    • Object Representations
    • Formatted Displays
    • A Hashable Vector2d
    • Saving Memory with __slots__
12. Special Methods for Sequences
    • Vector: A User-Defined Sequence Type
    • Protocols and Duck Typing
    • Vector Take #4: Hashing and a Faster ==
13. Interfaces, Protocols, and ABCs
    • Two Kinds of Protocols
    • Defensive Programming and “Fail Fast”
    • Structural Typing with ABCs
14. Inheritance: For Better or for Worse
    • The super() Function
    • Multiple Inheritance and Method Resolution Order
    • Coping with Inheritance
15. More About Type Hints
    • Overloaded Signatures
    • Variance and Generic Types
16. Operator Overloading
    • Operator Overloading 101
    • Rich Comparison Operators

Part IV. Control Flow

17. Iterators, Generators, and Classic Coroutines
    • Using iter with a Callable
    • Generator Functions in the Standard Library
    • Classic Coroutines
18. with, match, and else Blocks
    • Context Managers and with Blocks
    • Pattern Matching in lis.py: A Case Study
19. Concurrency Models in Python
    • Processes, Threads, and Python’s Infamous GIL
    • Spinner with Threads, Processes, and Coroutines
20. Concurrent Executors
    • Concurrent Web Downloads
    • Launching Processes with concurrent.futures
21. Asynchronous Programming
    • An asyncio Example: Probing Domains
    • Downloading with asyncio and HTTPX

Part V. Metaprogramming

22. Dynamic Attributes and Properties
    • Data Wrangling with Dynamic Attributes
    • Flexible Object Creation with __new__
    • Using a Property for Attribute Validation
23. Attribute Descriptors
    • Descriptor Example: Attribute Validation
    • Overriding Descriptors
24. Class Metaprogramming
    • Classes as Objects
    • type: The Built-In Class Factory
    • Metaclasses

Compiling CPython

In this section, you'll learn about contributing to Python by compiling CPython:

1. Compiling CPython
   • Compiling CPython on macOS
   • Compiling CPython on Linux
   • Installing a Custom Version
   • A Quick Primer on Make
   • CPython’s Make Targets
   • Compiling CPython on Windows
   • Profile-Guided Optimization
2. The Python Language and Grammar
   • Why CPython Is Written in C and Not Python
   • The Python Language Specification
   • The Parser Generator
   • Regenerating Grammar
3. Configuration and Input
   • Configuration State
   • Build Configuration
   • Building a Module From Input
4. Lexing and Parsing With Syntax Trees
   • Concrete Syntax Tree Generation
   • The CPython Parser-Tokenizer
   • Abstract Syntax Trees
   • Example: Adding an Almost-Equal Comparison Operator
5. The Compiler
   • Instantiating a Compiler
   • Core Compilation Process
   • Assembly
   • Creating a Code Object
   • Example: Implementing the Almost-Equal Operator
6. The Evaluation Loop
   • Constructing Thread State
   • Frame Execution
   • The Value Stack
   • Example: Adding an Item to a List
7. Memory Management
   • Memory Allocation in C
   • The CPython Memory Allocator
   • Reference Counting
   • Garbage Collection
8. Parallelism and Concurrency
   • Models of Parallelism and Concurrency
   • Multiprocess Parallelism
   • Multithreading
   • Asynchronous Programming
9. Objects and Types
   • Built-in Types
   • Object and Variable Object Types
   • The type Type
   • The Unicode String Type
   • The Dictionary Type
10. The Standard Library
    • Python Modules
    • Python and C Modules
11. The Test Suite
    • Running the Test Suite on Various OS
    • Test Flags and Utilities
12. Debugging
    • Using the Crash Handler
    • Compiling Debug Support
    • Using Debuggers (LLDB, GDB, Visual Studio, CLion)
13. Benchmarking, Profiling, and Tracing
    • Using timeit for Microbenchmarks
    • Using

the Python Benchmark Suite for Runtime Benchmarks - Profiling Python Code with cProfile - Profiling C Code with DTrace 14. Next Steps - Writing C Extensions for CPython - Improving Your Python Applications - Contributing to the CPython Project 15. Appendix: Introduction to C for Python Programmers - The C Preprocessor - Basic C Syntax

Contributing

Contributions are welcome! If you'd like to contribute to this repository, please follow these steps:

1. Fork this repository.
2. Create a new branch with your changes.
3. Open an issue first to discuss the changes you want to make.
4. After the discussion, submit a pull request with a detailed explanation of your contribution.

For major changes, please ensure that the issue is thoroughly discussed before starting the implementation.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Thank you for exploring Python Deep Dive! If you have any questions, suggestions, or feedback, feel free to open an issue or contact.
Regards: Muhammad Hashim