( Ebook PDF) Expert Python Programming: Learn Best Practices to Designing, Coding, and Distributing Your Python Software 1st edition by Tarek Ziade 184719494X 9781847194947 Full chapter
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ISBN 10: 184719494X
ISBN 13: 978-1847194947
Author: Tarek Ziade
Expert Python Programming: Learn Best Practices to Designing, Coding, and Distributing Your Python Software is a comprehensive guide aimed at experienced Python developers who wish to enhance their skills and adopt advanced techniques for building robust, efficient, and maintainable Python applications. This book covers a wide range of topics that will help developers refine their programming practices and learn how to optimize their Python code for performance, scalability, and security.
Key Features:
- Best Practices for Writing Python Code: Learn how to write clean, readable, and Pythonic code by following industry standards and applying design patterns that improve code quality.
- Advanced Python Techniques: Explore advanced features like metaprogramming, decorators, generators, and iterators to help you write more efficient and reusable code.
- Object-Oriented Programming: Dive into Python’s object-oriented programming model, exploring classes, inheritance, and polymorphism to structure your code in a more organized and maintainable way.
- Packaging and Distributing Software: Understand how to properly package your Python applications, create command-line interfaces, and distribute your software in a way that is easy for others to use.
- Performance Optimization: Learn techniques for profiling and optimizing your code to improve execution speed and memory usage, including how to use Cython for performance boosts.
- Building Web Applications: Get an overview of web frameworks like Django and Flask, and understand how to build and deploy web applications using Python.
- Testing and Debugging: Develop a deep understanding of testing, debugging, and code profiling to ensure your code is robust and error-free.
- Collaborating and Open Source: Learn how to contribute to open-source projects, use version control systems like Git, and manage Python projects efficiently using
pipand virtual environments.
Expert Python Programming: Learn Best Practices to Designing, Coding, and Distributing Your Python Software 1st Table of contents:
Chapter 1: Introduction to Python Programming
- Overview of Python
- Why Python for Expert Development
- Python’s Design Philosophy
Chapter 2: Mastering Python Fundamentals
- Python Syntax and Semantics
- Advanced Data Structures
- Functions, Modules, and Packages
- Exception Handling in Python
Chapter 3: Object-Oriented Programming (OOP) in Python
- Introduction to OOP Concepts
- Classes and Objects
- Inheritance and Polymorphism
- Encapsulation and Abstraction
- Special Methods in Python
Chapter 4: Pythonic Code and Best Practices
- Writing Readable and Maintainable Code
- Following the PEP-8 Guidelines
- Using List Comprehensions and Generators
- Decorators and Context Managers
- Managing Dependencies
Chapter 5: Advanced Python Features
- Metaprogramming
- Iterators and Generators
- Multiple Inheritance
- Using
__slots__for Memory Optimization
Chapter 6: Working with Data in Python
- Handling Files and Data Streams
- Using Databases in Python
- Working with JSON and XML Data
- Serialization with
pickleandjson
Chapter 7: Building and Distributing Python Software
- Packaging Python Code into Distributable Packages
- Creating Command-Line Interfaces (CLI)
- Unit Testing and Test-Driven Development (TDD)
- Debugging and Profiling Code
- Building and Using Virtual Environments
Chapter 8: Optimizing Python Performance
- Profiling and Benchmarking Python Code
- Optimizing Memory Usage
- Writing High-Performance Python Code
- Concurrency and Parallelism in Python
- Leveraging Cython for Performance Gains
Chapter 9: Working with Web Applications
- Introduction to Web Frameworks (Django, Flask)
- REST APIs and Web Services
- Python for Full-Stack Development
- Deploying Web Applications
Chapter 10: Python for Scientific and Numerical Computing
- Introduction to Scientific Libraries: NumPy, SciPy, Matplotlib
- Working with Pandas for Data Analysis
- Machine Learning with Python (scikit-learn, TensorFlow, PyTorch)
Chapter 11: Collaborating and Contributing to Open Source
- Using Version Control (Git)
- Contributing to Open Source Projects
- Managing Python Projects with
pipandvirtualenv
Chapter 12: Security and Best Practices in Python Development
- Securing Python Applications
- Managing Authentication and Authorization
- Using Secure Libraries and Frameworks
- Best Practices for Writing Secure Code
Chapter 13: Future of Python and Python Development
- Python’s Role in Emerging Technologies
- Staying Up-to-Date with Python’s Advancements
- Python and the Data Science Revolution
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