Advanced Python Programming

Advanced Python Programming


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Create distributed applications with clever design patterns to solve complex problems

Key Features
  • Set up and run distributed algorithms on a cluster using Dask and PySpark
  • Master skills to accurately implement concurrency in your code
  • Gain practical experience of Python design patterns with real-world examples
Book Description

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing.

By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems.

This Learning Path includes content from the following Packt products:

  • Python High Performance - Second Edition by Gabriele Lanaro
  • Mastering Concurrency in Python by Quan Nguyen
  • Mastering Python Design Patterns by Sakis Kasampalis
What you will learn
  • Use NumPy and pandas to import and manipulate datasets
  • Achieve native performance with Cython and Numba
  • Write asynchronous code using asyncio and RxPy
  • Design highly scalable programs with application scaffolding
  • Explore abstract methods to maintain data consistency
  • Clone objects using the prototype pattern
  • Use the adapter pattern to make incompatible interfaces compatible
  • Employ the strategy pattern to dynamically choose an algorithm
Who this book is for

This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.

Product Details

ISBN-13: 9781838551216
Publisher: Packt Publishing
Publication date: 02/22/2019
Pages: 672
Sales rank: 606,032
Product dimensions: 7.50(w) x 9.25(h) x 1.35(d)

About the Author

Dr. Gabriele Lanaro is passionate about good software and is the author of the chemlab and chemview open source packages. His interests span machine learning, numerical computing visualization, and web technologies. In 2013, he authored the first edition of the book High Performance Python Programming. He has been conducting research to study the formation and growth of crystals using medium and large-scale computer simulations. In 2017, he obtained his PhD in theoretical chemistry. Quan Nguyen is a Python enthusiast and data scientist. Currently, he works as a data analysis engineer at Micron Technology, Inc. With a strong background in mathematics and statistics, Quan is interested in the fields of scientific computing and machine learning. With data analysis being his focus, Quan also enjoys incorporating technology automation into everyday tasks through programming. Quan's passion for Python programming has led him to be heavily involved in the Python community. He started as a primary contributor for the Python for Scientists and Engineers book and various open source projects on GitHub. Quan is also a writer for the Python software foundation and an occasional content contributor for (part of Oracle). Sakis Kasampalis is a software engineer living in the Netherlands. He is not dogmatic about particular programming languages and tools; his principle is that the right tool should be used for the right job. One of his favorite tools is Python because he finds it very productive. Sakis has also technically reviewed the Mastering Object-oriented Python and Learning Python Design Patterns books, both published by Packt Publishing.

Table of Contents

Table of Contents

  1. Benchmarking and Profiling
  2. Pure Python Optimizations
  3. Fast Array Operations with NumPy and Pandas
  4. C Performance with Cython
  5. Exploring Compilers
  6. Implementing Concurrency
  7. Parallel Processing
  8. Advanced Introduction to Concurrent and Parallel Programming
  9. Amdahl's Law
  10. Working with Threads in Python
  11. Using the with Statement in Threads
  12. Concurrent Web Requests
  13. Working with Processes in Python
  14. Reduction Operators in Processes
  15. Concurrent Image Processing
  16. Introduction to Asynchronous Programming
  17. Implementing Asynchronous Programming in Python
  18. Building Communication Channels with asyncio
  19. Deadlocks
  20. Starvation
  21. Race Conditions
  22. The Global Interpreter Lock
  23. The Factory Pattern
  24. The Builder Pattern
  25. Other Creational Patterns
  26. The Adapter Pattern
  27. The Decorator Pattern
  28. The Bridge Pattern
  29. The Facade Pattern
  30. Other Structural Patterns
  31. The Chain of Responsibility Pattern
  32. The Command Pattern
  33. The Observer Pattern

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