Advanced Python Programming

Create distributed applications with clever design patterns to solve complex problemsKey FeaturesSet up and run distributed algorithms on a cluster using Dask and PySparkMaster skills to accurately implement concurrency in your codeGain practical experience of Python design patterns with real-world examplesBook DescriptionThis 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 LanaroMastering Concurrency in Python by Quan NguyenMastering Python Design Patterns by Sakis KasampalisWhat you will learnUse NumPy and pandas to import and manipulate datasetsAchieve native performance with Cython and NumbaWrite asynchronous code using asyncio and RxPyDesign highly scalable programs with application scaffoldingExplore abstract methods to maintain data consistencyClone objects using the prototype patternUse the adapter pattern to make incompatible interfaces compatibleEmploy the strategy pattern to dynamically choose an algorithmWho this book is forThis 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. 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 data scientist and Python enthusiast. He has a dual degree in mathematics and computer science with a minor in philosophy from DePauw University. Quan is interested in scientific computing and machine learning and enjoys incorporating technology automation into everyday tasks through programming. Quan's passion for Python has led him to be heavily involved in the Python community. He started as a primary contributor to the book Python for Scientists and Engineers and various open source projects on GitHub. Quan is also a writer for the Python Software Foundation and a content contributor for DataScience.com. He is currently pursuing a Ph.D. in computer science at Washington University in St. Louis.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 was also the technical reviewer of Mastering Object-oriented Python and Learning Python Design Patterns published by Packt Publishing.