Python High Performance Second Edition

Learn how to use Python to create efficient applications About This Book * Identify the bottlenecks in your applications and solve them using the best profiling techniques * Write efficient numerical code in NumPy Cython and Pandas * Adapt your programs to run on multiple processors and machines with parallel programming Who This Book Is For The book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expected What You Will Learn * Write efficient numerical code with the NumPy and Pandas libraries * Use Cython and Numba to achieve native performance * Find bottlenecks in your Python code using profilers * Write asynchronous code using Asyncio and RxPy * Use Tensorflow and Theano for automatic parallelism in Python * Set up and run distributed algorithms on a cluster using Dask and PySpark In Detail Python is a versatile language that has found applications in many industries. The clean syntax rich standard library and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn 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. By the end of the book readers will have learned to achieve performance and scale from their Python applications. Style and approach A step-by-step practical guide filled with real-world use cases and examples About the Author Dr. Gabriele Lanaro 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. His interests span machine learning numerical computing visualization and web technologies. He has a sheer passion for good software and is the author of the chemlab and chemview open source packages. In 2013 he authored the first edition of the book "High Performance Python Programming". I'd like to acknowledge the support from Packt editors including Vikas Tiwari. I would also like to thank my girlfriend Harani who had to tolerate the way-too-long writing nights and friends who provided company and support throughout. Also as always I'd love to thank my parents for giving me the opportunity to pursue my ambitions. Lastly I would like to thank Blenz coffee for powering the execution engine of this book through electricity and caffeine.