Python High Performance, Second Edition

Enhance Python applications using advanced techniques About This Book* Identify the bottlenecks in your applications and solve them using the best profiling techniques* Write efficient numerical code in NumPy and Cython* Adapt your programs to run on multiple processors with parallel programmingWho This Book Is For Python is a versatile language and has found its application in a variety of industries. Along with its strong language constructs, it is also supported by a massive selection of third-party libraries. 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 shows how to implement reactive programming to write robust and concurrent code. It also shows how to build microservices in Python. Readers will learn to perform machine learning using Tensorflow and Theano and use a cluster of computers for large scale computations. By the end of the book, readers will have learned to achieve the best performance from their Python applications. What you will learn* Effectively develop multidimensional arrays using the NumPy and Pandas libraries* Use Cython to get native performance from your code* Find bottlenecks in your Python code using profilers* Write asynchronous code using Asyncio and RxPy* Use Tensorflow and Theano for machine learning in Python* Implement native Python libraries to scale applications* Set up and run distributed processing libraries such as dask and pysparkIn Detail Python is a versatile language and has found its application in a variety of industries. Along with its strong language constructs, it is also supported by a massive selection of third-party libraries. 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 shows how to implement reactive programming to write robust and concurrent code. It also shows how to build microservices in Python. Readers will learn to perform machine learning using Tensorflow and Theano and use a cluster of computers for large scale computations. By the end of the book, readers will have learned to achieve the best performance from their Python applications.