What this book covers

Chapter 1, Benchmark and Profiling, will teach you how to assess the performance of Python programs and practical strategies on how to identify and isolate the slow sections of your code.

Chapter 2, Pure Python Optimizations, discusses how to improve your running times by order of magnitudes using the efficient data structures and algorithms available in the Python standard library and pure-Python third-party modules.

Chapter 3, Fast Array Operations with NumPy and Pandas, is a guide to the NumPy and Pandas packages. Mastery of these packages will allow you to implement fast numerical algorithms with an expressive, concise interface.

Chapter 4, C Performance with Cython, is a tutorial on Cython, a language that uses a Python-compatible syntax to generate efficient C code.

Chapter 5, Exploring Compilers, covers tools that can be used to compile Python to efficient machine code. The chapter will teach you how to use Numba, an optimizing compiler for Python functions, and PyPy, an alternative interpreter that can execute and optimize Python programs on the fly.

Chapter 6, Implementing Concurrency, is a guide to asynchronous and reactive programming. We will learn about key terms and concepts, and demonstrate how to write clean, concurrent code using the asyncio and RxPy frameworks.

Chapter 7, Parallel Processing, is an introduction to parallel programming on multi-core processors and GPUs. In this chapter, you will learn to achieve parallelism using the multiprocessing module and by expressing your code using Theano and Tensorflow.

Chapter 8, Distributed Processing, extends the content of the preceding chapter by focusing on running parallel algorithms on distributed systems for large-scale problems and big data. This chapter will cover the Dask, PySpark, and mpi4py libraries.

Chapter 9, Designing for High Performance, discusses general optimization strategies and best practices to develop, test, and deploy your high-performance Python applications.