Book Image

Python High Performance, Second Edition - Second Edition

By : Dr. Gabriele Lanaro
Book Image

Python High Performance, Second Edition - Second Edition

By: Dr. Gabriele Lanaro

Overview of this book

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.
Table of Contents (10 chapters)

Organizing your source code

The repository structure of a typical Python project consists, at a minimum, of a directory containing a README.md file, a Python module or package containing the source code for the application or library, and a setup.py file. Projects may also adopt different conventions to comply with company policies or specific frameworks in use. In this section, we will illustrate some common practices that are commonly found in community-driven Python projects which can include some of the tools we illustrated in the earlier chapters.

A typical directory structure for a Python project named myapp can look like this. Now, we will elucidate the role of each file and directory:

    myapp/ 
README.md
LICENSE
setup.py
myapp/
__init__.py
module1.py
cmodule1.pyx
module2/
__init__.py
src/
module.c
module.h
tests/
...