Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This 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 Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Context management


The new with statement was first introduced in Python 2.5 and has been in use for quite some time. However, there still seems to be confusion regarding its usage, even for experienced Python programmers. The with statement is most commonly used as a context manager that properly manages resources, which is essential in concurrent and parallel programming, where resources are shared across different entities in the concurrent or parallel application.

Starting from managing files

As an experienced Python user, you have probably seen the with statement being used to open and read external files inside Python programs. Looking at this problem at a lower level, the operation of opening an external file in Python will consume a resource—in this case, a file descriptor—and your operating system will set a limit on this resource. This means that there is an upper limit on how many files a single process running on your system can open simultaneously.

Let's consider a quick example...