Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Adding static types

In Python, a variable can be associated with objects of different types during the execution of the program. While this feature is desirable as it makes the language flexible and dynamic, it also adds significant overhead to the interpreter as it needs to look up the type and methods of the variables at runtime, making it difficult to perform various optimizations. Cython extends the Python language with explicit type declarations so that it can generate efficient C extensions through compilation.

The main way to declare data types in Cython is through cdef statements. The cdef keyword can be used in multiple contexts, such as variables, functions, and extension types (statically typed classes). We will see how to do this in the following subsections.

Declaring variables

In Cython, you can declare the type of a variable by prepending the variable with cdef and its respective type. For example, we can declare the i variable as a 16-bit integer in the following...