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

High-performance labeled data with xarray

With NumPy, we can manipulate multidimensional numerical data and perform mathematical computations that are highly optimized by low-level C and FORTRAN code. On the other hand, we have seen that pandas allows us to work with labeled, categorical data that resembles data tables using database-like operations.

These two tools complement each other: NumPy does not allow categorical data to be mixed in with numerical values, while pandas is mostly limited to two-dimensional, database-like datasets. Combining these tools can help address many data processing needs, but when we are faced with big, multidimensional data that is also labeled, many performance-related problems arise.

In the last section of this chapter, we will discuss xarray, a library that combines the best of both the NumPy and the pandas worlds and offers one of the best tools for working with labeled multidimensional data. We will explore some of its most prominent features...