Book Image

Mastering Numerical Computing with NumPy

By : Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu
Book Image

Mastering Numerical Computing with NumPy

By: Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu

Overview of this book

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations.
Table of Contents (11 chapters)

Why do we need a benchmark?

As you advance with your programming skills, you will start to implement more efficient programs. You will search dozens of code repositories to see how others are solving similar problems, and you will find those rare gems that will amaze you.

Throughout this progress of writing better software and implementing systems, you will need ways to measure and track the rate of improvement. You will generally consider your starting point as a baseline and see how the improvements you make will add up to performance metrics.

Once you set the baseline, you will benchmark several different implementations and will have a chance to compare these in terms of the performance metrics you choose. You can choose various metrics and need to decide these in advance.

Performance metrics for these benchmarks will be kept rather simple, and only the time spent metric will...