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)

NumPy internals

As you have seen in previous chapters, NumPy arrays make numerical computations efficient and its API is intuitive and easy to use. NumPy array are also core to other scientific libraries as many of them are built on top of NumPy arrays.

In order to write better and more efficient code, you need to understand the internals of data handling. A NumPy array and its metadata live in a data buffer, which is a dedicated block of memory with certain data items.

How does NumPy manage memory?

Once you initialize a NumPy array, its metadata and data are stored at allocated memory locations in Random Access Memory (RAM).

import numpy as np
array_x = np.array([100.12, 120.23, 130.91])

First, Python is a dynamically typed...