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 and pandas

When you think about it, NumPy is a fairly low-level array-manipulation library, and the majority of other Python libraries are written on top of it.

One of these libraries is pandas, which is a high-level data-manipulation library. When you are exploring a dataset, you usually perform operations such as calculating descriptive statistics, grouping by a certain characteristic, and merging. The pandas library has many friendly functions to perform these various useful operations.

Let's use a diabetes dataset in this example. The diabetes dataset in sklearn.datasets is standardized with a zero mean and unit L2 norm.

The dataset contains 442 records with 10 features: age, sex, body mass index, average blood pressure, and six blood serum measurements.

The target represents the disease progression after these baseline measures are taken. You can look at the data...