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Mastering Numerical Computing with NumPy

You're reading from Mastering Numerical Computing with NumPy Master scientific computing and perform complex operations with ease

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788993357

Length 248 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Scientific Computing

Authors (3):

Umit Mert Cakmak

Mert Cuhadaroglu

Tiago Antao

View More author details

Table of Contents (16) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Working with NumPy Arrays FREE CHAPTER

2. Linear Algebra with NumPy

3. Exploratory Data Analysis of Boston Housing Data with NumPy Statistics

4. Predicting Housing Prices Using Linear Regression

5. Clustering Clients of a Wholesale Distributor Using NumPy

6. NumPy, SciPy, Pandas, and Scikit-Learn

7. Advanced Numpy

8. Overview of High-Performance Numerical Computing Libraries

9. Performance Benchmarks

1. Other Books You May Enjoy

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Index

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 description at https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html and a related paper at http://web.stanford.edu...

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Authors (3)

Mert Cakmak

Mert Cakmak

Umit Mert Cakmak is a data scientist at IBM, where he excels at helping clients solve complex data science problems, from inception to delivery of deployable assets. His research spans multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

See other products by Mert Cakmak

Tiago Antao

Tiago Antao

Tiago Antao is a bioinformatician currently working in the field of genomics. A former computer scientist, Tiago moved into computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto (Portugal) and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine (UK). Postdoctoral, Tiago has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequencing data at the University of Oxford (UK), before helping to set up the bioinformatics infrastructure at the University of Montana. He currently works as a data engineer in the biotechnology field in Boston, MA. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.

See other products by Tiago Antao

Cuhadaroglu

Cuhadaroglu

Mert Cuhadaroglu is a BI Developer in EPAM, developing E2E analytics solutions for complex business problems in various industries, mostly investment banking, FMCG, media, communication, and pharma. He consistently uses advanced statistical models and ML algorithms to provide actionable insights. Throughout his career, he has worked in several other industries, such as banking and asset management. He continues his academic research in AI for trading algorithms.

See other products by Cuhadaroglu

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