Bibliography

This is a curated list of some NumPy related resources (articles, books & tutorials) addressing different aspects of NumPy. Some are very specific to NumPy/Scipy while some others offer a broader view on numerical computing.

Tutorials

• 100 Numpy exercises, Nicolas P. Rougier, 2016.
• Numpy tutorial, Nicolas P. Rougier, 2015.
• Python course, Bernd Klein, 2015.
• An introduction to Numpy and Scipy, M. Scott Shell, 2014.
• Python Numpy tutorial, Justin Johnson, 2014.
• Quickstart tutorial, Numpy developers, 2009.
• Numpy medkits, Stéfan van der Walt, 2008.

Articles

• The NumPy array: a structure for efficient numerical computation

  Stéfan van der Walt, Chris Colbert & Gael Varoquaux, Computing in Science and Engineering, 13(2), 2011.

  In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.

• Vectorised algorithms for spiking neural network simulation

  Romain Brette & Dan F. M. Goodman, Neural Computation, 23(6), 2010.

  High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.

• Python for Scientific Computing

  Travis E. Oliphant, Computing in Science & Engineering, 9(3), 2007.

  By itself, Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific and engineering code that's often fast enough to be immediately useful but also flexible enough to be sped up with additional extensions.

Books

• SciPy Lecture Notes,

  Gaël Varoquaux, Emmanuelle Gouillart, Olav Vahtras et al., 2016.

  One document to learn numerics, science, and data with Python. Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.

• Python Data Science Handbook

  Jake van der Plas, O'Reilly, 2016.

  The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data—intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: for example, how can you read this data format into your script? How can you manipulate, transform, and clean this data? How can you use this data to gain insight, answer questions, or to build statistical or machine learning models?

• Elegant SciPy: The Art of Scientific Python

  Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, O'Reilly, 2016.

  Welcome to Scientific Python and its community! With this practical book, you'll learn the fundamental parts of SciPy and related libraries, and get a taste of beautiful, easy-to-read code that you can use in practice. More and more scientists are programming, and the SciPy library is here to help. Finding useful functions and using them correctly, efficiently, and in easily readable code are two very different things. You'll learn by example with some of the best code available, selected to cover a wide range of SciPy and related libraries—including scikit-learn, scikit-image, toolz, and pandas.

• Learning IPython for Interactive Computing and Data Visualization

  Cyrille Rossant, Packt Publishing, 2015.

  This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.

• SciPy and NumPy

  Eli Bressert, O'Reilly Media, Inc., 2012

  Are you new to SciPy and NumPy? Do you want to learn it quickly and easily through examples and concise introduction? Then this is the book for you. You’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries.

• Python for Data Analysis

  Wes McKinney, O'Reilly Media, Inc., 2012.

  Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? This hands-on book is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using several Python libraries.*

• Guide to NumPy

  Travis Oliphant, 2006

  This book only briefly outlines some of the infrastructure that surrounds the basic objects in NumPy to provide the additional functionality contained in the older Numeric package (i.e. LinearAlgebra, RandomArray, FFT). This infrastructure in NumPy includes basic linear algebra routines, Fourier transform capabilities, and random number generators. In addition, the f2py module is described in its own documentation, and so is only briefly mentioned in the second part of the book.