• Title: Scientific Visualisation: Python and Matplotlib
• Author(s): Nicolas P. Rougier
• Publisher: AFNIL (November 13, 2021); eBook (HAL, Creative Commons Licensed, 2021)
• License(s): CC BY-NC-ND 4.0
• Hardcover/Paperback: 247 pages
• eBook: PDF (249 pages, 95Mb)
• Language: English
• ISBN-10/ASIN: 2957990105
• ISBN-13: 978-2957990108
• Share This:  

Book Description

Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Through practical, hands-on and straightforward examples, the book guides you through Data Visualization and Exploration using Python and Matplotlib.

• N/A

• Amazon

• Python Programming
• Data Analysis, Mining, and Visualization
• Visualization and GUI (Graphic User Interface) Programming
• Computational and Mathematical Simulations and Modeling

• Scientific Visualisation: Python and Matplotlib (Nicolas P. Rougier)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF
• Book Homepage

• Python and OpenGL for Scientific Visualization (Nicolas P. Rougier)

  The goal of this book is to reconcile Python programmers with OpenGL, providing both an introduction to modern OpenGL and a set of basic and advanced techniques in order to achieve both fast, scalable & beautiful scientific visualizations.

• Introduction to Scientific Programming with Python

  This book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, assuming little or no prior experience in programming.

• Fundamentals of Data Visualization: Informative Figures

  This book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures, teaches you the elements most critical to successful data visualization.

• Data Visualization: A Practical Introduction (Kieran Healy)

  It provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods.

• Python Data Science Handbook: Essential Tools (Jake VanderPlas)

  Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

• Python for Everybody: Exploring Data in Python 3

  This book is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.

• Automate the Boring Stuff with Python (Albert Sweigart)

  Learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You'll create Python programs that effortlessly perform useful and impressive feats of automation.