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Numerical Python

Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

  • Book
  • © 2024
  • Latest edition

Overview

  • Revised and updated with examples using the numerical and mathematical modules in Python and its standard library
  • Understand open-source numerical Python packages like NumPy, SciPy, SymPy, matplotlib and more
  • Applications include those from science, engineering, data analysis, and big data computing
  • 46k Accesses

  • 4 Citations

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About this book

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.

Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. 

After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.

What You'll Learn

  • Work with vectors and matrices using NumPy
  • Review Symbolic computing with SymPy
  • Plot and visualize data with Matplotlib
  • Perform data analysis tasks with Pandas and SciPy
  • Understand statistical modeling and machine learning with statsmodels and scikit-learn
  • Optimize Python code using Numba and Cython

Who This Book Is For

Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis. 

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Table of contents (19 chapters)

Authors and Affiliations

  • Urayasu-shi, Chiba, Japan

    Robert Johansson

About the author

Robert Johansson is an experienced Python programmer and computational scientist with a Ph.D. in Theoretical Physics from Chalmers University of Technology, Sweden. He has worked with scientific computing in academia and industry for over 15 years and participated in open source and proprietary research and development projects. His open-source contributions include work on QuTiP, a popular Python framework for simulating the dynamics of quantum systems, and he has also contributed to several other popular Python libraries in the scientific computing landscape. Robert is passionate about scientific computing and software development, teaching and communicating best practices for combining these fields with optimal outcomes: novel, reproducible, extensible, and impactful computational results.

Accessibility Information

PDF accessibility summary

This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.

EPUB accessibility summary

This ebook is designed with accessibility in mind, aiming to meet the ePub Accessibility 1.0 AA and WCAG 2.0 Level AA standards. Its features include described images and other non-text content, screenreader-friendly navigation and accessible math. Math is represented either as MathML, LaTeX or in images. If math is represented as image, Alt Text might not be present. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.

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