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  • © 2019

Numerical Python

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

Apress
  • Revised and updated with new examples using the numerical and mathematical modules in Python and its standard library

  • Understand open source numerical Python packages like NumPy, FiPy, Pillow, matplotlib and more

  • Applications include those from business management, big data/cloud computing, financial engineering and games

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-4246-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 54.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (19 chapters)

  1. Front Matter

    Pages i-xxiii
  2. Introduction to Computing with Python

    • Robert Johansson
    Pages 1-41
  3. Vectors, Matrices, and Multidimensional Arrays

    • Robert Johansson
    Pages 43-96
  4. Symbolic Computing

    • Robert Johansson
    Pages 97-134
  5. Plotting and Visualization

    • Robert Johansson
    Pages 135-181
  6. Equation Solving

    • Robert Johansson
    Pages 183-212
  7. Optimization

    • Robert Johansson
    Pages 213-242
  8. Interpolation

    • Robert Johansson
    Pages 243-265
  9. Integration

    • Robert Johansson
    Pages 267-293
  10. Ordinary Differential Equations

    • Robert Johansson
    Pages 295-333
  11. Sparse Matrices and Graphs

    • Robert Johansson
    Pages 335-361
  12. Partial Differential Equations

    • Robert Johansson
    Pages 363-404
  13. Data Processing and Analysis

    • Robert Johansson
    Pages 405-441
  14. Statistics

    • Robert Johansson
    Pages 443-470
  15. Statistical Modeling

    • Robert Johansson
    Pages 471-511
  16. Machine Learning

    • Robert Johansson
    Pages 513-541
  17. Bayesian Statistics

    • Robert Johansson
    Pages 543-572
  18. Signal Processing

    • Robert Johansson
    Pages 573-599
  19. Data Input and Output

    • Robert Johansson
    Pages 601-640
  20. Code Optimization

    • Robert Johansson
    Pages 641-665

About this book

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. 

Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for 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.

Keywords

  • Python
  • numerical
  • NumPy
  • SciPy
  • computation
  • algorithms
  • FEniCS
  • TensorFlow
  • Signal Processing
  • Image Processing
  • matplotlib
  • Jupyter
  • IPython
  • Machine learning

Reviews

“I would recommend the textbook to those interested in learning the Python ecosystem for numerical and scientific work. I enjoyed reading the style of examples where a few lines of code are explained at a time. This style feels like I'm getting a personalized lecture from Johansson while reading the book. It will be a very nice resource on the desk of any graduate student working with Python.” (Charles Jekel, SIAM Review, Vol. 62 (2), 2020)

Authors and Affiliations

  • Urayasu-shi, Chiba, Japan

    Robert Johansson

About the author

Robert Johansson is a numerical Python expert and computational scientist who has worked with SciPy, NumPy and QuTiP, an open-source Python framework for simulating the dynamics of quantum systems.

Bibliographic Information

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-4246-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 54.99
Price excludes VAT (USA)