Numerical Python

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

  • Robert┬áJohansson

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Robert Johansson
    Pages 1-41
  3. Robert Johansson
    Pages 43-96
  4. Robert Johansson
    Pages 97-134
  5. Robert Johansson
    Pages 135-181
  6. Robert Johansson
    Pages 183-212
  7. Robert Johansson
    Pages 213-242
  8. Robert Johansson
    Pages 243-265
  9. Robert Johansson
    Pages 267-293
  10. Robert Johansson
    Pages 295-333
  11. Robert Johansson
    Pages 335-361
  12. Robert Johansson
    Pages 363-404
  13. Robert Johansson
    Pages 405-441
  14. Robert Johansson
    Pages 443-470
  15. Robert Johansson
    Pages 471-511
  16. Robert Johansson
    Pages 513-541
  17. Robert Johansson
    Pages 543-572
  18. Robert Johansson
    Pages 573-599
  19. Robert Johansson
    Pages 601-640
  20. Robert Johansson
    Pages 641-665
  21. Back Matter
    Pages 667-700

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.


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

Authors and affiliations

  • Robert┬áJohansson
    • 1
  1. 1.Urayasu-shi, ChibaJapan

Bibliographic information

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