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Numerical Python

A Practical Techniques Approach for Industry

  • Robert┬áJohansson

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Robert Johansson
    Pages 1-24
  3. Robert Johansson
    Pages 25-62
  4. Robert Johansson
    Pages 63-88
  5. Robert Johansson
    Pages 89-123
  6. Robert Johansson
    Pages 125-145
  7. Robert Johansson
    Pages 147-168
  8. Robert Johansson
    Pages 169-186
  9. Robert Johansson
    Pages 187-206
  10. Robert Johansson
    Pages 207-234
  11. Robert Johansson
    Pages 235-254
  12. Robert Johansson
    Pages 255-284
  13. Robert Johansson
    Pages 285-311
  14. Robert Johansson
    Pages 313-332
  15. Robert Johansson
    Pages 333-362
  16. Robert Johansson
    Pages 363-382
  17. Robert Johansson
    Pages 383-404
  18. Robert Johansson
    Pages 405-424
  19. Robert Johansson
    Pages 425-451
  20. Robert Johansson
    Pages 453-470
  21. Robert Johansson
    Pages 471-479
  22. Back Matter
    Pages 471-487

About this book

Introduction

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.
Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work.
            
After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computat
ional methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include:
 
  • How to work with vectors and matrices using NumPy
  • How to work with symbolic computing using SymPy
  • How to plot and visualize data with Matplotlib
  • How to solve linear and nonlinear equations with SymPy and SciPy
  • How to solve solve optimization, interpolation, and integration problems using SciPy
  • How to solve ordinary and partial differential equations with SciPy and FEniCS
  • How to perform data analysis tasks and solve statistical problems with Pandas and SciPy
  • How to work with statistical modeling and machine learning with statsmodels and scikit-learn
  • How to handle file I/O using HDF5 and other common file formats for numerical data
  • How to optimize Python code using Numba and Cython

  • Keywords

    Python numerical NumPy SciPy computation algorithms FEniCS

    Authors and affiliations

    • Robert┬áJohansson
      • 1
    1. 1.Urayasu-shiJapan

    Bibliographic information

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