Numerical Python (eBook)
XXIII, 700 Seiten
Apress (Verlag)
978-1-4842-4246-9 (ISBN)
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.
What You'll Learn
- Work with vectors and matrices using NumPy
- Plot and visualize data with Matplotlib
- Perform data analysis tasks with Pandas and SciPy
- Review statistical modeling and machine learning with statsmodels and scikit-learn
- Optimize Python code using Numba and Cython
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.
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.What You'll LearnWork with vectors and matrices using NumPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyReview statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its related ecosystem for numerical computing.
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.
Numerical Python1. Introduction to Computing with Python2. Vectors, Matrices and Multidimensional Arrays3. Symbolic Computing4. Plotting and Visualization5. Equation Solving6. Optimization7. Interpolation8. Integration9. Ordinary Differential Equations10. Sparse Matrices and Graphs11. Partial Differential Equations12. Data Processing and Analysis13. Statistics14. Statistical Modeling15. Machine Learning16. Bayesian Statistics17. Signal and Image Processing18. Data Input and Output19. Code Optimization
| Erscheint lt. Verlag | 24.12.2018 |
|---|---|
| Zusatzinfo | XXIII, 700 p. 168 illus., 63 illus. in color. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Programmiersprachen / -werkzeuge ► Python | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| Schlagworte | algorithms • Computation • FEniCS • Image Processing • IPython • Jupyter • machine learning • matplotlib • Numerical • NumPy • Python • SciPy • Signal Processing • tensorflow |
| ISBN-10 | 1-4842-4246-7 / 1484242467 |
| ISBN-13 | 978-1-4842-4246-9 / 9781484242469 |
| Haben Sie eine Frage zum Produkt? |
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