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Numerical Python -  Robert Johansson

Numerical Python (eBook)

Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
eBook Download: PDF
2024 | 3., Third Edition
XX, 492 Seiten
Apress (Verlag)
979-8-8688-0413-7 (ISBN)
Systemvoraussetzungen
66,99 inkl. MwSt
(CHF 65,45)
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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. 


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 CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis. 
Erscheint lt. Verlag 27.9.2024
Zusatzinfo XX, 492 p. 165 illus., 155 illus. in color.
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Datenbanken
Informatik Programmiersprachen / -werkzeuge Python
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik Computerprogramme / Computeralgebra
Mathematik / Informatik Mathematik Statistik
Schlagworte algorithms • Computation • FEniCS • Image Processing • IPython • Jupyter • machine learning • matplotlib • Numerical • NumPy • Python • SciPy • Signal Processing • tensorflow
ISBN-13 979-8-8688-0413-7 / 9798868804137
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