Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib - Softcover

Johansson, Robert

 
9798868804120: Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Softcover
ISBN 13: 9798868804120
Verlag: Apress, 2024

Inhaltsangabe

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. 

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Robert Johansson is an experienced Python programmer and computational scientist with a Ph.D. in Theoretical Physics from Chalmers University of Technology, Sweden. He has worked with scientific computing in academia and industry for over 15 years and participated in open source and proprietary research and development projects. His open-source contributions include work on QuTiP, a popular Python framework for simulating the dynamics of quantum systems, and he has also contributed to several other popular Python libraries in the scientific computing landscape. Robert is passionate about scientific computing and software development, teaching and communicating best practices for combining these fields with optimal outcomes: novel, reproducible, extensible, and impactful computational results.

Von der hinteren Coverseite

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

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Apress
Erscheinungsdatum
2024
Sprache
Englisch
ISBN 13 
9798868804120
Einband
Tapa blanda
Auflage
3
Anzahl der Seiten
514
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar