Numerical python : Scientific computing and data science applications with numpy, SciPy and matplotlib / By Robert Johansson.

By:

Johansson, Robert

Material type: Text

TextPublication details: New York : Apress , 2022.Edition: 2nd edDescription: xxiii, 700p. ; 24 cmISBN:

9781484242452 (print)

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

005.1372 2 JOHR

LOC classification:

QA76.73.P98

Online resources:

View this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. An e-book available through full-text database.

Contents:

1. Introduction to Computing with Python -- 2. Vectors, Matrices and Multidimensional Arrays -- 3. Symbolic Computing -- 4. Plotting and Visualization -- 5. Equation Solving -- 6. Optimization -- 7. Interpolation -- 8. Integration -- 9. Ordinary Differential Equations -- 10. Sparse Matrices and Graphs -- 11. Partial Differential Equations -- 12. Data Processing and Analysis -- 13. Statistics -- 14. Statistical Modeling -- 15. Machine Learning -- 16. Bayesian Statistics -- 17. Signal and Image Processing -- 18. Data Input and Output -- 19. Code Optimization.

In: Springer eBooksSummary: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Barcode
Book	St Aloysius Institute of Management & Information Technology	MCA	005.1372 JOHR (Browse shelf(Opens below))	Available	MCA17063

Total holds: 0

Browsing St Aloysius Institute of Management & Information Technology shelves, Collection: MCA Close shelf browser (Hides shelf browser)

Previous								Next
Previous	005.1372 HOSC Powershell and python together : targeting digital investigations /	005.1372 JARK Python web scraping: fetching data from the web. Fetching data from the web.	005.1372 JOHP Make your own python text adventure : guide to learning programming /	005.1372 JOHR Numerical python : Scientific computing and data science applications with numpy, SciPy and matplotlib /	005.1372 JOSP Open cv with python by example: build real world computer vision applications and develop cool demos using open cv for python. Build real world computer vision applications and develop cool demos using open cv for python.	005.1372 JOSP OpenCV with python by example: build real world computer vision applications and develop cool demos using openCV for python. Build real world computer vision applications and develop cool demos using openCV for python.	005.1372 KALI Learn to program with python 3 : step by step guide to programming /	Next

Requires an SPL library card.

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.

Mode of access: World Wide Web.

There are no comments on this title.

to post a comment.