國立高雄大學圖資館 |

Language: English

Back

Numerical Pythonscientific computing...

Johansson, Robert.

Numerical Pythonscientific computing and data science applications with Numpy, SciPy and Matplotlib /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Numerical Pythonby Robert Johansson.
Reminder of title:	scientific computing and data science applications with Numpy, SciPy and Matplotlib /
Author:	Johansson, Robert.
Published:	Berkeley, CA :Apress :2024.
Description:	xx, 492 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Python (Computer program language)
Online resource:	https://doi.org/10.1007/979-8-8688-0413-7
ISBN:	9798868804137$q(electronic bk.)

Numerical Pythonscientific computing and data science applications with Numpy, SciPy and Matplotlib /
Johansson, Robert.

Numerical Pythonscientific computing and data science applications with Numpy, SciPy and Matplotlib /[electronic resource] :by Robert Johansson. - Third edition. - Berkeley, CA :Apress :2024. - xx, 492 p. :ill. (some col.), digital ;24 cm.

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 -- Appendix.

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.

ISBN: 9798868804137$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0413-7doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98 / J64 2024

Dewey Class. No.: 005.133

Numerical Pythonscientific computing and data science applications with Numpy, SciPy and Matplotlib /
LDR:03221nmm a2200337 a 4500 001 669768
003 DE-He213
005 20240928131748.0
006 m d
007 cr nn 008maaau
008 250120s2024 cau s 0 eng d
020 $a 9798868804137$q(electronic bk.)
020 $a 9798868804120$q(paper)
024 7 $a 10.1007/979-8-8688-0413-7 $2 doi
035 $a 979-8-8688-0413-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98 $b J64 2024
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b J65 2024
100 1 $a Johansson, Robert. $3 838488
245 1 0 $a Numerical Python $h [electronic resource] : $b scientific computing and data science applications with Numpy, SciPy and Matplotlib / $c by Robert Johansson.
250 $a Third edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xx, 492 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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 -- Appendix.
520 $a 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.
650 0 $a Python (Computer program language) $3 215247
650 0 $a Computer programming. $3 181992
650 1 4 $a Python. $3 763308
650 2 4 $a Mathematical Software. $3 279828
650 2 4 $a Big Data. $3 760530
650 2 4 $a Artificial Intelligence. $3 212515
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0413-7
950 $a Professional and Applied Computing (SpringerNature-12059)