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 :2019.
Description:	xxiii, 700 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Python (Computer program language)
Online resource:	https://doi.org/10.1007/978-1-4842-4246-9
ISBN:	9781484242469$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. - 2nd ed. - Berkeley, CA :Apress :2019. - xxiii, 700 p. :ill., 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.

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.

ISBN: 9781484242469$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-4246-9doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98 / J643 2019

Dewey Class. No.: 005.133

Numerical Pythonscientific computing and data science applications with Numpy, SciPy and Matplotlib /
LDR:02832nmm a2200337 a 4500 001 556043
003 DE-He213
005 20190718173325.0
006 m d
007 cr nn 008maaau
008 191121s2019 cau s 0 eng d
020 $a 9781484242469$q(electronic bk.)
020 $a 9781484242452$q(paper)
024 7 $a 10.1007/978-1-4842-4246-9 $2 doi
035 $a 978-1-4842-4246-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98 $b J643 2019
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 J653 2019
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 2nd ed.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xxiii, 700 p. : $b ill., 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.
520 $a 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.
650 0 $a Python (Computer program language) $3 215247
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 eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-4246-9
950 $a Professional and Applied Computing (Springer-12059)