國立虎尾科技大學 |

Numerical Python = A Practical Techniques Approach for Industry /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Numerical Python / by Robert Johansson.
Reminder of title:	A Practical Techniques Approach for Industry /
Author:	Johansson, Robert.
Description:	XXII, 487 p. 54 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Python (Computer program language). -
Online resource:	https://doi.org/10.1007/978-1-4842-0553-2
ISBN:	9781484205532

Numerical Python = A Practical Techniques Approach for Industry /
Johansson, Robert.

Numerical Python A Practical Techniques Approach for Industry /[electronic resource] :by Robert Johansson. - 1st ed. 2015. - XXII, 487 p. 54 illus. in color.online resource.

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 -- 20. Appendix: Installation.-.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include: How to work with vectors and matrices using NumPy How to work with symbolic computing using SymPy How to plot and visualize data with Matplotlib How to solve linear and nonlinear equations with SymPy and SciPy How to solve solve optimization, interpolation, and integration problems using SciPy How to solve ordinary and partial differential equations with SciPy and FEniCS How to perform data analysis tasks and solve statistical problems with Pandas and SciPy How to work with statistical modeling and machine learning with statsmodels and scikit-learn How to handle file I/O using HDF5 and other common file formats for numerical data How to optimize Python code using Numba and Cython.

ISBN: 9781484205532

Standard No.: 10.1007/978-1-4842-0553-2doiSubjects--Topical Terms:

1127623
Python (Computer program language).

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Numerical Python = A Practical Techniques Approach for Industry /
LDR:03981nam a22003855i 4500 001 968912
003 DE-He213
005 20200705164628.0
007 cr nn 008mamaa
008 201211s2015 xxu| s |||| 0|eng d
020 $a 9781484205532 $9 978-1-4842-0553-2
024 7 $a 10.1007/978-1-4842-0553-2 $2 doi
035 $a 978-1-4842-0553-2
050 4 $a QA76.73.P98
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
100 1 $a Johansson, Robert. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1070179
245 1 0 $a Numerical Python $h [electronic resource] : $b A Practical Techniques Approach for Industry / $c by Robert Johansson.
250 $a 1st ed. 2015.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2015.
300 $a XXII, 487 p. 54 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 -- 20. Appendix: Installation.-.
520 $a Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include: How to work with vectors and matrices using NumPy How to work with symbolic computing using SymPy How to plot and visualize data with Matplotlib How to solve linear and nonlinear equations with SymPy and SciPy How to solve solve optimization, interpolation, and integration problems using SciPy How to solve ordinary and partial differential equations with SciPy and FEniCS How to perform data analysis tasks and solve statistical problems with Pandas and SciPy How to work with statistical modeling and machine learning with statsmodels and scikit-learn How to handle file I/O using HDF5 and other common file formats for numerical data How to optimize Python code using Numba and Cython.
650 0 $a Python (Computer program language). $3 1127623
650 0 $a Programming languages (Electronic computers). $3 1127615
650 0 $a Computer software. $3 528062
650 1 4 $a Python. $3 1115944
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 669782
650 2 4 $a Mathematical Software. $3 672446
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484205549
776 0 8 $i Printed edition: $z 9781484205556
856 4 0 $u https://doi.org/10.1007/978-1-4842-0553-2
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)