The Resource Numerical Python : a practical techniques approach for industry, Robert Johansson
Numerical Python : a practical techniques approach for industry, Robert Johansson
Resource Information
The item Numerical Python : a practical techniques approach for industry, Robert Johansson represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Numerical Python : a practical techniques approach for industry, Robert Johansson represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Summary
 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 highlevel and easytoworkwith 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 arraybased and symbolic computing, allaround practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domainspecific 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 scikitlearn 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
 Language
 eng
 Extent
 1 online resource
 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
 20. Appendix: Installation.
 Isbn
 9781484205532
 Label
 Numerical Python : a practical techniques approach for industry
 Title
 Numerical Python
 Title remainder
 a practical techniques approach for industry
 Statement of responsibility
 Robert Johansson
 Language
 eng
 Summary
 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 highlevel and easytoworkwith 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 arraybased and symbolic computing, allaround practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domainspecific 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 scikitlearn 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
 Cataloging source
 N$T
 http://library.link/vocab/creatorName
 Johansson, Robert
 Dewey number
 005.13/3
 Index
 index present
 LC call number
 QA76.73.P98
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 The expert's voice in Python
 http://library.link/vocab/subjectName

 Python (Computer program language)
 Computer programming
 Programming & scripting languages: general
 Mathematical & statistical software
 COMPUTERS
 Computer programming
 Python (Computer program language)
 Label
 Numerical Python : a practical techniques approach for industry, Robert Johansson
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 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  20. Appendix: Installation.
 Control code
 923250349
 Dimensions
 unknown
 Extent
 1 online resource
 File format
 unknown
 Form of item
 online
 Isbn
 9781484205532
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9781484205532
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000673
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)923250349
 Label
 Numerical Python : a practical techniques approach for industry, Robert Johansson
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 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  20. Appendix: Installation.
 Control code
 923250349
 Dimensions
 unknown
 Extent
 1 online resource
 File format
 unknown
 Form of item
 online
 Isbn
 9781484205532
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9781484205532
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000673
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)923250349
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.missouri.edu/portal/NumericalPythonapracticaltechniques/TpTOu8WRwMc/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/NumericalPythonapracticaltechniques/TpTOu8WRwMc/">Numerical Python : a practical techniques approach for industry, Robert Johansson</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>