Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
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 numer...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
2024
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| Ausgabe: | Third Edition |
| Schlagworte: | |
| Zusammenfassung: | 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 CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis |
| Beschreibung: | X, 690 p. - 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 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. |
| Beschreibung: | 492 Seiten 254 mm |
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| 500 | |a X, 690 p. - 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 | ||
| 500 | |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 CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis | ||
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| spelling | Johansson, Robert Verfasser aut Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib Third Edition Berkeley, CA Apress 2024 492 Seiten 254 mm txt rdacontent n rdamedia nc rdacarrier X, 690 p. - 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 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 CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis bicssc bisacsh Computer software Big data Artificial intelligence Python (Computer program language) Hardcover, Softcover / Informatik, EDV/Programmiersprachen |
| spellingShingle | Johansson, Robert Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib bicssc bisacsh Computer software Big data Artificial intelligence Python (Computer program language) |
| title | Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| title_auth | Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| title_exact_search | Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| title_full | Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| title_fullStr | Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| title_full_unstemmed | Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| title_short | Numerical Python |
| title_sort | numerical python scientific computing and data science applications with numpy scipy and matplotlib |
| title_sub | Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib |
| topic | bicssc bisacsh Computer software Big data Artificial intelligence Python (Computer program language) |
| topic_facet | bicssc bisacsh Computer software Big data Artificial intelligence Python (Computer program language) |
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