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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.
What You'll Learn
Plot and visualize data with Matplotlib
Perform data analysis tasks with Pandas and SciPy
Review statistical modeling and machine learning with statsmodels and scikit-learn
Optimize Python code using Numba and Cython
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.
What You'll Learn
Plot and visualize data with Matplotlib
Perform data analysis tasks with Pandas and SciPy
Review statistical modeling and machine learning with statsmodels and scikit-learn
Optimize Python code using Numba and Cython
Revised and updated with new examples using the numerical and mathematical modules in Python and its standard library
Understand open source numerical Python packages like NumPy, FiPy, Pillow, matplotlib and more
Applications include those from business management, big data/cloud computing, financial engineering and games
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.
| Erscheinungsjahr: | 2018 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 700 |
| Inhalt: |
xxiii
700 S. 105 s/w Illustr. 63 farbige Illustr. 700 p. 168 illus. 63 illus. in color. |
| ISBN-13: | 9781484242452 |
| ISBN-10: | 1484242459 |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-4245-2 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Johansson, Robert |
| Auflage: | 2nd edition |
| Hersteller: | APRESS |
| Maße: | 254 x 178 x 38 mm |
| Von/Mit: | Robert Johansson |
| Erscheinungsdatum: | 25.12.2018 |
| Gewicht: | 1,337 kg |
Revised and updated with new examples using the numerical and mathematical modules in Python and its standard library
Understand open source numerical Python packages like NumPy, FiPy, Pillow, matplotlib and more
Applications include those from business management, big data/cloud computing, financial engineering and games
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.
| Erscheinungsjahr: | 2018 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 700 |
| Inhalt: |
xxiii
700 S. 105 s/w Illustr. 63 farbige Illustr. 700 p. 168 illus. 63 illus. in color. |
| ISBN-13: | 9781484242452 |
| ISBN-10: | 1484242459 |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-4245-2 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Johansson, Robert |
| Auflage: | 2nd edition |
| Hersteller: | APRESS |
| Maße: | 254 x 178 x 38 mm |
| Von/Mit: | Robert Johansson |
| Erscheinungsdatum: | 25.12.2018 |
| Gewicht: | 1,337 kg |
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