SciPy and NumPy. An Overview for Developers - Helion
ISBN: 978-14-493-6162-4
stron: 82, Format: ebook
Data wydania: 2012-11-15
Księgarnia: Helion
Cena książki: 33,92 zł (poprzednio: 39,44 zł)
Oszczędzasz: 14% (-5,52 zł)
Are you new to SciPy and NumPy? Do you want to learn it quickly and easily through examples and a concise introduction? Then this is the book for you. You’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries.
Ideal for data analysts and scientists in any field, this overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and loading and saving data. You’ll learn how SciPy helps you work with advanced mathematical functions such as optimization, interpolation, integration, clustering, statistics, and other tools that take scientific programming to a whole new level.
The new edition is now available, fully revised and updated in June 2013.
- Learn the capabilities of NumPy arrays, element-by-element operations, and core mathematical operations
- Solve minimization problems quickly with SciPy’s optimization package
- Use SciPy functions for interpolation, from simple univariate to complex multivariate cases
- Apply a variety of SciPy statistical tools such as distributions and functions
- Learn SciPy’s spatial and cluster analysis classes
- Save operation time and memory usage with sparse matrices
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Spis treści
SciPy and NumPy. An Overview for Developers eBook -- spis treści
- SciPy and NumPy
- Preface
- Audience
- Contents of this Book
- Conventions Used in This Book
- Using Code Examples
- Wed Like to Hear from You
- Safari Books Online
- Acknowledgments
- Content Updates
- January 31, 2013
- May 29, 2013
- 1. Introduction
- 1.1 Why SciPy and NumPy?
- 1.2 Getting NumPy and SciPy
- 1.3 Working with SciPy and NumPy
- 1.4 Source Code
- 2. NumPy
- 2.1 NumPy Arrays
- 2.1.1 Array Creation and Data Typing
- 2.1.2 Record Arrays
- 2.1.3 Indexing and Slicing
- 2.2 Boolean Statements and NumPy Arrays
- 2.3 Read and Write
- 2.3.1 Text Files
- 2.3.2 Binary Files
- 2.4 Math
- 2.4.1 Basic Operations
- 2.4.2 Linear Algebra
- 2.4.3 Random Sampling & Statistics
- 2.4.4 Polynomials
- 2.1 NumPy Arrays
- 3. SciPy
- 3.1 Optimization
- 3.1.1 Data Modeling and Fitting
- 3.1.2 Solutions to Functions
- 3.2 Interpolation
- 3.3 Integration
- 3.3.1 Analytic Integration
- 3.3.2 Numerical Integration
- 3.4 Statistics
- 3.4.1 Continuous and Discrete Distributions
- 3.4.2 Functions
- 3.5 Spatial and Clustering Analysis
- 3.5.1 Vector Quantization
- 3.5.2 Hierarchical Clustering
- 3.6 Signal and Image Processing
- 3.7 Sparse Matrices
- 3.8 Reading and Writing Files Beyond NumPy
- 3.1 Optimization
- 4. SciKit: Taking SciPy One Step Further
- 4.1 Scikit-Image
- 4.1.1 Dynamic Threshold
- 4.1.2 Local Maxima
- 4.2 Scikit-Learn
- 4.2.1 Linear Regression
- 4.2.2 Clustering
- 4.1 Scikit-Image
- 5. Conclusion
- 5.1 Summary
- 5.2 Whats Next?
- About the Author
- Colophon
- Copyright