SciPy and NumPy

Table of contents

1. SciPy and NumPy
2. Preface
   1. Audience
   2. Contents of this Book
   3. Conventions Used in This Book
   4. Using Code Examples
   5. We’d Like to Hear from You
   6. Safari® Books Online
   7. Acknowledgments
   8. Content Updates
      1. January 31, 2013
      2. May 29, 2013
3. 1. Introduction
   1. 1.1 Why SciPy and NumPy?
   2. 1.2 Getting NumPy and SciPy
   3. 1.3 Working with SciPy and NumPy
   4. 1.4 Source Code
4. 2. NumPy
   1. 2.1 NumPy Arrays
      1. 2.1.1 Array Creation and Data Typing
      2. 2.1.2 Record Arrays
      3. 2.1.3 Indexing and Slicing
   2. 2.2 Boolean Statements and NumPy Arrays
   3. 2.3 Read and Write
      1. 2.3.1 Text Files
      2. 2.3.2 Binary Files
   4. 2.4 Math
      1. 2.4.1 Basic Operations
      2. 2.4.2 Linear Algebra
      3. 2.4.3 Random Sampling & Statistics
      4. 2.4.4 Polynomials
5. 3. SciPy
   1. 3.1 Optimization
      1. 3.1.1 Data Modeling and Fitting
      2. 3.1.2 Solutions to Functions
   2. 3.2 Interpolation
   3. 3.3 Integration
      1. 3.3.1 Analytic Integration
      2. 3.3.2 Numerical Integration
   4. 3.4 Statistics
      1. 3.4.1 Continuous and Discrete Distributions
      2. 3.4.2 Functions
   5. 3.5 Spatial and Clustering Analysis
      1. 3.5.1 Vector Quantization
      2. 3.5.2 Hierarchical Clustering
   6. 3.6 Signal and Image Processing
   7. 3.7 Sparse Matrices
   8. 3.8 Reading and Writing Files Beyond NumPy
6. 4. SciKit: Taking SciPy One Step Further
   1. 4.1 Scikit-Image
      1. 4.1.1 Dynamic Threshold
      2. 4.1.2 Local Maxima
   2. 4.2 Scikit-Learn
      1. 4.2.1 Linear Regression
      2. 4.2.2 Clustering
7. 5. Conclusion
   1. 5.1 Summary
   2. 5.2 What’s Next?
8. About the Author
9. Colophon
10. Copyright