Data Science · Ebook

SciPy: Scientific Computing in Python

by Shriira Press

A comprehensive, self-contained guide to SciPy, the library that turns NumPy's fast arrays into a full scientific-computing toolkit — optimization, integration, interpolation, signal processing, statistics, linear algebra, sparse matrices, and more. Where NumPy gives you the ndarray and vectorized math, SciPy gives you the algorithms scientists and engineers actually need: fit a curve, minimize a function, solve a differential equation, filter a signal, run a hypothesis test, compute an FFT. This book teaches it module by module — optimize, integrate, interpolate, linalg, stats, signal, fft, sparse, spatial, ndimage — blending intuition (what each method is for), concepts (the numerical idea behind it), and runnable code.

Contents

1. 1Preface
2. 2Chapter 1 — What Is SciPy?
3. 3Chapter 2 — SciPy and NumPy: The Array Foundation
4. 4Chapter 3 — Special Functions and Constants
5. 5Chapter 4 — Optimization and Root Finding
6. 6Chapter 5 — Integration and Differential Equations
7. 7Chapter 6 — Interpolation
8. 8Chapter 7 — Linear Algebra
9. 9Chapter 8 — Statistics
10. 10Chapter 9 — Signal Processing
11. 11Chapter 10 — Fourier Transforms
12. 12Chapter 11 — Sparse Matrices and Spatial Algorithms
13. 13Chapter 12 — Image Processing and N-dimensional Data
14. 14Chapter 13 — SciPy in Practice and the Profession
15. 15Appendix A — Glossary and Submodule Map
16. 16Appendix B — Further Reading and Resources