-
Book Overview & Buying
-
Table Of Contents
Mastering Python Scientific Computing
By :
Mastering Python Scientific Computing
By:
Overview of this book
In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing.
At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python.
The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs.
Table of Contents (12 chapters)
Preface
Free Chapter
1. The Landscape of Scientific Computing – and Why Python?
2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes
3. Efficiently Fabricating and Managing Scientific Data
4. Scientific Computing APIs for Python
5. Performing Numerical Computing
6. Applying Python for Symbolic Computing
7. Data Analysis and Visualization
8. Parallel and Large-scale Scientific Computing
9. Revisiting Real-life Case Studies
10. Best Practices for Scientific Computing
Index
