This book is the teaching material on the scientific Python ecosystem, a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.
Beginning with general programming concepts such as loops and functions within the core Python language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of Python to build rich-media, shareable documents for scientific analysis.
Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.
Table of Contents:
- One document to learn numerics, science, and data with Python
- Getting started with Python for science
- Scientific computing with tools and workflow
- The Python language
- NumPy: creating and manipulating numerical data
- Matplotlib: plotting
- Scipy : high-level scientific computing
- Getting help and finding documentation
- Advanced topics
- Advanced Python Constructs
- Advanced Numpy
- Debugging code
- Optimizing code
- Sparse Matrices in SciPy
- Image manipulation and processing using Numpy and Scipy
- Mathematical optimization: finding minima of functions
- Interfacing with C
- Packages and applications
- Statistics in Python
- Sympy : Symbolic Mathematics in Python
- Scikit-image: image processing
- Traits: building interactive dialogs
- 3D plotting with Mayavi
- scikit-learn: machine learning in Python .
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