My new book, IPython Interactive Computing and Visualization Cookbook, has just been released! A sequel to my previous beginner-level book on Python for data analysis, this new 500-page book is a complete advanced-level guide to Python for data science. The 100+ recipes cover not only interactive and high-performance computing topics, but also data science methods in statistics, data mining, machine learning, signal processing, image processing, network analysis, and mathematical modeling.
Here is a glimpse of the topics addressed in this book:
- IPython notebook, interactive widgets in IPython 2+
- Best practices in interactive computing: version control, workflows with IPython, testing, debugging, continuous integration...
- Data analysis with pandas, NumPy/SciPy, and matplotlib
- Advanced data visualization with seaborn, Bokeh, mpld3, d3.js, Vispy...
- Code profiling and optimization
- High-performance computing with Numba, Cython, GPGPU with CUDA/OpenCL, MPI, HDF5, Julia...
- Statistical data analysis with SciPy, PyMC, R...
- Machine learning with scikit-learn
- Signal processing with SciPy, image processing with scikit-image and OpenCV
- Analysis of graphs and social networks with NetworkX
- Geographic Information Systems in Python
- Mathematical modeling: dynamical systems, symbolic mathematics with SymPy
All of the code is freely available as IPython notebooks on the book's GitHub repository. This repository is also the place where you can signal errata or propose improvements to any part of the book.
The book uses Python 3, although most of the code will work fine on Python 2.