Learning IPython for Interactive Computing and Data Visualization, Second Edition (Cyrille Rossant, http://ipython-books.github.io/minibook/). All the book's code examples are freely available on GitHub as Jupyter Notebooks. Chapter titles: 1. Getting started with IPython; 2. Interactive data analysis with pandas; 3. Numerical computing with NumPy; 4. Interactive plotting and Graphical Interfaces; 5. High-performance and parallel computing; and 6. Customizing IPython.
IPython Interactive Computing and Visualization Cookbook (Cyrille Rossant, http://ipython-books.github.io/cookbook/). This book assumes a basic knowledge of IPython, NumPy, pandas, and matplotlib. Again, all of the code is freely available on GitHub. Chapter titles: 1. A Tour of Interactive Computing with IPython; 2. Best practices in Interactive Computing; 3. Mastering the Notebook; 4. Profiling and Optimization; 5. High-Performance Computing; 6. Advanced Visualization; 7. Statistical Data Analysis; 8. Machine Learning; 9. Numerical Optimization; 10. Signal Processing; 11. Image and Audio Processing; 12. Deterministic Dynamical Systems; 13. Stochastic Dynamical Systems; 14. Graphs, Geometry, and Geographic Information Systems; and 15. Symbolic and Numerical Mathematics.
Chapter 7 covers Bayesian analysis. Section 7.7, "Fitting a Bayesian model by sampling from a posterior distribution with a Markov Chain Monte Carlo method", introduces the PyMC package. This package implements the Markov Chain Monte Carlo method for sampling from the posterior distribution given the observed data.