What this book is
This cookbook contains in excess of a hundred focused recipes, answering specific questions in numerical computing and data analysis with IPython on:
- How to explore a public dataset with pandas, PyMC, and SciPy
- How to create interactive plots, widgets, and Graphical User Interfaces in the IPython notebook
- How to create a configurable IPython extension with custom magic commands
- How to distribute asynchronous tasks in parallel with IPython
- How to accelerate code with OpenMP, MPI, Numba, Cython, OpenCL, CUDA, and the Julia programming language
- How to estimate a probability density from a dataset
- How to get started using the R statistical programming language in the notebook
- How to train a classifier or a regressor with scikit-learn
- How to find interesting projections in a high-dimensional dataset
- How to detect faces in an image
- How to simulate a reaction-diffusion system
- How to compute an itinerary in a road network
The choice made in this book was to introduce a wide range of different topics instead of delving into the details of a few methods. The goal is to give you a taste of the incredibly rich capabilities of Python for data science. All methods are applied on diverse real-world examples.
Every recipe of this book demonstrates not only how to apply a method, but also how and why it works. It is important to understand the mathematical concepts and ideas underlying the methods instead of merely applying them blindly.
Additionally, each recipe comes with many references for the interested reader who wants to know more. As online references change frequently, they will be kept up to date on the book's website (http://ipython-books.github.io).
