Introduction

IPython is an open source platform for interactive and parallel computing. It offers powerful interactive shells and a browser-based notebook. The notebook combines code, text, mathematical expressions, inline plots, interactive plots, and other rich media within a sharable web document. This platform provides an ideal framework for interactive scientific computing and data analysis. IPython has become essential to researchers, data scientists, and teachers.

IPython can be used with the Python programming language, but the platform also supports many other languages such as R, Julia, Haskell, or Ruby. The architecture of the project is indeed language-agnostic, consisting of messaging protocols and interactive clients (including the browser-based notebook). The clients are connected to kernels that implement the core interactive computing facilities. Therefore, the platform can be useful to technical and scientific communities that use languages other than Python.

In July 2014, Project Jupyter was announced by the IPython developers. This project will focus on the language-independent parts of IPython (including the notebook architecture), whereas the name IPython will be reserved to the Python kernel. In this book, for the sake of simplicity, we will just use the term IPython to refer to either the platform or the Python kernel.

Python is a high-level general-purpose language originally conceived by Guido van Rossum in the late 1980s (the name was inspired by the British comedy Monty Python's Flying Circus). This easy-to-use language is the basis of many scripting programs that glue different software components (glue language) together. In addition, Python comes with an extremely rich standard library (the batteries included philosophy), which covers string processing, Internet Protocols, operating system interfaces, and many other domains.

In the late 1990s, Travis Oliphant and others started to build efficient tools to deal with numerical data in Python: Numeric, Numarray, and finally, NumPy. SciPy, which implements many numerical computing algorithms, was also created on top of NumPy. In the early 2000s, John Hunter created matplotlib to bring scientific graphics to Python. At the same time, Fernando Perez created IPython to improve interactivity and productivity in Python. All the fundamental tools were here to turn Python into a great open source high-performance framework for scientific computing and data analysis.

In the late 2000s, Wes McKinney created pandas for the manipulation and analysis of numerical tables and time series. At the same time, the IPython developers started to work on a notebook client inspired by mathematical software such as Sage, Maple, and Mathematica. Finally, IPython 0.12, released in December 2011, introduced the HTML-based notebook that has now gone mainstream.

In 2013, the IPython team received a grant from the Sloan Foundation and a donation from Microsoft to support the development of the notebook. IPython 2.0, released in early 2014, brought many improvements and long-awaited features.

Here is a short summary of the changes brought by IPython 2.0 (succeeding v1.1):

IPython 3.0 and 4.0, planned for late 2014/early 2015, should facilitate the use of non-Python kernels and provide multiuser capabilities to the notebook.

Here are a few references: