IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are:

• Powerful interactive Python shells (terminal- and Qt-based).
• A web-based interactive notebook environment with all shell features plus support for embedded figures, animations and rich media.
• Support for interactive data visualization and use of GUI toolkits.
• Flexible, embeddable interpreters to load into your own projects.
• A high-performance library for high level and interactive parallel computing that works in multicore systems, clusters, supercomputing and cloud scenarios.

The enhanced interactive Python shells have the following main features:

• Comprehensive object introspection.
• Input history, persistent across sessions.
• Caching of output results during a session with automatically generated references.
• Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords.
• Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system.
• A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time).
• Session logging and reloading.
• Extensible syntax processing for special purpose situations.
• Access to the system shell with user-extensible alias system.
• Easily embeddable in other Python programs and GUIs.
• Integrated access to the pdb debugger and the Python profiler.

The parallel computing architecture has the following main features:

• Quickly parallelize Python code from an interactive Python/IPython session.
• A flexible and dynamic process model that be deployed on anything from multicore workstations to supercomputers.
• An architecture that supports many different styles of parallelism, from message passing to task farming.
• Both blocking and fully asynchronous interfaces.
• High level APIs that enable many things to be parallelized in a few lines of code.
• Share live parallel jobs with other users securely.
• Dynamically load balanced task farming system.
• Robust error handling in parallel code.

The latest development version is always available from IPython's GitHub site.