IPython: Productive Interactive Computing
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.
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