Introduction

Overview

One of Python’s most useful features is its interactive interpreter. This system allows very fast testing of ideas without the overhead of creating test files as is typical in most programming languages. However, the interpreter supplied with the standard Python distribution is somewhat limited for extended interactive use.

The goal of IPython is to create a comprehensive environment for interactive and exploratory computing. To support, this goal, IPython has two main components:

• An enhanced interactive Python shell.
• An architecture for interactive parallel computing.

All of IPython is open source (released under the revised BSD license).

Enhanced interactive Python shell

IPython’s interactive shell (ipython), has the following goals, amongst others:

1. Provide an interactive shell superior to Python’s default. IPython has many features for object introspection, system shell access, and its own special command system for adding functionality when working interactively. It tries to be a very efficient environment both for Python code development and for exploration of problems using Python objects (in situations like data analysis).
2. Serve as an embeddable, ready to use interpreter for your own programs. IPython can be started with a single call from inside another program, providing access to the current namespace. This can be very useful both for debugging purposes and for situations where a blend of batch-processing and interactive exploration are needed. New in the 0.9 version of IPython is a reusable wxPython based IPython widget.
3. Offer a flexible framework which can be used as the base environment for other systems with Python as the underlying language. Specifically scientific environments like Mathematica, IDL and Matlab inspired its design, but similar ideas can be useful in many fields.
4. Allow interactive testing of threaded graphical toolkits. IPython has support for interactive, non-blocking control of GTK, Qt and WX applications via special threading flags. The normal Python shell can only do this for Tkinter applications.

Main features of the interactive shell

• Dynamic object introspection. One can access docstrings, function definition prototypes, source code, source files and other details of any object accessible to the interpreter with a single keystroke (?, and using ?? provides additional detail).
• Searching through modules and namespaces with * wildcards, both when using the ? system and via the %psearch command.
• Completion in the local namespace, by typing TAB at the prompt. This works for keywords, modules, methods, variables and files in the current directory. This is supported via the readline library, and full access to configuring readline’s behavior is provided. Custom completers can be implemented easily for different purposes (system commands, magic arguments etc.)
• Numbered input/output prompts with command history (persistent across sessions and tied to each profile), full searching in this history and caching of all input and output.
• User-extensible ‘magic’ commands. A set of commands prefixed with % is available for controlling IPython itself and provides directory control, namespace information and many aliases to common system shell commands.
• Alias facility for defining your own system aliases.
• Complete system shell access. Lines starting with ! are passed directly to the system shell, and using !! or var = !cmd captures shell output into python variables for further use.
• Background execution of Python commands in a separate thread. IPython has an internal job manager called jobs, and a convenience backgrounding magic function called %bg.
• The ability to expand python variables when calling the system shell. In a shell command, any python variable prefixed with $ is expanded. A double $$ allows passing a literal $ to the shell (for access to shell and environment variables like PATH).
• Filesystem navigation, via a magic %cd command, along with a persistent bookmark system (using %bookmark) for fast access to frequently visited directories.
• A lightweight persistence framework via the %store command, which allows you to save arbitrary Python variables. These get restored automatically when your session restarts.
• Automatic indentation (optional) of code as you type (through the readline library).
• Macro system for quickly re-executing multiple lines of previous input with a single name. Macros can be stored persistently via %store and edited via %edit.
• Session logging (you can then later use these logs as code in your programs). Logs can optionally timestamp all input, and also store session output (marked as comments, so the log remains valid Python source code).
• Session restoring: logs can be replayed to restore a previous session to the state where you left it.
• Verbose and colored exception traceback printouts. Easier to parse visually, and in verbose mode they produce a lot of useful debugging information (basically a terminal version of the cgitb module).
• Auto-parentheses: callable objects can be executed without parentheses: sin 3 is automatically converted to sin(3).
• Auto-quoting: using ,, or ; as the first character forces auto-quoting of the rest of the line: ,my_function a b becomes automatically my_function("a","b"), while ;my_function a b becomes my_function("a b").
• Extensible input syntax. You can define filters that pre-process user input to simplify input in special situations. This allows for example pasting multi-line code fragments which start with >>> or ... such as those from other python sessions or the standard Python documentation.
• Flexible configuration system. It uses a configuration file which allows permanent setting of all command-line options, module loading, code and file execution. The system allows recursive file inclusion, so you can have a base file with defaults and layers which load other customizations for particular projects.
• Embeddable. You can call IPython as a python shell inside your own python programs. This can be used both for debugging code or for providing interactive abilities to your programs with knowledge about the local namespaces (very useful in debugging and data analysis situations).
• Easy debugger access. You can set IPython to call up an enhanced version of the Python debugger (pdb) every time there is an uncaught exception. This drops you inside the code which triggered the exception with all the data live and it is possible to navigate the stack to rapidly isolate the source of a bug. The %run magic command (with the -d option) can run any script under pdb’s control, automatically setting initial breakpoints for you. This version of pdb has IPython-specific improvements, including tab-completion and traceback coloring support. For even easier debugger access, try %debug after seeing an exception. winpdb is also supported, see ipy_winpdb extension.
• Profiler support. You can run single statements (similar to profile.run()) or complete programs under the profiler’s control. While this is possible with standard cProfile or profile modules, IPython wraps this functionality with magic commands (see %prun and %run -p) convenient for rapid interactive work.
• Doctest support. The special %doctest_mode command toggles a mode that allows you to paste existing doctests (with leading >>> prompts and whitespace) and uses doctest-compatible prompts and output, so you can use IPython sessions as doctest code.

Interactive parallel computing

Increasingly, parallel computer hardware, such as multicore CPUs, clusters and supercomputers, is becoming ubiquitous. Over the last 3 years, we have developed an architecture within IPython that allows such hardware to be used quickly and easily from Python. Moreover, this architecture is designed to support interactive and collaborative parallel computing.

The main features of this system are:

• 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. And all of these styles can be handled interactively.
• Both blocking and fully asynchronous interfaces.
• High level APIs that enable many things to be parallelized in a few lines of code.
• Write parallel code that will run unchanged on everything from multicore workstations to supercomputers.
• Full integration with Message Passing libraries (MPI).
• Capabilities based security model with full encryption of network connections.
• Share live parallel jobs with other users securely. We call this collaborative parallel computing.
• Dynamically load balanced task farming system.
• Robust error handling. Python exceptions raised in parallel execution are gathered and presented to the top-level code.

For more information, see our overview of using IPython for parallel computing.

Portability and Python requirements

As of the 0.9 release, IPython requires Python 2.4 or greater. We have not begun to test IPython on Python 2.6 or 3.0, but we expect it will work with some minor changes.

IPython is known to work on the following operating systems:

• Linux
• AIX
• Most other Unix-like OSs (Solaris, BSD, etc.)
• Mac OS X
• Windows (CygWin, XP, Vista, etc.)

See here for instructions on how to install IPython.