Introduction
Overview
One of Python’s most useful features is its interactive interpreter.
It allows for 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 three main components:
- An enhanced interactive Python shell.
- A decoupled two-process communication model, which
allows for multiple clients to connect to a computation kernel, most notably
the web-based notebook
- 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:
- Provide an interactive shell superior to Python’s default. IPython
has many features for tab-completion, object introspection, system shell
access, command history retrieval across sessions, 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).
- Serve as an embeddable, ready to use interpreter for your own
programs. An interactive IPython shell 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.
- Offer a flexible framework which can be used as the base
environment for working with other systems, with Python as the underlying
bridge language. Specifically scientific environments like Mathematica,
IDL and Matlab inspired its design, but similar ideas can be
useful in many fields.
- Allow interactive testing of threaded graphical toolkits. IPython
has support for interactive, non-blocking control of GTK, Qt, WX, GLUT, and
OS X 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.
- 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
when you run the %store -r command.
- 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 via the %macro command. 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 via the %autocall command: 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.
- 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.
- Simple timing information. You can use the %timeit command to get
the execution time of a Python statement or expression. This machinery is
intelligent enough to do more repetitions for commands that finish very
quickly in order to get a better estimate of their running time.
In [1]: %timeit 1+1
10000000 loops, best of 3: 25.5 ns per loop
In [2]: %timeit [math.sin(x) for x in range(5000)]
1000 loops, best of 3: 719 µs per loop
To get the timing information for more than one expression, use the
%%timeit cell magic command.
- Doctest support. The special %doctest_mode command toggles a mode
to use doctest-compatible prompts, so you can use IPython sessions as
doctest code. By default, IPython also allows you to paste existing
doctests, and strips out the leading >>> and ... prompts in
them.
Decoupled two-process model
IPython has abstracted and extended the notion of a traditional
Read-Evaluate-Print Loop (REPL) environment by decoupling the evaluation
into its own process. We call this process a kernel: it receives execution
instructions from clients and communicates the results back to them.
This decoupling allows us to have several clients connected to the same
kernel, and even allows clients and kernels to live on different machines.
With the exclusion of the traditional single process terminal-based IPython
(what you start if you run ipython without any subcommands), all
other IPython machinery uses this two-process model. This includes ipython
console, ipython qtconsole, and ipython notebook.
As an example, this means that when you start ipython qtconsole, you’re
really starting two processes, a kernel and a Qt-based client can send
commands to and receive results from that kernel. If there is already a kernel
running that you want to connect to, you can pass the --existing flag
which will skip initiating a new kernel and connect to the most recent kernel,
instead. To connect to a specific kernel once you have several kernels
running, use the %connect_info magic to get the unique connection file,
which will be something like --existing kernel-19732.json but with
different numbers which correspond to the Process ID of the kernel.
You can read more about using ipython qtconsole, and
ipython notebook. There is also a message spec which documents the protocol for communication between kernels
and clients.
Interactive parallel computing
Increasingly, parallel computer hardware, such as multicore CPUs, clusters and
supercomputers, is becoming ubiquitous. Over the last several 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 1.0 release, IPython works with Python 2.6, 2.7, 3.2 and 3.3.
Version 0.12 introduced full support for Python 3. Version 0.11 worked with
Python 2.6 and 2.7 only. Versions 0.9 and 0.10 worked with Python 2.4 and
above (not including Python 3).
IPython is known to work on the following operating systems:
- Linux
- Most other Unix-like OSs (AIX, Solaris, BSD, etc.)
- Mac OS X
- Windows (CygWin, XP, Vista, etc.)
See here for instructions on how to install IPython.
