This documentation covers IPython versions 6.0 and higher. Beginning with version 6.0, IPython stopped supporting compatibility with Python versions lower than 3.3 including all versions of Python 2.7.

If you are looking for an IPython version compatible with Python 2.7, please use the IPython 5.x LTS release and refer to its documentation (LTS is the long term support release).

IPython reference

Command-line usage

You start IPython with the command:

$ ipython [options] files

If invoked with no options, it executes all the files listed in sequence and exits. If you add the -i flag, it drops you into the interpreter while still acknowledging any options you may have set in your This behavior is different from standard Python, which when called as python -i will only execute one file and ignore your configuration setup.

Please note that some of the configuration options are not available at the command line, simply because they are not practical here. Look into your configuration files for details on those. There are separate configuration files for each profile, and the files look like or Profile directories look like profile_profilename and are typically installed in the IPYTHONDIR directory, which defaults to $HOME/.ipython. For Windows users, HOME resolves to C:\Users\YourUserName in most instances.

Command-line Options

To see the options IPython accepts, use ipython --help (and you probably should run the output through a pager such as ipython --help | less for more convenient reading). This shows all the options that have a single-word alias to control them, but IPython lets you configure all of its objects from the command-line by passing the full class name and a corresponding value; type ipython --help-all to see this full list. For example:

$ ipython --help-all
--matplotlib=<CaselessStrEnum> (InteractiveShellApp.matplotlib)
    Default: None
    Choices: ['auto', 'gtk', 'gtk3', 'inline', 'nbagg', 'notebook', 'osx', 'qt', 'qt4', 'qt5', 'tk', 'wx']
    Configure matplotlib for interactive use with the default matplotlib

Indicate that the following:

$ ipython --matplotlib qt

is equivalent to:

$ ipython --TerminalIPythonApp.matplotlib='qt'

Note that in the second form, you must use the equal sign, as the expression is evaluated as an actual Python assignment. While in the above example the short form is more convenient, only the most common options have a short form, while any configurable variable in IPython can be set at the command-line by using the long form. This long form is the same syntax used in the configuration files, if you want to set these options permanently.

Interactive use

IPython is meant to work as a drop-in replacement for the standard interactive interpreter. As such, any code which is valid python should execute normally under IPython (cases where this is not true should be reported as bugs). It does, however, offer many features which are not available at a standard python prompt. What follows is a list of these.

Caution for Windows users

Windows, unfortunately, uses the ‘' character as a path separator. This is a terrible choice, because ‘' also represents the escape character in most modern programming languages, including Python. For this reason, using ‘/’ character is recommended if you have problems with \. However, in Windows commands ‘/’ flags options, so you can not use it for the root directory. This means that paths beginning at the root must be typed in a contrived manner like: %copy \opt/foo/bar.txt \tmp

Magic command system

IPython will treat any line whose first character is a % as a special call to a ‘magic’ function. These allow you to control the behavior of IPython itself, plus a lot of system-type features. They are all prefixed with a % character, but parameters are given without parentheses or quotes.

Lines that begin with %% signal a cell magic: they take as arguments not only the rest of the current line, but all lines below them as well, in the current execution block. Cell magics can in fact make arbitrary modifications to the input they receive, which need not even be valid Python code at all. They receive the whole block as a single string.

As a line magic example, the cd magic works just like the OS command of the same name:

In [8]: %cd

The following uses the builtin timeit in cell mode:

In [10]: %%timeit x = range(10000)
    ...: min(x)
    ...: max(x)
1000 loops, best of 3: 438 us per loop

In this case, x = range(10000) is called as the line argument, and the block with min(x) and max(x) is called as the cell body. The timeit magic receives both.

If you have ‘automagic’ enabled (as it is by default), you don’t need to type in the single % explicitly for line magics; IPython will scan its internal list of magic functions and call one if it exists. With automagic on you can then just type cd mydir to go to directory ‘mydir’:

In [9]: cd mydir

Cell magics always require an explicit %% prefix, automagic calling only works for line magics.

The automagic system has the lowest possible precedence in name searches, so you can freely use variables with the same names as magic commands. If a magic command is ‘shadowed’ by a variable, you will need the explicit % prefix to use it:

In [1]: cd ipython     # %cd is called by automagic

In [2]: cd=1           # now cd is just a variable

In [3]: cd ..          # and doesn't work as a function anymore
File "<ipython-input-3-9fedb3aff56c>", line 1
  cd ..
SyntaxError: invalid syntax

In [4]: %cd ..         # but %cd always works

In [5]: del cd     # if you remove the cd variable, automagic works again

In [6]: cd ipython


Line magics, if they return a value, can be assigned to a variable using the syntax l = %sx ls (which in this particular case returns the result of ls as a python list). See below for more information.

Type %magic for more information, including a list of all available magic functions at any time and their docstrings. You can also type %magic_function_name? (see below for information on the ‘?’ system) to get information about any particular magic function you are interested in.

The API documentation for the IPython.core.magic module contains the full docstrings of all currently available magic commands.

See also

Built-in magic commands
A list of the line and cell magics available in IPython by default
Defining custom magics
How to define and register additional magic functions

Access to the standard Python help

Simply type help() to access Python’s standard help system. You can also type help(object) for information about a given object, or help('keyword') for information on a keyword. You may need to configure your PYTHONDOCS environment variable for this feature to work correctly.

Dynamic object information

Typing ?word or word? prints detailed information about an object. If certain strings in the object are too long (e.g. function signatures) they get snipped in the center for brevity. This system gives access variable types and values, docstrings, function prototypes and other useful information.

If the information will not fit in the terminal, it is displayed in a pager (less if available, otherwise a basic internal pager).

Typing ??word or word?? gives access to the full information, including the source code where possible. Long strings are not snipped.

The following magic functions are particularly useful for gathering information about your working environment:

  • pdoc <object>: Print (or run through a pager if too long) the docstring for an object. If the given object is a class, it will print both the class and the constructor docstrings.
  • pdef <object>: Print the call signature for any callable object. If the object is a class, print the constructor information.
  • psource <object>: Print (or run through a pager if too long) the source code for an object.
  • pfile <object>: Show the entire source file where an object was defined via a pager, opening it at the line where the object definition begins.
  • who/whos: These functions give information about identifiers you have defined interactively (not things you loaded or defined in your configuration files). %who just prints a list of identifiers and %whos prints a table with some basic details about each identifier.

The dynamic object information functions (?/??, %pdoc, %pfile, %pdef, %psource) work on object attributes, as well as directly on variables. For example, after doing import os, you can use os.path.abspath??.

Command line completion

At any time, hitting TAB will complete any available python commands or variable names, and show you a list of the possible completions if there’s no unambiguous one. It will also complete filenames in the current directory if no python names match what you’ve typed so far.

Search command history

IPython provides two ways for searching through previous input and thus reduce the need for repetitive typing:

  1. Start typing, and then use the up and down arrow keys (or Ctrl-p and Ctrl-n) to search through only the history items that match what you’ve typed so far.
  2. Hit Ctrl-r: to open a search prompt. Begin typing and the system searches your history for lines that contain what you’ve typed so far, completing as much as it can.

IPython will save your input history when it leaves and reload it next time you restart it. By default, the history file is named .ipython/profile_name/history.sqlite.


Starting with 5.0, IPython uses prompt_toolkit in place of readline, it thus can recognize lines ending in ‘:’ and indent the next line, while also un-indenting automatically after ‘raise’ or ‘return’, and support real multi-line editing as well as syntactic coloration during edition.

This feature does not use the readline library anymore, so it will not honor your ~/.inputrc configuration (or whatever file your INPUTRC environment variable points to).

In particular if you want to change the input mode to vi, you will need to set the TerminalInteractiveShell.editing_mode configuration option of IPython.

Session logging and restoring

You can log all input from a session either by starting IPython with the command line switch (see here) or by activating the logging at any moment with the magic function logstart.

Log files can later be reloaded by running them as scripts and IPython will attempt to ‘replay’ the log by executing all the lines in it, thus restoring the state of a previous session. This feature is not quite perfect, but can still be useful in many cases.

The log files can also be used as a way to have a permanent record of any code you wrote while experimenting. Log files are regular text files which you can later open in your favorite text editor to extract code or to ‘clean them up’ before using them to replay a session.

The logstart function for activating logging in mid-session is used as follows:

%logstart [log_name [log_mode]]

If no name is given, it defaults to a file named ‘’ in your current working directory, in ‘rotate’ mode (see below).

‘%logstart name’ saves to file ‘name’ in ‘backup’ mode. It saves your history up to that point and then continues logging.

%logstart takes a second optional parameter: logging mode. This can be one of (note that the modes are given unquoted):

  • [over:] overwrite existing log_name.
  • [backup:] rename (if exists) to log_name~ and start log_name.
  • [append:] well, that says it.
  • [rotate:] create rotating logs log_name.1~, log_name.2~, etc.

The logoff and logon functions allow you to temporarily stop and resume logging to a file which had previously been started with %logstart. They will fail (with an explanation) if you try to use them before logging has been started.

System shell access

Any input line beginning with a ! character is passed verbatim (minus the !, of course) to the underlying operating system. For example, typing !ls will run ‘ls’ in the current directory.

Manual capture of command output and magic output

You can assign the result of a system command to a Python variable with the syntax myfiles = !ls. Similarly, the result of a magic (as long as it returns a value) can be assigned to a variable. For example, the syntax myfiles = %sx ls is equivalent to the above system command example (the sx magic runs a shell command and captures the output). Each of these gets machine readable output from stdout (e.g. without colours), and splits on newlines. To explicitly get this sort of output without assigning to a variable, use two exclamation marks (!!ls) or the sx magic command without an assignment. (However, !! commands cannot be assigned to a variable.)

The captured list in this example has some convenience features. myfiles.n or myfiles.s returns a string delimited by newlines or spaces, respectively. myfiles.p produces path objects from the list items. See String lists for details.

IPython also allows you to expand the value of python variables when making system calls. Wrap variables or expressions in {braces}:

In [1]: pyvar = 'Hello world'
In [2]: !echo "A python variable: {pyvar}"
A python variable: Hello world
In [3]: import math
In [4]: x = 8
In [5]: !echo {math.factorial(x)}

For simple cases, you can alternatively prepend $ to a variable name:

In [6]: !echo $sys.argv
In [7]: !echo "A system variable: $$HOME"  # Use $$ for literal $
A system variable: /home/fperez

Note that $$ is used to represent a literal $.

System command aliases

The alias magic function allows you to define magic functions which are in fact system shell commands. These aliases can have parameters.

%alias alias_name cmd defines ‘alias_name’ as an alias for ‘cmd’

Then, typing alias_name params will execute the system command ‘cmd params’ (from your underlying operating system).

You can also define aliases with parameters using %s specifiers (one per parameter). The following example defines the parts function as an alias to the command echo first %s second %s where each %s will be replaced by a positional parameter to the call to %parts:

In [1]: %alias parts echo first %s second %s
In [2]: parts A B
first A second B
In [3]: parts A
ERROR: Alias <parts> requires 2 arguments, 1 given.

If called with no parameters, alias prints the table of currently defined aliases.

The rehashx magic allows you to load your entire $PATH as ipython aliases. See its docstring for further details.

Recursive reload

The IPython.lib.deepreload module allows you to recursively reload a module: changes made to any of its dependencies will be reloaded without having to exit. To start using it, do:

from IPython.lib.deepreload import reload as dreload

Verbose and colored exception traceback printouts

IPython provides the option to see very detailed exception tracebacks, which can be especially useful when debugging large programs. You can run any Python file with the %run function to benefit from these detailed tracebacks. Furthermore, both normal and verbose tracebacks can be colored (if your terminal supports it) which makes them much easier to parse visually.

See the magic xmode and colors functions for details.

These features are basically a terminal version of Ka-Ping Yee’s cgitb module, now part of the standard Python library.

Input caching system

IPython offers numbered prompts (In/Out) with input and output caching (also referred to as ‘input history’). All input is saved and can be retrieved as variables (besides the usual arrow key recall), in addition to the rep magic command that brings a history entry up for editing on the next command line.

The following variables always exist:

  • _i, _ii, _iii: store previous, next previous and next-next previous inputs.
  • In, _ih : a list of all inputs; _ih[n] is the input from line n. If you overwrite In with a variable of your own, you can remake the assignment to the internal list with a simple In=_ih.

Additionally, global variables named _i<n> are dynamically created (<n> being the prompt counter), so _i<n> == _ih[<n>] == In[<n>].

For example, what you typed at prompt 14 is available as _i14, _ih[14] and In[14].

This allows you to easily cut and paste multi line interactive prompts by printing them out: they print like a clean string, without prompt characters. You can also manipulate them like regular variables (they are strings), modify or exec them.

You can also re-execute multiple lines of input easily by using the magic rerun or macro functions. The macro system also allows you to re-execute previous lines which include magic function calls (which require special processing). Type %macro? for more details on the macro system.

A history function history allows you to see any part of your input history by printing a range of the _i variables.

You can also search (‘grep’) through your history by typing %hist -g somestring. This is handy for searching for URLs, IP addresses, etc. You can bring history entries listed by ‘%hist -g’ up for editing with the %recall command, or run them immediately with rerun.

Output caching system

For output that is returned from actions, a system similar to the input cache exists but using _ instead of _i. Only actions that produce a result (NOT assignments, for example) are cached. If you are familiar with Mathematica, IPython’s _ variables behave exactly like Mathematica’s % variables.

The following variables always exist:

  • [_] (a single underscore): stores previous output, like Python’s default interpreter.
  • [__] (two underscores): next previous.
  • [___] (three underscores): next-next previous.

Additionally, global variables named _<n> are dynamically created (<n> being the prompt counter), such that the result of output <n> is always available as _<n> (don’t use the angle brackets, just the number, e.g. _21).

These variables are also stored in a global dictionary (not a list, since it only has entries for lines which returned a result) available under the names _oh and Out (similar to _ih and In). So the output from line 12 can be obtained as _12, Out[12] or _oh[12]. If you accidentally overwrite the Out variable you can recover it by typing Out=_oh at the prompt.

This system obviously can potentially put heavy memory demands on your system, since it prevents Python’s garbage collector from removing any previously computed results. You can control how many results are kept in memory with the configuration option InteractiveShell.cache_size. If you set it to 0, output caching is disabled. You can also use the reset and xdel magics to clear large items from memory.

Directory history

Your history of visited directories is kept in the global list _dh, and the magic cd command can be used to go to any entry in that list. The dhist command allows you to view this history. Do cd -<TAB> to conveniently view the directory history.

Automatic parentheses and quotes

These features were adapted from Nathan Gray’s LazyPython. They are meant to allow less typing for common situations.

Callable objects (i.e. functions, methods, etc) can be invoked like this (notice the commas between the arguments):

In [1]: callable_ob arg1, arg2, arg3
------> callable_ob(arg1, arg2, arg3)


This feature is disabled by default. To enable it, use the %autocall magic command. The commands below with special prefixes will always work, however.

You can force automatic parentheses by using ‘/’ as the first character of a line. For example:

In [2]: /globals # becomes 'globals()'

Note that the ‘/’ MUST be the first character on the line! This won’t work:

In [3]: print /globals # syntax error

In most cases the automatic algorithm should work, so you should rarely need to explicitly invoke /. One notable exception is if you are trying to call a function with a list of tuples as arguments (the parenthesis will confuse IPython):

In [4]: zip (1,2,3),(4,5,6) # won't work

but this will work:

In [5]: /zip (1,2,3),(4,5,6)
------> zip ((1,2,3),(4,5,6))
Out[5]: [(1, 4), (2, 5), (3, 6)]

IPython tells you that it has altered your command line by displaying the new command line preceded by --->.

You can force automatic quoting of a function’s arguments by using , or ; as the first character of a line. For example:

In [1]: ,my_function /home/me  # becomes my_function("/home/me")

If you use ‘;’ the whole argument is quoted as a single string, while ‘,’ splits on whitespace:

In [2]: ,my_function a b c    # becomes my_function("a","b","c")

In [3]: ;my_function a b c    # becomes my_function("a b c")

Note that the ‘,’ or ‘;’ MUST be the first character on the line! This won’t work:

In [4]: x = ,my_function /home/me # syntax error

IPython as your default Python environment

Python honors the environment variable PYTHONSTARTUP and will execute at startup the file referenced by this variable. If you put the following code at the end of that file, then IPython will be your working environment anytime you start Python:

import os, IPython
os.environ['PYTHONSTARTUP'] = ''  # Prevent running this again
raise SystemExit

The raise SystemExit is needed to exit Python when it finishes, otherwise you’ll be back at the normal Python >>> prompt.

This is probably useful to developers who manage multiple Python versions and don’t want to have correspondingly multiple IPython versions. Note that in this mode, there is no way to pass IPython any command-line options, as those are trapped first by Python itself.

Embedding IPython

You can start a regular IPython session with

import IPython

at any point in your program. This will load IPython configuration, startup files, and everything, just as if it were a normal IPython session. For information on setting configuration options when running IPython from python, see Running IPython from Python.

It is also possible to embed an IPython shell in a namespace in your Python code. This allows you to evaluate dynamically the state of your code, operate with your variables, analyze them, etc. For example, if you run the following code snippet:

import IPython

a = 42

and within the IPython shell, you reassign a to 23 to do further testing of some sort, you can then exit:

>>> IPython.embed()
Python 3.6.2 (default, Jul 17 2017, 16:44:45)
Type 'copyright', 'credits' or 'license' for more information
IPython -- An enhanced Interactive Python. Type '?' for help.

In [1]: a = 23

In [2]: exit()

Once you exit and print a, the value 23 will be shown:

In: print(a)

It’s important to note that the code run in the embedded IPython shell will not change the state of your code and variables, unless the shell is contained within the global namespace. In the above example, a is changed because this is true.

To further exemplify this, consider the following example:

import IPython
def do():
    a = 42

Now if call the function and complete the state changes as we did above, the value 42 will be printed. Again, this is because it’s not in the global namespace:


Running a file with the above code can lead to the following session:

>>> do()
Python 3.6.2 (default, Jul 17 2017, 16:44:45)
Type 'copyright', 'credits' or 'license' for more information
IPython -- An enhanced Interactive Python. Type '?' for help.

In [1]: a = 23

In [2]: exit()


At present, embedding IPython cannot be done from inside IPython. Run the code samples below outside IPython.

This feature allows you to easily have a fully functional python environment for doing object introspection anywhere in your code with a simple function call. In some cases a simple print statement is enough, but if you need to do more detailed analysis of a code fragment this feature can be very valuable.

It can also be useful in scientific computing situations where it is common to need to do some automatic, computationally intensive part and then stop to look at data, plots, etc. Opening an IPython instance will give you full access to your data and functions, and you can resume program execution once you are done with the interactive part (perhaps to stop again later, as many times as needed).

The following code snippet is the bare minimum you need to include in your Python programs for this to work (detailed examples follow later):

from IPython import embed

embed() # this call anywhere in your program will start IPython

You can also embed an IPython kernel, for use with qtconsole, etc. via IPython.embed_kernel(). This should function work the same way, but you can connect an external frontend (ipython qtconsole or ipython console), rather than interacting with it in the terminal.

You can run embedded instances even in code which is itself being run at the IPython interactive prompt with ‘%run <filename>’. Since it’s easy to get lost as to where you are (in your top-level IPython or in your embedded one), it’s a good idea in such cases to set the in/out prompts to something different for the embedded instances. The code examples below illustrate this.

You can also have multiple IPython instances in your program and open them separately, for example with different options for data presentation. If you close and open the same instance multiple times, its prompt counters simply continue from each execution to the next.

Please look at the docstrings in the embed module for more details on the use of this system.

The following sample file illustrating how to use the embedding functionality is provided in the examples directory as It should be fairly self-explanatory:

#!/usr/bin/env python
"""An example of how to embed an IPython shell into a running program.

Please see the documentation in the IPython.Shell module for more details.

The accompanying file has quick code fragments for
embedding which you can cut and paste in your code once you understand how
things work.

The code in this file is deliberately extra-verbose, meant for learning."""

# The basics to get you going:

# IPython injects get_ipython into builtins, so you can know if you have nested
# copies running.

# Try running this code both at the command line and from inside IPython (with
# %run

from IPython.terminal.prompts import Prompts, Token

class CustomPrompt(Prompts):

    def in_prompt_tokens(self, cli=None):

       return [
            (Token.Prompt, 'In <'),
            (Token.PromptNum, str(,
            (Token.Prompt, '>: '),

    def out_prompt_tokens(self):
       return [
            (Token.OutPrompt, 'Out<'),
            (Token.OutPromptNum, str(,
            (Token.OutPrompt, '>: '),

from traitlets.config.loader import Config
except NameError:
    nested = 0
    cfg = Config()
    print("Running nested copies of IPython.")
    print("The prompts for the nested copy have been modified")
    cfg = Config()
    nested = 1

# First import the embeddable shell class
from IPython.terminal.embed import InteractiveShellEmbed

# Now create an instance of the embeddable shell. The first argument is a
# string with options exactly as you would type them if you were starting
# IPython at the system command line. Any parameters you want to define for
# configuration can thus be specified here.
ipshell = InteractiveShellEmbed(config=cfg,
                       banner1 = 'Dropping into IPython',
                       exit_msg = 'Leaving Interpreter, back to program.')

# Make a second instance, you can have as many as you want.
ipshell2 = InteractiveShellEmbed(config=cfg,
                        banner1 = 'Second IPython instance.')

print('\nHello. This is printed from the main controller program.\n')

# You can then call ipshell() anywhere you need it (with an optional
# message):
ipshell('***Called from top level. '
        'Hit Ctrl-D to exit interpreter and continue program.\n'
        'Note that if you use %kill_embedded, you can fully deactivate\n'
        'This embedded instance so it will never turn on again')

print('\nBack in caller program, moving along...\n')

# More details:

# InteractiveShellEmbed instances don't print the standard system banner and
# messages. The IPython banner (which actually may contain initialization
# messages) is available as get_ipython().banner in case you want it.

# InteractiveShellEmbed instances print the following information everytime they
# start:

# - A global startup banner.

# - A call-specific header string, which you can use to indicate where in the
# execution flow the shell is starting.

# They also print an exit message every time they exit.

# Both the startup banner and the exit message default to None, and can be set
# either at the instance constructor or at any other time with the
# by setting the banner and exit_msg attributes.

# The shell instance can be also put in 'dummy' mode globally or on a per-call
# basis. This gives you fine control for debugging without having to change
# code all over the place.

# The code below illustrates all this.

# This is how the global banner and exit_msg can be reset at any point
ipshell.banner2 = 'Entering interpreter - New Banner'
ipshell.exit_msg = 'Leaving interpreter - New exit_msg'

def foo(m):
    s = 'spam'
    ipshell('***In foo(). Try %whos, or print s or m:')
    print('foo says m = ',m)

def bar(n):
    s = 'eggs'
    ipshell('***In bar(). Try %whos, or print s or n:')
    print('bar says n = ',n)
# Some calls to the above functions which will trigger IPython:
print('Main program calling foo("eggs")\n')

# The shell can be put in 'dummy' mode where calls to it silently return. This
# allows you, for example, to globally turn off debugging for a program with a
# single call.
ipshell.dummy_mode = True
print('\nTrying to call IPython which is now "dummy":')
print('Nothing happened...')
# The global 'dummy' mode can still be overridden for a single call
print('\nOverriding dummy mode manually:')

# Reactivate the IPython shell
ipshell.dummy_mode = False

print('You can even have multiple embedded instances:')

print('\nMain program calling bar("spam")\n')

print('Main program finished. Bye!')

Once you understand how the system functions, you can use the following code fragments in your programs which are ready for cut and paste:

"""Quick code snippets for embedding IPython into other programs.

See for full details, this file has the bare minimum code for
cut and paste use once you understand how to use the system."""

# This code loads IPython but modifies a few things if it detects it's running
# embedded in another IPython session (helps avoid confusion)

except NameError:
    banner = '*** Nested interpreter ***'
    exit_msg = '*** Back in main IPython ***'

# First import the embed function
from IPython.terminal.embed import InteractiveShellEmbed
# Now create the IPython shell instance. Put ipshell() anywhere in your code
# where you want it to open.
ipshell = InteractiveShellEmbed(banner1=banner, exit_msg=exit_msg)

# This code will load an embeddable IPython shell always with no changes for
# nested embededings.

from IPython import embed
# Now embed() will open IPython anywhere in the code.

# This code loads an embeddable shell only if NOT running inside
# IPython. Inside IPython, the embeddable shell variable ipshell is just a
# dummy function.

except NameError:
    from IPython.terminal.embed import InteractiveShellEmbed
    ipshell = InteractiveShellEmbed()
    # Now ipshell() will open IPython anywhere in the code
    # Define a dummy ipshell() so the same code doesn't crash inside an
    # interactive IPython
    def ipshell(): pass

Using the Python debugger (pdb)

Running entire programs via pdb

pdb, the Python debugger, is a powerful interactive debugger which allows you to step through code, set breakpoints, watch variables, etc. IPython makes it very easy to start any script under the control of pdb, regardless of whether you have wrapped it into a ‘main()’ function or not. For this, simply type %run -d myscript at an IPython prompt. See the run command’s documentation for more details, including how to control where pdb will stop execution first.

For more information on the use of the pdb debugger, see Debugger Commands in the Python documentation.

IPython extends the debugger with a few useful additions, like coloring of tracebacks. The debugger will adopt the color scheme selected for IPython.

The where command has also been extended to take as argument the number of context line to show. This allows to a many line of context on shallow stack trace:

In [5]: def foo(x):
...:     1
...:     2
...:     3
...:     return 1/x+foo(x-1)
...:     5
...:     6
...:     7

In[6]: foo(1)
# ...
ipdb> where 8
----> 1 foo(1)

    1 def foo(x):
    2     1
    3     2
    4     3
----> 5     return 1/x+foo(x-1)
    6     5
    7     6
    8     7

> <ipython-input-5-7baadc3d1465>(5)foo()
    1 def foo(x):
    2     1
    3     2
    4     3
----> 5     return 1/x+foo(x-1)
    6     5
    7     6
    8     7

And less context on shallower Stack Trace:

ipdb> where 1
----> 1 foo(7)

----> 5     return 1/x+foo(x-1)

----> 5     return 1/x+foo(x-1)

----> 5     return 1/x+foo(x-1)

----> 5     return 1/x+foo(x-1)

Post-mortem debugging

Going into a debugger when an exception occurs can be extremely useful in order to find the origin of subtle bugs, because pdb opens up at the point in your code which triggered the exception, and while your program is at this point ‘dead’, all the data is still available and you can walk up and down the stack frame and understand the origin of the problem.

You can use the debug magic after an exception has occurred to start post-mortem debugging. IPython can also call debugger every time your code triggers an uncaught exception. This feature can be toggled with the pdb magic command, or you can start IPython with the --pdb option.

For a post-mortem debugger in your programs outside IPython, put the following lines toward the top of your ‘main’ routine:

import sys
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(mode='Verbose',
color_scheme='Linux', call_pdb=1)

The mode keyword can be either ‘Verbose’ or ‘Plain’, giving either very detailed or normal tracebacks respectively. The color_scheme keyword can be one of ‘NoColor’, ‘Linux’ (default) or ‘LightBG’. These are the same options which can be set in IPython with --colors and --xmode.

This will give any of your programs detailed, colored tracebacks with automatic invocation of pdb.

Pasting of code starting with Python or IPython prompts

IPython is smart enough to filter out input prompts, be they plain Python ones (>>> and ...) or IPython ones (In [N]: and ...:). You can therefore copy and paste from existing interactive sessions without worry.

The following is a ‘screenshot’ of how things work, copying an example from the standard Python tutorial:

In [1]: >>> # Fibonacci series:

In [2]: ... # the sum of two elements defines the next

In [3]: ... a, b = 0, 1

In [4]: >>> while b < 10:
   ...:     ...     print(b)
   ...:     ...     a, b = b, a+b

And pasting from IPython sessions works equally well:

In [1]: In [5]: def f(x):
   ...:        ...:     "A simple function"
   ...:        ...:     return x**2
   ...:    ...:

In [2]: f(3)
Out[2]: 9

GUI event loop support

IPython has excellent support for working interactively with Graphical User Interface (GUI) toolkits, such as wxPython, PyQt4/PySide, PyGTK and Tk. This is implemented by running the toolkit’s event loop while IPython is waiting for input.

For users, enabling GUI event loop integration is simple. You simple use the gui magic as follows:

%gui [GUINAME]

With no arguments, %gui removes all GUI support. Valid GUINAME arguments include wx, qt, qt5, gtk, gtk3 and tk.

Thus, to use wxPython interactively and create a running wx.App object, do:

%gui wx

You can also start IPython with an event loop set up using the --gui flag:

$ ipython --gui=qt

For information on IPython’s matplotlib integration (and the matplotlib mode) see this section.

For developers that want to integrate additional event loops with IPython, see Integrating with GUI event loops.

When running inside IPython with an integrated event loop, a GUI application should not start its own event loop. This means that applications that are meant to be used both in IPython and as standalone apps need to have special code to detects how the application is being run. We highly recommend using IPython’s support for this. Since the details vary slightly between toolkits, we point you to the various examples in our source directory examples/IPython Kernel/gui/ that demonstrate these capabilities.

PyQt and PySide

When you use --gui=qt or --matplotlib=qt, IPython can work with either PyQt4 or PySide. There are three options for configuration here, because PyQt4 has two APIs for QString and QVariant: v1, which is the default on Python 2, and the more natural v2, which is the only API supported by PySide. v2 is also the default for PyQt4 on Python 3. IPython’s code for the QtConsole uses v2, but you can still use any interface in your code, since the Qt frontend is in a different process.

The default will be to import PyQt4 without configuration of the APIs, thus matching what most applications would expect. It will fall back to PySide if PyQt4 is unavailable.

If specified, IPython will respect the environment variable QT_API used by ETS. ETS 4.0 also works with both PyQt4 and PySide, but it requires PyQt4 to use its v2 API. So if QT_API=pyside PySide will be used, and if QT_API=pyqt then PyQt4 will be used with the v2 API for QString and QVariant, so ETS codes like MayaVi will also work with IPython.

If you launch IPython in matplotlib mode with ipython --matplotlib=qt, then IPython will ask matplotlib which Qt library to use (only if QT_API is not set), via the ‘backend.qt4’ rcParam. If matplotlib is version 1.0.1 or older, then IPython will always use PyQt4 without setting the v2 APIs, since neither v2 PyQt nor PySide work.


Note that this means for ETS 4 to work with PyQt4, QT_API must be set to work with IPython’s qt integration, because otherwise PyQt4 will be loaded in an incompatible mode.

It also means that you must not have QT_API set if you want to use --gui=qt with code that requires PyQt4 API v1.

Plotting with matplotlib

matplotlib provides high quality 2D and 3D plotting for Python. matplotlib can produce plots on screen using a variety of GUI toolkits, including Tk, PyGTK, PyQt4 and wxPython. It also provides a number of commands useful for scientific computing, all with a syntax compatible with that of the popular Matlab program.

To start IPython with matplotlib support, use the --matplotlib switch. If IPython is already running, you can run the matplotlib magic. If no arguments are given, IPython will automatically detect your choice of matplotlib backend. You can also request a specific backend with %matplotlib backend, where backend must be one of: ‘tk’, ‘qt’, ‘wx’, ‘gtk’, ‘osx’. In the web notebook and Qt console, ‘inline’ is also a valid backend value, which produces static figures inlined inside the application window instead of matplotlib’s interactive figures that live in separate windows.

Interactive demos with IPython

IPython ships with a basic system for running scripts interactively in sections, useful when presenting code to audiences. A few tags embedded in comments (so that the script remains valid Python code) divide a file into separate blocks, and the demo can be run one block at a time, with IPython printing (with syntax highlighting) the block before executing it, and returning to the interactive prompt after each block. The interactive namespace is updated after each block is run with the contents of the demo’s namespace.

This allows you to show a piece of code, run it and then execute interactively commands based on the variables just created. Once you want to continue, you simply execute the next block of the demo. The following listing shows the markup necessary for dividing a script into sections for execution as a demo:

# -*- coding: utf-8 -*-
"""A simple interactive demo to illustrate the use of IPython's Demo class.

Any python script can be run as a demo, but that does little more than showing
it on-screen, syntax-highlighted in one shot.  If you add a little simple
markup, you can stop at specified intervals and return to the ipython prompt,
resuming execution later.

This is a unicode test, åäö

print('Hello, welcome to an interactive IPython demo.')
print('Executing this block should require confirmation before proceeding,')
print('unless auto_all has been set to true in the demo object')

# The mark below defines a block boundary, which is a point where IPython will
# stop execution and return to the interactive prompt.
# <demo> --- stop ---

x = 1
y = 2

# <demo> --- stop ---

# the mark below makes this block as silent
# <demo> silent

print('This is a silent block, which gets executed but not printed.')

# <demo> --- stop ---
# <demo> auto
print('This is an automatic block.')
print('It is executed without asking for confirmation, but printed.')
z = x+y


# <demo> --- stop ---
# This is just another normal block.
print('z is now:', z)


In order to run a file as a demo, you must first make a Demo object out of it. If the file is named, the following code will make a demo:

from IPython.lib.demo import Demo

mydemo = Demo('')

This creates the mydemo object, whose blocks you run one at a time by simply calling the object with no arguments. Then call it to run each step of the demo:


Demo objects can be restarted, you can move forward or back skipping blocks, re-execute the last block, etc. See the IPython.lib.demo module and the Demo class for details.

Limitations: These demos are limited to fairly simple uses. In particular, you cannot break up sections within indented code (loops, if statements, function definitions, etc.) Supporting something like this would basically require tracking the internal execution state of the Python interpreter, so only top-level divisions are allowed. If you want to be able to open an IPython instance at an arbitrary point in a program, you can use IPython’s embedding facilities.