This documentation covers a development version of IPython. The development version may differ significantly from the latest stable release.
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).
Custom input transformation¶
IPython extends Python syntax to allow things like magic commands, and help with
? syntax. There are several ways to customise how the user’s input is
processed into Python code to be executed.
These hooks are mainly for other projects using IPython as the core of their interactive interface. Using them carelessly can easily break IPython!
String based transformations¶
When the user enters code, it is first processed as a string. By the end of this stage, it must be valid Python syntax.
Changed in version 7.0: The API for string and token-based transformations has been completely redesigned. Any third party code extending input transformation will need to be rewritten. The new API is, hopefully, simpler.
String based transformations are functions which accept a list of strings: each string is a single line of the input cell, including its line ending. The transformation function should return output in the same structure.
These transformations are in two groups, accessible as attributes of
Each group is a list of transformation functions.
input_transformers_cleanuprun first on input, to do things like stripping prompts and leading indents from copied code. It may not be possible at this stage to parse the input as valid Python code.
Then IPython runs its own transformations to handle its special syntax, like
!systemcommands. This part does not expose extension points.
input_transformers_postrun as the last step, to do things like converting float literals into decimal objects. These may attempt to parse the input as Python code.
These transformers may raise
SyntaxError if the input code is invalid, but
in most cases it is clearer to pass unrecognised code through unmodified and let
Python’s own parser decide whether it is valid.
For example, imagine we want to obfuscate our code by reversing each line, so
)5(f =+ a instead of
a += f(5). Here’s how we could swap it
back the right way before IPython tries to run it:
def reverse_line_chars(lines): new_lines =  for line in lines: chars = line[:-1] # the newline needs to stay at the end new_lines.append(chars[::-1] + '\n') return new_lines
To start using this:
ip = get_ipython() ip.input_transformers_cleanup.append(reverse_line_chars)
New in version 7.17: input_transformers can now have an attribute
has_side_effects set to
True, which will prevent the transformers from being ran when IPython is
trying to guess whether the user input is complete.
After the code has been parsed as Python syntax, you can use Python’s powerful
Abstract Syntax Tree tools to modify it. Subclass
and add an instance to
This example wraps integer literals in an
Integer class, which is useful for
mathematical frameworks that want to handle e.g.
1/3 as a precise fraction:
class IntegerWrapper(ast.NodeTransformer): """Wraps all integers in a call to Integer()""" def visit_Num(self, node): if isinstance(node.n, int): return ast.Call(func=ast.Name(id='Integer', ctx=ast.Load()), args=[node], keywords=) return node