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 the ? 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 a line of code, it is first processed as a string. By the end of this stage, it must be valid Python syntax.

These transformers all subclass IPython.core.inputtransformer.InputTransformer, and are used by IPython.core.inputsplitter.IPythonInputSplitter.

These transformers act in three groups, stored separately as lists of instances in attributes of IPythonInputSplitter:

  • physical_line_transforms act on the lines as the user enters them. For example, these strip Python prompts from examples pasted in.
  • logical_line_transforms act on lines as connected by explicit line continuations, i.e. \ at the end of physical lines. They are skipped inside multiline Python statements. This is the point where IPython recognises %magic commands, for instance.
  • python_line_transforms act on blocks containing complete Python statements. Multi-line strings, lists and function calls are reassembled before being passed to these, but note that function and class definitions are still a series of separate statements. IPython does not use any of these by default.

An InteractiveShell instance actually has two IPythonInputSplitter instances, as the attributes input_splitter, to tell when a block of input is complete, and input_transformer_manager, to transform complete cells. If you add a transformer, you should make sure that it gets added to both, e.g.:


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.

Changed in version 2.0: Added the option to raise SyntaxError.

Stateless transformations

The simplest kind of transformations work one line at a time. Write a function which takes a line and returns a line, and decorate it with StatelessInputTransformer.wrap():

def my_special_commands(line):
    if line.startswith("¬"):
        return "specialcommand(" + repr(line) + ")"
    return line

The decorator returns a factory function which will produce instances of StatelessInputTransformer using your function.

Transforming a full block


Transforming a full block at once will break the automatic detection of whether a block of code is complete in interfaces relying on this functionality, such as terminal IPython. You will need to use a shortcut to force-execute your cells.

Transforming a full block of python code is possible by implementing a Inputtransformer and overwriting the push and reset methods. The reset method should send the full block of transformed text. As an example a transformer the reversed the lines from last to first.

from IPython.core.inputtransformer import InputTransformer

class ReverseLineTransformer(InputTransformer):

def __init__(self):
self.acc = []
def push(self, line):
self.acc.append(line) return None
def reset(self):
ret = ‘n’.join(self.acc[::-1]) self.acc = [] return ret

Coroutine transformers

More advanced transformers can be written as coroutines. The coroutine will be sent each line in turn, followed by None to reset it. It can yield lines, or None if it is accumulating text to yield at a later point. When reset, it should give up any code it has accumulated.

You may use CoroutineInputTransformer.wrap() to simplify the creation of such a transformer.

Here is a simple CoroutineInputTransformer that can be thought of being the identity:

from IPython.core.inputtransformer import CoroutineInputTransformer

def noop():
    line = ''
    while True:
        line = (yield line)

ip = get_ipython()


This code in IPython strips a constant amount of leading indentation from each line in a cell:

from IPython.core.inputtransformer import CoroutineInputTransformer

def leading_indent():
    """Remove leading indentation.

    If the first line starts with a spaces or tabs, the same whitespace will be
    removed from each following line until it is reset.
    space_re = re.compile(r'^[ \t]+')
    line = ''
    while True:
        line = (yield line)

        if line is None:

        m = space_re.match(line)
        if m:
            space =
            while line is not None:
                if line.startswith(space):
                    line = line[len(space):]
                line = (yield line)
            # No leading spaces - wait for reset
            while line is not None:
                line = (yield line)

Token-based transformers

There is an experimental framework that takes care of tokenizing and untokenizing lines of code. Define a function that accepts a list of tokens, and returns an iterable of output tokens, and decorate it with TokenInputTransformer.wrap(). These should only be used in python_line_transforms.

AST transformations

After the code has been parsed as Python syntax, you can use Python’s powerful Abstract Syntax Tree tools to modify it. Subclass ast.NodeTransformer, and add an instance to shell.ast_transformers.

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