What does the slash mean when help() is listing method signatures?

Question

What does the / mean in Python 3.4's help output for range before the closing parenthesis?

>>> help(range)
Help on class range in module builtins:

class range(object)
 |  range(stop) -> range object
 |  range(start, stop[, step]) -> range object
 |  
 |  Return a virtual sequence of numbers from start to stop by step.
 |  
 |  Methods defined here:
 |  
 |  __contains__(self, key, /)
 |      Return key in self.
 |  
 |  __eq__(self, value, /)
 |      Return self==value.

                                        ...

Answer 1

It signifies the end of the positional only parameters, parameters you cannot use as keyword parameters. Before Python 3.8, such parameters could only be specified in the C API.

It means the key argument to __contains__ can only be passed in by position (range(5).__contains__(3)), not as a keyword argument (range(5).__contains__(key=3)), something you can do with positional arguments in pure-python functions.

Also see the Argument Clinic documentation:

To mark all parameters as positional-only in Argument Clinic, add a / on a line by itself after the last parameter, indented the same as the parameter lines.

and the (very recent addition to) the Python FAQ:

A slash in the argument list of a function denotes that the parameters prior to it are positional-only. Positional-only parameters are the ones without an externally-usable name. Upon calling a function that accepts positional-only parameters, arguments are mapped to parameters based solely on their position.

The syntax is now part of the Python language specification, as of version 3.8, see PEP 570 – Python Positional-Only Parameters. Before PEP 570, the syntax was already reserved for possible future inclusion in Python, see PEP 457 - Syntax For Positional-Only Parameters.

Positional-only parameters can lead to cleaner and clearer APIs, make pure-Python implementations of otherwise C-only modules more consistent and easier to maintain, and because positional-only parameters require very little processing, they lead to faster Python code.