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Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
Demonstration doctests ===== This is just an example of what a README text looks like that can be used with the doctest.DocFileSuite() function from Python's doctest module. Normally, the README file would explain the API of the module, like this: >>> a = 1 >>> b = 2 >>> a + b 3 Notice, that we just demonstrated how to add two numbers in Python ...
The docstring for a Python code object (a module, class, or function) is the first statement of that code object, immediately following the definition (the 'def' or 'class' statement). The statement must be a bare string literal, not any other kind of expression. The docstring for the code object is available on that code object's __doc__ ...
Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels. [102]
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop.All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation:
It was developed for, and is used extensively by, the Python project for documentation. [9] Since its introduction in 2008, Sphinx has been adopted by many other important Python projects, including Bazaar, SQLAlchemy, MayaVi, SageMath, SciPy, Django and Pylons. It is also used for the Blender user manual [10] and Python API documentation. [11]
reStructuredText (RST, ReST, or reST) is a file format for textual data used primarily in the Python programming language community for technical documentation.. It is part of the Docutils project of the Python Doc-SIG (Documentation Special Interest Group), aimed at creating a set of tools for Python similar to Javadoc for Java or Plain Old Documentation (POD) for Perl.