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In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
There are subtle differences and distinctions in the use of the terms "generator" and "iterator", which vary between authors and languages. [5] In Python, a generator is an iterator constructor: a function that returns an iterator. An example of a Python generator returning an iterator for the Fibonacci numbers using Python's yield statement ...
cross-platform (Python) 2.1.1 BSD: Kid (templating language) Ryan Tomayko cross-platform (Python) 0.9.6 2006-12-20 Mako: Michael Bayer cross-platform (Python) 1.1.2 2020-03-01 MIT License: MATLAB: The MathWorks, Inc. Windows, macOS, Linux 1984 9.5 2018-09-12 Proprietary: M-Power: mrc cross-platform (Java) 2004 Proprietary: Microsoft Visual ...
Python 2.5 implements better support for coroutine-like functionality, based on extended generators Python 3.3 improves this ability, by supporting delegating to a subgenerator ( PEP 380 ) Python 3.4 introduces a comprehensive asynchronous I/O framework as standardized in PEP 3156 , which includes coroutines that leverage subgenerator delegation
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:
However, in the generic programming approach, each data structure returns a model of an iterator concept (a simple value type that can be dereferenced to retrieve the current value, or changed to point to another value in the sequence) and each algorithm is instead written generically with arguments of such iterators, e.g. a pair of iterators ...
Here, the list [0..] represents , x^2>3 represents the predicate, and 2*x represents the output expression.. List comprehensions give results in a defined order (unlike the members of sets); and list comprehensions may generate the members of a list in order, rather than produce the entirety of the list thus allowing, for example, the previous Haskell definition of the members of an infinite list.
In Design Patterns, an aggregate is not a design pattern but rather refers to an object such as a list, vector, or generator which provides an interface for creating iterators. The following example code is in Python .