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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.
For example, the iterator method is supposed to return an Iterator object, and the pull-one method is supposed to produce and return the next value if possible, or return the sentinel value IterationEnd if no more values could be produced. The following example shows an equivalent iteration over a collection using explicit iterators:
-- Getting rid of the global variables issue (cannot be used in parallel)-- from a set of old sources, without the need to change that code's-- logic or structure.--procedure Nesting_example_1 is type Buffer_type is array (Integer range <>) of Integer; procedure Decompress (compressed: in Buffer_type; decompressed: out Buffer_type) is-- Here ...
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
The iteration form of the Eiffel loop can also be used as a boolean expression when the keyword loop is replaced by either all (effecting universal quantification) or some (effecting existential quantification). This iteration is a boolean expression which is true if all items in my_list have counts greater than three:
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 Python, functions are first-class objects that can be created and passed around dynamically. Python's limited support for anonymous functions is the lambda construct. An example is the anonymous function which squares its input, called with the argument of 5:
Nested functions can be used for unstructured control flow, by using the return statement for general unstructured control flow.This can be used for finer-grained control than is possible with other built-in features of the language – for example, it can allow early termination of a for loop if break is not available, or early termination of a nested for loop if a multi-level break or ...