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Python uses the following syntax to express list comprehensions over finite lists: S = [ 2 * x for x in range ( 100 ) if x ** 2 > 3 ] A generator expression may be used in Python versions >= 2.4 which gives lazy evaluation over its input, and can be used with generators to iterate over 'infinite' input such as the count generator function which ...
Python does not contain the classical for loop, rather a foreach loop is used to iterate over the output of the built-in range() function which returns an iterable sequence of integers. for i in range ( 1 , 6 ): # gives i values from 1 to 5 inclusive (but not 6) # statements print ( i ) # if we want 6 we must do the following for i in range ( 1 ...
They can be used to iterate over any object that implements the NSFastEnumeration protocol, including NSArray, NSDictionary (iterates over keys), NSSet, etc. NSArray * a = [ NSArray new ]; // Any container class can be substituted for ( id obj in a ) { // Dynamic typing is used.
Some object-oriented languages such as C#, C++ (later versions), Delphi (later versions), Go, Java (later versions), Lua, Perl, Python, Ruby provide an intrinsic way of iterating through the elements of a collection without an explicit iterator. An iterator object may exist, but is not represented in the source code.
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.
Folds can be regarded as consistently replacing the structural components of a data structure with functions and values. Lists, for example, are built up in many functional languages from two primitives: any list is either an empty list, commonly called nil ([]), or is constructed by prefixing an element in front of another list, creating what is called a cons node ( Cons(X1,Cons(X2,Cons ...
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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.