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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 ...
For example, to perform an element by element sum of two arrays, a and b to produce a third c, it is only necessary to write c = a + b In addition to support for vectorized arithmetic and relational operations, these languages also vectorize common mathematical functions such as sine. For example, if x is an array, then y = sin (x)
In Java associative arrays are implemented as "maps", which are part of the Java collections framework. Since J2SE 5.0 and the introduction of generics into Java, collections can have a type specified; for example, an associative array that maps strings to strings might be specified as follows:
modified_identifier_list «As «non_array_type««array_rank_specifier»» (multiple declarator); valid declaration statements are of the form Dim declarator_list , where, for the purpose of semantic analysis, to convert the declarator_list to a list of only single declarators:
The foreach statement in some languages has some defined order, processing each item in the collection from the first to the last. The foreach statement in many other languages, especially array programming languages, does not have any particular order.
Programming languages or their standard libraries that support multi-dimensional arrays typically have a native row-major or column-major storage order for these arrays. Row-major order is used in C / C++ / Objective-C (for C-style arrays), PL/I , [ 4 ] Pascal , [ 5 ] Speakeasy , [ citation needed ] and SAS .
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
defines a primary function simpleFun that implicitly applies custom subfunction myCustomFun to each element of an array using built-in function arrayfun. Alternatively, it may be desirable to abstract the mechanisms of the array storage container from the user by defining a custom object-oriented MATLAB implementation of the Iterator Pattern.