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This comparison of programming languages (array) compares the features of array data structures or matrix processing for various computer programming languages. Syntax [ edit ]
The table shows a comparison of functional programming languages which compares various features and designs of different functional programming languages. Name Pure
The fundamental idea behind array programming is that operations apply at once to an entire set of values. This makes it a high-level programming model as it allows the programmer to think and operate on whole aggregates of data, without having to resort to explicit loops of individual scalar operations.
Purely functional data structures are often represented in a different way than their imperative counterparts. [6] For example, array with constant-time access and update is a basic component of most imperative languages and many imperative data-structures, such as hash table and binary heap , are based on arrays.
Formally, a purely functional data structure is a data structure which can be implemented in a purely functional language, such as Haskell. In practice, it means that the data structures must be built using only persistent data structures such as tuples, sum types , product types , and basic types such as integers, characters, strings.
Python uses the + operator for string concatenation. Python uses the * operator for duplicating a string a specified number of times. The @ infix operator is intended to be used by libraries such as NumPy for matrix multiplication. [104] [105] The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to ...
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
It is also not easy to create their equally efficient general-purpose immutable counterparts. For purely functional languages, the worst-case slowdown is logarithmic in the number of memory cells used, because mutable memory can be represented by a purely functional data structure with logarithmic access time (such as a balanced tree). [85]