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It supports 'lookup', 'remove', and 'insert' operations. The dictionary problem is the classic problem of designing efficient data structures that implement associative arrays. [2] The two major solutions to the dictionary problem are hash tables and search trees.
The erase–remove idiom cannot be used for containers that return const_iterator (e.g.: set) [6] std::remove and/or std::remove_if do not maintain elements that are removed (unlike std::partition, std::stable_partition). Thus, erase–remove can only be used with containers holding elements with full value semantics without incurring resource ...
In computing, a hash table is a data structure that implements an associative array, also called a dictionary or simply map; an associative array is an abstract data type that maps keys to values. [2] A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value ...
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})
While tries commonly store character strings, they can be adapted to work with any ordered sequence of elements, such as permutations of digits or shapes. A notable variant is the bitwise trie, which uses individual bits from fixed-length binary data (such as integers or memory addresses) as keys.
Python makes a distinction between lists and tuples. Lists are written as [1, 2, 3], are mutable, and cannot be used as the keys of dictionaries (dictionary keys must be immutable in Python). Tuples, written as (1, 2, 3), are immutable and thus can be used as keys of dictionaries, provided all of the tuple's elements are immutable.
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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.