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For example, an object that uses memoization to cache the results of expensive computations could still be considered an immutable object. Strings and other concrete objects are typically expressed as immutable objects to improve readability and runtime efficiency in object-oriented programming.
One example is mutability: whether the objects storing extrinsic flyweight state can change. Immutable objects are easily shared, but require creating new extrinsic objects whenever a change in state occurs. In contrast, mutable objects can share state. Mutability allows better object reuse via the caching and re-initialization of old, unused ...
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})
In computing, a persistent data structure or not ephemeral data structure is a data structure that always preserves the previous version of itself when it is modified. Such data structures are effectively immutable, as their operations do not (visibly) update the structure in-place, but instead always yield a new updated structure.
The standard type hierarchy of Python 3. In computer science and computer programming, a data type (or simply type) is a collection or grouping of data values, usually specified by a set of possible values, a set of allowed operations on these values, and/or a representation of these values as machine types. [1]
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. Arrays can be replaced by map or random access list , which admits purely functional implementation, but the access and update time is logarithmic .
Reference counts are also useful information to use as input to other runtime optimizations. For example, systems that depend heavily on immutable objects such as many functional programming languages can suffer an efficiency penalty due to frequent copies.
Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained—for example, a <= b <= c. Python uses and, or, and not as Boolean operators. Python has a type of expression named a list comprehension, and a more general expression named a generator expression. [78]