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This is a comparison of the features of the type systems and type checking of multiple programming languages.. Brief definitions A nominal type system means that the language decides whether types are compatible and/or equivalent based on explicit declarations and names.
In dynamically typed languages, values, rather than variables, have types. A weakly typed language has looser typing rules and may produce unpredictable or even erroneous results or may perform implicit type conversion at runtime. [2] A different but related concept is latent typing.
Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Readability counts. However, Python features regularly violate these principles and have received criticism for adding unnecessary language bloat. [81]
Since interpreters are usually easier to write than compilers, but run slower, this technique can make it easier to produce efficient implementations of programming languages. PyPy's meta-tracing toolchain is called RPython. PyPy officially supports Python 3.7 and 3.10 [4] and has a few diffrences in implementations compared to CPython. [5]
The process of verifying and enforcing the constraints of types—type checking—may occur at compile time (a static check) or at run-time (a dynamic check). If a language specification requires its typing rules strongly, more or less allowing only those automatic type conversions that do not lose information, one can refer to the process as strongly typed; if not, as weakly typed.
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})
Structural typing is a static typing system that determines type compatibility and equivalence by a type's structure, whereas duck typing is dynamic and determines type compatibility by only that part of a type's structure that is accessed during runtime. The TypeScript, [6] Elm [7] and Python [8] languages support structural typing to varying ...
In computer science, type safety and type soundness are the extent to which a programming language discourages or prevents type errors.Type safety is sometimes alternatively considered to be a property of facilities of a computer language; that is, some facilities are type-safe and their usage will not result in type errors, while other facilities in the same language may be type-unsafe and a ...