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A compiler can thus make almost all the conversions from source code semantics to the machine level once and for all (i.e. until the program has to be changed) while an interpreter has to do some of this conversion work every time a statement or function is executed. However, in an efficient interpreter, much of the translation work (including ...
An interpreter is a program that reads another program, typically as text, [4] as seen in languages like Python. [2] Interpreters read code, and produce the result directly. [8] Interpreters typically read code line by line, and parse it to convert and execute the code as operations and actions. [9]
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})
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...
In computer programming, a programming language implementation is a system for executing computer programs. There are two general approaches to programming language implementation: [1] Interpretation: The program is read as input by an interpreter, which performs the actions written in the program. [2]
There are clear benefits when translating high-level code with an interpreter. Since object code is not created in the interpretation process, less memory is required for the code. [5] Interpreter languages do not create machine-specific code and can be executed on any type of machine. [7]
Concurrency of Python code can only be achieved with separate CPython interpreter processes managed by a multitasking operating system. This complicates communication between concurrent Python processes , though the multiprocessing module mitigates this somewhat; it means that applications that really can benefit from concurrent Python-code ...
Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run in PyPy. PyPy itself is built using a technique known as meta-tracing, which is a mostly automatic transformation that takes an interpreter as input and produces a tracing just-in-time compiler as