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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 ...
and you can see that b, as visible from the closure's scope, retains the value it had; the changed binding of b inside the inner function did not propagate out. The way around this is to use a nonlocal b statement in bar. In Python 2 (which lacks nonlocal), the usual workaround is to use a mutable value and change that value, not the binding. E ...
A self-interpreter displays a universal function for the language in question, and can be helpful in learning certain aspects of the language. [2] A self-interpreter will provide a circular, vacuous definition of most language constructs and thus provides little insight into the interpreted language's semantics, for example evaluation strategy ...
In Python, == compares by value. 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 ...
The problem is evident: we did not keep track of the equality relationship between x and y; actually, this domain of intervals does not take into account any relationships between variables, and is thus a non-relational domain. Non-relational domains tend to be fast and simple to implement, but imprecise.
In computer science, a symbol table is a data structure used by a language translator such as a compiler or interpreter, where each identifier, symbol, constant, procedure and function in a program's source code is associated with information relating to its declaration or appearance in the source. In other words, the entries of a symbol table ...
Schematic representation of how threads work under GIL. Green - thread holding GIL, red - blocked threads. A global interpreter lock (GIL) is a mechanism used in computer-language interpreters to synchronize the execution of threads so that only one native thread (per process) can execute basic operations (such as memory allocation and reference counting) at a time. [1]
The non-Python library being called to perform the CPU-intensive task is not subject to the GIL and may concurrently execute many threads on multiple processors without restriction. Concurrency of Python code can only be achieved with separate CPython interpreter processes managed by a multitasking operating system.