Search results
Results From The WOW.Com Content Network
Although this program transforms a .py file to an .exe, it does not make it run faster because py2exe bundles the Python bytecode without converting it to machine-code. It may even run slower than using the Python interpreter directly because of startup overhead.
For premium support please call: 800-290-4726 more ways to reach us
Numba is used from Python, as a tool (enabled by adding a decorator to relevant Python code), a JIT compiler that translates a subset of Python and NumPy code into fast machine code. Pythran compiles a subset of Python 3 to C++ . [165] RPython can be compiled to C, and is used to build the PyPy interpreter of Python.
Thonny (/ ˈ θ ɒ n i / THON-ee) is a free and open-source integrated development environment for Python that is designed for beginners. It was created by Aivar Annamaa, an Estonian programmer. It was created by Aivar Annamaa, an Estonian programmer.
Programming languages can have multiple implementations. Different implementations can be written in different languages and can use different methods to compile or interpret code. For example, implementations of Python include: [9] CPython, the reference implementation of Python; IronPython, an implementation targeting the .NET Framework ...
Domain-specific languages which are called (at runtime) from programs written in general purpose languages like C or Perl, to perform a specific function, often returning the results of operation to the "host" programming language for further processing; generally, an interpreter or virtual machine for the domain-specific language is embedded ...
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 ...
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.