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Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Jupyter is financially sponsored by NumFOCUS. [1]
Visual Studio Code was first announced on April 29, 2015 by Microsoft at the 2015 Build conference. A preview build was released shortly thereafter. [13]On November 18, 2015, the project "Visual Studio Code — Open Source" (also known as "Code — OSS"), on which Visual Studio Code is based, was released under the open-source MIT License and made available on GitHub.
In addition to including the features, languages and plugins supported by VS Code, it has unique functionality built by Google. These include a built-in generative artificial intelligence assistant powered by Gemini , Nix integrations, and iOS and Android emulators .
Python Tools for Visual Studio (PTVS) is a free and open-source plug-in for versions of Visual Studio up to VS 2015 providing support for programming in Python. Since VS 2017, it is integrated in VS and called Python Support in Visual Studio. It supports IntelliSense, debugging, profiling, MPI cluster debugging, mixed C++/Python debugging, and ...
Spyder is extensible with first-party and third-party plugins, [8] and includes support for interactive tools for data inspection and embeds Python-specific code quality assurance and introspection instruments, such as Pyflakes, Pylint [9] and Rope. [10] [11] Spyder uses Qt for its GUI and is designed to use either of the PyQt or PySide Python ...
Nuitka compiles Python into C. [164] It works with Python 3.4 to 3.12 (and 2.6 and 2.7), for Python's main supported platforms (and Windows 7 or even Windows XP) and for Android. It claims complete support for Python 3.10, some support for 3.11 and 3.12 and experimental support for Python 3.13. It supports macOS including Apple Silicon-based.
PyPy (/ ˈ p aɪ p aɪ /) is an implementation of the Python programming language. [2] PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. [3] 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 ...
The listed languages are designed with varying degrees of OOP support. Some are highly focused in OOP while others support multiple paradigms including OOP. [1] For example, C++ is a multi-paradigm language including OOP; [2] however, it is less object-oriented than other languages such as Python [3] and Ruby. [4]