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] Some of the leading Java IDEs (such as IntelliJ and Eclipse) are also the basis for leading IDEs in other programming languages (e.g. for Python, IntelliJ is rebranded as PyCharm, and Eclipse has the PyDev plugin.)
It is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language.Spyder integrates with a number of prominent packages in the scientific Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy and Cython, as well as other open-source software.
Codelobster, a cross-platform IDE for various languages, including Python. EasyEclipse, an open source IDE for Python and other languages. Eclipse,with the Pydev plug-in. Eclipse supports many other languages as well. Emacs, with the built-in python-mode. [1] Eric, an IDE for Python and Ruby; Geany, IDE for Python development and other languages.
Anaconda is an open source [9] [10] data science and artificial intelligence distribution platform for Python and R programming languages.Developed by Anaconda, Inc., [11] an American company [1] founded in 2012, [11] the platform is used to develop and manage data science and AI projects. [9]
PyCharm is an integrated development environment (IDE) used for programming in Python.It provides code analysis, a graphical debugger, an integrated unit tester, integration with version control systems, and supports web development with Django.
eric-ide.python-projects.org eric is a free integrated development environment (IDE) used for computer programming . Since it is a full featured IDE, it provides by default all necessary tools needed for the writing of code and for the professional management of a software project.
modeFRONTIER – an integration platform for multi-objective and multidisciplinary optimization, which provides a seamless coupling with third party engineering tools, enables the automation of the design simulation process, and facilitates analytic decision-making. Maple – linear, quadratic, and nonlinear, continuous and integer optimization ...
It also allowed them to transform existing machine learning processes into reproducible DVC pipelines. DVC 0.6 solved most of the common problems that machine learning engineers and data scientists were facing: the reproducibility of machine learning experiments, as well as data versioning and low levels of collaboration between teams.