![]() While most IDEs are built for multiple fields, Spyder was designed specifically for data science and machine learning. It is a perfect starter for those who have not used an IDE before. Spyder is available for Windows, macOS, and major Linux distributions, like Debian, Fedora, and Ubuntu. Spyder (Scientific Python Development Environment) is an open-source, cross-platform Python IDE for data science. Sublime is not an IDE, but it has some quality features we cannot ignore. Sublime works well with old CPUs, unlike many heavyweight IDEs. For example, Sublime has this feature where from an excel sheet, the text can be converted to a dictionary using sublime commands in no time. ![]() Sublime has features specific to data science to optimize the developer’s work. Sublime can run Python, R, Julia, Octave, and SQL interactively, which is very useful for data science programmers. ![]() Sublime is available for Windows, macOS, and major Linux distributions, like Debian, Fedora, and Ubuntu. Jupyterlab vs spyder code#Sublime is a lightweight but powerful code editor. This feature is useful to understand local variables and call the stack of the function easily. When a function is called in Thonny, a separate window gets opened. Thonny has a powerful package manager that makes third-party installation easy and increases functionality. Thonny has features like code completion, debugger, and highlighting syntax errors. Thonny is a beginner-friendly IDE and is mostly used for educational purposes. This IDE was developed at The University of Tartu. Thonny is one of the best Python IDE that runs on Windows, macOS, and most Linux distribution systems. In this article, let us see what the top 10 Python IDEs for data science are. There are a lot of IDEs coming to market nowadays. Having a good IDE for data science workflow can help you maximize work efficiency and reduce the time taken to complete the work. Having a good IDE increases productivity and makes the workflow easy. These features are extremely useful for developers. Various processes of code writing can be implemented through IDEs like compiling, debugging, building executables, editing source code, etc. It provides multiple features which help in consolidating different aspects of programming. Jupyterlab vs spyder software#An IDE (Integrated Development Environment) is used in software development to ease the work of a programmer. ![]()
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