An IDE (Integrated Development Environment) is a software application that provides comprehensive facilities to computer programmers for software development. The easy debugging process, syntax highlighting, tool integration, keyboard shortcuts, and parsing available on IDEs make them an optimal coding tool for data scientists. This article highlights the top five IDEs for data scientists.
Spyder
Scientific Python Development Environment (Spyder) is an open-source, cross-platform IDE for Data Science. The IDEs essential building blocks, include advanced editing, code analytical tools, IPython Console, variable explorer, plots, debugger and the help icon, which makes Spyder an ideal choice for data scientists.
To install it, one must have Anaconda Environment in their system. The IDE integrates important libraries for data science- NumPy, SciPy, Matplotlib and IPython and can be extended to plugins- Spyder Notebook, Spyder Terminal, Spyder Unittest. According to data scientists, Spyder is very intuitive for scientific computing.
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JupyterLabs
JupyterLab is an open-source web application, which has been designed to provide a user interface based on Jupyter Notebook. It allows the user to work with documents on Jupyter Notebook, born out of IPython in 2014. Its flexible interface lets users configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design allows for extensions that expand and enrich functionality. The application is easy to use, has an interactive data science interface, and is user-friendly for presentation or educational tools.
PyCharm
PyCharm is an IDE for professional developers and data scientists. It has intelligent coding assistance that allows for smart code completion, code inspections, on-the-fly error highlighting and quick fixes, along with automated code refactorings and rich navigation capabilities. PyCharm has many tools:
- An integrated debugger and test runner
- A built-in terminal
- A Python profiler remote development capabilities with remote interpreters
- Integration with major VCS and built-in database tools
- Remote integration with Docker and Vagrant.
In addition, it also has an integrated library with tools such as NumPy and MatplotLib.
Visual Studio Code
VS Code is one of the most used Python IDEs. The IDE is known for its tools such as IntelliSense that allows features beyond syntax highlighting and smart completions based on variable types, imported modules, and functions definition. In addition, VS Code allows debugging code right from the editor with breakpoints, call stacks and an interactive console. Furthermore, VS Code is extensible and customisable, allowing for the addition of new languages, themes, and debuggers. The IDE also has built-in Git commands. VS Code is available in free and paid versions.
Atom
Atom is a formidable IDE for ML & DS professionals that supports many languages other than Python, such as C, C++, HTML, JavaScript, etc. The IDE includes features such as cross-platform editing, built-in package manager, smart autocompletion, file system browser, and multiple panes. Moreover, its plugins, languages, libraries, and tools are constantly updated, resulting in the Atom interface and experience being customisable and outstanding.
What do Experts suggest?
According to Chief Data Scientist at PayU Finance, Piyush Gupta, “At PayU, the developers tend to choose the platform of their own choice for development. However, the majority of them use a combination of Jupyter Notebooks and PyCharm. Jupyter is great for initial EDA and provides flexibility for a lot of basic tasks. Personally, I prefer to use PyCharm because of better environment management, more accurate refactoring, better package management, a dedicated python console, better navigation & UX, and advanced debugging capabilities.”
