Programmers have access to a wide variety of tools and information through the use of an integrated development environment (IDE). As a consequence of this, integrated development environments (IDEs) are the most effective coding tools for data scientists because of the user-friendliness of their interfaces and the presence of features such as syntax highlighting, tool integration, keyboard shortcuts, and parsing.
The best integrated development environments (IDEs) for data scientists are described here.
Jupyter notebook
Jupyter notebook is an open-source integrated development environment (IDE) that may be used for the creation of Jupyter documents, which can then be shared together with their live code. In addition to that, it features an interactive computing environment that can be accessed via the internet. The Jupyter notebook has the ability to handle many different languages for data research. Some of these languages are Python, Julia, Scala, and R.
Atom
In addition to Python, Atom is a powerful integrated development environment (IDE) that supports a number of other languages. The integrated development environment (IDE) provides functionality such as support for editing on multiple platforms, an integrated package management, intelligent auto-completion, a file system browser, and numerous tabs. In addition, the user experience and interface of Atom are extremely modifiable on account of the frequent upgrades that are applied to its plugins, languages, libraries, and tools.
Spyder
Spyder is an open-source integrated development environment (IDE) that was created and developed by Pierre Raybaut in 2009. It is compatible with a wide variety of Python packages, such as NumPy, SymPy, and SciPy, as well as IPython and pandas, amongst others. In addition to these features, the Spyder editor also provides code introspection and code completion capabilities, as well as syntax highlighting, horizontal and vertical splitting, and more.
Visual Studio Code
Visual Studio Code is a well-liked alternative when it comes to programming in the Python language. Popularity of this integrated development environment (IDE) can be attributed to its IntelliSense feature, which offers intelligent completions based on variable types, imported modules, and the declaration of functions. In addition, VS Code includes a call stack, breakpoints, and an interactive console, all of which make it possible to debug code without having to exit the editor. Because of its adaptability and the customization possibilities that it provides, VS Code also enables us to add new programming languages, themes, and debuggers. Additionally, the integrated Git commands are available for your usage within the IDE. There is a free edition of the Visual Studio Code IDE, and there is also a subscription edition.
Sublime text
We are restricted to using the text editor known as Sublime Text when working with the programming language known as Python. The text editor known as Sublime Text includes a wide variety of helpful features, such as options that are project-specific, swift navigation, and compatibility with plugins on several platforms, among other things. Although it is a quick text editor with a community that is willing to assist users, Sublime Text does require money.
Rodeo
Yhat’s Rodeo is an integrated development environment (IDE) that is open-source and geared at data science in Python. As a result, Rodeo gives its users access to Python tutorials as well as reference cheat sheets. A few of the features that come standard with Rodeo are syntax highlighting, auto-completion, easy manipulation of data frames and graphs, and support for IPython right out of the box.
JupyterLabs
An atmosphere comparable to that of the Jupyter Notebook was intended to be the result of the development of the open-source, web-based programme known as JupyterLab. The user’s document workspace is provided by Jupyter Notebook, which originated as IPython in 2014 and has since evolved. Users in the fields of data science, scientific computing, computational journalism, and machine learning can take advantage of its flexible interface to set up and reorganise workflows. The capability to add modules allows for the functionality to be expanded and enhanced. Because of its user-friendly and intuitive data science interface as well as its intuitive design, this programme is an ideal tool for use in teaching as well as giving presentations.
Thonny
A Python integrated development environment (IDE) called Thonny was developed at Tartu University. It was developed specifically for Python teachers and students who are just getting started with the language. Statement stepping without breakpoints is one of the capabilities offered by Thonny, along with a plain user interface (UI), line numbers, access to live variables, and more.
