The market for data science IDEs isn’t overly crowded. On the one hand, there’s Jupyter for maximal interactivity, and on the other, there’s PyCharm for a professional atmosphere. Text editors such as VSCode can also be used; however, they are time-consuming. Dataspell is a new entry on the block, an IDE designed specifically for data scientists. Let’s have a look at JupyterLab and JetBrains Dataspell’s functionality.
What is JupyterLab
JupyterLab is an open-source web application, described as “the cross-platform standalone application distribution of JupyterLab. “It is a self-contained desktop application that includes a Python environment and numerous prominent Python libraries that are pre-configured for use in scientific computing and data science operations.” Previously, JupyterLab was kept within a web browser environment, however, with the latest improvements, it is now a standalone application.
What is JetBrains DataSpell
JetBrains announced the release of new integrated development environments (IDEs) for data scientists who construct AI models using a variety of programming languages, including Python. The new IDEs will be offered to data scientists via an early access programme, enhancing the experience of regular notebooks. JetBrains DataSpell will provide data scientists with enhanced experience for managing and writing code. One can sign up for it here.
However, the new IDE will not be a replacement for Jupyter notebooks but rather work alongside them on local PCs. Jupyter notebooks are augmented with folding tracebacks, intelligent Python code aid, interactive tables, and out-of-the-box tables of contents, all of which make it easier to adhere to best practices.
Andrey Cheptsov, product manager at JetBrains, stated that “There has never been a dedicated IDE for data science in the Python ecosystem. Individuals engaged in data research were required to use editors, developer integrated development environments or standalone Jupyter notebooks”. He continued, “JetBrains anticipates that DataSpell will provide a more practical and efficient environment for working with data in general. The developer has indicated that features relating to data manipulation would be prioritised.”
Indeed, JupyterLab supports different languages and enables users to choose their display language using the language pack included with Jupyter. JupyterLab has switched to Jupyter Server as of the third version, a new Jupyter project built on the server element of the traditional Notebook server. This package includes a command palette that appears as a floating window on top of the JupyterLab workspace, allowing users to rapidly launch a command while leaving the sidebar closed or navigating between sidebar panels.
Platform Compatibility
JupyterLab App is compatible with Linux, macOS, and Windows operating systems based on Debian and Fedora. Each platform has a one-click installer. Jupyter’s current release includes a new visual debugger, as well as new methods for publishing and installing extensions via Python pip or Conda packages. Additionally, the company has enabled the installation of these without the need to construct JupyterLab with Node.js.
He added that JetBrains DataSpell works with both local Jupyter notebooks and remote Jupyter, JupyterHub, and JupyterLab servers. Additionally, DataSpell has Python scripting capabilities in addition to various tools for manipulating and viewing static and interactive data. Along with Python, JetBrains DataSpell has rudimentary support for the R programming language, with additional data science languages being added in the future. JetBrains’ new integrated development environment (IDE) complements rather than replaces Jupyter notebooks, Cheptsov explained.
Working Experience
The Jupyter notebook experience has been enhanced with intelligent Python coding aid, an out-of-the-box table of contents, folding tracebacks, and interactive tables. Cell outputs are compatible with both Markdown and JavaScript.
JetBrains’ DataSpell is geared toward the growing ranks of business data scientists, as opposed to other types of professionals who work with computer code. The data scientist team can optimise their workflow and deploy a small number of AI models successfully. They leverage industry-leading tools to navigate large information in less time, making it easier to work on numerous projects concurrently. The tools can assist businesses in attracting and retaining data scientist talent while taking into account a variety of aspects, including salary. Moreover, the tools are capable of writing complicated code. The goal is to increase data scientists’ productivity in order to launch numerous AI initiatives while increasing income and lowering costs. Additionally, the digital business transformation initiative can assist in navigating the data more readily without interfering with the code.
Conclusion
The overall impression of DataSpell is favourable since it extracts from PyCharm all of the critical functionality required for data science applications. DataSpell has significantly improved the Notebook experience. Additionally, DataSpell supports R, and the JetBrains team is working to improve their support for the R language and support for other data science-related languages, such as Julia. For all of these reasons, data scientists will undoubtedly give DataSpell a try once it is officially released.
