JetBrains, a software company for developers, presents in preview mode, its new development environment (IDE) called DataSpell. Dedicated to data science, the tool offers data analysis and machine learning model prototyping. This IDE is intended to be a real Swiss army knife for data science development and aims to improve the user experience with notebooks.
After PyCharm, JetBrains launches DataSpell, its new development environment
Unveiled on September 7 in preview mode, the DataSpell IDE is accessible from jetbrains.com. The interface has been designed to facilitate coding while focusing on data. The native Jupyter notebook support is emphasized in the context of using the IDE while offering an improved experience, according to the software company.
The notebook includes Markdown and JavaScript support cell output. Dataspell supports working with local Jupyter notebooks, as well as remote Jupyter, JupyterHub and JupyterLab servers. It supports Python scripting, providing a read, eval, print, loop (REPL) to execute the code correctly, as well as tools that will allow developers to work with interactive and static data visualization.
The JetBrains team has extensively studied the user experience with the notebooks. Here are the main improvements that have been made in this sense:
- Ability to hide individual results in a cell using a keyboard shortcut or the mouse.
- Improved scrolling of notebooks and their results.
- For table results, dedicated actions have been added to open the data in separate tabs.
- For images, dedicated actions have been added for saving images to a file.
- For unresolved packages in code cells, a new quick fix adds the import statement to the cell and executes it automatically so the user does not have to do it manually.
- Support for Chinese and Korean characters.
A tool designed to improve the user experience with notebooks
The company has recently begun to focus more on the Python interactive console. Now, when dataframes and graphs are evaluated in the Python console, the corresponding interactive results appear directly in the console. Exploratory data analysis is not limited to Jupyter notebooks and can often be done via Python scripts.
In DataSpell, the code base is considered a workspace rather than a project. In this workspace, the user will be able to switch between tasks and reuse notebooks just as they reuse configured environments. Overall, JetBrains wants to make DataSpell a more convenient and efficient environment for working with data. To provide the best possible experience, the company wants to give users the ability to choose their environment based on how they use its tools.
Note that most of the features of JetBrains DataSpell, including support for Jupyter notebooks, will soon be available in the professional edition of PyCharm, another IDE developed by JetBrains and used for programming in Python.
Translated from JetBrains propose DataSpell, son nouvel environnement de développement dédié à la data science
