With the growing popularity of Python as a data science language, it was only natural for JetBrains that PyCharm, its Python IDE, was enriched with data science capabilities. So, building on the feedback received over the past year, the company began working to significantly improve PyCharm’s data science support. During this process, JetBrains was able to better understand the differences between data scientists’ and software developers’ workflows and tool expectations. JetBrains therefore introduced a new integrated development environment for data science called DataSpell last March. After 5 months, the former DataSpell Early Access program is now open to everyone.
JetBrains DataSpell is JetBrains’ new IDE designed specifically for data science. Announced in March, it was previously available through a private Early Access Program (EAP). Program participants were able to use the IDE and help improve it before JetBrains made it public. Today, the developer software company announced that the DataSpell Early Access program is open to everyone. People who have already applied to participate in the program will therefore receive an invitation shortly. Also, downloading program builds from the JetBrains DataSpell website is now possible without registration. You can download the latest build of the program now.
Over the past few months, the DataSpell team has focused primarily on improving the experience with notebooks. This experience includes the ability to fluidly manipulate cells in Jupyter notebooks, access to all familiar keyboard shortcuts, and a crisp display of results and quick-fixes.
Here are the main improvements made to notebook support:
- It is now possible to hide individual results in a cell using keyboard shortcuts or the mouse.
- JetBrains has greatly improved notebook scrolling and results.
- Special actions have been added for table results, which allow you to open the data in separate tabs.
- For images, JetBrains has added dedicated actions for saving images to a file.
- For unresolved packages in code cells, a new quick-fix adds the import statement to the cell and runs it automatically, so the user doesn’t have to do it manually.
- Chinese and Korean characters are now displayed correctly in table results.
JetBrains has also been actively working on remote support for notebooks. DataSpell not only supports local notebooks running on the user’s computer, but also notebooks running on remote servers. During the Private Early Access program, remote notebook support was upgraded from an experimental feature to a standard, out-of-the-box feature. JetBrains plans to make even more improvements in this area in future updates.
The company has recently focused more on the interactive Python console. Now when data frames and charts 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. The interactive Python console is a very good tool for this, which we are constantly improving.
Finally, JetBrains has compiled a list of answers to frequently asked questions:
Q: Why launch a new IDE on top of PyCharm Professional Edition?
In addition to providing the desired quality features, JetBrains products aim to provide the best possible user experience. JetBrains DataSpell’s interface is therefore both data-driven and code-driven. Functions related to data work are preferred. This is reflected in the layout of tool windows, main menu, actions, etc.
In DataSpell, the codebase is viewed as a workspace rather than a project. In this workspace, the user can move from one task to another and reuse the notebooks just as he reuses the configured environments.
Overall, JetBrains aims to make DataSpell a more convenient and efficient environment for working with data. In order to offer the best possible experience, the company wants to give users the ability to choose their environment according to how they use their tools.
Q: How is JetBrains DataSpell better than other data science tools?
There has never been an IDE built specifically for data science in the Python ecosystem. Data scientists had to use standalone Jupyter editors, IDEs, or notebooks. Only the R ecosystem had a standalone IDE for data science. We’ve often heard data scientists who’ve used RStudio complain that there’s nothing quite like it in Python. JetBrains DataSpell is the IDE that meets the needs of these data scientists.
On the one hand, JetBrains DataSpell offers a wide range of data science tools, including notebooks, interactive REPL, dataset explorer and visualization, and conda support. On the other hand, JetBrains DataSpell offers intelligent coding support for Python and a variety of other tools, all seamlessly integrated into a unified user interface.
While Python support is a priority, JetBrains DataSpell is also open to supporting other languages. Currently it already has support for R and other languages may be supported in the future.
Q: Will JetBrains DataSpell features be available in PyCharm?
Yes, most of JetBrains DataSpell’s features, including support for Jupyter notebooks, will soon be available in PyCharm’s Professional Edition.
Q: How is JetBrains DataSpell different from PyCharm?
PyCharm’s user interface was designed with development workflows in mind. It requires setting up a project, running configurations, etc. JetBrains DataSpell is a much simpler IDE focused on data mining workflows.
If you use Python for pure data science, whether you work in fields as diverse as exploratory data analysis or prototyping machine learning models, JetBrains DataSpell is the tool for you. However, if you are planning to develop in Python, it is better to go for PyCharm.
Q: How much does JetBrains DataSpell cost?
DataSpell’s pricing is similar to other IntelliJ-based IDEs, such as DataGrip or PyCharm Professional Edition.
Download the DataSpell Early Access Program build