DataSpell 2026.1.3 — macOS

You have a CSV file that needs cleaning, a few plots to make, and maybe a quick model to test before you hand it off. For that kind of work, you want an environment that treats data as a first-class concern, not an afterthought inside a general IDE. DataSpell from JetBrains is built exactly for that.

How it works in practice

1. Open a notebook or a Python script. DataSpell gives you both Jupyter-style notebooks and a smart Python code editor in the same window. You can move between exploration and script writing without switching apps.
2. Use the interactive REPL console. When you need to test a transformation or run a quick calculation, the REPL is right there. No need to create a whole notebook cell for a one-liner.
3. Work with your data directly. The IDE includes a dataset viewer, so you can inspect columns, spot missing values, and see distributions without writing display code. Conda support means you can manage environments from inside the tool.
4. Export and share. Notebooks can be exported to PDF, and you can build visualizations inline. The 2026.1 release also adds AI agents to help with repetitive code tasks.

DataSpell is part of the JetBrains for Data family and runs on macOS 11 or later, with separate builds for Apple Silicon and Intel Macs. It is an IDE for data work — not a general development tool. Note that the product is scheduled for sunset on 2026-09-01, so plan accordingly if you are looking at a long-term setup.

Combines notebook and Python IDE features

If you have used JupyterLab, you know it works well for notebook-heavy workflows but leaves you on your own for script editing, debugging, and environment management. DataSpell gives you both the notebook interface and a proper Python IDE in one place, with code completion, refactoring, and built-in Conda support that JupyterLab does not offer out of the box.

VS Code with the Python and Jupyter extensions is a capable alternative, but it is a general-purpose editor first. You have to find and configure the right extensions for data work. DataSpell comes with those features already set up for data analysis: the dataset viewer, the REPL console, and database tools through the SQL plugin are there from the start. For someone who spends most of the day in data rather than general development, that saves time on configuration.

RStudio is a dedicated environment for R users, and DataSpell has basic R support, but it is not a replacement if R is your main language. For Python-focused data work, though, DataSpell covers more ground in one application.

Integrated workflow for Python notebook and script users

Data analysts who work mostly in Python notebooks and scripts will find the integrated workflow natural. You can clean a dataset in a script, explore it in a notebook, and check column types in the dataset viewer without leaving the IDE. Machine learning users who prototype models and need to switch between exploration and structured code also benefit from having both modes in one window.

It is also a good fit for teams that use Conda environments and SQL databases alongside Python. The environment management and database tools are built in, so you are not piecing together separate applications. If you mainly write general software in Python or other languages, a full IDE like PyCharm may suit you better. DataSpell is aimed at data work, not general development.

• AI agents support added for automating repetitive code tasks.
• PDF export for notebooks, making sharing results easier.
• Editor improvements and productivity tweaks throughout the IDE.

• macOS 11 or later
• Apple Silicon or Intel processor
• Interface language: English