Tuned for high interactivity
Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell.

Smart coding assistance
When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more.

Local and remote notebooks
Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE.

Scientific Python console
Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time.

Cells in Python scripts
Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook.

Data and visualization outputs
Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

Version control
Clone Git projects, commit and push changes, work with several branches, manage changelists, and stage updates before committing them.

Terminal
Work with the command line shell through the built-in Terminal that supports all of the same commands as your operating system.

Database tools
Access and query a database right from the IDE. Rely on smart coding assistance when editing SQL code, running queries, browsing data, and altering schemas.

Python
Regardless of whether you work in Jupyter notebooks or Python scripts, you will always be able to rely on intelligent code completion, on-the-fly error checking and quick-fixes, and easy code navigation.

Markdown
DataSpell supports editing and rendering Markdown in both notebook cells and in separate files. LaTeX support is not ready yet, but coming soon.

Interactive outputs
DataSpell fully supports both static and JavaScript-based outputs used by scientific libraries, such as Plotly, Bokeh, Altair, ipywidgets, and others. For DataFrames, DataSpell offers rich interactive table controls.

Conda
Built-in support for Conda makes it easy to create, manage, and reuse environments and dependencies.

Debugger
The Debugger is supported in both Jupyter notebooks and Python scripts. Stop at breakpoints, step through the code, and browse and manage the state of the variables.

SQL
Connect to your database to explore tables, perform refactorings, import/export data, and more.
Basic support for R includes a debugger, dataset and visualization explorer, package manager, intelligent coding assistance, and more.

Plugins
The Vim emulation, Docker, additional VCS, custom appearance themes, and much more is available through a universe of plugins.