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.

Interactive Python scripts

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.

Built-in tools and integrations

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.

Features

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.

FAQ

How do I choose which product to use: JetBrains DataSpell or PyCharm Professional Edition?

If you’re involved in both data science and development, we recommend you use PyCharm Professional Edition. If you’re involved more in data science than development, we recommend you use JetBrains DataSpell.

Does DataSpell support other languages than Python or R? How about Julia, and others?

Once the support for Python and R is polished enough, we may look at other languages too.

Does JetBrains DataSpell bundle database integration?

Yes.

Is DataSpell’s functionality available in PyCharm Professional Edition?

Since 2021.3, PyCharm Professional Edition includes support for local Jupyter notebooks. The support for remote Jupyter notebooks as well as other DataSpell’s features aren’t available yet with PyCharm Professional Edition.

Is DataSpell’s functionality available with PyCharm Community Edition?

No. There are no plans to include it in the community version.

Does DataSpell bundle web development capabilities?

No. PyCharm Professional Edition is the best option for web development combined with data science.

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