DataSpell Features

• Intelligent Jupyter notebooks
  • 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.
• 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.
• 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.
• R - 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.