DataSpell released

An IDE dedicated to exploratory data analysis and prototyping ML (machine learning) models.
December 2, 2021
New Product

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.
DataSpell released
DataSpell

DataSpell

The IDE for professional Data Scientists.

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