JetBrains DataSpell vs. Python vs. hug vs. marimo Comparison Chart

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. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

Drastically simplify API development over multiple interfaces. With hug, design and develop your API once, then expose it however your clients need to consume it. Be it locally, over HTTP, or through the command line - hug is the fastest and most modern way to create APIs on Python3. hug has been built from the ground up with performance in mind. It is built to consume resources only when necessary and is then compiled with Cython to achieve amazing performance. As a result, hug consistently benchmarks as one of the fastest Python frameworks and without question takes the crown as the fastest high-level framework for Python 3. hug makes it easy to expose multiple versions of your API. With hug you can simply specify what version or range of versions an endpoint supports and then automatically have that enforced and communicated to your API's users.