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Languages & runtimes · #jupyter
Learn how to connect Dataspell to a remote Jupyter kernel over SSH to keep the IDE responsive while working with large data sets and complex visualizations.
Learn how to diagnose and fix 'Kernel Died' errors in Jupyter by analyzing server logs, monitoring memory usage, and validating kernel specifications.
Learn how to use ipywidgets and the @interact decorator to replace manual cell re-execution with dynamic, real-time parameter tuning in Jupyter Notebooks.
Register each project's virtual environment as a named ipykernel so Jupyter notebooks execute against the exact interpreter and dependency versions. Covers activation, ipykernel installation, kernelspec registration, verification via sys.executable, and recovery from broken kernels.
Use Jupyter’s %timeit magic for fast, reliable benchmarks inside notebooks. Learn how it works, see a concrete example, understand its limits, and get practical tips for accurate performance checks.
Bind JupyterLab sliders and dropdowns to Plotly figures so charts update in place without rerunning cells or reloading the page.
Choose between %matplotlib inline and %matplotlib widget in Jupyter. Learn how the backends differ, what ipympl requires, and how to verify interactive plotting works.
Replace manual cell re-runs with live sliders and dropdowns using ipywidgets. Learn setup, a practical interact example, and the limits of widget state persistence.
Step-by-step guide to producing publication-ready PDFs from .ipynb files using nbconvert's LaTeX backend with custom templates for margins, fonts, and layout.
dataspell
python
plotly
matplotlib