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Languages & runtimes · #python
Use Agg before importing pyplot, close figures explicitly, and declare fonts to generate charts reliably in headless containers and long-running services.
Streamlit reruns your script on every interaction. Pick st.cache_data for serializable results and st.cache_resource for live connections and models—here is how to decide.
Learn how to implement and tune OpenCV's Canny Edge Detection to achieve single-pixel wide boundaries and avoid edge fragmentation using hysteresis thresholding.
When configuration values should never change after creation, frozen dataclasses with slots give you immutability, memory efficiency, and clear validation—all without boilerplate. Learn how to use them safely and what trade‑offs to watch for.
Choose between %matplotlib inline and %matplotlib widget in Jupyter. Learn how the backends differ, what ipympl requires, and how to verify interactive plotting works.
Learn how FastAPI's Depends and yield patterns eliminate duplicated setup code and guarantee cleanup of database sessions, auth tokens, and other shared resources.
Replace Plotly’s default hover text with rich, custom tooltips using hovertemplate. A step‑by‑step example, trade‑offs, and cross‑language tips to make your charts more informative.
Learn how to mount Google Drive in a Colab notebook so that models, datasets, and configs persist after runtime disconnections.
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.
matplotlib
streamlit
fastapi
sqlalchemy
plotly
googlecolab
jupyter