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DuckDB earns its place once files get large enough that pandas gets slow, or when it's easier to write the analysis as SQL than as DataFrame operations.",{"question":351,"answer":352},"When do I move past this stack to something more structured?","Once the analysis needs to run on a schedule, be shared as a live dashboard, or feed a model others depend on. That's the point to move to a dashboard stack, a proper ELT pipeline, or an ML training stack, depending on what the analysis was building toward.",{"question":354,"answer":355},"Jupyter Notebook, JupyterLab, or notebooks in VS Code?","They all open the same .ipynb files, so the choice is about the editor, not the analysis. Classic Jupyter Notebook is the simplest single-document view; current versions are built on JupyterLab's components. JupyterLab adds tabs, a file browser, and side-by-side notebooks and terminals, which helps once a project has several notebooks and data files. VS Code's notebook support suits people who already write Python there and want notebooks next to regular code with the same Git tooling. Switching later costs nothing, since the files don't change.",{"question":357,"answer":358},"Why does the notebook run out of memory when DuckDB handles large files?","Usually because the full result was pulled into pandas. DuckDB scans large Parquet or CSV files without loading them, but converting a query result to a DataFrame materializes every row in memory. Do the filtering and aggregation in DuckDB's SQL and bring only the small result into pandas for plotting or final touches. A related surprise: a default DuckDB connection lives in memory, so tables created in it disappear when the kernel restarts. Connect to a .duckdb file instead when intermediate tables should survive between sessions.",{"question":360,"answer":361},"How do I share an analysis with people who don't use Jupyter?","Export it, or turn it into a small app. Exporting the notebook to HTML (from the File menu or with nbconvert) produces a single file with all charts and tables that opens in any browser; hide the code cells if the audience only needs the findings. For a polished report, Quarto renders notebooks to documents and slides. When people need to change inputs and see updated results, move the logic into a Streamlit app, which the Python Dashboard Starter stack covers.",{"summary":363,"starting_cost_label":364,"has_free_tier":3,"line_items":365},"Jupyter Notebook, Pandas, NumPy, and DuckDB are all free, open-source Python libraries with no usage costs. 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