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…l datasets. Here's a summary: Dependencies added to pyproject.toml docs group: - pooch>=1.0 (downloads tutorial datasets) - netcdf4>=1.6 (reads NetCDF files) Datasets now used: ┌─────────────────┬───────────────────────────────────────────────────────┬────────────────────────────────────┐ │ Dataset │ Dimensions │ Use Case │ ├─────────────────┼───────────────────────────────────────────────────────┼────────────────────────────────────┤ │ air_temperature │ time (2920), lat (25), lon (53) │ Time series, heatmaps, climatology │ ├─────────────────┼───────────────────────────────────────────────────────┼────────────────────────────────────┤ │ eraint_uvz │ month (2), level (3), latitude (241), longitude (480) │ 4D data, pressure levels, faceting │ └─────────────────┴───────────────────────────────────────────────────────┴────────────────────────────────────┘ Key improvements: - getting-started.ipynb: Uses NCEP air temperature for basic intro - plot-types.ipynb: Demonstrates all plot types with both datasets, including 4D faceting and animations through pressure levels - advanced.ipynb: Shows xarray operations (rolling, groupby, resample) with real climate data, plus Hovmoller diagrams and temperature anomalies The notebooks now show meaningful patterns (seasonal cycles, jet streams, temperature gradients) instead of random noise.
Allow automerge of dependabot
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Caution Review failedThe pull request is closed. 📝 WalkthroughWalkthroughThis PR introduces automated dependency management via Dependabot configuration, updates example notebooks to emphasize Plotly Express workflows with stock data, and pins optional dependency versions to exact specifications in the project manifest. Changes
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Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes 📜 Recent review detailsConfiguration used: defaults Review profile: CHILL Plan: Pro 📒 Files selected for processing (6)
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Use plotly express data in notebooks
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