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Tutorials

Official notebooks live in the tutorials/ directory and are rendered into the website.

If you are new, work through them in numeric order. Each notebook builds on the previous one.

Before running notebooks

  1. Install ukpyn in your active environment.
  2. Set UKPN_API_KEY.
  3. Select the same environment kernel in VS Code/Jupyter.
  4. Run each notebook from the first cell to the last cell.

If cells fail with import errors, revisit Getting Started.

Notebook learning path

01-getting-started.ipynb

  • Audience: complete beginners
  • Outcome: run first API calls and inspect response objects
  • Focus: setup confidence and basic query flow

02-fetching-data.ipynb

  • Audience: beginners who completed notebook 01
  • Outcome: fetch domain data with practical filters
  • Focus: requesting and comparing subsets of records

03-analysis-patterns.ipynb

  • Audience: users comfortable with basic Python data handling
  • Outcome: repeatable analysis patterns for ODP records
  • Focus: lightweight transformations and interpretation

04-ltds-network-planning.ipynb

  • Audience: users exploring planning datasets
  • Outcome: LTDS-oriented analysis workflows
  • Focus: planning-centric tables and network context

05-flexibility-markets.ipynb

  • Audience: users interested in flexibility operations
  • Outcome: inspect dispatch and related market activity
  • Focus: flexibility domain queries and trend exploration

06-powerflow-timeseries.ipynb

  • Audience: users needing time-series network insights
  • Outcome: run circuit/transformer style time-series queries
  • Focus: granularity, period selection, and signal interpretation

07-curtailment-events.ipynb

  • Audience: users studying constraint and curtailment behavior
  • Outcome: fetch and analyse event-based datasets
  • Focus: event windows, frequency, and context

08-geospatial-data.ipynb

  • Audience: users who want spatial/network geography workflows
  • Outcome: explore GIS-style datasets and map-ready records
  • Focus: substations, lines, and location-driven analysis

09-pandapower-cim-import.ipynb

  • Audience: advanced users integrating with power systems tooling
  • Outcome: import and work with CIM/pandapower-related assets
  • Focus: interoperability and deeper modelling workflows

10-powerflow-quality-control.ipynb

  • Audience: users validating operational powerflow data quality
  • Outcome: cross-reference LTDS assets to monthly powerflow and run QC checks
  • Focus: redaction detection, month selection, gap/anomaly/step diagnostics, and line-vs-transformer balance

Troubleshooting tips for beginners

  • If a notebook imports fail, confirm kernel selection matches your install env.
  • If API calls fail, verify UKPN_API_KEY in the same environment/session.
  • If outputs differ from expectations, rerun all cells in order to reset state.

Notebook files in this docs section

Notebook pages available in the site navigation:

  • 01-getting-started.ipynb
  • 02-fetching-data.ipynb
  • 03-analysis-patterns.ipynb
  • 04-ltds-network-planning.ipynb
  • 05-flexibility-markets.ipynb
  • 06-powerflow-timeseries.ipynb
  • 07-curtailment-events.ipynb
  • 08-geospatial-data.ipynb
  • 09-pandapower-cim-import.ipynb
  • 10-powerflow-quality-control.ipynb

Use these notebooks as end-to-end, reproducible examples that complement the API guides in Orchestrators.