Develop a package similar in style and convenience to pandas
, but geared towards energy systems timeseries analysis and data processing.
Develop a suite of tools that are sufficiently powerful to be used for scripted scenario-results processing and automated validation, but which are also helpful when playing around in a notebook, diving into your data, exploring scenario results, …
- A wide range of visualization and plotting features (check out the gallery
- Regional and temporal aggregation and downscaling
- Validation and completeness checks
- Unit conversion
- Reading data directly from the World Bank Data Catalogue or any IIASA Scenario Explorer (think IPCC SR15 scenarios)
- Data format conversion between xlsx, csv, frictionless datapackage
Of course, pyam supports yearly data, subannual resolution with representative timesteps, and continuous-time timeseries data (Python datetime).
You may now think, well, I can program any of these features in 5 minutes myself in pandas or xarray or some other package, so why should I bother to take a look?
Well - because you can save 4 minutes and 30 seconds every time that you want to use one of these features, and you get the function with unit-testing, comprehensive documentation, and a set of explicit error messages when your input data doesn’t make sense...