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The point of the format is that you get json metadata to avoid the ambiguous issues (and it also has requirements on the csv format to use, although whether peo

The point of the format is that you get json metadata to avoid the ambiguous issues (and it also has requirements on the csv format to use, although whether people will follow them is a different matter). On the other hand, I used their python API a bit and for loading tables it's way too complicated, with the documentation going into great detail for faffing with metadata but not for actual loading (and nothing like a simple `read_datatable` function). That said, because it's just a folder with CSVs you can just read them individually, although then there's nothing to take advantage of the metadata automaticall.

_pwalsh 9y ago
(I work on the Frictionless Data specifications and tooling at Open Knowledge International.) Point taken about the API. The Data Package [1] and Table Schema [2] libraries are generally designed as low-level libraries for building higher-level applications using the specifications. goodtables-py [3] is an example of a higher-level application built on top. But, point taken, we will look at it, and we'd welcome your feedback on the issue tracker [4]. [1]: https://github.com/frictionlessdata/datapackage-py/issues https://github.com/frictionlessdata/datapackage-py/issues [2]: https://github.com/frictionlessdata/tableschema-py/issues https://github.com/frictionlessdata/tableschema-py/issues [3]: https://github.com/frictionlessdata/goodtables-py/issues https://github.com/frictionlessdata/goodtables-py/issues [4]: https://github.com/frictionlessdata/datapackage-py/issues https://github.com/frictionlessdata/datapackage-py/issues