[WIP] Add an on-disk format for datasets using Frictionless and feather

[larsyencken]

A challenge of the data catalog is to identify the metadata we need to get data into Grapher, and to make sure we have a format that can capture it all. This PR makes a first attempt at an on-disk format for datasets.

Frictionless + feather

Note: this is a prototype format meant as a starting point. We expect to be able to change this on-disk format arbitrarily in future.

• Each dataset gets its own folder; the name is not important
• The folder contains many tables in Feather format (dir/mytable1.feather, dir/mytable2.feather, ...)
• Metadata for the dataset as a whole, and for all individual tables, is stored in datapackage.json
• datapackage.json conforms to the Frictionless data standard, letting us use their tools for validation

Rich data frames and series

• Design goals:
  • We can have multiple serialisation formats for datasets
  • At any time, our tables and series behave and feel like pandas DataFrames and Series
  • We can start without any metadata at all, and incrementally add it
• For datasets, we introduce the Dataset protocol and AboutThisDataset class for metadata
• For tables, we introduce the RichDataFrame class and AboutThisTable class for metadata
• For variables, we introduce this RichDataSeries class and AboutThisSeries class for metadata
• For each of these levels, you get to the metadata object by obj.metadata

Todo

• Get RichDataFrame and RichSeries working and interoperating well
• Get Datasets serialising and deserialising from Frictionless + Feather
  • Translate RichDataFrame metadata into Frictionless resource format
  • Serialise dataset to Frictionless + Feather
  • Deserialise dataset from Frictionless + Feather
• Can create Frictionless + Feather datasets progressively

After this, the plan is to get review, merge this, then try to import the WHO GHO dataset into this format.

[larsyencken]

Closing this PR -- the work ended up forking and becoming the etl repository.