issue_comments: 345503897
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/simonw/datasette/issues/105#issuecomment-345503897 | https://api.github.com/repos/simonw/datasette/issues/105 | 345503897 | MDEyOklzc3VlQ29tbWVudDM0NTUwMzg5Nw== | 198537 | 2017-11-19T09:38:08Z | 2017-11-19T09:38:08Z | CONTRIBUTOR | Thanks, I wrote this very simple reader because the default approach as described on the Datahub pages seemed to complicated. I had metadata from the `datapackage.json` attached to the returned DataFrames but removed this due to some attribute handling change in the latest Pandas version. This could also be useful for getting from Data Package to SQL db: https://github.com/frictionlessdata/tableschema-sql-py I maintain a few climate science related dataset at https://github.com/openclimatedata/ The Data Retriever (mainly ecological data) by @ethanwhite et al. is also using the Data Package format for metadata and has some tooling for different dbs: https://frictionlessdata.io/articles/the-data-retriever/ https://github.com/weecology/retriever The Open Power System Data project also has a couple of datasets that show nicely how CSV is great for assembling and then already make SQLite files available. It's one of the first data sets I tried with Datasette, perfect for the use case of getting an API for putting power stations on a map ... https://data.open-power-system-data.org/ | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 274314940 |