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  • psychemedia · 2 ✖

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  • Integration with JupyterLab · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions issue performed_via_github_app
436042445 https://github.com/simonw/datasette/issues/370#issuecomment-436042445 https://api.github.com/repos/simonw/datasette/issues/370 MDEyOklzc3VlQ29tbWVudDQzNjA0MjQ0NQ== psychemedia 82988 2018-11-05T21:30:42Z 2018-11-05T21:31:48Z CONTRIBUTOR

Another route would be something like creating a datasette IPython magic for notebooks to take a dataframe and easily render it as a datasette. You'd need to run the app in the background rather than block execution in the notebook. Related to that, or to publishing a dataframe in notebook cell for use in other cells in a non-blocking way, there may be cribs in something like https://github.com/micahscopes/nbmultitask .

 
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 Integration with JupyterLab 377155320  
436037692 https://github.com/simonw/datasette/issues/370#issuecomment-436037692 https://api.github.com/repos/simonw/datasette/issues/370 MDEyOklzc3VlQ29tbWVudDQzNjAzNzY5Mg== psychemedia 82988 2018-11-05T21:15:47Z 2018-11-05T21:18:37Z CONTRIBUTOR

In terms of integration with pandas, I was pondering two different ways datasette/csvs_to_sqlite integration may work:

  • like pandasql, to provide a SQL query layer either by a direct connection to the sqlite db or via datasette API;
  • as an improvement of pandas.to_sql(), which is a bit ropey (e.g. pandas.to_sql_from_csvs(), routing the dataframe to sqlite via csvs_tosqlite rather than the dodgy mapping that pandas supports).

The pandas.publish_* idea could be quite interesting though... Would it be useful/fruitful to think about publish_ as a complement to pandas.to_?

 
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 Integration with JupyterLab 377155320  

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