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Dasks' out of core dataframes are just a thin wrapper around pandas dataframes (aided by the recent improvement in pandas to release the GIL on a bunch of operations)

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Uh, no they are not. They lazy- scale pandas to on disk and distributed files.

http://dask.pydata.org/en/latest/dataframe.html

"Dask dataframes look and feel like pandas dataframes, but operate on datasets larger than memory using multiple threads."

http://blaze.pydata.org/blog/2015/09/08/reddit-comments/

You haven't refuted anything I said. Internally the dask dataframe operations sit on top of pandas dataframes. All dask does is automatically handle the chunking into in-memory pandas dataframes and interpret dask workflows as a series of pandas operations.

Why doesn't Pandas have anything to save the entire workspace to disk (like .RData). There are all these cool file formats like Castra, HDF5, even the vanilla pickle - but I don't see anything with a one shot save of the workspace (something like Dill)

Is this an antipattern for Pandas?

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