I looked through the dataframe code and a couple of comments...
I had thought perhaps an app could read in the header info and type info from hdf5, and generate D struct definitions with column headers as symbol names. That would enable faster processing than with the associative arrays, as well as support the auto-completion that would be helpful in writing expressions.
The csv type info for columns could be inferred, or else stated in the reader call, as done as an option in julia.
In both cases the column names would have to be valid symbol names for this to work. I believe Julia also expects this, or else does some conversion on your column names to make them valid symbols. I think the D csv processing would also need to check if the
The jupyter interactive environment supports python pandas and Julia dataframe column names in the autocompletion, and so I think the D debugging environment would need to provide similar capability if it is to be considered as a fast-recompile substitute for interactive dataframe exploration.
It seems to me that your particular examples of stock data would eventually need to handle missing data, as supported in Julia dataframes and python pandas. They both provide ways to drop or fill missing values. Did you want to support that?
