dataframe implementations

[Jay Norwood via Digitalmars-d-learn]

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?