FWIW, I think it’s much easier to index structures if every row has an atomic existence that is independent of the table it is currently part of. (This is a big part of my interest in moving away from matrix semantics and towards relational model semantics.)
It’s a little harder to index DataFrames because the row indices change over time, so your index can’t just map values to indices. (Well, it can: but then it needs to be updated very frequently: potentially the entire index has to be rewritten if you delete the first row of a DataFrame.) — John On Sep 7, 2014, at 10:27 AM, Harlan Harris <[email protected]> wrote: > This was a feature that sorta existed for a while (see > https://github.com/JuliaStats/DataFrames.jl/issues/24 ), but nobody was very > happy with it, and I think John ripped it out as part of one of his > simplification passes. It's tricky to think about how best to implement this > sort of feature when you aspirationally want to support memory-mapped and > distributed structures too, and where you want a semantics that's explicitly > set-like, cf Pandas or R's data.tables. > > Also worth thinking about this in the context of John's just-announced goals: > https://gist.github.com/johnmyleswhite/ad5305ecaa9de01e317e > > > > On Sun, Sep 7, 2014 at 12:54 PM, John Myles White <[email protected]> > wrote: > No, DataFrames are not indexed. For now, you’d need to build a wrapper that > indexes a DataFrame to get that kind of functionality. > > — John > > On Sep 7, 2014, at 9:53 AM, Steven Sagaert <[email protected]> wrote: > > > Hi, > > I was wondering if searching in a dataframe is indexed (in the DB sense, > > not array sense. e.g. a tree index structure) or not? If so can you have > > multiple indices (on multiple columns) or not? > >
