Le mercredi 15 juin 2016 à 17:28 -0700, Tony Kelman a écrit :
> Try parse.([Int64], x)
> note that the output will be an Array{Any} because issue #4883 hasn't
> been fixed yet. The issue here is that broadcast doesn't treat types
> as "scalar-like."
Is the latter a separate bug? Should we open an issue for that?> > map of course works, but it is quite verbose. I’ve been working a > > group of new julia users lately, many of them from other languages > > like R, Python etc., and they roll their eyes when something that > > simple takes > > > > df[:x] = map(q->parse(Int64,q), df[:x]) > > > > It just is quite complicated for something pretty simple… Maybe > > there are other simple constructs for this? > > > > Thanks, > > David > > > > From: julia...@googlegroups.com [mailto:julia...@googlegroups.com] > > On Behalf Of John Myles White > > Sent: Wednesday, June 15, 2016 3:53 PM > > To: julia-users <julia...@googlegroups.com> > > Subject: [julia-users] Re: parse.(Int64, x) > > > > I would be careful combining element-wise function application with > > partial function application. Why not use map instead? > > > > On Wednesday, June 15, 2016 at 3:47:05 PM UTC-7, David Anthoff > > wrote: > > I just tried to use the new dot syntax for vectorising function > > calls in order to convert an array of strings into an array of > > Int64. For example, if this would work, it would be very, very > > handy: > > > > x = [“1”, “2”, “3”] > > parse.(Int64, x) > > > > Right now I get an error, but I wonder whether this could be > > enabled somehow in this new framework? If this would work for all > > sorts of parsing, type conversions etc. it would just be fantastic. > > Especially when working DataFrames and one is in the first phase of > > cleaning up data types of columns etc. this would make for a very > > nice and short notation. > > > > Thanks, > > David > > > > -- > > David Anthoff > > University of California, Berkeley > > > > http://www.david-anthoff.com > > > >
