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."
On Wednesday, June 15, 2016 at 4:19:09 PM UTC-7, David Anthoff wrote:
>
> 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 <javascript:> [mailto:
> julia...@googlegroups.com <javascript:>] *On Behalf Of *John Myles White
> *Sent:* Wednesday, June 15, 2016 3:53 PM
> *To:* julia-users <julia...@googlegroups.com <javascript:>>
> *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
>
>
>
>
