Each row of a DataFrame is itself a DataFrame.
Why not just store things in a vector of Line objects?
type Line{T}
a::T
pair::Int
end
df = DataFrame(A = Line[Line(1.0, 1), Line(2.0, 2)])
I've changed things from your code because there's a convention of using
uppercase letters to start the names of types.
-- John
On Apr 17, 2014, at 3:18 PM, Stéphane Laurent <[email protected]> wrote:
> Thank you. I need to extract the lines too. A line looks like
>
> type line{T}
> a::T
> pair:Int
> end
>
> This doesn't work, do you have something to propose :
>
> D = DataFrame(A = [1.,2.], B = [1,2])
> D[1,:]::line{Float64}
>
> ?
>
> Le jeudi 17 avril 2014 23:48:29 UTC+2, Simon Kornblith a écrit :
> The most performant approach would be to store the columns as vectors in a
> tuple or immutable. DataFrames can be nearly as performant if you:
>
> - Extract columns (df[:mycol]) and index into them whenever possible instead
> of indexing individual elements (df[1, :mycol])
> - Add typeasserts when you perform indexing operations
> (df[:mycol]::Vector{Int}), or pass the columns to another function
>
> Otherwise you will incur a slowdown because the compiler doesn't know the
> types.
>
> Simon
>
> On Thursday, April 17, 2014 5:34:24 PM UTC-4, John Myles White wrote:
> It's actually possible to place pure Julia vectors in a DataFrame, which
> might be convenient in this case. But you could always just store the columns
> in a Vector{Any}, which is what the DataFrame does behind the scenes anyway.
>
> -- John
>
> On Apr 17, 2014, at 2:27 PM, Stefan Karpinski <[email protected]> wrote:
>
>> A DataFrame does seem like a good option, but those have NA support that you
>> may not need. Can you elaborate a little more on the use case? Is it a fixed
>> set of column names and types? Or will you need to support different schemas?
>>
>>
>> On Thu, Apr 17, 2014 at 5:16 PM, Stéphane Laurent <[email protected]> wrote:
>> Hello,
>>
>> I need to deal with some objects represented as arrays whose some columns
>> are BigFloat, some columns are Int, some columns are logical. Is it a good
>> idea to use a DataFrame ? Is there a better solution ?This is for a
>> computationally intensive program.
>>
>
