df[:A] gives you a column, which is vector of Line objects.
df[:A][1] gives you the first entry of that vector.
df[:A][1].a gives you the a element of the first entry of that vector.
I think you're better off just using a raw vector of immutables. Instead of
defining type Line, define immutable Line.
-- John
On Apr 17, 2014, at 3:33 PM, Stéphane Laurent <laurent_s...@yahoo.fr> wrote:
> How do I extract a line and a column with this method ?
>
>
>
> Le vendredi 18 avril 2014 00:21:26 UTC+2, John Myles White a écrit :
> 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 <lauren...@yahoo.fr> 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 <ste...@karpinski.org> 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 <lauren...@yahoo.fr>
>>> 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.
>>>
>>
>
