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.
>>>
>>
>>
>>
