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
> <[email protected]<javascript:>>
> 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.
>>>>
>>>
>>>
>>>
>
