Thanks. I don't think that's really equivalent though. I believe python's
list
is not contiguous, and can efficiently handle insertions and deletions, both at
the ends and in the middle. Julia's array is a contiguous dense array, correct?
Jason Merrill wrote:
> julia> {}
> 0-element Array{Any,1}
>
> is like python's arrays in that it can hold values of heterogenous types,
> and you can grow it with push!, e.g.
>
> julia> a = {}
> 0-element Array{Any,1}
>
> julia> push!(a, "grapes")
> 1-element Array{Any,1}:
> "grapes"
>
> julia> push!(a, 3)
> 2-element Array{Any,1}:
> "grapes"
> 3
>
> If you know the type of all the things you'll be putting in your container,
> you will get better performance/memory efficiency with a typed array.
>
> julia> b = Float64[]
> 0-element Array{Float64,1}
>
> julia> push!(b, 1.0)
> 1-element Array{Float64,1}:
> 1.0
>
> julia> push!(b, NaN)
> 2-element Array{Float64,1}:
> 1.0
> NaN
>
> I think in either case, Julia is smart about growing the array by the right
> amount at the right time to get O(1) ammortized push!(), but if you have a
> guess about the eventual size of your array, you can help Julia out with
> `sizehint`.
>
> julia> sizehint(b, 100000)
>
> You don't have to be exactly right with sizehint--the worst that will
> happen is that you will use a little more memory than you needed to, or the
> array will have to be dynamically grown more often than it had to be. For
> this, I am most grateful. In Matlab, your choices are, AFAICT, 1) know the
> exact size of your container correctly ahead of time, 2) have terrible
> performance, or 3) allocate something that's definitely bigger than you
> need, and then keep track of how much of it you've used yourself and throw
> away the excess at the end.
>
>
> On Monday, August 4, 2014 4:16:27 PM UTC-7, Neal Becker wrote:
>>
>> What would be similar to python list type? A container that has an
>> efficient
>> append. A common use is construct an empty list, then grow it by calling
>> append.
>>
>>
