I’m still a little shaky about what’s stored in levels.
In general, the secrets to better performance are:
(1) Only index by column once to extract columns as a single chunk, then loop
over the column.
(2) Make sure you look at the `.data` components of a DataArray, rather than do
simple indexi
so i'm guessing i'm doing something wrong, this is much slower... i've
simplified things in my code a little to maybe help see what is going on
for c1 = levels[1], c2 = levels[2], c3 = levels[3], c4 = levels[4], c5 =
levels[5], c6 = levels[6], c7 = levels[7], c8 = levels[8], c9 = levels[9],
c
i will give this a shot. thank you for the reply and all your work on
Julia/DataFrames. It's much appreciated!
On Sunday, March 2, 2014 12:13:22 PM UTC-5, John Myles White wrote:
>
> I’m a little fuzzy still, but I think the answer is probably still that
> the problem you’re hitting is the ind
I’m a little fuzzy still, but I think the answer is probably still that the
problem you’re hitting is the indexing into the DataFrame isn’t sufficient to
let the compiler know that the return type of the index is always a Float64. So
you’ll want to try some of the tricks described in the thead I
the DataFrame contains floats and i'd ultimately like to have an array of
size nrow(data) with the sum of those 13 columns in it (the column
combination changes with each iteration).
Is that enough detail?
I've done the entire algorithm in c++ and at this point julia is a bit
slower, but i hav
Hi Jason,
Can you give a few more details about what objects are? What is data? What is
levels?
In general, the performance problems with DataFrames are actually performance
issues with DataArrays not letting type inference work well. We still haven’t
agreed on the right solution, but this thr
Hello everyone,
i am doing several millions of iterations over a dataframe and i need to
perform several computations over various combinations of columns. The
first of which is a simple sum of 13 column, this appears to be a slow
point of execution.
right now i'm doing something like this:
