Erick If you've found out by now what that macro does, you could send a PR
with a docstring for it! Something like:
"Creates a typed dictionary."
macro d(xs...)
@cond if VERSION < v"0.4-"
Expr(:typed_dict, :(Any=>Any), map(esc, xs)...)
else
:(Dict{Any, Any}($(map(x->esc(prockey(x)),
On Thu, Dec 24, 2015 at 8:22 PM, Yichao Yu wrote:
> On Thu, Dec 24, 2015 at 7:51 PM, Ismael Venegas Castelló
> wrote:
>> Something like this:
>>
>> julia> df = DataFrame(x = Int[1:10; typemax(Int8)]);
>>
>> julia> eltype(df[:x])
>> Int64
>>
>> julia>
I use it to load SIUnits and a module that I wrote with a bunch of
astronomical constants (e.g. masses and orbits of all the planets).
On Wednesday, 23 December 2015 21:38:44 UTC+1, SundaraRaman R wrote:
>
> I'm a newbie to Julia and just today learnt that there's a .juliarc.jl
> initialization
Where s is:
julia> s = "1.ts, 2.ts, , 00xyz.ts"
"1.ts, 2.ts, , 00xyz.ts"
El jueves, 24 de diciembre de 2015, 18:57:05 (UTC-6), Ismael Venegas
Castelló escribió:
>
> Is something like this what you are looking for?
>
> julia> matchall(r"(\d+?.+?\.ts)", s)
> 3-element
Is something like this what you are looking for?
julia> matchall(r"(\d+?.+?\.ts)", s)
3-element Array{SubString{UTF8String},1}:
"1.ts"
"2.ts"
"00xyz.ts"
El jueves, 24 de diciembre de 2015, 12:30:52 (UTC-6), Douglas Bates
escribió:
>
> Short version:
>
> I have a string that
Yichao, why is it bad idea to splat? Is it not performant?
El jueves, 24 de diciembre de 2015, 5:02:14 (UTC-6), Min-Woong Sohn
escribió:
>
> I want to reduce the amount of memory used by a dataframe that has lots of
> binary variables. What is the best way to achieve this? For example, how
>
You can reverse(the string), then use a reversed regex pattern, then use
the reverseind function to transform the indices of the matches back to
indices in the original string.
df[:x] = Int8[df[:x]...] doesn't seem to work with NAs.
On Thursday, December 24, 2015 at 6:02:14 AM UTC-5, Min-Woong Sohn wrote:
>
> I want to reduce the amount of memory used by a dataframe that has lots of
> binary variables. What is the best way to achieve this? For example, how
> can I
Something like this:
julia> df = DataFrame(x = Int[1:10; typemax(Int8)]);
julia> eltype(df[:x])
Int64
julia> df[:x] = Int8[df[:x]...];
julia> eltype(df[:x])
Int8
julia> df = DataFrame(x = Int[1:10; typemax(Int8) + 1]);
julia> eltype(df[:x])
Int64
julia> df[:x] = Int8[df[:x]...];
ERROR:
On Thu, Dec 24, 2015 at 7:51 PM, Ismael Venegas Castelló
wrote:
> Something like this:
>
> julia> df = DataFrame(x = Int[1:10; typemax(Int8)]);
>
> julia> eltype(df[:x])
> Int64
>
> julia> df[:x] = Int8[df[:x]...];
>
> julia> eltype(df[:x])
> Int8
>
> julia> df =
Is there a way to attach variable and value labels in a DataFrame?
Say I have a data file in plain text, which starts with some lines that
describe matrix sizes, etc, for the data that follows. For example, the
first line might be
9 2 2 1.3
To read this file, in C I would use something like this call to scanf:
scanf("%d%d%d%lf");
How do I do this in
If I want to do parrallel computing, how could I pay to get it?
On Thu, Dec 24, 2015 at 8:48 PM, Ismael Venegas Castelló
wrote:
> Yichao, why is it bad idea to splat? Is it not performant?
Yes, it's terrible for performance
```
julia> using Benchmarks
julia> f1(x) = Int8[x...]
f1 (generic function with 1 method)
julia> f2(x) =
Sorry for the delayed response, I didn't notice the push! sorry!
Glad you figured it out though.
On Tuesday, 22 December 2015 12:18:10 UTC-5, lmaga...@soasta.com wrote:
>
> Of course if I don't do *@everywhere push!(...)* then I get a
> BoundsError...
>
> Well, that was entirely my fault.
On Thursday, December 24, 2015 at 8:48:36 AM UTC+5:30, Stefan Karpinski
wrote:
>
> Mine's empty. I just take anything I want in .juliarc.jl and put it
> directly in base. j/k
>
Haha, I actually thought about something related when posting the question:
there's a lot of common boilerplate in
Short version:
I have a string that contains several instances of names of the form
1.ts, 2.ts, , 00xyz.ts and I want to find the last match.
That is, I want to find "00xyz.ts" or, alternatively, find all such names
in sequence..
Longer version:
These are file names of a series
Totally agree – one-letter macro names aren't the best form.
On Wed, Dec 23, 2015 at 11:43 PM, Tony Kelman wrote:
> May be grep-able, but doesn't change the point that a single letter macro
> name is not the greatest for readability.
What would be helpful is to know what kind of decisions you are thinking of
and what are the factors.
I suspect within 2 weeks for sure - but it's really for the Julia stats
folks to say. The idea is to get feedback and chart a course.
-viral
On 24 Dec 2015 10:07 p.m., "Lampkld"
An error message would be nice.
On Thursday, December 24, 2015 at 6:37:41 AM UTC-5, Tony Kelman wrote:
>
> Do you want an error, or truncation, or wraparound for values that are too
> large to fit in an Int8?
Sorry to bug you, but can we expect something this or next week? Would be
helpful in knowing until when to push some stuff off.
On Thursday, December 17, 2015 at 6:20:45 PM UTC-5, Viral Shah wrote:
>
>
> The JuliaStats team will be publishing a general plan on stats+df in a few
> days. I
I want to reduce the amount of memory used by a dataframe that has lots of
binary variables. What is the best way to achieve this? For example, how
can I convert a variable from Int64 to Int8 in a dataframe.
Thanks
Do you want an error, or truncation, or wraparound for values that are too
large to fit in an Int8?
Dear Stuart. Thanks for your help
On Wednesday, December 23, 2015 at 11:02:18 PM UTC+2, Stuart Brorson wrote:
>
> Julia is assuming your inputs are 32 bit integers. The result of
> multiplying them is larger than 2^32-1. Therefore, the integer
> multiplication is performed modulo 2^32, which
https://www.mythicsoft.com/agentransack
You won't have grepping problems again.
quinta-feira, 24 de Dezembro de 2015 às 05:41:05 UTC, Eric Forgy escreveu:
>
> Cool! Thanks Greg. I am also using Atom and happy to learn new tricks :)
>
> On Thursday, December 24, 2015 at 1:33:04 PM UTC+8, Greg
I too am interested in this. It might be worth building a "GrowableArray"
type for d>1. In the meantime, you can check out DataFrames, they do allow
growing. IIRC they are built as a vector of vector, so you should get
similar performance, but the syntax is nice if you have names for your
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