Performance tips#
using DataFrames
using BenchmarkTools
using CategoricalArrays
using PooledArrays
using Random
Access by column number is faster than by name#
x = DataFrame(rand(5, 1000), :auto)
@btime $x[!, 500]; ## Faster
3.827 ns (0 allocations: 0 bytes)
@btime $x.x500; ## Slower
15.630 ns (0 allocations: 0 bytes)
When working with data DataFrame use barrier functions or type annotation#
function f_bad() ## this function will be slow
Random.seed!(1)
x = DataFrame(rand(1000000, 2), :auto)
y, z = x[!, 1], x[!, 2]
p = 0.0
for i in 1:nrow(x)
p += y[i] * z[i]
end
p
end
@btime f_bad();
# if you run @code_warntype f_bad() then you notice
# that Julia does not know column types of `DataFrame`
149.432 ms (5999022 allocations: 122.06 MiB)
solution 1 is to use barrier function (it should be possible to use it in almost any code) for the calculation. You will notice much less memopry allocations and faster performance.
function f_inner(y, z)
p = 0.0
for i in eachindex(y, z)
p += y[i] * z[i]
end
p
end
function f_barrier()
Random.seed!(1)
x = DataFrame(rand(1000000, 2), :auto)
f_inner(x[!, 1], x[!, 2])
end
@btime f_barrier();
3.681 ms (44 allocations: 30.52 MiB)
or use inbuilt function if possible
using LinearAlgebra
function f_inbuilt()
Random.seed!(1)
x = DataFrame(rand(1000000, 2), :auto)
dot(x[!, 1], x[!, 2])
end
@btime f_inbuilt();
3.165 ms (44 allocations: 30.52 MiB)
solution 2 is to provide the types of extracted columns. However, there are cases in which you will not know these types.
function f_typed()
Random.seed!(1)
x = DataFrame(rand(1000000, 2), :auto)
y::Vector{Float64}, z::Vector{Float64} = x[!, 1], x[!, 2]
p = 0.0
for i in 1:nrow(x)
p += y[i] * z[i]
end
p
end
@btime f_typed();
3.763 ms (44 allocations: 30.52 MiB)
In general for tall and narrow tables it is often useful to use Tables.rowtable
, Tables.columntable
or Tables.namedtupleiterator
for intermediate processing of data in a type-stable way.
Consider using delayed DataFrame
creation technique#
also notice the difference in performance between copying vs non-copying data frame creation
function f1()
x = DataFrame([Vector{Float64}(undef, 10^4) for i in 1:100], :auto, copycols=false) ## we work with a DataFrame directly
for c in 1:ncol(x)
d = x[!, c]
for r in 1:nrow(x)
d[r] = rand()
end
end
x
end
function f1a()
x = DataFrame([Vector{Float64}(undef, 10^4) for i in 1:100], :auto) ## we work with a DataFrame directly
for c in 1:ncol(x)
d = x[!, c]
for r in 1:nrow(x)
d[r] = rand()
end
end
x
end
function f2()
x = Vector{Any}(undef, 100)
for c in 1:length(x)
d = Vector{Float64}(undef, 10^4)
for r in eachindex(d)
d[r] = rand()
end
x[c] = d
end
DataFrame(x, :auto, copycols=false) ## we delay creation of DataFrame after we have our job done
end
function f2a()
x = Vector{Any}(undef, 100)
for c in eachindex(x)
d = Vector{Float64}(undef, 10^4)
for r in eachindex(d)
d[r] = rand()
end
x[c] = d
end
DataFrame(x, :auto) ## we delay creation of DataFrame after we have our job done
end
@btime f1();
@btime f1a();
@btime f2();
@btime f2a();
29.541 ms (1949728 allocations: 37.40 MiB)
28.865 ms (1950028 allocations: 45.03 MiB)
1.142 ms (728 allocations: 7.66 MiB)
1.527 ms (1028 allocations: 15.29 MiB)
You can add rows to a DataFrame in place and it is fast#
x = DataFrame(rand(10^6, 5), :auto)
y = DataFrame(transpose(1.0:5.0), :auto)
z = [1.0:5.0;]
@btime vcat($x, $y); ## creates a new DataFrame - slow
@btime append!($x, $y); ## in place - fast
x = DataFrame(rand(10^6, 5), :auto) ## reset to the same starting point
@btime push!($x, $z); ## add a single row in place - fast
2.477 ms (212 allocations: 38.16 MiB)
1.124 μs (29 allocations: 1.50 KiB)
432.462 ns (16 allocations: 256 bytes)
Allowing missing as well as categorical slows down computations#
using StatsBase
function test(data) ## uses countmap function to test performance
println(eltype(data))
x = rand(data, 10^6)
y = categorical(x)
println(" raw:")
@btime countmap($x)
println(" categorical:")
@btime countmap($y)
nothing
end
test(1:10)
test([randstring() for i in 1:10])
test(allowmissing(1:10))
test(allowmissing([randstring() for i in 1:10]))
Int64
raw:
1.812 ms (8 allocations: 7.63 MiB)
categorical:
15.993 ms (1000004 allocations: 30.52 MiB)
String
raw:
21.960 ms (4 allocations: 448 bytes)
categorical:
32.089 ms (1000004 allocations: 30.52 MiB)
Union{Missing, Int64}
raw:
6.031 ms (4 allocations: 464 bytes)
categorical:
15.935 ms (1000004 allocations: 30.52 MiB)
Union{Missing, String}
raw:
22.191 ms (4 allocations: 448 bytes)
categorical:
33.176 ms (1000004 allocations: 30.52 MiB)
When aggregating use column selector and prefer integer, categorical, or pooled array grouping variable#
df = DataFrame(x=rand('a':'d', 10^7), y=1);
gdf = groupby(df, :x)
GroupedDataFrame with 4 groups based on key: x
Row | x | y |
---|---|---|
Char | Int64 | |
1 | b | 1 |
2 | b | 1 |
3 | b | 1 |
4 | b | 1 |
5 | b | 1 |
6 | b | 1 |
7 | b | 1 |
8 | b | 1 |
9 | b | 1 |
10 | b | 1 |
11 | b | 1 |
12 | b | 1 |
13 | b | 1 |
⋮ | ⋮ | ⋮ |
2500713 | b | 1 |
2500714 | b | 1 |
2500715 | b | 1 |
2500716 | b | 1 |
2500717 | b | 1 |
2500718 | b | 1 |
2500719 | b | 1 |
2500720 | b | 1 |
2500721 | b | 1 |
2500722 | b | 1 |
2500723 | b | 1 |
2500724 | b | 1 |
⋮
Row | x | y |
---|---|---|
Char | Int64 | |
1 | c | 1 |
2 | c | 1 |
3 | c | 1 |
4 | c | 1 |
5 | c | 1 |
6 | c | 1 |
7 | c | 1 |
8 | c | 1 |
9 | c | 1 |
10 | c | 1 |
11 | c | 1 |
12 | c | 1 |
13 | c | 1 |
⋮ | ⋮ | ⋮ |
2500293 | c | 1 |
2500294 | c | 1 |
2500295 | c | 1 |
2500296 | c | 1 |
2500297 | c | 1 |
2500298 | c | 1 |
2500299 | c | 1 |
2500300 | c | 1 |
2500301 | c | 1 |
2500302 | c | 1 |
2500303 | c | 1 |
2500304 | c | 1 |
traditional syntax, slow
@btime combine(v -> sum(v.y), $gdf)
15.846 ms (332 allocations: 19.09 MiB)
Row | x | x1 |
---|---|---|
Char | Int64 | |
1 | b | 2500724 |
2 | d | 2500237 |
3 | a | 2498735 |
4 | c | 2500304 |
use column selector
@btime combine($gdf, :y => sum)
6.916 ms (198 allocations: 10.14 KiB)
Row | x | y_sum |
---|---|---|
Char | Int64 | |
1 | b | 2500724 |
2 | d | 2500237 |
3 | a | 2498735 |
4 | c | 2500304 |
transform!(df, :x => categorical => :x);
gdf = groupby(df, :x)
GroupedDataFrame with 4 groups based on key: x
Row | x | y |
---|---|---|
Cat… | Int64 | |
1 | a | 1 |
2 | a | 1 |
3 | a | 1 |
4 | a | 1 |
5 | a | 1 |
6 | a | 1 |
7 | a | 1 |
8 | a | 1 |
9 | a | 1 |
10 | a | 1 |
11 | a | 1 |
12 | a | 1 |
13 | a | 1 |
⋮ | ⋮ | ⋮ |
2498724 | a | 1 |
2498725 | a | 1 |
2498726 | a | 1 |
2498727 | a | 1 |
2498728 | a | 1 |
2498729 | a | 1 |
2498730 | a | 1 |
2498731 | a | 1 |
2498732 | a | 1 |
2498733 | a | 1 |
2498734 | a | 1 |
2498735 | a | 1 |
⋮
Row | x | y |
---|---|---|
Cat… | Int64 | |
1 | d | 1 |
2 | d | 1 |
3 | d | 1 |
4 | d | 1 |
5 | d | 1 |
6 | d | 1 |
7 | d | 1 |
8 | d | 1 |
9 | d | 1 |
10 | d | 1 |
11 | d | 1 |
12 | d | 1 |
13 | d | 1 |
⋮ | ⋮ | ⋮ |
2500226 | d | 1 |
2500227 | d | 1 |
2500228 | d | 1 |
2500229 | d | 1 |
2500230 | d | 1 |
2500231 | d | 1 |
2500232 | d | 1 |
2500233 | d | 1 |
2500234 | d | 1 |
2500235 | d | 1 |
2500236 | d | 1 |
2500237 | d | 1 |
@btime combine($gdf, :y => sum)
6.866 ms (206 allocations: 10.62 KiB)
Row | x | y_sum |
---|---|---|
Cat… | Int64 | |
1 | a | 2498735 |
2 | b | 2500724 |
3 | c | 2500304 |
4 | d | 2500237 |
transform!(df, :x => PooledArray{Char} => :x)
Row | x | y |
---|---|---|
Char | Int64 | |
1 | b | 1 |
2 | b | 1 |
3 | d | 1 |
4 | a | 1 |
5 | c | 1 |
6 | b | 1 |
7 | c | 1 |
8 | b | 1 |
9 | a | 1 |
10 | c | 1 |
11 | a | 1 |
12 | a | 1 |
13 | d | 1 |
⋮ | ⋮ | ⋮ |
9999989 | d | 1 |
9999990 | a | 1 |
9999991 | c | 1 |
9999992 | d | 1 |
9999993 | c | 1 |
9999994 | d | 1 |
9999995 | b | 1 |
9999996 | b | 1 |
9999997 | b | 1 |
9999998 | a | 1 |
9999999 | a | 1 |
10000000 | a | 1 |
gdf = groupby(df, :x)
GroupedDataFrame with 4 groups based on key: x
Row | x | y |
---|---|---|
Char | Int64 | |
1 | b | 1 |
2 | b | 1 |
3 | b | 1 |
4 | b | 1 |
5 | b | 1 |
6 | b | 1 |
7 | b | 1 |
8 | b | 1 |
9 | b | 1 |
10 | b | 1 |
11 | b | 1 |
12 | b | 1 |
13 | b | 1 |
⋮ | ⋮ | ⋮ |
2500713 | b | 1 |
2500714 | b | 1 |
2500715 | b | 1 |
2500716 | b | 1 |
2500717 | b | 1 |
2500718 | b | 1 |
2500719 | b | 1 |
2500720 | b | 1 |
2500721 | b | 1 |
2500722 | b | 1 |
2500723 | b | 1 |
2500724 | b | 1 |
⋮
Row | x | y |
---|---|---|
Char | Int64 | |
1 | c | 1 |
2 | c | 1 |
3 | c | 1 |
4 | c | 1 |
5 | c | 1 |
6 | c | 1 |
7 | c | 1 |
8 | c | 1 |
9 | c | 1 |
10 | c | 1 |
11 | c | 1 |
12 | c | 1 |
13 | c | 1 |
⋮ | ⋮ | ⋮ |
2500293 | c | 1 |
2500294 | c | 1 |
2500295 | c | 1 |
2500296 | c | 1 |
2500297 | c | 1 |
2500298 | c | 1 |
2500299 | c | 1 |
2500300 | c | 1 |
2500301 | c | 1 |
2500302 | c | 1 |
2500303 | c | 1 |
2500304 | c | 1 |
@btime combine($gdf, :y => sum)
6.882 ms (200 allocations: 10.20 KiB)
Row | x | y_sum |
---|---|---|
Char | Int64 | |
1 | b | 2500724 |
2 | d | 2500237 |
3 | a | 2498735 |
4 | c | 2500304 |
Use views instead of materializing a new DataFrame#
x = DataFrame(rand(100, 1000), :auto)
@btime $x[1:1, :]
177.661 μs (3993 allocations: 159.07 KiB)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | x62 | x63 | x64 | x65 | x66 | x67 | x68 | x69 | x70 | x71 | x72 | x73 | x74 | x75 | x76 | x77 | x78 | x79 | x80 | x81 | x82 | x83 | x84 | x85 | x86 | x87 | x88 | x89 | x90 | x91 | x92 | x93 | x94 | x95 | x96 | x97 | x98 | x99 | x100 | ⋯ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | ⋯ | |
1 | 0.950058 | 0.587494 | 0.177994 | 0.655275 | 0.973326 | 0.409907 | 0.835926 | 0.649191 | 0.202792 | 0.808865 | 0.581285 | 0.34027 | 0.136758 | 0.660741 | 0.97527 | 0.579335 | 0.90986 | 0.848891 | 0.575617 | 0.627489 | 0.290292 | 0.728637 | 0.0211294 | 0.820952 | 0.846581 | 0.132386 | 0.107926 | 0.430422 | 0.961199 | 0.729527 | 0.59214 | 0.2748 | 0.334664 | 0.524227 | 0.82397 | 0.0176395 | 0.46553 | 0.135355 | 0.780029 | 0.58738 | 0.483812 | 0.65528 | 0.584815 | 0.0778716 | 0.537381 | 0.436296 | 0.0305809 | 0.462507 | 0.505382 | 0.170584 | 0.221943 | 0.535946 | 0.78498 | 0.730994 | 0.41515 | 0.418 | 0.450529 | 0.489728 | 0.791703 | 0.465304 | 0.26486 | 0.067945 | 0.508496 | 0.184748 | 0.461517 | 0.193533 | 0.387085 | 0.931858 | 0.425018 | 0.0951919 | 0.206334 | 0.918469 | 0.775217 | 0.865825 | 0.248852 | 0.430064 | 0.291865 | 0.100074 | 0.739642 | 0.685014 | 0.0949525 | 0.69768 | 0.466532 | 0.741147 | 0.563658 | 0.865301 | 0.540111 | 0.359868 | 0.0393546 | 0.540505 | 0.770954 | 0.873907 | 0.0977718 | 0.86035 | 0.567951 | 0.891952 | 0.0967576 | 0.345434 | 0.731782 | 0.21978 | ⋯ |
@btime $x[1, :]
22.833 ns (0 allocations: 0 bytes)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | x62 | x63 | x64 | x65 | x66 | x67 | x68 | x69 | x70 | x71 | x72 | x73 | x74 | x75 | x76 | x77 | x78 | x79 | x80 | x81 | x82 | x83 | x84 | x85 | x86 | x87 | x88 | x89 | x90 | x91 | x92 | x93 | x94 | x95 | x96 | x97 | x98 | x99 | x100 | ⋯ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | ⋯ | |
1 | 0.950058 | 0.587494 | 0.177994 | 0.655275 | 0.973326 | 0.409907 | 0.835926 | 0.649191 | 0.202792 | 0.808865 | 0.581285 | 0.34027 | 0.136758 | 0.660741 | 0.97527 | 0.579335 | 0.90986 | 0.848891 | 0.575617 | 0.627489 | 0.290292 | 0.728637 | 0.0211294 | 0.820952 | 0.846581 | 0.132386 | 0.107926 | 0.430422 | 0.961199 | 0.729527 | 0.59214 | 0.2748 | 0.334664 | 0.524227 | 0.82397 | 0.0176395 | 0.46553 | 0.135355 | 0.780029 | 0.58738 | 0.483812 | 0.65528 | 0.584815 | 0.0778716 | 0.537381 | 0.436296 | 0.0305809 | 0.462507 | 0.505382 | 0.170584 | 0.221943 | 0.535946 | 0.78498 | 0.730994 | 0.41515 | 0.418 | 0.450529 | 0.489728 | 0.791703 | 0.465304 | 0.26486 | 0.067945 | 0.508496 | 0.184748 | 0.461517 | 0.193533 | 0.387085 | 0.931858 | 0.425018 | 0.0951919 | 0.206334 | 0.918469 | 0.775217 | 0.865825 | 0.248852 | 0.430064 | 0.291865 | 0.100074 | 0.739642 | 0.685014 | 0.0949525 | 0.69768 | 0.466532 | 0.741147 | 0.563658 | 0.865301 | 0.540111 | 0.359868 | 0.0393546 | 0.540505 | 0.770954 | 0.873907 | 0.0977718 | 0.86035 | 0.567951 | 0.891952 | 0.0967576 | 0.345434 | 0.731782 | 0.21978 | ⋯ |
@btime view($x, 1:1, :)
21.908 ns (0 allocations: 0 bytes)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | x21 | x22 | x23 | x24 | x25 | x26 | x27 | x28 | x29 | x30 | x31 | x32 | x33 | x34 | x35 | x36 | x37 | x38 | x39 | x40 | x41 | x42 | x43 | x44 | x45 | x46 | x47 | x48 | x49 | x50 | x51 | x52 | x53 | x54 | x55 | x56 | x57 | x58 | x59 | x60 | x61 | x62 | x63 | x64 | x65 | x66 | x67 | x68 | x69 | x70 | x71 | x72 | x73 | x74 | x75 | x76 | x77 | x78 | x79 | x80 | x81 | x82 | x83 | x84 | x85 | x86 | x87 | x88 | x89 | x90 | x91 | x92 | x93 | x94 | x95 | x96 | x97 | x98 | x99 | x100 | ⋯ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | ⋯ | |
1 | 0.950058 | 0.587494 | 0.177994 | 0.655275 | 0.973326 | 0.409907 | 0.835926 | 0.649191 | 0.202792 | 0.808865 | 0.581285 | 0.34027 | 0.136758 | 0.660741 | 0.97527 | 0.579335 | 0.90986 | 0.848891 | 0.575617 | 0.627489 | 0.290292 | 0.728637 | 0.0211294 | 0.820952 | 0.846581 | 0.132386 | 0.107926 | 0.430422 | 0.961199 | 0.729527 | 0.59214 | 0.2748 | 0.334664 | 0.524227 | 0.82397 | 0.0176395 | 0.46553 | 0.135355 | 0.780029 | 0.58738 | 0.483812 | 0.65528 | 0.584815 | 0.0778716 | 0.537381 | 0.436296 | 0.0305809 | 0.462507 | 0.505382 | 0.170584 | 0.221943 | 0.535946 | 0.78498 | 0.730994 | 0.41515 | 0.418 | 0.450529 | 0.489728 | 0.791703 | 0.465304 | 0.26486 | 0.067945 | 0.508496 | 0.184748 | 0.461517 | 0.193533 | 0.387085 | 0.931858 | 0.425018 | 0.0951919 | 0.206334 | 0.918469 | 0.775217 | 0.865825 | 0.248852 | 0.430064 | 0.291865 | 0.100074 | 0.739642 | 0.685014 | 0.0949525 | 0.69768 | 0.466532 | 0.741147 | 0.563658 | 0.865301 | 0.540111 | 0.359868 | 0.0393546 | 0.540505 | 0.770954 | 0.873907 | 0.0977718 | 0.86035 | 0.567951 | 0.891952 | 0.0967576 | 0.345434 | 0.731782 | 0.21978 | ⋯ |
@btime $x[1:1, 1:20]
3.850 μs (70 allocations: 3.09 KiB)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
1 | 0.950058 | 0.587494 | 0.177994 | 0.655275 | 0.973326 | 0.409907 | 0.835926 | 0.649191 | 0.202792 | 0.808865 | 0.581285 | 0.34027 | 0.136758 | 0.660741 | 0.97527 | 0.579335 | 0.90986 | 0.848891 | 0.575617 | 0.627489 |
@btime $x[1, 1:20]
22.531 ns (0 allocations: 0 bytes)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
1 | 0.950058 | 0.587494 | 0.177994 | 0.655275 | 0.973326 | 0.409907 | 0.835926 | 0.649191 | 0.202792 | 0.808865 | 0.581285 | 0.34027 | 0.136758 | 0.660741 | 0.97527 | 0.579335 | 0.90986 | 0.848891 | 0.575617 | 0.627489 |
@btime view($x, 1:1, 1:20)
23.893 ns (0 allocations: 0 bytes)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
1 | 0.950058 | 0.587494 | 0.177994 | 0.655275 | 0.973326 | 0.409907 | 0.835926 | 0.649191 | 0.202792 | 0.808865 | 0.581285 | 0.34027 | 0.136758 | 0.660741 | 0.97527 | 0.579335 | 0.90986 | 0.848891 | 0.575617 | 0.627489 |
This notebook was generated using Literate.jl.