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
15.952 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`
101.876 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();
4.273 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();
3.681 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();
4.693 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();
27.122 ms (1949728 allocations: 37.40 MiB)
27.939 ms (1950028 allocations: 45.03 MiB)
1.147 ms (728 allocations: 7.66 MiB)
1.602 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, $z); ## add a single row in place - fast
2.827 ms (212 allocations: 38.16 MiB)
1.161 μs (29 allocations: 1.50 KiB)
432.384 ns (16 allocations: 256 bytes)
Allowing missing as well as categorical slows down computations#
using StatsBase
function test(data) ## uses countmap function to test performance
println(eltype(data))
x = rand(data, 10^6)
y = categorical(x)
println(" raw:")
@btime countmap($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:
1.863 ms (8 allocations: 7.63 MiB)
categorical:
15.800 ms (1000004 allocations: 30.52 MiB)
String
raw:
17.820 ms (4 allocations: 448 bytes)
categorical:
28.717 ms (1000004 allocations: 30.52 MiB)
Union{Missing, Int64}
raw:
6.757 ms (4 allocations: 464 bytes)
categorical:
16.287 ms (1000004 allocations: 30.52 MiB)
Union{Missing, String}
raw:
18.310 ms (4 allocations: 448 bytes)
categorical:
30.633 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 |
⋮ | ⋮ | ⋮ |
2500884 | b | 1 |
2500885 | b | 1 |
2500886 | b | 1 |
2500887 | b | 1 |
2500888 | b | 1 |
2500889 | b | 1 |
2500890 | b | 1 |
2500891 | b | 1 |
2500892 | b | 1 |
2500893 | b | 1 |
2500894 | b | 1 |
2500895 | b | 1 |
⋮
Row | x | y |
---|---|---|
Char | 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 |
⋮ | ⋮ | ⋮ |
2499674 | a | 1 |
2499675 | a | 1 |
2499676 | a | 1 |
2499677 | a | 1 |
2499678 | a | 1 |
2499679 | a | 1 |
2499680 | a | 1 |
2499681 | a | 1 |
2499682 | a | 1 |
2499683 | a | 1 |
2499684 | a | 1 |
2499685 | a | 1 |
traditional syntax, slow
@btime combine(v -> sum(v.y), $gdf)
17.078 ms (332 allocations: 19.09 MiB)
Row | x | x1 |
---|---|---|
Char | Int64 | |
1 | b | 2500895 |
2 | c | 2499377 |
3 | d | 2500043 |
4 | a | 2499685 |
use column selector
@btime combine($gdf, :y => sum)
6.889 ms (198 allocations: 10.14 KiB)
Row | x | y_sum |
---|---|---|
Char | Int64 | |
1 | b | 2500895 |
2 | c | 2499377 |
3 | d | 2500043 |
4 | a | 2499685 |
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 |
⋮ | ⋮ | ⋮ |
2499674 | a | 1 |
2499675 | a | 1 |
2499676 | a | 1 |
2499677 | a | 1 |
2499678 | a | 1 |
2499679 | a | 1 |
2499680 | a | 1 |
2499681 | a | 1 |
2499682 | a | 1 |
2499683 | a | 1 |
2499684 | a | 1 |
2499685 | 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 |
⋮ | ⋮ | ⋮ |
2500032 | d | 1 |
2500033 | d | 1 |
2500034 | d | 1 |
2500035 | d | 1 |
2500036 | d | 1 |
2500037 | d | 1 |
2500038 | d | 1 |
2500039 | d | 1 |
2500040 | d | 1 |
2500041 | d | 1 |
2500042 | d | 1 |
2500043 | d | 1 |
@btime combine($gdf, :y => sum)
6.921 ms (206 allocations: 10.62 KiB)
Row | x | y_sum |
---|---|---|
Cat… | Int64 | |
1 | a | 2499685 |
2 | b | 2500895 |
3 | c | 2499377 |
4 | d | 2500043 |
transform!(df, :x => PooledArray{Char} => :x)
Row | x | y |
---|---|---|
Char | Int64 | |
1 | b | 1 |
2 | b | 1 |
3 | c | 1 |
4 | d | 1 |
5 | c | 1 |
6 | a | 1 |
7 | c | 1 |
8 | c | 1 |
9 | a | 1 |
10 | b | 1 |
11 | d | 1 |
12 | a | 1 |
13 | d | 1 |
⋮ | ⋮ | ⋮ |
9999989 | a | 1 |
9999990 | b | 1 |
9999991 | d | 1 |
9999992 | c | 1 |
9999993 | c | 1 |
9999994 | c | 1 |
9999995 | a | 1 |
9999996 | a | 1 |
9999997 | c | 1 |
9999998 | c | 1 |
9999999 | a | 1 |
10000000 | d | 1 |
gdf = groupby(df, :x)
GroupedDataFrame with 4 groups based on key: x
Row | x | y |
---|---|---|
Char | Int64 | |
1 | b | 1 |
2 | b | 1 |
3 | b | 1 |
4 | b | 1 |
5 | b | 1 |
6 | b | 1 |
7 | b | 1 |
8 | b | 1 |
9 | b | 1 |
10 | b | 1 |
11 | b | 1 |
12 | b | 1 |
13 | b | 1 |
⋮ | ⋮ | ⋮ |
2500884 | b | 1 |
2500885 | b | 1 |
2500886 | b | 1 |
2500887 | b | 1 |
2500888 | b | 1 |
2500889 | b | 1 |
2500890 | b | 1 |
2500891 | b | 1 |
2500892 | b | 1 |
2500893 | b | 1 |
2500894 | b | 1 |
2500895 | b | 1 |
⋮
Row | x | y |
---|---|---|
Char | Int64 | |
1 | a | 1 |
2 | a | 1 |
3 | a | 1 |
4 | a | 1 |
5 | a | 1 |
6 | a | 1 |
7 | a | 1 |
8 | a | 1 |
9 | a | 1 |
10 | a | 1 |
11 | a | 1 |
12 | a | 1 |
13 | a | 1 |
⋮ | ⋮ | ⋮ |
2499674 | a | 1 |
2499675 | a | 1 |
2499676 | a | 1 |
2499677 | a | 1 |
2499678 | a | 1 |
2499679 | a | 1 |
2499680 | a | 1 |
2499681 | a | 1 |
2499682 | a | 1 |
2499683 | a | 1 |
2499684 | a | 1 |
2499685 | a | 1 |
@btime combine($gdf, :y => sum)
6.962 ms (200 allocations: 10.20 KiB)
Row | x | y_sum |
---|---|---|
Char | Int64 | |
1 | b | 2500895 |
2 | c | 2499377 |
3 | d | 2500043 |
4 | a | 2499685 |
Use views instead of materializing a new DataFrame#
x = DataFrame(rand(100, 1000), :auto)
@btime $x[1:1, :]
180.200 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 | 0.422801 | 0.0902145 | 0.464035 | 0.213822 | 0.662524 | 0.802374 | 0.998822 | 0.686711 | 0.470517 | 0.761153 | 0.135081 | 0.550665 | 0.315453 | 0.479952 | 0.554503 | 0.918138 | 0.81446 | 0.711045 | 0.0790613 | 0.140946 | 0.599258 | 0.229921 | 0.946416 | 0.638161 | 0.00474281 | 0.0441469 | 0.151772 | 0.791198 | 0.0143487 | 0.0523544 | 0.464115 | 0.714933 | 0.973773 | 0.202556 | 0.937359 | 0.719838 | 0.586283 | 0.986412 | 0.544319 | 0.850781 | 0.129103 | 0.377252 | 0.885368 | 0.875845 | 0.226687 | 0.731218 | 0.746306 | 0.707 | 0.325431 | 0.201631 | 0.0898095 | 0.312646 | 0.44582 | 0.0676193 | 0.953145 | 0.729716 | 0.319492 | 0.314294 | 0.893744 | 0.356202 | 0.623707 | 0.189512 | 0.276087 | 0.806748 | 0.764909 | 0.950339 | 0.509395 | 0.607226 | 0.716216 | 0.0637663 | 0.687426 | 0.766429 | 0.960237 | 0.590265 | 0.19368 | 0.392727 | 0.395247 | 0.497234 | 0.45752 | 0.985789 | 0.27788 | 0.732945 | 0.415065 | 0.333108 | 0.510974 | 0.146902 | 0.739298 | 0.384125 | 0.198563 | 0.298791 | 0.737801 | 0.124819 | 0.347573 | 0.198761 | 0.340509 | 0.453284 | 0.227376 | 0.880761 | 0.948952 | 0.656717 | ⋯ |
@btime $x[1, :]
23.227 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 | 0.422801 | 0.0902145 | 0.464035 | 0.213822 | 0.662524 | 0.802374 | 0.998822 | 0.686711 | 0.470517 | 0.761153 | 0.135081 | 0.550665 | 0.315453 | 0.479952 | 0.554503 | 0.918138 | 0.81446 | 0.711045 | 0.0790613 | 0.140946 | 0.599258 | 0.229921 | 0.946416 | 0.638161 | 0.00474281 | 0.0441469 | 0.151772 | 0.791198 | 0.0143487 | 0.0523544 | 0.464115 | 0.714933 | 0.973773 | 0.202556 | 0.937359 | 0.719838 | 0.586283 | 0.986412 | 0.544319 | 0.850781 | 0.129103 | 0.377252 | 0.885368 | 0.875845 | 0.226687 | 0.731218 | 0.746306 | 0.707 | 0.325431 | 0.201631 | 0.0898095 | 0.312646 | 0.44582 | 0.0676193 | 0.953145 | 0.729716 | 0.319492 | 0.314294 | 0.893744 | 0.356202 | 0.623707 | 0.189512 | 0.276087 | 0.806748 | 0.764909 | 0.950339 | 0.509395 | 0.607226 | 0.716216 | 0.0637663 | 0.687426 | 0.766429 | 0.960237 | 0.590265 | 0.19368 | 0.392727 | 0.395247 | 0.497234 | 0.45752 | 0.985789 | 0.27788 | 0.732945 | 0.415065 | 0.333108 | 0.510974 | 0.146902 | 0.739298 | 0.384125 | 0.198563 | 0.298791 | 0.737801 | 0.124819 | 0.347573 | 0.198761 | 0.340509 | 0.453284 | 0.227376 | 0.880761 | 0.948952 | 0.656717 | ⋯ |
@btime view($x, 1:1, :)
24.081 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 | 0.422801 | 0.0902145 | 0.464035 | 0.213822 | 0.662524 | 0.802374 | 0.998822 | 0.686711 | 0.470517 | 0.761153 | 0.135081 | 0.550665 | 0.315453 | 0.479952 | 0.554503 | 0.918138 | 0.81446 | 0.711045 | 0.0790613 | 0.140946 | 0.599258 | 0.229921 | 0.946416 | 0.638161 | 0.00474281 | 0.0441469 | 0.151772 | 0.791198 | 0.0143487 | 0.0523544 | 0.464115 | 0.714933 | 0.973773 | 0.202556 | 0.937359 | 0.719838 | 0.586283 | 0.986412 | 0.544319 | 0.850781 | 0.129103 | 0.377252 | 0.885368 | 0.875845 | 0.226687 | 0.731218 | 0.746306 | 0.707 | 0.325431 | 0.201631 | 0.0898095 | 0.312646 | 0.44582 | 0.0676193 | 0.953145 | 0.729716 | 0.319492 | 0.314294 | 0.893744 | 0.356202 | 0.623707 | 0.189512 | 0.276087 | 0.806748 | 0.764909 | 0.950339 | 0.509395 | 0.607226 | 0.716216 | 0.0637663 | 0.687426 | 0.766429 | 0.960237 | 0.590265 | 0.19368 | 0.392727 | 0.395247 | 0.497234 | 0.45752 | 0.985789 | 0.27788 | 0.732945 | 0.415065 | 0.333108 | 0.510974 | 0.146902 | 0.739298 | 0.384125 | 0.198563 | 0.298791 | 0.737801 | 0.124819 | 0.347573 | 0.198761 | 0.340509 | 0.453284 | 0.227376 | 0.880761 | 0.948952 | 0.656717 | ⋯ |
@btime $x[1:1, 1:20]
3.862 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 | 0.422801 | 0.0902145 | 0.464035 | 0.213822 | 0.662524 | 0.802374 | 0.998822 | 0.686711 | 0.470517 | 0.761153 | 0.135081 | 0.550665 | 0.315453 | 0.479952 | 0.554503 | 0.918138 | 0.81446 | 0.711045 | 0.0790613 | 0.140946 |
@btime $x[1, 1:20]
23.871 ns (0 allocations: 0 bytes)
Row | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
1 | 0.422801 | 0.0902145 | 0.464035 | 0.213822 | 0.662524 | 0.802374 | 0.998822 | 0.686711 | 0.470517 | 0.761153 | 0.135081 | 0.550665 | 0.315453 | 0.479952 | 0.554503 | 0.918138 | 0.81446 | 0.711045 | 0.0790613 | 0.140946 |
@btime view($x, 1:1, 1:20)
24.685 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 | 0.422801 | 0.0902145 | 0.464035 | 0.213822 | 0.662524 | 0.802374 | 0.998822 | 0.686711 | 0.470517 | 0.761153 | 0.135081 | 0.550665 | 0.315453 | 0.479952 | 0.554503 | 0.918138 | 0.81446 | 0.711045 | 0.0790613 | 0.140946 |
This notebook was generated using Literate.jl.