Hi all,
I am new with Julia my background is Stata, I am comparing different command and results. I wonder if exist some way to avoid display all database without ask for it.
Its complicated when I load millions of observations.
Maybe its my configuration??
I am using SublimeRepl with Sublime text 4
Example:
You can see that print all the rows and columns…
using Random, CategoricalArrays, DataFrames
# Generate sample data
n = 1000
Random.seed!(06515)
df = DataFrame(
y = randn(n),
x1 = randn(n),
x2 = randn(n),
group = categorical(rand(["A", "B", "C"], n)))
DataFrame adjusts display dynamically depending on the size of the display window. For example
julia> using CategoricalArrays
julia> using DataFrames
julia> using Random
julia> n = 1000
1000
julia> Random.seed!(06515)
TaskLocalRNG()
julia> df = DataFrame(
y = randn(n),
x1 = randn(n),
x2 = randn(n),
group = categorical(rand(["A", "B", "C"], n)))
1000×4 DataFrame
Row │ y x1 x2 group
│ Float64 Float64 Float64 Cat…
──────┼────────────────────────────────────────────
1 │ -1.702 0.630715 0.615604 A
2 │ -0.783141 3.32108 0.398537 A
3 │ -1.12673 0.741291 1.0854 A
4 │ -1.8922 -0.219293 0.275528 B
5 │ 0.251435 -0.168469 -0.384072 B
6 │ 1.32279 0.478072 -0.727269 C
7 │ 0.0177961 0.50748 0.511304 A
8 │ -0.00805389 1.32029 0.665092 B
9 │ 1.07988 0.664838 1.24314 A
10 │ 1.28176 1.24093 0.552596 A
11 │ -1.56424 -1.1748 1.26912 C
12 │ -0.101351 0.154412 0.245494 C
13 │ 0.1592 -0.291401 -0.370051 B
14 │ 0.305936 -1.95372 -1.67376 A
15 │ -0.455743 0.960887 0.232676 A
16 │ 1.46745 2.84029 0.278892 A
17 │ 0.145784 0.186623 0.11497 A
18 │ -1.04925 -0.494621 -0.182952 B
19 │ -1.50332 -1.70582 -0.358066 B
20 │ -0.725616 -0.0157264 -1.29976 B
21 │ -0.453116 0.611136 -2.54458 A
22 │ 0.435147 -1.11523 0.838568 C
⋮ │ ⋮ ⋮ ⋮ ⋮
979 │ 1.73312 0.45741 -0.394642 C
980 │ 0.297119 0.708152 -0.593984 A
981 │ -1.97116 -0.199694 0.572518 C
982 │ -1.80177 -0.294087 -0.627953 C
983 │ -1.14641 1.70589 -1.05738 C
984 │ -0.427469 0.199414 -1.53624 A
985 │ 0.443345 -0.446598 1.06973 C
986 │ 0.58093 -0.542177 0.709585 C
987 │ -0.942309 -3.04805 -1.38662 C
988 │ -0.317554 0.914196 0.283311 B
989 │ 0.802619 -1.84786 0.607003 A
990 │ 0.421096 -0.951215 0.382277 B
991 │ -0.632904 -1.4118 -0.294012 A
992 │ -0.260684 -0.526704 0.122641 B
993 │ -0.452213 -1.35053 -1.18115 B
994 │ -1.58226 0.420341 0.916672 B
995 │ -1.39596 0.339323 -1.45384 C
996 │ 1.36291 -1.30481 0.524041 B
997 │ 0.847221 0.0251756 0.781781 B
998 │ 1.21163 0.68933 0.0178197 A
999 │ -2.82673 -0.784539 0.156953 C
1000 │ 0.82131 -1.4174 -0.391486 B
956 rows omitted
julia>
and
julia> using CategoricalArrays
julia> using DataFrames
julia> using Random
julia> n = 1000
1000
julia> Random.seed!(06515)
TaskLocalRNG()
julia> df = DataFrame(
y = randn(n),
x1 = randn(n),
x2 = randn(n),
group = categorical(rand(["A", "B", "C"], n)))
1000×4 DataFrame
Row │ y x1 x2 group
│ Float64 Float64 Float64 Cat…
──────┼─────────────────────────────────────────────
1 │ -1.702 0.630715 0.615604 A
2 │ -0.783141 3.32108 0.398537 A
3 │ -1.12673 0.741291 1.0854 A
4 │ -1.8922 -0.219293 0.275528 B
5 │ 0.251435 -0.168469 -0.384072 B
6 │ 1.32279 0.478072 -0.727269 C
7 │ 0.0177961 0.50748 0.511304 A
8 │ -0.00805389 1.32029 0.665092 B
9 │ 1.07988 0.664838 1.24314 A
10 │ 1.28176 1.24093 0.552596 A
11 │ -1.56424 -1.1748 1.26912 C
12 │ -0.101351 0.154412 0.245494 C
13 │ 0.1592 -0.291401 -0.370051 B
14 │ 0.305936 -1.95372 -1.67376 A
15 │ -0.455743 0.960887 0.232676 A
16 │ 1.46745 2.84029 0.278892 A
17 │ 0.145784 0.186623 0.11497 A
18 │ -1.04925 -0.494621 -0.182952 B
19 │ -1.50332 -1.70582 -0.358066 B
20 │ -0.725616 -0.0157264 -1.29976 B
21 │ -0.453116 0.611136 -2.54458 A
22 │ 0.435147 -1.11523 0.838568 C
23 │ 0.501289 1.24776 0.647895 B
24 │ 0.402811 0.801254 1.27738 C
25 │ 0.211726 1.60119 -1.38694 B
26 │ -1.20796 -1.26969 1.2001 B
27 │ 0.177459 0.00575822 0.159947 C
28 │ 0.738264 -0.495383 -1.40506 B
29 │ -0.252979 -0.769827 0.527035 C
30 │ 0.266908 -0.246113 -0.827958 C
31 │ 0.0444857 0.0742844 -0.331827 C
⋮ │ ⋮ ⋮ ⋮ ⋮
971 │ -1.92257 -0.65397 -1.2593 B
972 │ -0.725117 -1.08739 -0.105488 A
973 │ -0.467188 0.420613 -0.485866 C
974 │ -0.791398 0.910714 -0.944314 B
975 │ -1.43344 0.891557 0.39844 A
976 │ -0.681357 -0.806813 2.01171 A
977 │ -1.41441 -1.57139 -0.260637 C
978 │ -0.420314 0.455886 -0.698848 A
979 │ 1.73312 0.45741 -0.394642 C
980 │ 0.297119 0.708152 -0.593984 A
981 │ -1.97116 -0.199694 0.572518 C
982 │ -1.80177 -0.294087 -0.627953 C
983 │ -1.14641 1.70589 -1.05738 C
984 │ -0.427469 0.199414 -1.53624 A
985 │ 0.443345 -0.446598 1.06973 C
986 │ 0.58093 -0.542177 0.709585 C
987 │ -0.942309 -3.04805 -1.38662 C
988 │ -0.317554 0.914196 0.283311 B
989 │ 0.802619 -1.84786 0.607003 A
990 │ 0.421096 -0.951215 0.382277 B
991 │ -0.632904 -1.4118 -0.294012 A
992 │ -0.260684 -0.526704 0.122641 B
993 │ -0.452213 -1.35053 -1.18115 B
994 │ -1.58226 0.420341 0.916672 B
995 │ -1.39596 0.339323 -1.45384 C
996 │ 1.36291 -1.30481 0.524041 B
997 │ 0.847221 0.0251756 0.781781 B
998 │ 1.21163 0.68933 0.0178197 A
999 │ -2.82673 -0.784539 0.156953 C
1000 │ 0.82131 -1.4174 -0.391486 B
939 rows omitted
are from the same terminal REPL session with the first having a smaller display than the second. I haven’t used Julia within Sublime so I don’t know how it differs from the REPL.
