A parallel coordinates plot visualizes multivariate data by representing each variable as a vertical axis and each observation as a line connecting values across all axes. This technique is powerful for identifying patterns, clusters, and outliers in high-dimensional datasets where traditional 2D plots fall short. It enables simultaneous comparison of multiple variables for each data point.
# anyplot.ai
# parallel-basic: Basic Parallel Coordinates Plot
# Library: makie 0.21.9 | Julia 1.11.9
# Quality: 88/100 | Created: 2026-07-24
using CairoMakie
using Colors
using Random
Random.seed!(42)
# Theme tokens
const THEME = get(ENV, "ANYPLOT_THEME", "light")
const PAGE_BG = THEME == "light" ? colorant"#FAF8F1" : colorant"#1A1A17"
const INK = THEME == "light" ? colorant"#1A1A17" : colorant"#F0EFE8"
const INK_SOFT = THEME == "light" ? colorant"#4A4A44" : colorant"#B8B7B0"
const INK_MUTED = THEME == "light" ? colorant"#6B6A63" : colorant"#A8A79F"
const IMPRINT_PALETTE = [
colorant"#009E73", # 1 — brand green, ALWAYS first series (Imprint palette)
colorant"#C475FD", # 2 — lavender
colorant"#4467A3", # 3 — blue
colorant"#BD8233", # 4 — ochre
colorant"#AE3030", # 5 — matte red
colorant"#2ABCCD", # 6 — cyan
colorant"#954477", # 7 — rose
colorant"#99B314", # 8 — lime
]
# Data — smartphone specs across three price tiers, six comparison dimensions.
# Axis order groups the four dimensions that move together with price
# (Price, Rating, Storage, Camera) so adjacent axes stay low-crossing, and
# places the two dimensions that buck that trend (Weight, Battery) together
# at the end instead of interleaved among the correlated block.
dimension_names = ["Price (\$)", "Rating (1-5)", "Storage (GB)", "Camera (MP)", "Weight (g)", "Battery (hrs)"]
n_dims = length(dimension_names)
tiers = ["Budget", "Mid-range", "Premium"]
n_per_tier = 20
tier_params = [
(300.0, 50.0, 3.5, 0.4, 20.0, 3.0, 190.0, 15.0, 64.0, 16.0, 12.0, 3.0),
(600.0, 80.0, 4.0, 0.3, 15.0, 3.0, 175.0, 12.0, 128.0, 20.0, 48.0, 8.0),
(1100.0, 120.0, 4.5, 0.25, 12.0, 2.5, 200.0, 10.0, 256.0, 32.0, 108.0, 15.0),
]
price = Float64[]
rating = Float64[]
battery = Float64[]
weight = Float64[]
storage = Float64[]
camera = Float64[]
tier = String[]
for (t, (p_mu, p_sd, r_mu, r_sd, b_mu, b_sd, w_mu, w_sd, s_mu, s_sd, c_mu, c_sd)) in zip(tiers, tier_params)
append!(price, p_mu .+ p_sd .* randn(n_per_tier))
append!(rating, clamp.(r_mu .+ r_sd .* randn(n_per_tier), 1.0, 5.0))
append!(battery, clamp.(b_mu .+ b_sd .* randn(n_per_tier), 4.0, 40.0))
append!(weight, clamp.(w_mu .+ w_sd .* randn(n_per_tier), 60.0, 400.0))
append!(storage, clamp.(s_mu .+ s_sd .* randn(n_per_tier), 16.0, 512.0))
append!(camera, clamp.(c_mu .+ c_sd .* randn(n_per_tier), 5.0, 200.0))
append!(tier, fill(t, n_per_tier))
end
data = hcat(price, rating, storage, camera, weight, battery)
n_obs = size(data, 1)
# Min-max normalize each dimension to a shared [0, 1] vertical scale
data_min = vec(minimum(data; dims = 1))
data_max = vec(maximum(data; dims = 1))
data_norm = (data .- data_min') ./ (data_max' .- data_min')
tier_color = Dict(tiers[i] => IMPRINT_PALETTE[i] for i in eachindex(tiers))
# Plot
fig = Figure(
size = (1600, 900),
fontsize = 14,
backgroundcolor = PAGE_BG,
)
ax = Axis(
fig[1, 1];
title = "parallel-basic · julia · makie · anyplot.ai",
titlesize = 20,
titlecolor = INK,
titlefont = :bold,
backgroundcolor = PAGE_BG,
xticks = (1:n_dims, dimension_names),
xticklabelsize = 13,
xticklabelcolor = INK_SOFT,
xticksvisible = false,
yticksvisible = false,
yticklabelsvisible = false,
ylabelvisible = false,
topspinevisible = false,
rightspinevisible = false,
leftspinevisible = false,
bottomspinevisible = false,
xgridvisible = false,
ygridvisible = false,
)
ylims!(ax, -0.12, 1.12)
xlims!(ax, 0.6, n_dims + 0.4)
# One vertical reference axis per dimension
vlines!(ax, 1:n_dims; color = INK_SOFT, linewidth = 1.2)
# One connecting line per observation, colored by tier, drawn brand-first
for t in tiers
idx = findall(==(t), tier)
for i in idx
lines!(ax, 1:n_dims, data_norm[i, :]; color = (tier_color[t], 0.38), linewidth = 1.5)
end
end
# Original min/max labels at each axis end — the normalized scale alone hides units
for j in 1:n_dims
text!(ax, j, 1.06; text = string(round(data_max[j]; digits = 1)),
align = (:center, :bottom), fontsize = 12, color = INK_MUTED)
text!(ax, j, -0.06; text = string(round(data_min[j]; digits = 1)),
align = (:center, :top), fontsize = 12, color = INK_MUTED)
end
# Legend
Legend(
fig[1, 2],
[LineElement(color = tier_color[t], linewidth = 3) for t in tiers],
tiers,
"Tier";
backgroundcolor = PAGE_BG,
framevisible = false,
labelcolor = INK_SOFT,
labelsize = 12,
titlecolor = INK,
titlesize = 13,
)
colsize!(fig.layout, 1, Relative(0.88))
# Save
save("plot-$(THEME).png", fig; px_per_unit = 2)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/parallel-basic/makie/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "parallel-basic",
"language": "julia",
"library": "makie",
"page": "https://anyplot.ai/parallel-basic/julia/makie",
"hub": "https://anyplot.ai/parallel-basic",
"code_json": "https://api.anyplot.ai/specs/parallel-basic/makie/code",
"spec_json": "https://api.anyplot.ai/specs/parallel-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/parallel-basic/julia/makie/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/parallel-basic/julia/makie/plot-dark.png",
"quality_score": 88.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Parallel Coordinates Plot on anyplot.ai.