A cumulative histogram (also known as an ogive or cumulative frequency histogram) displays the running total of observations up to each bin boundary. The y-axis shows cumulative count or proportion, creating a monotonically increasing step function that reaches the total sample size (or 1.0 for normalized).
# anyplot.ai
# histogram-cumulative: Cumulative Histogram
# Library: makie 0.21.9 | Julia 1.11.9
# Quality: 91/100 | Created: 2026-09-05
using CairoMakie
using Colors
using Random
using Statistics
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 BRAND = colorant"#009E73" # Imprint palette position 1 — ALWAYS first series
# --- Data ----------------------------------------------------------------
# Parcel delivery times (hours) for an e-commerce carrier — a right-skewed
# lead-time distribution typical of logistics data.
n_orders = 1400
delivery_hours = exp.(0.45 .* randn(n_orders) .+ log(28.0))
n_bins = 28
edges = range(0.0, quantile(delivery_hours, 0.99), length = n_bins + 1)
counts = zeros(Int, n_bins)
for t in delivery_hours
# Values past the 99th-percentile edge fall into the last bin, so the
# tail is still counted while the axis stays focused on the bulk of it.
idx = clamp(searchsortedlast(edges, t), 1, n_bins)
counts[idx] += 1
end
cum_proportion = cumsum(counts) ./ n_orders
# Ogive polyline: flat within each bin at its cumulative level, with a
# vertical rise at every bin edge — the standard cumulative-histogram step.
step_x = Float64[edges[1]]
step_y = Float64[0.0]
for i in 1:n_bins
push!(step_x, edges[i]); push!(step_y, cum_proportion[i])
push!(step_x, edges[i + 1]); push!(step_y, cum_proportion[i])
end
# --- Plot ----------------------------------------------------------------
fig = Figure(
size = (1600, 900),
fontsize = 14,
backgroundcolor = PAGE_BG,
)
ax = Axis(
fig[1, 1];
title = "histogram-cumulative · julia · makie · anyplot.ai",
titlesize = 20,
titlecolor = INK,
xlabel = "Delivery Time (hours)",
ylabel = "Cumulative Proportion of Orders",
xlabelsize = 14,
ylabelsize = 14,
xlabelcolor = INK,
ylabelcolor = INK,
xticklabelsize = 12,
yticklabelsize = 12,
xticklabelcolor = INK_SOFT,
yticklabelcolor = INK_SOFT,
xtickcolor = INK_SOFT,
ytickcolor = INK_SOFT,
backgroundcolor = PAGE_BG,
topspinevisible = false,
rightspinevisible = false,
leftspinecolor = INK_SOFT,
bottomspinecolor = INK_SOFT,
xgridvisible = false,
ygridcolor = RGBAf(INK.r, INK.g, INK.b, 0.15),
yminorgridvisible = false,
yticks = 0:0.25:1.0,
)
band!(ax, step_x, zeros(length(step_x)), step_y; color = (BRAND, 0.12))
# The step LINE itself uses Makie's native `stairs!` recipe (step = :pre)
# rather than a hand-drawn polyline — `:pre` jumps to each bin's cumulative
# level right at its left edge, reproducing the same ogive shape as step_x/
# step_y above while showcasing a Makie-distinctive step-plot primitive.
stairs!(ax, edges, vcat(0.0, cum_proportion); step = :pre, color = BRAND, linewidth = 3.5)
xlims!(ax, edges[1], edges[end])
ylims!(ax, 0.0, 1.02)
# --- Median reference guide ------------------------------------------------
# The spec calls out percentile-reading as a primary application; a dashed
# median guide with an inline label lets the viewer read a concrete value
# off the curve instead of eyeballing the shape alone.
median_hours = quantile(delivery_hours, 0.5)
lines!(ax, [median_hours, median_hours], [0.0, 0.5]; color = INK_SOFT, linestyle = :dash, linewidth = 1.5)
lines!(ax, [edges[1], median_hours], [0.5, 0.5]; color = INK_SOFT, linestyle = :dash, linewidth = 1.5)
text!(
ax, median_hours, 0.5;
text = "Median: $(round(median_hours, digits = 1))h",
align = (:left, :bottom),
offset = (8, 6),
color = INK,
fontsize = 13,
)
# --- Save ----------------------------------------------------------------
save("plot-$(THEME).png", fig; px_per_unit = 2)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/histogram-cumulative/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": "histogram-cumulative",
"language": "julia",
"library": "makie",
"page": "https://anyplot.ai/histogram-cumulative/julia/makie",
"hub": "https://anyplot.ai/histogram-cumulative",
"code_json": "https://api.anyplot.ai/specs/histogram-cumulative/makie/code",
"spec_json": "https://api.anyplot.ai/specs/histogram-cumulative",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/histogram-cumulative/julia/makie/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/histogram-cumulative/julia/makie/plot-dark.png",
"quality_score": 91.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Cumulative Histogram on anyplot.ai.