Because Julia passes by reference, you can update all linked plots simultaneously from a slider or live data feed. Let’s settle the debate. In Python, plotting 10M points with matplotlib is suicide (memory >8GB, render time >2min). In R, ggplot2 will choke on the backend grid engine. In Julia:
using GLMakie, Random Random.seed!(42) lats = 60.17 .+ randn(10_000_000) * 0.01 lons = 24.94 .+ randn(10_000_000) * 0.01 julia data kartta
Because GeoArray behaves like a regular Julia Array , all your linear algebra, FFTs, and statistical functions work out of the box. No rasterio boilerplate. A map without a legend is a puzzle. Makie’s composable Axis system (borrowing ideas from Grammar of Graphics) allows you to build legends as first-class objects. Because Julia passes by reference, you can update
Colorbar(fig[1, 2], plt) fig
using Statistics df.magnitude = coalesce.(df.magnitude, mean(skipmissing(df.magnitude))) This explicitness prevents the “swiss cheese map” phenomenon—where missing values create false gaps in your visualization. Matplotlib is a compass. ggplot2 is a sextant. Makie.jl is a satellite. In R, ggplot2 will choke on the backend grid engine
using DataFrames, CSV df = CSV.read("earthquakes.csv", DataFrame)
But here’s the cartographic insight: . Julia’s missing union type forces you to be explicit. No silent NaN propagation. You must decide: impute, drop, or mark.