poly_coords = [feature.geometry for feature in geojson]

The end. I hope you enjoyed this story!

The package is built on a rigorous mathematical foundation (Relational Algebra). This makes queries very robust and predictable. Once you learn the logic of "blocks" and "paths," the language stops surprising you.

using GLMakie, Random

A powerful, algebraic alternative to DataFrames.jl for complex, nested, and relational data queries. It is less about "tabular data" and more about "data navigation."

geojson = GeoJSON.read("europe_regions.geojson")

fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect()))

The Julia Data Kartta became legendary, attracting data scientists and analysts from far and wide. Together, they explored the vast expanse of data, unlocking secrets and driving innovation in the land of Computaria.

The general workflow for creating a data map in Julia follows a logical progression from data loading to aesthetic refinement. 1. Loading Your Data