Euclidean distance is straight-line distance — Pythagoras extended to as many dimensions as you like:
1d = sqrt(Σ (a[i] - b[i])²)
Task: write euclidean_distance(point_a, point_b) returning the distance, rounded to 4 decimal places.
It's the default distance almost everywhere — k-NN, k-means, nearest-neighbour search — but it gets unhelpful in very high dimensions, where all pairwise distances drift toward each other and "nearest" stops meaning much. That's why embeddings are usually compared by cosine instead.
Once your code passes every test, a reference solution shows up here so you can compare it with yours.
Two ways to measure the distance between the same two points on a city grid.