Lists

Use literals to create lists, length to count them, and get when an index may be absent.

scores: List[Int] = [82, 91, 77]
count: Int = scores.length() -- 3

match scores.get(0):
    Some(score): print(score) -- 82
    None: print("empty")

append returns a list with one more element. Rebind a var when accumulating values.

func main(args: List[String]) -> Void:
	var scores: List[Int] = []

	scores = scores.append(82)

	scores = scores.append(91)

Use method-style list combinators when the work is a data pipeline.

scores = [1000, 77, 50, 85, 11, 200, 400, 12]


func main(args: List[String]):
	lowest_passing_scores = scores
		.filter(func(score): score >= 70)
		.sort()
		.take(3)
		.map(func(score): score.to_string())

	print(lowest_passing_scores.join(", ")) -- prints: 77, 85, 200

Use .parallel when each element can be processed independently. It is especially useful for large lists with expensive per-item work; small lists may not repay the scheduling overhead.

results = scores.parallel(func(chunk):
	chunk
		.filter(func(score): score >= 70)
		.map(func(score): expensive_score(score))
)

Build a new list with explicit iteration when the control flow matters.

pure func passing(scores: List[Int]) -> List[Int]:
	var result: List[Int] = []
	for score in scores:
		if score >= 70:
			result = result.append(score)
	result


func main(args: List[String]) -> Void:
	scores: List[Int] = [82, 61, 91]
	good: List[Int] = passing(scores)
	match good.get(0):
		Some(score): print("first passing: ${score}") -- prints: first passing: 82
		None: print("no passing scores")