Covariance and contravariance (computer science)

Alright kiddo, let's talk about covariance and contravariance in computer science!

Think of a box of shapes - squares, circles, and triangles. Let's say you want to put all those shapes into another box, but this new box only fits squares. You can only put squares into the new box, but not the circles or triangles, right?

Now let's apply this concept to computer science. We have types of boxes called data types, and they can hold different types of objects. Sometimes we want to put objects of one data type into another data type.

Covariance means that the objects in one data type can be treated as objects of another data type, as long as the second data type is a "wide" version of the first data type. Going back to our shape example, if we had a box that fits both squares and circles, and we filled it with squares, we could say that it's also a box of circles because squares are a "wide" version of circles (since they both have an area).

Contravariance, on the other hand, means that the objects in one data type can be treated as objects of another data type, as long as the second data type is a "narrow" version of the first data type. Going back to our shape example again, let's say we have a box that only fits squares, but we can also use it as a box of triangles because squares are a "narrow" version of triangles (since both have sides).

So in short, covariance and contravariance describe how objects of different data types can be treated as if they belong to another data type based on their similarities. It's like fitting a square into a circle, if the circle is big enough, or fitting a square and a triangle into a box that only fits squares.