These Are Not Your Regular Trees!

This week we learned about trees. Not about your regular trees, but ones we use to organize data. They are perhaps called trees because they, too, branch out.

What I particularly hate about trees is all the terminology we had to learn. There's some straightforward terminology such as parent and root, perhaps siblings as well. Others not so much such as branching factor, internal node, etc. But after revising the terminology it does start to make sense. Perhaps the biggest challenge this week was  understanding the ordering types since trees can be represented as lists. There's preorder, inorder, and postorder traversal. All relative to the root of the tree, of course.

This week our lab was based on python idioms. Something we did a while back. I hadn't revised it all prior to the lab, but despite that I did not find the lab very difficult. I was able to use the python shell to my advantage and figure out what the idiom statements did.

How are trees different from other types of data structures that we've learned? Unlike everything we've learned before, namely stacks and queues, trees are different because they are not linear structures of data. They are hierarchical. We could represent lists linearly in lists, hence the different traversal types.