Data structures: Introduction to Trees

Hello everyone ! In this lesson, we will introduce
you to an interesting data structure that has got its application in a wide number of
scenarios in computer science and this data structure is tree. So, far in this series,
we have talked about what we can call linear data structures. Array, Linked List, stack
and queue, all of these are linear data structures. All of these are basically collections of
different kinds in which data is arranged in a sequential manner. In all these structures
that I am showing here, we have a logical start and a logical end and then an element
in any of these collections can have a next element and e previous element. So, all in
all we have linear or sequential arrangement. Now, as we understand, these data structures
are ways to store and organize data in computers. For different kinds of data, we use different
kinds of data structure. Our choice of data structure depends upon a number of factors.
First of all, its about what needs to be stored. A certain data structure can be best fit for
a particular kind of data. Then, we may care for the cost of operations. Quite often, we
want to minimize the cost of most frequently performed operations. For example, lets say
we have a simple list and we are searching for an element in the list most of the time.
Then, we may want to store the list or collection as an array in sorted order, so we can perform
something like binary search really fast. Another factor can be memory consumption.
Sometimes, we may want to minimize the memory usage and finally we may also choose a data
structure for ease of implementation, although this may not be the best strategy. Tree is
one data structure that’s quite often used to represent hierarchical data. For example,
lets say we want to show employees in an organization and their positions in organizational hierarchy,
then we can show it something like this. Lets say this is organization hierarchy of some
company. In this company, John is CEO and john has two direct reports – Steve and Rama.
Then Steve has 3 direct reports. Steve is manager of Lee, Bob and Ella. They may be
having some designation. Rama also has two direct reports. Then Bob has two direct reports
and then Tom has 1 direct report. This particular logical structure that I have drawn here is
a Tree. Well, you have to look at this structure upside down and then it will resemble a real
tree. The root here is at top and we are branching out in downward direction. Logical representation
of tree data structure is always like this. Root at top and branching out in downward
direction. Ok, so tree is an efficient way of storing and organizing data that is naturally
hierarchical, but this is not the only application of tree in computer science. We will talk
about other applications and some of the implementation details like how we can create such a logical
structure in computer’s memory later. First I want to define tree as a logical model.
Tree data structure can be defined as a collection of entities called nodes linked together to
simulate hierarchy. Tree is a non-linear data structure. Its a hierarchical structure. The
topmost node in the tree is called root of the tree. Each node will contain some data
and this can be data of any type. In the tree that I am showing in right here data is name
of employee and designation. So, we can have an object with two string fields one to store
name and another to store designation. Okay, so each node will contain some data and may
contain link or reference to some other nodes that can be called its children. Now I am
introducing you to some vocabulary that we use for tree data structure. What I am going
to do here is , I am going to number these Nodes in the left tree. So, I can refer to
these Nodes using these numbers. I am numbering these nodes only for my convenience. its not
to show any order. Ok, coming back, as i had said each node will have some data. We call
fill in some data in these circles. It can be data of any type. it can be an integer
or a character or a string or we can simple assume that there is some data filled inside
these nodes and we are not showing it. Ok, as we were discussing, a node may have link
or reference to some other nodes that will be called its children. Each arrow in this
structure here is a link. Ok, now as you can see, the root node which is numbered 1 by
me and once again this number is not indicative of any order. I could have called the root
node number 10 also. So, root node has link to these two nodes numbered 2 and 3. So, 2
and 3 will be called children of 1 and node 1 will be called parent of nodes 2 and 3.
I’ll write down all these terms that I am talking about. We mentioned root, children
and parent. In this tree, one is a parent of , 1 is parent of 2 and 3. 2 is child of
1. And now, 4 , 5 and 6 are children of 2. So, node 2 is child of node 1, but parent
of nodes 4, 5 and 6. Children of same parent are called sibling. I am showing siblings
in same color here. 2 and 3 are sibling. Then, 4, 5 and 6 are sibling, then 7,8 are sibling
and finally 9 and 10 are sibling. I hope you are clear with these terms now. The topmost
node in the tree is called root. Root would be the only node without a parent. And then,
if a node has a direct link to some other node, then we have a parent child relationship
between the nodes. Any node in the tree that does not have a child is called leaf node.
All these nodes marked in black here are leaves. So, leaf is one more term. All other nodes
with at least one child can be called internal nodes. And we can have some more relationships
like parent of parent can be called grand-parent. So, 1 is grand-parent of 4 and 4 is grand-child
of 1. In general, if we can go from node A to B walking through the links and remember
these links are not bidirectional. We have a link from 1 to 2, so we can go from 1 to
2, but we cannot go from 2 to 1. When we are walking the tree, we can walk in only one
direction. OK, so if we can go from node A to node B, then A can be called ancestor of
B and B can be called descendant of A. Lets pick up this node numbered 10. 1, 2 and 5
are all ancestors of 10 and 10 is a descendant of all of these nodes. We can walk from any
of these nodes to 10. Ok, let me now ask you some questions to make sure you understand
things. What are the common ancestors of 4 and 9? Ancestors of 4 are 1 and 2 and ancestors
of 9 are 1,2 and 5. So, common ancestors will be 1 and 2. Ok, next question. Are 6 and 7
sibling? Sibling must have same parent. 6 and 7 do not have same parent. They have same
grand-parent. one is grandparent of both. Nodes not having same parent but having same
grandparent can be called cousins. So, 6 and 7 are cousins. These relationships are really
interesting. We can also say that node number 3 is uncle of node number 6 because its sibling
of 2 which is father of 6 or i should say parent of 6. So, we have quite some terms
in vocabulary of tree. Ok, now I will talk about some properties of tree. Tree can be
called a recursive data structure. We can define tree recursively as a structure that
consists of a distinguished node called root and some sub-trees and the arrangement is
such that root of the tree contains link to roots of all the sub-trees. T1, T2 and T3
in this figure are sub-trees. In the tree that I have drawn in left here, we have 2
sub-trees for root node. I am showing the root node in red, the left sub-tree in brown
and right sub-tree in yellow. We can further split the left sub-tree and look at it like
node number 2 is root of this sub-tree and this particular tree with node number 2 as
root has 3 sub-trees. i am showing the three sub-trees in 3 different colors. Recursion
basically is reducing something in a self similar manner. This recursive property of
tree will be used everywhere in all implementation and usage of tree. The next property that
I want to talk about is – in a tree with n nodes, there will be exactly n-1 links or
edges. Each arrow in this figure can be called a link or an edge. All nodes except the root
node will have exactly 1 incoming edge. If you can see, I’ll pick this node numbered
2. There is only one incoming link. This is incoming link and these three are outgoing
links. There will be one link for each parent-child relationship. So, in a valid tree if there
are n nodes, there will be exactly n-1 edges. One incoming edge for each node except the
root. Ok, now i want to talk about these two properties called depth and height. Depth
of some node X in a tree can be defined as length of the path from root to Node X. Each
edge in the path will contribute one unit to the length. So, we can also say number
of edges in path from root to X. The depth of root node will be zero. Lets pick some
other node. For this node, numbered 5, we have 2 edges in the path from root. So, the
depth of this node is 2. In this tree here, depth of nodes 2 and 3 is 1. Depth of nodes
4,5,6,7 and 8 is 2 and the depth of nodes 9, 10 and 11 is 3. Ok, now height of a node
in tree can be defined as number of edges in longest path from that node to a leaf node.
So, height of some node X will be equal to number of edges in longest path from X to
a leaf. In this figure, for node 3, the longest path from this node to any leaf is 2. So,
height of node 3 is 2. Node 8 is also a leaf node. I’ll mark all the leaf nodes here. A
leaf node is a node with zero child. The longest path from node 3 to any of the leaf nodes
is 2. So, the height of Node 3 is 2. Height of leaf nodes will be 0. So, what will be
the height of root node in this tree. We can reach all the leaves from root node. number
of edges in longest path is 3. So, height of the root node here is 3. We also define
height of a tree. Height of tree is defined as height of root node. Height of this tree
that I am showing here is 3. Height and depth are different properties and height and depth
of a node may or may not be same. We often confuse between the two. Based on properties,
trees are classified into various categories. There are different kinds of trees that are
used in different scenarios. Simplest and most common kind of tree is a tree with this
property that any node can have at most 2 children. In this figure, node 2 has 3 children.
I am getting rid of some nodes and now this is a binary tree. Binary tree is most famous
and throughout this series, we will mostly be talking about binary trees. The most common
way of implementing tree is dynamically created nodes linked using pointers or references,
just the way we do for linked list. We can look at the tree like this. in this structure
that I have drawn in right here, node has 3 fields. one of the fields is to store data.
Lets say middle cell is to store data. The left cell is to store the address of the left
child. And the right cell is to store address of right child. Because this is a binary tree,
we cannot have more than two children. We can all one of the children left child and
another right child. Programmatically, in C or C++, we can define a node as a structure
like this. We have three fields here – one to store data, lets say data type is integer.
I have filled in some data in these nodes. So, in each node, we have 3 fields. We have
an integer variable to store the data and then we have 2 pointers to Node, one to store
the address of the left child that will be the root of the left sub-tree and another
to store the address of the right child. We have kept only 2 pointers because we can have
at most 2 children in binary tree. This particular definition of Node can be used only for a
binary tree. For generic trees that can have any number of children, we use some other
structure and I’ll talk about it in later lessons. In fact, we will discuss implementation
in detail in later lessons. This is just to give you a brief idea of how things will be
like in implementation. Ok, so this is cool. We understand what a tree data structure is,
but in the beginning we had said that storing naturally hierarchical data is not the only
application of tree. So, lets quickly have a look at some of the applications of tree
in computer science. First application of course is storing naturally hierarchical data.
For example, the file system on your disc drive, the file and folder hierarchy is naturally
hierarchical data. its stored in the form of tree. Next application is organizing data,
organizing collections for quick search, insertion and deletion. For example, binary search tree
that we’ll be discussing a lot in next couple of lessons can give us order of log N time
for searching an element in it. A special kind of tree called Trie is used , is use
to store dictionary. Its really fast and efficient and is used for dynamic spell checking. Tree
data structure is also used in network routing algorithms and this list goes on. We’ll talk
about different kinds of trees and their applications in later lessons. I’ll stop here now. This
is good for an introduction. In next couple of lessons, we will talk about binary search
trees and its implementation. This is it for this lesson. Thanks for watching !

Glenn Chapman


  1. Do you know how to create ctree with excel I have added my dataset but when I click build it fails to work. Any help appreciated

  2. Hello,
    Is an Ordered Tree always a Positional Tree? Or are there Ordered Trees that are not Positional Trees?

  3. Yarr hindi me video bna leta to thodi …hindi medium ke students bhi sikh lete . Kya yrr

  4. U can complete the vedio in less time .. about 10 minutes u were repeating unnecessarly … But the vedio was good

  5. Owsm sir…….!!!um totally speechless about ua oll de lectures that i had watched….!!! keep it up sir… n tnkui so mch…..!!!

  6. Make videos on Hash Tables as well, we all are having a lot of problems in it.

  7. nice video,I do not understand the high terminotogy, would you explain it to me?

  8. You are a great man…. Nice explanation. Now, I can understand book easily.

  9. best explanation of theory of tree, very useful for examination point of view

  10. we all need a teacher like him…. thank you so much sir…. you have made Data Structures very interesting to learn…. Not all HEROES wear capes and he is the perfect example of it

  11. never heard the terms cousin and uncle in context of tree relationships

  12. OMG!! Explained everything that my lecturer taught in 12 hours of stupid boring lecture.

  13. C++ Binary tree file download https://dadanp4.blogspot.com/2018/07/algoritma-ii.html?m=1

  14. Bro, you are doing an awesome job here, I would like to know which software you used to make the presentation.

  15. Hi, It seems a mistake, In Binary tree, the right node is always bigger than left node.. The Root node 2 has right node as 1 and left node as 4

  16. This is an excellent video. You explain it very well. I had to rewind several times because I'm easily distracted, but I have definitely learned. Thank you so much for your efforts.

  17. Awesome , to the point explanation . Your videos make even hardest topic very easy to understand .

  18. It was really good the way you explained in simplest way tysm for this lesson 🤟

  19. Thank you so much, I had a class today, in which I came to know about Binary Tree, and your video helped alot to know to what it is.

  20. This video is super awesome. Tomorrow i will be sitting data structure exam.

  21. Normally I don't comment but I'm so happy that I finally found a person who explains everything structurally and in understandable way. Thank you a lot! <3

  22. Super bhayaa. Gud information so useful to me. Tq so much bhayaa

  23. If anyone is having confusion between depth and height, think of the analogy that we measure the 'depth' of sea from it's surface and the 'height' of a person from toe to head.

  24. Although I don't like your accent, your explanation is the best!

  25. The only channel that comes to mind when recommending data structures and algorithms

  26. I am just following your lecture instead of my university lectures bc these are way better than my university lectureres…..Thank you..Keep Sharing

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