Algorithms and data structures in Python

What are Algorithms and data structures in Python? | Explanation | Best Books | Courses

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What are Algorithms and Data Structure in Python? | Explanation | Best Books | Courses

The motive of this post is to give an idea about the Algorithms and Data Structures in Python.
This topic plays a vital role in the professional life of data scientist in order to help them for solving machine learning models in a more productive way.

I am here with you all to share about built-in-data structure , user defined data structure.

Built-in Data Structure

As it is quite clear by the name, Data structure is used to store, organise, and manage data for easy access and modification.
Here we are going to take a look on build in data structures. There are four types of built in data structures in Python : List, Tuple, Set and Dictionary.


We can define a list by using square brackets and it holds data that is separated by commas. The List is mutable and arranged. It can contains a mixture of distinct data types.

# empty list
my_list = []

# list of integers
my_list = [1, 2, 3]

# list with mixed data types
my_list = [1, "Hello", 3.4]


A tuple is a another blockchain . It is a data type for immutable and ordered sequence of elements as it does not allow to add or omit elements in the tuple.
It is as similar as list only difference is list is mutable and it is not .

tuple1 = ("apple", "banana", "cherry")
tuple2 = (1, 5, 7, 9, 3)
tuple3 = (True, False, False)


Set is a mutable and unordered of extraordinary components. It can allow us to eliminate copy rapidly from a rundown.
A set with strings , integers and Boolean value.

set1 = {"abc", 34, True, 40, "male"}


Dictionary is an alterable and unordered information structure. It licenses putting away a couple
of things (for example keys and values).
In the dictionary , it is feasible to incorporate holders into different compartments to make
compound information structures. For your convenience here is a example below:

Car= { "brand": "Ford", "model": "Mustang","year":1964 }

User Defined Data Structures

Now let’s move towards the next part that is user defined data structure. there are three types of user-defined data structure : Queue , Stack and Tree.

Stack Using Arrays:

The stack is a direct information structure where components are organized consecutively. It follows the instrument L.I.F.O which means toward the end in first out. In this way, the last component embedded will be taken out as the first.

The operations are:
Push → inserting an element into the stack
Pop → removing an element from the stack

The conditions to Check :

  • Overflow Condition – This condition happens when we attempt to place another
    component into a stack that is as of now having most extreme components.
  • Underflow Condition – This condition happen when we try to delete the elements when stack is a null .

Queue Using Arrays:

The Queue is a direct information structure where components are in a consecutive way. It follows the F.I.F.O instrument that implies earliest in, earliest out.

Think when you go to the film with your companions, as you can envision the first of you that give the ticket is likewise the principal that get out of the line. The system of the Queue is something very similar.

Beneath the angles that describe the queue :

Two ends:

  • front → points to starting element
  • rear → points to the last element

There are two operations:

  • enqueue – embeddings a component into the line. It will be done at the back.
  • dequeue – Removing an element from the queue. It will be done from front .

There are two conditions:

  • overflow → insertion into a queue that is full
  • underflow → deletion from the empty queue


Trees are utilized to characterize progressive system. It begins with the root nodes and goes further down, the last nodes are called young nodes. In this article, I center around the binary tree.

The binary tree is a tree information structure in which every hub has all things considered two youngsters, which are referred to as the left kid and the right kid.


A Linked list is a linear data structure, in which the components are not put away at adjacent memory areas.


A Graph is a non-linear data structure comprising of hubs and edges. The hubs are now and then likewise alluded to as vertices and the edges are lines or bends that interface any two hubs in the Graph.

A Graph comprises of a limited arrangement of vertices(or hubs) and set of Edges which associate a couple of hubs.

Till Here It was all about the brief guide of Data Structure Now let’s move together towards our
second part ALGORITHMS.


The algorithms has existed since vestige. Truth be told, the old Egyptians utilized it to tackle their issues. Then, at that point they showed this way to deal with the Greeks.

As you definitely realize Algorithms are directions that are formed in a limited and successive request to tackle issues.

At the point when we compose a algorithm, we need to realize what is the specific issue, figure out where we need to begin and pause and define the middle of the road steps.

There are three main ways to execute the algorithms:

Divide and Conquer – It isolates the issue into sub-parts and tackles every one independently.

Dynamic programming – It partitions the issue into sub-parts recollects the consequences of the sub-parts and applies it to similar ones.

Greedy algorithms – It Involves by taking the simplest step to solve a problem without worrying about the complexity of the future steps.


Trees in python are non-linear data structures. They are described by roots and hubs. I take the class I developed before for the binary tree.
Tree Crossing refers to visiting every hub present in the tree precisely once, to refresh or genuinely look at them(update and check).


The sorting algorithms is utilized to sort information in some provided request. It tends to be ordered in Merge Sort and Bubble Sort.

  • Merge Sort – It addresses to the divide and Conquer rule. The given list is first separated into more small records and analyzes contiguous records and afterward, reorders them in the ideal succession . Thus in lists as unordered components as input , we need to have
    ordered elements as output.
  • Bubble Sort – It first analyzes and afterward sorts contiguous components in case they are not in the predetermined request.
  • Insertion Sort – It picks one item of a given list at the time and places it at the exact spot where it is to be placed.

There are also some other sorting algorithms like selection sort and Shell sort.


Looking through algorithms are utilized to look for certain components present in a given dataset.

There are many types of search algorithms such as Linear Search, Binary Search, Exponential Search, Interpolation Search, and so on.

In this part, we will see the Linear Search and Binary Search.

  • Linear Search – In a first dimensional exhibit we need to look through a specific key component. The info is the gathering of components and the key component that we need to discover. In this way, we need to compare the key element and every element of the gathering.
  • Binary Search – in this Algorithms , we accept that the list is in increasing sequence . Thus, if the worth of the Search key is not exactly the element in the list, we slender the stretch to the lower half. Else, we tight to the upper half. We proceed with our check until the worth is found or the list is unfilled.

Books and Courses







So this was all about data structures and algorithms in python. I hope all your basics is clear by now. however if you’re still left with any doubt or query let us know in the comments section. we will try to get to you ASAP.

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