Data Structures and Algorithms in Python – All You Need to Know

Python is a high-level, object-oriented programming language. It is a general-purpose language that is used in a variety of applications such as software testing, web development, data science, machine learning, and more. Data structures and algorithms are the building blocks of Python. While Python data structures organize and store data in the memory, Python algorithms are a set of instructions that are executed to get the solution to a given problem. In this article, we will discuss some of the common data structures and algorithms in Python.

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What are Data Structures in Python?

Data structures in Python allow programmers to organize, store and manage data so that it can be accessed and modified efficiently. Python has four types of built-in data structures, list, tuple, set, and dictionary. Python also has user-defined data structures. Users can create their own data structures in Python and have full control over their functionality.

Built-in Data Structures

Lists

Lists in Python store data of different data types in a sequential manner. A list is defined using square brackets and holds data that is separated by commas. Each element of the list has an address, called an index. The indexing starts from 0 and goes till the last element. We can modify, add, and remove elements in a list after it has been created.

For Example

list1 = [“red”, “blue”, “27”] #creating list print(list1)

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Tuple

Python tuples are like lists except they are immutable. While items in python lists can be changed, Python tuple elements can’t be changed or modified. Tuple items are defined inside parentheses, unlike lists that are defined in square brackets. The length of tuples is fixed.

For Example

tup1 = (1, 2, 3, 4, 5); #creating tuple print(tup1)

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Dictionary

Python dictionaries consist of key-value pairs. The keys are unique and immutable objects such as strings, numbers, or tuples. The values can be of any type. The key identifies an item and the value stores the values of those items. The value can be accessed by a unique key in the dictionary. Each key is separated from its value by a colon. The items are separated by commas. All the key-value pairs are enclosed in curly braces.

Syntax

dictionary = {“key name”: value}

For Example

dict = {‘Name’: ‘John’, ‘Age’: 24, ‘Salary’: 25000} print “dict[‘Name’]: “, dict[‘Name’] print “dict[‘Salary’]: “, dict[‘Salary’]

Output

dict[‘Name’]:  John dict[‘Salary’]: 25000

Set

Python sets are an unordered and mutable collection of unique elements. Sets are defined in square brackets. Two values cannot be the same in a set.

For Example

set1 = {1, 2, 2, 3, 3, 4, 5, 6, 6} #creating set print(set1)

Output

{1, 2, 3, 4, 5, 6}

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User-defined Data Structures in Python

User-defined data structures are those data structures that are defined by the user. The user has full control over how the data should be saved and manipulated.

Stacks

These are the linear data structures in Python that follow the Last-In-First-Out (LIFO) approach. When a new element is inserted into a stack, it is added to the top of the stack. The top element is removed first from a stack. The basic operations on the stack include push and pop for insertion and deletion respectively.

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Queue

Python queues are linear data structures that store elements following the First-In-First-Out (FIFO) approach. The element that was added first will be removed first. The basic operations on queues include Enqueue and Dequeue for adding and deleting elements from the queue.

Linked List

A linked list is a linear data structure that consists of a series of data elements that are joined together through links. Each data element is connected to another data element with a pointer. Linked lists are not there in Python’s standard library. This is why we implement linked lists using the concept of nodes.

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Tree

Python trees are non-linear data structures. They are comprised of nodes connected by edges. The root node is called the Parent node while the left node and right node as Child nodes. Python binary trees are those whose elements have at most two child nodes.

Graph

A graph visually represents a set of objects, with some object pairs connected by links. The objects that are interconnected are represented by vertices. The links joining the vertices are called edges. Thus, a graph in Python is a combination of vertices and edges where a single point may have connections with a single point or multiple points.

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HashMap or Hash Tables

A hash table or a hashmap is a data structure that maps keys to its value pairs. In Python, hash tables or hash maps are implemented through the built-in dictionary data type. The hash function creates the index value that holds the elements to be inserted or removed, etc. The index value acts as the key to the data value, enabling easier and faster access to data. Hash tables store key-value pairs and the hash function generates the key.

What are Algorithms in Python?

Algorithms in Python are a set of guidelines that are executed for solving a problem. Algorithms are not language-specific, we can implement them in different programming languages. They have some common code constructs, such as flow-control (if-else) and loops (do, for, while).

Below are some common algorithms used in Python:

Sorting Algorithm

The sort algorithm sorts data in a certain order. It represents the ways to arrange data in a particular order. It helps in optimizing data to a high level and presents it in a readable format. There are five types of sorting algorithms in Python:

• Bubble Sort
• Insertion Sort
• Merge Sort
• Shell Sort
• Selection Sort

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Searching Algorithm

This algorithm is used for searching an item in the data order. It helps to search or retrieve an element from any data structure where it is stored. Searching algorithms are divided into categories, based on the type of search operation:

• Sequential Search
• Interpolation Search

Graph Algorithm

Graphs are a collection of nodes and connections. Graph search algorithms offer a better way of dealing with abstract concepts and are widely used in computer science. There are two ways of traversing graphs using their edges:

• Depth-first Traversal (DFS): Graph is traversed in a depth-ward motion.
• Breadth-first Traversal (BFS): Graph is traversed in a breadth-ward motion.

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Conclusion

In this article, we covered the different data structures in Python such as linked lists, stacks, and queues, binary search trees, heap, searching, and hashing. We also discussed various algorithms in Python. We hope this article will help you build a strong foundation to work on Python projects, write efficient codes, and crack coding interviews questions related to data structures and algorithms in python.

FAQs about Data Structures and Algorithms in Python

Q1. How can I learn more about data structures and algorithms in Python?

Ans. There are many resources to strengthen your knowledge of data structures and algorithms in Python. You can read blogs and books or watch YouTube tutorials to learn more about data structures and algorithms in Python. You can also take up online courses to gain an in-depth understanding of data structures and algorithms in Python. Another way is to learn hands-on by working on Python projects online.

Q2. How long does it take to learn data structures and algorithms in Python?

Ans. Depending on the time you dedicate to learning and the resource you choose, it can take you around three to six months to learn data structures and algorithms in Python.

Q3. What are the properties of an algorithm?

Ans. The following are the six properties of an algorithm including:

1. Input specified: Input is the data that is transformed during computation while producing the output.
2. Output specified: It is the data that results from the computation. An algorithm should have one or more well-defined outputs.
3. Definiteness: It means specifying the sequence of operations to turn inputs into outputs. The algorithm must be clear and unambiguous.
4. Effectiveness: All steps that are required for output in an algorithm should be feasible with available resources.
5. Finiteness: The algorithm is supposed to eventually stop. You should either get the expected output or you get a response about no possible solution.
6. Independent: An algorithm should have a stepwise direction that should be independent of any programming code.