In computer science, two fundamental concepts stand out: data structures and algorithms. These are essential tools for any programmer. Data structures in Python are all about organizing and storing data in the computer’s memory while our program is running. On the other hand, Python algorithms are like a set of detailed instructions that guide the computer in processing the data for a specific purpose.
Let us consider data structures as containers that hold information in a particular way, like lists or dictionaries. Conversely, algorithms are like step-by-step recipes that tell the computer how to work with the data stored in those containers.
Algorithms use different data structures to solve specific problems related to data analysis. Whether it’s a real-world problem or a coding challenge, understanding data structures and algorithms in Python is vital to coming up with accurate solutions. This article will delve into various Python algorithms and data structures, giving a detailed discussion to help you grasp their importance and usage.
What Are Data Structures?
When making data easily accessible and allowing efficient changes, the key is organizing, managing, and storing the data properly. That’s where Data Structures come into play. They help us arrange the data in a way that allows us to store collections of information, establish relationships between them, and perform operations on them as needed. In other words, Data Structures help us sort out our data so that we can find and retrieve it quickly when required.
However, no single Data Structure can handle all the scenarios we encounter. That’s why we have multiple data structures suited for various situations and purposes. These different options allow us to choose the most suitable one for the task and ensure efficient and effective data management.
Why do we need Data Structures?
Imagine you have a file explorer with thousands of documents stored and want to find a specific document. One way to do this is by going through each document individually, which can be quite slow and time-consuming.
However, there’s a smarter way to handle this situation. Your Operating System (OS) uses a technique called “Indexing” and a type of data structure known as “hash tables.” These data structures help the OS quickly jump to where the document is stored or related documents are present.
By using indexing and hash tables, the operating system can then significantly reduce the time it takes to search for a document, even when many files are in the file explorer. This is a prime example of why Data Structures are so important. They allow us to organize and store data in ways that optimize performance and make our tasks much more efficient.
Types Of Data Structure
There are many data structures available to us, such as lists, dictionaries, tuples, linked lists, stacks, etc. Each type of data structure has its own advantages. Let us know some of the major types of data structures.
1. Tuples
Tuples are ordered collections of elements. They are immutable, but unlike lists, they cannot be modified once they are created. They are used to group one or more related groups of data together. You can create a tuple by enclosing a comma-separated sequence of values in parentheses.
| Tuples In Python |
| # Creating a tuple
my_tuple = (1, 2, 3, “Hello”, “World”) # Accessing elements in a tuple print(my_tuple[0]) # Output: 1 print(my_tuple[3]) # Output: Hello # Iterating through a tuple for item in my_tuple: print(item) # Tuple with a single element (note the trailing comma) single_element_tuple = (42,) # Tuple unpacking a, b, c, d, e = my_tuple print(a, b, c, d, e) # Output: 1 2 3 Hello World |
2. List
Lists are ordered collections of data. It is mutable, and you can carry out modifications inside it even after creation, unlike tuples. In python, lists can contain different types of data. You can enclose all data inside a square bracket in lists.
| Lists In Python |
| # Creating a list
my_list = [1, 2, 3, “Hello”, “World”] # Accessing elements in a list print(my_list[0]) # Output: 1 print(my_list[3]) # Output: Hello # Modifying elements in a list my_list[1] = 42 print(my_list) # Output: [1, 42, 3, ‘Hello’, ‘World’] # Iterating through a list for item in my_list: print(item) # List methods my_list.append(“Python”) print(my_list) Output: [1, 42, 3, ‘Hello’, ‘World’, ‘Python’] my_list.remove(3) print(my_list) Output: [1, 42, ‘Hello’, ‘World’, ‘Python’] |
3. Dictionary
The dictionary is based on the Key-value pair. It is an unordered collection of data. Each key in the dictionary is unique and is mapped to a specific value in the dataset. The time complexity generally is of O(1) for this data structure. It helps us store and retrieve data based on key rather than an index. It helps in searching for and mapping the required data. You can create data with the help of a built-in dict( ) constructor.
| Dictionary In Python |
| # Creating a dictionary
my_dict = { “name”: “John”, “age”: 30, “city”: “New York” } # Accessing values in a dictionary print(my_dict[“name”]) Output: John print(my_dict[“age”]) Output: 30 # Modifying values in a dictionary my_dict[“age”] = 31 print(my_dict) Output: {‘name’: ‘John’, ‘age’: 31, ‘city’: ‘New York’} # Adding a new key-value pair my_dict[“country”] = “USA” print(my_dict) Output: {‘name’: ‘John’, ‘age’: 31, ‘city’: ‘New York’, ‘country’: ‘USA’} # Removing a key-value pair del my_dict[“city”] print(my_dict) # Output: {‘name’: ‘John’, ‘age’: 31, ‘country’: ‘USA’} # Checking if a key exists in a dictionary if “name” in my_dict: print(“Name:”, my_dict[“name”]) Output: Name: John |
4. Set
A set is an unordered collection of unique data. This data structure does not contain duplicate elements. Sets are often used when you don’t want any duplicate items in your datasets. It can be created using built in set () constructor.
| Set In Python |
| # Creating a set
my_set = {1, 2, 3, 3, 4, 5} # Printing the set (duplicates are automatically removed) print(my_set) # Output: {1, 2, 3, 4, 5} # Checking if an element is in the set print(2 in my_set) Output: True print(6 in my_set) Output: False # Adding elements to a set my_set.add(6) my_set.add(7) print(my_set) # Output: {1, 2, 3, 4, 5, 6, 7} # Removing elements from a set my_set.remove(3) print(my_set) # Output: {1, 2, 4, 5, 6, 7} |
5. Linked List
This is a non-contagious data structure and consists of various nodes. Each node is connected to another node, forming a chain like structure. It provides a flexible way of storing and manipulating data. It also helps insert and delete elements in a data set faster.
| Linked List In Python |
| class Node:
def __init__(self, data): self.data = data self.next = None class LinkedList: def __init__(self): self.head = None # Method to insert a new node at the end of the linked list def append(self, data): new_node = Node(data) if not self.head: self.head = new_node return current = self.head while current.next: current = current.next current.next = new_node # Method to display the linked list def display(self): current = self.head while current: print(current.data, end=” -> “) current = current.next print(“None”) # Create a linked list and append some elements my_linked_list = LinkedList() my_linked_list.append(1) my_linked_list.append(2) my_linked_list.append(3) # Display the linked list my_linked_list.display() |
6. Binary Tree
They are hierarchical data structures in Python that consist of nodes and edges. Each node in the binary tree has a maximum of two children. Binary trees in data structures have various applications in computer science. Each node in a binary tree consists of three main parts.
- Data Element
- Address of the left element
- Address of the right element
| Binary Tree In Python |
| class Node:
def __init__(self, key): self.left = None self.right = None self.val = key # Creating a binary tree root = Node(1) root.left = Node(2) root.right = Node(3) root.left.left = Node(4) root.left.right = Node(5) |
Binary Tree Data Structure
Resources To Learn Data Structures
Many online tutorials and free courses are available nowadays to learn and practice data structure. But we need to be careful while selecting a course. Reviews can be a great help, though.
PhysicsWallah provides the best study and practice environment for data structures. Along with this, it also offers various data structures and related courses at a very affordable price. You will get various benefits with 24-hour doubt support. Check various courses on PW Skills
What Are Data Structures and Algorithms FAQs
With Python, how to learn data structures and algorithms?
Introduction to data structure and algorithms. In Python, you'll start to learn about data structures and algorithms. Nodes. You can learn more about the node and building block data structure—linked Lists. In Python, you'll learn about linked lists and how to create them—doubly Linked Lists, Queues, Stacks Hash Maps.
What is it you need to know about data structures and algorithms?
Before starting any data structure or algorithm, you must know how to express and use this data structure. Now, it's the knowledge of all programming languages that we need to begin with. So you'll get an idea of program complexity, one of the main and most commonly used concepts for DSA.
Is it useful to learn Python data structures and algorithms?
In developing optimized code, learning data structures will help. Therefore, writing the best possible solutions for time and space is important. Choose the best-fit data structure, which needs less space and an algorithm that runs code as fast as possible.
For all languages, is DSA the same?
You don't need any language specifically for your data structures and algorithms, but you can do it in all languages: JavaScript, C, C++, Java, or Python. You're supposed to be comfortable with the syntax of your language, and you can go.
What's the best method of using Python to learn DSA?
In this Python class, you will learn the following main things:
- How to create a common data structure and algorithm in Python
- computer science fundamentals
- Advice and tricks to tackle difficult questions in a programming interview
- 100+ coding interview questions with explanations
- A huge O, time and space complexity
- Recursion
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