Key Takeaways:
- Learning about the basic Data Structures in Python Programming Language
- Gaining the knowledge and difference of Lists, Tuples, and Sets.
- Understanding the advantages of using Data Structures, why to use and when to use them?
What are Python Data Structures?
Data Structures in Python are like containers that hold information in a neat and organized way.
Using Data Structures in Python makes it easier for programmers to work with data. Data Structures help in tasks like sorting, searching, and accessing data quickly. Examples of Data Structures in Python include lists, tuples, and sets.
Advantages Of Using Data Structures In Python
Using data structures in Python offers various advantages to developers, some of them are listed below:
- Efficient Data Handling: Data structures like lists, sets, and tuples are optimized for storing and manipulating data efficiently. They provide built-in methods for common operations, making tasks easier and faster.
- Flexibility: Python’s data structures are versatile and can hold different types of data, including numbers, strings, and complex objects. This flexibility allows developers to work with diverse datasets within the same program.
- Memory Management: Python’s memory management is optimized for data structures, automatically handling memory allocation and deallocation.
- Built-in Functionality: Python’s standard library provides a rich set of functions and methods specifically designed for data structures. This includes sorting, searching, iterating, and performing mathematical operations, which helps enhance productivity and code readability.
- Easy to Use: Data structures in Python are user-friendly, making it easy for beginners to learn and use.
Types Of Data Structures In Python
Data structures in Python are of basically three types which include:
- Lists
- Sets
- Tuples
We will be discussing each of these types in detail below for your better understanding of this concept.
Lists
A list in Python Data Structures is defined as an Ordered Collection of items, and it is considered as one of the most important elements of Data Structures when used in Python.
Lists in Python are like containers that hold collections of items such as numbers, strings, or other objects, in a specific order. Using lists allows for easy access and modification of items which makes them capable of storing and organizing data in programs.
While Creating a list in a Python program, all the elements that are to be included in the list should be inside square brackets and separated by commas. The basic syntax for creating a list is written below for your reference.
List_Fruits = [Apple, Mango, Banana, Pineapple, Orange]
Here we have created a list of Fruits in which elements kept inside the Square brackets and separated by commas will act as a list.
You can create a list of up to N elements.
Lists Can be Nested
A list in Python Data Structures can be nested, which means you can have a sub-list inside another list and that list can also be a sub-list of any other list. This nesting allows developers to create complex data structures where each element of the outer list can itself be a list.
Nested lists are commonly used for representing hierarchical or multi-dimensional data structures in a Python programming language.
Lists are Mutable:
The ability to change the contents of the list is called “mutable,” mutable is nothing but a fancy word for saying that the list can be modified or updated easily even after creation.
To understand it in a better way, let us understand it more clearly with an example. Lists in Python are like boxes that can hold many things inside. One cool thing about these boxes is that we can change what’s inside them whenever we want even after packing a box. This means if we have a list of toys, we can add new toys to the list or take some out. It’s like having a magic box that can grow bigger or smaller depending on what we put in or take out.
Tuples
A tuple is another type of data structures in Python. Quite similar to the list, tuples are also an ordered collection of objects. But unlike lists, tuples have some limited functionality.
The primary factor that makes tuples and lists different from one another is mutability. Lists are mutable, whereas tuples are immutable, this means we can not modify add, or delete elements inside tuples once they are created but in Lists we can modify them whenever we want even after creation.
Lists are kept inside the square brackets and separated by commas. whereas the use of brackets in creating tuples is optional but it is recommended to use brackets as it helps in distinguishing between the start and end of the tuple and reduces complexity.
An example of a tuple is written below for your reference:
tuple_A = (item 1, item 2, item 3,…, item n)
Sets
Sets in Python are like special containers where each item is uniquely defined, this means there are no duplicates allowed. You can add or remove items from a set easily, and sets are helpful for checking if something is in the group or not. They’re handy for tasks like counting unique things or checking membership quickly.
Like lists, sets are also mutable which means they can also be modified, added, replaced, or deleted after creation. An example of a set is written below for your reference:
set_name= {“1”, “2”, “3”,….., “n”}
Here In this example, Set_name is the name of the sets whereas 1,2,3….,n are the elements inside the set. Keep in mind that you can not create duplicate elements in a set so every element you define here should be unique.
Why Tuples are Preferred Over Lists?
Tuples are often preferred over lists in Python for several reasons. One key advantage is that tuples are immutable in nature, meaning once they are created, their elements cannot be changed. This immutable property makes tuples safer to use in situations where you don’t want the data to be accidentally modified. Additionally, because tuples are immutable, they are faster to iterate over and can be used as keys in dictionaries as dictionaries require immutable keys. also, tuples use less memory, and make program execution faster as compared to using lists. Lists are slower than tuples because every time a new execution is done with lists, new objects are created, and the objects are not interpreted just once.
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Data Structures in Python FAQs
What are data structures in Python?
Data Structures in Python are like containers that hold information in a neat and organized way. Using Data Structures in Python makes it easier for programmers to work with data storing it efficiently and for easy manipulation.
How do I choose the right data structure for my needs?
The choice of the right data structure depends on factors like the type of data, required operations (e.g., searching, sorting), and memory efficiency.
Can data structures in Python be modified?
Yes, some data structures like lists and sets are mutable, this means you can change their contents after creation whereas tuples are immutable which means you cannot change them once created.
What are the advantages of using data structures in Python?
Data structures offer advantages like efficient data handling, memory management, built-in functionality, scalability, and ease of use for further detailed explanation of advantages you can refer above in the article.
