Python data structures organize and group data according to type. There are four main types of Python data structures.
Python Data Structures: What Are the 4 Main Types?
- Lists
- Sets
- Tuples
- Dictionaries
What Are the 4 Built-In Python Data Structures?
The four primary data structures utilized in Python are lists, sets, tuples and dictionaries.
Lists
Lists are a type of data structure containing an ordered collection of items. They are crucial to executing projects in Python.
Every item contained within a list has an inherent order used to identify them, which remains consistent throughout the life of the list. Lists are mutable, allowing elements to be searched, added, moved and deleted after creation. Lists can also be nested, allowing them to contain any object, including other lists and sublists.
Tuples
A tuple contains much of the same functionality as a list, albeit with limited functionality. The primary difference between the two is that a list is immutable, meaning it cannot be modified or deleted. Tuples are best when a user intends to keep an object intact throughout its lifetime to prevent the modification or addition of data.
Sets
A set is a collection of unique elements with no defined order, which are utilized when an object only needs to exist within a collection of objects and its order or number of appearances are not important.
Dictionaries
Dictionaries are unique and immutable objects that consist of key value pairs and are accessible through unique keys in the dictionary.
What Are User-Defined Data Structures in Python?
User-defined data structures add additional functionality to Python, thereby allowing users to access, modify or preserve data in specific ways. In addition to Python’s built-in data structures, there are a number of user-defined data structures you can use, such as arrays, stacks, queues, trees and more.
- Arrays: Arrays function similarly to lists, but allow only homogeneous elements to be stored within them, whereas lists may contain heterogeneous elements.
- Stacks: Stacks are linear data structures in which the data that is published last may be accessed first. Stacks are commonly utilized in recursive programming and for undo functions in word processors.
- Queues: A queue works oppositely of a stack and is based on the First-In-First-Out principle, that is the data entered first may be accessed last.
- Trees: Trees are non-linear data structures that incorporate roots and nodes to create a hierarchy of data. These structures are used heavily on HTML pages.
Python Data Structures: Advantages and Disadvantages
Each data structure offers a different way of completing tasks such as sorting, inserting and finding, but efficiency depends on the situation.
No data structure is ultimately better than another, but using one for a task it is not designed to support may lead to longer workflows, or worse, skewed data.
- Linked lists are purpose-built for inserting and deleting data but only offer sequential access to this data, which means searching and sorting will be problematic.
- Similar to a list, tuples can be used for outputting either an entire tuple or individual elements, but they use less memory space and do not allow sorting, adding, replacing or deleting elements.
- Sets are intentionally designed to be limited but excel in checking for a value’s existence and avoiding duplicates in a set.
- Dictionaries allow data to be collected in key-value pairs, making them excellent for quick retrievals in unstructured documents, but are too limited to work with large amounts of tabular data.
- Arrays are easy to create and excellent for completing tasks that involve working with sequential data but searching, sorting, inserting and deleting will pose items once you shift items.
Stacks are excellent for adding or removing data that was last entered within the database, and queues are meant to add or remove data first entered in the data set, but if you want to pull an item from the middle of the set, you’ll benefit from using a different data structure.
Binary search trees allow you to access, sort and delete data quickly while maintaining the sorted order of elements once retrieved. Despite these advantages, binary search trees require a tedious amount of work to create and manage.
