Data Structure in Python || List || Tuple - Day 9 Data Science Course
In Python, data structures are used to organize and store data in a specific format, making it easier to access and manipulate the data. Python offers several built-in data structures that serve different purposes. Here's a short description of some common data structures in Python:
1. List: Lists are ordered collections that can hold elements of different data types. They are mutable, meaning you can add, remove, or modify elements after creation. Lists are denoted by square brackets `[]`.
2. Tuple: Tuples are similar to lists, but they are immutable, meaning their elements cannot be changed after creation. They are denoted by parentheses `()`.
3. Dictionary: Dictionaries are unordered collections of key-value pairs. Each value in the dictionary is associated with a unique key. They are useful for fast data retrieval based on keys. Dictionaries are denoted by curly braces `{}`.
4. Set: Sets are unordered collections of unique elements. They do not allow duplicate values. Sets are helpful for performing mathematical set operations like union, intersection, etc. Sets are denoted by curly braces `{}`.
5. String: Strings are sequences of characters and are used to represent text data. They are immutable, meaning their contents cannot be changed after creation.
6. Stack: A stack is a Last-In-First-Out (LIFO) data structure. Elements can be added or removed only from the top of the stack.
7. Queue: A queue is a First-In-First-Out (FIFO) data structure. Elements are added at the end and removed from the front.
8. Linked List: A linked list is a data structure consisting of elements, where each element (node) points to the next one in the sequence.
9. Tree: A tree is a hierarchical data structure with nodes connected by edges. It has a root node at the top and child nodes branching out from it.
10. Graph: A graph is a collection of nodes (vertices) connected by edges (links). It can be either directed or undirected, and it is a flexible data structure used to represent complex relationships.
Python's rich selection of built-in data structures allows programmers to efficiently store, retrieve, and manipulate data for various use cases. Additionally, Python's versatility enables developers to implement custom data structures when needed.
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