What are Data Structures

In computer science, a data structure is a way of organizing and storing data in a computer so that it can be accessed and modified efficiently. Different types of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. Some common data structures include:

Although python doesn't implement the above-mentioned data structures out of the box, we can most likely implement them manually.

Some standard data structures in computer science include:

• Arrays: An array is a collection of items that are stored in contiguous memory locations. Each item in the array has a specific index, and the items can be accessed directly by their index.

• Linked lists: A linked list is a collection of items that are stored in separate memory locations, but are linked together using pointers. Each item in a linked list has a data element and a pointer to the next item in the list.

• Stacks: A stack is a data structure that follows the Last In, First Out (LIFO) principle. Items are added to the top of the stack (pushed) and removed from the top (popped).

• Queues: A queue is a data structure that follows the First In, First Out (FIFO) principle. Items are added to the end of the queue (enqueued) and removed from the front (dequeued).

• Trees: A tree is a data structure that consists of nodes arranged in a hierarchy. The top node is called the root, and the nodes below it are called child nodes. Each node in a tree can have zero or more child nodes.

• Graphs: A graph is a data structure that consists of a set of vertices (nodes) and a set of edges that connect the vertices. Graphs can be used to represent a wide variety of real-world relationships, such as social connections, transportation networks, and data dependencies.

Data structures are an important part of computer science because they provide the foundation for efficient algorithms and enable computers to store and manipulate large amounts of data.

Python Data Structures

In Python, there are several built-in data structures that you can use to store and organize data:

• Lists: Lists are ordered collections of objects. They can store elements of different data types, and are implemented as arrays in Python. Lists are mutable, which means you can change their contents after they have been created.

• Tuples: Tuples are also ordered collections of objects, but they are immutable, which means you cannot change their contents after they have been created. Tuples are often used to store data that should not be modified, such as records in a database.

• Dictionaries: Dictionaries are unordered collections of key-value pairs. They are implemented using hash tables in Python, and are very efficient for looking up values based on their keys.

• Sets: Sets are unordered collections of unique elements. They are useful for storing data that does not need to be in a particular order, and for removing duplicates from a list.

• Strings: Strings are sequences of characters. They are immutable, which means you cannot change the contents of a string after it has been created. Strings are often used to store and manipulate text data.

In addition to these built-in data structures, Python also has several modules in its standard library that provide additional data structures, such as the collections module, which includes other specialized data structures.

You can use these data structures to store and organize data in your Python programs. For example, you might use a list to store a set of names, a tuple to store a record of information about a person (such as their name, age, and address), a set to store a list of unique items, or a dictionary to store data about a set of objects (such as a set of employees and their salaries).