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Data Structures In Python Include Collections, String Types, Lists, Dictionaries, Tuples, Queues, And Stacks. Each structure has properties and operations for managing and processing data in Python. Each data structure in Python supports operations such as insertion, deletion, search, and sorting.

Using this, you can manage your data. Depending on the requirements and use case, select the appropriate data structure and apply it.

Python has various data structures, each suited to specific applications. Some of the critical data structures in Python are as follows:

List

A list is one of Python’s most straightforward data structures and can contain any data. It can be managed by adding, removing, and modifying list content operators. A list in Python is a sequence of values ​​containing any data type. To create a list in Python, you can use the [] operator to insert the importance ​​of the list. For example:

my_list = [1, 2, 3, 'four,' 5.6]

In this example, a list called “my_list” contains five elements: numbers, strings, and decimals.

To access the list’s values, use their indices. The first index of the list starts with the number zero, and the index of the last element is equal to the total number of elements minus one. For example, to access the third value of the list “my_list,” you can use index 2:

print(my_list[2]) # Output: 3

Also, you can perform various operations on the list using list-specific functions. For example, the “append” function adds a value to the end of the list, and the “remove” function removes a value from the list. For example:

my_list.append(6)

print(my_list) # Output: [1, 2, 3, 'four,' 5.6, 6]

my_list.remove('four')

print(my_list) # Output: [1, 2, 3, 5.6, 6]

In this example, the “append” function adds the value 6 to the end of the list, and the “remove” function removes the value “four” from the list.

What can be done with the Python programming language?

Dictionary

A dictionary is a data structure that maps keys to values. Each key is associated with a deal, and this data structure is suitable for storing and accessing the data identified by the key. A dictionary in Python is implemented using key-value pairs.

To create a dictionary in Python, you can use the {} operator and put key-value pairs inside it. For example:

my_dict = {'name': 'John,' 'age': 30, 'city': 'New York'}

In this example, a dictionary named “my_dict” is created, which contains three key-value pairs. The key of each pair is a string, and the corresponding value can be any data type.

To access the value of a key in the dictionary, you can use the key name inside the [] operator. For example:

print(my_dict['name']) # output: 'John'

Also, you can perform various operations on the dictionary by using dictionary-specific functions. For example, the “keys” function returns all dictionary keys, and the “values” function returns all dictionary values. For example:

print(my_dict.keys()) # Output: dict_keys(['name,' 'age,' 'city'])

print(my_dict.values()) # Output: dict_values(['John', 30, 'New York'])

In this example, all dictionary keys are returned using the “keys” function, and all their values ​​are returned using the “values” function.

Tuple

A tuple is similar to a list, except that once defined, its contents cannot be changed. This data structure is suitable for storing data that must remain constant. To be more precise, a tuple is like a list of data structures in Python, except that a tuple is immutable; that is, its values cannot be changed after it is created.

In other words, the tuple can only be read and not written. To create a tuple in Python, you can use the () operator and put the desired values ​​inside it. For example:

my_tuple = (1, 2, 'three,' 4.0)

In this example, a tuple named “my_tuple” is created that contains four values.

To access tuple values, you can use their indices. The first index of the tuple starts with the number zero, and the index of the last element is equal to the total number of elements minus one. For example, to access the third value of the tuple “my_tuple,” you can use index 2:

print(my_tuple[2]) # output: 'three'

Also, you can perform various operations on the tuple by using tuple-specific functions. For example, the “count” function returns the number of values ​​in a tuple, and the “index” function returns the index of a value in the tuple. For example:

my_tuple = (1, 2, 'three,' 4.0, 'three')

print(my_tuple.count('three')) # Output: 2

print(my_tuple.index(4.0)) # Output: 3

In this example, the “count” function is used to count the number of “three” values in the tuple, and the “index” function is used to return the index corresponding to the value 4.0.

Set

The Set consists of a series of unique elements randomly placed in the Set. This data structure is suitable for operations such as set union, set difference, and set intersection. In other words, a Set is a data structure in Python that holds a set of unique elements; each element exists only once in the Set. Collections in Python are implemented using the {} operator and do not repeat values.

To create an array in Python, you can use the {} operator and put the desired values ​​inside it. For example:

my_set = {1, 2, 'three,' 4.0}

In this example, a set named “my_set” is created that contains four elements. Notice that the element ‘three’ exists once in the Set. You can access the collection’s elements using a “for” loop. For example:

For an item in my_set:

print(item)

Also, you can perform various operations on the collection using collection-specific functions. For example, the “add” function adds an element to the collection, and the “remove” function is used to remove an element. For example:

my_set.add(5)

print(my_set) # Output: {1, 2, 4.0, 5, 'three'}

my_set.remove('three')

print(my_set) # Output: {1, 2, 4.0, 5}

In this example, element five has been added to the Set using the “add” function, and part ‘three’ has been removed from the Set using the “remove” function.

Queue

A queue is a data structure suitable for storing data in a first-in, first-out (FIFO) manner. This data structure is well-suited for handling system messages and requests. A queue is a data structure that stores and manages a sequence of elements.

One common application of lines is in scheduling algorithms. In Python, you can create a queue in the “Queue” class in the “queue” module.

To create a queue in Python, you must first add the “queue” module to your code. You can then create a queue using the “Queue” class. For example:

import queue

my_queue = queue.Queue()

In this example, a queue named “my_queue” is created.

You can use the “put” function to add an element to the queue. For example:

my_queue.put('first')

my_queue.put('second')

In this example, two elements, ‘first’ and ‘second,’ are added to the queue. You can use the “get” function to remove an element from the queue. For example:

print(my_queue.get()) # output: 'First'

In this example, the first element of the queue is removed using the “get” function and printed as output. Also, you can check if the queue is empty using the “empty” function. For example:

print(my_queue.empty()) # Output: False

In this example, using the “empty” function, it is checked that the queue “my_queue” is not empty.

Stack

The stack contains a series of elements that are placed in it in LIFO (Last-In-First-Out) order. This data structure is suitable for things like managing the history of steps a user has taken.

A stack is a data structure that stores and manages a sequence of elements. You can use the “Stack” class in the “collections” module to create a stack in Python. To create a stack in Python, you must first add the “collections” module to your code. You can then create a stack using the “Stack” class. For example:

from collections import queue

my_stack = queue()

In this example, a stack named “my_stack” is created. You can use the “append” function to add an element to the stack. For example:

my_stack.append('first')

my_stack.append('second')

In this example, two elements, ‘first’ and ‘second,’ are added to the stack. The “pop” function can remove an element from the stack. For example:

print(my_stack.pop()) # output: 'second'

In this example, the last element of the stack is removed using the “pop” function and printed as output. Also, you can get the number of stack elements using the “len” function. For example:

print(len(my_stack)) # Output: 1

In this example, using the “len” function, the number of elements of the “my_stack” stack has been calculated and printed as output.

This data structure is used in many Python programs and allows users to store and manage their data in an orderly and organized manner.

How to implement data structures in Python?

To implement data structures in Python, you can use classes. In this method, a class is created as a data structure, and then the members of that class are implemented as members of your desired data structure. For example, the implementation of a list is as follows:

Class Node:

def __init__(self, data):

self.data = data

self.next = None

Class LinkedList:

def __init__(self):

self.head = None

def add_node(self, data):

new_node = Node(data)

If self. The head is None:

self.head = new_node

Otherwise:

current_node = self.head

while current_node.next is None:

current_node = current_node.next

current_node.next = new_node

In this example, a class called “Node” is created, which contains two members, “data” and “next.” The “data” member is associated with the value of the data to be stored, and the “next” member is related to the address value of the next node in the linked list.

Also, a class named “LinkedList” contains a member “head,” the first node of the linked list. The “add_node” member is implemented as a function to add a new node to the linked list.

To use this data structure, you can create an object of the “LinkedList” class and then add new nodes to the linked list using the “add_node” function:

linked_list = LinkedList()

linked_list.add_node(10)

linked_list.add_node(20)

linked_list.add_node(30)

In this example, an object of the “LinkedList” class is created, and then using the “add_node” function, nodes with values ​​10, 20, and 30 are added to the linked list.

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