Queue in Python - W3spoint

In Python, a queue can be implemented using the built-in queue module.

Here’s an example of how to create and use a queue:

import queue

# Create a new queue
q = queue.Queue()

# Add items to the queue
q.put(1)
q.put(2)
q.put(3)

# Get the first item in the queue
item = q.get()
print(item)  # Output: 1

# Check if the queue is empty
if q.empty():
    print("Queue is empty")
else:
    print("Queue is not empty")

# Get the next item in the queue
item = q.get()
print(item)  # Output: 2

# Get the next item in the queue
item = q.get()
print(item)  # Output: 3

# Check if the queue is empty
if q.empty():
    print("Queue is empty")
else:
    print("Queue is not empty")

This code creates a queue using the Queue class from the queue module. Items are added to the queue using the put() method, and retrieved from the queue using the get() method. The empty() method is used to check if the queue is empty.

What is the Queue?:

In computer science, a queue is an abstract data type that represents a collection of elements, where the addition of new elements happens at one end of the collection, and the removal of existing elements occurs at the other end. The elements that are added first are the ones that are removed first, which gives the queue its characteristic behavior of “first in, first out” (FIFO).

Queues are often used in computing applications where there is a need to manage a list of tasks or events that need to be processed in a specific order. For example, in operating systems, queues are used to manage processes that are waiting for CPU time or to manage requests to access shared resources such as a printer or a network connection. Queues can also be used in web applications to manage requests from clients, where the first request that arrives is the first to be served.

Operations in Python:

In Python, a queue can be implemented using the built-in queue module. The module provides various classes to create and manage queues. Some of the common operations supported by these classes are:

put(item) – adds an item to the end of the queue.
get() – removes and returns the first item from the queue. If the queue is empty, this operation blocks until an item is available.
get_nowait() – removes and returns the first item from the queue. If the queue is empty, this operation raises the queue.Empty exception.
empty() – returns True if the queue is empty, and False otherwise.
qsize() – returns the number of items in the queue.
full() – returns True if the queue is full, and False otherwise. This operation is only applicable to bounded queues.
put_nowait(item) – adds an item to the end of the queue. If the queue is full, this operation raises the queue.Full exception.
task_done() – indicates that a task previously added to the queue has been completed. This is used in conjunction with the join() method.
join() – blocks until all tasks in the queue have been completed and marked as done using the task_done() method.

Note that some of these operations are specific to certain types of queues, such as bounded or unbounded queues. It’s also important to consider the potential for concurrency issues when using queues in multi-threaded or multi-process applications.

Methods Available in Queue:

The queue module in Python provides various classes for implementing different types of queues, and each of these classes support a set of methods for managing the queue. Here are some of the common methods available in the Queue class, which is a basic implementation of a FIFO queue:

put(item[, block[, timeout]]): Adds an item to the end of the queue. If the optional block argument is set to True (default), the method blocks until there is space available in the queue. If block is set to False, the method raises the queue.Full exception if the queue is full. The optional timeout argument sets the maximum amount of time to block for, after which the method raises the queue.Full exception if the queue is still full.
get([block[, timeout]]): Removes and returns the first item from the queue. If the optional block argument is set to True (default), the method blocks until an item is available in the queue. If block is set to False, the method raises the queue.Empty exception if the queue is empty. The optional timeout argument sets the maximum amount of time to block for, after which the method raises the queue.Empty exception if the queue is still empty.
empty(): Returns True if the queue is empty, and False otherwise.
full(): Returns True if the queue is full, and False otherwise. Note that this method is only applicable to bounded queues.
qsize(): Returns the number of items in the queue.
put_nowait(item): Adds an item to the end of the queue. Equivalent to calling put(item, False).
get_nowait(): Removes and returns the first item from the queue. Equivalent to calling get(False).

In addition to these methods, the Queue class also provides the task_done() and join() methods, which are used in conjunction with the Queue class to implement task management. The task_done() method indicates that a task previously added to the queue has been completed, while the join() method blocks until all tasks in the queue have been completed and marked as done using the task_done() method.

The built-in Python List:

In Python, a list is a built-in data type that represents a collection of elements, which can be of any type. Lists are mutable, meaning their contents can be changed, and they support various operations such as adding or removing elements, accessing elements by index, and slicing. Here are some of the common methods and operations supported by Python lists:

len(list): Returns the number of elements in the list.
list[index]: Returns the element at the specified index. Negative indexes count from the end of the list.
list[start:end:step]: Returns a slice of the list, starting at the start index, up to but not including the end index, with a step size of step.
list.append(item): Adds an item to the end of the list.
list.insert(index, item): Inserts an item at the specified index, shifting the existing elements to the right.
list.remove(item): Removes the first occurrence of the specified item from the list. Raises a ValueError if the item is not found.
list.pop(index): Removes and returns the element at the specified index. If no index is specified, removes and returns the last element.
list.sort(): Sorts the list in ascending order. The reverse argument can be set to True to sort in descending order.
list.reverse(): Reverses the order of the elements in the list.
list.copy(): Returns a shallow copy of the list.
list.clear(): Removes all elements from the list.
list.extend(iterable): Adds the elements from the iterable to the end of the list.

Note that lists are implemented as dynamic arrays in Python, meaning that they can automatically resize themselves as elements are added or removed. However, this can result in performance issues for large lists, as resizing requires allocating a new block of memory and copying the existing elements. In such cases, it may be more efficient to use other data structures such as arrays, dequeues, or linked lists, depending on the specific use case.

Adding Element to a Queue:

To add an element to a queue in Python, you can use the put() method provided by the queue module. Here’s an example:

import queue

# create a new queue
q = queue.Queue()

# add elements to the queue
q.put(10)
q.put(20)
q.put(30)

# print the queue contents
while not q.empty():
    print(q.get())

In this example, we first import the queue module and create a new Queue object. We then add three elements to the queue using the put() method, which adds the elements to the end of the queue. Finally, we use a while loop and the get() method to remove and print the elements from the queue in the order they were added.

The output of this program would be:

10
20
30

Note that if the queue is full when you try to add an element using put(), the method will block until there is space available in the queue. You can also use the put_nowait() method if you want to add an element without blocking, but this will raise a queue.Full exception if the queue is full.

Removing Element from a Queue:

To remove an element from a queue in Python, you can use the get() method provided by the queue module. Here’s an example:

import queue

# create a new queue
q = queue.Queue()

# add elements to the queue
q.put(10)
q.put(20)
q.put(30)

# remove and print the first element from the queue
print(q.get())

# remove and print the second element from the queue
print(q.get())

# remove and print the third element from the queue
print(q.get())

In this example, we first import the queue module and create a new Queue object. We then add three elements to the queue using the put() method. To remove and print the elements from the queue, we use the get() method, which removes and returns the first element from the queue.

The output of this program would be:

10
20
30

Note that if the queue is empty when you try to remove an element using get(), the method will block until an element is available in the queue. You can also use the get_nowait() method if you want to remove an element without blocking, but this will raise a queue.Empty exception if the queue is empty.

Sorting the Queue:

In Python, a queue data structure (as implemented by the queue module) is not designed to be sorted. However, you can still sort the contents of a queue by removing all elements from the queue, sorting them using the built-in sorted() function or other sorting algorithms, and then adding them back to the queue. Here’s an example:

import queue

# create a new queue
q = queue.Queue()

# add elements to the queue
q.put(10)
q.put(20)
q.put(5)
q.put(30)

# remove all elements from the queue and store them in a list
lst = []
while not q.empty():
    lst.append(q.get())

# sort the list
lst.sort()

# add the sorted elements back to the queue
for item in lst:
    q.put(item)

# print the sorted queue contents
while not q.empty():
    print(q.get())

In this example, we first import the queue module and create a new Queue object. We then add four elements to the queue using the put() method. To sort the elements, we remove all elements from the queue using a while loop and the get() method, and store them in a list. We then sort the list using the built-in sort() method, and add the sorted elements back to the queue using a for loop and the put() method. Finally, we use another while loop and the get() method to remove and print the elements from the queue in sorted order.

The output of this program would be:

Note that this approach may not be the most efficient way to sort elements, especially for very large queues. In such cases, you may want to consider using a different data structure or sorting algorithm that is better suited to your specific use case.

The Queue Module:

The queue module in Python provides a thread-safe implementation of the queue data structure. This module provides several classes for implementing different types of queues, including:

queue.Queue: This is the most basic type of queue, which uses a simple FIFO (first-in, first-out) ordering of elements. This class provides several methods for adding and removing elements from the queue.
queue.LifoQueue: This is a last-in, first-out (LIFO) queue, also known as a stack. It provides the same methods as queue.Queue, but elements are removed from the opposite end of the queue.
queue.PriorityQueue: This is a priority queue, which orders elements based on their priority. Elements with higher priority are removed before those with lower priority. This class requires elements to be comparable, and provides a put() method that takes a tuple of (priority, value) as its argument.
queue.SimpleQueue: This is a simpler, more lightweight version of queue.Queue that is only available in Python 3.7 and later.

All of these classes are thread-safe, which means that they can be used in a multi-threaded environment without the need for additional synchronization. The queue module also provides several synchronization primitives for use with these classes, including Lock, RLock, and Condition, which can be used to coordinate access to the queue between multiple threads.

Overall, the queue module provides a powerful and flexible way to implement queue data structures in Python, with support for different types of ordering and synchronization mechanisms.

Working With queue.Queue Class:

The queue.Queue class is a simple implementation of the queue data structure in Python, using a FIFO (first-in, first-out) ordering of elements. Here’s an example of how to use the Queue class:

import queue

# create a new queue
q = queue.Queue()

# add elements to the queue
q.put(10)
q.put(20)
q.put(30)

# get and remove the first element from the queue
print(q.get())

# get and remove the second element from the queue
print(q.get())

# get and remove the third element from the queue
print(q.get())

The output of this program would be:

10
20
30

The Queue class also provides several other useful methods for working with queues, including:

q.qsize(): Returns the number of elements currently in the queue.
q.empty(): Returns True if the queue is empty, False otherwise.
q.full(): Returns True if the queue is full (i.e., the maximum size has been reached), False otherwise.
q.put(item, block=True, timeout=None): Adds an element to the end of the queue. If block is True, this method will block until there is room in the queue. If timeout is not None, it specifies the maximum time (in seconds) to wait for space to become available in the queue.
q.put_nowait(item): Adds an element to the end of the queue, without blocking.
q.get(block=True, timeout=None): Removes and returns the first element from the queue. If block is True, this method will block until an element is available. If timeout is not None, it specifies the maximum time (in seconds) to wait for an element to become available.
q.get_nowait(): Removes and returns the first element from the queue, without blocking.

These methods provide a flexible way to work with queues in Python, and can be used to implement a variety of different algorithms and data structures.

Working With collection.deque Class:

The collections.deque class in Python provides an implementation of a double-ended queue (deque) data structure. This data structure is similar to a list or queue, but allows for efficient insertion and deletion of elements from both ends of the sequence.

Here’s an example of how to use the deque class:

from collections import deque

# create a new deque
d = deque()

# add elements to the deque
d.append(10)
d.append(20)
d.append(30)

# get and remove the first element from the deque
print(d.popleft())

# get and remove the last element from the deque
print(d.pop())

# get and remove the next element from the deque
print(d.popleft())

In this example, we first import the collections module and create a new deque object. We then add three elements to the deque using the append() method, which adds elements to the right end of the deque. To remove and print elements from the deque, we use the popleft() and pop() methods, which remove and return elements from the left and right ends of the deque, respectively.

The output of this program would be:

10
30
20

The deque class also provides several other useful methods for working with deques, including:

d.appendleft(item): Adds an element to the left end of the deque.
d.extend(iterable): Adds multiple elements to the right end of the deque, from an iterable.
d.extendleft(iterable): Adds multiple elements to the left end of the deque, from an iterable. The elements are added in reverse order.
d.rotate(n): Rotates the deque n steps to the right (if n is positive) or to the left (if n is negative).
d.clear(): Removes all elements from the deque.
d.count(item): Returns the number of occurrences of item in the deque.
d.index(item[, start[, stop]]): Returns the index of the first occurrence of item in the deque, between the optional start and stop indices.

These methods provide a flexible way to work with deques in Python, and can be used to implement a variety of different algorithms and data structures.

The multiprocessing.Queue Class:

The multiprocessing.Queue class in Python provides a way to implement inter-process communication between multiple Python processes using a queue data structure. This queue is similar to the queue.Queue class we saw earlier, but is designed to be used with Python’s multiprocessing module, which allows for parallel processing and distributed computing.

Here’s an example of how to use the multiprocessing.Queue class:

from multiprocessing import Process, Queue

# define a worker function to be run in a separate process
def worker(q):
    while True:
        item = q.get()
        if item is None:
            break
        print(f"Worker got item: {item}")

# create a new queue
q = Queue()

# start the worker process
p = Process(target=worker, args=(q,))
p.start()

# add items to the queue
for i in range(10):
    q.put(i)

# signal the worker to stop
q.put(None)

# wait for the worker to finish
p.join()

In this example, we first import the Process and Queue classes from the multiprocessing module. We then define a worker() function that takes a Queue object as an argument, and repeatedly reads and processes items from the queue until it receives a None sentinel value. To start the worker process, we create a new Process object and pass it the worker() function and the Queue object as arguments. We then start the process using the start() method.

Next, we add 10 items to the queue using the put() method. To signal the worker to stop, we add a None sentinel value to the queue. Finally, we wait for the worker process to finish using the join() method.

The output of this program would be:

Worker got item: 0
Worker got item: 1
Worker got item: 2
Worker got item: 3
Worker got item: 4
Worker got item: 5
Worker got item: 6
Worker got item: 7
Worker got item: 8
Worker got item: 9
Worker got item: 0
Worker got item: 1
Worker got item: 2
Worker got item: 3
Worker got item: 4
Worker got item: 5
Worker got item: 6
Worker got item: 7
Worker got item: 8
Worker got item: 9

The multiprocessing.Queue class also provides several other useful methods for working with inter-process queues, including:

q.put(item[, block[, timeout]]): Adds an element to the end of the queue. If block is True, this method will block until there is room in the queue. If timeout is not None, it specifies the maximum time (in seconds) to wait for space to become available in the queue.
q.get([block[, timeout]]): Removes and returns the first element from the queue. If block is True, this method will block until an element is available. If timeout is not None, it specifies the maximum time (in seconds) to wait for an element to become available.
q.empty(): Returns True if the queue is empty, False otherwise.
q.qsize(): Returns the number of elements currently in the queue.
q.close(): Closes the queue and prevents any more data from being added to it.
q.join_thread(): Joins the thread(s) that put items into the queue.

These methods provide a flexible way to implement inter-process communication using queues in Python.

Priority Queue in Python:

A priority queue is a data structure that stores a collection of items, each with an associated priority value. The priority value determines the order in which the items are processed. Items with a higher priority value are processed before items with a lower priority value.

In Python, the heapq module provides a simple way to implement a priority queue using a heap data structure. Here’s an example of how to use heapq to create a priority queue:

import heapq

# create an empty priority queue
pq = []

# add items to the priority queue
heapq.heappush(pq, (3, "three"))
heapq.heappush(pq, (1, "one"))
heapq.heappush(pq, (4, "four"))
heapq.heappush(pq, (2, "two"))

# remove items from the priority queue in order of priority
while pq:
    item = heapq.heappop(pq)
    print(item[1])

In this example, we first import the heapq module. We then create an empty priority queue using a Python list. To add items to the priority queue, we use the heappush() function, which takes two arguments: the queue to add the item to, and the item itself (represented as a tuple with the priority value first and the item value second).

To remove items from the priority queue in order of priority, we use the heappop() function, which removes and returns the item with the lowest priority value from the queue.

The output of this program would be:

one
two
three
four

In addition to heappush() and heappop(), the heapq module provides several other useful functions for working with priority queues, including:

heappushpop(heap, item): Pushes the item onto the heap, and then removes and returns the smallest item.
heapreplace(heap, item): Removes and returns the smallest item from the heap, and then pushes the item onto the heap.
nlargest(n, iterable[, key]): Returns a list of the n largest elements from the iterable.
nsmallest(n, iterable[, key]): Returns a list of the n smallest elements from the iterable.

Using these functions, it’s easy to implement a priority queue in Python using the heapq module.

Manually Sorted List:

A manually sorted list is a Python list that has been sorted manually, without using the built-in sorted() function or any other sorting algorithm provided by Python libraries.

To sort a list manually, you can use a simple sorting algorithm such as the bubble sort algorithm, which works by repeatedly swapping adjacent elements if they are in the wrong order. Here’s an example of how to sort a list using the bubble sort algorithm:

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]

# Example usage
my_list = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
bubble_sort(my_list)
print(my_list)
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]

# Example usage
my_list = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
bubble_sort(my_list)
print(my_list)

In this example, we define the bubble_sort() function, which takes a list as its argument and sorts the list in place using the bubble sort algorithm. The for loop on line 3 iterates over the list n times, where n is the length of the list. The inner for loop on line 4 iterates over the list from the first element to the n-i-1-th element, where i is the current iteration of the outer loop. If two adjacent elements are in the wrong order, they are swapped using a tuple assignment on line 5.

We then create an example list my_list and sort it using the bubble_sort() function. Finally, we print the sorted list.

Note that while manually sorting a list can be a useful exercise to gain a deeper understanding of sorting algorithms, it is generally not recommended to use manually sorted lists in production code, as there are many efficient sorting algorithms built into Python that are more reliable and performant.

The queue.PriorityQueue Class:

The queue.PriorityQueue class is a built-in Python class that provides an implementation of a priority queue. It uses the heap data structure to maintain the priority queue, which allows for efficient insertion and removal of elements.

Here’s an example of how to use the queue.PriorityQueue class:

import queue

# create a new priority queue
pq = queue.PriorityQueue()

# add items to the priority queue
pq.put((3, "three"))
pq.put((1, "one"))
pq.put((4, "four"))
pq.put((2, "two"))

# remove items from the priority queue in order of priority
while not pq.empty():
    item = pq.get()
    print(item[1])

In this example, we first import the queue module. We then create a new PriorityQueue object, pq.

To add items to the priority queue, we use the put() method, which takes a single argument representing the item to be added (in the form of a tuple, with the priority value first and the item value second).

To remove items from the priority queue in order of priority, we use the get() method, which removes and returns the item with the highest priority value from the queue.

The output of this program would be:

one
two
three
four

The queue.PriorityQueue class also provides several other useful methods, including:

qsize(): Returns the number of items in the queue.
empty(): Returns True if the queue is empty, False otherwise.
full(): Returns True if the queue is full, False otherwise.
put_nowait(item): Adds an item to the queue without blocking.
get_nowait(): Removes and returns an item from the queue without blocking.

Using the queue.PriorityQueue class provides a simple and efficient way to implement a priority queue in Python, without having to manually sort a list or implement a sorting algorithm.

Conclusion:

In conclusion, queues are an essential data structure in computer science that provide a way to store and manage collections of elements in a first-in-first-out (FIFO) order. Python provides several built-in classes and modules for working with queues, including the queue.Queue, collection.deque, and queue.PriorityQueue classes.

The queue.Queue and collection.deque classes are useful for implementing simple queues in Python, while the queue.PriorityQueue class is specifically designed for managing priority queues.

When working with queues, it’s important to consider the specific needs of your application and choose the appropriate data structure and implementation to ensure efficient and effective processing of data.

Overall, understanding how to work with queues is an important skill for any programmer, as they are frequently used in a wide range of applications, from web development to scientific computing to data analysis.