Python
docs.python.org โบ 3 โบ library โบ queue.html
queue โ A synchronized queue class
February 23, 2026 - Source code: Lib/queue.py The queue module implements multi-producer, multi-consumer queues. It is especially useful in threaded programming when information must be exchanged safely between multip...
Videos
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Queues in Python Explained [ Step-by-Step Guide to Data Structures ...
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Queue - Data Structures in Python #3 - YouTube
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Working with Queues in Python - An introductory guide - YouTube
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Python QUEUEs | Queue implementation example - YouTube
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Python Queue Module | Python FIFO Queue | Python LIFO queue | Python ...
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Queue - Data Structures & Algorithms Tutorials In Python #8 - YouTube
W3Schools
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Queues with Python
Queues can be used to implement job scheduling for an office printer, order processing for e-tickets, or to create algorithms for breadth-first search in graphs. Queues are often mentioned together with Stacks, which is a similar data structure described on the previous page. For Python lists (and arrays), a Queue can look and behave like this:
O'Reilly
oreilly.com โบ library โบ view โบ python-standard-library โบ 0596000960 โบ ch03s03.html
The Queue Module - Python Standard Library [Book]
May 10, 2001 - File: queue-example-1.py import threading import Queue import time, random WORKERS = 2 class Worker(threading.Thread): def _ _init_ _(self, queue): self._ _queue = queue threading.Thread._ _init_ _(self) def run(self): while 1: item = self._ _queue.get() if item is None: break # reached end of queue # pretend we're doing something that takes 10โ100 ms time.sleep(random.randint(10, 100) / 1000.0) print "task", item, "finished" # # try it queue = Queue.Queue(0) for i in range(WORKERS): Worker(queue).start() # start a worker for i in range(10): queue.put(i) for i in range(WORKERS): queue.put(None) # add end-of-queue markers task 1 finished task 0 finished task 3 finished task 2 finished task 4 finished task 5 finished task 7 finished task 6 finished task 9 finished task 8 finished
Author ย Fredrik Lundh
Published ย 2001
Pages ย 304
W3Schools
w3schools.com โบ python โบ ref_module_queue.asp
Python queue Module
Python Examples Python Compiler Python Exercises Python Quiz Python Challenges Python Practice Problems Python Server Python Syllabus Python Study Plan Python Interview Q&A Python Bootcamp Python Training ... The queue module provides synchronized queue classes for multi-producer, multi-consumer scenarios.
Python
docs.python.org โบ 3.0 โบ library โบ queue.html
queue โ A synchronized queue class โ Python v3.0.1 documentation
The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer thread calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks. Example of how ...
Runestone Academy
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4.12. Implementing a Queue in Python โ Problem Solving with Algorithms and Data Structures
CodeLens 1 shows the Queue class in action as we perform the sequence of operations from Table 1. Activity: CodeLens Example Queue Operations (ququeuetest)
RQ
python-rq.org
RQ: Simple job queues for Python
from rq.repeat import Repeat # Repeat job 3 times after successful completion, with 60 second intervals job = queue.enqueue(say_hello, repeat=Repeat(times=3, interval=60)) # Use different intervals between repetitions job = queue.enqueue(say_hello, repeat=Repeat(times=3, interval=[10, 30, 60]))
Real Python
realpython.com โบ queue-in-python
Python Stacks, Queues, and Priority Queues in Practice โ Real Python
December 1, 2023 - To get the most out of this tutorial, you should be familiar with Pythonโs sequence types, such as lists and tuples, and the higher-level collections in the standard library. You can download the complete source code for this tutorial with the associated sample data by clicking the link in the box below: Get Source Code: Click here to get access to the source code and sample data that youโll use to explore queues in Python.
Simplilearn
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Queue in Python: Working With Queue Data Structure in Python
March 5, 2026 - A queue is a built-in module of python used in threaded programming. It stores items sequentially in a FIFO manner. Learn all about the queue in python now!
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Top answer 1 of 2
23
The for loop is launching a number of worker threads to perform the function defined by "worker". Here is working code that should run on your system in python 2.7.
import Queue
import threading
# input queue to be processed by many threads
q_in = Queue.Queue(maxsize=0)
# output queue to be processed by one thread
q_out = Queue.Queue(maxsize=0)
# number of worker threads to complete the processing
num_worker_threads = 10
# process that each worker thread will execute until the Queue is empty
def worker():
while True:
# get item from queue, do work on it, let queue know processing is done for one item
item = q_in.get()
q_out.put(do_work(item))
q_in.task_done()
# squares a number and returns the number and its square
def do_work(item):
return (item,item*item)
# another queued thread we will use to print output
def printer():
while True:
# get an item processed by worker threads and print the result. Let queue know item has been processed
item = q_out.get()
print "%d squared is : %d" % item
q_out.task_done()
# launch all of our queued processes
def main():
# Launches a number of worker threads to perform operations using the queue of inputs
for i in range(num_worker_threads):
t = threading.Thread(target=worker)
t.daemon = True
t.start()
# launches a single "printer" thread to output the result (makes things neater)
t = threading.Thread(target=printer)
t.daemon = True
t.start()
# put items on the input queue (numbers to be squared)
for item in range(10):
q_in.put(item)
# wait for two queues to be emptied (and workers to close)
q_in.join() # block until all tasks are done
q_out.join()
print "Processing Complete"
main()
Python 3 version per @handle
import queue
import threading
# input queue to be processed by many threads
q_in = queue.Queue(maxsize=0)
# output queue to be processed by one thread
q_out = queue.Queue(maxsize=0)
# number of worker threads to complete the processing
num_worker_threads = 10
# process that each worker thread will execute until the Queue is empty
def worker():
while True:
# get item from queue, do work on it, let queue know processing is done for one item
item = q_in.get()
q_out.put(do_work(item))
q_in.task_done()
# squares a number and returns the number and its square
def do_work(item):
return (item,item*item)
# another queued thread we will use to print output
def printer():
while True:
# get an item processed by worker threads and print the result. Let queue know item has been processed
item = q_out.get()
print("{0[0]} squared is : {0[1]}".format(item) )
q_out.task_done()
# launch all of our queued processes
def main():
# Launches a number of worker threads to perform operations using the queue of inputs
for i in range(num_worker_threads):
t = threading.Thread(target=worker)
t.daemon = True
t.start()
# launches a single "printer" thread to output the result (makes things neater)
t = threading.Thread(target=printer)
t.daemon = True
t.start()
# put items on the input queue (numbers to be squared)
for item in range(10):
q_in.put(item)
# wait for two queues to be emptied (and workers to close)
q_in.join() # block until all tasks are done
q_out.join()
print( "Processing Complete" )
main()
2 of 2
3
You can think of the number of worker threads as the number of bank tellers at a bank. So people (your items) stand in line (your queue) to be processed by a bank teller (your worker thread). Queues are actually an easy and well understood mechanism to manage complexities in threads.
I have adjusted your code a bit to show how it works.
import queue
import time
from threading import Thread
def do_work(item):
print("processing", item)
def source():
item = 1
while True:
print("starting", item)
yield item
time.sleep(0.2)
item += 1
def worker():
while True:
item = q.get()
do_work(item)
q.task_done()
q = queue.Queue(maxsize=0)
def main():
for i in range(2):
t = Thread(target=worker)
t.daemon = True
t.start()
for item in source():
q.put(item)
q.join() # block until all tasks are done
main()
Python Module of the Week
pymotw.com โบ 2 โบ Queue
Queue โ A thread-safe FIFO implementation - Python Module of the Week
In this single-threaded example, the jobs are pulled out of the queue in strictly priority order. If there were multiple threads consuming the jobs, they would be processed based on the priority of items in the queue at the time get() was called. $ python Queue_priority.py New job: Mid-level ...
Guru99
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Python Queue: FIFO, LIFO Example
August 12, 2024 - The module is available by default with python, and you donโt need any additional installation to start working with the queue. There are 2 types of queue FIFO (first in first out) and LIFO (last in first out). Step 2) To work with FIFO queue , call the Queue class using the queue module imported as shown below: ... In the case of first in first out, the element that goes first will be the first to come out. Let us work on an example to add an item in a queue.
Python
docs.python.org โบ 3.5 โบ library โบ asyncio-queue.html
18.5.8. Queues โ Python 3.5.10 documentation
December 18, 2020 - If it is an integer greater than 0, then yield from put() will block when the queue reaches maxsize, until an item is removed by get(). Unlike the standard library queue, you can reliably know this Queueโs size with qsize(), since your single-threaded asyncio application wonโt be interrupted between calling qsize() and doing an operation on the Queue.