Today's session will be an exciting exploration of the Queue data structure. Much like stacks, which operate on the "Last-In, First-Out" or LIFO principle, queues function on the opposite principle of "First-In, First-Out" or FIFO. Consequently, the first item entered into the queue is also the first one out.
The real-world analogy would be a queue at a grocery store checkout. The person who has been waiting in line the longest is served first, then the next person, and so on. The last person to join the queue will be the last one to be served. In computer science, we utilize the same concept.
In this lesson, we aim to delve deeper into this concept - understanding Queues, uncovering their Pythonic implementation, and analyzing the time and space complexities associated with their functionalities.
At its core, a Queue is a sequential collection of elements that follows a "First-In, First-Out" (FIFO) principle. It operates much like real-world queues or lines, where the first element inserted is the first one to be removed. For example, consider a line of people waiting to buy tickets at a theater. The person who arrives first gets their ticket first. In computer science, a Queue works in exactly the same way.
In Python, we can implement Queues using built-in data types. Indeed, the Python list datatype comes in handy here. Python lists, however, have a significant drawback: the pop(0) method has time complexity, while we would like it to be . There is another Python module named that offers , a flexible container that serves both as queue and stack implementations. We will use the data structure to implement the queue in this lesson.
