Priority Queue Introduction and Concept in DSA Python - Build from Scratch

Priority Queue Introduction and Concept

📖 Scenario: Imagine you are managing a hospital emergency room. Patients arrive with different levels of urgency. You want to treat the most urgent patients first. This is a perfect use case for a priority queue.

🎯 Goal: You will create a simple priority queue using a list of tuples where each tuple contains a patient's name and their priority level. Then, you will extract patients in order of their priority.

📋 What You'll Learn

Create a list called patients with given patient names and priority levels

Create a variable called highest_priority to track the highest priority found

Write a loop to find the patient with the highest priority

Print the patient with the highest priority

💡 Why This Matters

🌍 Real World

Priority queues are used in hospitals, task scheduling, and anywhere urgent tasks must be handled first.

💼 Career

Understanding priority queues helps in roles like software development, system design, and operations where task prioritization is key.

1

Create the patient list

Create a list called patients with these exact entries: ("Alice", 2), ("Bob", 5), ("Charlie", 1), ("Diana", 4)

DSA Python

# Create the patients list with names and priority levels
# Your code here

Hint

Use a list of tuples where each tuple has a name and a number for priority.

2

Set up the highest priority variable

Create a variable called highest_priority and set it to 0 to start tracking the highest priority

DSA Python

patients = [("Alice", 2), ("Bob", 5), ("Charlie", 1), ("Diana", 4)]
# Create highest_priority variable and set it to 0
# Your code here

Hint

Start with 0 because priorities are positive numbers and we want to find the maximum.

3

Find the patient with the highest priority

Use a for loop with variables name and priority to iterate over patients. Inside the loop, update highest_priority if priority is greater than highest_priority. Also, create a variable called top_patient to store the name of the patient with the highest priority.

DSA Python

patients = [("Alice", 2), ("Bob", 5), ("Charlie", 1), ("Diana", 4)]
highest_priority = 0
# Use a for loop to find the patient with the highest priority
# Your code here

Hint

Compare each patient's priority to the current highest and update both the highest priority and the top patient name.

4

Print the patient with the highest priority

Write a print statement to display the text: "Patient with highest priority: " followed by the value of top_patient

DSA Python

patients = [("Alice", 2), ("Bob", 5), ("Charlie", 1), ("Diana", 4)]
highest_priority = 0
for name, priority in patients:
    if priority > highest_priority:
        highest_priority = priority
        top_patient = name
# Print the patient with the highest priority
# Your code here

Hint

Use string concatenation or f-string to combine the message and the patient name.

Practice

1. What is the main feature of a priority queue compared to a regular queue?

easy

A. It does not allow insertion after creation

B. Elements are always dequeued in the order they were inserted

C. It only stores elements of the same value

D. Elements are dequeued based on their priority, not just order of insertion

Solution

1. Step 1: Understand queue behavior

   A regular queue removes elements in the order they were added (FIFO).

2. Step 2: Understand priority queue behavior

   A priority queue removes elements based on priority, not just order.

3. Final Answer:

   Elements are dequeued based on their priority, not just order of insertion -> Option D

4. Quick Check:

   Priority queue = priority-based removal [OK]

Hint: Priority queue removes highest priority first, not oldest [OK]

Common Mistakes:

• Confusing priority queue with FIFO queue
• Thinking priority queue only stores equal values
• Assuming no insertion allowed after creation

2. Which of the following is the correct way to import and create a min-priority queue using Python's heapq module?

easy

A. from queue import PriorityQueue; pq = PriorityQueue()

B. import heapq; pq = []

C. import heapq; pq = heapq.PriorityQueue()

D. import queue; pq = queue.heapq()

Solution

1. Step 1: Identify correct module for heapq min-priority queue

   Python's heapq module implements min-heap using lists.

2. Step 2: Check import and creation syntax

   import heapq; pq = [] correctly imports heapq and creates an empty list to use as heap.

3. Final Answer:

   import heapq; pq = [] -> Option B

4. Quick Check:

   heapq module with empty list = correct [OK]

Hint: heapq uses empty list [] for min-heap [OK]

Common Mistakes:

• Confusing heapq (functions) with PriorityQueue class
• Trying to call heapq.PriorityQueue() which doesn't exist
• Wrong import statements

3. What will be the output of the following code?

import heapq
pq = []
heapq.heappush(pq, 20)
heapq.heappush(pq, 5)
heapq.heappush(pq, 15)
print(heapq.heappop(pq))

medium

A. 5

B. 20

C. 15

D. Error

Solution

1. Step 1: Understand heapq behavior

   heapq creates a min-heap, so smallest element is popped first.

2. Step 2: Trace the pushes and pop

   Elements pushed: 20, 5, 15. The smallest is 5, so heappop returns 5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   Min-heap pop = smallest element = 5 [OK]

Hint: heapq pops smallest element first [OK]

Common Mistakes:

• Thinking heapq pops largest element
• Confusing push order with pop order
• Expecting error due to empty heap

4. Identify the error in the following code that tries to implement a max-priority queue using heapq:

import heapq
pq = []
heapq.heappush(pq, 10)
heapq.heappush(pq, 30)
heapq.heappush(pq, 20)
max_val = heapq.heappop(pq)
print(max_val)

medium

A. pq should be a list of tuples, not integers

B. heapq does not support push operation

C. heapq.heappop returns the smallest element, not the largest

D. Missing call to heapq.heapify before pushing

Solution

1. Step 1: Understand heapq default behavior

   heapq implements a min-heap, so heappop returns smallest element.

2. Step 2: Check code for max-priority queue logic

   The code pushes values directly and pops, expecting max but gets min.

3. Final Answer:

   heapq.heappop returns the smallest element, not the largest -> Option C

4. Quick Check:

   heapq pop = smallest, not max [OK]

Hint: heapq pops smallest; invert values for max-heap [OK]

Common Mistakes:

• Expecting heappop to return max without inversion
• Not using negative values to simulate max-heap
• Assuming heapq needs heapify before every push

5. You want to implement a max-priority queue using Python's heapq module. Which approach correctly achieves this?

hard

A. Push negative values and pop normally, then negate again

B. Use heapq as is, it supports max-heap by default

C. Sort the list after every push to get max element

D. Use a separate max() function after popping from heapq

Solution

1. Step 1: Understand heapq default behavior

   heapq is a min-heap, so it pops smallest element by default.

2. Step 2: Implement max-heap using negation

   By pushing negative values, the smallest negative corresponds to largest original value. Pop and negate again to get max.

3. Final Answer:

   Push negative values and pop normally, then negate again -> Option A

4. Quick Check:

   Negate values to simulate max-heap with heapq [OK]

Hint: Invert values (negate) to use min-heap as max-heap [OK]

Common Mistakes:

• Assuming heapq supports max-heap natively
• Sorting list after every push is inefficient
• Using max() after pop breaks priority queue logic