Priority Queue Introduction and Concept in DSA Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an empty priority queue using Python's heapq module.

DSA Python

import heapq
pq = [1]

Drag options to blanks, or click blank then click option'

A[]

B{}

C()

Dset()

Attempts:

3 left

💡 Hint

Common Mistakes

Using a dictionary or set instead of a list to store the priority queue.

Priority queues in Python can be implemented using a list with the heapq module. An empty list [] is used to start the queue.

2fill in blank

medium

Complete the code to add an element with priority 5 to the priority queue.

DSA Python

import heapq
pq = []
heapq.[1](pq, 5)

Drag options to blanks, or click blank then click option'

Apop

Binsert

Cheappush

Dpush

Attempts:

3 left

💡 Hint

Common Mistakes

Using pop or insert which are not heapq functions for adding elements.

The heappush function adds an element to the heap while maintaining the heap property.

3fill in blank

hard

Fix the error in the code to remove the smallest element from the priority queue.

DSA Python

import heapq
pq = [3, 5, 7]
heapq.heapify(pq)
smallest = heapq.[1](pq)
print(smallest)

Drag options to blanks, or click blank then click option'

Apopitem

Bpop

Cremove

Dheappop

Attempts:

3 left

💡 Hint

Common Mistakes

Using pop which removes the last element, not the smallest.

The heappop function removes and returns the smallest element from the heap.

4fill in blank

hard

Fill both blanks to create a priority queue from a list and then add a new element 2.

DSA Python

import heapq
pq = [4, 1, 7]
heapq.[1](pq)
heapq.[2](pq, 2)

Drag options to blanks, or click blank then click option'

Aheapify

Bheappush

Csorted

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using sorted instead of heapify to create a heap.

heapify converts a list into a heap in-place. Then heappush adds a new element while keeping the heap property.

5fill in blank

hard

Fill all three blanks to pop the smallest element, then push 6, and finally peek at the smallest element without removing it.

DSA Python

import heapq
pq = [3, 8, 5]
heapq.heapify(pq)
smallest = heapq.[1](pq)
heapq.[2](pq, 6)
peek = pq[3]

Drag options to blanks, or click blank then click option'

Aheappop

Bheappush

C[0]

Dpop

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to peek using pop() which removes elements.

heappop removes the smallest element, heappush adds a new element, and accessing pq[0] peeks at the smallest element without removing it.

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