Min Stack Design in DSA Python - Time & Space Complexity

Time Complexity: Min Stack Design

O(1)

Understanding Time Complexity

We want to understand how fast the Min Stack operations run as the number of elements grows.

Specifically, how the time to push, pop, or get the minimum changes with more items.

Scenario Under Consideration

Analyze the time complexity of the following Min Stack implementation.

class MinStack:
    def __init__(self):
        self.stack = []
        self.min_stack = []

    def push(self, val):
        self.stack.append(val)
        if not self.min_stack or val <= self.min_stack[-1]:
            self.min_stack.append(val)

    def pop(self):
        if self.stack:
            val = self.stack.pop()
            if val == self.min_stack[-1]:
                self.min_stack.pop()

    def get_min(self):
        return self.min_stack[-1] if self.min_stack else None

This code keeps track of the minimum value at each step using a second stack.

Identify Repeating Operations

Look at the main operations that repeat when the stack changes.

• Primary operation: Adding or removing elements from the main and min stacks.
• How many times: Each push or pop runs once per call, no loops inside.

How Execution Grows With Input

Each operation does a fixed number of steps regardless of stack size.

Input Size (n)	Approx. Operations
10	10 push or pop steps
100	100 push or pop steps
1000	1000 push or pop steps

Pattern observation: The time per operation stays the same no matter how big the stack is.

Final Time Complexity

Time Complexity: O(1)

This means each push, pop, or get_min operation takes the same short time no matter how many items are in the stack.

Common Mistake

[X] Wrong: "Finding the minimum must check all elements, so it takes longer as the stack grows."

[OK] Correct: The min stack keeps track of minimums as we go, so get_min just looks at the top of min_stack instantly.

Interview Connect

Knowing how to keep track of minimums efficiently shows you can design smart data structures that save time.

This skill helps you solve problems where quick access to special values is needed.

Self-Check

"What if we stored minimums only when they change, not every time? How would that affect time complexity?"

Practice

1. What is the main purpose of a Min Stack data structure?

easy

A. To store only the minimum elements pushed so far

B. To sort elements in ascending order automatically

C. To retrieve the minimum element in constant time along with stack operations

D. To reverse the order of elements pushed into the stack

Solution

1. Step 1: Understand the Min Stack concept

   A Min Stack is designed to support normal stack operations and also return the minimum element quickly.

2. Step 2: Identify the key feature

   The key feature is retrieving the minimum element in constant time, not sorting or reversing.

3. Final Answer:

   To retrieve the minimum element in constant time along with stack operations -> Option C

4. Quick Check:

   Min Stack = fast minimum retrieval [OK]

Hint: Min Stack always tracks minimum for O(1) retrieval [OK]

Common Mistakes:

• Thinking Min Stack sorts elements
• Assuming it stores only minimum elements
• Confusing with reversing stack order

2. Which of the following is the correct way to initialize a Min Stack class in Python?

easy

A. class MinStack:
def __init__(self):
stack = []
min_stack = []

B. class MinStack:
def __init__(self):
self = []

C. class MinStack:
def __init__(self):
self.stack = None
self.min_stack = None

D. class MinStack:
def __init__(self):
self.stack = []
self.min_stack = []

Solution

1. Step 1: Check correct attribute initialization

   Attributes must be initialized with self to be instance variables.

2. Step 2: Verify list initialization

   Both stack and min_stack should be empty lists initialized with self.

3. Final Answer:

   class MinStack:\n def __init__(self):\n self.stack = []\n self.min_stack = [] -> Option D

4. Quick Check:

   Use self for instance variables [OK]

Hint: Always use self to define instance variables in __init__ [OK]

Common Mistakes:

• Not using self for attributes
• Initializing attributes as None instead of lists
• Assigning self to a list directly

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

min_stack = MinStack()
min_stack.push(3)
min_stack.push(5)
min_stack.push(2)
min_stack.push(1)
min_stack.pop()
print(min_stack.get_min())

medium

A. 2

B. 1

C. 3

D. 5

Solution

1. Step 1: Trace push operations and min tracking

   Push 3 (min=3), push 5 (min=3), push 2 (min=2), push 1 (min=1).

2. Step 2: Pop removes 1, min updates to 2

   After popping 1, the minimum is the next smallest, which is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Pop removes min 1, next min = 2 [OK]

Hint: Pop min, next min is previous min in min stack [OK]

Common Mistakes:

• Assuming min stays 1 after pop
• Confusing push order with min updates
• Ignoring min stack updates on pop

4. Identify the bug in this Min Stack pop method and choose the correct fix:

def pop(self):
    if self.stack:
        self.stack.pop()
        self.min_stack.pop()

medium

A. No bug; code is correct

B. Pop from min_stack only if popped element equals current min

C. Pop from min_stack before popping from stack

D. Check if min_stack is empty before popping it

Solution

1. Step 1: Understand pop logic in Min Stack

   Pop from min_stack only if the popped element is the current minimum to keep min_stack accurate.

2. Step 2: Identify bug and fix

   Current code always pops min_stack, which can cause errors if min_stack is out of sync.

3. Final Answer:

   Pop from min_stack only if popped element equals current min -> Option B

4. Quick Check:

   Pop min_stack conditionally to keep min correct [OK]

Hint: Pop min_stack only if popped value equals current min [OK]

Common Mistakes:

• Always popping min_stack without condition
• Not checking min_stack emptiness
• Changing pop order incorrectly

5. You want to design a Min Stack that supports push, pop, top, and get_min all in O(1) time. Which approach is best?

hard

A. Use two stacks: one for all elements, one for current minimums

B. Use a queue to store elements and a variable for min

C. Use one stack and scan all elements to find min on get_min

D. Use a linked list and traverse to find min on each get_min

Solution

1. Step 1: Analyze time complexity requirements

   All operations must be O(1), so scanning or traversing is not allowed.

2. Step 2: Choose data structure approach

   Using two stacks, one for elements and one for minimums, allows constant time min retrieval.

3. Final Answer:

   Use two stacks: one for all elements, one for current minimums -> Option A

4. Quick Check:

   Two stacks = O(1) min retrieval [OK]

Hint: Two stacks track elements and minimums for O(1) min [OK]

Common Mistakes:

• Using scanning which is O(n)
• Using queue which doesn't support stack operations
• Using linked list traversal for min