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Design2- Minstack #2620

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Design2- Minstack #2620

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766609c
Completed Design Hashset implementation
varnika-98 Jan 22, 2026
45c6ae2
Completed Design-2 problem
varnika-98 Jan 22, 2026
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28 changes: 28 additions & 0 deletions DesignHashSet.py
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Original file line number Diff line number Diff line change
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# This approach uses an array of fixed size and maps each key to an index using a hash function (key % size).
# Each index stores a bucket to handle collisions using chaining.
# All operations: add, remove, contains—run in O(1) average time because each bucket stays small.

class MyHashSet:

def __init__(self):
self.size = 1000
self.buckets = [[] for _ in range(self.size)]

def _hash(self, key):
return key % self.size

def add(self, key: int) -> None:
idx = self._hash(key)
bucket = self.buckets[idx]
if key not in bucket:
bucket.append(key)

def remove(self, key: int) -> None:
idx = self._hash(key)
bucket = self.buckets[idx]
if key in bucket:
bucket.remove(key)

def contains(self, key: int) -> bool:
idx = self._hash(key)
return key in self.buckets[idx]
28 changes: 28 additions & 0 deletions minStack.py
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# two stacks: one for all values and one for tracking the minimum at each level.
# Every time we push a value, we also push the new minimum so far onto the min stack.
# This ensures that getMin(), top(), and pop() all run in O(1) time with no extra computation.

class MinStack:

def __init__(self):
self.stack = [] # main stack
self.min_stack = [] # stack of minimums

def push(self, val: int) -> None:
self.stack.append(val)
# If min_stack is empty or val is smaller/equal, push it as the new minimum
if not self.min_stack or val <= self.min_stack[-1]:
self.min_stack.append(val)
else:
# Repeat the current minimum to keep stacks aligned
self.min_stack.append(self.min_stack[-1])

def pop(self) -> None:
self.stack.pop()
self.min_stack.pop()

def top(self) -> int:
return self.stack[-1]

def getMin(self) -> int:
return self.min_stack[-1]