Union Find Disjoint Set Data Structure in DSA Python - Practice Problems & Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Union Find after union operations

What is the printed parent array after performing the union operations shown below?

DSA Python

class UnionFind:
    def __init__(self, size):
        self.parent = list(range(size))
        self.rank = [0] * size

    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]

    def union(self, x, y):
        rootX = self.find(x)
        rootY = self.find(y)
        if rootX != rootY:
            if self.rank[rootX] > self.rank[rootY]:
                self.parent[rootY] = rootX
            elif self.rank[rootX] < self.rank[rootY]:
                self.parent[rootX] = rootY
            else:
                self.parent[rootY] = rootX
                self.rank[rootX] += 1

uf = UnionFind(5)
uf.union(0, 1)
uf.union(1, 2)
uf.union(3, 4)
print(uf.parent)

A [1, 1, 1, 4, 4]

B [0, 1, 2, 3, 4]

C [0, 0, 0, 3, 3]

D [0, 0, 1, 3, 4]

💡 Hint

Remember that union by rank attaches the smaller tree under the root of the larger tree.

After union(0,1) and union(1,2), nodes 0,1,2 share the same root 0. After union(3,4), nodes 3 and 4 share root 3. So parent array is [0,0,0,3,3].

🧠 Conceptual

intermediate

Number of connected components after unions

Given a Union Find structure initialized with 6 elements (0 to 5), after performing union(0,1), union(2,3), union(4,5), and union(1,2), how many connected components remain?

A 3

B 1

C 4

D 2

💡 Hint

Each union merges two components into one.

Initially 6 components. union(0,1) merges 0 and 1 → 5 components. union(2,3) merges 2 and 3 → 4 components. union(4,5) merges 4 and 5 → 3 components. union(1,2) merges sets containing 0,1 and 2,3 → 2 components remain.

🔧 Debug

advanced

Identify the error in this Union Find implementation

What error will this code produce when calling uf.union(0, 1)?

DSA Python

class UnionFind:
    def __init__(self, size):
        self.parent = list(range(size))
        self.rank = [0] * size

    def find(self, x):
        while self.parent[x] != x:
            x = self.parent[x]
        return x

    def union(self, x, y):
        rootX = self.find(x)
        rootY = self.find(y)
        if rootX != rootY:
            if self.rank[rootX] > self.rank[rootY]:
                self.parent[rootY] = rootX
            elif self.rank[rootX] < self.rank[rootY]:
                self.parent[rootX] = rootY
            else:
                self.parent[rootY] = rootX
                self.rank[rootX] += 1

uf = UnionFind(3)
uf.union(0, 1)

A Logical error: find does not do path compression

B RecursionError due to infinite recursion in find

C AttributeError because 'parent' is not defined

D No error; code runs correctly

💡 Hint

Check how find method updates parents during traversal.

The find method uses a loop but does not update parent pointers to root, so path compression is missing. This is a logical inefficiency, not a runtime error.

📝 Syntax

advanced

Identify the syntax error in this Union Find code snippet

Which option correctly identifies the syntax error in the code below?

DSA Python

class UnionFind:
    def __init__(self, size):
        self.parent = list(range(size))
        self.rank = [0] * size

    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]

A Indentation error in __init__ method

B Missing colon ':' after if statement in find method

C Missing parentheses in print statement

D Incorrect use of list initialization for parent

💡 Hint

Look carefully at the if statement syntax.

The if statement in find method lacks a colon at the end, causing a SyntaxError.

🚀 Application

expert

Using Union Find to detect cycle in an undirected graph

Given edges of an undirected graph: [(0,1), (1,2), (2,3), (3,0)], which union find operation detects a cycle when processing these edges in order?

DSA Python

class UnionFind:
    def __init__(self, size):
        self.parent = list(range(size))
        self.rank = [0] * size

    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]

    def union(self, x, y):
        rootX = self.find(x)
        rootY = self.find(y)
        if rootX == rootY:
            return False  # cycle detected
        if self.rank[rootX] > self.rank[rootY]:
            self.parent[rootY] = rootX
        elif self.rank[rootX] < self.rank[rootY]:
            self.parent[rootX] = rootY
        else:
            self.parent[rootY] = rootX
            self.rank[rootX] += 1
        return True

uf = UnionFind(4)
edges = [(0,1), (1,2), (2,3), (3,0)]
for i, (u,v) in enumerate(edges):
    if not uf.union(u,v):
        print(i)

A 3

B 0

C 2

D 1

💡 Hint

A cycle is detected when union returns False because both nodes share the same root.

Edges 0,1,2 connect nodes without cycle. Edge (3,0) connects nodes already connected, so union returns False and prints index 3.

Practice

1. What is the main purpose of the Union Find Disjoint Set data structure?

easy

A. To efficiently track and merge groups of connected elements

B. To sort elements in ascending order

C. To store key-value pairs for fast lookup

D. To implement a queue with fast enqueue and dequeue

Solution

1. Step 1: Understand the purpose of Union Find

   The Union Find data structure is designed to keep track of which elements belong to which groups or sets, especially when these groups merge.

2. Step 2: Compare with other data structures

   Sorting, key-value storage, and queue operations are unrelated to Union Find's main goal.

3. Final Answer:

   To efficiently track and merge groups of connected elements -> Option A

4. Quick Check:

   Union Find = track and merge groups [OK]

Hint: Union Find = manage connected groups fast [OK]

Common Mistakes:

• Confusing Union Find with sorting algorithms
• Thinking it stores key-value pairs
• Mixing it up with queue or stack data structures

2. Which of the following is the correct way to initialize a Union Find parent array for 5 elements in Python?

easy

A. parent = [None] * 5

B. parent = [1, 1, 1, 1, 1]

C. parent = [5, 5, 5, 5, 5]

D. parent = [0, 1, 2, 3, 4]

Solution

1. Step 1: Understand initialization of Union Find

   Each element starts as its own parent, so the parent array should have each index equal to itself.

2. Step 2: Check each option

   parent = [0, 1, 2, 3, 4] correctly sets parent[i] = i for i in 0 to 4. Others do not represent this.

3. Final Answer:

   parent = [0, 1, 2, 3, 4] -> Option D

4. Quick Check:

   Parent array init = self indices [OK]

Hint: Parent array starts with self indices [OK]

Common Mistakes:

• Setting all parents to 1 or a fixed number
• Using None which breaks find operations
• Confusing size array with parent array

3. What is the output of the following code?

class UnionFind:
    def __init__(self, n):
        self.parent = list(range(n))
    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]
    def union(self, a, b):
        rootA = self.find(a)
        rootB = self.find(b)
        if rootA != rootB:
            self.parent[rootB] = rootA

uf = UnionFind(4)
uf.union(0, 1)
uf.union(2, 3)
uf.union(1, 3)
print(uf.parent)

medium

A. [0, 0, 0, 2]

B. [0, 0, 0, 0]

C. [0, 0, 2, 2]

D. [0, 1, 2, 3]

Solution

1. Step 1: Trace unions step-by-step

   Init: [0,1,2,3]
   Union(0,1): find(0)=0, find(1)=1 -> parent[1]=0 -> [0,0,2,3]
   Union(2,3): find(2)=2, find(3)=3 -> parent[3]=2 -> [0,0,2,2]
   Union(1,3): find(1)=0, find(3)=2 (sets parent[3]=2) -> parent[2]=0 -> [0,0,0,2]

2. Step 2: Verify print output

   No additional find calls before print. parent[3] points to 2 (now root 0), but not fully compressed to 0. Output: [0, 0, 0, 2]

3. Final Answer:

   [0, 0, 0, 2] -> Option A

4. Quick Check:

   parent = [0,0,0,2] after partial compression [OK]

Hint: Trace find calls and compression timing during union [OK]

Common Mistakes:

• Forgetting last union sets parent[2]=0 -> [0,0,2,2]
• Over-applying path compression -> [0,0,0,0]
• Ignoring unions entirely

4. Identify the bug in this Union Find union method:

def union(self, a, b):
    rootA = self.find(a)
    rootB = self.find(b)
    if rootA != rootB:
        self.parent[rootA] = rootB

medium

A. The find method is not called correctly

B. It should assign parent of rootB to rootA, not the other way

C. The if condition should be rootA == rootB

D. The method is missing a return statement

Solution

1. Step 1: Understand union direction

   Union should attach one root to the other to merge sets. Usually, parent of rootB is set to rootA or vice versa consistently.

2. Step 2: Check given code

   Code sets parent[rootA] = rootB, which reverses the usual convention and can cause incorrect tree structure or confusion.

3. Final Answer:

   It should assign parent of rootB to rootA, not the other way -> Option B

4. Quick Check:

   Union sets parent[rootB] = rootA [OK]

Hint: Union sets parent of second root to first root [OK]

Common Mistakes:

• Swapping roots incorrectly in union
• Not calling find before union
• Confusing equality check in if condition

5. Given a graph with 6 nodes and edges [(0,1), (1,2), (3,4)], how many connected components does the Union Find structure report after union operations on these edges?

hard

A. 4

B. 2

C. 3

D. 5

Solution

1. Step 1: Initialize 6 separate nodes

   Initially, each node is its own component: 6 components total.

2. Step 2: Perform unions for edges

   Union(0,1) merges nodes 0 and 1 -> components reduce to 5.
   Union(1,2) merges node 2 with 0-1 group -> components reduce to 4.
   Union(3,4) merges nodes 3 and 4 -> components reduce to 3.

3. Step 3: Count remaining components

   Node 5 remains alone -> total components = 3.

4. Final Answer:

   3 -> Option C

5. Quick Check:

   6 nodes - 3 unions = 3 components [OK]

Hint: Components = nodes - unions done [OK]

Common Mistakes:

• Counting edges instead of components
• Forgetting isolated nodes count as components
• Miscounting after unions