Why Number of Islands BFS and DFS in DSA Python?

The Big Idea

What if you could instantly find all islands on a map without missing or double-counting any?

The Scenario

Imagine you have a big map made of water and land squares. You want to count how many separate islands are on the map. Doing this by looking at each square and trying to remember which land belongs to which island can be very confusing and tiring.

The Problem

Manually checking each land square and trying to connect it to an island is slow and easy to mess up. You might count the same island twice or miss some parts because it's hard to keep track of all connected lands without a clear method.

The Solution

Using BFS (Breadth-First Search) or DFS (Depth-First Search) helps you explore each island fully by visiting all connected land squares step-by-step. This way, you can mark the entire island once and avoid counting it multiple times, making the counting fast and accurate.

Before vs After

✗ Before

count = 0
for each cell in grid:
  if cell is land and not visited:
    count += 1
    # manually check neighbors and mark visited

✓ After

def dfs(r, c):
  if out of bounds or water or visited:
    return
  mark visited
  dfs(neighboring cells)

for each cell in grid:
  if cell is land and not visited:
    dfs(cell)
    count += 1

What It Enables

This method lets you quickly and correctly find how many islands are on any map, no matter how big or complex.

Real Life Example

Think about a drone flying over a flooded area trying to count safe land patches. Using BFS or DFS helps the drone's software quickly identify each separate safe zone without confusion.

Key Takeaways

Manually counting islands is confusing and error-prone.

BFS and DFS explore connected lands systematically.

This approach ensures fast and accurate island counting.

Practice

1. What does the "Number of Islands" problem ask you to find in a grid of '1's and '0's?

easy

A. Count all '0's in the grid

B. Find the largest island size

C. Count how many groups of connected '1's (islands) exist

D. Calculate the perimeter of all islands

Solution

1. Step 1: Understand the grid representation

   The grid contains '1's representing land and '0's representing water.

2. Step 2: Identify what the problem asks

   It asks to count how many separate groups of connected '1's (islands) are present.

3. Final Answer:

   Count how many groups of connected '1's (islands) exist -> Option C

4. Quick Check:

   Number of islands = count of connected '1' groups [OK]

Hint: Islands = groups of connected '1's, count them [OK]

Common Mistakes:

• Confusing island count with island size
• Counting '0's instead of '1's
• Calculating perimeter instead of count

2. Which of the following Python code snippets correctly starts a DFS from cell (i, j) in a grid to mark connected land?

easy

A. def dfs(i, j): if i<0 or j<0 or i>=len(grid) or j>=len(grid[0]) or grid[i][j] == '0': return; grid[i][j] = '0'; dfs(i+1,j); dfs(i-1,j); dfs(i,j+1); dfs(i,j-1)

B. def dfs(i, j): while grid[i][j] == '1': grid[i][j] = '0'; dfs(i+1,j); dfs(i-1,j)

C. def dfs(i, j): if grid[i][j] == '1': return; dfs(i+1,j); dfs(i-1,j)

D. def dfs(i, j): for x in range(len(grid)): for y in range(len(grid[0])): dfs(x,y)

Solution

1. Step 1: Check boundary and water conditions

   def dfs(i, j): if i<0 or j<0 or i>=len(grid) or j>=len(grid[0]) or grid[i][j] == '0': return; grid[i][j] = '0'; dfs(i+1,j); dfs(i-1,j); dfs(i,j+1); dfs(i,j-1) correctly checks if indices are out of bounds or cell is water ('0') and returns.

2. Step 2: Mark current land as visited and recurse

   def dfs(i, j): if i<0 or j<0 or i>=len(grid) or j>=len(grid[0]) or grid[i][j] == '0': return; grid[i][j] = '0'; dfs(i+1,j); dfs(i-1,j); dfs(i,j+1); dfs(i,j-1) marks grid[i][j] = '0' to avoid revisiting and calls dfs on all four neighbors.

3. Final Answer:

   Option A code correctly implements DFS with boundary checks and marking visited -> Option A

4. Quick Check:

   DFS with boundary and visited marking = def dfs(i, j): if i<0 or j<0 or i>=len(grid) or j>=len(grid[0]) or grid[i][j] == '0': return; grid[i][j] = '0'; dfs(i+1,j); dfs(i-1,j); dfs(i,j+1); dfs(i,j-1) [OK]

Hint: Check boundaries and mark visited before recursive DFS calls [OK]

Common Mistakes:

• Not checking boundaries before recursion
• Not marking visited cells
• Using loops incorrectly inside DFS

3. What is the output of the following code snippet for the given grid?

grid = [
  ['1','1','0','0'],
  ['1','0','0','1'],
  ['0','0','1','1'],
  ['0','0','0','0']
]

count = 0
for i in range(len(grid)):
    for j in range(len(grid[0])):
        if grid[i][j] == '1':
            count += 1
            stack = [(i,j)]
            while stack:
                x,y = stack.pop()
                if 0 <= x < len(grid) and 0 <= y < len(grid[0]) and grid[x][y] == '1':
                    grid[x][y] = '0'
                    stack.extend([(x+1,y),(x-1,y),(x,y+1),(x,y-1)])
print(count)

medium

A. 2

B. 3

C. 4

D. 5

Solution

1. Step 1: Identify islands in the grid

   The grid has three islands: top-left (cells (0,0),(0,1),(1,0)), single cell island at (1,3), and bottom-right island (cells (2,2),(2,3)).

2. Step 2: Trace the DFS using stack

   Each '1' found triggers DFS marking connected '1's as '0'. The count increments for each new island found. Total islands counted = 3.

3. Final Answer:

   3 -> Option B

4. Quick Check:

   Number of islands found by DFS = 3 [OK]

Hint: Count connected '1' groups using DFS stack traversal [OK]

Common Mistakes:

• Counting each '1' without marking visited
• Misidentifying connected cells
• Off-by-one errors in loops

4. The following DFS code for counting islands has a bug. What is the error?

def dfs(i, j):
    if i < 0 or j < 0 or i >= len(grid) or j >= len(grid[0]):
        return
    if grid[i][j] == '0':
        return
    dfs(i+1, j)
    dfs(i-1, j)
    dfs(i, j+1)
    dfs(i, j-1)

count = 0
for i in range(len(grid)):
    for j in range(len(grid[0])):
        if grid[i][j] == '1':
            count += 1
            dfs(i, j)
print(count)

medium

A. The DFS only checks horizontal neighbors

B. The boundary checks are incorrect

C. The count variable is not incremented properly

D. The code does not mark visited cells, causing infinite recursion

Solution

1. Step 1: Analyze DFS function behavior

   The DFS checks boundaries and water but never marks visited land cells as '0' or visited.

2. Step 2: Identify consequence of missing visited marking

   Without marking visited, DFS revisits same cells repeatedly, causing infinite recursion or stack overflow.

3. Final Answer:

   The code does not mark visited cells, causing infinite recursion -> Option D

4. Quick Check:

   Missing visited marking causes infinite recursion [OK]

Hint: Always mark visited cells to avoid infinite loops in DFS [OK]

Common Mistakes:

• Forgetting to mark visited cells
• Incorrect boundary conditions
• Incrementing count inside DFS

5. Given a grid where diagonal connections also count as connected land, how would you modify the BFS or DFS approach to count islands correctly?

hard

A. Add diagonal neighbors (i+1,j+1), (i+1,j-1), (i-1,j+1), (i-1,j-1) to the search directions

B. Only count horizontal neighbors and ignore vertical and diagonal

C. Use BFS but skip marking visited cells

D. Count islands by summing all '1's without traversal

Solution

1. Step 1: Understand diagonal connectivity

   Diagonal connections mean neighbors include cells diagonally adjacent, not just up/down/left/right.

2. Step 2: Modify search directions

   Add diagonal neighbors (i+1,j+1), (i+1,j-1), (i-1,j+1), (i-1,j-1) to BFS or DFS neighbor checks to include diagonals.

3. Final Answer:

   Add diagonal neighbors (i+1,j+1), (i+1,j-1), (i-1,j+1), (i-1,j-1) to the search directions -> Option A

4. Quick Check:

   Include diagonals in neighbors for diagonal connectivity [OK]

Hint: Include diagonal neighbors in BFS/DFS directions [OK]

Common Mistakes:

• Ignoring diagonal neighbors
• Not marking visited cells
• Counting without traversal