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Summary - DFS

Run DFS on a graph and print the order in a mini task.

Summary - DFS

DFS dives deep with a stack or recursion and backtracks at dead ends. It is used for pathfinding, cycle detection and topological sort.

In this mini task you will run DFS and print the visit order; combine all steps.

DFS walks deep graphs efficiently; this is why it is preferred for connectivity analysis and sorting.

Practice Print DFS

Write dfs_print(graph, start). Run stack-based DFS and print the visit order with print(). Output: [0, 2, 3, 1].

Starter code

solution.py
def dfs_print(graph, start):
    # Print DFS order

Sample solution

solution.py
def dfs_print(graph, start):
    # Print DFS order
    visited = []
    stack = [start]
    while stack:
        node = stack.pop()
        if node not in visited:
            visited.append(node)
            for nb in reversed(graph[node]):
                if nb not in visited:
                    stack.append(nb)
    print(visited)
dfs_print({0: [1, 2], 1: [0, 3], 2: [0, 3], 3: [1, 2]}, 0)

Expected output

[0, 2, 3, 1]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 Summary - DFS: match the terms

Term Meaning
DFS Stack
recursion Self-call
backtrack Return
visited No repeat

2 Stack op

Question 
node = stack.____
Answer 
node = stack.pop()

Explanation: pop() is right.

3 What is DFS's time complexity?

  • O(V log V)
  • O(V + E) Correct answer
  • O(E^2)
  • O(V!)

Explanation: O(V + E).

All lessons in Intermediate

1 Merge sort

  1. Divide and conquer
  2. Merge operation
  3. Full merge sort
  4. Summary - Merge sort

2 Quick sort

  1. Pivot selection
  2. Partitioning
  3. Recursive quick sort
  4. Summary - Quick sort

3 Heap sort

  1. What is a heap?
  2. Heapify
  3. Build heap and sort
  4. Summary - Heap sort

4 Counting sort

  1. Counting idea
  2. Count array
  3. Output array
  4. Summary - Counting sort

5 Radix sort

  1. Radix idea
  2. Digit pass
  3. Stable sub-sort
  4. Summary - Radix sort

6 Sorting project

  1. Project: Overview
  2. Score array
  3. Apply merge sort
  4. Print statistics

7 Tree

  1. What is a tree?
  2. Node and edge
  3. Binary tree
  4. Summary - Trees

8 Binary search tree

  1. BST rule
  2. BST insert
  3. BST search
  4. Summary - BST

9 AVL tree

  1. Balanced tree
  2. Rotation
  3. Balance after insert
  4. Summary - AVL tree

10 Heap

  1. Min and max heap
  2. Insert
  3. Extract
  4. Summary - Heap

11 Priority queue

  1. What is a priority queue?
  2. Heap-based implementation
  3. Push and pop
  4. Summary - Priority queue

12 Data structures project

  1. Project: Overview
  2. Undo stack
  3. Pending queue
  4. Print the state

13 Graph basics

  1. What is a graph?
  2. Adjacency list
  3. Adjacency matrix
  4. Summary - Graph

14 Breadth-first search

  1. BFS idea
  2. Using a queue
  3. Marking visited
  4. Summary - BFS

15 Depth-first search

  1. DFS idea
  2. Stack or recursion
  3. Backtracking
  4. Summary - DFS

16 Greedy algorithms

  1. Greedy approach
  2. Coin change
  3. Greedy limits
  4. Summary - Greedy

17 Divide and conquer

  1. Three steps
  2. Recursive structure
  3. Master theorem
  4. Summary - Divide and conquer

18 Backtracking

  1. What is backtracking?
  2. State tree
  3. Pruning
  4. Summary - Backtracking

19 Graph project

  1. Project: Overview
  2. City graph
  3. BFS for reachability
  4. Print the result

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4