By Minh Vu
Updated Feb 08, 2026
Minh Vu
Software Engineer
Hi 👋, I'm a software engineer specializing in backend systems, distributed systems, and scalable architecture. My blog shares practical tutorials based on 3+ years of experience. LeetCode 1756 (Top 10%). Actively seeking SDE roles — let's get in touch!
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Breadth-First Search (BFS) is a fundamental algorithm for searching or traversing tree and graph data structures.
The algorithm works as below:
In rush? Go to the full code for the Python BFS implementation.
BFS is useful for finding the shortest path of an unweighted graph.
In this tutorial, I will show you how to implement the BFS algorithm in Python the easy way. Keep reading!
Follow the steps below to implement the BFS algorithm in Python.
Before we dive into the BFS algorithm, we need a graph to work with. I will use the graph as the one shown in the visualization above.
For simplicity, we'll use an adjacency list to represent a graph, where each node maps to a list of its neighbors.
graph = { 'A': ['B', 'C'], 'B': ['D', 'E'], 'C': ['F', 'G'], 'D': [], 'E': [], 'F': [], 'G': [], }
The BFS algorithm pseudo-code can be described as follows:
Common BFS implementation uses a queue to store the nodes to be visited. So I will use deque (pronounced as DECK) from the collections module to implement the queue for efficient pop and append operations.
Let's implement the BFS algorithm in Python.
from collections import deque def bfs(graph, start): visited = set() # Use a set to keep track of visited nodes for efficiency. queue = deque([start]) # Initialize a queue with the start node. while queue: vertex = queue.popleft() # Pop a vertex off the queue. if vertex not in visited: visited.add(vertex) print(vertex, end=" ") # Process the vertex (for example, print it out). # Skip if the vertex does not have any neighbors. if vertex not in graph: continue # Add neighbors of the vertex to the queue if they haven't been visited. for neighbor in graph[vertex]: if neighbor not in visited: queue.append(neighbor)
Let's test the bfs function with the graph we defined earlier. Here is the full code.
from collections import deque def bfs(graph, start): visited = set() # Use a set to keep track of visited nodes for efficiency. queue = deque([start]) # Initialize a queue with the start node. while queue: vertex = queue.popleft() # Pop a vertex off the queue. if vertex not in visited: visited.add(vertex) print(vertex, end=" ") # Process the vertex (for example, print it out). # Skip if the vertex does not have any neighbors. if vertex not in graph: continue # Add neighbors of the vertex to the queue if they haven't been visited. for neighbor in graph[vertex]: if neighbor not in visited: queue.append(neighbor) graph = { 'A': ['B', 'C'], 'B': ['D', 'E'], 'C': ['F', 'G'], 'D': [], 'E': [], 'F': [], 'G': [], } # Assuming the graph is defined as shown in the previous section. bfs(graph, 'A') # Start the BFS traversal from node 'A'.
You can run this code by using the command: python bfs.py.
The output would be:
A B C D E F G
This output represents the order in which the nodes are visited when starting the BFS from node 'A'.
In this tutorial, we have learned how to implement the BFS algorithm in Python using a queue.
I know algorithms are hard to learn, but this is one of the easiest algorithms. So read it again if you don't understand it the first time.
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