How to do BFS search in python? write a full explanation

Breadth-First Search (BFS) is a fundamental algorithm used for traversing graphs or trees. It starts from a given node or "root" and explores all the neighboring nodes at the present depth level before moving on to explore the nodes at the next depth level.

BFS can be implemented using a queue data structure to keep track of the nodes to be visited. Here is a step-by-step guide to implementing BFS in Python:

Step 1: Create a Queue

Create a queue to keep track of the nodes to be visited. In Python, you can use the `collections` module's `deque` function to create a queue.

Step 2: Create a Graph

Create a graph as a dictionary where each key represents a node and its corresponding value represents a list of its neighboring nodes.

Step 3: Enqueue the Starting Node

Enqueue the starting node or "root" node to the queue.

Step 4: Dequeue and Explore Neighbors

Dequeue a node from the queue and explore all its neighboring nodes. Mark the node as visited to avoid revisiting it.

Step 5: Enqueue Unvisited Neighbors

Enqueue all unvisited neighboring nodes of the dequeued node.

Step 6: Repeat Steps 4-5

Repeat steps 4-5 until the queue is empty, meaning all nodes have been visited.

Here is a Python implementation of BFS:

```

from collections import deque

def bfs(graph, root):

visited = set()

queue = deque([root])

while queue:

node = queue.popleft()

if node not in visited:

visited.add(node)

print(node, end=" ")

for neighbor in graph[node]:

if neighbor not in visited:

queue.append(neighbor)

# Example graph

graph = {

'A' : ['B','C'],

'B' : ['D', 'E'],

'C' : ['F'],

'D' : [],

'E' : ['F'],

'F' : []

}

bfs(graph, 'A') # Output: A B C D E F

```

In this implementation, we use a `deque` to create a queue and a `set` to keep track of visited nodes. We start by enqueueing the root node and then iterate through the queue, dequeuing nodes and exploring their neighbors. We mark each node as visited to avoid revisiting it.