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Use deque as queue in breadth_first_search_shortest_path_2.py (#12861)

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* Fixes #12857 Use collections.deque as queue in graphs BFS shortest path 2

* Use collections.deque as queue in the correct syntax: queue = deque([start])

* Fix CI error due to HTTP 404 on https://finance.yahoo.com/quote/GOOG/\?p\=GOOG

* Undo change in workflows/build.yml as it's fixed in PR 12864

---------

Co-authored-by: Maxim Smolskiy <mithridatus@mail.ru>
This commit is contained in:
John Liu
2025-08-24 14:59:44 -07:00
committed by GitHub
parent 561cc383ec
commit b0be304433
1 changed files with 12 additions and 10 deletions
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22
graphs/breadth_first_search_shortest_path_2.py
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@@ -1,10 +1,12 @@
"""Breadth-first search shortest path implementations.
"""Breadth-first search the shortest path implementations.
doctest:
python -m doctest -v bfs_shortest_path.py
python -m doctest -v breadth_first_search_shortest_path_2.py
Manual test:
python bfs_shortest_path.py
python breadth_first_search_shortest_path_2.py
"""
from collections import deque
demo_graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
@@ -17,7 +19,7 @@ demo_graph = {
def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
"""Find shortest path between `start` and `goal` nodes.
"""Find the shortest path between `start` and `goal` nodes.
Args:
graph (dict): node/list of neighboring nodes key/value pairs.
start: start node.
@@ -36,7 +38,7 @@ def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
# keep track of explored nodes
explored = set()
# keep track of all the paths to be checked
queue = [[start]]
queue = deque([[start]])
# return path if start is goal
if start == goal:
@@ -45,7 +47,7 @@ def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
path = queue.pop(0)
path = queue.popleft()
# get the last node from the path
node = path[-1]
if node not in explored:
@@ -68,13 +70,13 @@ def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
def bfs_shortest_path_distance(graph: dict, start, target) -> int:
"""Find shortest path distance between `start` and `target` nodes.
"""Find the shortest path distance between `start` and `target` nodes.
Args:
graph: node/list of neighboring nodes key/value pairs.
start: node to start search from.
target: node to search for.
Returns:
Number of edges in shortest path between `start` and `target` nodes.
Number of edges in the shortest path between `start` and `target` nodes.
-1 if no path exists.
Example:
>>> bfs_shortest_path_distance(demo_graph, "G", "D")
@@ -88,12 +90,12 @@ def bfs_shortest_path_distance(graph: dict, start, target) -> int:
return -1
if start == target:
return 0
queue = [start]
queue = deque([start])
visited = set(start)
# Keep tab on distances from `start` node.
dist = {start: 0, target: -1}
while queue:
node = queue.pop(0)
node = queue.popleft()
if node == target:
dist[target] = (
dist[node] if dist[target] == -1 else min(dist[target], dist[node])