Hello there!

I just finished the Shortest Path Algorithm project in Python curriculum. To understand it deeper I’ve readen some materials on this topic and decided to try to write my own implementation using adjecency matrix.

Here is my code. Would appreciate any comments and suggests how it could be improved!

```
import numpy as np
na = np.nan
graph = np.array([[0, 7, 9, na, na, 14],
[7, 0, 10, 15, na, na],
[9, 10, 0, 11, na, 2],
[na, 15, 11, 0, 6, na],
[na, na, na, 6, 0, 9],
[14, na, 2, na, 9, 0]])
def dijkstra(graph, start):
distances = np.fromiter((0 if i == start else np.inf for i in range(len(graph))), dtype='float')
visited = np.full_like(graph[start], 0, dtype='bool')
paths = dict.fromkeys(range(len(graph)), [str(start)])
for _ in range(len(graph)):
min_distance_node = np.where(distances == distances[~visited].min())[0][0]
for node in range(len(graph)):
if not graph[min_distance_node, node] > 0:
continue
distance_to_node = distances[min_distance_node] + graph[min_distance_node, node]
if distance_to_node < distances[node] and visited[node] == False:
distances[node] = distance_to_node
paths[node] = paths[min_distance_node].copy()
paths[node].append(str(node))
visited[min_distance_node] = True
for node, path in paths.items():
print(f'The shortest path from node {start} to node {node} is:\n{' -> '.join(path)}\nTotal distance: {distances[node]}', end='\n\n')
return paths
```