<｜begin▁of▁sentence｜>### Instruction:
Certainly, let's put this into context.

Imagine you're a seasoned sales representative, and you've been tasked with optimizing the travel itinerary for a cross-country sales tour. Picture the locations as nodes in a travel network, with the routes between them as directed edges. For example, you're planning to start at location 's' and need to reach location 'v', with the possible routes and distance costs between them as follows:

- From 's' to 'u', the travel cost is 10.
- From 's' to 'x', the travel cost is 5.
- From 'u' to 'v', the travel cost is 1.
- From 'u' to 'x', the travel cost is 2.
- From 'v' to 'y', the travel cost is 1.
- From 'x' to 'u', the travel cost is 3.
- From 'x' to 'v', the travel cost is 5.
- From 'x' to 'y', the travel cost is 2.
- From 'y' to 's', the travel cost is 7.
- From 'y' to 'v', the travel cost is 6.

The challenge here is to determine the most cost-effective path between all pairs of locations, leveraging Floyd’s algorithm to ensure that your sales route is as efficient as possible, thus maximizing the potential for sales while minimizing travel expenses. Using our available tools, how could we apply the Floyd-Warshall algorithm in this travel network to find the shortest path from 's' to 'v'? Once we've calculated the shortest paths, can we take a closer look and actually delineate the step-by-step route from 's' to 'v' that you will travel?
### Response:
import networkx as nx

# Create a weighted directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([
    ('s', 'u', 10),
    ('s', 'x', 5),
    ('u', 'v', 1),
    ('u', 'x', 2),
    ('v', 'y', 1),
    ('x', 'u', 3),
    ('x', 'v', 5),
    ('x', 'y', 2),
    ('y', 's', 7),
    ('y', 'v', 6),
])

# Compute shortest paths and distances using Floyd's algorithm
predecessor, distance = nx.floyd_warshall_predecessor_and_distance(G)

# Reconstruct and print the shortest path from 's' to 'v'
path = nx.reconstruct_path('s', 'v', predecessor)
print('Shortest path from s to v:', path)
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