<｜begin▁of▁sentence｜>### Instruction:
As a property appraiser, you're tasked with determining the value of real estate properties for various purposes such as sale, purchase, or taxation. Let's say you're evaluating a neighborhood where properties are represented as vertices in a network. Each property is connected to its neighboring properties via roads, akin to edges in a graph. Now, imagine you've identified three properties labeled as 0, 1, and 2. These properties are linked by roads, each road having a specific length, which can be considered as the weight of the edges in the graph.

To put it into perspective, consider Property 0 as the focal point. It's connected to Property 1 via a road with a length of 0.2 units, to Property 2 via a road with a length of 0.8 units, and to Property 1 via another road with a length of 0.5 units. Now, if you were to apply a specialized tool like the Vertex Diversity function in igraph, you could analyze the diversity of connections Property 0 has based on the lengths of its road connections. This insight might prove invaluable in understanding the centrality and connectivity of Property 0 within the neighborhood network.
### Response:
from igraph import Graph

# Create a graph with edge weights
g = Graph(edges=[(0,1), (1,2), (0,2)], directed=False)
g.es['weight'] = [0.2, 0.5, 0.8]

# Calculate vertex diversity based on edge weights (e.g. for vertex 0)
vertex_diversity = g.vs[0].diversity()
print(vertex_diversity)
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