<｜begin▁of▁sentence｜>### Instruction:
As a personal chef, imagine you're preparing meals for a group of friends hosting a dinner party. Each friend is represented by a seat at the dining table, and the connections between them represent who knows each other well enough to engage in conversation during the party. You've been asked to gauge how closely connected each person is to their immediate neighbors in the seating arrangement.

Utilizing the "GraphBase.transitivity_local_undirected" function, you need to calculate and communicate the level of familiarity or "clustering coefficient" for each friend, indicating how likely they are to engage in conversation with their nearby acquaintances at the party.
### Response:
from igraph import Graph

# Create a graph from an edge list
edges = [(0, 1), (1, 2), (2, 3), (3, 0), (1, 3)]
g = Graph(edges)

# Calculate the local transitivity for all vertices in the graph
transitivity_local = g.transitivity_local_undirected()

# Print the local transitivity for each vertex
print(transitivity_local)
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