<｜begin▁of▁sentence｜>### Instruction:
As a Project Coordinator overseeing the analytical dynamics of our latest project, I have encountered a data set that captures a key performance metric over a sequence of time: [3, 1, 4, 1, 5, 9, 2, 6, 5, 3]. I'd like to visualize this data by translating it into a Visibility Graph to comprehend the interconnectedness of our metrics at every point in time. Could you assist in devising this graph using NetworkX, ensuring each value converts to a node, and identifying edges based on the visibility principle from each data point? The final graph will help us discern the underlying pattern and cross-time interactions, providing deeper insights for our project review meeting.
### Response:
import networkx as nx

# Time series data
series = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3]

# Convert time series data to a visibility graph
G = nx.visibility_graph(series)

# Print the resulting graph
print('Nodes:', G.nodes())
print('Edges:', G.edges())
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