<｜begin▁of▁sentence｜>### Instruction:
Imagine you're choreographing a contemporary dance piece entitled "The Connection", symbolizing the bonds between individuals in a society. You've decided to represent the dancers as a community where each dancer represents a node, labelled {0, 1, 2, 3, 4}. The interactions between some dancers are defined by your chosen choreography's movements, much like edges in a network, with the pairings being {(0, 1), (1, 2), (3, 4)}. 

To add a layer of complexity to your performance, you want to capture the invisible bonds within the group's dynamics through the ebbs and flows of the dance. You think of this in terms of the 'vibrational frequencies' of the dance troupe—akin to the eigenvalues of the Laplacian matrix of the dancers' interaction network.

You're curious to find out the rounded 'vibrational frequencies' to use them as a metaphor for the unseen connections. These frequencies correlate with the number of distinct groups or sub-dances (connected components) that exist within "The Connection". How would these frequencies manifest in your choreography, and what would they say about the number of separate interactions present in your dance piece?
### Response:
import networkx as nx
import numpy as np

# Create a graph with 5 nodes and 2 connected components
G = nx.Graph()
G.add_nodes_from(range(5))
G.add_edges_from([(0, 1), (1, 2), (3, 4)])

# Get the Laplacian spectrum
spectrum = nx.laplacian_spectrum(G)

# Convert to a list and round the eigenvalues
spectrum_rounded = np.round(spectrum, 3)

print('Laplacian spectrum of the graph:', spectrum_rounded)
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