<｜begin▁of▁sentence｜>### Instruction:
In the realm of real estate analysis, consider a network akin to a newly planned community with 100 plots (nodes) and 200 pathways (edges) connecting them. As an appraiser, if you were interested in assessing the connectivity within this community — much like examining the layout for potential value implications based on accessibility — you might visualize this web of connections by observing the "degree distribution," essentially how many pathways each plot has.

To translate this into more technical terms, if one wished, for instance, to use the graspologic library, specifically its 'degreeplot' function, how could they effectively generate a visual representation of this network's degree distribution? This would provide a histogram-like insight where you could see how many plots have abundant pathways versus those that might be considered more isolated, which in turn could influence your property value assessments.
### Response:
import matplotlib.pyplot as plt
import networkx as nx
from graspologic.plot import degreeplot

# Generate a random graph
G = nx.gnm_random_graph(100, 200)

# Get the degree for each node
degrees = [deg for _, deg in G.degree()]

# Plot the degree distribution
ax = degreeplot(degrees, title='Degree Distribution', context='talk', font_scale=1.25, figsize=(8, 6), palette='Set1')
plt.show()
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