<｜begin▁of▁sentence｜>### Instruction:
Imagine you're planning a charity fundraiser event where the goal is to match five major donors with three different charitable causes. You want to ensure that each donor is properly introduced to all causes, thereby maximizing the potential for donations. We could visualize this scenario with a graph where the five donors and three causes are interconnected. In preparation for the event, you're looking to analyze the structure of these connections to ensure everyone is equally engaged.

To help you plan this networking event effectively, could you utilize NetworkX to evaluate the interaction opportunities available? Specifically, we'd like to calculate how many causes each donor will be introduced to and vice versa. This will help us understand if the event is designed in a way that every cause gets equal attention from all the donors.
### Response:
from networkx.algorithms import bipartite
import networkx as nx

# Create a bipartite graph
B = nx.complete_bipartite_graph(5, 3)

# Define one set of nodes (the 'bottom' nodes)
bottom_nodes = set([5, 6, 7])

# Get the degrees of the nodes
degX, degY = bipartite.degrees(B, bottom_nodes)

# Convert the degree information to dictionary for easy viewing
degrees_bottom = dict(degY)
degrees_top = dict(degX)

print('Degrees of the bottom set:', degrees_bottom)
print('Degrees of the top set:', degrees_top)
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