<｜begin▁of▁sentence｜>### Instruction:
Certainly! Here's the rephrased question:

Imagine we're representing a trio of athletes, each with unique interactions and endorsement deals connecting them. The clients are labeled as 0, 1, and 2. These connections resemble a competition where each interaction can occur more than once, akin to repeat matchups, and some of these matchups come with various financial incentives, while others do not. In this representation, athlete 0 has a deal with athlete 1, athlete 1 reciprocates with athlete 0, and athlete 2 has an agreement with themselves.

To manage these deals effectively and foresee potential earnings, we want to draft a comprehensive table — much like an adjacency matrix using a Pandas DataFrame — that summarizes the financial weight of each possible connection, accounting for both direct and indirect deals. In this matrix, the sum of multiple deals between the same athletes should be combined, ensuring a clear picture of their total engagements. Deals that do not exist should be clearly marked with a zero to reflect no financial exchange. All this needs to be organized while considering a pre-defined list of clients [0,1,2].

How would one visualize this complex network of deals for easy interpretation and management using the data provided?
### Response:
import networkx as nx
import pandas as pd
G = nx.MultiDiGraph()
G.add_edge(0, 1, weight=2)
G.add_edge(1, 0)
G.add_edge(2, 2, weight=3)
df = nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], multigraph_weight=sum, weight='weight', nonedge=0.0)
print(df)
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