<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working in a healthcare setting with a network of communication pathways between different departments, such as Nursing, Administration, and Lab Services. Picture this as a direction-oriented flowchart where the pathways represent lines of information exchange or delegation, each with a specific 'load' value indicating the frequency or importance of the interactions.

In this scenario, we have a chart that includes pathways such as Nursing (0) to Administration (1) with a load value of 2 represented by (0, 1, {'weight': 2}), a reciprocal pathway from Administration (1) to Nursing (0) with no specified load (1, 0, {}), and a unique situation where Lab Services (2) has a self-directed pathway indicating internal interactions with a combined load value of 3, represented by (2, 2, {'weight': 3}) and (2, 2, {}).

To evaluate the combined 'load' values of these interactions and present it in a simplified format, we're looking at consolidating this information into a table — much like how patient information is recorded in a chart. Can you consider how we might compile a tabulated representation or 'chart' (akin to a Pandas DataFrame) reflecting these departmental communication pathways (nodes [0, 1, 2]) while accurately combining the load values (weights) where multiple pathways exist between the same departments? This would give us an overview to possibly optimize our inter-departmental communications.
### Response:
import networkx as nx
import pandas as pd

# Create a multigraph
G = nx.MultiDiGraph()
G.add_edge(0, 1, weight=2)
G.add_edge(1, 0)
G.add_edge(2, 2, weight=3)
G.add_edge(2, 2)

# Convert to Pandas DataFrame
adj_df = nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], dtype=int)

print(adj_df)
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