<｜begin▁of▁sentence｜>### Instruction:
Imagine you're preparing for an avant-garde performance that illustrates the complex interplay of relationships within a networked society. For this, you've conceptualized a piece where each performer represents a node within this network: 'A', 'B', and 'C'. Your choreography involves sequences where the performers interact, symbolizing the exchange of information or energy between them.

Consider these interactions as connections or ties with varying intensities; some exchanges are more significant (heavier) than others. For instance, when 'A' engages with 'B', you could envision a prop being passed with a significance of 5. Yet, if 'B' reciprocates to 'A', they do so with a lesser intensity, a value of 3. At times, the performers interact with themselves, such as 'B' with a weighted 4 expression of self-reflection, or 'C' with an introspective moment carrying a weight of 2.

Some interactions are unplanned or spontaneous, indicated by the connection ('A', 'C'), which will be signified with the lightest touch, an improvised default weight one might presume is 1.

Now, imagine distilling this intricate dance into a visual schematic, akin to an adjacency matrix. For this tableau to be insightful, amalgamate the exchanges between the performers whenever they interact more than once, summing their prop's weights to represent the cumulative intensity of their relationship. This schematic must also convey the spontaneity of unplanned interactions with the assumed baseline weight.

Your endeavor is to transform this matrix, a rigid numerical representation, into a fluid, interpretable DataFrame, much like how one might transpose a musical score into a dance routine. Remember, this is not merely a task of tabulation; it's a translation of relationships into resonant data, encapsulating the ebbs and flows of your envisioned communal narrative. How would you articulate this matrix to serve as a guiding framework for your performance's structure?
### Response:
import networkx as nx
import pandas as pd

# Create a MultiDiGraph
g = nx.MultiDiGraph()
g.add_edge('A', 'B', weight=5)
g.add_edge('B', 'A', weight=3)
g.add_edge('B', 'B', weight=4)
g.add_edge('C', 'C', weight=2)
g.add_edge('A', 'C')

# Convert to pandas adjacency DataFrame
df = nx.to_pandas_adjacency(g, nodelist=['A', 'B', 'C'], weight='weight', multigraph_weight=sum)

print(df)
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