<｜begin▁of▁sentence｜>### Instruction:
Given a directed multigraph G where some edges may have weights and others may not, and a specified nodelist of [0, 1, 2], how would you represent the graph adjacency matrix as a Pandas DataFrame, aggregating the weight of parallel edges as their sum, filling non-edges with 0, and considering unweighted edges as having a weight of 1?
### Response:
import networkx as nx
import pandas as pd

# Create a MultiDiGraph
G = nx.MultiDiGraph()

# Add edges to the graph
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
# 0 1 2 are nodes in the graph
# The DataFrame will have the weights as values, with non-edge entries as 0.
df = nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], dtype=int)

# Output the resulting DataFrame
print(df)
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