<｜begin▁of▁sentence｜>### Instruction:
As a Blockchain Developer working on developing an intricate system for secure transactions, I'm currently exploring ways to visualize and analyze the network of transaction pathways. To optimize the data flow, I have collated a series of nodes and transactional pathways in a pandas DataFrame. This DataFrame contains the columns 'source', 'target', and 'weight', each designating the initiating node of a transaction, the receiving node, and the quantitative measure of the transaction size, respectively.

Could you illustrate how one might engender a NetworkX graph encapsulating these weighted pathways, ensuring the graph encapsulates the transactional weights ascribed within the DataFrame? The operational integrity of the blockchain system hinges on the accuracy of this representation, and your advice would be instrumental in advancing this endeavor.
### Response:
import pandas as pd
import networkx as nx

# Create a Pandas DataFrame with edge data
edge_data = {'source': ['A', 'B', 'C'], 'target': ['B', 'C', 'D'], 'weight': [3, 4, 5]}
df_edges = pd.DataFrame(edge_data)

# Use from_pandas_edgelist to create a graph
G = nx.from_pandas_edgelist(df_edges, 'source', 'target', edge_attr=True)

# Access edge attributes
edge_weight_AB = G['A']['B']['weight']
print('Weight of edge A-B:', edge_weight_AB)
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