<｜begin▁of▁sentence｜>### Instruction:
Suppose we're conceptualizing a new digital marketing campaign strategy, visualizing the journey of engagement from initial contact to final conversion as a directed flowchart. We're tracking various interactions along the way: from the initial touchdown at 'Node 0', being a customer's first visit to our website, then moving through different touchpoints – 'Node 1' might represent signing up for a newsletter, 'Node 2' could be browsing products, 'Node 3' captures adding items to a cart, and 'Node 4' is the final transaction.

As we analyze the data, we notice that customers loop through certain phases, such as revisiting the product section after adding items to their cart or reevaluating choices before finalizing the purchase. These interactions can be mapped as edges in our analysis: [(0, 1), (1, 2), (2, 3), (3, 4), (4, 2)] - illustrating a recursive loop where customers oscillate between considering and adding products.

In order to streamline this flowchart and prepare a focused, effective marketing strategy, I'm interested in how we can distill this diagram into a 'condensation graph.' This would involve merging these iterative engagements into single representative nodes, consolidating each cluster of repeated interactions (or 'Strongly Connected Components') into singular entities. How can we achieve this amalgamation, ensuring our strategy graph represents the clearest path to successful conversion without the complexities of the iterative decision phase our customers experience?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 2)])

# Calculate the condensation of the graph
C = nx.condensation(G)

# Print condensation graph nodes and their data
print(C.nodes.data())
# Print the mapping from the original nodes to the nodes in the condensation graph
print(C.graph['mapping'])
<|EOT|>
