<｜begin▁of▁sentence｜>### Instruction:
As a network administrator, imagine you've been tasked with analyzing the traffic flow within a company's communication network. The current system map consists of a series of directed communication pathways, specifically outlined by the connections: [('A', 'B'), ('B', 'C'), ('C', 'A'), ('D', 'E')]. To optimize routing and troubleshoot connectivity issues, you need to determine the different groups of systems that can communicate with each other, regardless of the direction of the pathways. Could you devise a strategy using NetworkX to identify these groupings within the network, referred to as weakly connected components? This would assist in ensuring robustness in the network's communication infrastructure.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges
G.add_edge('A', 'B')
G.add_edge('B', 'C')
G.add_edge('C', 'A')
G.add_edge('D', 'E')

# Generate weakly connected components
components = list(nx.weakly_connected_components(G))

print('Weakly connected components:', components)
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