<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a Sales Associate at a bustling retail analytics firm, and one of your innovative projects involves mapping the interactions between different departments to optimize the sales strategy. You've been handed a dataset, rather like a detailed client interaction network, with connections that represent interactions between departments numbered 1-7, similar to how certain products might be recommended in conjunction with others.

The connections are as follows: there are transactions from department 1 to 2, then from 1 to 3, department 2 recommends customers back to 3, department 3 often refers clients back to 1, department 5 introduces customers to 6, then suggests options from 4, and finally, department 6 brings clients over to 7.

Your task is to utilize the store's analytics tool, NetworkX, specifically calling upon its 'triads_by_type' function, to categorize and list out all possible three-department collaborative patterns, or triads, within this network. Each triad type highlights a different kind of collaborative behavior between the departments. By doing so, you can better understand the dynamics of inter-departmental recommendations and design strategies to enhance cross-selling and up-selling opportunities. Could you organize our departmental interaction data using this NetworkX function and give us a rundown of the types of triads present?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 1), (5, 6), (5, 4), (6, 7)])

# Calculate triads by type
triads_dict = nx.triads_by_type(G)

# Access triad type '120C'
triad_120C_edges = triads_dict['120C'][0].edges()
print('Edges for triad type 120C:', triad_120C_edges)

# Access triad type '012'
triad_012_edges = triads_dict['012'][0].edges()
print('Edges for triad type 012:', triad_012_edges)
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