<｜begin▁of▁sentence｜>### Instruction:
As a keen financial analyst at our bank, you've provided me with a transaction matrix that illustrates the relationships between our clients and investment opportunities, where a '0' represents no investment and '1' indicates an investment. The matrix you've compiled is as follows:

```
0 1 1
1 0 0
0 0 1
```

You've asked me to visualize these relationships to better understand our clients' investment patterns. To accomplish this, I'll use NetworkX to create a bipartite graph based on the investment matrix you've supplied. I'll then list the edges of the graph for your review, which will represent the connections between clients and the investment opportunities they've chosen. Once this bipartite graph is complete, it will serve as an efficient tool for you to analyze the investment strategies and potentially offer more tailored financial advice to our clientele.
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a SciPy sparse array (biadjacency matrix)
biadjacency_matrix = csr_matrix([[0, 1, 1], [1, 0, 0], [0, 0, 1]])

# Use NetworkX to create a bipartite graph from the biadjacency matrix
B = nx.from_biadjacency_matrix(biadjacency_matrix)

# Print the edges of the bipartite graph
print(B.edges(data=True))
<|EOT|>
