<｜begin▁of▁sentence｜>### Instruction:
Imagine you're evaluating two bustling marketplaces, each with a network of traders and customers intersecting much like social networks. To understand the intricacies of these market interactions, you're examining the 'busyness' of each marketplace - in other words, how connected everyone is. Think of it like checking how many friends each person has in two different social circles. 

To dive into this, you're adopting a common model akin to a tool from the world of network science - the Erdos-Renyi model - to simulate the chance meetings and connections between people in these markets. In this scenario, you're using the NetworkX API to construct these simulated networks.

Now, your goal is to discern if the level of connectedness in one market is noticeably different from that in the other. What you're looking for is whether the sheer number of potential deals or interactions (also known as network density) differs significantly between the two marketplaces, based on your Erdos-Renyi modelled assumptions.

You're poised with your data, ready to apply statistical rigor through Python to conclude if one marketplace's network is genuinely more bustling with interaction than the other. How would you proceed with this econometric inquiry using the networks you've mapped out in NetworkX?
### Response:
import numpy as np
from graspologic.inference import density_test

# Generate two random adjacency matrices
np.random.seed(42)
A1 = np.random.binomial(1, 0.5, size=(5, 5))
A2 = np.random.binomial(1, 0.4, size=(5, 5))

# Perform the density test between the two networks
result = density_test(A1, A2, method='fisher')

# Print the results
print(f'Statistic: {result.stat}')
print(f'p-value: {result.pvalue}')
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