<｜begin▁of▁sentence｜>### Instruction:
In the workshop of a furniture maker renowned for their commitment to personalized, handcrafted designs, a new project unfolds. The goal is to create a unique set of three interconnected furniture pieces, each with its own distinct character but part of a cohesive narrative series. To pay homage to their craft, the artisan decides to assign each piece a gender motif: the first piece in the series will carry masculine features, while the latter two will embrace feminine aesthetics. 

The furniture maker, also a keen enthusiast of how individual pieces interact within a space to create harmony or contrast, wishes to analyze the "gender" attributes of their creations to better understand the mixture of masculine and feminine elements in their series. This analysis could help in conceptualizing future projects where the blending of attributes plays a crucial role in the design philosophy.

How does the artisan calculate the "gender" attribute mixing matrix for their series of furniture pieces? Specifically, what is the mixing value from pieces attributed as male to those attributed as female, using this gender classification as an integral part of their design narrative? This calculation would not only enrich the current project but also inform the furniture maker's approach to blending design motifs in future creations.
### Response:
import networkx as nx
import numpy as np

# Create a graph
G=nx.path_graph(3)

# Set attributes for each node
gender = {0: 'male', 1: 'female', 2: 'female'}
nx.set_node_attributes(G, gender, 'gender')

# Define a mapping from gender to an integer index
mapping = {'male': 0, 'female': 1}

# Compute the attribute mixing matrix
mix_mat = nx.attribute_mixing_matrix(G, 'gender', mapping=mapping)

# Print the mixing matrix
print(mix_mat)

# Access mixing from male nodes to female nodes
print('Mixing from male to female:', mix_mat[mapping['male'], mapping['female']])
<|EOT|>
