<｜begin▁of▁sentence｜>### Instruction:
In the midst of evaluating a range of medical imaging scans, suppose we have a set of diagnostic features with 10 individual cases, each characterized by 4 distinct markers. These cases are categorized into two diagnosis groups: 'A' for one health condition and 'B' for another. As a radiologist seeking a comprehensive understanding of how these markers correlate with each other across our two groups, how might we employ graspologic’s pairplot function to create a visual representation that elucidates the inter-relationships of features within our dataset, as well as the distribution tendencies of each feature in our diagnostic categories?
### Response:
import numpy as np
import graspologic

# Generate synthetic data
np.random.seed(42)
X = np.random.rand(10, 4)  # 10 samples with 4 features
labels = np.array(['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'])  # Labels for each sample

# Feature names
col_names = ['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4']

# Create the pairplot
pairplot = graspologic.plot.pairplot(X, labels=labels, col_names=col_names, title='Pairplot', legend_name='Type', height=10, context='talk', font_scale=1.2, palette='Set1', alpha=0.7, size=50, marker='o')
pairplot.savefig('pairplot.png')
<|EOT|>
