<｜begin▁of▁sentence｜>### Instruction:
As an orthopedic surgeon precisely understands the intricate relationships between various components of the musculoskeletal system, a biologist, with an analogous meticulousness, seeks to dissect the interrelations among distinct characteristics of iris species. Picture this: The biologist is armed with a dataset - a meticulously curated collection of measurements, ranging from sepal length to petal width, derived from the Setosa, Versicolor, and Virginica iris species. With the end goal of shedding light on the complex web of pairwise associations and the distinctive distributions of these botanical traits among the different iris classes, the biologist looks towards a powerful tool - the 'graspologic.plot.pairplot' function.

Dr. Biologist, how might you employ the 'graspologic.plot.pairplot' function to portray this delicate dance of data points visually, thereby bringing the silent stories written in the irises' dimensions to a compelling, colorful canvas?
### Response:
import graspologic
import seaborn as sns
import numpy as np
from graspologic.plot import pairplot

# Load Iris dataset
iris = sns.load_dataset('iris')
# Obtain features (first 4 columns) and class labels
X = iris.iloc[:, 0:4].to_numpy()
labels = iris['species'].to_numpy()
col_names = iris.columns[0:4]

# Create a pairplot
pairplot_result = pairplot(X, labels=labels, col_names=col_names,
                         title='Iris Pairplot', legend_name='Species',
                         variables=None, height=2.5, context='talk',
                         font_scale=1, palette='Set1', alpha=0.7, size=20,
                         marker='o')

# Display the pairplot
import matplotlib.pyplot as plt
plt.show()
<|EOT|>
