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Program 6: Multi-dimensional data representation using different charts and graphs

# Import necessary libraries
import seaborn as sns

import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import parallel_coordinates

# Load the Iris dataset

iris = sns.load_dataset('iris')

# 1. Pair Plot
sns.pairplot(iris, hue='species', palette='Set2')

plt.suptitle("Pair Plot of Iris Dataset", y=1.02)
plt.show()

# 2. Scatter Plot
plt.figure(figsize=(8,6))

sns.scatterplot(data=iris, x='sepal_length', y='petal_length', hue='species',
style='species', s=100)

plt.title("Scatter Plot of Sepal Length vs Petal Length")
plt.show()

# 3. Bubble Chart
plt.figure(figsize=(8,6))
# Bubble size based on petal_width
sns.scatterplot(data=iris, x='sepal_length', y='petal_length', size='petal_width',
hue='species', sizes=(50,300), alpha=0.6, palette='Set1')
plt.title("Bubble Chart of Sepal Length vs Petal Length (size = Petal Width)")
plt.show()

# 4. Heatmap (Correlation Matrix)
plt.figure(figsize=(6,5))
corr = iris.iloc[:,0:4].corr() # Only numerical features

sns.heatmap(corr, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title("Heatmap of Feature Correlations")
plt.show()

# 5. Parallel Coordinates Plot

plt.figure(figsize=(10,6))

180 Touchpad Artificial Intelligence - XI