# main.py

# A demonstration of advanced statistical visualization using Seaborn.

#

# Before running, you may need to install seaborn, pandas, and scikit-learn:

# pip install seaborn pandas scikit-learn matplotlib

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

import os

from sklearn.datasets import load_iris

print("--- Starting Advanced Statistical Visualization Demonstration with Seaborn ---")

# --- Section 1: Load the Dataset ---

# We will use the Iris dataset, which is perfect for this kind of visualization

# as it has multiple numerical features and a categorical target.

print("\n--- 1. Loading the Iris Dataset ---")

iris = load_iris()

# Create a pandas DataFrame for easier manipulation with Seaborn

df = pd.DataFrame(data=iris.data, columns=iris.feature_names)

df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)

print("Dataset loaded successfully. First 5 rows:")

print(df.head())

# --- Section 2: Create a Pair Plot ---

# A pair plot (or scatterplot matrix) creates a grid of axes such that each

# variable in the data is shared across the y-axes on a single row and

# the x-axes on a single column.

# The diagonal plots show the distribution of each variable (as a histogram or KDE).

# The off-diagonal plots show the relationship between pairs of variables (as a scatter plot).

print("\n--- 2. Generating a Pair Plot ---")

try:

    # sns.pairplot() is the core function.

    # The 'hue' parameter colors the data points based on the 'species' column,

    # making it easy to see how the different species are clustered.

    sns.pairplot(df, hue='species', palette='viridis')

    plt.suptitle('Pairwise Relationships in the Iris Dataset', y=1.02) # Add a title above the plot

    # Save the plot to a file

    plot_filename = 'iris_pairplot.png'

    plt.savefig(plot_filename)

    print(f"\nPair plot saved as '{plot_filename}'")

    plt.show() # Display the plot

    plt.close()

except Exception as e:

    print(f"An error occurred during visualization: {e}")

# --- Clean up the created image file ---

finally:

    print("\n--- Cleaning up created image file ---")

    if 'plot_filename' in locals() and os.path.exists(plot_filename):

        os.remove(plot_filename)

        print(f"Removed '{plot_filename}'")

print("\n--- End of Demonstration ---")