# Complex demo: Multi-plot AI data visualization with NumPy and Pandas
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def visualize_ai_data(data):
    df = pd.DataFrame(data)
    fig, axes = plt.subplots(2, 2, figsize=(12, 8))
    axes[0, 0].plot(df['X'], df['Y1'], label='Sin Curve', color='blue', linestyle='--')
    axes[0, 0].legend()
    axes[0, 1].scatter(df['X'], df['Y2'], c='red', marker='o', label='Scatter Points')
    axes[0, 1].legend()
    axes[1, 0].bar(df['Category'], df['Value'], color='green')
    axes[1, 1].hist(df['Value'], bins=5, color='purple')
    plt.suptitle('AI Data Visualization')
    plt.tight_layout()
    plt.show()

# Generate complex AI-like data
x = np.linspace(0, 10, 50)
y1 = np.sin(x)
y2 = np.random.rand(50) * 10
category = ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A']
value = np.random.randint(1, 10, 10)
data = {"X": x, "Y1": y1, "Y2": y2, "Category": category[:50], "Value": np.concatenate([value, np.random.randint(1, 10, 40)])}
visualize_ai_data(data)