Automotive Engine Correlogram

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

np.random.seed(987)

n = 170
fuel = np.random.choice(['Gasoline', 'Diesel', 'Hybrid'], n)
data = {
    'MPG': np.where(fuel == 'Hybrid', np.random.normal(50, 8, n),
                   np.where(fuel == 'Diesel', np.random.normal(35, 5, n), np.random.normal(28, 6, n))),
    'HP': np.where(fuel == 'Gasoline', np.random.normal(250, 60, n),
                  np.where(fuel == 'Diesel', np.random.normal(200, 40, n), np.random.normal(180, 30, n))),
    'Torque': np.where(fuel == 'Diesel', np.random.normal(350, 70, n),
                      np.where(fuel == 'Gasoline', np.random.normal(270, 50, n), np.random.normal(220, 40, n))),
    'Weight (lbs)': np.where(fuel == 'Hybrid', np.random.normal(3800, 300, n),
                            np.where(fuel == 'Diesel', np.random.normal(4200, 400, n), np.random.normal(3500, 350, n))),
    'Fuel': fuel
}
df = pd.DataFrame(data)

sns.set_style("whitegrid", {'axes.facecolor': '#ffffff', 'figure.facecolor': '#ffffff', 'grid.color': '#eeeeee'})

palette = {'Gasoline': '#F54927', 'Diesel': '#4927F5', 'Hybrid': '#6CF527'}

g = sns.pairplot(
    df,
    hue='Fuel',
    palette=palette,
    height=2,
    kind='scatter',
    diag_kind='kde',
    markers=['o', 's', 'D'],
    plot_kws={'alpha': 0.75, 's': 55, 'edgecolor': 'white', 'linewidths': 0.6},
    diag_kws={'alpha': 0.5, 'linewidth': 2.5, 'fill': True}
)

g.fig.set_facecolor('#ffffff')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#ffffff')
        for spine in ax.spines.values():
            spine.set_color('#dddddd')
        ax.tick_params(colors='#666666')
        ax.xaxis.label.set_color('#333333')
        ax.yaxis.label.set_color('#333333')

g.fig.suptitle('Automotive Performance Metrics', fontsize=14, fontweight='bold', color='#1a1a1a', y=1.02)

plt.tight_layout()
plt.show()