Stellar Classification Features

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

np.random.seed(987)

n = 160
spectral = np.random.choice(['O/B', 'F/G', 'K/M'], n)
data = {
    'Temp (K)': np.where(spectral == 'O/B', np.random.normal(25000, 8000, n),
                        np.where(spectral == 'F/G', np.random.normal(6000, 800, n), np.random.normal(4000, 500, n))),
    'Luminosity': np.where(spectral == 'O/B', np.random.lognormal(4, 1, n),
                          np.where(spectral == 'F/G', np.random.lognormal(0, 0.5, n), np.random.lognormal(-1, 0.4, n))),
    'Mass (M☉)': np.where(spectral == 'O/B', np.random.normal(15, 5, n),
                         np.where(spectral == 'F/G', np.random.normal(1, 0.2, n), np.random.normal(0.5, 0.15, n))),
    'Radius (R☉)': np.where(spectral == 'O/B', np.random.normal(8, 3, n),
                           np.where(spectral == 'F/G', np.random.normal(1, 0.2, n), np.random.normal(0.6, 0.1, n))),
    'Class': spectral
}
df = pd.DataFrame(data)

plt.style.use('dark_background')
sns.set_style("darkgrid", {'axes.facecolor': '#0a0a0f', 'figure.facecolor': '#0a0a0f', 'grid.color': '#333333'})

palette = {'O/B': '#27D3F5', 'F/G': '#F5D327', 'K/M': '#C82909'}

g = sns.pairplot(
    df,
    hue='Class',
    palette=palette,
    height=2,
    kind='scatter',
    diag_kind='kde',
    markers=['*', 'o', 's'],
    plot_kws={'alpha': 0.75, 's': 60, 'edgecolor': 'white', 'linewidths': 0.5},
    diag_kws={'alpha': 0.5, 'linewidth': 2.5, 'fill': True}
)

g.fig.set_facecolor('#0a0a0f')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#0a0a0f')
        for spine in ax.spines.values():
            spine.set_color('#333333')
        ax.tick_params(colors='#888888')
        ax.xaxis.label.set_color('white')
        ax.yaxis.label.set_color('white')

g.fig.suptitle('Stellar Properties by Spectral Class', fontsize=14, fontweight='bold', color='white', y=1.02)
plt.setp(g._legend.get_title(), color='white')
plt.setp(g._legend.get_texts(), color='white')

plt.tight_layout()
plt.show()