Athletic Performance Metrics

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

np.random.seed(456)

n = 160
athlete_types = np.random.choice(['Sprinter', 'Endurance', 'Power'], n)
data = {
    'VO2max': np.where(athlete_types == 'Endurance', np.random.normal(65, 5, n),
                      np.where(athlete_types == 'Sprinter', np.random.normal(50, 4, n), np.random.normal(45, 5, n))),
    'Power (W/kg)': np.where(athlete_types == 'Power', np.random.normal(18, 2, n),
                            np.where(athlete_types == 'Sprinter', np.random.normal(15, 2, n), np.random.normal(12, 1.5, n))),
    'Lactate (mmol)': np.random.normal(4, 1.5, n),
    'HR Max': np.random.normal(185, 10, n),
    'Type': athlete_types
}
df = pd.DataFrame(data)

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

palette = {'Sprinter': '#F5276C', 'Endurance': '#27D3F5', 'Power': '#F5D327'}

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

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('Athlete Physiological Profiles', 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()