Athlete Performance by Sport

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

np.random.seed(654)

sports = ['Swimming', 'Cycling', 'Running', 'Rowing']
data = []
for sport in sports:
    n = 38
    training_hours = np.random.uniform(5, 30, n)
    gains = {'Swimming': 2.8, 'Cycling': 2.5, 'Running': 3.0, 'Rowing': 2.6}
    performance = gains[sport] * np.sqrt(training_hours) + np.random.normal(0, 1.5, n)
    for t, p in zip(training_hours, performance):
        data.append({'Training Hours/Week': t, 'Performance Gain (%)': p, 'Sport': sport})

df = pd.DataFrame(data)

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

palette = ['#27D3F5', '#F5276C', '#6CF527', '#F5D327']

g = sns.lmplot(
    data=df,
    x='Training Hours/Week',
    y='Performance Gain (%)',
    hue='Sport',
    col='Sport',
    col_wrap=2,
    height=3.5,
    aspect=1.2,
    palette=palette,
    scatter_kws={'alpha': 0.75, 's': 55, 'edgecolor': 'white', 'linewidths': 0.4},
    line_kws={'linewidth': 2.5},
    ci=95
)

g.fig.set_facecolor('#0a0a0f')
for ax in g.axes.flat:
    ax.set_facecolor('#0a0a0f')
    for spine in ax.spines.values():
        spine.set_color('#333333')

g.fig.suptitle('Training Response by Discipline', fontsize=14, fontweight='bold', color='white', y=1.02)
plt.tight_layout()
plt.show()