Boxplot

Renewable Power Output

Power generation variability across renewable energy sources

Output
Renewable Power Output
Python
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
sources = ['Solar', 'Wind', 'Hydro', 'Geothermal', 'Biomass']
data = [
    np.random.beta(2, 3, 365) * 100,
    np.random.beta(3, 2, 365) * 100,
    np.random.beta(5, 1.5, 365) * 100,
    np.random.beta(8, 1, 365) * 100,
    np.random.beta(6, 2, 365) * 100
]

fig, ax = plt.subplots(figsize=(10, 6), dpi=100)
ax.set_facecolor('#0a0a0f')
fig.patch.set_facecolor('#0a0a0f')

colors = ['#F5D327', '#27D3F5', '#276CF5', '#F54927', '#6CF527']
bp = ax.boxplot(data, widths=0.55, patch_artist=True, showfliers=False, notch=True,
                medianprops=dict(color='white', linewidth=2.5))

for patch, color in zip(bp['boxes'], colors):
    patch.set_facecolor(color)
    patch.set_alpha(0.85)
    patch.set_edgecolor('white')
for i, color in enumerate(colors):
    bp['whiskers'][i*2].set_color(color)
    bp['whiskers'][i*2+1].set_color(color)
    bp['caps'][i*2].set_color(color)
    bp['caps'][i*2+1].set_color(color)

ax.set_xticklabels(sources)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('#333333')
ax.spines['bottom'].set_color('#333333')
ax.yaxis.grid(True, color='#1a1a2e', linewidth=0.5, zorder=0)
ax.set_axisbelow(True)
ax.tick_params(axis='both', colors='#888888', labelsize=9, length=0, pad=8)
ax.set_ylabel('Capacity Factor (%)', fontsize=11, color='white', fontweight='500')
ax.set_title('Renewable Energy Capacity Factors', fontsize=14, color='white', fontweight='bold', pad=15)

plt.tight_layout()
plt.show()
Library

Matplotlib

Category

Statistical

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