Boxplot

Batch Processing Time

Job execution analysis.

Output
Batch Processing Time
Python
import matplotlib.pyplot as plt
import numpy as np

COLORS = {'small': '#10B981', 'medium': '#6366F1', 'large': '#F59E0B', 'xlarge': '#EF4444', 'median': '#FFFFFF', 'background': '#FFFFFF', 'text': '#1E293B', 'text_muted': '#64748B', 'grid': '#F1F5F9'}

np.random.seed(42)
data = [np.random.gamma(s, 10, 50) for s in [2, 5, 10, 20]]
colors = [COLORS['small'], COLORS['medium'], COLORS['large'], COLORS['xlarge']]

fig, ax = plt.subplots(figsize=(10, 6), dpi=100)
ax.set_facecolor(COLORS['background'])
fig.patch.set_facecolor(COLORS['background'])

bp = ax.boxplot(data, widths=0.5, patch_artist=True, showfliers=False,
                medianprops=dict(color=COLORS['median'], linewidth=2))

for patch, whisker1, whisker2, cap1, cap2, color in zip(bp['boxes'], bp['whiskers'][::2], bp['whiskers'][1::2], bp['caps'][::2], bp['caps'][1::2], colors):
    patch.set_facecolor(color)
    patch.set_edgecolor('white')
    whisker1.set_color(color)
    whisker2.set_color(color)
    cap1.set_color(color)
    cap2.set_color(color)

ax.set_xticklabels(['Small', 'Medium', 'Large', 'X-Large'])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color(COLORS['grid'])
ax.spines['bottom'].set_color(COLORS['grid'])
ax.yaxis.grid(True, color=COLORS['grid'], linewidth=1, zorder=0)
ax.set_axisbelow(True)
ax.tick_params(axis='both', colors=COLORS['text_muted'], labelsize=9, length=0, pad=8)
ax.set_xlabel('Batch Size', fontsize=10, color=COLORS['text'], labelpad=10)
ax.set_ylabel('Processing Time (s)', fontsize=10, color=COLORS['text'], labelpad=10)

plt.tight_layout()
plt.show()
Library

Matplotlib

Category

Statistical

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