Raincloud Plot

Gaming Network Latency Raincloud

Comparing network latency distributions across gaming server regions.

Output
Gaming Network Latency Raincloud
Python
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats

np.random.seed(789)

# Ping in milliseconds
na_west = np.random.lognormal(2.8, 0.3, 120)
na_east = np.random.lognormal(3.0, 0.35, 115)
eu_west = np.random.lognormal(3.4, 0.4, 100)
asia = np.random.lognormal(3.8, 0.45, 90)

na_west = np.clip(na_west, 8, 50)
na_east = np.clip(na_east, 10, 60)
eu_west = np.clip(eu_west, 15, 90)
asia = np.clip(asia, 25, 150)

F_stat, p_value = stats.f_oneway(na_west, na_east, eu_west, asia)

BG_COLOR = "#0a0a0f"
COLOR_SCALE = ["#6CF527", "#27D3F5", "#F5B027", "#F5276C"]

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

y_data = [na_west, na_east, eu_west, asia]
positions = [0, 1, 2, 3]
labels = ["NA-West", "NA-East", "EU-West", "Asia-Pacific"]

for h in [20, 50, 100]:
    ax.axhline(h, color='#333333', ls=(0, (5, 5)), alpha=0.5, zorder=0)

# Quality zones
ax.axhspan(0, 30, alpha=0.08, color='#22c55e')
ax.axhspan(30, 60, alpha=0.05, color='#fbbf24')
ax.axhspan(60, 160, alpha=0.05, color='#ef4444')
ax.text(3.55, 15, "Excellent", color='#22c55e', fontsize=9, va='center')
ax.text(3.55, 45, "Good", color='#fbbf24', fontsize=9, va='center')
ax.text(3.55, 100, "Playable", color='#ef4444', fontsize=9, va='center')

violins = ax.violinplot(y_data, positions=positions, widths=0.5, 
                         bw_method="silverman", showmeans=False, 
                         showmedians=False, showextrema=False)
for pc in violins["bodies"]:
    pc.set_facecolor("none")
    pc.set_edgecolor("white")
    pc.set_linewidth(1.8)

bp = ax.boxplot(y_data, positions=positions, showfliers=False, showcaps=False,
                medianprops=dict(linewidth=3, color='#F5D327'),
                whiskerprops=dict(linewidth=2, color='#555555'),
                boxprops=dict(linewidth=2, color='#555555'))

for i, (y, color) in enumerate(zip(y_data, COLOR_SCALE)):
    x_jitter = np.array([i] * len(y)) + stats.t(df=6, scale=0.04).rvs(len(y))
    ax.scatter(x_jitter, y, s=50, color=color, alpha=0.5, zorder=2)

means = [y.mean() for y in y_data]
for i, (mean, color) in enumerate(zip(means, COLOR_SCALE)):
    ax.scatter(i, mean, s=180, color='#C82909', zorder=5, edgecolors='white', linewidths=2)
    ax.plot([i, i + 0.28], [mean, mean], ls="dashdot", color="white", zorder=3, lw=1.5)
    ax.text(i + 0.3, mean, f"μ={mean:.0f}ms", fontsize=10, va="center", color='white',
            bbox=dict(facecolor=BG_COLOR, edgecolor=color, boxstyle="round,pad=0.15", lw=2))

# Comparison brackets
tick = 5
ax.plot([0, 0, 3, 3], [145-tick, 145, 145, 145-tick], c="white", lw=1.5)
ax.text(1.5, 148, "p < 0.001, Tukey HSD", fontsize=10, va="bottom", ha="center", color='#F5D327')

ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["left"].set_color('#444444')
ax.spines["bottom"].set_color('#444444')
ax.tick_params(colors='#888888', length=0)

xlabels = [f"{l}\n(n={len(y_data[i])})" for i, l in enumerate(labels)]
ax.set_xticks(positions)
ax.set_xticklabels(xlabels, size=11, color='white')
ax.set_ylabel("Ping Latency (ms)", size=14, color='white', fontweight='bold')

ax.set_title("Gaming Network Latency by Region", fontsize=14, color="white", fontweight="bold", pad=20)


ax.set_ylim(0, 165)
plt.tight_layout()
plt.show()
Library

Matplotlib

Category

Statistical

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