Imported from Ryukijano/agent-skills (.cursor/skills/data-visualization/SKILL.md). Install upstream with npx skills add Ryukijano/agent-skills --skill data-visualization. Copyright stays with the author.

Data Visualization

Library Selection

matplotlib Quick Start

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(10, 6))
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Sine Wave')
ax.legend()
plt.tight_layout()
plt.savefig('plot.png', dpi=150)

seaborn for Statistical Plots

import seaborn as sns
import pandas as pd

df = pd.read_csv('data.csv')
sns.boxplot(data=df, x='group', y='value')
plt.savefig('boxplot.png')

plotly for Interactive

import plotly.graph_objects as go

fig = go.Figure(data=go.Scatter(x=[1,2,3], y=[4,5,6], mode='markers'))
fig.write_html('interactive.html')

Best Practices

• Choose the right chart type for the data
• Label axes and include units
• Use color intentionally (not just for decoration)
• Avoid chart junk: 3D effects, excessive gridlines
• Ensure accessibility: color-blind friendly palettes
• Save at 150+ DPI for print quality