Data Visualization Skill
Create publication-quality analytical charts using matplotlib and seaborn in a headless GPU sandbox. Charts are saved as PNG files to /workspace/ for retrieval.
When to Use This Skill
Use this skill when:
- Visualizing results from cuDF analysis or cuML models
- Creating charts (bar, line, scatter, heatmap, histogram, box plot)
- Building multi-panel analysis summaries
- The user asks for visual output, plots, graphs, or charts
- Presenting statistical findings with figures
Initialization (REQUIRED)
MUST call matplotlib.use('Agg') BEFORE importing pyplot. This enables headless rendering.
import matplotlib
matplotlib.use('Agg') # Headless backend — MUST be before pyplot import
import matplotlib.pyplot as plt
import numpy as np
# Publication-quality defaults
plt.rcParams.update({
'figure.dpi': 100,
'savefig.dpi': 300,
'font.size': 11,
'axes.labelsize': 12,
'axes.titlesize': 14,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'legend.fontsize': 10,
'figure.constrained_layout.use': True,
})
# Colorblind-safe palette (Okabe-Ito)
COLORS = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC',
'#CA9161', '#FBAFE4', '#949494', '#ECE133', '#56B4E9']Saving Charts
Always save to /workspace/ with these settings:
plt.savefig('/workspace/chart_name.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlinedpi=300for print qualitybbox_inches='tight'removes excess whitespacefacecolor='white'ensures white background- Always call
plt.close()after saving to free memory
Displaying Charts (REQUIRED)
After saving any chart, you MUST call read_file on it to display it inline in the conversation:
read_file("/workspace/chart_name.png")Users cannot see charts unless you do this. Every chart you save MUST be followed by a read_file call.
Quick Reference
Bar Chart (from groupby results)
# After: result = to_pd(df.groupby("category")["value"].mean())
fig, ax = plt.subplots(figsize=(8, 5))
bars = ax.bar(result.index, result.values, color=COLORS[:len(result)],
edgecolor='black', linewidth=0.8)
for bar in bars:
height = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2., height,
f'{height:.1f}', ha='center', va='bottom', fontsize=9)
ax.set_ylabel('Mean Value', fontweight='bold')
ax.set_xlabel('Category', fontweight='bold')
ax.set_title('Average Value by Category', fontweight='bold')
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.set_axisbelow(True)
plt.savefig('/workspace/bar_chart.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineLine Chart (trends over time)
fig, ax = plt.subplots(figsize=(10, 5))
for i, col in enumerate(columns_to_plot):
ax.plot(df["date"], df[col], label=col, color=COLORS[i], linewidth=2,
marker='o', markersize=3, markevery=max(1, len(df)//20))
ax.set_ylabel('Values', fontweight='bold')
ax.set_xlabel('Date', fontweight='bold')
ax.set_title('Trends Over Time', fontweight='bold')
ax.legend(frameon=True, shadow=False)
ax.grid(True, alpha=0.3, linestyle='--')
ax.set_axisbelow(True)
plt.xticks(rotation=45, ha='right')
plt.savefig('/workspace/line_chart.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineScatter Plot — Continuous Color (correlations)
fig, ax = plt.subplots(figsize=(8, 6))
scatter = ax.scatter(df["x"], df["y"], c=df["value"], cmap='viridis',
s=40, alpha=0.7, edgecolors='black', linewidth=0.3)
plt.colorbar(scatter, ax=ax, label='Value')
# Optional: trend line
z = np.polyfit(df["x"], df["y"], 1)
ax.plot(df["x"].sort_values(), np.poly1d(z)(df["x"].sort_values()),
"r--", linewidth=2, label=f'y={z[0]:.2f}x+{z[1]:.2f}')
ax.set_xlabel('X', fontweight='bold')
ax.set_ylabel('Y', fontweight='bold')
ax.set_title('Correlation Analysis', fontweight='bold')
ax.legend()
ax.grid(True, alpha=0.3, linestyle='--')
plt.savefig('/workspace/scatter_correlation.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineScatter Plot — Categorical Color (clusters)
fig, ax = plt.subplots(figsize=(8, 6))
for i, label in enumerate(sorted(df["cluster"].unique())):
mask = df["cluster"] == label
ax.scatter(df.loc[mask, "x"], df.loc[mask, "y"],
c=COLORS[i], label=f'Cluster {label}', s=40, alpha=0.7)
ax.set_xlabel('X', fontweight='bold')
ax.set_ylabel('Y', fontweight='bold')
ax.set_title('Cluster Visualization', fontweight='bold')
ax.legend()
ax.grid(True, alpha=0.3, linestyle='--')
plt.savefig('/workspace/scatter_clusters.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineHeatmap (correlation matrix or confusion matrix)
import seaborn as sns
fig, ax = plt.subplots(figsize=(8, 7))
# corr_matrix = to_pd(df[numeric_cols].corr())
sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='RdBu_r', center=0,
square=True, linewidths=1, vmin=-1, vmax=1,
cbar_kws={'label': 'Correlation'}, ax=ax)
ax.set_title('Correlation Matrix', fontweight='bold')
plt.savefig('/workspace/heatmap.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineHistogram with KDE
fig, ax = plt.subplots(figsize=(8, 5))
ax.hist(df["value"], bins=30, color=COLORS[0], alpha=0.7,
edgecolor='black', linewidth=0.5, density=True, label='Distribution')
# Add KDE curve
from scipy.stats import gaussian_kde
kde = gaussian_kde(df["value"].dropna())
x_range = np.linspace(df["value"].min(), df["value"].max(), 200)
ax.plot(x_range, kde(x_range), color=COLORS[1], linewidth=2, label='KDE')
ax.set_xlabel('Value', fontweight='bold')
ax.set_ylabel('Density', fontweight='bold')
ax.set_title('Value Distribution', fontweight='bold')
ax.legend()
ax.grid(axis='y', alpha=0.3, linestyle='--')
plt.savefig('/workspace/histogram.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineBox Plot (compare groups)
fig, ax = plt.subplots(figsize=(8, 5))
groups = [df[df["group"] == g]["value"].values for g in group_names]
bp = ax.boxplot(groups, labels=group_names, patch_artist=True,
widths=0.6, showmeans=True,
meanprops=dict(marker='D', markerfacecolor='red', markersize=6))
for i, patch in enumerate(bp['boxes']):
patch.set_facecolor(COLORS[i % len(COLORS)])
patch.set_alpha(0.7)
ax.set_ylabel('Value', fontweight='bold')
ax.set_title('Distribution by Group', fontweight='bold')
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.set_axisbelow(True)
plt.savefig('/workspace/boxplot.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineMulti-Panel Analysis Summary
Use this to create a single image with multiple charts — the most effective way to present a complete analysis.
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
# Top-left: Distribution
axes[0, 0].hist(df["value"], bins=30, color=COLORS[0], alpha=0.7, edgecolor='black', linewidth=0.5)
axes[0, 0].set_title('Value Distribution', fontweight='bold')
axes[0, 0].set_xlabel('Value')
axes[0, 0].grid(axis='y', alpha=0.3, linestyle='--')
# Top-right: Scatter
axes[0, 1].scatter(df["x"], df["y"], c=COLORS[0], s=30, alpha=0.5)
axes[0, 1].set_title('X vs Y', fontweight='bold')
axes[0, 1].set_xlabel('X')
axes[0, 1].set_ylabel('Y')
axes[0, 1].grid(True, alpha=0.3, linestyle='--')
# Bottom-left: Bar chart
group_means = df.groupby("category")["value"].mean()
axes[1, 0].bar(group_means.index, group_means.values, color=COLORS[:len(group_means)])
axes[1, 0].set_title('Mean by Category', fontweight='bold')
axes[1, 0].set_xlabel('Category')
axes[1, 0].grid(axis='y', alpha=0.3, linestyle='--')
# Bottom-right: Box plot
axes[1, 1].boxplot([df[df["category"] == c]["value"].values for c in categories],
labels=categories, patch_artist=True)
axes[1, 1].set_title('Distribution by Category', fontweight='bold')
axes[1, 1].grid(axis='y', alpha=0.3, linestyle='--')
fig.suptitle('Analysis Summary', fontsize=16, fontweight='bold')
plt.savefig('/workspace/analysis_summary.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineFeature Importance Chart (from cuML model)
fig, ax = plt.subplots(figsize=(8, max(4, len(feature_names) * 0.35)))
# importances = to_pd(model.feature_importances_)
sorted_idx = np.argsort(importances)
ax.barh(np.array(feature_names)[sorted_idx], importances[sorted_idx],
color=COLORS[0], edgecolor='black', linewidth=0.5)
ax.set_xlabel('Importance', fontweight='bold')
ax.set_title('Feature Importances', fontweight='bold')
ax.grid(axis='x', alpha=0.3, linestyle='--')
ax.set_axisbelow(True)
plt.savefig('/workspace/feature_importance.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineConfusion Matrix (from cuML classification)
import seaborn as sns
fig, ax = plt.subplots(figsize=(7, 6))
# cm = confusion_matrix(to_pd(y_test), to_pd(predictions))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', square=True,
xticklabels=class_names, yticklabels=class_names,
linewidths=1, cbar_kws={'label': 'Count'}, ax=ax)
ax.set_xlabel('Predicted', fontweight='bold')
ax.set_ylabel('Actual', fontweight='bold')
ax.set_title('Confusion Matrix', fontweight='bold')
plt.savefig('/workspace/confusion_matrix.png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inlineStyle Rules
- Use
COLORSpalette (colorblind-safe) — never rely on color alone to distinguish elements - No pie charts (bar charts are always clearer)
- No 3D plots (distort data perception)
- Grid lines at
alpha=0.3, linestyle='--'withax.set_axisbelow(True) - Bold axis labels and titles (
fontweight='bold') - White background for all exports
- 1-4 charts per analysis is typical; use multi-panel for more
Output Guidelines
- Save all charts to
/workspace/as PNG - Print file paths after saving so the agent can reference them
- For multi-panel summaries, use
figsize=(14, 10)for 2×2 layouts - Keep chart titles descriptive but concise
- Include units in axis labels when applicable