Chart

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import boto3

# IMPORTANT: Replace with the ACTUAL s3_uri value returned by artifact_path
S3_URI = "s3://my-bucket/user123/proj456/artifacts/art_abc123/chart.png"  # <- paste the actual s3_uri here

# Parse S3 URI into bucket and key
BUCKET, KEY = S3_URI.replace("s3://", "").split("/", 1)

# Create chart
fig, ax = plt.subplots(figsize=(10, 6))
# ... build chart ...
fig.savefig('./chart.png', dpi=150, bbox_inches='tight', facecolor='white')
plt.close(fig)

# Upload to S3
s3 = boto3.client('s3')
with open('./chart.png', 'rb') as f:
    s3.upload_fileobj(
        f, BUCKET, KEY,
        ExtraArgs={'ContentType': 'image/png'}
    )

4. Report the artifact_ref to the user

Headless Setup (Required)

The code_interpreter has no display. Always set the Agg backend before importing pyplot:

import matplotlib
matplotlib.use('Agg')  # MUST be before importing pyplot
import matplotlib.pyplot as plt

Always use fig.savefig() — never call plt.show(). Always call plt.close(fig) after saving to free memory.

Chart Types

Bar Chart

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

categories = ['Q1', 'Q2', 'Q3', 'Q4']
values = [4500, 5500, 6200, 7100]
colors = ['#2196F3', '#4CAF50', '#FF9800', '#E91E63']

bars = ax.bar(categories, values, color=colors, width=0.6, edgecolor='white', linewidth=0.5)

# Add value labels on bars
for bar, val in zip(bars, values):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 100,
            f'{val:,}', ha='center', va='bottom', fontsize=11, fontweight='bold')

ax.set_title('Quarterly Revenue', fontsize=16, fontweight='bold', pad=15)
ax.set_ylabel('Revenue ($)', fontsize=12)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x:,.0f}'))

Horizontal Bar Chart

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

categories = ['Product A', 'Product B', 'Product C', 'Product D']
values = [320, 450, 280, 510]

bars = ax.barh(categories, values, color='#2196F3', height=0.5)

for bar, val in zip(bars, values):
    ax.text(val + 5, bar.get_y() + bar.get_height()/2,
            f'{val}', va='center', fontsize=11)

ax.set_title('Sales by Product', fontsize=16, fontweight='bold')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.invert_yaxis()

Grouped Bar Chart

import numpy as np

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

categories = ['Q1', 'Q2', 'Q3', 'Q4']
series1 = [300, 400, 350, 500]
series2 = [250, 350, 400, 450]

x = np.arange(len(categories))
width = 0.35

ax.bar(x - width/2, series1, width, label='2024', color='#2196F3')
ax.bar(x + width/2, series2, width, label='2025', color='#FF9800')

ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()
ax.set_title('Year-over-Year Comparison', fontsize=16, fontweight='bold')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Line Chart

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

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
values = [120, 135, 148, 162, 155, 178]

ax.plot(months, values, color='#2196F3', linewidth=2.5, marker='o', markersize=8)
ax.fill_between(range(len(months)), values, alpha=0.1, color='#2196F3')

ax.set_title('Monthly Growth', fontsize=16, fontweight='bold', pad=15)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(axis='y', alpha=0.3)

Multi-Line Chart

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

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
line1 = [120, 135, 148, 162, 155, 178]
line2 = [100, 115, 130, 140, 150, 160]

ax.plot(months, line1, color='#2196F3', linewidth=2.5, marker='o', label='Product A')
ax.plot(months, line2, color='#E91E63', linewidth=2.5, marker='s', label='Product B')

ax.legend(frameon=False)
ax.set_title('Product Comparison', fontsize=16, fontweight='bold')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(axis='y', alpha=0.3)

Pie Chart

fig, ax = plt.subplots(figsize=(8, 8))

labels = ['Product A', 'Product B', 'Product C', 'Other']
sizes = [35, 30, 20, 15]
colors = ['#2196F3', '#4CAF50', '#FF9800', '#9E9E9E']
explode = (0.05, 0, 0, 0)  # Slightly pull out the largest slice

wedges, texts, autotexts = ax.pie(
    sizes, labels=labels, colors=colors, explode=explode,
    autopct='%1.0f%%', startangle=90,
    textprops={'fontsize': 12}
)
for autotext in autotexts:
    autotext.set_fontweight('bold')

ax.set_title('Market Share', fontsize=16, fontweight='bold', pad=20)

Donut Chart

fig, ax = plt.subplots(figsize=(8, 8))

labels = ['Complete', 'In Progress', 'Pending']
sizes = [65, 20, 15]
colors = ['#4CAF50', '#FF9800', '#E0E0E0']

wedges, texts, autotexts = ax.pie(
    sizes, labels=labels, colors=colors,
    autopct='%1.0f%%', startangle=90, pctdistance=0.8,
    wedgeprops=dict(width=0.4)
)

# Center label
ax.text(0, 0, '65%', ha='center', va='center', fontsize=28, fontweight='bold', color='#4CAF50')
ax.text(0, -0.12, 'Complete', ha='center', va='center', fontsize=12, color='#666666')

Scatter Plot

import numpy as np

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

np.random.seed(42)
x = np.random.randn(100) * 10 + 50
y = x * 0.8 + np.random.randn(100) * 5
sizes = np.random.randint(20, 200, 100)

scatter = ax.scatter(x, y, s=sizes, c=y, cmap='viridis', alpha=0.7, edgecolors='white', linewidth=0.5)
fig.colorbar(scatter, ax=ax, label='Value')

ax.set_title('Scatter Analysis', fontsize=16, fontweight='bold')
ax.set_xlabel('Variable X', fontsize=12)
ax.set_ylabel('Variable Y', fontsize=12)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Heatmap

import numpy as np

fig, ax = plt.subplots(figsize=(10, 8))

data = np.random.rand(6, 8)
row_labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']
col_labels = ['9am', '10am', '11am', '12pm', '1pm', '2pm', '3pm', '4pm']

im = ax.imshow(data, cmap='YlOrRd', aspect='auto')
fig.colorbar(im, ax=ax)

ax.set_xticks(range(len(col_labels)))
ax.set_yticks(range(len(row_labels)))
ax.set_xticklabels(col_labels)
ax.set_yticklabels(row_labels)

# Add value labels
for i in range(len(row_labels)):
    for j in range(len(col_labels)):
        color = 'white' if data[i, j] > 0.6 else 'black'
        ax.text(j, i, f'{data[i,j]:.1f}', ha='center', va='center', color=color, fontsize=9)

ax.set_title('Activity Heatmap', fontsize=16, fontweight='bold', pad=15)

Histogram

import numpy as np

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

np.random.seed(42)
data = np.random.normal(100, 15, 1000)

ax.hist(data, bins=30, color='#2196F3', edgecolor='white', linewidth=0.5, alpha=0.8)
ax.axvline(np.mean(data), color='#E91E63', linestyle='--', linewidth=2, label=f'Mean: {np.mean(data):.1f}')

ax.set_title('Distribution', fontsize=16, fontweight='bold')
ax.set_xlabel('Value', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)
ax.legend(frameon=False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Stacked Bar Chart

import numpy as np

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

categories = ['Q1', 'Q2', 'Q3', 'Q4']
seg1 = [200, 250, 300, 350]
seg2 = [150, 180, 200, 220]
seg3 = [100, 120, 140, 160]

ax.bar(categories, seg1, label='Segment A', color='#2196F3')
ax.bar(categories, seg2, bottom=seg1, label='Segment B', color='#4CAF50')
ax.bar(categories, seg3, bottom=np.array(seg1)+np.array(seg2), label='Segment C', color='#FF9800')

ax.set_title('Revenue Breakdown', fontsize=16, fontweight='bold')
ax.legend(frameon=False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Styling Guide

Color Palettes

Typography

# Title
ax.set_title('Chart Title', fontsize=16, fontweight='bold', pad=15)

# Axis labels
ax.set_xlabel('X Label', fontsize=12)
ax.set_ylabel('Y Label', fontsize=12)

# Tick labels
ax.tick_params(labelsize=10)

# Annotation
ax.annotate('Peak', xy=(x, y), fontsize=11, fontweight='bold',
            arrowprops=dict(arrowstyle='->', color='#333333'))

Clean Style Rules

Apply these to every chart for a polished, professional look:

# Remove top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Subtle gridlines (y-axis only for bar/line charts)
ax.grid(axis='y', alpha=0.3, linestyle='-', linewidth=0.5)

# White background
fig.patch.set_facecolor('white')
ax.set_facecolor('white')

# Tight layout to prevent label clipping
fig.tight_layout()

Number Formatting

# Currency
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x:,.0f}'))

# Percentage
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:.0f}%'))

# Thousands (K)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x/1000:.0f}K'))

# Millions (M)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x/1e6:.1f}M'))

Multiple Charts (Subplots)

fig, axes = plt.subplots(1, 3, figsize=(18, 6))

# Chart 1
axes[0].bar(['A', 'B', 'C'], [10, 20, 15], color='#2196F3')
axes[0].set_title('Bar Chart')

# Chart 2
axes[1].plot([1, 2, 3, 4], [10, 15, 12, 18], color='#4CAF50', marker='o')
axes[1].set_title('Line Chart')

# Chart 3
axes[2].pie([40, 30, 30], labels=['X', 'Y', 'Z'], colors=['#E91E63', '#FF9800', '#9E9E9E'])
axes[2].set_title('Pie Chart')

for ax in axes[:2]:
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

fig.suptitle('Dashboard Overview', fontsize=18, fontweight='bold', y=1.02)
fig.tight_layout()

Embedding in Other Documents

Charts created with this skill produce PNG images that can be embedded in:

• PPTX — use the pptx skill's add_picture() with the chart image
• DOCX — use the docx skill's doc.add_picture() with the chart image
• Markdown — use ![Chart description](image_url) syntax

When creating charts for embedding, save the image locally first, then reference it in the parent document's code_interpreter script.

Common Pitfalls

• Always set matplotlib.use('Agg') before importing pyplot — code_interpreter has no display
• Never call plt.show() — it does nothing in headless mode and may hang
• Always call plt.close(fig) after saving — prevents memory leaks
• Use bbox_inches='tight' in savefig() — prevents label clipping
• Use facecolor='white' in savefig() — default transparent background renders poorly in some viewers
• Use fig, ax = plt.subplots() over plt.plot() — explicit figure/axes gives more control
• Import numpy for any data manipulation — it's always available alongside matplotlib

Dependencies

All dependencies should be installed within code_interpreter:

!pip install matplotlib numpy