chart

name

chart

description

Data chart and graph creation skill using Matplotlib. Use when the user wants to create data visualizations — bar charts, line charts, pie charts, scatter plots, heatmaps, histograms, or any data-driven graph. Triggers include: any mention of 'chart', 'graph', 'plot', 'visualization', 'visualize', or requests to display data visually. Also when the user provides numeric data and asks to 'show' or 'compare' it. Do NOT use for diagrams (flowcharts, sequence diagrams, ER diagrams) — those belong to a diagram skill.

Chart creation with Matplotlib

Execution Rules

• ALL code execution MUST use the code_interpreter tool. Do NOT use the shell tool.
• NEVER call !pip install. matplotlib, numpy, pandas, boto3, Pillow are pre-installed in the AgentCore Code Interpreter sandbox. Import directly. If an import fails, stop and report the error to the user — do not attempt to install anything.
• Generate the chart and upload to S3 in a SINGLE code_interpreter call. Do NOT split into multiple calls.
• Before calling code_interpreter, call artifact_path(filename="chart.png") to get the S3 bucket and key.
• After completion, report the artifact_ref to the user.
• If code_interpreter fails with an error, do NOT retry automatically. Report the error to the user and ask for clarification or guidance.

Workflow

1. Call artifact_path(filename="chart.png") — returns { s3_uri, bucket, key, artifact_ref }
2. Copy the actual s3_uri string value from the artifact_path result and hardcode it as a string literal in your code_interpreter script. Do NOT use variable references — the code_interpreter runs in an isolated sandbox and cannot access the agent's tool results.
3. Call code_interpreter ONCE with a single script that creates the chart, saves it, and uploads to S3.

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

Palette	Colors	Use Case
Professional Blue	#1565C0, #42A5F5, #90CAF9	Business reports
Traffic Light	#4CAF50, #FF9800, #F44336	Status, good/warn/bad
Warm	#E91E63, #FF5722, #FF9800, #FFC107	Marketing, engagement
Cool	#0D47A1, #1565C0, #1E88E5, #42A5F5	Technology, finance
Earth	#5D4037, #795548, #8D6E63, #A1887F	Natural, organic themes
Vibrant	#2196F3, #4CAF50, #FF9800, #E91E63	General purpose
Grayscale	#212121, #616161, #9E9E9E, #E0E0E0	Print, formal docs

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()

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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.

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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

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Dependencies

matplotlib, numpy, pandas, boto3, Pillow are pre-installed in the Code Interpreter sandbox. Do NOT call !pip install — import directly.