Data Visualization Creator
Create publication-quality data visualizations with matplotlib, plotly, or Chart.js
Your matplotlib charts look like homework assignments — default colors, tiny labels, and no styling. Making them presentation-ready means 50 lines of formatting code you'll forget by next week.
Who it's for: researchers creating figures for papers, analysts building executive reports, data scientists presenting to non-technical stakeholders, students preparing thesis visualizations, anyone whose default charts get rejected for being 'not polished enough'
Example
"Create a publication-quality chart showing user growth by cohort" → Right chart type selected, professional styling with colorblind-friendly palette, proper labels and annotations, export-ready figure, and the Python code to reproduce it
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add data-visualization-creator# Data Visualization Creator
## Your Role
You are a data visualization assistant. You create publication-quality charts and graphs from data using Python (matplotlib, seaborn, plotly) or JavaScript (Chart.js). You select the right chart type, apply best practices for clarity and accuracy, and produce visuals ready for reports and presentations.
## Workflow
### 1. Understand the Request
Determine:
- **Data source**: Query results, pasted data, CSV/Excel file, or data to be queried
- **Chart type**: Explicitly requested or needs to be recommendedGet new playbooks like this one
One email a week with new Claude Code workflows. Free, like everything here.
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What This Does
Creates publication-quality data visualizations from your data using Python (matplotlib, seaborn, plotly). The assistant selects the right chart type for your data, applies professional styling with colorblind-friendly palettes, and produces charts ready for reports, presentations, or dashboards. It also provides the code so you can modify and reuse the visualizations.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Set Up Your Project
Create a project folder and place the template inside:
mkdir -p ~/Projects/data-viz
mv ~/Downloads/CLAUDE.md ~/Projects/data-viz/
Add your data files (CSV, Excel, JSON) to the same folder.
Step 3: Start Working
cd ~/Projects/data-viz
claude
Say: "Create a chart showing monthly revenue trends from sales_data.csv"
Chart Type Selection Guide
The assistant automatically recommends the right chart type based on your data:
| Data Relationship | Recommended Chart |
|---|---|
| Trend over time | Line chart |
| Comparison across categories | Bar chart |
| Part-to-whole composition | Stacked bar or area chart |
| Distribution of values | Histogram or box plot |
| Correlation between variables | Scatter plot |
| Ranking | Horizontal bar chart |
| Matrix of relationships | Heatmap |
| Geographic data | Choropleth map |
Design Best Practices Applied
- Colorblind-friendly palettes used by default
- Insight-driven titles ("Revenue grew 23% YoY" instead of "Revenue by Month")
- Clean formatting -- no chart junk, appropriate whitespace, readable axis labels
- Smart number formatting -- currencies as
$1.2M, percentages as45.2%, large numbers as2.3K - Accurate scales -- bar charts start at zero, consistent scales across comparison panels
Tips
- Request "interactive" to get plotly charts with hover, zoom, and filter capabilities
- Specify "presentation" for larger fonts and higher contrast suitable for slides
- You can request multiple charts at once (e.g., "create a 2x2 grid of charts showing quarterly metrics")
- Charts are saved as PNG files to your current directory for easy inclusion in reports
Example Prompts
"Show monthly revenue for the last 12 months as a line chart with the trend highlighted"
"Create a horizontal bar chart ranking products by NPS score from this data"
"Make a heatmap of order volume by day-of-week and hour from orders.csv"
"Build an interactive scatter plot showing the correlation between spend and revenue"
"Create a presentation-ready 2x2 grid: revenue, users, conversion rate, and churn by quarter"