AutoPlot - Automatic Data Visualization#

Transform any data file into beautiful charts with one command. No Python, no coding, just results.

When to Use#

✅ Use this skill when:

• "Visualize this CSV file"
• "Create a chart from this data"
• "Plot these sales numbers"
• "Show me trends in this dataset"
• "Make a bar chart from this Excel file"
• "Generate an infographic from this JSON"
• "Analyze and visualize this data"

❌ Don't use when:

• Real-time streaming data visualization
• Complex statistical analysis (use dedicated tools)
• 3D visualization (not supported yet)

Features#

1. Auto-Detection#

• File format - CSV, Excel (.xlsx/.xls), JSON
• Data types - Numeric, categorical, datetime, text
• Relationships - Correlations, trends, distributions

2. Smart Chart Selection#

Data PatternAuto-Selected ChartTime seriesLine chart with trendCategories + valuesBar chartDistributionsHistogram / Box plotCorrelationsScatter plot + regressionProportionsPie / Donut chartGeographicMap visualization

3. Output Formats#

• PNG - Static image, email-ready
• SVG - Scalable, web-ready
• HTML - Interactive, zoom/pan/hover
• PDF - Publication quality

4. Styling#

• Professional themes (default, minimal, dark)
• Auto-color palettes
• Responsive sizing
• Annotations and labels

Quick Start#

Check installation#

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py --version

Visualize a file#

bash

# Auto-detect and visualize
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize data.csv

# Specify output format
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize sales.xlsx --format html

# Custom title and theme
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize metrics.json --title "Q4 Performance" --theme dark

Commands#

visualize#

Create visualization from data file.

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize <file> [options]

Options:
  --chart-type    Override auto-detection (bar, line, scatter, pie, histogram, box)
  --x-column      Specify X-axis column
  --y-column      Specify Y-axis column
  --title         Chart title
  --theme         Visual theme (default, minimal, dark, colorful)
  --format        Output format (png, svg, html, pdf)
  --output        Output file path
  --width         Chart width in pixels (default: 1200)
  --height        Chart height in pixels (default: 800)

analyze#

Analyze data structure and suggest visualizations.

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py analyze <file> [options]

Options:
  --detailed      Show detailed statistics
  --sample N      Show first N rows

dashboard#

Generate multi-chart dashboard.

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py dashboard <file> [options]

Options:
  --charts N      Number of charts to include
  --layout        Layout style (grid, vertical, horizontal)

Examples#

Example 1: Sales Data#

Input: sales.csv

csv

Month,Revenue,Profit,Customers
Jan,10000,3000,150
Feb,12000,4000,180
Mar,15000,5000,220

Command:

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize sales.csv --title "Q1 Sales Performance"

Output:

• Line chart for Revenue/Profit trend
• Bar chart for Customers by month
• Correlation heatmap

Example 2: Survey Results#

Input: survey.xlsx with categorical data

Command:

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize survey.xlsx --chart-type pie

Output: Pie chart showing response distribution

Example 3: Interactive Dashboard#

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py dashboard metrics.json --format html

Output: metrics_dashboard.html - Open in browser for interactive exploration

Supported Data Formats#

CSV#

• Comma or tab delimited
• Headers auto-detected
• Date columns auto-parsed

Excel#

• .xlsx and .xls
• Multiple sheets (specify with --sheet)
• Formulas evaluated

JSON#

• Array of objects
• Nested objects flattened
• Metadata preserved

Chart Types#

Available Charts#

• Line - Time series, trends
• Bar - Comparisons, rankings
• Scatter - Correlations, clusters
• Pie/Donut - Proportions
• Histogram - Distributions
• Box/Violin - Statistical summaries
• Heatmap - Correlation matrices
• Area - Cumulative trends
• Bubble - Multi-dimensional

Auto-Selection Logic#

text

if datetime_column:
    -> Line chart
elif categorical + numeric:
    -> Bar chart
elif two_numeric:
    if correlation_detected:
        -> Scatter + regression
    else:
        -> Scatter
elif single_numeric:
    -> Histogram
elif categorical_only:
    -> Count plot / Pie

Styling Options#

Themes#

• default - Clean, professional
• minimal - No grid, minimal labels
• dark - Dark background, neon colors
• colorful - Vibrant palette
• print - B&W optimized

Customization#

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize data.csv \
  --theme dark \
  --title "Revenue Analysis" \
  --width 1600 \
  --height 900

Output Examples#

PNG (Static)#

• Email attachments
• Documents
• Social media

SVG (Vector)#

• Scalable without quality loss
• Web embedding
• Print materials

HTML (Interactive)#

• Zoom and pan
• Hover tooltips
• Toggle data series
• Export to PNG

PDF (Publication)#

• Research papers
• Reports
• Presentations

Advanced Usage#

Multi-Series Charts#

bash

# Plot multiple columns
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize data.csv \
  --y-column Revenue,Profit,Expenses \
  --chart-type line

Aggregations#

bash

# Group by category and aggregate
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize data.csv \
  --group-by Category \
  --aggregate sum

Filtering#

bash

# Filter data before plotting
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize data.csv \
  --filter "Year >= 2020" \
  --filter "Status = 'Active'"

Integration with OpenClaw#

In conversation#

text

User: "Show me the trend in sales.csv"
AI: [Uses autoplot to generate line chart]

User: "Create a dashboard from this Excel file"
AI: [Generates multi-chart HTML dashboard]

Batch processing#

bash

# Visualize all CSV files in directory
for file in *.csv; do
    python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize "$file"
done

Requirements#

• Python 3.8+
• pandas
• plotly / matplotlib
• openpyxl (for Excel files)

Installation#

bash

# Install dependencies
pip install -r ~/.openclaw/workspace/autoplot/requirements.txt

# Verify installation
python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py --version

Troubleshooting#

Large datasets (>100k rows)#

Use sampling:

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize big.csv --sample 10000

Memory issues#

Process in chunks or use aggregation.

Date parsing errors#

Specify date format:

bash

python3 ~/.openclaw/workspace/autoplot/scripts/autoplot.py visualize data.csv --date-format "%Y-%m-%d"

Roadmap#

• 3D visualization support
• Animation/timelapse charts
• Real-time data streaming
• SQL database connections
• Custom chart templates
• API server mode

License#

MIT License - See LICENSE file

Credits#

Built with Plotly, Pandas, and OpenClaw