Agent skill
autoviz
Automatic exploratory data analysis and visualization with a single line of code - generates comprehensive charts, detects patterns, and exports to HTML/notebooks
Install this agent skill to your Project
npx add-skill https://github.com/vamseeachanta/workspace-hub/tree/main/.claude/skills/data/analysis/autoviz
SKILL.md
Autoviz
When to Use This Skill
USE AutoViz when:
- Quick EDA - Need rapid insights into a new dataset
- Initial exploration - Starting analysis on unfamiliar data
- Pattern discovery - Automatically detect relationships between variables
- Presentation prep - Need charts quickly for stakeholder meetings
- Large datasets - Built-in sampling handles big data efficiently
- Feature analysis - Understanding distribution and importance of features
- Correlation hunting - Finding relationships without manual chart creation
- Report generation - Export comprehensive HTML reports
DON'T USE AutoViz when:
- Custom visualizations - Need highly specific chart designs
- Interactive dashboards - Use Streamlit or Dash instead
- Real-time data - Streaming visualization requirements
- Production systems - Charts for automated pipelines (use Plotly/Altair)
- Precise statistical tests - Need formal hypothesis testing
- Domain-specific plots - Specialized visualizations not in standard EDA
Prerequisites
# Basic installation
pip install autoviz
# With all visualization backends
pip install autoviz matplotlib seaborn plotly bokeh
# Using uv (recommended)
uv pip install autoviz pandas matplotlib seaborn plotly
# Jupyter notebook support
pip install autoviz ipywidgets notebook
# Verify installation
python -c "from autoviz import AutoViz_Class; print('AutoViz ready!')"
Complete Examples
Example 1: Sales Data EDA Pipeline
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import os
def sales_eda_pipeline(
data_path: str,
output_dir: str,
*See sub-skills for full details.*
### Example 2: Machine Learning Feature Analysis
```python
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
from sklearn.datasets import make_classification, make_regression
import os
def ml_feature_analysis(
X: pd.DataFrame,
y: pd.Series,
*See sub-skills for full details.*
### Example 3: Multi-Dataset Comparison
```python
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
import os
from datetime import datetime
def compare_datasets(
datasets: dict,
output_dir: str = "comparison_output"
*See sub-skills for full details.*
## Version History
- **1.0.0** (2026-01-17): Initial release
- Basic one-line EDA functionality
- Chart format options (png, svg, html, bokeh, server)
- Large dataset handling with sampling
- Feature distribution analysis
- Correlation detection
- Outlier identification
- HTML and notebook export
- Complete pipeline examples
- Integration with Streamlit and Polars
- Best practices and troubleshooting
## Resources
- **Official Documentation**: https://github.com/AutoViML/AutoViz
- **PyPI**: https://pypi.org/project/autoviz/
- **Tutorial**: https://towardsdatascience.com/autoviz-a-new-tool-for-automated-visualization-ec9c1744a6ad
- **Examples**: https://github.com/AutoViML/AutoViz/tree/master/examples
---
**Automate your exploratory data analysis with AutoViz - one line of code, comprehensive insights!**
## Sub-Skills
- [1. Basic One-Line EDA](1-basic-one-line-eda/SKILL.md)
- [2. Chart Format and Output Options (+1)](2-chart-format-and-output-options/SKILL.md)
- [4. Feature Analysis and Distribution Plots](4-feature-analysis-and-distribution-plots/SKILL.md)
- [5. Correlation Detection](5-correlation-detection/SKILL.md)
- [6. Outlier Detection and Highlighting](6-outlier-detection-and-highlighting/SKILL.md)
- [7. Export to HTML and Notebooks](7-export-to-html-and-notebooks/SKILL.md)
- [AutoViz with Streamlit (+1)](autoviz-with-streamlit/SKILL.md)
- [1. Sample Large Datasets (+3)](1-sample-large-datasets/SKILL.md)
- [Common Issues](common-issues/SKILL.md)
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