autoviz

name	autoviz
version	1.0.0
description	Automatic exploratory data analysis and visualization with a single line of code - generates comprehensive charts, detects patterns, and exports to HTML/notebooks
type	reference
author	workspace-hub
category	data-analysis
capabilities	["One-line automatic EDA","Feature distribution analysis","Correlation detection and visualization","Outlier identification and highlighting","Automated chart type selection","Export to HTML and Jupyter notebooks","Support for large datasets with sampling","Categorical and numerical feature analysis"]
tools	["autoviz","pandas","matplotlib","seaborn","plotly"]
tags	["autoviz","eda","exploratory-data-analysis","visualization","automated","charts","correlation","distribution","outliers","feature-analysis"]
platforms	["python"]
related_skills	["ydata-profiling","pandas-data-processing","polars","plotly","streamlit"]
requires	[]
scripts_exempt	true

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

 (