---
id: autovizwidget
version: "0.23.0"
license: BSD 3-clause
license_treatment: permissive
maintenance: aging
---

# autovizwidget — AutoVizWidget: An Auto-Visualization library for pandas dataframes

License: permissive · Maintenance: aging · Downloads: 1.6M/mo

## What it is and what it does

Autovizwidget is a visualization component built for Sparkmagic that automatically renders pandas dataframes as interactive charts within Jupyter notebooks. It sits atop plotly, ipywidgets, and pandas to provide a no-code visualization layer—users pass a dataframe and the widget selects appropriate chart types and renders them interactively without requiring manual plotting code.

The package is designed primarily for exploratory data analysis in Jupyter environments, especially when working with Spark clusters via Sparkmagic's remote execution model. It depends on notebook, hdijupyterutils, and the visualization stack to function, making it a specialized tool for interactive notebook workflows rather than a general-purpose plotting library.

Use it for:

- Quickly explore query results from remote Spark clusters without writing visualization code in Sparkmagic notebooks.
- Generate interactive charts from pandas dataframes returned by Spark SQL queries for ad-hoc analysis.
- Build exploratory dashboards in Jupyter that automatically visualize data without manual plot configuration.
- Prototype data insights in notebooks before scaling to production Spark jobs.
- Share interactive data exploration notebooks with non-technical stakeholders using automatic visualizations.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Autovizwidget automatically generates interactive visualizations for pandas dataframes in Jupyter notebooks, integrating with Sparkmagic's remote Spark cluster execution.

Yes, if you use Sparkmagic in Jupyter notebooks and want automatic dataframe visualization without writing plotting code. The low install friction and permissive license make it a low-risk addition. However, the aging maintenance status (403 days since last release) and lack of recent updates suggest you should verify it works with your current Jupyter and ipywidgets versions before relying on it for critical workflows. No known security vulnerabilities.

## Install

pip install autovizwidget
uv add autovizwidget
poetry add autovizwidget

## Installing autovizwidget

Before you install: Low install friction with a pure-Python wheel distribution. Maintenance status is aging—last release was 403 days ago—but the repository remains active and unarchived with 1366 stars, suggesting continued community interest despite infrequent updates.

License in practice: BSD 3-clause permissive license allows use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart:

pip install autovizwidget

import pandas as pd
from autovizwidget.widget.autovizwidget import AutoVizWidget

df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
AutoVizWidget(df)

Requires Jupyter notebook environment with ipywidgets properly installed and enabled; works within Sparkmagic's Jupyter integration but not in standalone Python scripts.

Verify before relying:

- Whether autovizwidget works independently or only as part of Sparkmagic's kernel/magic ecosystem.
- Specific Python version support beyond the classifier indicating 3.7; modern Python compatibility unspecified.
- Performance characteristics with large dataframes or complex data types.
- Whether visualization types and interactivity options are configurable or fully automatic.

## Package facts

- License: BSD 3-clause (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: aging
- Downloads: 1.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas dataframe visualization jupyter, automatic chart generation notebooks, interactive plots pandas data, sparkmagic visualization widget, jupyter dataframe explorer, auto-visualization pandas, interactive data display jupyter, jupyter-notebook, data-exploration, spark-integration

[View on SkillFed](https://skillfed.io/packages/autovizwidget) · [View on PyPI](https://pypi.org/project/autovizwidget/)