# AutoViML/AutoViz

Automatically Visualize any dataset, any size with a single line of code.  Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Repository: https://github.com/AutoViML/AutoViz
Canonical: https://ross.abutalabs.com/products/autoviz
Language: Python
License: Apache-2.0
License Family: permissive
Topics: visualization, python3, python, xgboost, automl, scikit-learn, machine-learning, tableau, automl-algorithms, tpot, auto-sklearn, automated-machine-learning
Last push: 2024-06-10T12:08:43+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2604, "days_push": 814, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1898, forks 214 (observed 2026-08-28T04:05:51.077869+00:00)

## What it is
AutoViz is a Python library that automatically generates visualizations for any dataset, of any size, with a single line of code. It also includes a FixDQ() function for assessing and fixing data quality issues.

## Use cases
- automatically visualize a csv dataset with one line of code
- quick exploratory data analysis of a pandas dataframe
- generate charts for large datasets without manual plotting
- assess and fix data quality issues before modeling
- explore datasets as a beginner without writing matplotlib code

## When to choose
- you want fast automated EDA charts with minimal code
- you need quick insight into a new dataset's distributions and relationships
- you want a lightweight automated visualization companion to an AutoML workflow

## When to avoid
- you need fully customized, publication-quality figures
- you require fine-grained control over chart types and styling
- your project depends on a specific visualization stack like plotly or seaborn

## Facets
- artifact type: library
- maturity: active
- function: data-visualization
- domain: data-visualization, data-science, machine-learning
- platform: python, cross-platform
- tags: automl, eda, exploratory-data-analysis, charts, one-liner, data-quality

## Member repositories
- AutoViML/AutoViz (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:51.077869+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:12:12.305411+00:00, confidence not recorded.
  - readme: https://github.com/AutoViML/AutoViz (fetched 2026-08-28T04:05:51.077869+00:00, sha cad7f646a4b6)
  - registry_pypi: https://pypi.org/pypi/autoviz/json (fetched 2026-08-29T10:51:21.136623+00:00, sha d954829aa4ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.