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Autoviz

AutoViz is an open-source Python library that generates exploratory visualizations from CSV, TXT and JSON files or pandas dataframes, and includes FixDQ() for dataset-quality assessment and addressing quality issues.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Autoviz — OpenViST project profile in AI Data & Analytics.
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Understand the project

Practical overview

When it fits

Use AutoViz during exploratory data analysis when you want an initial set of charts from a supported dataset, or when a workflow also needs a dataset-quality check.

Who it is for

  • Python users exploring datasets through automated charts.
  • Data analysts who want a quick visual overview or dataset-quality assessment in an exploratory analysis workflow.

Documented uses

  • Generate exploratory visualizations from a file or pandas dataframe.
  • Display charts in a Jupyter Notebook or save chart output locally or as HTML.
  • Limit rows or columns analyzed when working with a large dataset.
  • Assess dataset quality and use FixDQ() to address data-quality issues.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. Repository setup instructions list Anaconda and Python-version-specific dependency files.

  • AutoViz runs as a Python library and is documented for use from a Jupyter Notebook or terminal, rather than as a hosted analytics workspace.
  • It accepts either a supported filename or a pandas dataframe; the documentation says to use only one of these input methods per call.
  • The documented chart options include static image formats, interactive Bokeh output, browser dashboards and HTML files.
  • For large datasets, row and column sampling settings can be used to speed up visualization.
  • The documented dependency installation varies by Python version.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate exploratory charts from a dataset

Use AutoViz to create visualizations from a supported file or pandas dataframe.

Before you start

  • Anaconda, as listed in the repository prerequisites.
  • A Python environment with AutoViz dependencies installed using the requirements file for its Python version.
  • A CSV, TXT or JSON file, or a pandas dataframe.

Steps

  1. Clone or download the AutoViz repository and create an Anaconda environment, following the repository installation instructions.

  2. Install dependencies with requirements.txt for Python versions below 3.10, requirements-py310.txt for Python 3.10, or requirements-py311.txt for Python 3.11 and above.

  3. In a notebook or terminal, instantiate AutoViz_Class and call AutoViz with a filename, or provide a pandas dataframe using the documented dataframe argument.

  4. Choose chart output settings as needed, such as static image charts, interactive Bokeh charts or HTML output.

Expected outcome

AutoViz generates exploratory charts, which can be displayed in a notebook or saved in the documented output formats.

Guide limitations

  • For a dataframe input, the documentation says to use an empty filename and provide the dataframe argument; use only one input method.
  • Large datasets can be sampled with max_rows_analyzed and max_cols_analyzed.
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Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
AutoViML/AutoViz · Official homepage title · retained in its source language
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