autoviz

AutoViz

Use this skill when a user wants to explore a tabular dataset with AutoViz, fix data-quality problems with FixDQ, or generate wordclouds from string columns. Keep this skill router-like: send the user to the most specific sub-skill, then use the bundled references and scripts for details.

First decision

• If the task is about loading a file or DataFrame and producing plots, use sub-skills/eda-visualization/SKILL.md.
• If the task is about data-quality reporting or FixDQ, use sub-skills/data-quality-fixes/SKILL.md.
• If the task is about text columns, NLP cleanup, or wordclouds, use sub-skills/text-wordclouds/SKILL.md.

Minimal usage shape

from autoviz import AutoViz_Class

AV = AutoViz_Class()
dft = AV.AutoViz(
    filename,
    sep=",",
    depVar="",
    dfte=df,
    header=0,
    verbose=1,
    lowess=False,
    chart_format="svg",
    max_rows_analyzed=150000,
    max_cols_analyzed=30,
    save_plot_dir=None,
)

What to read next

• Read references/overview.md for the package surface and the quickest route to the right sub-skill.
• Read references/install-and-compatibility.md if imports, pip check, xgboost, pandas, or interactive-backend dependencies fail.
• Read references/chart-formats.md when the user asks for png, svg, jpg, bokeh, server, or html output behavior.
• Read references/troubleshooting.md for import, environment, plotting, and text-column failure modes.
• Read references/api-reference.md when you need exact signatures or helper names.
• Read references/repo-provenance.md when you need to check source freshness or decide whether the skill should be refreshed.
• Read references/repo-routing-metadata.json when you need the managed router placement metadata used during import.
• Read scripts/inspect_install.py when you need a quick runtime snapshot from a prepared environment.

Common routing cues

• AutoViz_Class, AutoViz_Main, filename, dfte, chart_format, save_plot_dir, max_rows_analyzed, or max_cols_analyzed usually mean the EDA sub-skill.
• FixDQ, data_cleaning_suggestions, duplicate rows, infinity, rare categories, mixed types, or leakage usually mean the data-quality sub-skill.
• wordcloud, nltk, textblob, emoji, stopwords, discrete string, or NLP columns usually mean the text sub-skill.
• If the user asks about installation, package versions, or why import autoviz fails, route to the relevant references first and then return to the right sub-skill.

Import note

import autoviz prints a banner in this repository version. That is expected and not a failure.

Quick expectations

• The package is CPU-oriented.
• Interactive chart formats depend on hvplot, holoviews, panel, bokeh, and IPython.
• data_cleaning_suggestions uses pandas_dq.
• FixDQ is the transformer-style path for reusable data-quality cleanup.
• Wordcloud generation may trigger an NLTK download if string columns are present.
• A tiny toy DataFrame can be classified in surprising ways, so use the bundled smoke scripts when debugging.

What not to do

• Do not point users at the original source checkout as a required runtime dependency.
• Do not suggest source notebooks or repo tests as the primary user workflow when a bundled script exists.
• Do not claim accelerator requirements; this skill's covered workflows are CPU-based.
• Do not hide compatibility warnings in prose when pip check or import output shows a concrete failure.

Handoff shape

When the user is still deciding, keep the response short and route them to the right sub-skill plus the correct reference. When the user wants action, use the bundled script for that sub-skill, verify the runtime, and then summarize the result in package terms rather than source-file terms.

Escalation

If the user wants a broader comparison with another package, first finish the AutoViz route and then explain the boundary clearly. If the issue is really a repository install or compatibility failure, point them to the install-and-compatibility and troubleshooting references before changing the workflow.