---

name: sweetviz description: "Use Sweetviz to generate pandas EDA reports, compare tabular datasets, configure feature typing, and troubleshoot HTML/notebook output." disable-model-invocation: true metadata: disco-role: operating license: MIT

Sweetviz repo skill

Use this skill when a task involves Sweetviz, pandas exploratory data analysis reports, target-aware tabular profiling, train/test dataset comparison, feature type overrides, Sweetviz config files, or Sweetviz HTML/notebook output troubleshooting.

Quick start

1. Install Sweetviz in the active project environment:

   python -m pip install sweetviz
   

   For source checkouts or local fixes, use an editable install from that checkout:

   python -m pip install -e .
   

2. Verify the public import and signatures before writing guidance that depends on exact parameters:

   import sweetviz as sv
   print(sv.__version__)
   print(sv.analyze)
   

   Or run the bundled environment checker:

   python scripts/check_sweetviz_install.py --json
   

3. Route to a sub-skill for the actual workflow.

Route map

• Use sub-skills/report-generation/ when the user wants to create a Sweetviz report from one pandas DataFrame, choose a target, save HTML with show_html(), embed with show_notebook(), control layout/scale, avoid browser launches, or understand optional Comet logging.
• Use sub-skills/dataset-comparison/ when the user wants to compare train/test DataFrames, baseline/current datasets, or two groups from one DataFrame with compare() or compare_intra().
• Use sub-skills/configuration-and-data-handling/ when the user mentions FeatureConfig, skipped/forced feature types, wrong type inference, config overrides, quiet/progress-only output, duplicate columns, target NaNs, mixed types, CJK fonts, or preflight validation.

Common tasks often need two routes: validate/force feature types with configuration-and-data-handling, then render with report-generation or compare with dataset-comparison.

Public API orientation

Sweetviz 2.3.3 exposes these primary objects from import sweetviz as sv:

• sv.analyze(source, target_feat=None, feat_cfg=None, pairwise_analysis='auto')
• sv.compare(source, compare, target_feat=None, feat_cfg=None, pairwise_analysis='auto')
• sv.compare_intra(source_df, condition_series, names, target_feat=None, feat_cfg=None, pairwise_analysis='auto')
• sv.FeatureConfig(skip=None, force_cat=None, force_text=None, force_num=None)
• sv.DataframeReport with show_html(), show_notebook(), and log_comet() methods
• sv.config_parser for INI-style defaults such as verbosity, layouts, CJK fonts, and type thresholds

Do not pass verbosity= to public sv.analyze() or sv.compare() in this version. Use [General] default_verbosity through sv.config_parser or the lower-level DataframeReport(..., verbosity=...) path when that trade-off is intentional.

Shared references and scripts

• Read compatibility and assets for package dependencies, Python support, optional extras, packaged templates/fonts/styles, and why there is no Sweetviz CLI route.
• Read troubleshooting for install/import failures, shadowed modules, package-data/template errors, font/browser/notebook issues, optional Comet behavior, and broad data compatibility symptoms.
• Read repo provenance before deciding whether this skill is current for a Sweetviz checkout or should be refreshed.
• Run check_sweetviz_install.py to verify importability, version metadata, public signatures, packaged assets, and optional tiny HTML rendering without opening a browser.

Cautions

• Targets must exist in the source data, must not be skipped, must contain no missing values, and must be boolean or numeric after Sweetviz type detection/forcing.
• Numeric columns with at most the configured distinct-value threshold infer as categorical by default. Force known numeric low-cardinality columns with FeatureConfig(force_num=[...]) when needed.
• pairwise_analysis='auto' can warn and return early for wide data; use 'off' for deterministic smoke reports or 'on' only when the user explicitly wants associations and accepts the cost.
• Sweetviz has no console entry point in this package version. Use the Python API or bundled skill helper scripts.
• Optional Comet.ml logging is external and credentialed. Do not require it for local report generation or verification unless the user explicitly asks for Comet integration.