# fbdesignpro/sweetviz

Visualize and compare datasets, target values and associations, with one line of code.

Repository: https://github.com/fbdesignpro/sweetviz
Canonical: https://ross.abutalabs.com/products/sweetviz
Language: Python
License: MIT
License Family: permissive
Topics: pandas-dataframe, eda, pandas, data-exploration, data-analysis, data-science, data-visualization, machine-learning, data-profiling, exploratory-data-analysis, statistics, exploration, python
Last push: 2026-04-11T19:21:12+00:00

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 76, release rhythm 78, longevity 100
- inputs: {"age_days": 2307, "days_push": 144, "days_rel": 144, "gap_med": 6, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3119, forks 288 (observed 2026-08-28T04:07:44.242195+00:00)

## What it is
Sweetviz is a Python library that generates high-density HTML visualizations for exploratory data analysis in just a couple of lines of code. It specializes in target analysis, dataset comparison (e.g. train vs test), and mixed-type feature associations.

## Use cases
- profile a pandas dataframe with one line of code
- compare training vs test datasets
- analyze how a target variable relates to features
- generate an EDA report as a self-contained HTML file
- compute correlations across numerical and categorical features
- explore a dataset before building a machine learning model

## When to choose
- you need fast, automated exploratory data analysis on pandas dataframes
- you want to compare two datasets or dataset subsets visually
- you need a shareable, self-contained HTML profiling report
- you want automatic type inference and mixed-type correlations

## When to avoid
- you need interactive dashboards or live-updating visualizations
- your data is not in pandas or a tabular format
- you need production-grade BI reporting rather than quick EDA
- datasets are too large to profile in memory

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, analytics, data-science
- domain: data-science, data-visualization, analytics, machine-learning
- platform: python
- tags: eda, data-profiling, pandas, exploratory-data-analysis, html-report, statistics

## Member repositories
- fbdesignpro/sweetviz (main) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:44.242195+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 2026-08-30T07:26:37.722610+00:00, confidence not recorded.
  - readme: https://github.com/fbdesignpro/sweetviz (fetched 2026-08-28T04:07:44.242195+00:00, sha bbea021f12b5)
  - registry_pypi: https://pypi.org/pypi/sweetviz/json (fetched 2026-08-29T09:41:35.558283+00:00, sha 651f378ccc0e)
- Data as of 2026-08-30T08:39:29.467469+00:00.