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Sweetviz

Sweetviz is an open-source Python library that generates visual exploratory-data-analysis reports from pandas dataframes, including feature summaries, target analysis, associations and dataset comparisons.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Sweetviz — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Sweetviz when working with pandas dataframes in Python and you want a visual dataset profile or a comparison report without assembling those summaries manually.

Who it is for

  • Python data practitioners inspecting datasets before or during machine-learning preparation
  • Analysts comparing training and test data or contrasting groups within a dataset

Documented uses

  • Summarize feature types, missing and unique values, duplicate rows, frequent values and numerical statistics.
  • Inspect how a boolean or numerical target relates to other features.
  • Compare distinct datasets or groups within a dataset.
  • Review numerical, categorical and mixed-type associations in a visual report.
Before you choose

Requirements & limits

  • The README lists Python 3.6+ and pandas 0.25.3+ as requirements.
  • Reports can be rendered as HTML or in notebook contexts; HTML output is described as self-contained.
  • Automatic type inference can be overridden or features can be skipped through FeatureConfig.
  • Target analysis accepts boolean and numerical target features only.
  • The README flags report-output limitations in custom environments such as Google Colab that require custom file operations.

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

Create an HTML report for one dataframe

Generate a single-dataset exploratory analysis report from a pandas dataframe.

Before you start

  • Python 3.6 or later
  • pandas 0.25.3 or later
  • Sweetviz installed in the Python environment
  • A pandas dataframe available as my_dataframe

Steps

  1. Install the library with pip install sweetviz.

  2. Import Sweetviz with import sweetviz as sv.

  3. Create a report with my_report = sv.analyze(my_dataframe).

  4. Render it with my_report.show_html().

Expected outcome

Sweetviz generates an HTML report named SWEETVIZ_REPORT.html by default and opens it in the default browser.

Guide limitations

  • The README notes that custom environments such as Google Colab may require file operations that are not supported for report output in this mode.
  • A target feature, if specified, must be boolean or numerical.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
fbdesignpro/sweetviz · Official homepage title · retained in its source language
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