{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/16"},{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"}],"enrichment":{"capability":"Sweetviz generates interactive HTML visualizations for exploratory data analysis (EDA) in two lines of code, supporting target analysis, dataset comparison, and correlation detection across numerical, categorical, and mixed-type features.","skillfed_tags":["data-profiling","visualization","eda"],"use_cases":["Generate a quick summary report of a new dataset to understand distributions, missing values, and feature types without writing custom code.","Compare training and test datasets side-by-side to identify data drift or distribution mismatches before model training.","Analyze how a target variable (e.g., loan default, customer churn) relates to other features to guide feature engineering.","Visualize correlations and associations across mixed data types to identify potential multicollinearity or feature interactions.","Create an interactive HTML report for stakeholders to explore data characteristics without requiring Python knowledge."],"what_it_does":"Sweetviz is a pandas-based Python library that automates exploratory data analysis by generating self-contained, interactive HTML reports from dataframes. It detects feature types automatically (numerical, categorical, text), computes summary statistics and associations, and visualizes relationships between features and optional target variables. The library is designed around two main workflows: analyzing a single dataset with an optional target feature, and comparing two datasets (e.g., training vs. test splits) or subsets within a dataset (e.g., male vs. female rows).\n\nThe package handles mixed-type associations seamlessly\u2014Pearson correlation for numerical features, uncertainty coefficient for categorical features, and correlation ratio for categorical-numerical pairs. Output is a fully self-contained HTML application that renders in a browser or notebook environment, making it useful for quick data characterization tasks without manual visualization code. It depends on standard scientific Python libraries (pandas, numpy, scipy, matplotlib) and template rendering (jinja2).","worth_installing":"Yes. Sweetviz is actively maintained, permissively licensed, has low install friction, and solves a common pain point in data science workflows. It is well-suited for rapid EDA when you need quick visual summaries of datasets and their relationships. Install it if you work regularly with pandas dataframes and want to reduce boilerplate visualization code; skip it if you need fine-grained control over plot aesthetics or work primarily in restricted environments."},"id":"sweetviz","links":{"html":"https://skillfed.io/packages/sweetviz","md":"https://skillfed.io/packages/sweetviz.md","pypi":"https://pypi.org/project/sweetviz/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-11","license_spdx":"MIT","license_treatment":"permissive","name":"sweetviz","python_support":"supports_current","summary":"A pandas-based library to visualize and compare datasets."},"popularity":{"monthly_downloads":207279,"position":9556,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.3"}