{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Build Tools","url":"https://skillfed.io/packages/category/software-development-build-tools"}],"enrichment":{"capability":"Generates comprehensive exploratory data analysis reports for pandas DataFrames, automatically detecting data types, computing statistics, and producing visualizations exportable as HTML, JSON, or Jupyter widgets.","skillfed_tags":["data-profiling","eda","pandas-integration"],"use_cases":["Generate a quick statistical summary and quality report on a new dataset before cleaning or modeling","Compare two versions of the same dataset to identify changes in distributions, missing values, or data quality","Profile time-series data to detect seasonality, autocorrelation, and temporal patterns in one command","Embed interactive profiling widgets in Jupyter notebooks for exploratory analysis during data investigation","Export a shareable HTML report documenting data characteristics and potential issues for stakeholders or documentation"],"what_it_does":"ydata-profiling automates exploratory data analysis by generating a single-line report on a pandas DataFrame. It infers column data types, computes descriptive statistics, detects data quality issues (missing values, duplicates, skewness, high correlations), and produces visualizations for univariate and multivariate analysis. The package handles specialized data including time-series (with autocorrelation and seasonality analysis), text (script and block detection), images (dimensions, EXIF metadata), and files (sizes, creation dates).\n\nReports export to HTML for sharing, JSON for automation, or Jupyter widgets for interactive exploration. The package includes dataset comparison, sensitive data handling, and configuration options for large datasets. It depends on a substantial stack: pandas, numpy, scipy, statsmodels for statistics; matplotlib, seaborn, wordcloud for visualization; pydantic for validation; and specialized libraries like phik for correlation, imagehash for image analysis, and numba for performance.","worth_installing":"Yes, with a migration caveat. The package is actively maintained, has no known vulnerabilities, and provides genuine value for exploratory data analysis workflows. However, install fg-data-profiling instead\u2014ydata-profiling is deprecated and will not receive future updates. If you are already using ydata-profiling, plan migration soon; if starting fresh, use the renamed package directly."},"id":"ydata-profiling","links":{"html":"https://skillfed.io/packages/ydata-profiling","md":"https://skillfed.io/packages/ydata-profiling.md","pypi":"https://pypi.org/project/ydata-profiling/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-22","license_spdx":null,"license_treatment":"permissive","name":"ydata-profiling","python_support":"supports_current","summary":"Generate profile report for pandas DataFrame"},"popularity":{"monthly_downloads":1895918,"position":3452,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"4.18.4"}