{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Generates summary statistics for dataset features and creates interactive visualizations of data distributions, supporting both numeric and categorical columns from pandas DataFrames or TensorFlow records.","skillfed_tags":["data-profiling","exploratory-analysis","abandoned"],"use_cases":["Explore feature distributions and detect data quality issues in a pandas DataFrame before training an ML model.","Compare summary statistics across multiple datasets side-by-side to identify distribution shifts or anomalies.","Generate weighted statistics for datasets where examples have importance weights, toggling between weighted and unweighted views.","Analyze TensorFlow Example records from TfRecord files to understand feature statistics at scale in a TensorFlow pipeline.","Create interactive reports of dataset characteristics in Jupyter notebooks for data documentation and team review."],"what_it_does":"Facets Overview is a Python library that computes and visualizes summary statistics for dataset features in an interactive Jupyter notebook interface. It accepts data as pandas DataFrames or TensorFlow Example protocol buffers from TfRecord files, then generates a protocol buffer containing statistics like min, mean, median, max, and standard deviation for numeric features, and metrics like average length, unique value counts, and mode for string features. The visualization displays these statistics in two tables\u2014one for numeric and one for categorical features\u2014with sortable rows, distribution charts, and optional weighted statistics if example weights are provided.\n\nThe package is designed for exploratory data analysis and data quality assessment in machine learning workflows. It highlights potentially problematic statistics (such as missing values) in red and offers multiple chart types including histograms, deciles, and cumulative distribution functions. However, the package is abandoned and has not been updated since May 2023, meaning it may face compatibility issues with newer versions of its dependencies (numpy, pandas, protobuf) or modern Jupyter environments.","worth_installing":"Yes, but with caution. The package is useful for interactive data exploration and visualization in Jupyter notebooks, has low install friction, and carries no known vulnerabilities. However, it is abandoned and unmaintained since May 2023, so compatibility with current versions of numpy, pandas, protobuf, and Jupyter is uncertain. Install only if you can tolerate potential breakage or are working in a stable, locked environment. For active projects, consider maintained alternatives."},"id":"facets-overview","links":{"html":"https://skillfed.io/packages/facets-overview","md":"https://skillfed.io/packages/facets-overview.md","pypi":"https://pypi.org/project/facets-overview/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2023-05-24","license_spdx":null,"license_treatment":"permissive","name":"facets-overview","python_support":"unspecified","summary":"Python code to support the Facets Overview visualization"},"popularity":{"monthly_downloads":239275,"position":8923,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.1"}