{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/3"},{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Detects and replaces personally identifiable information (names, emails, phone numbers, credit cards, dates of birth, social security numbers, and more) in free text with anonymized placeholders.","skillfed_tags":["pii-detection","data-anonymization","text-processing"],"use_cases":["Prepare real customer datasets for sharing with external teams or for use in testing environments without exposing actual PII.","Anonymize support tickets, chat logs, or user feedback before storing them in non-secure systems or using them for model training.","Redact sensitive information from documents before publishing case studies, examples, or internal documentation.","Generate synthetic test data by scrubbing production logs and then using faker to regenerate realistic but non-real personal details.","Audit and clean data pipelines to ensure PII is not accidentally leaked in logs, error messages, or intermediate outputs."],"what_it_does":"Scrubadub is a text anonymization library that identifies and replaces sensitive personal information in unstructured text. It ships with built-in detectors for common PII categories\u2014names, emails, phone numbers, credit card numbers, dates of birth, social security numbers, and others\u2014and replaces matches with standardized placeholders like {{EMAIL}} or {{PHONE}}. The library is highly configurable, allowing you to enable or disable specific detectors, customize replacement strategies, and adapt behavior for different languages and regions through optional extensions.\n\nThe package depends on several NLP and data-parsing libraries (textblob, phonenumbers, python-stdnum, dateparser, scikit-learn, faker, catalogue, and typing-extensions) to perform pattern matching and validation across its detector suite. It is intended for workflows where you need to prepare datasets for sharing, testing, or analysis while protecting individual privacy\u2014common in data science, compliance, and software testing contexts.","worth_installing":"Yes, with conditions. Scrubadub is stable, permissively licensed, and has no known security vulnerabilities, making it suitable for production use in established data anonymization workflows. However, maintenance is dormant, so if you need active bug fixes or feature development, evaluate whether the package's current detector coverage meets your needs. For standard use cases (email, phone, credit card, SSN redaction), it remains a solid choice."},"id":"scrubadub","links":{"html":"https://skillfed.io/packages/scrubadub","md":"https://skillfed.io/packages/scrubadub.md","pypi":"https://pypi.org/project/scrubadub/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-09-01","license_spdx":null,"license_treatment":"permissive","name":"scrubadub","python_support":"unspecified","summary":"Clean personally identifiable information from dirty dirty text."},"popularity":{"monthly_downloads":2470289,"position":3049,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.0.1"}