{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/11"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/12"}],"enrichment":{"capability":"Provides pure-Python implementations of probabilistic data structures including Bloom filters, Count-Min sketches, Cuckoo filters, and Quotient filters for memory-efficient membership testing and frequency estimation.","skillfed_tags":["data-structures","algorithms","memory-efficient"],"use_cases":["Implement a URL deduplicator for web crawlers to avoid revisiting pages without storing every URL in memory.","Track which user IDs have already been processed in a stream-processing pipeline with bounded memory.","Estimate word frequencies in large text corpora using Count-Min sketch without storing exact counts.","Build a cache-miss detector or negative-lookup filter for database queries to avoid expensive lookups.","Detect duplicate network packets or log entries in real-time monitoring systems with minimal overhead."],"what_it_does":"pyprobables is a pure-Python library implementing common probabilistic data structures\u2014Bloom filters, Count-Min sketches, Cuckoo filters, and Quotient filters. These structures trade a small, tunable false-positive rate for dramatic memory savings compared to exact data structures, making them useful when you need fast approximate answers about set membership or frequency counts without storing the full dataset.\n\nThe library is straightforward to use: instantiate a structure with your parameters (element count, false-positive tolerance, or capacity), then add items and query them. The documentation notes that C-compiled hashing algorithms can improve raw performance if needed. The package is actively maintained, supports modern Python versions, and carries no external dependencies.","worth_installing":"Yes. pyprobables is a solid, actively maintained library with zero dependencies, permissive MIT licensing, and clean API design. Install it if you need any probabilistic data structure for approximate membership testing or frequency estimation. No security vulnerabilities reported."},"id":"pyprobables","links":{"html":"https://skillfed.io/packages/pyprobables","md":"https://skillfed.io/packages/pyprobables.md","pypi":"https://pypi.org/project/pyprobables/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-08","license_spdx":"MIT","license_treatment":"permissive","name":"pyprobables","python_support":"supports_current","summary":"Probabilistic data structures in python"},"popularity":{"monthly_downloads":75177,"position":14745,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.0"}