pyprobables
Probabilistic data structures in python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Installs cleanly with no runtime dependencies.
- Actively maintained with a recent release and ongoing commits.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
last release 2026-02-08 (187 days) · last repo commit 2026-07-23 · 123 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 75,177 downloads/mo, #14,745 on PyPI
Alternatives
Verify before relying
pip install pyprobables
from probables import BloomFilter
blm = BloomFilter(est_elements=1000, false_positive_rate=0.05)
blm.add('google.com')
print(blm.check('google.com')) # True- Whether the pure-Python implementation meets latency or throughput requirements without optional C-compiled hash libraries.
- Memory overhead and scalability limits for each data structure at production scale.
- Performance characteristics compared to other probabilistic data structure implementations.
What it is and what it does
pyprobables is a pure-Python library implementing common probabilistic data structures—Bloom 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
pyprobables on PyPI
Before you install
Installs cleanly with no runtime dependencies. Actively maintained with a recent release and ongoing commits. Supports current Python versions (3.10–3.14+).
Requires Python 3.10 or later.
License in practice
MIT license is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
Quickstart
pip install pyprobables
from probables import BloomFilter
blm = BloomFilter(est_elements=1000, false_positive_rate=0.05)
blm.add('google.com')
print(blm.check('google.com')) # TrueVerify before relying
- Whether the pure-Python implementation meets latency or throughput requirements without optional C-compiled hash libraries.
- Memory overhead and scalability limits for each data structure at production scale.
- Performance characteristics compared to other probabilistic data structure implementations.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 187 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 75,177 / month, #14,745 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: LibrariesTopic :: Utilities |
Evidence: pyprobables-0.7.0-py3-none-any.whl
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See also bloom-filter2 · pybloom-live · bloomfilter-py · madoka · datasketch · rbloom · eth-bloom · dict-hash · siphash · preshed