---
id: fastdigest
version: "0.12.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# fastdigest — A fast t-digest library for Python built on Rust.

License: permissive · Maintenance: active · Downloads: 152.6K/mo

## What it is and what it does

fastdigest is a Python wrapper around a Rust implementation of the t-digest data structure, designed for computing approximate quantiles and other online statistics on streaming or distributed data. It trades exact computation for speed and memory efficiency, making it suitable for scenarios where you need to estimate percentiles, medians, or trimmed means from large datasets without storing all values in memory.

The package provides methods to initialize a digest from values, incrementally update it with new data (with optional weighting), merge multiple digests together, and query statistics like quantiles, cumulative distribution function values, means, and median absolute deviation. It supports serialization to/from dictionaries and pickle format, and aims for API compatibility with the tdigest library to ease migration.

Use it for:

- Estimate percentiles from streaming sensor or log data without storing all raw values
- Aggregate quantile statistics across distributed compute jobs via digest merging
- Detect anomalies by tracking CDF shifts or trimmed means over time
- Compute real-time analytics dashboards that need fast quantile updates on large datasets
- Replace slower pure-Python t-digest implementations in existing codebases

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

fastdigest provides a Rust-backed t-digest implementation for computing streaming quantiles, percentiles, and online statistics on large or distributed datasets with minimal memory overhead.

Yes. fastdigest is actively maintained, has no security vulnerabilities, carries a permissive MIT license, and offers substantial speed gains (480x faster than tdigest in the documented benchmark) with a straightforward API. Install it if you need streaming quantile estimation or online statistics on large datasets and want a fast, dependency-free solution.

## Install

pip install fastdigest
uv add fastdigest
poetry add fastdigest

## Installing fastdigest

Before you install: Medium install friction due to compiled wheels; prebuilt binaries are available for Python 3.7–3.14 across macOS, Linux (including musl), and Windows architectures. Active maintenance with recent commits; no runtime dependencies.

License in practice: MIT license (permissive); you may use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart:

pip install fastdigest

from fastdigest import TDigest

digest = TDigest.from_values(range(1001))
print(digest.quantile(0.99))  # 99th percentile
print(digest.cdf(990))        # rank of value 990

Verify before relying:

- Accuracy bounds or error guarantees for quantile estimates compared to exact computation
- Memory consumption scaling with digest size or data distribution
- Whether the package supports Python 3.14 and 3.13 in practice or only declares support

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 152.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

t-digest quantile estimation, streaming statistics python, percentile calculation online, distributed data aggregation, memory-efficient quantiles, real-time analytics digest, rust-powered statistics, streaming-analytics, quantile-estimation, rust-extension

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