driftfx 0.1.0

Zero-false-positive data drift guardrails using DAFSA + BK-tree

driftfx

🚦 Zero-false-positive drift detection for analytics pipelines.

Fast: Optimized with Cython - handles 10,000+ unique values in under 2 seconds.

Get Started

pip install driftfx

Python Usage

import pandas as pd, driftfx as dr

dr.snapshot(df_baseline, "baseline", cols=["name"])
result = dr.check(df_new, "baseline", cols=["name"])

if not result.is_clean():
    if result.renames:
        print("Renames / typos:", result.renames[:5])     # first 5 examples
    if result.brand_new:
        print("Brand-new names:", result.brand_new[:5])   # first 5 examples

CLI Usage

# snapshot baseline
$ driftfx snapshot --input data.parquet --cols name --baseline baseline/
$ Snapshot complete ✓

# check new batch
$ driftfx check --input new.parquet --cols name --baseline baseline/
$ [✖] name: 17 renames / 31 new codes
$ Drift detected: 48 anomalies 

Performance

With Cython-optimized Levenshtein distance calculations:

Operation	Time	Throughput	Dataset
Snapshot	1.6s	5,264 rows/s	10,000 unique values
Check	1.2s	7,893 rows/s	10,050 rows

The Cython implementation provides an 8x speedup for snapshot operations compared to pure Python.