dq-prof 0.1.20

Prebuilt dq-prof binary wrapper

dq-prof 0.1.8

Fast, zero-config data sanity checks for pipelines.

Run one command and instantly see if your data is broken. Like Ruff, but for datasets.

Quick example

dq-prof data.parquet
DATA HEALTH: WARN

CRITICAL
- created_at: stale timestamps (last value 2025-03-01)

WARNING
- revenue: nulls 12% (expected <5%)
- region: skew (US = 78%)

Titanic (real public data):

./examples/download_titanic.sh
dq-prof examples/titanic.csv --fail-on warning

DATA HEALTH: FAIL
Rows: 891 sampled of 891 (mode=Head)

CRITICAL
- age: high null ratio 19.87% (obs=0.1987, exp=< 0.05)
- deck: high null ratio 77.22% (obs=0.7722, exp=< 0.05)

WARNING
- sibsp: outlier ratio 3.37% (obs=0.0337, exp=< 0.03)
- survived: distinct ratio very low (obs=0.0022, exp=> 0.01)
- pclass: distinct ratio very low (obs=0.0034, exp=> 0.01)
- sex: distinct ratio very low (obs=0.0022, exp=> 0.01)
- sibsp: distinct ratio very low (obs=0.0079, exp=> 0.01)

Baseline vs drift example:

dq-prof examples/clean_sales.csv --full-scan --save-baseline baseline_clean.json
dq-prof examples/drift_sales.csv --baseline baseline_clean.json --fail-on warning --color never

DATA HEALTH: WARN
CRITICAL
- region: top value dominates 100.0% of rows (obs=1.0000, exp=< 0.75)

WARNING
- region: top value share increased by 40.0pp vs baseline (obs=1.0000, exp=0.6000)

Why dq-prof

• Zero config – no YAML, no expectations to write
• Fast – runs in seconds on sampled data
• Catches real issues – null spikes, skew, outliers, freshness, schema drift
• Baseline-aware – detect changes vs previous runs
• CI-friendly – fail pipelines when data looks wrong

Install

Download a release binary and run:

chmod +x dq-prof
./dq-prof data.parquet

Or pip:

pip install dq-prof
dq-prof --help

Examples

Inspect a file:

dq-prof examples/clean_sales.csv

Compare to baseline (full scan required to save):

dq-prof data.csv --full-scan --save-baseline baseline.json
dq-prof data.csv --baseline baseline.json --fail-on warning

Postgres:

dq-prof public.sales \
  --pg-url postgres://user:pass@host/db \
  --sample-rows 50000

JSON output:

dq-prof data.parquet --format json

Output

Text or JSON with severity:

• CRITICAL – likely broken data
• WARNING – suspicious change

Philosophy

dq-prof is not a data observability platform. It’s a fast sanity check you run inline — a linter for data — before or after a pipeline step to catch issues immediately.