whylogs
Open-source Python library that profiles data and logs its statistics to track quality, drift and model inputs.
How the 6.6 was reached
- Quality of output5/10
Apache-2.0 project with 2,833 stars and profiling features, but last push was January 2025, suggesting slowed activity. github.com
- Ease of use8/10
Single pip install and a one-line quick start (why.log(df)); no separate docs site was found. github.com
- Pricing value9/10
Apache-2.0 open source is free; the WhyLabs Starter edition is also free. github.com
- API and integration quality6/10
Python library with a Java implementation and data-pipeline integrations; no separate reference docs were found on the pages checked. github.com
- Solves the problem it claims to solve5/10
Only GitHub figures are independent; modest stars and no commits since January 2025 leave the claim partly supported by vendor description. github.com
6.6/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- Apache-2.0 licensed library
- Python package with a Java implementation
- Local data profiling
- Hosted monitoring edition, listed as always free
Key Features
- Creates profiles of distributions, missing values and custom metrics
- Data constraints to check data matches its expected form
- Drift detection and training-serving skew identification
- One-line start with why.log(df) after pip install
- Integrations for data pipelines
Pros & Cons
Pros
- Install and first log take one pip command and one line of code
- Free under Apache-2.0, with a free WhyLabs Starter edition
- Profiles capture statistics instead of raw rows, per the project description
- Java implementation exists next to the Python library
Cons
- Last push was January 2025, which points to slowed activity
- No separate docs site or API reference was found
- 2,833 GitHub stars is modest next to larger monitoring projects
- Little independent evidence backs the claims
whylogs suits Python teams that want a cheap, quick way to profile data and catch drift in pipelines. Teams that need an actively developed project with full reference docs should check recent commit history first and weigh other options.
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