---
id: whylogs
version: "1.6.4"
license: Apache-2.0
license_treatment: permissive
maintenance: dormant
---

# whylogs — Profile and monitor your ML data pipeline end-to-end

License: permissive · Maintenance: dormant · Downloads: 172.1K/mo

## What it is and what it does

whylogs is a data profiling library that generates statistical summaries (called profiles) of datasets to enable monitoring and validation in machine learning workflows. It depends on protobuf for serialization, requests for HTTP communication, and whylabs-client for integration with WhyLabs services. The core use case is creating profiles from data, then using those profiles to track data distributions over time, detect anomalies, and validate that incoming data meets expected constraints.

The library supports Python 3.7.1 through 3.11 and is designed for data scientists, ML engineers, and data engineers working on data quality, drift detection, and pipeline monitoring. However, maintenance is dormant—the last release was 619 days ago—so the package receives no active updates, bug fixes, or compatibility maintenance.

Use it for:

- Detect data drift in model input features by comparing profiles across time periods
- Validate data quality constraints in a data pipeline before feeding data to models
- Perform exploratory data analysis of large datasets by generating summary statistics
- Track training-serving skew by profiling training data and comparing to production data
- Enable data auditing and governance by logging dataset profiles for compliance

## Worth the install?

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

whylogs generates statistical profiles of datasets to track data distributions, detect drift, and validate data quality across machine learning pipelines.

Yes, if you need data profiling and drift detection for a stable ML pipeline and can tolerate dormant maintenance. The low install friction, permissive license, and absence of known vulnerabilities make it safe to adopt. However, the 619-day gap since the last release means no active support for new Python versions, dependency updates, or bug fixes—verify compatibility with your current environment before relying on it for production.

## Install

pip install whylogs
uv add whylogs
poetry add whylogs

## Installing whylogs

Before you install: Low install friction with a pure-Python wheel and eight runtime dependencies including protobuf, requests, and whylabs-client. Maintenance is dormant—no release in 619 days—so expect no active bug fixes or feature updates.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; attribution required.

Quickstart:

pip install whylogs

import whylogs as why

results = why.log(data)

Verify before relying:

- Whether dormant status (619 days since last release) affects compatibility with recent versions of runtime dependencies
- Whether whylabs-client dependency requires API credentials or can function offline
- Performance characteristics on very large datasets

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 172.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

data profiling and monitoring, detect data drift, data quality validation, ml data pipeline monitoring, dataset statistics logging, training serving skew detection, data distribution tracking, data-profiling, ml-monitoring, drift-detection

[View on SkillFed](https://skillfed.io/packages/whylogs) · [View on PyPI](https://pypi.org/project/whylogs/)