The open standard for data logging

Documentation • Slack Community • Python Quickstart • WhyLabs Quickstart

What is whylogs

whylogs is an open source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to:

1. Track changes in their dataset
2. Create data constraints to know whether their data looks the way it should
3. Quickly visualize key summary statistics about their datasets

These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:

• Detect data drift in model input features
• Detect training-serving skew, concept drift, and model performance degradation
• Validate data quality in model inputs or in a data pipeline
• Perform exploratory data analysis of massive datasets
• Track data distributions & data quality for ML experiments
• Enable data auditing and governance across the organization
• Standardize data documentation practices across the organization
• And more

If you have any questions, comments, or just want to hang out with us, please join our Slack Community. In addition to joining the Slack Community, you can also help this project by giving us a ⭐ in the upper right corner of this page.

Python Quickstart

import whylogs as why
import pandas as pd

#dataframe
df = pd.read_csv("path/to/file.csv")
results = why.log(df)

• whylogs Profiles
• Data Constraints
• Profile Visualization
• Integrations
• Supported Data Types
• Examples
• Usage Statistics
• Community
• Contribute

whylogs Profiles