254  whylogs(1) - Kailash Tool Reference

254.1 whylogs

whylogs - WhyLabs whylogs — privacy-preserving data and ML/LLM logging profiles for drift and quality monitoring.

Layer DEFENCE
Category d-02-detection-monitoring-observability
Safety class safe
Licence Apache-2.0
Upstream github.com/whylabs/whylogs

254.1.1 NAME

whylogs - WhyLabs whylogs — privacy-preserving data and ML/LLM logging profiles for drift and quality monitoring.

254.1.2 SYNOPSIS & usage

Derived from the upstream README at generation time; verify against --help where the tool is installed.

import whylogs as why
import pandas as pd

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

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

254.1.3 DESCRIPTION (upstream)

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 d

254.1.4 CONFIGURATION (detected)

Config-surface markers detected in the upstream README (2 matches, e.g. whylogs-ci.yml, environment variable). A full config reference page for this tool is queued under the config-page policy — this line is the detection record, not the page.

254.1.5 SEE ALSO

ref: pin - verified 2026-10-14