Building Modern EDA Pipelines with Pingouin

2026-05-11 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

This article details how to construct a robust Exploratory Data Analysis (EDA) pipeline using the Pingouin Python library, which bridges SciPy and pandas. It emphasizes the "garbage in, garbage out" principle in machine learning, advocating for rigorous statistical validation beyond basic visualizations. The guide demonstrates checking for univariate and multivariate normality, homoscedasticity using Levene's test, sphericity with Mauchly's test, and multicollinearity via Pingouin's `rcorr` function. Using a wine quality dataset, the examples illustrate how to identify common data issues like non-normality and heteroscedasticity, providing insights into suitable data transformations or model choices for downstream machine learning tasks.

Key takeaway

For Data Scientists building machine learning models, understanding and validating data properties statistically is crucial. Your models' effectiveness hinges on data quality; use Pingouin to automate checks for normality, homoscedasticity, sphericity, and multicollinearity. This proactive approach helps you select appropriate data transformations or non-parametric models, preventing flawed outcomes and ensuring your downstream analyses are built on solid statistical ground.

Key insights

Pingouin enables rigorous statistical validation of data properties for robust EDA pipelines.

Principles

• Validate data against mathematical assumptions.
• Detect data issues before model training.
• "Garbage in, garbage out" applies to ML.

Method

Install Pingouin, load data, then apply `pg.normality()`, `pg.multivariate_normality()`, `pg.homoscedasticity()`, `pg.sphericity()`, and `pg.rcorr()` to validate data properties.

In practice

• Use `pg.normality()` for Shapiro-Wilk tests.
• Apply `pg.homoscedasticity()` for Levene's test.
• Check multicollinearity with `pg.rcorr()`.

Topics

• Pingouin
• Exploratory Data Analysis
• Statistical Testing
• Data Preprocessing
• Normality Testing

Best for: Data Scientist, Machine Learning Engineer, AI Student

Related on AIssential

• The “Robust” Data Scientist: Winning with Messy Data and Pingouin — Robust statistics provide reliable results from messy data that violate classical statistical assumptions.
• spaCy meets Transformers — Transformer models offer high NLP accuracy with less data, but require careful pipeline integration for performance.
• When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews — AI-assisted interviews require statistical validation to correct mapping errors and ensure reliable structured data.
• How are you handling training data when public datasets don't match your use case? [D] — Public datasets often fail to meet specific ML project needs, necessitating alternative data sourcing and generation strategies.
• I Built a Data Quality Tool Because I Was Tired of Debugging Bad Data — Fast, opinionated first-contact profiling for tabular data prevents downstream ML issues.
• Goal Analytics: A Fully Built Data Pipeline, Six Models, and a Real Backtest — A robust sports forecasting system integrates diverse models, rigorous point-in-time backtesting, and a meticulously cleaned data pipeline.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.