skrub
Python library for preprocessing and feature engineering on dataframes.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is skrub?
Skrub is a Python library that simplifies the process of preprocessing and feature engineering, making it easier to prepare data for machine learning tasks. It focuses on enhancing the quality and usability of dataframe-based datasets.
Key differentiator
“Skrub stands out by providing a streamlined approach to dataframe preprocessing, making it easier for developers to focus on model development rather than data preparation.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Skrub heavily integrates with Pandas, requiring a deep understanding of its DataFrame structure and operations.
The official documentation lacks comprehensive tutorials or example use cases, making it difficult for new users to understand advanced features.
Skrub's operations can be slow when applied to very large dataframes due to the overhead of Python and Pandas.
Certain feature engineering tasks require additional dependencies like NumPy or SciPy, which may complicate setup and maintenance.
Fit analysis
Who is it for?
✓ Best for
Developers working with Python who need to preprocess large datasets efficiently.
Teams that require robust feature engineering capabilities without complex setup.
✕ Not a fit for
Projects requiring real-time data processing and transformation.
Users looking for a web-based UI for data preprocessing tasks.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with skrub
Step-by-step setup guide with code examples and common gotchas.