sklearndf

What it is and what it does

sklearndf is a wrapper library that enhances scikit-learn estimators to preserve pandas DataFrames and feature names through transformations. When you use scikit-learn's transformers, they typically return numpy arrays even if your input was a DataFrame, losing column names in the process. This makes it hard to trace which features went where, especially in complex pipelines with feature engineering steps. sklearndf solves this by providing drop-in replacements (e.g., StandardScalerDF instead of StandardScaler) that return DataFrames with feature names intact.

The library depends on numpy, pandas, scipy, scikit-learn, packaging, and gamma-pytools. It supports Python 3.9 through 3.13 and is marked Production/Stable. The main value is in model inspection and debugging: you can see exactly which original features contributed to each output feature, which is crucial when transformers create new features (like one-hot encoding) or when you need to audit a pipeline's behavior.

Use it for

• Inspect and debug scikit-learn pipelines by keeping track of feature names through all transformation steps.
• One-hot encode categorical features while maintaining a clear mapping from encoded columns back to original features.
• Audit feature engineering in production models to understand which raw inputs drive each final feature.
• Build interpretable machine learning workflows where feature lineage is essential for model validation.
• Integrate scikit-learn into data analysis notebooks where preserving DataFrame structure simplifies downstream exploration.

Worth the install?

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Install

sklearndf on PyPI

Before you install

Low install friction with a pure-Python wheel. Maintenance is aging—last release was 345 days ago—but the repository remains active and marked Production/Stable. Depends on well-established libraries: numpy, pandas, scipy, scikit-learn, packaging, and gamma-pytools.

Requires Python >=3.9 and <4a; scikit-learn, pandas, scipy, numpy, and packaging must be installed.

License in practice

Licensed under Apache Software License v2.0 (permissive). You may use, modify, and distribute freely in commercial and private projects, provided you include a copy of the license and state significant changes.

Quickstart

pip install sklearndf

from sklearndf.preprocessing import StandardScalerDF
import pandas as pd

X = pd.DataFrame({'a': [2, 3], 'b': [4, 5]})
scaler = StandardScalerDF()
X_scaled = scaler.fit_transform(X)  # Returns DataFrame with feature names preserved

Package facts

Evidence: sklearndf-2.4.2-py3-none-any.whl

Tags

More Scientific/Engineering packages

See also sklearn-pandas · skops · datasieve · sklearn-compat · sklearn2pmml · sklearn-crfsuite · scikit-learn-stubs · sagemaker-scikit-learn-extension · category-encoders