pretab is a modular, extensible, and scikit-learn-compatible preprocessing library for tabular data. It supports all sklearn transformers out of the box, and extends functionality with a rich set of custom encoders, splines, and neural basis expansions.

• 🔢 Numerical preprocessing via:

  • Polynomial and spline expansions: B-splines, natural cubic splines, thin plate splines, tensor product splines, P-splines
  • Neural-inspired basis: RBF, ReLU, Sigmoid, Tanh
  • Custom binning: rule-based or tree-based
  • Piecewise Linear Encoding (PLE)

• 🌤 Categorical preprocessing:

  • Ordinal encodings
  • One-hot encodings
  • Language embeddings (pretrained vectorizers)
  • Custom encoders like OneHotFromOrdinalTransformer

• 🔧 Composable pipeline interface:

  • Fully compatible with sklearn.pipeline.Pipeline and sklearn.compose.ColumnTransformer
  • Accepts all sklearn-native transformers and parameters seamlessly

• 🧠 Smart preprocessing:

  • Automatically detects feature types (categorical vs numerical)
  • Supports both pandas.DataFrame and numpy.ndarray inputs

• 🧪 Comprehensive test coverage

• 🤝 Community-driven and open to contributions

Install via pip:

pip install pretab

Or install in editable mode for development:

git clone https://github.com/OpenTabular/pretab.git cd pretab pip install -e .

import pandas as pd import numpy as np from pretab.preprocessor import Preprocessor # Simulated tabular dataset df = pd.DataFrame({ "age": np.random.randint(18, 65, size=100), "income": np.random.normal(60000, 15000, size=100).astype(int), "job": np.random.choice(["nurse", "engineer", "scientist", "teacher", "artist", "manager"], size=100), "city": np.random.choice(["Berlin", "Munich", "Hamburg", "Cologne"], size=100), "experience": np.random.randint(0, 40, size=100) }) y = np.random.randn(100, 1) # Optional feature-specific preprocessing config config = { "age": "ple", "income": "rbf", "experience": "quantile", "job": "one-hot", "city": "none" } # Initialize Preprocessor preprocessor = Preprocessor( feature_preprocessing=config, task="regression" ) # Fit and transform the data into a dictionary of feature arrays X_dict = preprocessor.fit_transform(df, y) # Optionally get a stacked array instead of a dictionary X_array = preprocessor.transform(df, return_array=True) # Get feature metadata preprocessor.get_feature_info(verbose=True)

pretab includes both sklearn-native and custom-built transformers:

• CubicSplineTransformer
• NaturalCubicSplineTransformer
• PSplineTransformer
• TensorProductSplineTransformer
• ThinPlateSplineTransformer

• RBFExpansionTransformer
• ReLUExpansionTransformer
• SigmoidExpansionTransformer
• TanhExpansionTransformer

• PLETransformer
• CustomBinTransformer
• OneHotFromOrdinalTransformer
• ContinuousOrdinalTransformer
• LanguageEmbeddingTransformer

• NoTransformer
• ToFloatTransformer

Plus: any sklearn transformer can be passed directly with full support for hyperparameters.

Using the transformers follows the standard sklearn.preprocessing steps. I.e. using PLE

import numpy as np from pretab.transformers import PLETransformer x = np.random.randn(100, 1) y = np.random.randn(100, 1) x_ple = PLETransformer(n_bins=15, task="regression").fit_transform(x, y) assert x_ple.shape[1] == 15

For splines, the penalty matrices can be extracted via .get_penalty_matrix()

import numpy as np from pretab.transformers import ThinPlateSplineTransformer x = np.random.randn(100, 1) tp = ThinPlateSplineTransformer(n_basis=15) x_tp = tp.fit_transform(x) assert x_tp.shape[1] == 15 penalty = tp.get_penalty_matrix()

pytest --maxfail=2 --disable-warnings -v

pretab is community-driven! Whether you’re fixing bugs, adding new encoders, or improving the docs — contributions are welcome.

git clone https://github.com/OpenTabular/pretab.git cd pretab pip install -e ".[dev]"

Then create a pull request 🚀

MIT License. See LICENSE for details.

pretab builds on the strengths of:

• scikit-learn