type-preserving-scaler 0.0.2

Type Preserving Scaler

Type Preserving Scaler

type_preserving_scaler provides a small wrapper around scikit-learn's StandardScaler for projects that want predictable output container types.

What it is

The package exposes one class:

from type_preserving_scaler import StandardScalerThatPreservesInputType

StandardScalerThatPreservesInputType behaves like sklearn.preprocessing.StandardScaler, with one added convention:

• fit on a pandas DataFrame, and transform() / fit_transform() return a pandas DataFrame
• fit on a NumPy array, and transform() / fit_transform() return a NumPy ndarray

For DataFrame inputs, the tested behavior preserves the output shape, columns, and index.

Why it exists

Plain StandardScaler can return NumPy arrays even when the caller is working with pandas data. That can force downstream code to manually rebuild DataFrames or carry column/index metadata separately.

This package keeps the common "pandas in, pandas out" workflow while retaining the familiar StandardScaler API.

How it works

The class subclasses sklearn.preprocessing.StandardScaler. During fit(), it delegates to the parent scaler, then calls scikit-learn's set_output() API:

• set_output(transform="pandas") when fit() receives a pandas DataFrame
• set_output(transform="default") otherwise

The output type is therefore determined by the data passed to fit(), not by each later call to transform().

Installation

pip install type_preserving_scaler

The package requires Python 3.8 or newer and depends on NumPy, pandas, and scikit-learn.

Usage

import pandas as pd
from type_preserving_scaler import StandardScalerThatPreservesInputType

df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 20, 30]})

scaler = StandardScalerThatPreservesInputType()
scaled = scaler.fit_transform(df)

assert isinstance(scaled, pd.DataFrame)
assert scaled.columns.equals(df.columns)
assert scaled.index.equals(df.index)

NumPy inputs keep NumPy outputs:

import numpy as np
from type_preserving_scaler import StandardScalerThatPreservesInputType

array = np.array([[1.0, 2.0], [3.0, 4.0]])
scaled = StandardScalerThatPreservesInputType().fit_transform(array)

assert isinstance(scaled, np.ndarray)

Development

Install the development dependencies and the package in editable mode:

pip install -r requirements_dev.txt
pip install -e .

Common local commands:

make test
make lint
make docs

Limitations

• This package only wraps StandardScaler; it is not a general adapter for all scikit-learn transformers.
• The output type is chosen when fit() runs.
• The installed scikit-learn version must provide the set_output() API used by the implementation.
• The package preserves the output container type. It does not change StandardScaler's scaling behavior.

License

MIT.

Changelog

0.0.1

• First release on PyPI.