A data normalization package
Project description
normscalers
A package for data normalization including the methods of MinMaxScaler, MaxAbsScaler, RobustScaler, StandardScaler and Normalizer in Scikit-learning, and DecimalScaler. The package can automatically detect the one-hot encoded variables and skip them to be normalized.
Install
pip install normscaler
use
(1) import one or more scalers by their names
- MinMaxScaler
- MaxAbsScaler
- RobustScaler
- StandardScaler
- Normalizer
- DecimalScaler
For example, import DecimalScaler by
from normascaler.scaler import DecimalScaler
(2) Use Decimal scaling method
X_train_scaled, X_train_scaled = DecimalScaler(X_train, X-test)
(3) Display the normalized X_train data in Pandas DataFrame
X_train_scaled
(4) Display the normalized X_test data in Pandas DataFrame
X_test_scaled
Documentation
Examples of a Jupyter note in GitHub: https://github.com/shoukewei/normscaler/blob/main/docs/examples.ipynb
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