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
1 maintainer
Metadata
1 maintainer
Release files for normscaler 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| normscaler-0.0.2.tar.gz | 4.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| normscaler-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.6 kB
Release files / normscaler-0.0.2.tar.gz
| Download URL | normscaler-0.0.2.tar.gz |
|---|---|
| Size | 4.0 kB |
| Tags | Source |
|
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Release files / normscaler-0.0.2-py3-none-any.whl
| Download URL | normscaler-0.0.2-py3-none-any.whl |
|---|---|
| Size | 3.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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