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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file normscaler-0.0.2.tar.gz.
File metadata
- Download URL: normscaler-0.0.2.tar.gz
- Upload date:
- Size: 4.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1b944460838c636da0f32b78af21364d9d420a5cdcd1b4fc8d16bc74cd07817a
|
|
| MD5 |
f1fc0d6696812c3ccca63f5e7918d41a
|
|
| BLAKE2b-256 |
f5bd80b5698ee76c81f18fbbef3bab9feba89869536b4afca799eda3032bdd3f
|
File details
Details for the file normscaler-0.0.2-py3-none-any.whl.
File metadata
- Download URL: normscaler-0.0.2-py3-none-any.whl
- Upload date:
- Size: 3.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
53b4a92fcec9568d50a04abbad1955e4627ef6a63ca2ebcadd28d9c9b68f247e
|
|
| MD5 |
80a7cbe7509d22b7eacf78fad3bd806d
|
|
| BLAKE2b-256 |
318b334c6164f4d96a54b30fc4b93305f66e7f5765445895a880b4ce14eef4e6
|
