Normascaler has different data normalization methods including MinMaxScaler, MaxAbsScaler, RobustScaler, StandardScaler, Normalizer, DecimalScaler

It usually cost efforts and times to write codes to normalize dataset when there are encoded categorical, string or dummy variables, even using the built-in normalization scaler functions of scikit-learn. In addition, decimal scaling method is also very important normalization methods, which makes traditional statistic linear model much easier and more meaningful to explain. However, it seems that there is not a Python package including decimal scaling normalization. This method seem easy, but it will cost time to write code to calculate the scaler for each variable when there are many variables.

Based on these reasons, Dr. Shouke Wei in Deepsim created a normalization package to deal with above issues. The package is named as normscaler, standing for normalization scalers, which has been used by myself for a couple of years, but was just published publicly before I wrote this post.

You can find it on the PyPI page and in the GitHub depository, where you can also find detailed information. A concrete example on how to use this package can be found in the post





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