Group Scalers

The group scalers use the same scaling parameters for a group of similar variables rather than scaling each column independently. This is useful for situations where variables are related, such as temperatures at different height levels.

Groups are specified as a list of column ids, which can be column names for pandas dataframes or column indices for numpy arrays.

For example:

from bridgescaler.group import GroupStandardScaler
import pandas as pd
import numpy as np
x_rand = np.random.random(size=(100, 5))
data = pd.DataFrame(data=x_rand,
                    columns=["a", "b", "c", "d", "e"])
groups = [["a", "b"], ["c", "d"], "e"]
group_scaler = GroupStandardScaler()
x_transformed = group_scaler.fit_transform(data, groups=groups)

“a” and “b” are a single group and all values of both will be included when calculating the mean and standard deviation for that group.