MeanNormalizationScaler

MeanNormalizationScaler() scales variables using mean normalization. With mean normalization, we center the distribution around 0, and rescale the distribution to the variable’s value range, so that its values vary between -1 and 1. This is accomplished by subtracting the mean of the feature and then dividing by its range (i.e., the difference between the maximum and minimum values).

The MeanNormalizationScaler() only works with non-constant numerical variables. If the variable is constant, the scaler will raise an error.

Python example

We’ll show how to use MeanNormalizationScaler() through a toy dataset. Let’s create a toy dataset:

import pandas as pd
from feature_engine.scaling import MeanNormalizationScaler

df = pd.DataFrame.from_dict(
    {
        "Name": ["tom", "nick", "krish", "jack"],
        "City": ["London", "Manchester", "Liverpool", "Bristol"],
        "Age": [20, 21, 19, 18],
        "Height": [1.80, 1.77, 1.90, 2.00],
        "Marks": [0.9, 0.8, 0.7, 0.6],
        "dob": pd.date_range("2020-02-24", periods=4, freq="min"),
    })

print(df)

The dataset looks like this:

    Name        City  Age  Height  Marks                 dob
0    tom      London   20    1.80    0.9 2020-02-24 00:00:00
1   nick  Manchester   21    1.77    0.8 2020-02-24 00:01:00
2  krish   Liverpool   19    1.90    0.7 2020-02-24 00:02:00
3   jack     Bristol   18    2.00    0.6 2020-02-24 00:03:00

We see that the only numerical features in this dataset are Age, Marks, and Height. We want to scale them using mean normalization.

First, let’s make a list with the variable names:

vars = [
  'Age',
  'Marks',
  'Height',
]

Now, let’s set up MeanNormalizationScaler():

# set up the scaler
scaler = MeanNormalizationScaler(variables = vars)

# fit the scaler
scaler.fit(df)

The scaler learns the mean of every column in vars and their respective range. Note that we can access these values in the following way:

# access the parameters learned by the scaler
print(f'Means: {scaler.mean_}')
print(f'Ranges: {scaler.range_}')

We see the features’ mean and value ranges in the following output:

Means: {'Age': 19.5, 'Marks': 0.7500000000000001, 'Height': 1.8675000000000002}
Ranges: {'Age': 3.0, 'Marks': 0.30000000000000004, 'Height': 0.22999999999999998}

We can now go ahead and scale the variables:

# scale the data
df = scaler.transform(df)
print(df)

In the following output, we can see the scaled variables:

    Name        City       Age    Height     Marks                 dob
0    tom      London  0.166667 -0.293478  0.500000 2020-02-24 00:00:00
1   nick  Manchester  0.500000 -0.423913  0.166667 2020-02-24 00:01:00
2  krish   Liverpool -0.166667  0.141304 -0.166667 2020-02-24 00:02:00
3   jack     Bristol -0.500000  0.576087 -0.500000 2020-02-24 00:03:00

We can restore the data to itsoriginal values using the inverse transformation:

# inverse transform the dataframe
df = scaler.inverse_transform(df)
print(df)

In the following data, we see the scaled variables returned to their oridinal representation:

    Name        City  Age  Height  Marks                 dob
0    tom      London   20    1.80    0.9 2020-02-24 00:00:00
1   nick  Manchester   21    1.77    0.8 2020-02-24 00:01:00
2  krish   Liverpool   19    1.90    0.7 2020-02-24 00:02:00
3   jack     Bristol   18    2.00    0.6 2020-02-24 00:03:00

Additional resources

For more details about this and other feature engineering methods check out these resources:

Feature Engineering for Machine Learning

Or read our book:

Python Feature Engineering Cookbook

Both our book and course are suitable for beginners and more advanced data scientists alike. By purchasing them you are supporting Sole, the main developer of Feature-engine.