Mean Centering on a DataFrame in Pandas

Introduction

Data preprocessing is an essential step in machine learning and data analysis. One common technique used for feature scaling is mean centering, which involves subtracting the mean value of each column from the corresponding values in that column. In this article, we will explore how to perform mean centering on a DataFrame using pandas.

Background

Standardization, as performed by StandardScaler from sklearn.preprocessing, scales the data to have a mean of 0 and standard deviation of 1. While standardization is useful for certain machine learning algorithms, it may not be suitable for all types of models or datasets. Mean centering, on the other hand, can help reduce the effect of outliers and improve model performance.

Types of Data Centering

There are two main types of data centering: mean centering and median centering. Mean centering involves subtracting the mean value of each column from the corresponding values in that column, while median centering involves subtracting the median value of each column from the corresponding values in that column.

Mean Centering

Why is Mean Centering Used?

Mean centering is used to:

Reduce the effect of outliers: Outliers can greatly affect the mean and skew the distribution of the data. By subtracting the mean, we reduce the impact of these outliers.
Improve model performance: Some machine learning algorithms perform better when the data is centered around zero.

How is Mean Centering Performed?

Mean centering involves subtracting the mean value of each column from the corresponding values in that column. This can be achieved using pandas’ vectorized operations or manually using loops.

Pandas Implementation

{<
# Load necessary libraries
import pandas as pd

# Create a sample DataFrame
dataxx = {'Name':['Tom', 'gik','Tom','Tom','Terry','Jerry','Abel','Dula','Abel'], 
          'Age':[20, 21, 19, 18,88,89,95,96,97],'gg':[1, 1,1, 30, 30,30,40,40,40]} 
dfxx = pd.DataFrame(dataxx)

# Calculate the mean of each column
mean_values = dfxx.mean()

# Subtract the mean values from the corresponding columns
dfxx["meancentered"] = dfxx.Age - dfxx.Age.mean()

Example Output

	Name	Age	gg	meancentered
0	Tom	20	1	-40.333333
1	gik	21	1	-39.333333
2	Tom	19	1	-41.333333
3	Tom	18	30	-42.333333
4	Terry	88	30	27.666667
5	Jerry	89	30	28.666667
6	Abel	95	40	34.666667
7	Dula	96	40	35.666667
8	Abel	97	40	36.666667

Standardization vs Mean Centering

# Standardize the data using StandardScaler from sklearn.preprocessing
from sklearn.preprocessing import StandardScaler

standard = dfxx.copy()
standard.iloc[:,1:-1] = StandardScaler().fit_transform(standard.iloc[:,1:-1])

	Name	Age	gg
0	Tom	20	1
1	gik	21	1
2	Tom	19	1
3	Tom	18	30
4	Terry	88	30
5	Jerry	89	30
6	Abel	95	40
7	Dula	96	40
8	Abel	97	40

While standardization scales the data to have a mean of zero and standard deviation of one, mean centering subtracts the mean value from each column. The choice between these two techniques depends on the specific problem and dataset.

Conclusion

Mean centering is an essential technique in data preprocessing that can help improve model performance and reduce the effect of outliers. By understanding how to perform mean centering using pandas, you can better prepare your data for analysis and modeling tasks. Remember to choose the right scaling technique depending on your specific problem and dataset.

Last modified on 2024-01-02