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Data Normalization with Pandas
  • Last Updated : 11 Dec, 2020

In this article, we will learn how to normalize data in Pandas. Let’s discuss some concepts first :

  • Pandas: Pandas is an open-source library that’s built on top of NumPy library. it is a Python package that provides various data structures and operations for manipulating numerical data and statistics. It’s mainly popular for importing and analyzing data much easier. Pandas is fast and it’s high-performance & productive for users.
  • Data Normalization: Data Normalization could also be a typical practice in machine learning which consists of transforming numeric columns to a standard scale. In machine learning, some feature values differ from others multiple times. The features with higher values will dominate the learning process.

Steps Needed

Here, we will apply some techniques to normalize the data and discuss these with the help of examples. For this, let’s understand the steps needed for data normalization with Pandas.

  1. Import Library (Pandas)
  2. Import / Load / Create data.
  3. Use the technique to normalize the data.

Examples

Here, we create data by some random values and apply some normalization techniques to it.

Python3




# importing packages
import pandas as pd
  
# create data
df = pd.DataFrame([
                   [180000, 110, 18.9, 1400], 
                   [360000, 905, 23.4, 1800], 
                   [230000, 230, 14.0, 1300], 
                   [60000, 450, 13.5, 1500]], 
    
                   columns=['Col A', 'Col B',
                            'Col C', 'Col D'])
  
# view data
display(df)

Output:



See the plot of this dataframe:

Python3




import matplotlib.pyplot as plt
df.plot(kind = 'bar')

Let’s apply normalization techniques one by one.

Using The maximum absolute scaling

The maximum absolute scaling rescales each feature between -1 and 1 by dividing every observation by its maximum absolute value. We can apply the maximum absolute scaling in Pandas using the .max() and .abs() methods, as shown below.

Python3




# copy the data
df_max_scaled = df.copy()
  
# apply normalization techniques
for column in df_max_scaled.columns:
    df_max_scaled[column] = df_max_scaled[column]  / df_max_scaled[column].abs().max()
      
# view normalized data
display(df_max_scaled)

Output :



See the plot of this dataframe:

Python3




import matplotlib.pyplot as plt
df_max_scaled.plot(kind = 'bar')

Output:

Using The min-max feature scaling

The min-max approach (often called normalization) rescales the feature to a hard and fast range of [0,1] by subtracting the minimum value of the feature then dividing by the range. We can apply the min-max scaling in Pandas using the .min() and .max() methods.

Python3




# copy the data
df_min_max_scaled = df.copy()
  
# apply normalization techniques
for column in df_min_max_scaled.columns:
    df_min_max_scaled[column] = (df_min_max_scaled[column] - df_min_max_scaled[column].min()) / (df_min_max_scaled[column].max() - df_min_max_scaled[column].min())    
  
# view normalized data
print(df_min_max_scaled)

Output :

Let’s draw a plot with this dataframe:

Python3






import matplotlib.pyplot as plt
df_min_max_scaled.plot(kind = 'bar')

Using The z-score method

The z-score method (often called standardization) transforms the info into distribution with a mean of 0 and a typical deviation of 1. Each standardized value is computed by subtracting the mean of the corresponding feature then dividing by the quality deviation.

Python3




# copy the data
df_z_scaled = df.copy()
  
# apply normalization techniques
for column in df_z_scaled.columns:
    df_z_scaled[column] = (df_z_scaled[column] -
                           df_z_scaled[column].mean()) / df_z_scaled[column].std()    
  
# view normalized data   
display(df_z_scaled)

Output : 

Let’s draw a plot with this dataframe.

Python3




import matplotlib.pyplot as plt
df_z_scaled.plot(kind='bar')

Summary

Data normalization consists of remodeling numeric columns to a standard scale. In Python, we will implement data normalization in a very simple way. The Pandas library contains multiple built-in methods for calculating the foremost common descriptive statistical functions which make data normalization techniques very easy to implement.

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