Standardizing numerical data using Z-Score scaling in Pandas

Last update on December 21 2024 07:41:43 (UTC/GMT +8 hours)

Pandas: Machine Learning Integration Exercise-8 with Solution

Write a Pandas program to standardize numerical data using Z-Score scaling.

This exercise shows how to standardize numerical data using Z-score scaling (StandardScaler).

Sample Solution :

Code :

import pandas as pd
from sklearn.preprocessing import StandardScaler

# Load the dataset
df = pd.read_csv('data.csv')

# Initialize the StandardScaler
scaler = StandardScaler()

# Apply Z-score scaling to the 'Age' and 'Salary' columns
df[['Age', 'Salary']] = scaler.fit_transform(df[['Age', 'Salary']])

# Output the standardized dataset
print(df)

Output:

   ID      Name       Age  Gender    Salary  Target
0   1      Sara -0.719874  Female -1.207020       0
1   2    Ophrah  0.404929    Male -0.278543       1
2   3    Torben -1.394756    Male  0.649934       0
3   4  Masaharu  1.529732    Male  1.578410       1
4   5      Kaya       NaN  Female -0.742781       0
5   6   Abaddon  0.179969    Male       NaN       1

Explanation:

• Loaded the dataset using Pandas.
• Initialized the StandardScaler from Scikit-learn.
• Applied Z-score scaling (standardization) to the 'Age' and 'Salary' columns, centering them around zero.
• Displayed the standardized dataset.

Python-Pandas Code Editor:

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