Pandas - Normalizing data in a DataFrame using Min-Max scaling

Last update on April 09 2025 12:54:43 (UTC/GMT +8 hours)

6. Normalizing Data with Min-Max Scaling

Write a Pandas program that normalizes data with Min-Max scaling.

In this exercise, we have normalized data using min-max scaling, scaling each value to a range between 0 and 1.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with numerical values
df = pd.DataFrame({
    'Age': [25, 30, 22, 45],
    'Salary': [50000, 60000, 70000, 80000]
})

# Apply min-max scaling to normalize the values between 0 and 1
df_normalized = (df - df.min()) / (df.max() - df.min())

# Output the result
print(df_normalized)

Output:

        Age    Salary
0  0.130435  0.000000
1  0.347826  0.333333
2  0.000000  0.666667
3  1.000000  1.000000

Explanation:

• Created a DataFrame with numerical data.
• Applied min-max scaling to normalize each value between 0 and 1 using the formula (x - min) / (max - min).
• Outputted the normalized DataFrame.

For more Practice: Solve these Related Problems:

• Write a Pandas program to apply Min-Max scaling to a numeric column and store the results in a new column.
• Write a Pandas program to normalize multiple columns simultaneously using Min-Max scaling.
• Write a Pandas program to compare original and normalized values by plotting them side-by-side.
• Write a Pandas program to selectively apply Min-Max scaling only on columns with a specific data type.

Python-Pandas Code Editor:

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