Pandas - Normalizing data in a DataFrame using a custom function

Last update on April 09 2025 12:53:07 (UTC/GMT +8 hours)

17. Normalize Data in a DataFrame with a Custom Function

Write a Pandas program that applies a Custom function to Normalize data in a DataFrame.

This exercise demonstrates how to apply a custom function to normalize (scale) the values in a DataFrame.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [40, 50, 60]
})

# Define a custom function to normalize values
def normalize(column):
    return (column - column.min()) / (column.max() - column.min())

# Apply the function column-wise
df_normalized = df.apply(normalize, axis=0)

# Output the result
print(df_normalized)

Output:

     A    B
0  0.0  0.0
1  0.5  0.5
2  1.0  1.0                                

Explanation:

• Created a DataFrame with two columns 'A' and 'B'.
• Defined a function normalize() to normalize values by scaling them between 0 and 1.
• Applied the normalization function column-wise using apply() with axis=0.
• Returned a DataFrame with normalized values.

For more Practice: Solve these Related Problems:

• Write a Pandas program to apply a custom normalization function to a numeric column using apply() and then compare the before-and-after statistics.
• Write a Pandas program to use apply() to normalize all numeric columns in a DataFrame using min-max scaling.
• Write a Pandas program to create a custom function that standardizes data and then apply it across the DataFrame using apply().
• Write a Pandas program to compare the performance of a vectorized normalization versus one implemented with apply() and a custom function.

Python-Pandas Code Editor:

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