Pandas - Applying Custom Function to Modify Data Based on Column Types

Last update on April 09 2025 12:53:10 (UTC/GMT +8 hours)

11. Custom Function to Modify Data Based on Column Types

Write a Pandas program to create a custom function to modify data based on column types.

This exercise shows how to apply different operations on columns based on their data types using apply().

Sample Solution:

Code :

import pandas as pd

# Create a sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': ['x', 'y', 'z'],
    'C': [1.1, 2.2, 3.3]
})

# Define a custom function to modify data based on type
def modify_based_on_type(x):
    if isinstance(x, int):
        return x + 10  # Add 10 if integer
    elif isinstance(x, float):
        return x * 2  # Double if float
    else:
        return x.upper()  # Uppercase if string

# Apply the function to each element using applymap()
df_modified = df.applymap(modify_based_on_type)

# Output the result
print(df_modified)

Output:

    A  B    C
0  11  X  2.2
1  12  Y  4.4
2  13  Z  6.6                           

Explanation:

• Created a DataFrame with integer, string, and float columns.
• Defined a function modify_based_on_type() that modifies data based on its type:
• Adds 10 to integers.
• Doubles floats.
• Converts strings to uppercase.
• Applied the function to each element in the DataFrame using applymap().
• Displayed the updated DataFrame with modified values.

For more Practice: Solve these Related Problems:

• Write a Pandas program to apply a custom function that modifies numeric columns by scaling them and converts object columns to lowercase.
• Write a Pandas program to create a function that checks the data type of each column and applies an appropriate transformation using apply().
• Write a Pandas program to modify a DataFrame by applying different functions based on the column type, then output the transformed DataFrame.
• Write a Pandas program to use apply() with a custom function that normalizes numeric columns and formats date columns.

Python-Pandas Code Editor:

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