Pandas - Conditionally Applying a Function to DataFrame Rows with apply()

Last update on December 21 2024 07:41:13 (UTC/GMT +8 hours)

Pandas: Custom Function Exercise-8 with Solution

Write a Pandas program that conditionally apply a function to a DataFrame rows.

This exercise demonstrates how to apply a custom function to rows based on a condition.

Sample Solution:

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [10, 20, 30],
    'C': [100, 200, 300]
})

# Define a custom function that adds columns A and B if a condition is met
def add_if_condition(row):
    return row['A'] + row['B'] if row['C'] > 150 else row['A']

# Apply the custom function row-wise
df['Conditional_Sum'] = df.apply(add_if_condition, axis=1)

# Output the result
print(df)

Output:

   A   B    C  Conditional_Sum
0  1  10  100                1
1  2  20  200               22
2  3  30  300               33                             

Explanation:

• Created a DataFrame with columns 'A', 'B', and 'C'.
• Defined a custom function add_if_condition() that adds columns 'A' and 'B' if column 'C' is greater than 150.
• Applied the function row-wise using apply() with axis=1.
• Added the result as a new column 'Conditional_Sum' to the DataFrame.

Python-Pandas Code Editor:

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