Pandas - Applying Conditional Logic to DataFrame Rows with apply()

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

12. Conditional Logic with apply() in DataFrame

Write a Pandas program that uses apply() to work with conditional logic in DataFrame.

In this exercise we have applied a custom function that uses conditional logic to set values based on a threshold.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [5, 15, 25]
})

# Define a function with conditional logic
def threshold(row):
    return 'High' if row['A'] > 15 else 'Low'

# Apply the function to the rows
df['A_threshold'] = df.apply(threshold, axis=1)

# Output the result
print(df)

Output:

    A   B A_threshold
0  10   5         Low
1  20  15        High
2  30  25        High                                     

Explanation:

• Created a DataFrame with columns 'A' and 'B'.
• Defined a function threshold() that labels 'A' as 'High' if it’s greater than 15, otherwise 'Low'.
• Applied this function row-wise using apply() with axis=1.
• Added the labels as a new column 'A_threshold' in the DataFrame.

For more Practice: Solve these Related Problems:

• Write a Pandas program to use apply() with a custom function that assigns labels to rows based on multiple conditions.
• Write a Pandas program to apply conditional logic to a DataFrame column and return different values based on the condition using apply().
• Write a Pandas program to create a new column by applying a function that uses if-else logic on each row via apply().
• Write a Pandas program to conditionally update values in a DataFrame by applying a lambda function with if-else statements using apply().

Python-Pandas Code Editor:

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