Pandas - Apply Custom Function with Multiple Conditions in a DataFrame

Last update on April 09 2025 12:53:40 (UTC/GMT +8 hours)

14. Create New Column Based on Multiple Conditions Using apply()

Write a Pandas program that uses apply() function to create a new column based on multiple conditions.

This exercise demonstrates how to apply a custom function with multiple conditions to create a new column in a DataFrame.

Sample Solution :

Code :

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

# Define a function with multiple conditions
def categorize(row):
    if row['A'] > 20 and row['B'] > 20:
        return 'High-High'
    elif row['A'] > 20:
        return 'High-Low'
    elif row['B'] > 20:
        return 'Low-High'
    else:
        return 'Low-Low'

# Apply the function row-wise to create a new category column
df['Category'] = df.apply(categorize, axis=1)

# Output the result
print(df)

Output:

    A   B  Category
0  10  20   Low-Low
1  15  25  Low-High
2  30  10  High-Low                                   

Explanation:

• Created a DataFrame with columns 'A' and 'B'.
• Defined a function categorize() that checks multiple conditions based on the values in columns 'A' and 'B'.
• Applied the function row-wise using apply() with axis=1.
• Created a new column 'Category' containing labels based on the conditions.

For more Practice: Solve these Related Problems:

• Write a Pandas program to create a new column that categorizes rows based on conditions across two columns using apply().
• Write a Pandas program to use apply() to combine values from multiple columns into a new column based on conditional logic.
• Write a Pandas program to add a column that flags rows as 'High' or 'Low' based on conditions applied to two different columns using apply().
• Write a Pandas program to create a new column by applying a function that returns different text labels depending on multiple column values.

Python-Pandas Code Editor:

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