Pandas - Applying a Custom Function to Multiple Columns in DataFrame

Last update on December 21 2024 07:41:03 (UTC/GMT +8 hours)

Pandas: Custom Function Exercise-7 with Solution

Write a Pandas program that applies a custom function to multiple columns using apply().

In this exercise, we have applied a custom function to multiple columns of a DataFrame, modifying their values based on a specific 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 subtracts a value from two columns
def subtract_values(row):
    return row['A'] - row['B']

# Apply the custom function across columns 'A' and 'B' for each row
df['A_minus_B'] = df.apply(subtract_values, axis=1)

# Output the result
print(df)

Output:

   A   B    C  A_minus_B
0  1  10  100         -9
1  2  20  200        -18
2  3  30  300        -27                               

Explanation:

• Created a DataFrame with columns 'A', 'B', and 'C'.
• Defined a custom function subtract_values() that subtracts column 'B' from column 'A'.
• Applied this function across columns using apply() with axis=1.
• Added the results as a new column 'A_minus_B' in the DataFrame.

Python-Pandas Code Editor:

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