Pandas - Apply Different Functions on DataFrame Columns with apply()

Last update on April 09 2025 12:53:43 (UTC/GMT +8 hours)

5. Apply Different Functions on DataFrame Columns Using apply()

Write a Pandas program that applies different functions on DataFrame columns using apply().

Apply different functions to different columns of a DataFrame by passing a dictionary to apply().

Sample Solution:

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Define different custom functions
def add_one(x):
    return x + 1

def multiply_by_two(x):
    return x * 2

# Apply different functions to columns
df['A'] = df['A'].apply(add_one)
df['B'] = df['B'].apply(multiply_by_two)

# Output the result
print(df)

Output:

   A   B  C
0  2   8  7
1  3  10  8
2  4  12  9                               

Explanation:

• Created a DataFrame with columns 'A', 'B', 'C'.
• Defined two functions: add_one() and multiply_by_two().
• Applied add_one() to column 'A' and multiply_by_two() to column 'B'.
• Displayed the modified DataFrame.

For more Practice: Solve these Related Problems:

• Write a Pandas program to apply the mean function on numeric columns and the mode on non-numeric columns using apply() with a custom lambda.
• Write a Pandas program to apply multiple aggregation functions to different columns by passing a dictionary to apply().
• Write a Pandas program to compute the range for one column and the median for another by selectively applying functions using apply().
• Write a Pandas program to use apply() to apply a different transformation to each column based on its data type.

Python-Pandas Code Editor:

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