Pandas - Applying a Custom Function to Rows using apply()
Pandas: Custom Function Exercise-2 with Solution
Write a Pandas program that apply a custom function to each row using apply() function.
In this exercise, we have applied a custom function that calculates the sum of each row in a DataFrame using apply() function.
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 a custom function to calculate the sum of a row
def row_sum(row):
return row.sum()
# Apply the custom function row-wise
df['Row_Sum'] = df.apply(row_sum, axis=1)
# Output the result
print(df)
Output:
A B C Row_Sum 0 1 4 7 12 1 2 5 8 15 2 3 6 9 18
Explanation:
- Created a DataFrame with columns 'A', 'B', 'C'.
- Defined a function row_sum() to calculate the sum of a row.
- Applied row_sum() row-wise using apply() with axis=1.
- Added the row sums as a new column to the DataFrame.
Python-Pandas Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
Previous: Pandas - Applying a Custom Function Element-wise with applymap().
Next: Pandas - Applying a Custom Function to Columns using apply().
What is the difficulty level of this exercise?
Test your Programming skills with w3resource's quiz.
It will be nice if you may share this link in any developer community or anywhere else, from where other developers may find this content. Thanks.
https://www.w3resource.com/python-exercises/pandas/apply-a-custom-function-to-rows-in-pandas-dataframe-using-apply.php
- Weekly Trends and Language Statistics
- Weekly Trends and Language Statistics