Pandas - Conditionally Applying a Function to DataFrame Rows with apply()
Pandas: Custom Function Exercise-8 with Solution
Write a Pandas program that conditionally apply a function to a DataFrame rows.
This exercise demonstrates how to apply a custom function to rows based on a 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 adds columns A and B if a condition is met
def add_if_condition(row):
return row['A'] + row['B'] if row['C'] > 150 else row['A']
# Apply the custom function row-wise
df['Conditional_Sum'] = df.apply(add_if_condition, axis=1)
# Output the result
print(df)
Output:
A B C Conditional_Sum 0 1 10 100 1 1 2 20 200 22 2 3 30 300 33
Explanation:
- Created a DataFrame with columns 'A', 'B', and 'C'.
- Defined a custom function add_if_condition() that adds columns 'A' and 'B' if column 'C' is greater than 150.
- Applied the function row-wise using apply() with axis=1.
- Added the result as a new column 'Conditional_Sum' to the DataFrame.
Python-Pandas Code Editor:
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