# 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:**

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**Previous:** Pandas - Applying a Custom Function Element-wise with applymap().

**Next:** Pandas - Applying a Custom Function to Columns using apply().

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