Write a Python function which accepts DataFrame Age, Salary columns second, third and fourth rows as input and find the mean, product of values

This tutorial shows how to write a Python function that accepts a DataFrame and calculates the mean and product of specific rows from Age and Salary columns using iloc for row slicing.

Sample DataFrame

Let's start with a sample DataFrame containing employee data ?

import pandas as pd

data = [[1,27,40000],[2,22,25000],[3,25,40000],[4,23,35000],[5,24,30000],
        [6,32,30000],[7,30,50000],[8,28,20000],[9,29,32000],[10,27,23000]]
df = pd.DataFrame(data, columns=('Id','Age','salary'))
print(df)

   Id  Age  salary
0   1   27   40000
1   2   22   25000
2   3   25   40000
3   4   23   35000
4   5   24   30000
5   6   32   30000
6   7   30   50000
7   8   28   20000
8   9   29   32000
9  10   27   23000

Function to Calculate Mean and Product

We'll create a function that slices the second, third, and fourth rows (index 1, 2, 3) from Age and Salary columns ?

import pandas as pd

def find_mean_prod():
    data = [[1,27,40000],[2,22,25000],[3,25,40000],[4,23,35000],[5,24,30000],
            [6,32,30000],[7,30,50000],[8,28,20000],[9,29,32000],[10,27,23000]]
    df = pd.DataFrame(data, columns=('Id','Age','salary'))
    
    print("Original DataFrame:")
    print(df)
    print("\nSlicing second, third and fourth rows of Age and Salary columns:")
    
    # Select rows 1:4 (index 1,2,3) and columns 1: (Age, salary)
    result = df.iloc[1:4, 1:]
    print(result)
    
    print("\nMean is:")
    print(result.mean())
    
    print("\nProduct is:")
    print(result.prod())

find_mean_prod()

Original DataFrame:
   Id  Age  salary
0   1   27   40000
1   2   22   25000
2   3   25   40000
3   4   23   35000
4   5   24   30000
5   6   32   30000
6   7   30   50000
7   8   28   20000
8   9   29   32000
9  10   27   23000

Slicing second, third and fourth rows of Age and Salary columns:
   Age  salary
1   22   25000
2   25   40000
3   23   35000

Mean is:
Age          23.333333
salary    33333.333333
dtype: float64

Product is:
Age              12650
salary    35000000000000
dtype: int64

How It Works

The function uses df.iloc[1:4, 1:] for slicing:

• 1:4 selects rows at index 1, 2, 3 (second, third, fourth rows)
• 1: selects all columns starting from index 1 (Age and salary columns)
• mean() calculates the average value for each column
• prod() calculates the product of all values in each column

Conclusion

Use iloc for position-based DataFrame slicing, then apply mean() and prod() methods to calculate statistics on the selected data subset.