FOR EACH ROW: Looping Through Data in Databases and Spreadsheets

In a database or spreadsheet program, the phrase 'FOR EACH ROW' is typically used to indicate that a certain action or calculation should be performed for every row in a given table or dataset. This is often used in programming or scripting languages to iterate through each row and perform some operation on the data within that row.

For example, if we have a table of sales data with columns for 'Product', 'Quantity', and 'Price', we might use the phrase 'FOR EACH ROW' to calculate the total revenue for each product by multiplying the quantity and price for each row.

Here is an example of how this could be implemented in Python code:

import pandas as pd

# Load the sales data into a DataFrame
sales_data = pd.read_csv('sales_data.csv')

# Iterate through each row in the DataFrame
for index, row in sales_data.iterrows():
    # Perform some operation on the data within each row
    total_revenue = row['Quantity'] * row['Price']
    
    # Output the result for each row
    print(f"Total revenue for {row['Product']}: {total_revenue}")

In this example, the code will iterate through each row in the sales_data DataFrame, calculate the total revenue for each row by multiplying the quantity and price, and then output the result for each row. The 'FOR EACH ROW' phrase is implied by the for index, row in sales_data.iterrows(): line, which indicates that the following block of code should be executed for each row in the DataFrame.