# Question 8: It assumes that the CSV file being read 
# (tomato_sales_error.csv) has a column named "Tomato_Sales." 
# If the column doesn't exist in the CSV file or if there is an issue 
# with the file itself, an error will be raised.

# Question 9: 
import pandas as pd

def read_tomato_sales(file_name):
    data = pd.read_csv(file_name)

    # Ensure required columns exist
    required_columns = ["Store", "Region", "Tomato_Sales"]
    missing_columns = set(required_columns) - set(data.columns)
    if missing_columns:
        raise ValueError(f"Invalid CSV format: Missing columns: {', '.join(missing_columns)}")

    # Validate Tomato_Sales column
    tomato_sales = data["Tomato_Sales"]
    if not tomato_sales.dtype == int and not tomato_sales.dtype == float:
        raise ValueError("Invalid CSV format: 'Tomato_Sales' column must contain numerical values.")

    # Calculate total tomato sales
    total_sales = tomato_sales.sum()
    print(f"Total tomato sales: {total_sales}")

# Question 10: Bytes represent raw binary data, while strings represent 
# textual data. Using bytes allows us to handle various character 
# encodings. By working with bytes, we can ensure that the data remains in 
# its raw encoded form, allowing for accurate handling of different 
# encodings.

# In the interactive Python REPL (Read-Eval-Print Loop), the "_" variable 
# is a special variable that is automatically assigned the result of the 
# last expression evaluated. The type of the "_" variable reflects the 
# type of the last expression evaluated in the REPL.