The CSV Processor

Welcome to your real-world graduation test.

In previous chapters, your data was pristine. In the Office, data is notoriously messy. A major part of an Architect's job is ensuring the system doesn't collapse when handed bad data.

What is a CSV?

CSV (Comma-Separated Values) is the standard format for exporting spreadsheets (like Excel). It is fundamentally just a text file where each line is a row, and columns are separated by commas ,.

Example: 101,Alice,Engineering,8500

Index 0: 101 (ID)
Index 1: Alice (Name)
Index 2: Engineering (Department)
Index 3: 8500 (Salary)

CSV Explanation Diagram

The Challenge: Fault Tolerance

Finance has sent you salaries.csv. You need to read it, extract the salary from the 4th column (Index 3), and sum it all up into total_payroll.

The Catch: The file is corrupted. It contains empty lines, missing salaries, and salaries spelled out as words instead of numbers (which will cause a ValueError if you try to int() them).

Your Task

Read the File

Open salaries.csv. Remember to next(file) to skip the header row!

Parse Rows

Loop over the file. Strip the whitespace and .split(',') by commas.

Defend the System

Use a try...except ValueError: block. Try to convert the salary to an int and add it to total_payroll. If it fails (or if the line is empty/broken), gracefully continue to the next row without crashing!

Suggested Solution

Expand

Solution:

By catching the exception, we prevent one bad row from destroying the entire data pipeline.

total_payroll = 0

with open('salaries.csv', 'r') as file:
  next(file) # Skip header
  
  for line in file:
      clean_line = line.strip()
      
      # Skip completely empty lines
      if not clean_line:
          continue
          
      parts = clean_line.split(',')
      
      # Defense! Try to parse, fail gracefully.
      try:
          salary_str = parts[3]
          salary = int(salary_str)
          total_payroll += salary
      except (ValueError, IndexError):
          # Catches both bad numbers and malformed rows missing columns
          print(f"Skipping bad data row: {clean_line}")

print("Total Valid Payroll:", total_payroll)