Day 32 - File I/O

Skills: None

Pre-reading: NEED TO WRITE! -- New section in DCIC about manual file I/O, to supplement existing content loading CSVs from files.

Intro (10 mins)

• Today we’ll learn how to read and write CSV files "by hand" in Python, using only basic file I/O and string methods—no Pandas.
• This helps you understand what's happening under the hood when you use high-level libraries.
• Example: Reading a CSV file and filtering for dine-in orders.

  # Read the file
  # First download the file from the URL:
  # https://pdi.run/f25-restaurant-orders.csv
  # and save it as 'restaurant_orders.csv' in the same directory as this file.
  file = open('restaurant_orders.csv', 'r')
  lines = file.readlines()
  file.close()
  
  # Convert lines to list of lists
  data = []
  for line in lines:
      cells = line.strip().split(',')
      data.append(cells)
  
  # Separate header and data
  headers = data[0]
  orders = data[1:]
  
  # Convert quantity to int and filter for dine-in
  dine_in_orders = []
  for row in orders:
      row[2] = int(row[2])
      if row[3] == 'dine-in':
          dine_in_orders.append(row)
  
  # Write filtered data to a new CSV
  output = [headers] + dine_in_orders
  out_file = open('dine_in_orders.csv', 'w')
  for row in output:
      out_file.write(','.join([str(cell) for cell in row]) + '\n')
  out_file.close()
• All of these steps—reading, splitting, cleaning, filtering, and writing—are done manually here, but are automated by Pandas.

Class Exercises (35 mins)

• Filter the data to include only orders where order_type is "takeout". Print the filtered rows.
• Write the filtered data (including the header) to a new CSV file called takeout_orders.csv.
• What happens if you forget to close the file after writing? Try it and see if you get an error or warning.
• Add a new column to each row called total_price (assume each dish costs $10). Write the updated data to a new CSV file.
• Try reading a CSV file that is missing a column in one row. What happens? How could you handle this?
• Change your code to skip rows that are missing data, and write only the complete rows to a new file.
• Write code to count the total number of orders in the file (excluding the header).
• Write code to count how many orders are for each unique order_type (e.g., "dine-in", "takeout").
• Write code to find the dish with the highest total quantity ordered across all rows.
• Write code to filter for orders where quantity is greater than 2, and write these rows (with the header) to a new CSV file called large_orders.csv.
• Write code to compute the total revenue (sum of total_price) for all orders, assuming each dish costs $10.
• Write code to find and print all unique dish names in the file.
• Write code to sort the orders by quantity (largest first) and write the sorted data (with header) to a new CSV file.
• Write code to handle a file where some rows are missing the order_type column: skip those rows and print a warning for each skipped row.
• (Optional) Write code to add a new column called order_label that is "big" if quantity ≥ 3, "medium" if quantity is 2, and "small" otherwise, and write the updated data to a new CSV file.

Wrap-up (5 mins)

• Manual file I/O gives you insight into how data is read, cleaned, filtered, and written in Python.
• Libraries like Pandas automate these steps, but it’s important to understand what’s happening behind the scenes.