Lab: build a utility

Build a CSV column extractor from scratch — read structured input, apply a transformation, write clean output with proper error handling.

This is an optional lab. No new concepts — just hands-on practice applying everything from the Utility Thinking module. Work through it at your own pace. Each checkpoint has a Check button so you can test as you go.

You are going to build a CSV column extractor: a utility that takes CSV text as input, extracts one or more named columns, and writes the values as plain lines to stdout. It is small enough to finish in a session and real enough that you could actually use it.

Here is what it will do when finished:

# Input (stdin):
name,score,city
alice,91,london
bob,74,paris
carol,88,london

# Command:
python extract.py score

# Output (stdout):
score
91
74
88

One column name as a positional argument. Clean lines out. Errors to stderr. Exit 1 if the column does not exist.

Warm up: parse one CSV row

Before the graded work, make sure you can split a CSV line. Python's str.split is not safe for CSV (it breaks on commas inside quoted fields), but for simple data without embedded commas, line.split(",") is fine. For real CSV, use the csv module.

Play with this before moving on:

Python — editable, runs in your browser

Notice how the header gives you the column names and each data row is a parallel list of values. To extract column "score", you find its index in the header, then pull that index from every data row.

Checkpoint 1 — find a column index

Write column_index(header, name) that returns the integer index of name in the header list, or -1 if it is not found.

Find column indexPython

Write column_index(header, name) that returns the index of name in header, or -1 if not found.

column_index(["name","score","city"], "score") → 1column_index(["name","score","city"], "age") → -1

The built-in list.index() raises a ValueError if the item is absent — you will need to handle that, or use a different approach. A try/except around list.index() is one clean option.

Checkpoint 2 — extract a column

Write extract_column(csv_text, column_name) that returns a list of values for the named column. The first element should be the column name itself (the header), followed by one value per data row. If the column does not exist, return an empty list.

Extract a columnPython

Write extract_column(csv_text, column_name) that returns a list: [header_value, row1_value, row2_value, ...]. Return [] if the column is not found.

extract_column(csv, "score") → ["score", "91", "74", "88"]extract_column(csv, "age") → []

Checkpoint 3 — wire up the utility

Now put it all together. Write the complete run_extractor(csv_text, column_name) function that:

Returns the extracted lines joined by newlines when the column exists
Returns None when the column does not exist (the caller should then exit 1)

Complete extractorPython

Write run_extractor(csv_text, column_name) that returns the column values joined by newlines, or None if the column does not exist.

run_extractor(csv, "score") → "score\n91\n74"run_extractor(csv, "age") → None

Putting it all together

Here is a complete utility that uses your functions. Notice how short main() is — all the real work is in the helper functions that you just tested:

Python — editable, runs in your browser

import sys
import argparse

def column_index(header, name):
  try:
      return header.index(name)
  except ValueError:
      return -1

def extract_column(csv_text, column_name):
  lines = csv_text.strip().splitlines()
  if not lines:
      return []
  header = lines[0].split(",")
  idx = column_index(header, column_name)
  if idx == -1:
      return []
  result = [header[idx]]
  for line in lines[1:]:
      row = line.split(",")
      result.append(row[idx])
  return result

def run_extractor(csv_text, column_name):
  values = extract_column(csv_text, column_name)
  if not values:
      return None
  return "
".join(values)

def main():
  parser = argparse.ArgumentParser(description="Extract a CSV column.")
  parser.add_argument("column", help="Name of the column to extract")
  args = parser.parse_args(["score"])   # simulate: python extract.py score

csv_text = "name,score,city
alice,91,london
bob,74,paris
carol,88,london
"

result = run_extractor(csv_text, args.column)
  if result is None:
      sys.stderr.write("error: column not found: " + args.column + "
")
      sys.exit(1)

print(result)
  sys.exit(0)

main()

Try changing parse_args(["score"]) to parse_args(["city"]) or parse_args(["age"]) to see the success and failure paths.

Where to go next

Your utility works — and it is composable. Next, the packaging and sharing module covers how to turn this script into an installable command that you can run by name from anywhere.

Finished reading? Mark it complete to track your progress.