CSV Files: Tables as Text

Why this matters

CSV (comma-separated values) is the everyday format for tables: spreadsheets, exports from banks and shops, datasets. It is just text, one row per line, with commas between the cells. Python’s csv module reads and writes it correctly, including the awkward cases like a comma inside a cell.

The idea

A file scores.csv:

name,score
Ada,90
Bo,85

Read it with DictReader, which uses the header row as the keys:

import csv

with open("scores.csv", newline="") as file:
    for row in csv.DictReader(file):
        print(row["name"], row["score"])   # every value is TEXT: "90", not 90

Write it with DictWriter:

with open("out.csv", "w", newline="") as file:
    writer = csv.DictWriter(file, fieldnames=["name", "score"])
    writer.writeheader()
    writer.writerow({"name": "Ada", "score": 90})

Two details to remember:

• Open CSV files with newline="" so the module controls line endings.
• Everything read from a CSV is a string. Convert numbers with int() or float().

There is also csv.reader / csv.writer which use plain lists instead of dictionaries.

Try it

Create a small CSV file with write_text, read it with DictReader, and print the average of a numeric column.

Your turn

The checks give your functions CSV files with a name,score header. Write three functions:

• load_students(path): a list of (name, score) tuples with the score as an int.
• class_average(path): the average score as a number.
• save_passed(source, destination, minimum): write a new CSV (with the same name,score header) containing only the students whose score is at least minimum.

Recap

• csv.DictReader gives each row as a dict keyed by the header; DictWriter writes dicts.
• Open CSV files with newline=""; values come in as text.
• Convert, filter and write back: the heart of simple data processing.

Common mistakes

• Doing maths on strings: "90" + "85" is "9085". Convert first.
• Forgetting writeheader(), so the file has no header row.
• Splitting lines by hand with .split(","): it breaks on quoted commas. Use csv.