CSV Files — The Spreadsheet of Code

• Recognise the CSV format and its three rules (header, comma, newline).
• Read a CSV with csv.reader — each row becomes a list.
• Read a CSV with csv.DictReader — each row becomes a dict.
• Handle the gotchas: the newline="" open-arg and converting strings to numbers.

CSV stands for Comma-Separated Values. Here is a complete CSV file:

name,age,score
Aisyah,13,88
Wei Jie,14,75
Suresh,13,92
Mei,14,80

Three rules:

• The first row is usually the header — the column names.
• Each row is one record, fields separated by commas.
• Newline ends a row.

Save the four lines above as students.csv. We'll use it for the rest of the lesson.

csv.reader → each row is a list

import csv

with open("students.csv", newline="") as f:
    reader = csv.reader(f)
    header = next(reader)        # ['name', 'age', 'score']
    print("Header:", header)
    for row in reader:
        print(row)

Header: ['name', 'age', 'score']
['Aisyah', '13', '88']
['Wei Jie', '14', '75']
['Suresh', '13', '92']
['Mei', '14', '80']

Every value is a string, including the numbers. Converting comes next.

csv.DictReader → each row is a dict

import csv

with open("students.csv", newline="") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row)

{'name': 'Aisyah', 'age': '13', 'score': '88'}
{'name': 'Wei Jie', 'age': '14', 'score': '75'}
{'name': 'Suresh', 'age': '13', 'score': '92'}
{'name': 'Mei', 'age': '14', 'score': '80'}

DictReader uses the header as keys. You access columns by name (row["score"]) instead of by position. Most of the time that's what you want.

Why newline=""?

The csv module handles end-of-line characters itself. If you open the file in Python's default text mode (which converts newlines), some Excel-saved CSVs get extra blank rows. Always pass newline="" when working with CSVs — make it a habit.

Converting strings to numbers

Every CSV value is text. To do maths, you convert:

with open("students.csv", newline="") as f:
    reader = csv.DictReader(f)
    total = 0
    count = 0
    for row in reader:
        total += int(row["score"])
        count += 1
    print(f"Average score: {total / count:.1f}")

Average score: 83.8

# class_stats.py — read a CSV and report
import csv

scores = []
with open("students.csv", newline="") as f:
    for row in csv.DictReader(f):
        scores.append({
            "name":  row["name"],
            "age":   int(row["age"]),
            "score": int(row["score"]),
        })

print(f"📊 {len(scores)} students loaded\n")

# Top scorer
top = max(scores, key=lambda s: s["score"])
print(f"🏆 Top:     {top['name']:<10} → {top['score']}")

# Lowest
low = min(scores, key=lambda s: s["score"])
print(f"📉 Lowest:  {low['name']:<10} → {low['score']}")

# Average
avg = sum(s["score"] for s in scores) / len(scores)
print(f"📈 Average: {avg:.1f}")

# Group by age
print("\nBy age:")
ages = sorted({s["age"] for s in scores})
for a in ages:
    these = [s for s in scores if s["age"] == a]
    avg_a = sum(s["score"] for s in these) / len(these)
    print(f"  age {a}: n={len(these)}, avg={avg_a:.1f}")

Sample output

📊 4 students loaded

🏆 Top:     Suresh     → 92
📉 Lowest:  Wei Jie    → 75
📈 Average: 83.8

By age:
  age 13: n=2, avg=90.0
  age 14: n=2, avg=77.5

Read the diff

Four passes. The load pass reads + converts strings to ints. min/max/sum work on the cleaned list. The group-by at the bottom is a classic pattern: collect the distinct keys, then for each key build a sub-list. You'll see this 100 times before Level 4 is done.

CSV is a text file where each row is one record and fields are comma-separated. Python's csv module reads it two ways: csv.reader gives lists, csv.DictReader gives dicts keyed by the header. Always open with newline="". Every value is a string — convert to int/float before doing maths. Tomorrow we write CSVs ourselves.