Python CSV – Reading and Writing CSV Files

Reading CSV Files

csv.reader reads each row as a list of strings.

import csv

# Example CSV (students.csv):
# name,grade,score
# Alice,A,95
# Bob,B,82
# Charlie,A,91

with open("students.csv", "r") as f:
    reader = csv.reader(f)
    header = next(reader)     # Skip header row
    print(f"Columns: {header}")
    for row in reader:
        print(f"{row[0]}: {row[2]}")

DictReader – Rows as Dictionaries

csv.DictReader maps each row to a dict using the header row as keys.

import csv

with open("students.csv", "r") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(f"{row['name']} scored {row['score']} (grade {row['grade']})")

Writing CSV Files

csv.writer writes lists as CSV rows.

import csv

students = [
    ["name", "grade", "score"],  # header
    ["Alice", "A", 95],
    ["Bob", "B", 82],
    ["Charlie", "A", 91]
]

with open("output.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerows(students)

print("CSV written successfully")

DictWriter – Write from Dicts

Use DictWriter when your data is in dictionary form.

import csv

rows = [
    {"name": "Alice", "grade": "A", "score": 95},
    {"name": "Bob",   "grade": "B", "score": 82},
]

with open("output.csv", "w", newline="") as f:
    fields = ["name", "grade", "score"]
    writer = csv.DictWriter(f, fieldnames=fields)
    writer.writeheader()     # Write column names
    writer.writerows(rows)   # Write all rows

Real-World: Analyse CSV Data

Compute statistics from a CSV without pandas.

import csv

total, count = 0, 0
with open("students.csv", "r") as f:
    for row in csv.DictReader(f):
        total += int(row["score"])
        count += 1

print(f"Average score: {total/count:.1f}")

Reading and Writing CSV the Right Way

CSV looks simple — comma-separated lines — but hand-parsing with .split(",") breaks the moment a field contains a comma or a quote. Python's csv module handles all those edge cases correctly.

import csv

# read as dictionaries keyed by header row
with open("data.csv", newline="") as f:
    for row in csv.DictReader(f):
        print(row["name"], row["email"])

# write
with open("out.csv", "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["name", "email"])
    writer.writeheader()
    writer.writerow({"name": "Ann", "email": "a@b.com"})

Why not split(","): a value like "Smith, John" is one quoted field with a comma inside — split wrongly cuts it in two. The csv module respects quoting and escaping. Two must-dos: always open files with newline="" (prevents blank rows on Windows), and use a with block so the file closes even on error. DictReader is usually nicest — access columns by name (row["email"]) instead of fragile numeric indices. For anything beyond simple I/O (filtering, analysis, large files), pandas.read_csv() is far more powerful.

Interview Questions on CSV Files

• What module does Python use to work with CSV files?
• What is the difference between csv.reader and csv.DictReader?
• Why should you pass newline="" when opening a CSV file for writing?
• How do you handle CSV files with different delimiters?
• How do you write a header row with DictWriter?
• Why might you use pandas instead of the csv module?
• How do you handle quoted fields containing commas?

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