csv-files.md

Python by Example: CSV Files

CSV (comma-separated values) is a plain-text format for tabular data. Python's csv module handles quoting and escaping automatically. Use csv.writer and csv.reader for row-based access; use csv.DictReader when you want named fields.

What you'll learn:

• Writing rows with csv.writer
• Reading rows with csv.reader
• Named-field access with csv.DictReader

import csv


with open("people.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["name", "age", "city"])
    writer.writerows([
        ["Alice", 30, "New York"],
        ["Bob", 25, "London"],
        ["Carol", 35, "Tokyo"],
    ])


with open("people.csv") as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)


# DictReader maps each row to a dict using the header
with open("people.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["name"], row["age"])

Pass newline="" when opening for writing—the csv module handles its own line endings. All values come back as strings when reading; convert them explicitly if you need numbers.

To run this program:

$ python source/csv-files.py
['name', 'age', 'city']
['Alice', '30', 'New York']
['Bob', '25', 'London']
['Carol', '35', 'Tokyo']
Alice 30
Bob 25
Carol 35

Tip: For tab-separated files, pass delimiter="\t" to csv.writer and csv.reader.

Try it: Read the CSV back and print only the people older than 28.

Source: csv-files.py

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