csv library

Official documentation

The csv module reads and writes CSV ("comma-separated values") files — a plain-text table format that spreadsheets and databases can both open. Every example below actually runs in your browser: Pyodide gives each page its own in-memory filesystem, so open() works exactly like it would on a real computer, just without anything being saved outside this page.

Install

csv ships with Python's standard library — nothing to install.

Import

The whole module is used through the csv. prefix, so a plain import is all you need.

import csv

Tool	Reads/writes rows as	Use it for
csv.writer	A list per row	Writing plain rows of values
csv.reader	A list per row	Reading plain rows of values
csv.DictWriter	A dict per row	Writing rows keyed by column name
csv.DictReader	A dict per row	Reading rows keyed by column name

Writing CSV files

csv.writer wraps an open file and turns each list you pass to .writerow() into one comma-separated line.

import csv

with open("snakes.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["species", "length_ft"])
    writer.writerow(["ball", 4.5])
    writer.writerow(["burmese", 12])

print("wrote snakes.csv")

Writing multiple rows at once

Takes a list of rows and writes them all in one call — .writerows(), instead of looping over .writerow() yourself.

rows = [["ball", 4.5], ["burmese", 12], ["boa", 8]]
writer.writerows(rows)

Run a writing CSV example

All the examples above, combined into one script:

import csv

with open("snakes.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["species", "length_ft"])
    writer.writerow(["ball", 4.5])
    writer.writerow(["burmese", 12])

print("wrote snakes.csv")

import csv

rows = [["ball", 4.5], ["burmese", 12], ["boa", 8]]

with open("snakes.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["species", "length_ft"])
    writer.writerows(rows)

Reading CSV files

csv.reader gives back each row as a plain list of strings — including the header row, which is usually skipped over explicitly.

import csv

with open("snakes.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["species", "length_ft"])
    writer.writerow(["ball", 4.5])
    writer.writerow(["burmese", 12])

with open("snakes.csv", newline="") as file:
    reader = csv.reader(file)
    header = next(reader)
    for row in reader:
        print(row)

Reading rows as dictionaries

Uses the first row as column names automatically, so each row comes back as a dict. You can look up values by column name instead of by position. Every value is still read as a plain string — convert it (e.g. with float()) if you need to do math on it.

for row in csv.DictReader(file):
    row["species"]      # "ball"
    row["length_ft"]    # "4.5" — still a string, not a float

Run a reading CSV example

All the examples above, combined into one script:

import csv

with open("snakes.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["species", "length_ft"])
    writer.writerow(["ball", 4.5])
    writer.writerow(["burmese", 12])

with open("snakes.csv", newline="") as file:
    reader = csv.reader(file)
    header = next(reader)
    for row in reader:
        print(row)

import csv

with open("snakes.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["species", "length_ft"])
    writer.writerow(["ball", 4.5])
    writer.writerow(["burmese", 12])

with open("snakes.csv", newline="") as file:
    for row in csv.DictReader(file):
        print(row["species"], float(row["length_ft"]))