csv — CSV File Reading and Writing

The csv module implements classes to read and write tabular data in CSV format.

import csv

Reading CSV Files

import csv

# Example: data.csv contains:
# Name,Age,City
# Alice,30,New York
# Bob,25,London

with open('data.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    header = next(reader)  # Read header row
    for row in reader:
        print(row)
        # ['Alice', '30', 'New York']
        # ['Bob', '25', 'London']

Writing CSV Files

import csv

data = [
    ['Name', 'Age', 'City'],
    ['Alice', 30, 'New York'],
    ['Bob', 25, 'London']
]

# Important: newline='' is required to prevent double-spacing on Windows
with open('output.csv', mode='w', encoding='utf-8', newline='') as file:
    writer = csv.writer(file, quoting=csv.QUOTE_MINIMAL)
    writer.writerows(data)

Using DictReader (Recommended)

Maps the information in each row to a dict whose keys are given by the first row.

import csv

with open('data.csv', mode='r', encoding='utf-8') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row['Name'], row['Age'])
        # Alice 30
        # Bob 25

Using DictWriter

import csv

data = [
    {'Name': 'Alice', 'Age': 30, 'City': 'New York'},
    {'Name': 'Bob', 'Age': 25, 'City': 'London'}
]

with open('output.csv', mode='w', encoding='utf-8', newline='') as file:
    fieldnames = ['Name', 'Age', 'City']
    writer = csv.DictWriter(file, fieldnames=fieldnames)
    
    writer.writeheader()  # Write the column names
    writer.writerows(data)

Custom Dialects / Delimiters

import csv

# Using TSV (Tab-Separated Values)
with open('data.tsv', mode='r') as file:
    reader = csv.reader(file, delimiter='\t')
    
# Using semicolons
with open('data.csv', mode='w', newline='') as file:
    writer = csv.writer(file, delimiter=';')

Common Pitfalls

• Not using newline='' when writing will cause blank lines between rows on Windows.
• Numbers are read as strings — you must manually convert them via int() or float().

Tip

For large datasets and data analysis, the pandas library (pandas.read_csv()) is vastly more powerful and handles type conversion automatically.

Official Documentation

csv — CSV File Reading and Writing

API Reference

Reader Objects

Writer Objects