CSV files in Python: 3 Essential Techniques

CSV (Comma-Separated Values) files are a common and versatile format for storing tabular data. Python’s built-in csv module makes it easy to work with these files, whether you need to read, write, or manipulate their contents. This guide will walk you through essential techniques for handling CSV files in Python, enabling you to extract valuable insights from your data.

1. Reading CSV Files: The csv.reader

The csv.reader class is your gateway to reading CSV data. It treats the file as a sequence of rows, allowing you to iterate over them easily.

import csv

with open('10_02_us.csv', 'r') as file:
    reader = csv.reader(file, delimiter='\t')  # Use '\t' for tab-delimited files
    for row in reader:
        print(row) 

Key Features:

• Iteration: Conveniently loop through rows.
• Custom Delimiters: Handle tab-separated values or other delimiters.
• Skipping Headers: Use next(reader) to skip the first row if it contains column names.

2. Working with CSV Data as Dictionaries: The csv.DictReader

For more structured access, consider using csv.DictReader:

with open('10_02_us.csv', 'r') as file:
    reader = csv.DictReader(file, delimiter='\t')
    for row in reader:
        print(row['place name'], row['state code'])  # Access columns by name

Key Features:

• Column Names: Access columns using their names from the header row.
• Data as Dictionaries: Each row is returned as a dictionary, making it easy to work with individual fields.

3. Writing CSV Files: The csv.writer

To write data to a CSV file, use csv.writer:

with open('ma_prime.csv', 'w', newline='') as file:  
    writer = csv.writer(file)
    writer.writerow(['Place Name', 'County'])  # Write the header row
    for row in data:
        writer.writerow([row['place name'], row['county']])  # Write each row as a list

Key Features:

• writerow(list): Writes a single row of data to the file.
• Newline Handling: Use newline='' on Windows to avoid extra blank lines between rows.
• Custom Delimiters: Specify different delimiters (e.g., tabs) if needed.

4. Advanced Filtering: List Comprehensions and CSV

Combine the power of list comprehensions with CSV data to filter and transform information:

primes = [num for num in range(2, 100000) if all(num % i != 0 for i in range(2, int(num**0.5) + 1))]

data = [row for row in data if int(row['postal code']) in primes and row['state code'] == 'MA']

This example finds prime postal codes in Massachusetts.

Frequently Asked Questions (FAQ)