Reading from and writing to text and CSV files is one of the most common tasks in data processing and automation. Python makes file handling seamless with built-in methods for working with .txt files and the powerful csv module for .csv files.

In this guide, you'll learn how to open, read, write, and manipulate text and CSV files using Python efficiently and safely.

1. Working with .txt Files

Reading from a .txt File

with open("example.txt", "r") as file:
    content = file.read()
    print(content)

Writing to a .txt File

with open("example.txt", "w") as file:
    file.write("This is the first line.\n")
    file.write("This is the second line.")

Appending to a .txt File

with open("example.txt", "a") as file:
    file.write("\nThis line is appended.")

Reading Line-by-Line

with open("example.txt", "r") as file:
    for line in file:
        print(line.strip())

2. Working with .csv Files Using the csv Module

Python’s csv module provides functionality to read and write CSV (Comma-Separated Values) files in a clean and structured way.

Import the Module

import csv

Writing to a CSV File

import csv

with open("data.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Name", "Age", "City"])
    writer.writerow(["Alice", 30, "New York"])
    writer.writerow(["Bob", 25, "London"])

This creates a file data.csv with column headers and rows of data.

Appending to a CSV File

with open("data.csv", "a", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Charlie", 22, "Paris"])

Reading from a CSV File

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Reading as Dictionary

DictReader reads each row as a dictionary with column headers as keys.

with open("data.csv", "r") as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row["Name"], row["City"])

Writing with Dictionary

with open("employees.csv", "w", newline="") as file:
    fieldnames = ["ID", "Name", "Department"]
    writer = csv.DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    writer.writerow({"ID": 101, "Name": "John", "Department": "Sales"})
    writer.writerow({"ID": 102, "Name": "Daisy", "Department": "HR"})

3. Best Practices

• Always use with statement to handle file closing automatically.
• Use newline="" while writing CSVs to avoid blank lines on Windows.
• Use csv.DictReader and DictWriter for cleaner, header-based access.
• Handle file not found or permission errors using try-except.

4. Summary Table

5. Final Thoughts

Python provides a clean and consistent approach to handling both text and CSV files. Whether you’re managing logs, storing structured data, or importing datasets, mastering .txt and .csv file operations is essential for everyday Python development.