A CSV (Comma-Separated Values) file is used to store tabular data such as spreadsheets and databases.
Each line represents a row, and values are separated by commas.

Python provides a built-in csv module to work with CSV files easily.

IMPORTING CSV MODULE

import csv

READING A CSV FILE

Example CSV File (data.csv)

Name,Age,City
Hira,25,Karachi
Ali,30,Lahore
Sara,22,Islamabad

Reading CSV File

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

Each row is returned as a list.

SKIPPING HEADER ROW

import csvwith open("data.csv", "r") as file:
    reader = csv.reader(file)
    next(reader)   # Skip header
    for row in reader:
        print(row)

USING DICTREADER

DictReader reads CSV data as dictionaries.

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

This makes it easier to access columns by name.

WRITING TO A CSV FILE

import csvwith open("new_data.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Name", "Age", "City"])
    writer.writerow(["Hira", 25, "Karachi"])
    writer.writerow(["Ali", 30, "Lahore"])

newline="" prevents extra blank lines on some systems.

WRITING USING DICTWRITER

import csvdata = [
    {"Name": "Hira", "Age": 25, "City": "Karachi"},
    {"Name": "Ali", "Age": 30, "City": "Lahore"}
]with open("dict_data.csv", "w", newline="") as file:
    fieldnames = ["Name", "Age", "City"]
    writer = csv.DictWriter(file, fieldnames=fieldnames)    writer.writeheader()
    writer.writerows(data)

APPENDING TO A CSV FILE

import csvwith open("data.csv", "a", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Sara", 22, "Islamabad"])

IMPORTANT POINTS

• Always use with to handle files safely
• Use newline="" when writing CSV files
• Use DictReader and DictWriter for better readability
• CSV files store data in plain text format

WHY CSV FILES ARE IMPORTANT

• Common format for data exchange
• Used in Excel and databases
• Essential for data analysis
• Widely used in business and automation

Understanding how to work with CSV files is essential for handling structured data in Python applications.