Python CSV

CSV stands for Comma-Separated Values.

A CSV file is a simple text file used to store tabular data in rows and columns.

For example, a student dataset might look like this:

name,age,marks
Ali,20,85
Sara,22,92
Ahmed,21,78

Each line represents a row, while commas separate individual values.

CSV files are extremely common in business, data analysis and software development.

CSV is particularly useful because almost every spreadsheet application can open CSV files.

Microsoft Excel, Google Sheets and many data-analysis tools can work with CSV files.

Consider this CSV file:

name,department,salary
Ali,Sales,60000
Sara,Marketing,75000
Ahmed,IT,90000

The first row is commonly used as the header row.

name     | department | salary
---------|------------|-------
Ali      | Sales      | 60000
Sara     | Marketing  | 75000
Ahmed    | IT         | 90000

The header tells us what each column represents.

Python includes a built-in module named csv.

import csv

No external installation is required. The module is included with Python.

The first step is to open the CSV file using Python's open() function.

import csv

with open("students.csv", "r", encoding="utf-8") as file:
    reader = csv.reader(file)

    for row in reader:
        print(row)

Each row returned by csv.reader() is represented as a list.

['name', 'age', 'marks']
['Ali', '20', '85']
['Sara', '22', '92']
['Ahmed', '21', '78']

csv.reader() is useful when you want direct access to each row and column by position.

import csv

with open("students.csv", "r", encoding="utf-8") as file:

    reader = csv.reader(file)

    next(reader)

    for row in reader:
        name = row[0]
        age = int(row[1])
        marks = int(row[2])

        print(name, marks)

Ali 85
Sara 92
Ahmed 78

next(reader) skips the header row.

Many CSV files contain a header row. You can capture the header separately.

import csv

with open("students.csv", "r", encoding="utf-8") as file:

    reader = csv.reader(file)

    headers = next(reader)

    print("Headers:", headers)

    for row in reader:
        print(row)

Headers: ['name', 'age', 'marks']
['Ali', '20', '85']
['Sara', '22', '92']
['Ahmed', '21', '78']

DictReader makes CSV data easier to work with because each row becomes a dictionary.

import csv

with open("students.csv", "r", encoding="utf-8") as file:

    reader = csv.DictReader(file)

    for student in reader:
        print(student["name"])
        print(student["marks"])

Ali
85
Sara
92
Ahmed
78

Python can also create CSV files.

The basic workflow is:

import csv

with open("students.csv", "w", newline="", encoding="utf-8") as file:

    writer = csv.writer(file)

    writer.writerow(["name", "age", "marks"])

    writer.writerow(["Ali", 20, 85])
    writer.writerow(["Sara", 22, 92])
    writer.writerow(["Ahmed", 21, 78])

csv.writer() provides methods for writing rows into a CSV file.

writerow()

writer.writerow(["Ali", 20, 85])

writerows()

Use writerows() to write multiple rows.

rows = [
    ["Ali", 20, 85],
    ["Sara", 22, 92],
    ["Ahmed", 21, 78]
]

writer.writerows(rows)

DictWriter allows you to write dictionaries directly into a CSV file.

import csv

fieldnames = ["name", "age", "marks"]

students = [
    {"name": "Ali", "age": 20, "marks": 85},
    {"name": "Sara", "age": 22, "marks": 92},
    {"name": "Ahmed", "age": 21, "marks": 78}
]

with open("students.csv", "w", newline="", encoding="utf-8") as file:

    writer = csv.DictWriter(
        file,
        fieldnames=fieldnames
    )

    writer.writeheader()
    writer.writerows(students)

Use mode "a" when you want to append new rows to an existing CSV file.

import csv

with open("students.csv", "a", newline="", encoding="utf-8") as file:

    writer = csv.writer(file)

    writer.writerow(["Usman", 23, 88])

Although CSV commonly means comma-separated values, files can use other delimiters such as semicolons or tabs.

For example:

name;age;marks
Ali;20;85
Sara;22;92

You can specify the delimiter when reading the file.

import csv

with open("students.csv", "r", encoding="utf-8") as file:

    reader = csv.reader(
        file,
        delimiter=";"
    )

    for row in reader:
        print(row)

CSV files may contain text that itself includes commas. Quoting allows the CSV format to distinguish between commas inside a value and commas separating columns.

name,address
Ali,"House 10, Street 5, Multan"
Sara,"House 20, Street 8, Lahore"

Python's csv module handles CSV quoting rules for you.

import csv

with open("customers.csv", "w", newline="", encoding="utf-8") as file:

    writer = csv.writer(file)

    writer.writerow(["name", "address"])

    writer.writerow([
        "Ali",
        "House 10, Street 5, Multan"
    ])

When writing CSV files in Python, it is recommended to use:

with open(
    "students.csv",
    "w",
    newline="",
    encoding="utf-8"
) as file:

    writer = csv.writer(file)

The newline="" setting allows the CSV module to handle line endings correctly.

CSV files may contain names, addresses and other information in many languages.

Using UTF-8 encoding helps Python handle a wide range of characters.

with open(
    "customers.csv",
    "r",
    encoding="utf-8"
) as file:

    reader = csv.reader(file)

    for row in reader:
        print(row)

CSV becomes much more powerful when combined with Python's calculations, conditions and loops.

Example — Calculate Average Marks

import csv

total = 0
count = 0

with open("students.csv", "r", encoding="utf-8") as file:

    reader = csv.DictReader(file)

    for student in reader:

        marks = int(student["marks"])

        total += marks
        count += 1

average = total / count

print("Average:", average)

Average: 85.0

Example — Find High Scorers

import csv

with open("students.csv", "r", encoding="utf-8") as file:

    reader = csv.DictReader(file)

    for student in reader:

        marks = int(student["marks"])

        if marks >= 90:
            print(student["name"])

Sara

1. Sales Reports

A business might export its sales information into a CSV file:

product,quantity,price
Laptop,5,85000
Mouse,20,2500
Keyboard,12,4500

2. Customer Records

name,email,city
Ali,ali@example.com,Multan
Sara,sara@example.com,Lahore
Ahmed,ahmed@example.com,Islamabad

3. Data Analysis

Analysts frequently receive datasets as CSV files. Python can read these files and calculate totals, averages, filter records and generate reports.

Build a small Python program that reads sales information from a CSV file and generates a report.

Suggested CSV

product,quantity,price
Laptop,5,85000
Mouse,20,2500
Keyboard,12,4500
Monitor,4,35000

Expected Report

===== SALES REPORT =====

Total Units Sold: 41
Total Revenue: 618000

Top Product:
Laptop

=========================

You now have the skills to create, read and process practical CSV datasets using Python.