CSV files are one of the simplest and most widely used formats for storing structured data. From customer records and sales reports to analytics exports and application data, CSV files are everywhere.

Python makes it easy to read and write CSV files using its built-in csv module. You can work with CSV data without installing any external library, making Python a practical choice for automation, data processing, and backend development.

In this guide, you’ll learn how to read and write CSV files with Python, including practical examples, different CSV-reading techniques, writing data, handling headers, common mistakes, and best practices.

What Is a CSV File?

CSV stands for Comma-Separated Values.

A CSV file stores data in rows and columns, similar to a spreadsheet. Each row typically represents a record, while commas separate individual values.

For example:

name,email,age
John,john@example.com,25
Sarah,sarah@example.com,28
Mike,mike@example.com,31

Here:

name, email, and age are column headers.
Each following line represents one record.
Commas separate the values.

Although CSV usually means “comma-separated values,” other delimiters such as tabs or semicolons can also be used.

Why Use CSV Files in Python?

CSV is popular because it is:

Simple and human-readable
Lightweight
Supported by Excel and Google Sheets
Easy to import and export
Supported by almost every programming language
Useful for data migration and automation

Python’s built-in csv module provides everything you need for basic CSV operations.

Common Use Cases

You can use Python CSV processing for:

Importing customer data
Exporting application reports
Processing sales records
Migrating data between systems
Generating reports
Cleaning datasets
Automating spreadsheet workflows
Reading configuration or structured data

Python CSV Module

Python includes a built-in module called csv for working with CSV files.

You don’t need to install anything.

import csv

The module provides useful functions and classes such as:

Function/Class	Purpose
`csv.reader()`	Reads CSV rows
`csv.writer()`	Writes CSV rows
`csv.DictReader()`	Reads rows as dictionaries
`csv.DictWriter()`	Writes dictionaries to CSV
`csv.writer()`	Creates a CSV writer
`csv.reader()`	Creates a CSV reader

For most applications, reader, writer, DictReader, and DictWriter are the most useful tools.

How to Read a CSV File With Python

Let’s start with the simplest example.

Suppose you have a file named users.csv:

name,email,age
John,john@example.com,25
Sarah,sarah@example.com,28
Mike,mike@example.com,31

You can read this file using csv.reader().

Reading CSV Using `csv.reader()`

import csv

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

    for row in reader:
        print(row)

Output

['name', 'email', 'age']
['John', 'john@example.com', '25']
['Sarah', 'sarah@example.com', '28']
['Mike', 'mike@example.com', '31']

Each CSV row is returned as a Python list.

How This Code Works

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

This opens the CSV file in read mode.

"r" means read mode.
newline="" helps the CSV module handle line endings correctly.
encoding="utf-8" supports a wide range of characters.
with automatically closes the file after processing.

Then:

reader = csv.reader(file)

creates a CSV reader.

Finally:

for row in reader:
    print(row)

loops through each row.

How to Skip the CSV Header

Often, you don’t want to process the header as regular data.

You can skip it using next().

import csv

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

    next(reader)

    for row in reader:
        print(row)

Now the output starts with:

['John', 'john@example.com', '25']
['Sarah', 'sarah@example.com', '28']
['Mike', 'mike@example.com', '31']

This approach is useful when you already know the structure of the CSV file.

Reading CSV Files Using `DictReader`

For many applications, csv.DictReader() is easier to work with.

Instead of returning lists, it returns each row as a dictionary.

import csv

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

    for row in reader:
        print(row["name"], row["email"], row["age"])

Output

John john@example.com 25
Sarah sarah@example.com 28
Mike mike@example.com 31

The first row automatically becomes the dictionary keys.

For example:

{
    "name": "John",
    "email": "john@example.com",
    "age": "25"
}

This makes DictReader especially useful when your CSV contains many columns.

`csv.reader()` vs `csv.DictReader()`

Choosing the right reader can make your code cleaner.

Feature	`csv.reader()`	`csv.DictReader()`
Returns	Lists	Dictionaries
Access data	By index	By column name
Header handling	Manual	Automatic
Easy for beginners	Yes	Yes
Best for	Simple CSV processing	Structured records
Readability	Moderate	High

For example:

Using `reader`

print(row[0])

Using `DictReader`

print(row["name"])

For larger applications, DictReader is often easier to maintain.

How to Write a CSV File With Python

Python also makes it simple to create CSV files.

Suppose you want to create a file containing employee information.

import csv

employees = [
    ["John", "Developer", 50000],
    ["Sarah", "Designer", 55000],
    ["Mike", "Manager", 70000]
]

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

    writer.writerow(["Name", "Role", "Salary"])
    writer.writerows(employees)

This creates:

Name,Role,Salary
John,Developer,50000
Sarah,Designer,55000
Mike,Manager,70000

Writing a Single CSV Row

Use writerow() when you want to write one row.

import csv

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

    writer.writerow(["Name", "Email", "Age"])
    writer.writerow(["John", "john@example.com", 25])

The resulting CSV file will contain:

Name,Email,Age
John,john@example.com,25

Writing Multiple CSV Rows

Use writerows() when you have multiple rows.

import csv

users = [
    ["John", "john@example.com", 25],
    ["Sarah", "sarah@example.com", 28],
    ["Mike", "mike@example.com", 31]
]

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

    writer.writerow(["Name", "Email", "Age"])
    writer.writerows(users)

`writerow()` vs `writerows()`

Method	Use
`writerow()`	Write one row
`writerows()`	Write multiple rows

Writing CSV Files Using `DictWriter`

If your data is represented using dictionaries, csv.DictWriter() is a convenient option.

import csv

users = [
    {
        "name": "John",
        "email": "john@example.com",
        "age": 25
    },
    {
        "name": "Sarah",
        "email": "sarah@example.com",
        "age": 28
    }
]

with open("users.csv", "w", newline="", encoding="utf-8") as file:
    fieldnames = ["name", "email", "age"]

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

    writer.writeheader()
    writer.writerows(users)

The generated file looks like:

name,email,age
John,john@example.com,25
Sarah,sarah@example.com,28

Why Use `DictWriter()`?

It allows you to work with meaningful column names rather than remembering column positions.

row["email"]

is easier to understand than:

row[1]

Append Data to an Existing CSV File

Sometimes you don’t want to overwrite an existing CSV file. Instead, you want to add new records.

Use append mode:

import csv

new_user = ["David", "david@example.com", 35]

with open("users.csv", "a", newline="", encoding="utf-8") as file:
    writer = csv.writer(file)
    writer.writerow(new_user)

The "a" mode means append.

Common File Modes

Mode	Meaning
`"r"`	Read
`"w"`	Write and overwrite
`"a"`	Append
`"r+"`	Read and write

Be careful with "w" because it replaces the existing contents of the file.

Reading CSV Files With a Different Delimiter

Not every CSV file uses commas.

Some files use semicolons:

name;email;age
John;john@example.com;25

You can specify the delimiter:

import csv

with open("users.csv", "r", newline="", encoding="utf-8") as file:
    reader = csv.reader(file, delimiter=";")

    for row in reader:
        print(row)

You can also work with tab-separated data:

reader = csv.reader(file, delimiter="\t")

This makes Python’s CSV module useful for many delimited text formats.

Handling CSV Data With Quoted Values

CSV files can contain commas inside a field.

For example:

name,address
John,"New York, USA"
Sarah,"London, UK"

Using the CSV module correctly handles these values:

import csv

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

    for row in reader:
        print(row["address"])

Output:

New York, USA
London, UK

This is one reason you should use Python’s csv module instead of manually splitting lines with split(",").

Why You Shouldn’t Use `split(",")` for CSV Files

A common beginner approach is:

line.split(",")

It looks simple, but it can break when CSV values contain commas.

For example:

John,"New York, USA"

A simple split(",") can incorrectly treat the comma inside the address as a separator.

The csv module understands CSV quoting rules and handles these cases correctly.

Better Approach

import csv

reader = csv.reader(file)

Best practice: Use Python’s CSV parser rather than manually parsing CSV strings.

Real-World Example: Generate a Sales Report

Let’s create a practical example.

Suppose your application receives sales data:

product,quantity,price
Laptop,2,75000
Mouse,5,1200
Keyboard,3,2500

You can calculate the total value of each product.

import csv

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

    for row in reader:
        quantity = int(row["quantity"])
        price = float(row["price"])

        total = quantity * price

        print(f"{row['product']}: ₹{total:,.2f}")

Output

Laptop: ₹150,000.00
Mouse: ₹6,000.00
Keyboard: ₹7,500.00

Notice that values read from CSV files are strings by default.

That’s why we convert:

int(row["quantity"])

and:

float(row["price"])

before performing calculations.

Important: CSV Values Are Usually Strings

When you read a CSV file:

row["age"]

may return:

"25"

not:

If you need a number, convert it explicitly:

age = int(row["age"])

For decimal values:

price = float(row["price"])

This is an important consideration when processing CSV data in Python.

CSV Reading and Writing Workflow

A typical Python CSV workflow looks like this:

        CSV File
           │
           ▼
     Open the File
           │
           ▼
      CSV Reader
           │
           ▼
   Process / Validate Data
           │
           ▼
     Transform Data
           │
           ▼
      CSV Writer
           │
           ▼
      Output File

This pattern is useful for data-import scripts, reporting tools, ETL processes, and automation.

Handling Missing or Invalid Data

Real-world CSV files are rarely perfect.

You may encounter missing values:

name,email,age
John,john@example.com,25
Sarah,,28
Mike,mike@example.com,

You should validate data before processing it.

import csv

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

    for row in reader:
        if not row["email"]:
            print(f"Missing email for {row['name']}")

        if not row["age"]:
            print(f"Missing age for {row['name']}")

For production applications, you should also validate data types and handle malformed rows appropriately.

Handling FileNotFoundError

If the CSV file doesn’t exist, Python raises FileNotFoundError.

You can handle it gracefully:

import csv

try:
    with open("users.csv", "r", newline="", encoding="utf-8") as file:
        reader = csv.DictReader(file)

        for row in reader:
            print(row)

except FileNotFoundError:
    print("CSV file was not found.")

This prevents the application from crashing without a useful message.

Working With Large CSV Files

CSV files can contain thousands or even millions of rows.

Avoid loading the entire file into memory when you don’t need to.

Instead, process rows one at a time:

import csv

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

    for row in reader:
        process_data(row)

This approach allows Python to process records incrementally.

For very large datasets, you can also consider specialized data-processing tools such as pandas, databases, or dedicated ETL systems depending on your workload.

Python `csv` Module vs Pandas

Both approaches are useful, but they serve different purposes.

Feature	Python `csv`	Pandas
Built into Python	Yes	No
Installation required	No	Yes
Simple CSV operations	Excellent	Excellent
Data analysis	Basic	Excellent
Memory efficiency	Good for streaming	Depends on dataset
DataFrame support	No	Yes
Best for	File processing	Data analysis

For simple file operations, Python’s built-in csv module is often enough.

For complex data analysis, filtering, grouping, and statistical operations, pandas can be more convenient.

Common Mistakes When Working With CSV Files

1. Forgetting `newline=""`

When writing CSV files, use:

open("file.csv", "w", newline="", encoding="utf-8")

This helps avoid unwanted blank lines on some platforms.

2. Using the Wrong File Mode

This:

open("users.csv", "w")

can overwrite the existing file.

If you want to add data, use:

open("users.csv", "a")

3. Forgetting Encoding

Using:

encoding="utf-8"

is a good default for modern applications.

It helps when CSV files contain characters such as:

José
東京
भारत

4. Assuming Everything Is a Number

CSV values are generally read as strings.

This won’t perform numeric addition correctly:

quantity = row["quantity"]

Convert it when necessary:

quantity = int(row["quantity"])

5. Parsing CSV With String Methods

Avoid:

line.split(",")

Use:

csv.reader()

or:

csv.DictReader()

instead.

Best Practices for Reading and Writing CSV Files in Python

Follow these practices when working with CSV files:

1. Use the built-in `csv` module

Don’t manually parse CSV data unless you have a specific reason.

2. Prefer `DictReader` for structured records

It makes your code easier to understand.

3. Use UTF-8 encoding

encoding="utf-8"

4. Use `newline=""`

Especially when writing CSV files.

5. Validate imported data

Check for missing or invalid values before processing.

6. Convert data types explicitly

age = int(row["age"])
price = float(row["price"])

7. Avoid loading huge files unnecessarily

Process records incrementally when possible.

8. Don’t overwrite files accidentally

Understand the difference between:

r → read
w → overwrite/write
a → append

Quick Reference: Python CSV Operations

Task	Python Code
Import CSV module	`import csv`
Read CSV	`csv.reader()`
Read CSV as dictionaries	`csv.DictReader()`
Write CSV	`csv.writer()`
Write dictionaries	`csv.DictWriter()`
Write one row	`writer.writerow()`
Write multiple rows	`writer.writerows()`
Add header	`writer.writeheader()`
Append file	`open(..., "a")`
Read file	`open(..., "r")`
Overwrite file	`open(..., "w")`

Frequently Asked Questions

1. How do I read a CSV file in Python?

You can use Python’s built-in csv.reader():

import csv

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

    for row in reader:
        print(row)

For column-based access, csv.DictReader() is often more convenient.

2. How do I write a CSV file in Python?

Use csv.writer():

import csv

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

    writer.writerow(["Name", "Age"])
    writer.writerow(["John", 25])

3. What is the difference between `csv.reader()` and `csv.DictReader()`?

csv.reader() returns each row as a list, while csv.DictReader() returns each row as a dictionary using the CSV header as the keys.

Use DictReader when you want to access columns by name.

4. How do I append data to a CSV file in Python?

Open the file using append mode:

with open("data.csv", "a", newline="", encoding="utf-8") as file:
    writer = csv.writer(file)
    writer.writerow(["John", 25])

The "a" mode adds new data without replacing the existing contents.

5. Can Python read CSV files without installing a library?

Yes. Python includes the csv module in its standard library, so you don’t need to install an external package for basic CSV processing.

6. How do I handle commas inside CSV values?

Use Python’s csv module rather than split(",").

The CSV parser correctly handles quoted values such as:

John,"New York, USA"

7. Should I use CSV or pandas in Python?

Use the built-in csv module for straightforward file reading, writing, imports, exports, and streaming operations.

Use pandas when you need advanced data analysis, filtering, grouping, transformations, or DataFrame functionality.

8. Why are numbers read as strings from a CSV file?

CSV is a text-based format, so values are generally returned as strings.

Convert them explicitly:

age = int(row["age"])
price = float(row["price"])

Key Takeaways

Here are the most important things to remember:

Python has a built-in csv module for working with CSV files.
Use csv.reader() for simple row-based processing.
Use csv.DictReader() when working with column names.
Use csv.writer() to create CSV files.
Use csv.DictWriter() when your data is stored as dictionaries.
Use "a" mode to append instead of overwriting.
Use newline="" and encoding="utf-8" when opening CSV files.
CSV values are generally read as strings, so convert data types when necessary.
Avoid parsing CSV files manually with split(",").
Validate real-world CSV data before processing it.

Conclusion

Learning how to read and write CSV files with Python is an essential skill for beginners and developers working with data, automation, APIs, reporting systems, and backend applications.

Python’s built-in csv module provides a simple and reliable way to read, write, append, and process CSV data without installing additional libraries.

For basic CSV processing, start with csv.reader() and csv.writer(). When your data has meaningful column names, csv.DictReader() and csv.DictWriter() can make your code significantly cleaner and easier to maintain.

Once you understand these fundamentals, you can build more advanced workflows such as automated reports, data imports, CSV validation tools, and data-processing pipelines.

How to Read and Write CSV Files With Python

What Is a CSV File?

Why Use CSV Files in Python?

Common Use Cases

Python CSV Module

How to Read a CSV File With Python

Reading CSV Using csv.reader()

Output

How This Code Works

How to Skip the CSV Header

Reading CSV Files Using DictReader

Output

csv.reader() vs csv.DictReader()

Using reader

Using DictReader

How to Write a CSV File With Python

Writing a Single CSV Row

Writing Multiple CSV Rows

writerow() vs writerows()

Writing CSV Files Using DictWriter

Why Use DictWriter()?

Append Data to an Existing CSV File

Common File Modes

Reading CSV Files With a Different Delimiter

Handling CSV Data With Quoted Values

Why You Shouldn’t Use split(",") for CSV Files

Better Approach

Real-World Example: Generate a Sales Report

Output

Important: CSV Values Are Usually Strings

CSV Reading and Writing Workflow

Handling Missing or Invalid Data

Handling FileNotFoundError

Working With Large CSV Files

Python csv Module vs Pandas

Common Mistakes When Working With CSV Files

1. Forgetting newline=""

2. Using the Wrong File Mode

3. Forgetting Encoding

4. Assuming Everything Is a Number

5. Parsing CSV With String Methods

Best Practices for Reading and Writing CSV Files in Python

1. Use the built-in csv module

2. Prefer DictReader for structured records

3. Use UTF-8 encoding

4. Use newline=""

5. Validate imported data

6. Convert data types explicitly

7. Avoid loading huge files unnecessarily

8. Don’t overwrite files accidentally

Quick Reference: Python CSV Operations

Frequently Asked Questions

1. How do I read a CSV file in Python?

2. How do I write a CSV file in Python?

3. What is the difference between csv.reader() and csv.DictReader()?

4. How do I append data to a CSV file in Python?

5. Can Python read CSV files without installing a library?

6. How do I handle commas inside CSV values?

7. Should I use CSV or pandas in Python?

8. Why are numbers read as strings from a CSV file?

Key Takeaways

Conclusion

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Reading CSV Using `csv.reader()`

Reading CSV Files Using `DictReader`

`csv.reader()` vs `csv.DictReader()`

Using `reader`

Using `DictReader`

`writerow()` vs `writerows()`

Writing CSV Files Using `DictWriter`

Why Use `DictWriter()`?

Why You Shouldn’t Use `split(",")` for CSV Files

Python `csv` Module vs Pandas

1. Forgetting `newline=""`

1. Use the built-in `csv` module

2. Prefer `DictReader` for structured records

4. Use `newline=""`

3. What is the difference between `csv.reader()` and `csv.DictReader()`?