Day 44/100 – Working with CSV Files in Python

Welcome back to 100 Days, 100 Python Topics! Yesterday, we explored file modes and error handling. Today, we’ll focus on CSV files — a very common format for storing tabular data. Whether you’re dealing with spreadsheets, datasets, or exporting data from an app, Python’s csv module makes working with CSV files a breeze.

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1. What is a CSV File?

A CSV (Comma-Separated Values) file is a plain text file where each line represents a row of data, and each value in the row is separated by a comma (or sometimes other delimiters like ; or \t).

Example:

Name,Age,Country
John,25,USA
Emma,30,UK
Liam,28,Canada

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2. Reading CSV Files

Python provides the built-in csv module to read CSV files easily.

import csv

# Reading a CSV file
with open('data.csv', mode='r') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Notes:

• csv.reader() returns each row as a list of strings.
• The first row often contains headers, which you might want to skip using next(reader).

Skipping the header example:

with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    next(reader)  # Skip the first row
    for row in reader:
        print(row)

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3. Writing to CSV Files

To create or modify a CSV file, use csv.writer().

import csv

# Writing to a CSV file
data = [
    ["Name", "Age", "Country"],
    ["John", 25, "USA"],
    ["Emma", 30, "UK"]
]

with open('output.csv', mode='w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)

Key points:

• newline='' prevents adding extra blank lines on some systems.
• Use writer.writerow() for a single row, writer.writerows() for multiple rows.

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4. Using DictReader and DictWriter

Instead of working with lists, DictReader and DictWriter let you use column headers as keys.

Reading CSV as dictionaries:

import csv

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

Writing CSV from dictionaries:

import csv

data = [
    {"Name": "John", "Age": 25, "Country": "USA"},
    {"Name": "Emma", "Age": 30, "Country": "UK"}
]

with open('dict_output.csv', mode='w', newline='') as file:
    fieldnames = ["Name", "Age", "Country"]
    writer = csv.DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    writer.writerows(data)

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5. Handling Different Delimiters

Not all CSVs use commas. Some use semicolons or tabs.

import csv

with open('semicolon_data.csv', mode='r') as file:
    reader = csv.reader(file, delimiter=';')
    for row in reader:
        print(row)

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6. Best Practices

✅ Always use with open() for automatic file closing.
✅ Use newline='' when writing to avoid extra blank lines.
✅ Consider pandas for large datasets.
✅ Handle encoding (e.g., encoding='utf-8') if your CSV contains special characters.

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Challenge for You

1. Create a CSV file containing at least 5 rows of employee data.
2. Write a script to:

• Read the CSV file using DictReader.
• Add a new column for "Salary".
• Save it back to a new CSV file using DictWriter.

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