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Getting Started with CSV

3 min read
By Olivia Bennett

Introduction: Why CSV Still Matters

In a world of complex data formats, CSV is the quiet workhorse. Whether you’re pulling quick exports, exchanging datasets between teams, or running ETL jobs, CSV’s simplicity makes it a first choice for many engineers.

What is a CSV File?

A CSV (Comma-Separated Values) file is a plain text file that encodes tabular data. Each line corresponds to a row, and fields within a row are separated by commas (,). It’s human-readable and easy for machines to parse.

CSV Format Essentials

Structure and Syntax

  • Header row: Optional, but typically contains column names.
  • Data rows: Values separated by commas.
  • Quoted fields: Fields containing commas, new lines, or quotes should be enclosed in double quotes.

Example:

plain
name,age,city
Alice,30,New York
Bob,25,San Francisco

Variations in Delimiters

While the standard uses commas, you might encounter:

  • Semicolon (;) in European datasets
  • Tab (\\t) in TSV files
  • Pipe (|) when avoiding both commas and semicolons

Pros and Cons of CSV

Pros:

  • Lightweight and human-readable
  • Easy to generate and parse
  • Supported by almost every language and tool

Cons:

  • No formal schema or datatype enforcement
  • Difficult to store nested or complex data
  • Data integrity issues if not validated

Working with CSV in Python

Reading CSV Files

Python’s built-in csv module makes reading easy:

python
import csv
with open('data.csv', newline='') as csvfile:
  reader = csv.DictReader(csvfile)
  for row in reader:
    print(row['name'], row['age'])

Writing CSV Files

python
import csv
data = [
  {'name': 'Alice', 'age': 30},
  {'name': 'Bob', 'age': 25}
]
with open('out.csv', 'w', newline='') as csvfile:
	writer = csv.DictWriter(csvfile, fieldnames=['name', 'age'])
	writer.writeheader()
	writer.writerows(data)

Using Pandas for CSV

import pandas as pd

Read CSV

df = pd.read_csv('data.csv')

Process data

df['age'] = df['age'] + 1

Save back

df.to_csv('data_updated.csv', index=False)

Handling CSV in Excel

Excel supports CSV import and export directly:

  • Open CSV: Double-click, or use Data > From Text/CSV
  • Export as CSV: File > Save As > CSV (Comma delimited)

Gotcha: Excel may auto-format dates or large numbers, changing values unintentionally. Always verify your data after import/export.

CSV in Big Data Workflows

Data Exchange

Many APIs and data providers deliver bulk datasets as CSV due to its universality.

Batch Imports

ETL pipelines often stage intermediate datasets as CSV for compatibility across systems.

Reporting Pipelines

CSV is common as a final output from analytics jobs, feeding into BI tools or stakeholder reports.

Best Practices for Clean CSV Handling

  • Always include a header row for clarity.
  • Use UTF-8 encoding to avoid character issues.
  • Escape fields containing delimiters or line breaks.
  • Validate before importing into critical systems.

Final Thoughts

CSV may be old-school, but it remains indispensable. Its balance of simplicity, portability, and tool support makes it a go-to format for quick data exchange and integration. Mastering CSV manipulation in Python, Excel, and your big data stack ensures smoother workflows and fewer integration headaches.

Getting Started with CSV | JuheAPI