What is CSV?
CSV stands for Comma-Separated Values. It's a plain text file format that stores data in rows and columns, with commas separating each value. Think of it as a spreadsheet stripped down to its basics. No fancy formatting, no formulas, just raw data organized in a way that virtually any program can read.
Each line in a CSV file represents one row of data. Within that row, commas divide the information into separate columns. The first row usually contains headers that label what each column represents, though this isn't required.
Why CSV matters for web scraping
When you scrape data from websites, you need somewhere to store it. CSV files are the default choice for most scraping projects because they solve a critical problem: turning messy web data into clean, organized tables.
Say you're scraping product listings from an e-commerce site. You extract product names, prices, ratings, and availability. A CSV file lets you export all that information into a simple table where each row is a product and each column is a different attribute. You can then open it in Excel, import it into a database, or feed it into an analysis tool without any conversion headaches.
Web scrapers love CSV files because they work everywhere. You don't need special software to open them, and you don't need to worry about compatibility issues when sharing data with colleagues or clients.
How CSV files are structured
Here's what a basic CSV file looks like:
The first line contains headers: Product Name, Price, Rating
The second line contains data: Laptop, 899.99, 4.5
The third line contains more data: Mouse, 29.99, 4.8
That's it. Each piece of information is separated by a comma, and each new line starts a new record. If a field contains a comma (like a price written as "1,299.99"), you wrap it in quotation marks so the system knows to treat it as a single value.
Advantages of using CSV
CSV files are popular for good reasons. They're lightweight, taking up minimal storage space compared to Excel files or databases. You can open them in any text editor, spreadsheet program, or programming language without special tools.
They're also incredibly easy to create and edit. No complex formatting rules or proprietary software needed. This simplicity makes them perfect for quick data transfers between different systems.
For web scraping specifically, CSV files let you start analyzing data immediately. Most scraping tools can export directly to CSV, which means you can have your scraped data in a usable format within seconds.
Limitations to watch for
CSV files have their downsides. They can't store formatting like bold text, colors, or fonts. If you need your data to look pretty, CSV isn't your format.
They also struggle with complex data structures. If you're scraping nested information (like products with multiple variants, each with their own attributes), flattening everything into a simple table can get messy. You might need to create multiple CSV files or use a more sophisticated format like JSON.
Another issue: CSV files treat everything as text. A number might look like "100" but the system doesn't automatically know it's a number versus the text string "100". This can cause problems when you import the data into other tools that need to perform calculations.
And if your data contains special characters, commas, or line breaks, you'll need to handle them carefully with quotation marks and escape characters, which adds complexity.
Common uses beyond web scraping
While we focus on web scraping, CSV files show up everywhere in data work. Businesses use them to manage customer lists, inventory records, and transaction histories. Data analysts use them to move data between different analysis tools. Developers use them to seed databases or transfer information between incompatible systems.
Pretty much any time you need to share structured data between different programs or people, CSV is a safe bet.
How Browse AI handles CSV exports
Browse AI lets you extract data from websites without writing code and automatically exports your scraped data to CSV format. You can set up a scraper in minutes, run it on schedule, and download your results as a clean CSV file ready for analysis. The platform handles all the formatting complexities, so you get properly structured data every time. You can also connect your scraped data directly to Google Sheets or other tools through integrations, but CSV export gives you maximum flexibility for working with the data however you want.
