A simple, universal format for tabular data exchange and storage
CSV (Comma-Separated Values) is a simple, text-based file format used to store tabular data. As the name suggests, it typically uses commas to separate values, though other delimiters like semicolons, tabs, or pipes can also be used depending on regional settings and specific implementations.
The format's origin dates back to the early days of computing, with no single standardized specification until relatively recently. CSV gained widespread adoption due to its simplicity and compatibility with virtually all database systems, spreadsheet applications, and programming languages.
Despite its apparent simplicity, CSV has numerous variations and edge cases that can cause compatibility issues. In 2005, RFC 4180 was published to provide some standardization, but many implementations still vary in how they handle quotation, escaping special characters, line breaks, and other details.
A CSV file consists of rows (records) and columns (fields). Each row is on a new line, and columns are separated by the delimiter character (typically a comma). Fields containing the delimiter, quotes, or line breaks are usually enclosed in double quotes. According to RFC 4180, a double quote character within a quoted field should be represented by two double quotes. The first row may contain column headers, but this is not required by the format itself.
CSV is the de facto standard for exchanging tabular data between different systems, applications, and organizations. Its universal compatibility and simple structure make it ideal for transferring data across platforms without requiring specialized software or complex conversion processes.
Virtually all database systems support importing from and exporting to CSV format. This makes CSV an essential tool for database migrations, backups, and transferring data between different database systems or versions.
CSV files are widely used in data analysis workflows. They can be easily imported into analysis tools like Excel, Google Sheets, R, Python (pandas), and specialized data analysis software. The format's simplicity facilitates rapid data exploration and visualization.
Many machine learning datasets are distributed as CSV files due to their widespread compatibility and easy parsing. The format is well-supported by all major machine learning libraries and frameworks, making it convenient for both data preparation and model training.
CSV is commonly used for exporting logs, sensor readings, and other machine-generated data in a structured format that can be easily processed by analytics systems or imported into databases for storage and analysis.
CSV enjoys excellent compatibility across virtually all data-handling applications:
Despite its widespread use, CSV implementations can vary in several ways:
| Feature | CSV | Excel (XLSX) | JSON | XML | SQLite |
|---|---|---|---|---|---|
| Simplicity | |||||
| Human Readability | |||||
| Data Structure Support | |||||
| File Size Efficiency | |||||
| Data Type Support | |||||
| Cross-Platform Support |
CSV excels in simplicity and universal compatibility, making it ideal for basic data exchange. Excel files offer better user experience and formatting for human users. JSON and XML provide support for complex, hierarchical data structures, with JSON being more compact and easier to parse. SQLite offers the most powerful querying capabilities and data integrity features but requires specific software for access.
When converting from Excel to CSV, be aware that you'll lose formatting, formulas, multiple sheets, and other Excel-specific features. For data with complex formatting or calculations, consider pre-processing to convert formulas to values and simplify formatting. Choose the appropriate delimiter based on your data and target system (comma or semicolon).
When exporting from databases to CSV, ensure character encoding settings match your requirements (UTF-8 recommended for most cases). If possible, export using SQL queries that format and filter data appropriately rather than dumping entire tables. Consider the implications of NULL values, which may be represented differently across systems.
Converting from hierarchical formats like JSON or XML to flat CSV requires careful planning. You'll need to decide how to flatten nested structures, which may involve creating multiple CSV files or denormalizing data. For complex structures, consider creating a mapping document to explain the transformation logic.
When importing CSV into Excel, pay attention to data type detection. Excel tries to guess data types, which can lead to issues with number formatting, dates, and values beginning with special characters. Use Excel's Text Import Wizard or Power Query for more control over the import process, particularly for preserving leading zeros and date formats.
Before importing CSV data into a database, create a proper table schema that defines appropriate data types and constraints. Use database-specific bulk import tools for better performance with large datasets. Consider validating and cleaning the data before import to avoid integrity issues.
When converting CSV to hierarchical formats like JSON or XML, the first row is typically used for property/element names. Decide how to handle data types, as CSV stores everything as text. You may need to parse numbers, dates, booleans, etc. Also consider whether the data should be represented as an array of objects or in a different structure.