xsv — Fast CSV Toolkit Written in Rust

What it is

xsv is a command-line toolkit for working with CSV data, written in Rust. It provides a suite of subcommands for viewing headers, slicing rows, selecting columns, searching with regex, joining files, computing statistics, and reformatting output. Rust compilation gives xsv the ability to process millions of rows per second on commodity hardware.

xsv targets data engineers, analysts, and developers who work with CSV files in the terminal. It fills the gap between simple tools like cut and awk (which do not understand CSV quoting) and full data processing frameworks (which require writing programs).

How it saves time or tokens

xsv operates on CSV data natively, handling quoted fields, escaped commas, and multi-line values correctly. A single xsv command replaces multi-step awk/sed pipelines. The index subcommand creates a binary index for instant random access into large files. Statistics computation runs in streaming mode without loading the entire file into memory.

How to use

1. Install xsv via brew install xsv on macOS or cargo install xsv from source.
2. Explore CSV structure with xsv headers data.csv and xsv stats data.csv.
3. Chain subcommands with pipes: select, filter, sort, and aggregate in a single pipeline.

Example

# View CSV headers
xsv headers data.csv

# First 10 rows, pretty-printed
xsv slice -l 10 data.csv | xsv table

# Select specific columns
xsv select name,email,age data.csv

# Search rows by regex
xsv search -s status 'active' data.csv

# Statistics for all columns
xsv stats data.csv | xsv table

# Join two CSV files on a common column
xsv join id users.csv user_id orders.csv

# Count rows by category
xsv frequency -s category data.csv

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Common pitfalls

• xsv is CSV-only. It does not support JSON, TSV with tab delimiters (use --delimiter flag), or other formats. For multi-format needs, consider Miller.
• The xsv project has not received major updates recently. For active development, check out qsv, a maintained fork with additional features.
• Large file joins load the smaller file into memory. Ensure adequate RAM when joining against files with millions of rows.