Tass

A pager for tabular data

tass - it's like less, but for tables! It can read CSV/TSV and parquet files, and even ND-JSON in a pinch. It looks like this:

You can see tass's schema inference in action here. The fourth and fifth columns only contain a small number of unique values, so tass colour-codes them automatically. The columns to the right of those are numerical, so tass right-aligns them and shows negative values in red.

This demo shows data being piped into (an older version of) tass. You can also open files which are still being appended to, and new rows will appear as the file grows.

Installing

If you already have rust, you can install tass like this:

$ cargo install tass

Usage

You can pass a filename, or pipe data to stdin:

$ tass mydata.csv
$ cat mydata.csv | tass

Key	Action
Up/j, PageUp	Move up one row, page
Down/k,PageDown	Move down one row, page
Left/h	Move left one column
Right/l	Move right one column
Home, End	Move to start/end of file
number g	Move to line $number
f	Move to end and auto-scroll as new rows come in
/, ?	Search, reverse-search for string
n, N	Jump to next, previous match
q/Esc	Quit

Comparison to other tools

Tool	Functionality	Convenience	Filetypes	Loads whole file into memory	Live view growing data
tass	⭐ Viewing data, basic searching	🚀 Snappy TUI	CSV/TSV, parquet, JSON	no 😌	✔️
csvlens	⭐ Similar to tass	🚀 Similar to tass	CSV/TSV	no 😌	✔️
VisiData	⭐⭐ Summary stats, plots, etc.	TUI, a bit clunky	CSV/TSV, parquet, JSON, ...	yes 😱	✔️
Excel/Calc/Numbers/Google Sheets	⭐⭐ It's a spreadsheet!	⏳ Launch a GUI app	CSV/TSV, xls, ods, ...	yes 😱	❌
Pandas/Polars/DataFrame.jl	⭐⭐⭐ It's a dataframe library!	⏳ A bunch of typing	CSV/TSV, parquet, ...	it depends	❌
duckdb	⭐⭐⭐ It's a database!	⏳ A bunch of typing	CSV/TSV, parquet, ...	it depends	❌

For manipulating data, my advice is to use a spreadsheet (for small datasets) or dataframe library (for larger datasets). Sometimes you just want to quickly inspect the contents of a file though, and that's what tass is for.

Tips & tricks

Here are some more tips for working with large CSV files:

• If you want to see summary statistics but don't care about the underlying data itself, you can still use VisiData/Excel/etc.: just downsample it first. This will reduce the precision of your stats and plots, but not the overall shape (probably). xsv has a subcommand that can do this for you.
• If you want exact answers to complex questions, use a dataframe library. Alternatively, consider dumping your CSV into a sqlite database and working with that instead.
• For ad-hoc computations on CSV files, take a look at frawk - it's really nice.