csvkit

csvkit

csvkit — Python CSV toolkit (csvlook, csvsql, csvjoin, csvstat)

When to Use

Reach for csvlook when the task is in the data category and the agent would otherwise call an MCP tool or read large outputs into context.

Token Cost

CLI tools return structured stdout that fits in <1KB for typical queries; equivalent MCP calls regularly exceed 10KB. Reference: Anthropic engineering (Nov 4 2025) — code-execution-over-MCP yields 98.7% token reduction.

Recipes

csvcut -c name,email records.csv

Project two named columns from a CSV — column-name addressing instead of positional.

csvstat records.csv

One-shot column-by-column summary: type, count, null %, min/max/mean. Drop-in EDA.

csvgrep -c status -m active records.csv

Row filter where column status matches literal active.

csvjoin -c id a.csv b.csv

Join two CSVs on column id (inner join by default; --left / --outer available).

csvsql --query 'SELECT name FROM data WHERE active = 1' data.csv

Run SQL directly against a CSV using an in-memory SQLite — no schema file required.

Wrong Choice When

• Files larger than ~1M rows: csvkit is Python-startup-heavy; hatch3r-cli-duckdb (tier 2) loads and queries the same file in a fraction of the time.
• Production SQL workloads: csvsql is convenient but evaluates against in-memory SQLite — use a real database for anything served.
• Single-column slice or count under a few hundred MB: hatch3r-cli-qsv (tier 2) is faster with lower memory pressure.

Alternatives

Tool	When to prefer
hatch3r-cli-duckdb (tier 2)	Large files, analytical SQL, Parquet, multi-file joins
hatch3r-cli-qsv (tier 2)	Fast column slicing, sampling, deduping
hatch3r-cli-miller (tier 3)	Streaming put/filter DSL, format conversion

Detection / Install

Verify with:

command -v csvlook

Install (mac):

# pipx
pipx install csvkit

Homepage: https://csvkit.readthedocs.io/