csv-wrangling

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Standard workflow order, tool selection matrix, and composition patterns for qsv CSV data wrangling

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CSV Wrangling with qsv

Standard Workflow Order

Always follow this sequence when processing CSV data:

Setup (Cowork) - If relative paths don't resolve, call mcp__qsv__qsv_get_working_dir and mcp__qsv__qsv_set_working_dir to sync
Index - index (enables fast random access for subsequent commands)
Discover - sniff (detect format, encoding, delimiter) -> headers -> count
Profile - stats --cardinality --stats-jsonl (creates cache used by smart commands)
Inspect - slice --len 5 (preview rows), frequency --frequency-jsonl (value distributions with cache for reuse)
Transform - select, sort, dedup, rename, replace, search, sqlp, etc.
Validate - validate (against JSON Schema), stats (verify results)
Export - tojsonl, table, mcp__qsv__qsv_to_parquet, to (xlsx/sqlite/postgres/ods/datapackage)
Document - describegpt --all (AI-generated Data Dictionary, Description & Tags)

Tool Selection Matrix

Task	Best Tool	Alternative	When to Use Alternative
Select columns	`select`	`sqlp`	Need computed columns
Filter rows	`search`	`sqlp`	Complex WHERE conditions
Sort data	`sort`	`sqlp`	Need ORDER BY with LIMIT
Remove duplicates	`dedup`	`sqlp`	Need GROUP BY dedup
Join two files	`joinp`	`join`	`join` for memory-constrained
Aggregate/GROUP BY	`sqlp`	`frequency`	`frequency` for simple counts; `--frequency-jsonl` creates cache
Column stats	`stats`	`moarstats`	`moarstats` for extended stats
Find/replace	`replace`	`sqlp`	`sqlp` for conditional replace
Reshape wide->long	`transpose --long`	-	DuckDB UNPIVOT (external) for complex reshaping
Reshape long->wide	`pivotp`	`sqlp`	Complex pivots
Concatenate files	`cat rows`	`cat rowskey`	Different column orders
Sample rows	`sample`	`slice`	`slice` for positional ranges
Document dataset	`describegpt`	—	AI-generated Data Dictionary, Description & Tags

qsv Selection Syntax

Used by select, search, sort, dedup, frequency, and other commands:

Syntax	Meaning	Example
`name`	Column by name	`select "City"`
`1`	Column by 1-based index	`select 1`
`1,3,5`	Multiple columns	`select 1,3,5`
`1-5`	Range (inclusive)	`select 1-5`
`!col`	Exclude column	`select '!SSN'`
`!1-3`	Exclude range	`select '!1-3'`
`/regex/`	Match column names	`select '/^price/'`

Common Pipeline Patterns

Clean and Deduplicate

sniff -> index -> safenames -> fixlengths -> sqlp (TRIM) -> dedup -> validate

Profile and Analyze

sniff -> index -> stats --cardinality --stats-jsonl -> read .stats.csv -> frequency (on key columns) -> sqlp (GROUP BY queries)

Before writing SQL: read .stats.csv to learn column types, cardinality, nullcount, min/max, sort order. Run frequency on columns you'll GROUP BY or filter on. Use this to write precise WHERE clauses, correct type casts, and avoid unnecessary COALESCE.

For repeated SQL queries on large CSV (> 10MB), consider converting to Parquet: sniff -> index -> stats -> to_parquet -> sqlp (using read_parquet()). Note: sqlp can query CSV of any size directly.

Join and Enrich

index (both files) -> stats (both) -> joinp -> select (keep needed columns) -> sort

Profile and Document

sniff -> index -> stats --cardinality --stats-jsonl -> describegpt --all

Convert and Export

excel (to CSV) -> index -> stats -> select -> tojsonl / qsv_to_parquet

Batch Convert to Multiple Formats

excel (to CSV) -> index -> stats -> to xlsx report.xlsx
excel (to CSV) -> index -> stats -> to sqlite report.db
excel (to CSV) -> index -> stats -> to parquet parquet_output_dir

File Integrity Verification

blake3 file.csv > checksums.b3 (before transfer) -> blake3 --check checksums.b3 (after transfer)

Delimiter Handling

CSV (,): default, no flag needed
TSV (\t): use --delimiter '\t' or file extension .tsv
SSV (;): use --delimiter ';' or file extension .ssv
Auto-detect: set QSV_SNIFF_DELIMITER=1 environment variable

Important Notes

Column indices are 1-based, not 0-based
--no-headers flag changes behavior significantly - most commands assume headers exist
Output goes to stdout by default; use --output file.csv to write to file
Many commands auto-detect .sz (Snappy compressed) files transparently
cat rows requires same column order; use cat rowskey for different schemas
dedup loads all data into memory and sorts internally; use --sorted flag if input is already sorted to enable streaming mode with constant memory
sort loads entire file into memory; for huge files use sqlp with ORDER BY
For repeated SQL queries on large CSV (> 10MB), consider converting to Parquet with mcp__qsv__qsv_to_parquet for faster performance. Parquet works ONLY with sqlp and DuckDB — all other qsv commands need CSV/TSV/SSV input

Tool Discovery

Use mcp__qsv__qsv_search_tools to discover commands beyond the initially loaded core tools. There are 55 qsv skill-based commands covering selection, filtering, transformation, aggregation, joining, validation, formatting, conversion, and more.

Operational Notes

Timeout: Default operation timeout is 10 minutes (configurable via QSV_MCP_OPERATION_TIMEOUT_MS, max 30 min). Allow operations to run to completion.
Memory: dedup, sort, reverse, table, transpose, pragmastat, and stats (with extended stats) load entire files into memory. For files >1GB, prefer extdedup/extsort via mcp__qsv__qsv_command.
Cowork path architecture: qsv runs on the HOST machine. File paths must be valid on the host. Always verify with mcp__qsv__qsv_get_working_dir.
Sequential operations: Prefer sequential over parallel qsv calls to avoid queuing delays: index → stats → analysis.
Large files (>5GB): Let mcp__qsv__qsv_frequency run to completion. Only fall back to mcp__qsv__qsv_sqlp with GROUP BY if the server timeout is exceeded.
Context window: Save outputs to files rather than returning to chat. Use mcp__qsv__qsv_slice or mcp__qsv__qsv_sqlp with LIMIT to inspect subsets.