Skip to main content

csvql-query

CI License: MIT PyPI

Query CSV files with SQL from Python — powered by a Zig/SIMD engine.

Zero-copy mmap reads + SIMD parsing happen before Python ever sees the data. Faster than DuckDB on typical workloads, no dependencies required.

Installation

pip install csvql-query

Quick Start

import csvql

# Returns a list of dicts (like csv.DictReader, but with SQL)
rows = csvql.query("SELECT name, salary FROM 'employees.csv' WHERE salary > 100000 ORDER BY salary DESC")
# [{'name': 'Alice', 'salary': '185000'}, ...]

# Raw CSV string
csv_str = csvql.query_csv("SELECT * FROM 'data.csv' LIMIT 10")

# pandas DataFrame (pandas must be installed)
df = csvql.query_df("SELECT category, COUNT(*) as n FROM 'sales.csv' GROUP BY category")

# (headers, rows) tuples — no dependencies
headers, rows = csvql.query_tuples("SELECT name, age FROM 'users.csv' WHERE age > 25")

API

Function Returns Description
query(sql) list[dict] Execute SQL, get list of dicts
query_csv(sql) str Execute SQL, get raw CSV string
query_df(sql) DataFrame Execute SQL, get pandas DataFrame
query_tuples(sql) (list[str], list[tuple]) Execute SQL, get (headers, rows)

SQL Support

The SQL path is embedded in the query string (same as the CLI):

# Filtering, ordering, limiting
csvql.query("SELECT name, city FROM 'data.csv' WHERE age > 30 ORDER BY name LIMIT 5")

# Aggregation
csvql.query("SELECT department, AVG(salary) FROM 'emp.csv' GROUP BY department")

# Unix pipes — use '-' as the filename
import subprocess, sys
# or just pass stdin data via the engine directly

Full SQL reference: SIMPLE_QUERY_LANGUAGE.md

Performance

  • mmap + SIMD parsing — data is never copied into Python memory
  • Parallel chunk processing on multi-core machines
  • Typically 5–9x faster than DuckDB on 1M-row CSVs

Requirements

  • Python ≥ 3.10
  • macOS (x86_64 / arm64) or Linux (x86_64)
  • pandas optional — only needed for query_df()

Links

Metadata

Release files for csvql-query 2.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for csvql-query 2.0.2
File Interpreter ABI Platform
csvql_query-2.0.2-py3-none-manylinux_2_17_x86_64.whl Python 3 none Linux glibc 2.17+ x86-64 Details
csvql_query-2.0.2-py3-none-macosx_12_0_x86_64.whl Python 3 none macOS 12.0+ x86-64 Details
csvql_query-2.0.2-py3-none-macosx_12_0_arm64.whl Python 3 none macOS 12.0+ ARM64 Details

Total release size: 2.8 MB

Release files / csvql_query-2.0.2-py3-none-manylinux_2_17_x86_64.whl

Download URL csvql_query-2.0.2-py3-none-manylinux_2_17_x86_64.whl
Size 2.0 MB
Tags Linux glibc 2.17+ x86-64 Python 3
SHA-256 checksum
How to use checksums
cf2333a72b7c1a5d294f8de20172302d24278d3b0b7169d2854b521beb86945c
BLAKE2b-256 checksum
How to use checksums
474ee1e010385c16d1755f505b0602a2151bb052fd98847890d7a936319ac135
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / csvql_query-2.0.2-py3-none-macosx_12_0_x86_64.whl

Download URL csvql_query-2.0.2-py3-none-macosx_12_0_x86_64.whl
Size 389.5 kB
Tags Python 3 macOS 12.0+ x86-64
SHA-256 checksum
How to use checksums
365fcaadd51adcc90e4695a0d59b61f218e9ba4051ce5ed65f5e25c9e7776c05
BLAKE2b-256 checksum
How to use checksums
146ccee5debac7c45ee02be41c3999f132e02b9c77c9848f6644635bfd33830e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / csvql_query-2.0.2-py3-none-macosx_12_0_arm64.whl

Download URL csvql_query-2.0.2-py3-none-macosx_12_0_arm64.whl
Size 389.5 kB
Tags Python 3 macOS 12.0+ ARM64
SHA-256 checksum
How to use checksums
21c34df12984a42e570c3ad53213f1b97e1f04629b2b606207f1b2763fd57fe7
BLAKE2b-256 checksum
How to use checksums
05166fb18037add88a434fb273de285b6cd9fd14df8d664e16fa54ec51ad0aa0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release history Release notifications | RSS feed

2.9.0

5 release files

2.8.1

5 release files

2.8.0

5 release files

2.7.0

5 release files

2.6.3

5 release files

2.6.2

5 release files

2.6.0

3 release files

2.5.0

3 release files

2.4.3

3 release files

2.4.1

3 release files

2.4.0

3 release files

2.3.0

3 release files

2.2.0

3 release files

2.1.1

3 release files

2.1.0

3 release files

2.0.4

3 release files

2.0.3

3 release files

This release

2.0.2 This release

3 release files

2.0.1

3 release files

2.0.0

3 release files

1.9.0

3 release files

1.8.0

3 release files

1.7.2

3 release files

1.7.1

3 release files

1.7.0

3 release files

1.6.1

3 release files

1.6.0

3 release files

1.5.13

3 release files

1.5.12

3 release files

1.5.11

3 release files

1.5.10

3 release files

1.5.9

3 release files

1.5.8

3 release files

1.5.7

3 release files

1.5.6

3 release files

1.5.5

3 release files

1.5.1

3 release files

1.5.0

3 release files

1.4.0

3 release files

1.3.0

3 release files

1.1.1

3 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page