# petl-developers/petl

Python Extract Transform and Load Tables of Data

Repository: https://github.com/petl-developers/petl
Canonical: https://ross.abutalabs.com/products/petl
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-19T20:56:33+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 98, longevity 100
- inputs: {"age_days": 5493, "days_push": 14, "days_rel": 14, "gap_med": 10.5, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1316, forks 207 (observed 2026-08-28T04:04:20.838459+00:00)

## What it is
petl is a general-purpose Python package for extracting, transforming and loading tables of data. It provides a lightweight, pure-Python toolkit for reading, converting, and writing tabular data across formats like CSV, JSON, XML, and SQL databases.

## Use cases
- convert csv files to
- clean and transform tabular data in python
- load data from csv into a sql database
- filter and sort rows in a large table
- join and merge datasets from different files
- extract transform load without pandas
- deduplicate records in a data file

## When to choose
- you need lightweight pure-Python ETL without heavy dependencies
- you work with tabular data in many formats (csv, , xml, sql)
- you want lazy evaluation pipelines that handle large files
- you need simple data cleaning, sorting, joining, and conversion

## When to avoid
- you need distributed or parallel processing at scale (use Spark/Dask)
- you want dataframe-style numeric analysis (use pandas)
- you need a full orchestration or scheduling ETL platform

## Facets
- artifact type: library
- maturity: stable
- function: etl, csv, data-science
- domain: data-science, files
- platform: python, cross-platform
- tags: etl, tables, data-pipelines, tabular-data, data-engineering

## Member repositories
- petl-developers/petl (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.838459+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:48:30.493252+00:00, confidence not recorded.
  - readme: https://github.com/petl-developers/petl (fetched 2026-08-28T04:04:20.838459+00:00, sha 0ab0f63f9454)
  - registry_pypi: https://pypi.org/pypi/petl/json (fetched 2026-08-29T12:07:02.352469+00:00, sha cb9d9bdeb0d2)
- Data as of 2026-08-30T08:39:29.467469+00:00.