# machow/siuba

Python library for using dplyr like syntax with pandas and SQL

Repository: https://github.com/machow/siuba
Canonical: https://ross.abutalabs.com/products/siuba
Homepage: https://siuba.org
Language: Python
License: MIT
License Family: permissive
Topics: data-analysis, python, pandas, sql, dplyr
Last push: 2025-09-24T02:15:01+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 43, release rhythm 8, longevity 100
- inputs: {"age_days": 2762, "days_push": 344, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1183, forks 54 (observed 2026-08-28T04:03:54.456971+00:00)

## What it is
siuba is a Python library that ports R's dplyr syntax to pandas DataFrames and SQL databases, using pipe (>>) chaining and lazy expressions. The same analysis code runs on local DataFrames or generates SQL queries against backends like postgres, sqlite, duckdb, and snowflake.

## Use cases
- analyze pandas dataframes with dplyr-style verbs
- write data analysis code that runs on both pandas and SQL
- generate SQL queries from Python expressions
- group and summarize tabular data with a concise pipe syntax
- port R dplyr workflows to Python
- avoid verbose pandas lambda syntax
- run the same analysis on a local dataframe and a remote database

## When to choose
- you know dplyr and want similar ergonomics in Python
- you want one analysis codebase for pandas and SQL backends
- you find pandas method chaining with lambdas too verbose
- you need lazy SQL query generation from dataframe-style code

## When to avoid
- you prefer idiomatic pandas or polars APIs
- you need a mature, widely-adopted tool with large ecosystem support
- your SQL backend is not among the supported dialects
- you need advanced pandas features outside siuba's verb set

## Facets
- artifact type: library
- maturity: active
- function: data-science, etl, database-driver
- domain: data-science, databases
- platform: python, cross-platform
- tags: dplyr, pandas, sql, dataframe, tidyverse, data-analysis, sql-query-generation, data-engineering

## Member repositories
- machow/siuba (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:54.456971+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 2026-08-30T06:24:35.289043+00:00, confidence not recorded.
  - readme: https://github.com/machow/siuba (fetched 2026-08-28T04:03:54.456971+00:00, sha 6e9e58d92495)
  - homepage: https://siuba.org (fetched 2026-08-29T12:31:49.536085+00:00, sha 027ed6027b7f)
  - site_page: https://siuba.org/about/key_features (fetched 2026-08-29T12:31:49.545264+00:00, sha 79ac31f4f976)
  - site_page: https://siuba.org/guide/basics-sql (fetched 2026-08-29T12:31:49.547576+00:00, sha a4477c34c8b0)
  - registry_pypi: https://pypi.org/pypi/siuba/json (fetched 2026-08-29T12:31:49.549264+00:00, sha b1b70fd7b3ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.