pins

What it is and what it does

Pins is a data and model versioning system that lets you save Python objects to a "pin board" — a storage backend you choose — and retrieve them later with automatic version tracking. It bridges collaboration by letting multiple people or projects access the same pinned data, and it works across Python and R. You can pin to local folders, shared drives, or cloud services like S3, Google Cloud Storage, and Azure; Posit Connect users can also share pins through their organization's server.

The typical workflow is simple: create a board (e.g., `board_folder()` for local storage or `board_s3()` for AWS), write objects with `.pin_write()` specifying a name and format (CSV, Parquet, joblib, JSON), and read them back with `.pin_read()`. Each pin is automatically versioned, so you can track what changed, revert to old versions, and audit data lineage. It's built on top of fsspec for flexible storage backends and integrates with pandas, joblib, and YAML for metadata.

Use it for:

• Share cleaned datasets across multiple analysis projects without duplicating files or managing manual sync.
• Version model artifacts and training data so teams can reproduce results from any historical checkpoint.
• Publish reports or dashboards to Posit Connect that read shared pins, keeping data in sync automatically.
• Store intermediate results in S3 or GCS during data pipelines so downstream jobs can retrieve them without re-computation.
• Enable R and Python teams to exchange data seamlessly using the same pin board and versioning scheme.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Pins publishes and shares Python objects (data, models, etc.) to versioned pin boards stored locally, on cloud services (S3, GCS, Azure), or Posit Connect, with automatic versioning and cross-language (Python/R) compatibility.

Yes. Pins solves a real problem in collaborative data work—versioned, multi-backend data sharing—with low friction (pure Python, permissive license, active maintenance). Use it if you need to share datasets or models across projects or people, or if you want automatic versioning without managing a database. Not needed for single-user, single-project workflows.

Install

pins on PyPI

pip install pins

uv add pins

poetry add pins

Installing pins

Before you install

Low install friction; pure Python wheel with 13 runtime dependencies including pandas, requests, and joblib. Actively maintained with recent commits and permissive licensing.

License in practice

Permissive license (MIT per classifiers) means you can use, modify, and distribute pins freely in commercial and private projects without restriction.

Quickstart

pip install pins

import pins
board = pins.board_temp()
board.pin_write(data, "my_data", type="csv")
board.pin_read("my_data")

Requires Python 3.9 or later; cloud board usage (S3, GCS, Azure) requires appropriate credentials and network access.

Verify before relying

• Whether cross-language (Python/R) pin compatibility is production-ready and what version of pins for R is required.
• Performance characteristics and scalability limits for large datasets or high-frequency pin operations.
• Whether Posit Connect integration requires specific server versions or authentication methods beyond environment variables.

Package facts

Evidence: pins-0.9.1-py2.py3-none-any.whl

Keywords: data, tidyverse

Tags

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