---
id: nominal
version: "1.160.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# nominal — Automate Nominal workflows in Python

License: permissive · Maintenance: active · Downloads: 201.8K/mo

## What it is and what it does

Nominal is a Python client library for the Nominal platform, which appears to be a hosted service for managing test data, storage, and compute workflows. The package provides programmatic access to Nominal's APIs, allowing you to automate and integrate Nominal workflows into Python applications. It bundles a substantial dependency tree including pandas and polars for data manipulation, requests and urllib3 for HTTP communication, and ffmpeg-python for media handling, suggesting the platform handles diverse data types and formats.

The package is actively maintained (last commit 2026-08-14) and supports current Python versions (3.10–3.14). Installation is straightforward via pip. The documentation is hosted at docs.nominal.io, and the project is open-source under Apache-2.0. With no known security vulnerabilities and low install friction, it is positioned as a stable integration point for Nominal workflows, though the exact scope of 'test data' and 'compute' operations is not detailed in the fact sheet.

Use it for:

- Automate test data generation and management workflows within Python applications that integrate with the Nominal platform.
- Build data pipelines that combine Nominal storage operations with pandas or polars for local data transformation.
- Integrate Nominal compute tasks into CI/CD or testing frameworks to orchestrate remote processing.
- Programmatically manage multimedia or complex data types through Nominal's storage layer via the Python SDK.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Nominal is a Python SDK for automating workflows involving test data, storage, and compute operations through the Nominal platform.

Yes, if you use the Nominal platform. The package is actively maintained, has low install friction, carries permissive licensing, and has no known vulnerabilities. The large dependency tree suggests rapid iteration, so verify that the API surface matches your use case before committing to production. If you do not use Nominal, this package has no standalone value.

## Install

pip install nominal
uv add nominal
poetry add nominal

## Installing nominal

Before you install: Low friction installation with a pure-Python wheel. Active maintenance—released 2026-08-13, last commit 2026-08-14—and supports modern Python versions (3.10 through 3.14). Carries 17 runtime dependencies including data libraries (pandas, polars), HTTP clients (requests, urllib3), and media tools (ffmpeg-python).

License in practice: Licensed under Apache-2.0 (permissive). You may use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and state any significant changes.

Quickstart:

pip install nominal

import nominal
# See https://docs.nominal.io/core/sdk/python-client/quickstart for usage examples

Requires Python 3.10 or later (supports up to 3.14). ffmpeg-python dependency may require ffmpeg system library installed separately.

Verify before relying:

- Whether the 17 runtime dependencies are all required for basic usage or if many are optional.
- What 'test data, storage, and compute' operations concretely mean—whether this is for ML/AI workflows, general data pipelines, or something else.
- Whether the package is stable for production use or still evolving rapidly given its version history.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 201.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

test data management SDK, workflow automation Python, data storage compute platform, nominal API client, test data pipeline, workflow-automation, data-platform-client

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