nominal
Automate Nominal workflows in Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14).
- ffmpeg-python dependency may require ffmpeg system library installed separately.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-08-13 (1 days) · last repo commit 2026-08-14 · 12 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 201,775 downloads/mo, #9,663 on PyPI
Alternatives
Verify before relying
pip install nominal
import nominal
# See https://docs.nominal.io/core/sdk/python-client/quickstart for usage examples- 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.
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 on this page. Verify before relying on it.
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
nominal on PyPI
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).
Requires Python 3.10 or later (supports up to 3.14). ffmpeg-python dependency may require ffmpeg system library installed separately.
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 examplesVerify 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 the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesclickconjure-python-clientexceptiongroupffmpeg-pythonnominal-api-protosnominal-apinominal-streamingpandaspolarspython-dateutilpyyamlrequestsrichtabulatetruststoretyping-extensionsurllib3 |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 201,775 / month, #9,663 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: nominal-1.160.0-py3-none-any.whl
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