papermill
Parameterize and run Jupyter and nteract Notebooks
Decision gist · record as of 2026-08-14
Yes. Papermill is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for anyone using Jupyter Notebooks in production pipelines or reporting workflows. The permissive BSD license poses no restrictions. Install it if you need to automate notebook execution with varying inputs or integrate notebooks into larger workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; notebook must contain a cell tagged 'parameters' for parameter injection to work as intended.
- Low friction installation with a pure-Python wheel.
- Actively maintained with recent releases; last commit 2026-07-06 and 6469 repository stars indicate sustained community use.
License · maintenance · safety
BSD (permissive) — BSD license is permissive; you can use, modify, and distribute papermill with minimal restrictions in both open-source and commercial projects.
last release 2026-02-27 (168 days) · last repo commit 2026-07-06 · 6,469 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 15,388,058 downloads/mo, #1,188 on PyPI
Alternatives
Verify before relying
pip install papermill
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters=dict(alpha=0.6, ratio=0.1)
)- Whether optional I/O bundles (s3, azure, all) are required for your cloud storage use case or if local/HTTP execution alone suffices.
- Performance characteristics when executing large notebooks or running many notebooks in parallel workflows.
What it is and what it does
Papermill is a tool for turning Jupyter Notebooks into parameterized, reusable components in data pipelines. It lets you inject parameters into a notebook before execution, then run it and capture the output—all without manual intervention. This is useful when you have a notebook-based analysis or report that needs to run with different inputs (e.g., different dates, thresholds, or datasets) or when you want to chain multiple notebooks together in a workflow.
The package works by looking for a cell tagged 'parameters' in your notebook, then injecting a new cell with your supplied parameter values before execution. You can pass parameters via the Python API, command-line flags, YAML files, or base64-encoded strings. It supports reading from and writing to local files, HTTP URLs, AWS S3, Azure storage, and Google Cloud Storage, making it suitable for both local development and cloud-based data pipelines.
Use it for
- Run a financial or business report notebook on different dates (month-end, year-end) with parameters controlling the date range.
- Build a data pipeline where one notebook's output feeds into another notebook's parameters, automating multi-step analyses.
- Batch-execute the same analysis notebook across many datasets or cohorts, storing results in cloud storage.
- Integrate notebook-based models or analyses into scheduled jobs or CI/CD workflows without rewriting the logic.
- Generate parameterized reports on demand, injecting user-supplied values (thresholds, filters, model hyperparameters) at runtime.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Papermill is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for anyone using Jupyter Notebooks in production pipelines or reporting workflows. The permissive BSD license poses no restrictions. Install it if you need to automate notebook execution with varying inputs or integrate notebooks into larger workflows.
Install
papermill on PyPI
Before you install
Low friction installation with a pure-Python wheel. Actively maintained with recent releases; last commit 2026-07-06 and 6469 repository stars indicate sustained community use.
Requires Python 3.10 or later; notebook must contain a cell tagged 'parameters' for parameter injection to work as intended.
License in practice
BSD license is permissive; you can use, modify, and distribute papermill with minimal restrictions in both open-source and commercial projects.
Quickstart
pip install papermill
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters=dict(alpha=0.6, ratio=0.1)
)Verify before relying
- Whether optional I/O bundles (s3, azure, all) are required for your cloud storage use case or if local/HTTP execution alone suffices.
- Performance characteristics when executing large notebooks or running many notebooks in parallel workflows.
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesaiohttpclickentrypointsnbclientnbformatpyyamlrequeststenacitytqdm |
| Maintenance | Actively maintained 168 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 15,388,058 / month, #1,188 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsLicense :: OSI Approved :: BSD LicenseProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: papermill-2.7.0-py3-none-any.whl
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See also apache-airflow-providers-papermill · scrapbook · execnb · jupyter-mcp-server · mkdocs-jupyter · testbook · jupyter-nbmodel-client · nbclient · jupyter-mcp-tools · json-e